## **Advances in Postharvest Process Systems**

Edited by Daniel I. Onwude and Guangnan Chen Printed Edition of the Special Issue Published in *Processes*

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## **Advances in Postharvest Process Systems**

## **Advances in Postharvest Process Systems**

Editors

**Daniel I. Onwude Guangnan Chen**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Daniel I. Onwude Empa-Swiss Federal Laboratories of Material Science and Technology Switzerland Guangnan Chen University of Southern Queensland Australia

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## **Contents**




## **About the Editors**

**Dr. Daniel I. Onwude** is currently a Research Scientist at the Swiss Federal Laboratories For Material Science and Technology, Empa, ETHz domain, Switzerland. He is also a Lecturer in the Department of Agricultural and Food Engineering, University of Uyo, Nigeria. Dr. Onwude received his Ph.D. degree in Agricultural Process Engineering from Universiti Putra Malaysia in 2018. In 2019, he received a post-doctoral scholarship at ETH Zurich, Switzerland, with joint affiliation with Empa, under the Swiss Government Excellence Scholarship.

Dr. Onwude has published three book chapters and over 50 articles in international peer-reviewed journals, which were cited over 1000 times, and resulted in a Scopus h-index of 14. He has been a principal investigator in several national and international research projects. He is currently an Associate Editor for the *Journal of Thermal Science and Engineering Progress* (TSEP-Elsevier), and an Editorial Board Member in the *Journal of Agricultural and Food Engineering* (JAFE). He is a member of international Food and Agricultural organizations including the American Society of Agricultural and Biological Engineers (ASABE), Nigerian Institution of Agricultural Engineers (NIAE), Institute of Food Technologist (IFT), International Society for Horticultural Science (ISHS), and Institution of Engineering and Technology (IET). His research interests include food preservation and processing, drying technologies, computational fluid dynamic, and mathematical modelling and computer simulations.

**Dr Guangnan Chen** is currently an Associate Professor and Discipline Leader in agricultural engineering at the University of Southern Queensland, Australia. He graduated from the University of Sydney with a PhD degree in 1994. Before joining the University of Southern Queensland in early 2002, he worked for two years as a post-doctoral fellow and for more than five years in a private consulting company based in New Zealand.

Dr Chen teaches and researches the subjects of agricultural machinery, agricultural materials and post-harvest technologies, and sustainable agriculture. He has published more than 140 papers in various international journals and conferences, including three edited books published by the CRC Press, Taylor & Francis Books and 10 invited book chapters. He is currently also the Secretary of Board of Technical Section IV (Energy in Agriculture), Commission Internationale du Genie Rural (CIGR), which is one of the world's top professional bodies in agricultural and biosystems engineering.

### *Editorial* **Special Issue "Advances in Postharvest Process Systems"**

**Daniel I. Onwude 1,2,\* and Guangnan Chen 3,\***


The world population is predicted to increase from the present 7.7 billion to 9.7 billion in 2050, demanding a significant increase in food supply and production. However, around 25–30% of food is wasted worldwide every year due to poor postharvest supply chain design and management in different stages of the food supply chain, including postharvest handling, processing, and storage systems.

This special issue presents state-of-the-art information on the important innovations and research in the agricultural and food industry. Different novel technologies and their implementation to optimize postharvest processes and reduce losses are reviewed and explored. In particular, it examines a range of recently developed and improved technologies and systems to help the industry and growers to manage and minimize postharvest losses, enhance reliability and sustainability in the postharvest food value chain, and generate high-quality products that are both healthy and appealing to consumers.

This special issue consists of three sections, focusing on food storage and preservation technologies [1–4], food processing technologies [5–8], and the applications of advanced mathematical modeling and computer simulations [9–11]. We wish to acknowledge the expert contributions of all authors here. We also wish to acknowledge and thank MDPI staff for their professional assistance in editing the published articles. We sincerely hope that this special issue will assist all readers and stakeholders working in or are associated with the fields of agriculture, agri-food chain, and technology development and promotion. After all, efficient postharvest technology is an essential and key factor underlying future global food security, and ultimately human survival and development.

#### **1. Food Storage and Preservation Technologies**

There are many ways to store and preserve food, each with its benefits and limitations. Different food and crops also need to be stored and preserved in particular ways to maintain their quality best. Onwude et al. [1] reviews the recent advances in technologies to reduce the quality loss of fresh agricultural produce in the supply chain, including the applications of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. The Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) are particularly identified as emerging technologies that can help monitor and control the quality during postharvest.

Ndukwu et al. [2] investigates the effectiveness of different household storage strategies and also the plant-based preservatives for dehulled and sun-dried breadfruit seeds. The obtained results revealed the high potential of alligator pepper (*Zingiberaceaeaframomum melegueta*) as a botanical insecticide in preventing insect infestation and mold growth. An aluminum silo bin with alligator pepper powder is subsequently recommended to store dried and dehulled breadfruit seeds as a baseline for other tropical crops.

**Citation:** Onwude, D.I.; Chen, G. Special Issue "Advances in Postharvest Process Systems". *Processes* **2021**, *9*, 1426. https:// doi.org/10.3390/pr9081426

Received: 2 August 2021 Accepted: 2 August 2021 Published: 18 August 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Tomlins et al. [3] evaluates the various factors contributing to the storage shelf-life of fresh Cassava roots. In this study, three different types of bag materials were tested. Microclimate related to temperature, humidity, and carbon dioxide (CO2) was monitored. The results showed that fresh cassava roots could be stored for 8 days, with minimal postharvest physiological deterioration and starch loss. It was also shown that carbon dioxide concentration in the stores was significantly correlated with the starch loss in fresh cassava roots and is therefore proposed as a possible method for continuously and remotely monitoring starch loss in large-scale commercial operations and reducing postharvest losses.

Esua et al. [4] reviews the applications of ultraviolet-C radiation and ultrasound technology for postharvest preservation and handling of fruits and vegetables. It was identified that scale-up and optimization of the process would provide considerable opportunities for industry-led collaborations and chart effective commercialization paths.

#### **2. Food Processing Technologies**

Food processing technologies include the applications of different physical, chemical, or microbiological methods and techniques to enhance the food quality and also to ensure more diversity for the increasing demand of different consumers. Raut et al. [5] discusses the impact of process parameters and bulk properties on the quality of dried hops. The results showed that it is important to define and consider optimum bulk and process parameters in order to optimize the hop drying process to improve the process efficiency as well the product quality.

Gurdil et al. [6] evaluates the postharvest processing of hazelnut kernel oil extraction using uniaxial pressure and organic solvent. It was found that the increased speed caused a serration effect on the force–deformation curve, resulting in lower oil yield. Lower and upper oil point forces were also observed to be useful for predicting the pressure for maximum output oil. It was further found that the peroxide value and free fatty acid content of kernel oil decreased with increasing temperature. In designing new presses, it is suggested that there is a need to consider compression and relaxation processes to reduce the residual kernel cake oil.

Tan et al. [7] explores the physicochemical changes, microbiological properties, and storage shelf life of cow and goat milk from industrial high-pressure processing (HPP). No significant changes were found in the physicochemical properties of the treated milk except for pH. HPP-treated cow and goat milk both achieved microbial shelf life of 22 days at 8 ◦C storage temperature.

Kwofie et al. [8] investigates the relationship between the classification and force deformation characteristics of common beans and the influence of bean softeners on cooking time. This work classified ten bean cultivars as either easy-to-cook (ETC) or hard-to-cook (HTC) based on a traditional subjective finger pressing test and a scientific objective hardness test. The results showed that a modified three-parameter non-linear regression model could accurately predict the rate of bean softening. The influence of bean softeners, such as potassium carbonate (K2CO3) and sodium chloride (NaCl), to reduce cooking time was also investigated in this paper.

#### **3. Application of Mathematical Modeling and Computer Simulations in Postharvest Technology**

With increased computing power, it is now very attractive to investigate the applications of mathematical modeling and computer simulations in the postharvest processes. Many models have now been successfully developed and employed in food and crop monitoring, grading, and classification, predicting and modeling quality properties, and forecasting chemical, physical, and nutrient characteristics during processing and postharvest storage. Khaled et al. [9] demonstrates the application of computational intelligence in describing the drying kinetics of persimmon fruit during vacuum and hot air-drying processes. Kinetic models were developed using selected thin layer models and computational intelligence methods, including multi-layer feed-forward artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbors (kNN). The validation

results indicated good agreement could be obtained from the computational intelligence methods between the predicted values and the experimental data.

Azman et al. [10] studies the physical properties and mass modeling of pepper berries at different maturity levels. It was found that the quadratic model was best fitted for mass prediction at all mass maturity levels (immature, mature, and ripe). The results showed that mass modeling based on the actual volume of pepper berries was more applicable compared to other properties. The findings would be potentially useful in developing improved grading, handling, and packaging systems.

Due to the numerous side effects of synthetic pesticides, including environmental pollution, threats to human health, harmful effects on non-target organisms, and pest resistance, the use of alternative healthy, available, and efficient agents in pest management strategies is attracting increased attention. Ebadollahi et al. [11] examines the optimization and modeling of susceptibility of Tribolium castaneum (Coleoptera: Tenebrionidae) to the fumigation of two essential Satureja oils. The insecticidal properties of the essential oils were modeled and optimized using response surface methodology. It was suggested that the essential oils of S. hortensis and S. intermedia could be offered as promising pesticidal agents against Tribolium castaneum in the management of such pests instead of detrimental synthetic pesticides.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Review* **Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce**

**Daniel I. Onwude 1,2,\*, Guangnan Chen 3, Nnanna Eke-emezie 4, Abraham Kabutey 5, Alfadhl Yahya Khaled <sup>6</sup> and Barbara Sturm <sup>7</sup>**


Received: 12 September 2020; Accepted: 5 November 2020; Published: 9 November 2020

**Abstract:** Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (≈50%) still occur during the packaging, pre-cooling, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins for multiple shipments in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables.

**Keywords:** food security; food quality; agricultural production; crop storage and processing; food distribution; smart digital technology; industry 4.0; refrigeration

#### **1. Introduction**

Food losses in the postharvest supply chain amount to a great loss of investments in the packaging, transportation, and storage operations. About 25–30% of global food produced is lost between on-farm food production and its storage at a retail facility, largely as a result of poor chain management and spoilage [1,2]. Food losses occur due to a reduction in quality and safety standards driven by consumer preferences, particularly in developed countries [3]. A high amount of losses (up to 30% per year) is often experienced during the postharvest handling of fresh agricultural produce, such as fruits and vegetables [4]. Advanced technologies are required to reduce the losses of fruits and vegetables in the postharvest supply chain. The reduction of these losses would increase the number of fresh produce available for consumption.

Fruits and vegetables are important sources of nutrients such as vitamins, minerals, and bioactive compounds, which provide many health benefits [5–8]. However, they are highly perishable goods that need to be appropriately preserved, to reduce the degradation of macro and micro-nutrients and extend shelf life [7,9,10]. As a result, fruits and vegetables are often packaged and kept in a desired low-temperature range using various refrigeration systems during the transportation and storage postharvest handling processes. This process delays or reduces microbial growth and enzymatic reaction, thereby improving overall quality, reducing mass loss, and extending shelf-life. The succession of refrigeration steps along these chains can be referred to as a postharvest cold chain of fruits and vegetables [11]. A description of the postharvest cold chain of fruits and vegetables is shown in Figure 1.

**Figure 1.** Postharvest refrigerated supply chain of fruits and vegetables.

Refrigeration is a key element in enhancing the quality of fresh produce and extending the shelf-life, thereby enabling their adequate supply to an increasingly urbanized world [11–13]. However, more than 90% of perishable goods are still not refrigerated [1,14]. Inadequate refrigeration infrastructure or access to energy accounts for more than 20% losses of perishable goods [15]. These losses also encompass a huge amount of energy and water losses, but also carbon dioxide emissions [15,16]. Therefore, sustainable cold chain technologies in terms of being more resource-efficient, improving product quality retention, and reducing induced food losses become indispensable.

Several studies have been conducted on the postharvest cold chain of fruits and vegetables with a view to gain more insight on ways to address these technological and developmental challenges. The losses in the mass and nutritional qualities of strawberries, raspberries, red currants, drupes, cherries, and sour cherries were reduced using refrigerated containers at 4 ◦C when compared to storage

at room temperature [17]. Packaging methods such as edible coating, active modified atmosphere packaging (MAP), nano-composite based packaging (NCP), and polypropylene/polyethylene bags have been used to reduce quality losses of cherry tomato, kiwifruits, guava, mushroom, cucumber, and berries during cold chain processes [18–23]. Recently, active packaging such as oxygen scavengers, ethylene absorbers, moisture regulators, and intelligent packaging including the use of chemical sensors, temperature, freshness and gas indicators, barcodes and radio frequency identification devices (RFID), have been developed to maintain the quality and improve the safety of fresh produce [24–27]. These different packaging methods are simple to design, easily affordable and can help to extend product shelf life [15]. However, retailers in the food supply chain are increasingly looking for ways to minimize or eliminate the use of packaging, to project sustainable eco-friendly products [28]. Consequently, the negative impact of most packaging materials (e.g., plastic packaging) is largely overestimated by consumers in comparison to other actions with much higher impacts [29–31]. As an example, the controversy between paper bags versus plastic packaging comes to mind. Paper bags hold a much higher environmental impact, due to its higher weight [32], but are often perceived to be more eco-friendly by the consumer. In a similar manner, a life cycle analysis of a commonly consumed fruit or vegetable with and without packaging will show that the environmental impact of plastic packaging, for example, is by far smaller than the impact of the food losses [33,34]. In addition, plastic packaging presently reduces food losses by up to 4.8% at retail and also reduces induced food losses at households as a result of prolonged shelf life [35]. Despite all these improvements and awareness from peer-reviewed literature, the question of why a significant amount of food losses in the postharvest supply chain (see above) arises, suggesting that more insight and advances into cold chain technology are required to further reduce food losses by preventing excessive quality loss of fresh produce.

Key drivers that accelerate food losses during the postharvest supply chain of fruits and vegetables include lack of innovative packaging materials, inadequate monitoring technology, variations in the temperature, approach air velocity and relative humidity in cold chain systems, rate of metabolism, long shipment duration and the heterogeneity of fruits and vegetables. During shipments, there is often a wide variation of temperature and relative humidity at different locations in a cold chain system. Great variations in the approach airspeed of different fruits and vegetables are often observed as a result of the heterogeneous nature of refrigeration equipment, food properties, and packaging container. These variations can affect the final mass loss, overall quality, and the remaining shelf life of fresh produce [36–38].

Understanding the physics behind different phenomena that occur during the different postharvest supply chains and linking these phenomena to measurable output using sensors that provide actionable data may be the key in optimizing the design of packaging, storage, and transport processes for fruits and vegetables. Unfortunately, studies on these advances are limited.

This paper aims to explore ways on how food losses can further be reduced in the postharvest supply chain of fruits and vegetables. Particularly, we discuss the current state-of-the-art in monitoring and optimizing cold chain systems for a reduction in quality loss during the packaging, transportation, and storage of fruits and vegetables. We also analyze the potential of applying emerging technologies such as the Internet of Things (IoT) and digital twins for reducing food losses. We then put forward how the future should look towards reducing food losses during the packaging, storage, and transportation supply chain.

#### **2. The Need to Reduce Food Losses in the Postharvest Supply Chain of Fruits and Vegetables**

Food losses can be referred to as "the reduction in the amount of fresh fruits and vegetables that was originally meant for human consumption" [39–41]. Globally, one third to half of all food produced is lost or wasted along postharvest supply chains, with packaging, storage, and transportation value chains the most impacted [42,43]. Losses of fruits and vegetables worldwide are between 40% and 50% of which 54% occur in stages of production, postharvest handling, and storage [3,44,45].

During packaging, transportation, and storage of fresh agricultural produce, food losses are often induced as a result of a reduction in the quality (e.g., color, texture, mass) of the produce. These postharvest handling operations affect the nutritional and sensory quality of the agricultural produce, the mass of the fresh produce as well as the quantity of fresh produce available to the consumers. The quality of fresh agricultural produce can be referred to as the excellent characteristics of such products that are acceptable to a consumer [46]. Consumers typically purchase fresh agricultural produce based on their biochemical characteristics such as appearance, texture, flavor, and nutritive value [46,47]. Fresh agricultural produce such as fruits and vegetables provide an essential part of human nutrition, as they are important sources of vitamins, dietary fibers, minerals, and other biochemical (e.g., carbohydrate, protein, etc.) with tremendous health benefits [48]. Adequate in-transit monitoring of environmental conditions and changes in the quality attributes of fresh produce during transport and storage will help reduce food losses and ensure the availability and accessibility of fresh fruits and vegetables with high nutritional density to the consumers [49,50]. Therefore, emerging technologies are needed to help reduce the overall quality loss of fresh agricultural produce, thereby reducing food losses in the postharvest supply chain.

#### **3. Advanced Technologies for Quality Assessment in Postharvest Supply Chain: State-Of-The-Art**

In recent decades, several modern food quality techniques have been applied to monitor, control, and predict the quality evolution of various fruits and vegetables in postharvest supply chains. These techniques include imaging systems, spectroscopy, multi-sensors, electronic nose (E-nose), acoustic impulse response (AIR), radio frequency identification (RFID), printed sensors (PTS), and mathematical modeling. In this section, we analyze the application of these techniques in advancing cold chain operations and process optimization during the packaging, storage, and transportation of fruits and vegetables within the past 10 years.

#### *3.1. Application of Imaging Technology, Spectroscopy, Multi-Sensors, E-Nose, AIR, RFID and PTS in the Postharvest Supply Chain of Fruits and Vegetables*

Imaging technology is an advanced method used by the food and agro-allied industries to monitor changes in food quality [51]. This technology includes computer vision (CV), hyperspectral imaging (HSI), multispectral imaging (MSI), thermography, and X-ray imaging. Image technology is particularly useful in detecting and evaluating the external quality attributes (color, geometrical, size, appearance, and surface structure) [52,53], and in some cases, the internal structures (X-rays and hyperspectral imaging) of fruits and vegetables. This technology involves collecting and analyzing spatial information gained from captured images of products, such as color, geometrical, size, appearance, and surface structure. The application of imaging technology in postharvest supply chains is mainly limited to surface detection. The surface properties of an object can be detected due to the interaction of light. A typical imaging system consists of a CCD camera, a light source, a computer, and related software (Figure 2). The camera captures the images of the product based on the region of interest. The captured images are then processed to evaluate and quantify the quality changes that have occurred during a particular postharvest operation. The image processing steps often consist of image acquisition, segmentation, feature extraction and recognition, classification, and interpretation [54–57].

**Figure 2.** Typical set-up of an imaging system for monitoring the quality of fresh agricultural produce.

In order to adequately discriminate and analyze the captured numerous images, chemometrics and deep learning methods are often employed. These methods have already been found reliable in quantifying the accuracy of processed images and associated quality changes of fruits and vegetables in the postharvest supply chains (Table 1). They include Savitsky–Golay (SG), Standard Normal Variate (SNV), Principal Component Analysis (PCA), Partial Least Squares Regression (PLSR), Multiple Scatter Correction (MSC), Partial Least Squares Discriminant Analysis (PLS-DA), Artificial Neural Network (ANN), Convolutional Neural Networks (CNN), Linear Discriminant Analysis (LDA), k-Nearest Neighbors (kNN), Correlation-based Feature Subset Selection (CFS), Gini Impurity Algorithm (GIA), Sequential Forward Selection (SFS), Backpropagation Neural Network (BPNN), Extreme Learning Machine (ELM), Sparse Logistic Regression (SLR), Support Vector Machine(SVM), Radial Basis Function (RBF), Neural Networks (NN), Genetic Algorithm (GA), Support Vector Regression (SVR), Student–Newman–Keuls (SNK), Least Squares Support Vector Machines (LS-SVM), and Random Forest (RF).

The applications of different imaging and smart digital technologies in monitoring the quality of fruits and vegetables in postharvest cold chains are summarized in Table 1. These technologies include computer vision (CV), hyperspectral imaging (HSI), multispectral imaging (MSI), X-ray imaging, spectroscopy, multi-sensors, E-nose, and acoustic impulse response.

From Table 1, the majority of the study was done using CV [58–66]. This involves the capturing of images of a product using a digital camera, and the ability of computers to understand the processed image data using computational intelligence tools (e.g., chemometrics, deep learning) [54,55]. This imaging method is rapid, reliable, and consistent. However, this technique has some limitations such as the use of artificial lighting during image capturing and the inability to detect internal attributes. Table 1 further shows that the bulk of the studies using CV was on cold storage, mostly to monitor and detect spoilage, chilling injury, and shelf-life of grapes, lettuce, tomato, zucchini, banana, strawberry, oranges, and mango [58–64,66]. CV with several chemometric and statistical analytic approaches was able to quantify quality losses with 75–92% accuracy (Table 1). Only a single study explored the application of CV in quantifying the quality losses of lettuce as a result of packaging material used, with 86% accuracy (Table 1) [65].

Hyperspectral and multispectral imaging (HSI and MSI) are advancements of computer vision, which involves the capturing of image data at a different wavelength (e.g., continuous 400–1700 nm in

steps of 1 nm for HSI and targeted 400–1100 nm in steps of 20 nm for MSI) across the electromagnetic spectrum [67–70]. Hyperspectral imaging particularly integrates both imaging and spectroscopy features to simultaneously gather spectral and spatial information from a product, thus making it a more powerful imaging technology compare to CV and multispectral imaging [71]. Both hyperspectral and multispectral imaging technologies can detect internal and external quality attributes of fresh produce. However, they also require artificial lightning, are very sensitive to environmental conditions, have limited penetration depth, and are very expensive to use. Closely following CV, several studies have been conducted on the application of hyperspectral imaging in monitoring the quality of spinach, mushrooms, cucumber, mango, apples, and citrus fruit during cold storage and packaging (Table 1) [72–75]. This imaging system coupled with PCA, CFS, GIA, SFS, SLR, LDA, kNN, and NN was able to give 89–98% accuracy (Table 1). However, not so much for multispectral imaging, as only two studies quantified the quality losses of mangoes during the cold storage, with a classification rate of ≈92% using PLS-DA (Table 1) [70,76].

Spectroscopy, which is the study of the interaction of electromagnetic waves, including ultraviolet, visible, and infrared spectra, has been applied to monitor and optimize the cold storage process of peach and mango (Table 1). Although only a few studies have been carried out (Table 1) [77,78], this spectral approach can however give an accurate prediction (≈96%) of the total soluble solids, and phenolic content of peaches during cold storage. This technique gives the advantage of repeatable spectral data and provides high resolution of spectra. Additionally, this method is toxic-free. Nevertheless, the spectra data often contains redundant information due to hundreds of spectral variables, limited sensitivity to minor components, and complicated analysis.

X-ray imaging was applied to detect both the internal disorder and external changes (firmness) of pears and kiwifruit during cold storage, respectively (Table 1) [11,79]. This technology involves the production of electromagnetic radiation by an X-ray tube when passed through a product to absorb part of X-ray beam energy [80]. Using SVM, FEA, NN, and LDA, X-ray adequately quantified the columella firmness of kiwifruit and discriminated healthy pear from defective ones, with accuracy ranging from 90% to 95% (Table 1).

Other techniques used to monitor the quality of fruits and vegetables in postharvest cold chains are multi-sensors, electronic nose (E-nose), acoustic impulse response, radio frequency identification (RFID), and printed sensors (PTS). In this study, multi-sensors involves the use of numerous sensors placed at different locations on the produce and in the cold chain equipment (storage container or transport vehicle) to capture important quality attributes (e.g., color, firmness) and food losses (weight loss, temperature, time) metrics [81]. Data from the sensors are processed using sensor fusion (soft sensors). Soft sensors are virtual software code to process multiple sensor information for identified quality classifiers and for the development of warning systems (e.g., quality decline in fruits) [82]. They can be developed using different methods including mechanistic modeling based on physics of specific measured quality and food loss metrics, statistical modeling based on low-level representations in the feature space, and chemometrics or deep learning-based sparse representation techniques for multi-modal event modeling. Figure 3 depicts the use of multiple sensors (e.g., temperature, humidity) coupled with imaging technology during the cold storage of fruits and vegetables. From Table 1, only two studies applied multi-sensors to improve the accuracy of continuous sensor data acquisition in order to enhance transparency and traceability of the cold storage and transportation logistics of pear [83,84]. The multi-sensors monitored critical parameters that affect the quality attributes of fresh produce, including temperature and relative humidity (using portable low-energy-demanding temperature and humidity sensors). The detection of these parameters during shipment allows for effective control of safety and quality changes of the pear. Similarly, a study was reported on the use of an electronic nose (an instrument used to detect volatile organic compounds) [85] and acoustic impulse response to evaluate the quality of tomato and apple during cold storage, respectively (Table 1). The accuracy of the measured quality attribute (firmness) and mass loss data using ANN was ≈85%. This value is lower than those obtained using the aforementioned imaging technologies. This could

be because of the complex nature of the method, which is based on the measurement of the sound emitted by fruit as it vibrates in response to a gentle tap with a small pendulum [86].

**Figure 3.** Schematics of multi-sensors coupled with imaging system for monitoring the quality of fruits and vegetables during cold storage.

RFID has also been applied as an advanced tool for identifying internal and external changes in the physical, biochemical, and physiological processes of packaged food [87–89]. This non-contact identification communication technology can automatically identify multiple objects moving at high-speed, and therefore can be applied in the transport cold chain, specifically as an IoT enabler [90]. Similarly, PTS which uses the printing process, such as inkjet printing, nanoimprinting, screen printing, etc., to prepare electronic circuits on a flexible substrate enables the monitoring of temperature, moisture, pressure, and motion of fresh produce [91]. This technology has the advantage of flexibility when printed on substrates, ease of distribution, and low cost especially when compared to RFID [91]. However, their application as tools in monitoring and optimizing cold chain processes is scarce.

Furthermore, there is no study on the application of imaging technology and smart digital technologies to monitor food quality losses during the transportation of fruits and vegetables. This is surprising considering that food losses in the transportation stage of the food supply chain can be as high as 30% as in the case of Poland, for example [92]. For this reason, future studies on the application of imaging technology, spectroscopy, multi-sensors, electronic nose, acoustic impulse response, RFID, PTS in the postharvest cold chain of fruits and vegetables should focus on the transportation chain.



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#### *3.2. Application of Mathematical Modeling Techniques in the Postharvest Supply Chain of Fruits and Vegetables*

Modeling is the act of representing phenomena or processes in such a way as to explicitly describe an observed system and to predict or optimize different behaviors, parameters, and conditions [94]. Mathematical modeling is essential for efficient engineering design and optimization. With adequate mathematical models, undesirable effects that significantly causes food losses such as weight loss, or quality changes can be predicted, thus cold chain logistics can be optimized or controlled.

Mathematical modeling techniques are becoming increasingly popular as an alternative to expensive and difficult experiments of postharvest cold chain operations as a result of the sophistication and reliability of computers as well as the affordability and availability of modeling software [95–99]. Agricultural and food engineers, and other researchers have over the years developed different mathematical models for postharvest supply chains. Depending on the complexity, these different modeling techniques have been developed to predict heat and mass transfer, fluid flow, and quality changes in and around fresh produce. Gas exchange and in-depth understanding of migration from packaging material to fresh produce have been described using mathematical models. Additionally, several deterministic, stochastic and kinetic models have also been developed to predict the overall quality of fresh produce, mass loss, fluid flow, and heat and mass transfer during the transportation and storage of fruits and vegetables [98,100–106].

In this study, mathematical models used to enhance cold chain operations chain can be separated into six different types based on their specific process application, namely: migration models (MM), membrane gas separation (MGS) model, heat and mass transfer (HMT) model, structural behavior models (SBM), stochastic models (SM), and kinetics rate models (KRM) (Table 2). MM are often used to study the migration of organic compounds such as Benzophenone, Diisobutyl phthalate, and Phenanthrene from packaging material to fresh produce. On the other hand, MGS models are often used in a modified atmosphere (e.g., modified atmosphere storage or modified atmosphere packaging) to study the lifespan of fresh produce by reducing the respiration rate through the adequate regulation of atmospheric conditions (e.g., CO2, O2). Additionally, to abstract the packaging of different fruits and vegetables [107]. This type of model works on four ideal flow patterns for a mixed gas module including co-current flow, cross flow, counter-current flow, and perfect mixing [108–110].

HMT modeling also called hygrothermal modeling involves using a numerical physics-based method such as computational fluid dynamic (CFD) to solve the governing partial differential equations of heat and mass transport phenomena in a system, often using finite element analysis (FEA) [100,105,111,112]. HMT models describe the underlining physics inside fresh produce and how they are affected by the surrounding conditions (Figure 4). They generally are independent of experimental calibration and validation. HMT models can also be used to investigate the impact of different packaging designs on the convective heat transfer rate of fruits and the surrounding [105]. By reducing heat transfer from the outside environment, effective packaging can help to shield the product from temperature variation in the storage and transport process. In addition, increasing the packaging heat transfer resistance can also ensure temperature stability of the fresh agricultural produce [113,114]. There is therefore a need for simulation tools and numerical models that analyze all factors affecting optimum packaging design.

**Figure 4.** Simulation domain showing loaded fruits in stacked cartons in a virtual wind tunnel system with internal and external surrounding conditions [101].

SBM involves the study of structural and mechanical properties of packaging materials for fresh produce using FEA (Table 2). This modeling approach encompasses a geometric representation, material representation, and boundary conditions (loading and restraints).

SM involves predicting the variability of certain generated data following their probability distribution, and then evaluating of results statistics until the minimum error becomes constant [115]. These models are often used to analyze the effect of biological variability on food quality and losses during the supply chain, and also to quantify the efficiency of the cold chain technology [102,103,116]. Although they do not provide a fundamental understanding of the underlying physics, they are however very reliable and flexible.

KRM are temperature-dependent and are frequently used to study the combination of the rate of reaction with the material balance to predict the behavior of a particular system. Nutritional and sensory qualities of fresh agricultural produce in the postharvest supply chain can be quantified based on kinetics, such as zero-order, first-order, second-order, mixed order, or higher-order reactions [117].

From Table 2, most modeling studies were conducted on packaging followed by storage with very little modeling studies on the transportation of fruits and vegetables. Over 25 modeling studies on the packaging of fruits and vegetables during the postharvest cold chain were conducted within the last decade (Table 2). The products studied were apple, tomato, carrots, strawberries, capsicum, citrus, avocado, grape, feijoa fruits, and pears [100,101,105,107,112,116,118–137]. The bulk of the studies used HMT models to describe the cooling process in a packaged material during storage [100,101,104,105,112,119,120,130,131,137–140]. Seven studies applied MGS models to modify the atmospheric conditions of fresh agricultural produce in packaging material (Table 2) [118,121,125–127,135]. While, four studies examined the migration of chemical compounds (e.g., Benzophenone, Diisobutyl phthalate, and Phenanthrene) from packaging material to fresh produce using MM (Table 2) [122,124,134,136]. Several researchers also reported the application of KRM, SBM, and SM during the packaging of fruits and vegetables (Table 2) [116,123,128,129,132,141].

**Table 2.** Summary of recent literature on the application of mathematical modeling in monitoring quality loss of fruits and vegetables in postharvest supply chain fruitsandvegetables.







With respect to storage, several researchers developed various models to predict quality loss during the storage of carrots, strawberries, spinach, apricots, peaches, capsicums, banana, cabbage, pears, citrus, and broccoli, however not all together (Table 2)[98,102–106,116,118,119,121,122,124,126,133,138–140,142–144]. A bulk of the models used were based on heat and mass transfer simulations of the weight loss, and temperature distribution; KRM for quality decay and shelf-life prediction (Table 2) [98,104–106,112,119,133,138–140,142,144].

Concerning the transportation supply chain, only a few authors have applied SM, HMT models, and KRMto estimate heat generation, cooling efficiency, temperature distribution, and the expectedfraction of perishable products (Table 2). These products include spinach, peaches, and banana [103,104,143].

The above analysis shows that mathematical models have been widely developed and applied in the packaging and storage of fruits and vegetables with the view to improve quality and reduce food losses. However, not so much modeling study for the transportation supply chain of fruits and vegetables. Future mathematical modeling studies should focus on the transportation supply chain taking into account the shipment time, the varying environmental conditions (e.g., temperature, humidity, and airflow), packaging, and vehicular movement. In addition, the KRM could be integrated with HMT models to give more insight into what extent the quality attributes of fruits and vegetables are preserved better and also to quantify the effect of other drivers for decay processes (e.g., relative humidity, light) in the fresh produce supply chain. This can be achieved by developing a digital twin of the product (see Section 4.2).

#### **4. Emerging Opportunities in Reducing Food Losses in the Postharvest Supply Chain**

The application of IoT and digital twins in optimizing shelf-life and reducing food losses during an entire shipment has gained significant interest in recent years. This section analyzes the potential of applying the Internet of Things (IoT) and digital twins in reducing food losses in the postharvest supply chains of fruits and vegetables.

#### *4.1. Application of IoT in the Postharvest Supply Chain of Fruits and Vegetables*

IoT has emerged in different fields such as e-commerce [145,146], manufacturing [147,148], education [149–151], medicine and healthcare [152–158], and agriculture [159,160]. This is because of the enormous number of devices connected to the Internet, as well as the widely available internet and data storage service providers [148,161,162]. Basically, IoT allows humans, objects, and things to connect and communicate at any time and anywhere. The European Commission Information Society defined IoT as different things exhibiting identical and virtual personalities, connecting and communicating in a smart space using intelligent interfaces within social, economical, and user contexts [163].

The IoT system consists of networks of physical objects that contain embedded technology to sense, communicate, and interact with their internal states or the external environment [164]. The key enablers for a typical IoT system include RFID, printed sensors, web service, machine-to-machine communication (M2M), WSN, imaging system, multi-sensors, cloud, blockchain, among others, but not necessarily altogether [87,91,147,161,165,166].

The application of IoT is well-established in various agricultural production sectors such as controlled environment agriculture, open-field agriculture, and livestock applications [167,168]. In recent years, the use of IoT has gained significant interest in the food industry for product tracking, traceability and the monitoring of environmental conditions (e.g., temperature, humidity), weight loss, and the overall quality loss in the postharvest supply chain [87,161,165,169–171]. This technology has also received significant attention in developing intelligent packaging in the food sector [172–174].

Intelligent packaging involves the use of sensors (biosensor, printed, chemical, and gas sensor) and indicators (time-temperature indicators, freshness indicators, gas indicators, and integrity indicators) to detect biological, chemical, or gaseous changes from packaged fresh produce [24,25,27,91,170,174–176]. The sensor-based RFID tags as an example can detect hygrothermal and chemical changes

(e.g., temperature, CO2, light exposure, pH, etc.) of the fresh produce in the post-harvest supply chain [25,87,175]. The timely information obtained within the package system can be used to inform stakeholders in the supply chain of an event that may damage the packaging material or the fresh produce itself.

Generally, the application of IoT in different cold chain processes results in a large amount of real-time data which can pave the way for new computational approaches such as artificial intelligence and big data analytics [161,177]. This data will help various stakeholders in the supply chain control and optimize the cold chain technology to reduce quality loss and also make informed decisions regarding food safety. However, the application of IoT in controlling cold chain technologies in order to reduce food losses in the supply chain of fruits and vegetables is still inadequate.

Table 3 shows that several studies applied IoT in tracking and tracing temperature changes and food quality during the shipment of fruits and vegetables in the past decade [20,159,165,178,179]. Two studies applied IoT on the packaging of fruits and vegetables, as well as during cold storage [159,178]. The application of IoT in the shipment of fruits and vegetables is accompanied by multi-sensors such as temperature and humidity sensors, light exposure sensors, and global positioning system (GPS) sensors (Figure 5) during shipment of fruits and vegetables (Table 3). These sensors are installed in the food containers to monitor the changes in the environmental cold chain conditions such as air temperature, airspeed, light exposure, and relative humidity using a sensor data fusion (soft sensors). They are connected to a wireless network and computers to communicate with control stations, producers, or other stakeholders in the supply chain. The collected data can then serve as input data in analyzing the changes in the food attributes (e.g., weight loss, shelf life, nutritional, or sensory qualities), using a mechanistic physics-based model or a digital twin. It is worth mentioning that multi-sensors (e.g., chemical sensors, biosensors, etc.), imaging systems (see Section 3.1), E-nose (see Section 3.1), spectroscopy (see Section 3.1), and AIR (see Section 3.1) can also be used to directly measure changes in some quality attributes of fresh produce in the postharvest supply chain. IoT has become a very important tool in monitoring and controlling the process conditions of food, allowing the controllers to implement proper decisions. All of these can help to significantly reduce food losses. More so, the reduced cost of software and hardware wireless devices [180], digital sensors, accompanied by IoT technology in food transportation, packaging, and/or storage already increases the potential of IoT as a veritable and sustainable tool for reducing food losses.

**Figure 5.** The implementation of Internet of Things (IoT) during shipment of fresh fruits and vegetables.

#### *4.2. Digital Twin as an Advanced Tool in Reducing Food Losses in the Postharvest Supply Chain of Fruits and Vegetables*

Digital twins have recently gained significant interest in postharvest engineering, as a way of expanding mathematical models and computer simulations by linking input data to the solutions implemented after the simulation study [181]. Simply, a digital twin of a product can be defined as a virtual model of the product's real-life representation containing all realistic characteristics. The virtual model contains all essential elements, including geometrical components and material properties, and accurately and realistically simulates all relevant physics and their kinetics throughout the product's life-cycle. Digital twins can be mechanistic (physics-based), statistical (empirical-based), and intelligent (e.g., machine learning, deep learning) in nature. However, only the physics-based mechanistic digital twins can adequately evaluate the processes that cause quality loss in fresh produce. This involves linking measured sensor data of the environmental conditions (e.g., the air temperature around the fruit), as input data to the currently still uncharted product's quality evolution of fresh produce (Figure 6), preferable in a real-time update, using a physics-based model. In this way, the digital replica reacts hygrothermally and biologically in the same way as its physical counterpart (a real fresh fruit or vegetable).

**Figure 6.** A schematic of a simple digital twin for fruit during the postharvest supply chain.

By enriching current real-time monitoring capabilities using sensors, digital twins can be used to diagnose and predict potential problems in the supply chain that will increase food losses. These problems can be caused by physiological (e.g., chilling injury), hygrothermal (e.g., mass loss), biotic (e.g., phytosanitary pests, pathogens), and mechanical (e.g., puncture injury, bruising) effects. This unique attribute shows that the digital twin has the potential of incorporating several physics-based thermal, physiological, mechanical, biological, and decay models for corresponding quality and shelf-life metrics. This insight can then help remotely analyze the quality performance of the fresh produce in each shipment and also predict the remaining shelf life days. Based on the analysis, a proactive preventive measure can be taken early to reduce quality losses throughout the cold chain. Such measures can also help predict and optimize future product quality and process design.

As a next step, digital twins can be implemented in real-time with actual multiple shipments. This is expedited with the integration of the already available big data technologies (e.g., IoT devices, blockchain devices, soft sensors, cloud systems, etc.) [182,183]. However, such a system is not yet in place, to the best of our knowledge (Table 3). From Table 3, only two studies developed a digital twin for mango. The mechanistic models developed for these studies included HMT models, as well as KMR for various quality attributes such as firmness, soluble solids content, and vitamin content [184] [185]. The air temperature data of the actual mango cold chain, collected as input from a temperature sensor was linked to these models to create a digital twin of a virtual mango fruit [184,185]. With the digital twins, the fruit quality evolution was quantified for multiple overseas shipments. However, the twin did not use other significant input data history such as the humidity of the products at the different supply chains. The temperature data collected was not in real-time, but offline, so a-posteriori. In addition, the digital twin did not integrate models to estimate the mass loss, chilling injury, and other biochemical models which are important for quantifying food losses in the entire cold chain (Table 2).

**Table 3.** Summary of recent literature on the application of IoT and digital twins in reducing food quality losses in the postharvest supply chain of fruits and vegetables in the last 10 years.


#### **5. Future Opportunities to Reduce Food Losses in the Postharvest Supply Chain of Fruits and Vegetables**

With the gradual depletion of resources, there is a need to look at sustainable ways of achieving food security by reducing food losses in the postharvest supply chain. Looking ahead, the major challenges that cause food losses in the postharvest supply chain have to be addressed.

One emerging field is the development of intelligent packaging systems to reduce food losses, especially fresh agricultural produce. Intelligent packaging systems through the use of internal and external monitors (sensors, nanosensors, and indicators) provide valuable information on the interaction of food with the packaging material and the environment at different phases of processing, transportation, and storage. It also takes into consideration the ergonomic features of the packaging to reduce inconvenience in the transportation, storage, use, and eventual disposal of the packaging material [174,186]. With the recent interest and development in intelligent packaging, there is a need to integrate the sensors, indicators, and data carriers technologically to provide real-time information about fresh food in different cold chain logistics through the use of IoT based technologies and digital twin.

Although the potential of a digital twin in minimizing quality losses and increasing the shelf-life of fresh produce has been demonstrated (Table 3), the holistic implementation of digital twins in the entire value chain (from planting-fork) and for a wide range of fresh produce is yet to be demonstrated. The existing digital twins (Table 3) should be improved upon to include other relevant models that simulate thermal, physiological, mechanical, and biological damages that cause food losses in the postharvest supply chain. For example, a mass loss model can be included to quantify the salable weight at the end of the chain. This can help quantify the market value of fresh produce due to the subjective acceptable consumer product appearance. Additionally, tropical fruits such as banana or papaya experience chilling injury due to low-temperature storage and long cold chain process (Table 1). Therefore, thermal damage models predicting chilling injury during cold chain processes should be included. The potential of linking pathogens with decay severity should also be a future focus. Future digital twins should also capture the biological variability of fresh produce in order to give more realistic actionable metrics as multiple fresh produce have different individual pre-harvest and postharvest history. This can be achieved by integrating stochastic simulations (e.g., Monte Carlo simulations) with the existing digital twin physics-based models.

An additional future focus is to integrate IoT systems (including soft sensors) in real-time with digital twins. This real-time coupling will enable stakeholders to monitor and control each supply chain shipment at all times and take dynamically corrective measures to reduce quality loss and increase the remaining shelf-life days. Furthermore, by adding more "intelligence" to the coupled IoT and digital twins system, the cold chain technology (e.g., a refrigerated container) can independently optimize its process parameters to increase the shelf life of fresh produce and reduce food losses of the entire shipment. This added value can be easily quantified especially in this current time, considering the COVID-19 situation. Due to COVID-19, food producers have seen a decrease in the timely distribution of fresh produce to supply chain retailers. This is attributed to the decrease in transport labor, and longer shipment time because of shipment re-routing. This development has led to increased food losses. As a consequence, for example, about 5 billion US dollar worth of fresh fruits and vegetables were lost in the USA alone during the COVID-19 peak period from March 2020 to June 2020 [187]. With intelligent coupled IoT and digital twins, different processes and cold chain technology can be optimized to reduce the dependency on human labor, faster shipment duration, and possible damages caused by physiological, hygrothermal, mechanical, and biotic factors. A reduction in damages on the fresh produce implies a reduction in food losses.

#### **6. Conclusions**

Fruits and vegetables are important sources of nutrients such as vitamins, minerals, and bioactive compounds, which provide many health benefits. However, due to poor postharvest management processes, large quantities of fruits and vegetables perish before they reach the consumer. Of all the techniques for extending the shelf life of perishable produce, storage at low temperatures is by far the most effective.

This study, therefore, provides in-depth insight on the application of advanced technology in improving food security, by reducing food losses during postharvest cold chain processes for fruits and vegetables. It has been found that:


The augmented insight on the application of emerging technologies can serve as a roadmap for future cold chain studies on fresh agricultural produce.

**Author Contributions:** Conceptualization, D.I.O. and G.C.; methodology, D.I.O.; investigation, D.I.O., N.E.-e., A.K., A.Y.K., B.S.; data curation, D.I.O., N.E.-e., A.K., A.Y.K.; writing—original draft preparation, D.I.O.; writing—review and editing, D.I.O., N.E.-e., A.K., A.Y.K., G.C., B.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Nomenclature**



#### **References**


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## *Article* **The Effectiveness of Different Household Storage Strategies and Plant-Based Preservatives for Dehulled and Sun-Dried Breadfruit Seeds**

**Macmanus C. Ndukwu 1, Daniel I. Onwude 2,\*, James Ehiem 1, Ugochukwu C. Abada 3, Inemesit E. Ekop <sup>4</sup> and Guangnan Chen <sup>5</sup>**


**Abstract:** In a tropical rainforest environment, different storage strategies are often adopted in the preservation of primary processed food crops, such as maize, sorghum, etc., after drying and dehulling to increase shelf-life. For breadfruit seeds (*Treculia Africana*), the current challenge is identifying the most appropriate short-term storage and packaging methods that can retain the quality of stored products and extend shelf-life. In this regard, we compared the performance of a plastic container, a weaved silo bag and a locally developed silo bin for the short-term storage of parboiled, dehulled and dried breadfruit seeds treated with locally sourced and affordable alligator pepper (*Zingiberaceae aframomum melegueta*) and bitter kola (*garcinia*) powder as preservatives. We show that the concentration of CO2 was lower in the silo bin treated with 150 g alligator pepper and higher in the silo bag-treated with 100 g bitter kola nut. A higher CO2 concentration resulted in limited oxygen availability, higher water vapor, and a higher heat release rate. Non-treated bag storage had the highest average mold count of 1.093 <sup>×</sup> 103 CFU/mL, while silo bin-stored breadfruit treated with 150 g of alligator pepper had the lowest mold count of 2.6 <sup>×</sup> 102 CFU/mL. The storage time and botanical treatments influenced both the crude protein and crude fiber content. Average insect infestations were low (0–4.5) in the silo bin with breadfruits treated with alligator pepper powder, as the seeds seemed to continue to desorb moisture in storage, unlike in other treatments. The obtained results revealed the high potential of alligator pepper (*Zingiberaceae aframomum melegueta*) as a botanical insecticide in preventing insect infestation and mold growth in stored breadfruit instead of using synthetic insecticide. An aluminum silo bin with alligator pepper powder is recommended to store dried and dehulled breadfruit seeds as a baseline for other tropical crops.

**Keywords:** postharvest storage; food packaging and shelf-life; bitter kola; food preservation; alligator pepper; underutilized seeds

#### **1. Introduction**

Breadfruit belongs to the family of *Moraceae* and is a multipurpose tropical agroforestry tree crop with about 120 varieties. Prominent among them in Nigeria is African breadfruit (*Treculia africana*) [1,2]. The seeds are rich in oil, carbohydrates, proteins, calcium, phosphorus, minerals, and vitamins [1]. African breadfruit seeds are prepared for

**Citation:** Ndukwu, M.C.; Onwude, D.I.; Ehiem, J.; Abada, U.C.; Ekop, I.E.; Chen, G. The Effectiveness of Different Household Storage Strategies and Plant-Based Preservatives for Dehulled and Sun-Dried Breadfruit Seeds. *Processes* **2021**, *9*, 380. https://doi.org/10.3390/ pr9020380

Academic Editor: Lina Cossignani Received: 14 December 2020 Accepted: 14 February 2021 Published: 19 February 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

consumption by cooking (either fresh or dried), roasting, or breadfruit flour. This highly nutritional but underutilized crop is native to the West Indies, Jamaica, as well as many West African countries, including Nigeria, Ghana and Sierra Leone [3]. In 2011, Nigeria accounted for about 32% of African breadfruits' global production [4]. Food prepared from breadfruit seeds is highly appreciated and sought out because it serves as a common source of calories among over 60 million people in the Central African Republic, southern Nigeria, and southern Cameroun [5]. The processing of this underutilized crop involves the fermentation or dehulling of the fresh fruit to extract the seed, washing, heat treatment, and threshing of the seed to reveal an edible greenish cotyledon [6]. This is then either cooked fresh or dried to a moisture content of about 8–10% wet basis before storage [6]. However, drying unhulled seeds makes dehulling very difficult even if heat treatment is applied. This is because of the adhesive layer that attaches the hull to the kernel [7].

Traditionally, in Nigeria, after drying African breadfruits, various households store dried breadfruits under room conditions in plastic containers, weaved silo bags, bottles, hanging above the cooking attic, spreading on the open floor or on a mat, or keeping them in basins [6,8]. However, storing the dried seeds for a long period is a challenge due to the hygroscopic nature of the seeds even after drying, which makes them prone to quick fungi and insect damage, discoloration, etc. [6,9]. Apart from managing the pest in enclosed storage, temperature increase, high humidity and the reaction of chemical and gaseous constituents from the product can cause considerable deterioration [9]. Investigations were carried out by Adindu and Williams [6] on the nutritional quality of non-dehulled dried breadfruit seed stored in tight plastic buckets, polythene bags, closed and open raffia baskets, and tied jute bags. However, in most homes, breadfruits are parboiled, dehulled and dried before storage, as in rice processing. Therefore, storing pretreated and processed African breadfruit seeds is still a challenge as there is a lack of adequate data for food processors.

Several studies on the different postharvest management practices on stored agroproduct exist in the scientific literature [10–12]. However, these studies are limited mainly to grains. Furthermore, many storage approaches have been adopted in the literature for the shelf-life extension of stored products by researchers, ranging from the use of botanical insecticides (oil from Vittallaria paradoxa seed, coconut, mustard seed), to Diatomaceous earth, hermetic bags, metal silos, and charcoal ash [12–19]. However, insight gained from these studies revealed that most of these storage approaches are crop-specific with a particular focus on cereals, including maize, cowpea, wheat, etc. [11–21]. To the best of our knowledge, information on the most suitable storage strategy for primary processed and raw African breadfruit seeds under household conditions is non-existent. Again, the respiratory activities in the interstitial space of the storage ecosystem of biological products influence product storage conditions. Temperature changes in the interstitial space have been adduced to respiratory activities within the storage ecosystem, which is a function of the external conditions. With this knowledge of the interstitial temperature, the gaseous exchange within the space can be accounted for. However, this measurement of the interstitial temperature requires constant measurement and probing of the storage system, which distorts the tightness of the storage system. Therefore, employing a mathematical model that will account for interstitial temperature and all the desired components of gaseous exchange involved in the respiration activities using the external conditions will solve this problem. In this way, the rigorous and cumbersome experimental measurement process is eliminated [22–24]. In addition, using this modeling approach will enhance future optimization and process design [25].

Locally, in Nigeria, dried alligator pepper (*Zingiberaceae aframomum melegueta*) powder has been integrated into the storage bag to drive away insects and pests in maize stored in weaved bags under the market environment [12]. In addition to alligator pepper, bitter kola (*garcinia*) has also been used locally for storage because its extracts have shown antimicrobial, antifungal and anti-pest properties, along with their pungent, peppery and bitter taste [26–31]. Therefore, as food processors reduce the dependency on synthetic insecticides

due to the attendant health challenges, evaluating the efficacy of these affordable edible plant seeds as alternatives become indispensable.

This research aims to identify the most effective storage strategies and treatments for an on-the-shelf postharvest preservation approach for breadfruits seeds, as adopted in Nigeria. A plastic container, a weaved bag and a locally developed silo bin were adopted for the short-term storage of parboiled, dehulled, and sun-dried breadfruit seeds treated with alligator pepper (*Zingiberaceae aframomum melegueta*) and bitter kola (*garcinia*) powder as preservatives. The effectiveness of the storage strategies was evaluated in terms of shelflife duration, nutritional product quality, mold count and insect enumeration. The result of this study will provide valuable information to improve the all-year-round availability and affordability of breadfruits seeds in major breadfruit-producing countries.

#### **2. Materials and Methods**

#### *2.1. Description of Storage Strategies*

The experiment was carried out for 3 months (14 weeks) (September–December 2019) at the crop processing and storage laboratory of Michael Okpara University of Agriculture Umudike, Nigeria, 5.53◦ N, 7.49◦ E. The local storage strategies adopted include a weaved silo bag, a plastic drum and a locally developed aluminum silo bin. Figure 1 shows the schematic view of the aluminum silo and its components. It consists of a storage container equipped with a wooden stirrer force-fitted into the silo through a sealed bearing. Loading and unloading points were created at the top and bottom of the silo and plastic container. The plastic buckets and aluminum silos were airtight with a side opening for material sampling. The loading and unloading opening of the silo was covered with a metal cover sheet, while that of the plastic drum was sealed with masking tape.

**Figure 1.** Schematic view of the locally developed aluminum storage silo.

#### *2.2. Sourcing, Heat Treatments and Drying of Breadfruit Seeds*

The breadfruit used for this experiment was obtained directly from the farmers in Umudike, southeastern Nigeria. All the seeds were sourced from the same lot as recommended in the International Rules of Seed Testing [32] and there was no requirement for additional homogenization. The seeds were harvested off-season and were not treated with any pesticide. The farmers allow matured fruit to naturally fall from the tree in the

wild, after which it is allowed to ferment from two to three weeks. Fermented fruits were washed with clean water to remove the pulp from the seeds. The seeds were manually selected to remove any bad seed or dirt, after which they were spread on a stainless tray and kept under the sun for three hours. The seeds were parboiled in hot water for 5 min with clean water, after which the water was filtered out. The parboiled seeds were spread under the sun and allowed to cool before manual threshing using a local wooden thresher to reveal the cotyledon. Parboiling the seed unstuck the chaff from the cotyledon for easy separation, and it also kills any larvae of pests from the farm or the fermentation process. Parboiling of the seed for a short time of less than 10 min causes no significant difference in the seed's nutritional composition [33]. The threshed mixture of cotyledon and chaff was separated manually by winnowing using a stainless tray. After winnowing, broken cotyledon was manually removed while the rest was dried under the sun until the moisture content reached about 8 ± 2.2%. Initial moisture content was checked with a grain moisture meter (Kongskilde, series 20849) initially calibrated with a laboratory oven (DHG-9053A Ocean med+ England).

#### *2.3. Preparation of Botanical Treatments*

Dried pulverized alligator pepper seed (*Zingiberaceae aframomum melegueta*) at 8% moisture content and dried pulverized bitter kola seed (*Garcinia*) at 10% moisture content were introduced as botanical preservatives. These two plant materials were harvested from the trees in southeastern Nigeria, and care was taken to select seeds of premium quality. Before milling, the two seeds were dried in the sun for 7 days and further in the oven at 40 ◦C. They were milled separately using a hammer mill (Animal ration shredder hammer mill foliage, Model: TRF 400; 1.5 kW; 10 swinging hammers). The milled seeds were sieved with 90 μm sieve openings and kept in an airtight vessel before application.

#### *2.4. Experimental Test*

Each of the storage strategies received two botanical treatments (pest management) consisting of mixing 2 kg of dried breadfruit with 100 and 150 g of dried pulverized alligator pepper (*Zingiberaceae aframomum melegueta*), and 100 and 150 g dried pulverized bitter kola (*Garcinia*), respectively. In addition, a similar experiment without treatment was set up to serve as a control. Therefore, a total of 15 experiments were set up. The treatments were categorized as follows: breadfruits stored in the aluminum silo and treated with various mass of pulverized alligator pepper, labeled as SLAP100 and SLAP150; breadfruits stored in the aluminum silo and treated with pulverized bitter kola were labeled as SLBK100 and SLBK150, while SL0 was the control. In addition, breadfruits stored in the plastic drum and treated with pulverized alligator pepper were labeled as RBAP100 and RBAP150, and breadfruits stored in the plastic drum and treated with pulverized bitter kola were labeled as RBBK100 and RBBK150. At the same time, RB0 serves as the control. In addition, breadfruit stored in the woven bag and treated with pulverized alligator pepper were labeled as BGAP100 and BGAP150, and breadfruit stored in the woven bag and treated with pulverized bitter kola were labeled as BGBK100 and BGBK150, while BG0 served as the control. All containers received 2 kg of heat-treated, dehulled and dried breadfruit seed with the botanical treatment applied once at the beginning of storage. Figure 2 shows the storage strategies.

**Figure 2.** The different storage strategies for household preservation of breadfruit seeds.

#### *2.5. Weight Loss and Moisture Content*

The moisture content (MC) of the seeds was determined using a grain moisture meter (Kongskilde, series 20849) previously calibrated with a laboratory oven for accuracy. MC was respectively measured at 0, 6, and 12 weeks of storage. Weight loss in storage was determined by direct weighing (Camry weighing balance, ACS, 56; China) of the entire sample at the beginning of storage and after 4, 8, and 12 weeks of sampling.

#### *2.6. Enumeration of Insects*

The number of adult insects present in the stored product was counted at 6 weeks and 12 weeks of storage by sifting the 100 g sample through a sieve pan. The insects were retrieved and counted manually.

#### *2.7. Total Mold Count*

Sabouraud dextrose agar and Yeast extract agar (BDH, London, UK) for fungi and yeast count were used as the media for isolating the microorganisms. The media were prepared according to the manufacturer's instructions by sterilization at a temperature of 121 ◦C for 15 min in an autoclave. According to the dilution-plating method described by Cowan and Steel [34], the total mold count was determined. Tenfold serial dilution of the sample was prepared with distilled water, and 9 mL measures of diluent were placed into 9 sterile test-tubes. About 1 g of each sample was uniformly mixed and 1.0 mL was transferred into the first tube of diluent. This was done for the remaining dilutions using a fresh pipette for each to obtain different concentrations. Using the third and fifth (10−<sup>3</sup> and 10−<sup>5</sup> concentration) dilutions, 0.1 mL amounts of each dilution were pipetted into each of three Petri dishes. The Sabouraud dextrose agar and Yeast extract agar were added using the spread plate method. The plates were incubated at 37 ◦C for 24 h aerobically. Counts were taken of the colonies in the three plates that were inoculated with the dilutions of 50 and 500 colonies per plate. The discrete colonies were purified by repeated subculturing onto Sabouraud dextrose agar and Yeast extract agar until pure cultures were obtained. Pure mold isolates were streaked onto new culture plates and incubated for 48 h. The pure isolates were stored on Sabouraud dextrose agar. The average number per plate was multiplied by the dilution factor to obtain the viable count per gram of the sample [34]. The cut-off point for contamination was taken as >102 CFU/mL [35]. The samples were subjected to microbiological analysis to monitor the dynamic changes in the fungal populations. Mold isolates were identified and characterized by microscopic

morphological observations. The morphological characteristics of mold include the shape of colonies, colonial outline, colonial evaluation, color, consistency and size.

#### *2.8. Product Quality*

Fresh and stored dried breadfruit seeds were analyzed for ash, carbohydrate, fat, protein, and crude fiber content. Prior to nutritional analysis, the seeds were washed with clean water to remove any traces of the botanicals used for the treatment. The ash content was deduced by the furnace incineration gravimetric method, while the fat content was inferred via the continuous solvent extraction gravimetric method using Soxhlet apparatus as described in the Association of Official Analytical Chemists (AOAC 2007) manual [36]. The crude fiber content was deduced using the Wende method described by James [37], while the crude protein was determined by the Kjedahl method [38]. The carbohydrate content was calculated via the nitrogen-free extraction method as described by James [37].

#### *2.9. Determination of Minerals*

The dried breadfruit's mineral content was determined using the dry ash acid extraction method, as described by Carpenter and Hendricks [39]. A 5g measure of each breadfruit sample was burned to ashes in a muffle furnace at 500 ◦C. The ash produced was dissolved in 10 mL of 2M HCl solution and diluted to 100 mL with distilled water in a volumetric flask and filtered. The filtrate was used for mineral analysis. The mineral analysis was carried out according to Ref. [40] by employing atomic absorption spectroscopy for magnesium (Mg), calcium (Ca), phosphorus (P), iron (Fe) and zinc (Zn), and flame emission photometry for sodium (Na) and potassium (K).

#### *2.10. Data Analysis*

All the accumulated data were subjected to two-way ANOVA to test the variation in the mean between different storage approaches, periods and treatments. Turkey's HSD (*p* < 0.05) test was used to detect the mean differences between the examined traits.

#### **3. Mathematical Modeling**

#### *3.1. Modeling of Interstitial Gaseous Exchange*

According to Abelon et al. [41], the concentration of gas in a storage system is dependent on the degree of consumption or entrance of oxygen, which leads to the total combustion of carbohydrates, so the production or loss of carbon dioxide, water and heat within the storage system is as follows:

$$\rm C\_6H\_{12}O\_6 + 6O\_2 \to 6CO\_2 + 6H\_2O + 2835k/mol \tag{1}$$

$$180\text{g} + 192\text{g} \to 264\text{g} + 108\text{g} + 2835\text{kJ/mol} \tag{2}$$

The gas exchange between the system and the environment is a function of the partial pressure difference and the permeability of the systems. Assuming uniformly distributed temperature and moisture, and ignoring respiration from insects and carbon dioxide sorption [42], the rate of heat produced during respiration, the water vapor produced and the oxygen consumed were calculated from Equations (1) and (2). These equations were used to deduce Equations (3)–(5) considering the molecular weight of carbohydrate, oxygen, carbon dioxide and water, and the heat produced according to Gaston et al. [42], as follows:

$$\mathbf{Y}\_{\rm res} = \mathbf{q}\_{\rm h} \mathbf{Y}\_{\rm CO\_2} \tag{3}$$

$$\mathbf{Y\_{H\_2O}} = \mathbf{q\_w}\mathbf{Y\_{CO\_2}}\tag{4}$$

$$\mathbf{Y}\_{\text{O}\_2} = \mathbf{q}\_{\text{o}} \mathbf{Y}\_{\text{CO}\_2} \tag{5}$$

where qh, qw, and qo are the parameters deduced from Equation (1), while YCO2 is the CO2 release rate in mg (CO2) kg−<sup>1</sup> (dry matter) in 24 h. The YCO2 was given from a generic equation presented in Ref. [43] for the oxidation of hexose, as follows:

$$\log \Upsilon\_{\rm CO\_2=} \mathbf{q}\_1 + \mathbf{q}\_2 \mathbf{T}\_{\rm C} - \mathbf{q}\_3 \theta + \mathbf{q}\_4 \theta^2 + \mathbf{q}\_5 \mathbf{M} \tag{6}$$

where q1, q2, q3, q4 and q5 are the regression constants presented in Table 1. Equation (6) can be adapted to suit any stored product by adding a non-unite parameter ζ, as follows:

$$\log \Upsilon\_{\rm CO\_2=e} \zeta \left( \mathbf{q}\_1 + \mathbf{q}\_2 \mathbf{T}\_{\rm C} - \mathbf{q}\_3 \theta + \mathbf{q}\_4 \theta^2 + \mathbf{q}\_5 \mathbf{M} \right) \tag{7}$$

The total amount of CO2 produced or oxygen consumed over the storage period (% *v*/*v*) was calculated by the integration of Equation (8) overtime in days, and the result is presented in Equation (9), according to Gaston et al. [42] and Abelon et al. [41].

$$\mathbf{m}\_{\rm CO\_2}(\mathbf{t}) = \int\_0^\mathbf{t} \nabla\_{\rm CO\_{2(\\_t')d\\_t'}} \tag{8}$$

$$\text{V}\begin{array}{l}\text{X}\\\text{CO}\_{2}-\frac{\text{Y}\_{\text{CO}\_{2}}(t)}{\text{TOTON}\_{\text{CO}\_{2}}}\rho\_{\text{b}}\times\frac{\text{RT}}{\text{s}}\,\frac{(t)}{\text{P}\_{\text{at}}}\end{array} \tag{9}$$

where MCO2 is molecular weight (44 g/mol), T is the intergranular temperature (K), Pat is the atmospheric pressure (1 atm = 101 325 Pa), R is the gas constant (8.314J/mol K), *ε* is the porosity and ρ<sup>b</sup> is the bulk density of the stored product (kg/m3), given as follows:

$$
\rho\_\mathbf{b} = (1 - \varepsilon)\rho\_\mathbf{sp} + \varepsilon\rho\_\mathbf{a} \tag{10}
$$

where sp is the density of the parboiled breadfruit, *ε* is the porosity of parboiled breadfruit, and <sup>a</sup> is the density of intergranular air (kg/m3) adapted from the density of a heated space given by Simo-Tagne et al. [44], as follows:

$$
\mathfrak{p}\_{\mathbf{a}} = \frac{\mathbf{b}}{T} \tag{11}
$$

where T is the temperature (K) and b is given as 353 (kg K/m3).

**Table 1.** Input parameters for the determination of gaseous exchange.


#### *3.2. Intergranular Temperature and Relative Humidity*

The building (ambient) temperature and relative humidity were measured with a temperature and humidity clock (DTH-82; TLX, Guandong China). The intergranular dry bulb temperature was determined three times daily with a temperature probe (Extech, Taiwan China) inserted through the small hole made at the top of the silo and plastic container. In contrast, for the bag storage, it was inserted carefully through the body. After each measurement, the hole was sealed correctly. However, the interstitial relative humidity was determined from the vapor pressure equations, according to Abe and Basunia [46] for a stored product, as follows:

$$\text{RH} = \frac{\text{h}\_{\text{VP}}}{\text{h}\_{\text{ds}}} \tag{12}$$

where hvp and hds are the vapor pressure and saturated vapor pressure at the dry bulb temperatures given in Equations (13) and (15), respectively, by Abe and Basunia [46] as follows:

$$\mathbf{h\_{vp}} = \mathbf{h\_{ws}} - 0.5(\mathbf{T\_{db}} - \mathbf{T\_{wb}}) \times \left(\frac{760}{755}\right) \tag{13}$$

where Tdb and Twb are the intergranular dry and wet bulb temperature, and hws is the saturated vapor pressure at the wet bulb temperature, given as:

$$\mathbf{h\_{ws}} = 4.58 \times 10^{(7.5 \text{T}\_{wb})/(237 + \text{T}\_{wb})} \tag{14}$$

$$\mathbf{h\_{ds}} = 4.58 \times 10^{(7.5 \,\mathrm{T\_{db}})/(237 + \mathrm{T\_{db}})} \tag{15}$$

The wet bulb temperature (◦C) of the intergranular air was calculated from the empirical relationship presented by Fouda and Melikyan [47] for moist air, as follows,

$$\rm{T\_{wb}} = 2.65(1.97 + 4.3T\_{db} + 1000 \,\mathrm{d})^{1/2} - 14.85 \tag{16}$$

where d is the pressure parameter given in Equation (17), as follows,

$$\mathbf{d} = \frac{0.622 \mathbf{p\_{st}}}{\mathbf{p\_{atm}} - \mathbf{p\_{st}}} \tag{17}$$

where patm is the atmospheric pressure (Pa) and pst is the saturated vapor pressure at the surface of the storage containers, given as Equation (18) as follows,

$$\mathbf{p}\_{\rm st} = \exp\left(23.196 - \frac{3816.44}{\mathrm{T} - 46.13}\right) \tag{18}$$

where T is the temperature (K).

#### **4. Results and Discussions**

#### *4.1. Temperature, Relative Humidity and Moisture Distribution*

The initial breadfruit seeds moisture was 10.2% w.b. (wet basis), while the intergranular temperature of bulk seed was recorded as 28.3 ◦C at the beginning of storage. The intergranular temperature variations among the storage strategies and botanical treatments are shown in Figure 3A–F. The building temperature (ambient temperature) was marginally higher than the intergranular temperatures for the botanically treated seeds, but lower than the temperature of the untreated seeds stored by different methods. This showed the stability of seeds treated botanically. Monitoring the storage temperature in bin silos has been reported as an effective way of measuring stored products' storage conditions [41]. A sharp rise in core temperature is an indication of localized heating as a result of spoilage, as shown in non-treated bin silos (Figure 3A,B), while in the case of bags, it is as a result of gaseous exchange between the core and the environment, which is influenced by the climatic conditions [48]. The mean value for the building temperature was determined as 28.78 ◦C, while SL0, SLAP100, SLAP150, SLBK100, SLBK150, RB0, RBAP100, RBAP150, RBBK100, RBBK150, BG0, BGAP100, BGAP150, BGBK100 and BGBK150 were recorded as 29.49, 28.59, 28.66, 28.61, 28.69, 29.42, 28.64,28.73, 28.68, 28.62, 29.38, 28.68,28.72, 28.67 and 28.77 ◦C, respectively, with a mean building relative humidity of 73%. The observed temperature is within the temperature range of seeds stored in bins and silo bags, which is less than 30 ◦C [48]. It was also observed with the inclusion of non-treated samples that there was a mean temperature increase from 28.25 to 29.46 ◦C in the first week to a range of 28.62–29.63 ◦C in the fourth week. However, this average value decreased to a range of 28.59–29.49 ◦C in the 12th week. Nevertheless, for the storage strategies adopted, the maximum temperature difference between the building (storage room) and the intergranular temperature is less than 1.0 ◦C. This could be due to the small size of storage [46].

**Figure 3.** Intergranular temperature of the stored breadfruits for different storage methods and treatments. (**A**) Breadfruit stored in the silo and treated with aligator pepper. (**B**) Breadfruit stored in the silo and treated with bitter kola. (**C**) Breadfruit stored in the rubber and treated with aligator pepper. (**D**) Breadfruit stored in the rubber and treated with bitter kola. (**E**) Breadfruit stored in the bag and treated with bitter kola. (**F**) Breadfruit stored in the bag and treated with aligator pepper.

Stored food's shelf-life can be influenced by the initial product moisture, temperature, and weather variation during storage, which is modified by the building conditions [49–51]. Moisture uptick was recorded for all non-botanical treated stored breadfruits, as shown in Figure 4. The increase ranged from 0.09 to 5.04%, with the product stored in the silo recording the lowest moisture increase, while the silo bag recorded the highest value after 12 weeks of storage. This might result from the marginally increased temperature observed in non-botanical-treated storage compared to the ambient values (Figure 3). However, considering the moisture content of the botanically treated breadfruits, the moisture contents of those treated with alligator pepper decreased consistently. In contrast, those treated with bitter kola increased within the first six weeks but decreased later when checked at 12 weeks. The variation in the seed moisture content of the different storage methods and pest management strategies could be due to the possible condensation of air occasioned by the temperature drop in the intergranular spaces, or between the storage spaces [52]. Besides this, the vapor emitted by biomaterials due to biological processes might have contributed to the uptick in seed moisture [53,54]. On the other hand, seed moisture with alligator pepper tends to drop due to the lower biological activities. Therefore, it can be stated that the alligator pepper can serve as a drying agent in stored

products. A significant (*p* < 0.05) moisture decrease was observed between those treated with alligator pepper when the amount of alligator pepper increased from 100 to 150 g, while it was not significant (*p* < 0.05) when compared, under the same storage method, with those treated with bitter kola. However, a significant difference (*p* < 0.05) exists between the moisture levels measured for those treated with the same amounts of alligator pepper and bitter kola. Again, for different silo storage strategies, there were significant differences (*p* > 0.05) among the same amounts of treated material.

**Figure 4.** Variation in moisture content for different storage strategies within selected weeks.

The ambient relative humidity is shown in Figure 5. The weather pattern highly influences relative humidity in the storage room. The room relative humidity will increase during the ambient peak humidity, while the temperature drops, and vice versa. The ambient relative humidity ranged from 71 to 76%, with an average value of 73%. This is relatively high, which is common in most tropical rainforest zones, making it difficult to achieve stability in natural storage because of the influence of relative humidity on microbial developments. The value of the ambient relative humidity will also influence the interstitial humidity due to environmental interactions between the storage space and the external environment. In addition, the internal heat of the storage product can also affect the relative humidity of the intergranular space.

The interstitial relative humidity was predicted using Equations (12)–(18), and is presented in Figure 6A–F for different storage strategies. The simulated relative humidity and wet bulb temperature were deduced from the measured interstitial temperature, and therefore varied as the interstitial temperature varied. In addition, from Figure 4, we can see that the products desorb or adsorb moisture from the interacting environment due to the storage strategies and management approach adopted, which might have affected the intergranular temperature vis-a-vis the interstitial relative humidity profile. For all the storage strategies, the interstitial relative humidity ranged from 29.86 to 34.25%. The lower relative humidity obtained is a result of the dried nature of the product. However, due to the high ambient relative humidity and temperature, it is possible to have water accumulate on the walls of the silo bin and the plastic containers. The introduction of alligator pepper, in particular, seemed to function as a drying agent for the product.

**Figure 5.** Ambient relative humidity during the storage of breadfruit seeds.

**Figure 6.** *Cont*.

**Figure 6.** Relative humidity of different storage methods and treatments. (**a**) Breadfruit stored in the silo and treated with aligator pepper. (**b**) Breadfruit stored in the silo and treated with bitter kola. (**c**) Breadfruit stored in the rubber and treated with aligator pepper. (**d**) Breadfruit stored in the rubber and treated with bitter kola. (**e**) Breadfruit stored in the bag and treated with aligator pepper. (**f**) Breadfruit stored in the bag and treated with bitter kola.

#### *4.2. Insect Enumeration*

At the beginning of the storage period, no insect infestations were observed, but after four weeks, *Tribolium casteneum* (Figure 7) was observed in all storage strategies except for the untreated and alligator pepper-treated breadfruits stored in silo, as shown in Table 2. These values significantly (*p* < 0.05) increased by 12 weeks of storage, with infestations now noticed in all storage strategies. However, during the counting of the insects, it was observed that not all the insects were alive, even though the ratio of dead and living insects was not separated; the total insects in the infestations were enumerated together. Quantifying the ratio of dead and living insects for future insect mortality prediction in the treated samples could be a future gap to be filled.

Nonetheless, silo storage treatment with alligator pepper demonstrated lower total insect infestation, followed by rubber containers, while infestations were very high in bag storage. It has been reported that metal silos can eliminate insects from the stored product due to a shortage of oxygen [19]. However, since the silo is not fully filled and there is the possibility of space between the bearing and the stirring shaft, air could have been introduced into the silo. This is because as little as 15% oxygen is enough for insects to thrive [55]. Therefore, from this study, we deduce that none of the storage strategies could stop the proliferation of insects beyond four weeks.

**Figure 7.** *Tribolium casteneum* on the stored breadfruit seeds after four weeks.


**Table 2.** Average live and dead insects (*Tribolium casteneum*) for different storage strategies for selected weeks.

#### *4.3. Microbial Analysis*

The mold counts after 4, 8 and 12 weeks of storage are presented in Table 3. The mold counts increased as the weeks went by. While the increase in carbon dioxide or the lower oxygen levels slowed the insect infestation on a modified atmosphere-stored product, this was not efficient in stopping mold proliferation. However, the mold's ability to attack the product tissues can be delayed [48,56]. From the results presented in Table 3, we see that treating the product with 100 g of alligator pepper and bitter kola increased the mold count. However, when the treatments were increased to 150 g, the fungi count decreased under the same storage condition. However, non-treated bag storage had the highest average mold count of 1.093 × 103 CFU/mL, while silo-stored breadfruit treated with 150 g alligator pepper had the lowet mold count of 0.26 × 103 CFU/mL. The lower mold count observed in alligator pepper-treated samples can be associated with its lower moisture content compared to others, as shown in Figure 4. When products treated with 100 g botanicals were compared to non-treated products for silo and plastic container storage, we saw that non-treated products had lower mold count. This implies that the botanicals, being biological material, are subject to decay over time, and might have contributed to the increased mold count. Three mold species were predominantly isolated from the mold analysis for each storage strategy, i.e., *Aspergillus niger*, *Aspergillus sp* and *Rhodotorula sp.* However, *Aspergillus sp,* was predominantly isolated for most of the storage methods and treatments. The predominance of *Rhodotorula sp* in the plastic container (RBAP100), unlike other storage methods, might be due to its opportunistic infection nature and affinity to plastic, as reported in the literature [57]. However, none of the storage strategies could stop mold growth after four weeks of storage completely.

**Table 3.** Average fungi counts for different storage strategies and dominant mold species detected.



**Table 3.** *Cont.*

#### *4.4. Proximate Analysis and Mineral Composition*

Proximate composition assessment was carried out at the beginning, and after 6 weeks and 12 weeks of storage, and the concentrations of crude protein, fat, ash, and carbohydrates are presented in Table 4. The maximum decrease in crude protein concentration from the initial value was about 30.22% in the silo storage-treated samples with and without alligator pepper, and the reduction was about 75.27% for those treated with bitter kola within the first six weeks of storage. A similar magnitude of depreciation in crude protein was observed for other treatments with the same storage methods. Storage time influenced the nutritional composition. The chemical process occurs during the storage of crops due to either respiration or chemical decomposition, reducing the nutritional values [58]. Among storage methods with the same types of botanical treatments, crude protein values were not significant (*p* < 0.05), but they were significant (*p* > 0.05) when compared among the different botanical treatments of the same storage method (Table 4). The crude fiber increased for all storage strategies, while crude fat decreased. The loss in crude fat for botanical treatment with and without alligator pepper was steeper, reaching a 61.42 to 65.64% loss in crude fat for all storage strategies. Samples treated with bitter kola lost between 25.4 and 34.84%, which shows that the bioconversion effect of bitter kola on crude fat is high, and might have contributed to the smaller reduction observed than in the non-treated samples. The ash content increased for those treated with or without alligator pepper, but decreased for those treated with bitter kola. However, the carbohydrate and crude fiber contents did not significantly (*p* > 0.05) change for all treatments. The mineral compositions of sodium (Na), magnesium (Mg), calcium (Ca), phosphorus (P), potassium (K), iron (Fe) and zinc (Zn) of the stored products remained inferior to the initial values, except for phosphorous, which increased as shown in Table 5. The values of P, K, and Fe were higher in the alligator pepper-treated products stored in the silo than in other storage methods. This might be due to more negligible microbial and fungi attacks on products treated with alligator pepper. Alligator pepper contains alkaloids, tannins, saponin, steroids, cardiacglycoside, flavonoid and terpenoids, among other antimicrobial and antifungal agents [27]. Therefore, due to the antimicrobial and antifungal potency, alligator pepper-treated products stored in the silo will contain higher mineral contents than products stored using other methods in this study. Similar observations have been made by Ubani et al. [59] in the storage of oilseeds. An adequately stored oil seed with 6–8% moisture content always shows chemical and mineral stability [59].



**Table 5.** Variation in the mineral compositions of the stored breadfruits using different strategies.



**Table 5.** *Cont.*

#### *4.5. Analysis of Gaseous Exchange*

The gaseous concentrations in the stored product showed changes as the days progressed. The CO2 concentration at the beginning of storage was 0, but increased to 0.13–0.14 (% *v*/*v*) in the first week of storage, as shown in Figure 8A. The CO2 concentration ranged from 0.16 to 0.47 (% *v*/*v*) at the sixth weeks, and 0.57 to 3.98 (% *v*/*v*) in the twelfth week for all treatments in the silo bin storage. Other storage strategies ranged from 0.12–1.09 (% *v*/*v*) to 1.09–9.6 (% *v*/*v*) for plastic container, and 0.19–6.55 (% *v*/*v*) to 5.12–10.81 (% *v*/*v*) for silo bag, respectively. The concentration of CO2 was lower in the silo bin-treated samples with 150 g of alligator pepper, and higher in the silo bag-treated samples with 100 g bitter kola. A higher CO2 concentration results in limited oxygen availability as more oxygen is consumed, higher water vapor production, and a higher heat release rate, as shown in Figure 8B–D for blanched and dehulled breadfruits storage. The concentration of CO2 and O2 availability depends on the respiratory capacity of the stored seed, the infiltration of external O2 and the loss of CO2 to the storage building [41].

Storage strategies with higher moisture content showed higher CO2 concentration, higher O2 consumption with more water vapor, and higher energy release rate. As such, a greater tendency to spoilage from fungi infestation could have resulted in the higher fungi count observed in Table 2. This is in tandem with other similar studies that reported that higher moisture content increases the CO2 concentration of stored bio-products [48,60]. In the first week of storage, the moisture content was almost the same. The observed CO2 concentration showed no significant difference (*p* < 0.05), but a significant difference was observed as the storage time increased to 6 weeks and 12 weeks. Higher respiration occurs due to the increased moisture content and depletion of oxygen, as observed in Figure 8B,C. For the weeks analyzed, respiration reached its peak at 6 weeks and decreased in the 12th week, which was reflected in the high oxygen depletion peaking in the same week (6) before going down in week 12 for all storage strategies. Due to the lower moisture content of the silo bin with alligator pepper, they also had lower respiration heat and CO2 concentration values. At the same time, the bag storage method had a higher CO2 concentration due to the higher moisture content, which resulted probably from environmental interference.

**Figure 8.** Gaseous exchange of breadfruit seeds for different storage strategies. (**A**) CO2 concentration. (**B**) O2 consumption. (**C**) Water vaper production. (**D**) Heat release.

#### **5. Conclusions**

The present study evaluates the effectiveness of different local storage methods and botanical treatments to preserve blanched, dehulled, and dried breadfruit seeds under small-scale household conditions in a tropical rainforest environment with very high ambient humidity. Non-treated bag storage had the highest average mold count of 1.093 × <sup>10</sup><sup>2</sup> CFU/mL, while silo bin-stored breadfruit treated with 150 g of alligator pepper had the lowest mold count of 0.26 × 102 CFU/mL. *Aspergillus niger*, *Aspergillus sp* and *Rhodotorula sp* were the predominant mold species isolated; however, *Aspergillus sp* was dominant. The storage time influenced both the nutritional and mineral quality; regardless, products stored in a plastic container and weaved silo bags were inferior compared to those stored in the aluminum silo bin. Insect infestations were low in alligator pepper-treated seeds, as the seed continued to desorb moisture in storage, unlike other treatments. Silo bins had a comparatively lower *Tribolium casteneum* attack, and this did not happen until after 2 months. The concentration of CO2 was lower in the silo bin samples treated with 150 g alligator pepper, and higher in the silo bag-treated samples with 100 g bitter kola nut. A higher CO2 concentration resulted in limited oxygen availability, higher water vapor production, and a higher heat release rate. The analysis of the various results showed that adopting the mini silo bin storage with treated alligator pepper can help in the short-term

storage of blanched breadfruit, achieving better quality and an extended shelf-life, which most households aim to achieve.

**Author Contributions:** M.C.N.: conceptualization, methodology, supervision, project administration, data analysis, writing—original draft, review and editing. D.I.O.: methodology, review and editing. J.E.: review and editing: U.C.A.: review and editing. I.E.E.: review and editing. G.C.: review and editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data is contained within the article.

**Acknowledgments:** The authors acknowledge Ndkuwu's 2018/2019 undergraduate project students and Postgraduate Diploma students for helping in data collection for this project.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Article* **Enhancing the Shelf-Life of Fresh Cassava Roots: A Field Evaluation of Simple Storage Bags**

**Keith Tomlins 1, Aditya Parmar 1,\*, Celestina Ibitayo Omohimi 2, Lateef Oladimeji Sanni 2, Adekola Felix Adegoke 2, Abdul-Rasaq Adesola Adebowale <sup>2</sup> and Ben Bennett <sup>1</sup>**


**Abstract:** Postharvest physiological deterioration (PPD) of fresh cassava roots limits their shelf-life to about 48 h. There is a demand for simple, cheap, and logistically feasible solutions for extending the shelf life of fresh cassava roots in industrial processes. In this study, three different types of bag materials were tested, namely woven polypropylene, tarpaulin, and jute as a potential storage solution for cassava roots with different levels of mechanical damage. Microclimate related to temperature, humidity, and carbon di-oxide (CO2) was monitored in order to understand the storage conditions for up to 12 days. The results showed that fresh cassava roots could be stored for 8 days, with minimal PPD and starch loss (2.4%). However, roots with significant mechanical damage in the form of cuts and breakages had a considerably shorter shelf life in the storage bag, compared to whole roots and roots with retained stalk (peduncle) where roots are connected to the main plant. Wetting of the roots and bag material were not significant factors in determining the shelf life and starch loss. Carbon dioxide concentration in the stores was significantly correlated with the starch loss in fresh cassava roots and is proposed as a possible method for continuously and remotely monitoring starch loss in large-scale commercial operations and reducing postharvest losses.

**Keywords:** cassava; storage; PPD; starch; shelf-life; postharvest losses

#### **1. Introduction**

The short shelf-life of cassava (*Manihot esculenta C*.) roots is primarily attributed to postharvest physiological deterioration (PPD), which is triggered as a wound response shortly after harvest [1–3]. PPD reduces the quality and quantity of starch and renders the cassava roots unmarketable and inedible. PPD is a complex process, and its exact mechanism is still not fully understood [2,4,5]; however, it is known that it involves enzymatic stress responses to wounds and changes in gene expression. PPD can be accompanied by moisture and starch loss [4,6,7]. It results in the formation of blue-black internal root discolouration (vascular streaking) because of the combination of insoluble precipitates formed from scopoletin reacting with hydrogen peroxide. Cassava roots can also suffer from fungal rots. Cassava varieties have been reported to differ in storability [2,8] concerning PPD.

Various attempts have been made in the past to store fresh cassava in various conditions to control PPD and enhance the overall shelf life of fresh cassava roots. Several factors appear to be important for storing roots under ambient tropical conditions; these include curing, cassava variety, chemical treatments, the container, or bag that the roots are stored in, and physical damage to the roots due to harvesting and handling. Curing of the fresh cassava roots is a critical factor; it is the process of wound healing and has been

**Citation:** Tomlins, K.; Parmar, A.; Omohimi, C.I.; Sanni, L.O.; Adegoke, A.F.; Adebowale, A.-R.A.; Bennett, B. Enhancing the Shelf-Life of Fresh Cassava Roots: A Field Evaluation of Simple Storage Bags. *Processes* **2021**, *9*, 577. https:// doi.org/doi:10.3390/pr9040577

Academic Editors: Daniel I. Onwude and Guangnan Chen

Received: 9 March 2021 Accepted: 23 March 2021 Published: 26 March 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

shown to reduce storage losses of slightly damaged roots [4,7]. The optimum conditions for curing are a humidity of 80–85% and a temperature of 25 to 35 ◦C [9–11]. Previous studies on the storage of fresh cassava roots have reported durations of 11 days [12–14], 11 to 21 days [15], 14 days [16], one month, and two months under cooler conditions. However, these experiments were undertaken in laboratory conditions with small quantities and lacked practical applications in a commercial setting where tons of roots arrive every day at the processing plant. The limited research at a larger scale, such as the storage of 300 kg of roots in Colombia [12,13] and 500 kg in Tanzania, indicates that the roots can store well in larger quantities, but there was no replication and hence the information is limited.

Since cassava is a low-value crop, the challenge is to find a reliable solution at a low cost. The hypothesis for this study was that PPD in fresh cassava can be restricted at scale by the application of a low-cost simple bag storage method along with control measures that might include bag design and the control of moisture, root damage, and variety. To test this, we addressed the following questions for storage at scale:


#### **2. Materials and Methods**

The research was undertaken during the main harvest period in Nigeria in 2018 and 2019. All experiments were conducted at the Federal University of Agriculture, Abeokuta, Ogun State, Nigeria in an experimental barn for root and tuber crops and at ambient temperature (23–32 ◦C) and humidity (56–100%). Due to the need to harvest, sort, weigh, transport, and handle large quantities of fresh cassava roots, a stepwise approach to minimize logistical challenges was adopted (Figure 1).

**Figure 1.** Stepwise progression in experiments to investigate shelf-life at scale.

#### *2.1. Cassava Roots*

Fresh roots of variety TME (Tropical Manihot Esculenta) 419 were used in the experiments unless stated otherwise. They were manually harvested and sorted. In the field, exposure of the roots to direct sunlight was avoided and roots were transported from the field to the research station within 2–3 h of harvest. The variety TME 419 was selected to develop the storage method because it is mostly preferred by farmers and commercial industries in Nigeria. It has high yields, higher starch content, and matures early in the growing season [3]. Two additional varieties, namely, TMS (Tropical Manihot Species) 0581 and TMS 1632, were used for the varietal comparison.

#### *2.2. Bag Material and the Effect of Wetting*

Three types of bag material were compared: woven polypropylene (1390 mm long by 789 mm wide by 0.7 mm thick), tarpaulin (1180 mm long by 853 mm wide by 0.6 mm thick), and jute (1083 mm long by 823 mm wide by 2.1 mm thick). These were selected based on previous research [13] and availability in Nigeria. Bags were locally purchased. Additionally, fresh roots were either wetted with water (to provide high humidity) or not wetted. Controls were roots heaped on the ground without using any bag materials. Table 1 illustrates the experimental design for bag material and the effect of wetting.


**Table 1.** Experiment design for bag material and wetting of roots.

Fresh cassava root samples were either wetted (dipping in clean water) or not wetted. Roots (50 kg) were placed in three bag types, constructed of either polypropylene, jute, or tarpaulin, and control roots (wetted and unwetted) were heaped on the floor. All treatments were replicated five times. Roots from each treatment (5 kg) were randomly sampled at 48 h intervals for weight loss, PPD, fungal rot, and starch content measurements for a duration of up to 12 days.

The carbon dioxide (CO2), temperature (◦C), and relative humidity (RH %) levels were measured using a Rotronics CP11 datalogger (Rotronics Instruments (UK) Ltd., Crawley, West Sussex, UK). They were set to record continuously during the experiments. The data loggers were fitted inside a ventilated plastic tube (68 mm × 300 mm) which was situated in the centre of each sack or a pile of roots.

#### *2.3. Effect of Mechanical Root Damage*

Woven polypropylene sacks were filled with 50 kg wetted fresh cassava roots (TME 419) with the following treatments being either one break, two breaks, no breaks but slight bruising, no breaks with extensive bruising, and roots with no breaks and no bruising. The breaks were obtained by cutting roots with a knife. All treatments were replicated five times. Cassava roots (5 kg) were sampled for a period of 12 days, every two days. Table 2 illustrates the categories of mechanical damage.


#### **Table 2.** Categories of mechanical damage to cassava roots.

\* Where yellow area represents bruising of the surface of the root.

#### *2.4. Harvesting with and without the Peduncle*

Woven polypropylene sacks were filled with 50 kg wetted fresh cassava roots (TME 419) with the following treatments being either root with the peduncle still attached or root without (Table 3). Control roots were not stored in sacks. All treatments were replicated five times. Cassava roots (5 kg) were sampled at zero, two, four, six and eight, ten, and twelve days.

**Table 3.** Cassava roots harvested with and without the peduncle.


#### *2.5. Varietal Comparison*

Woven polypropylene sacks were filled with 50 kg wetted fresh cassava roots (TMS 0581, TMS 1632, and TME 419) with the following treatments being either root with the peduncle still attached or root without peduncle. Control sample cassava roots were not stored in sacks. All treatments were replicated five times. Cassava roots (5 kg) were sampled at zero, two, four, six, eight, ten, and twelve days.

#### *2.6. Weight Loss, PPD, and Fungal Rot Measurement*

The weight of the fresh cassava roots was recorded every 24 h for a period of 12 days to measure the weight loss during storage in different bag materials and without any bags (control). The PPD for each treatment was measured based on seven transversal slices

of 2 cm thickness which were cut along the root, starting from the proximal end [7]. A score of 0–1 was assigned to each slice by an expert panel of three people (cassava crop physiologists and postharvest scientists), corresponding to the percentage of the transversal cut surface showing vascular discolouration (which represents PPD) (0.1 = 10%, 0.2 = 20%, etc.). The mean score of PPD for each root was calculated as the final measurement. For fungal rotting incidence, roots were visually scored from 0 to 5 (by the same expert panel), where 0 means no rotting and 5 severe rotting.

#### *2.7. Starch Content Measurement*

Starch was measured using the same method as used by the industry in Nigeria, that is, the Reimann Balance (Specific Gravity) Method (1986) (Figure 2). This is recommended by the International Starch Institute, Science Park, Aarhus, Denmark. The calculated data is in per cent, and deviates less than 0.05 from the values readout of the EU table enforced by the European Commission covering potatoes with 13% to 23% starch dry matter. A calibrated Kern CH 15K20 weighing balance was used (RS Components Ltd., Northants, UK).

**Figure 2.** Reimann Balance (Specific Gravity) Method as constructed at the Federal University of Agriculture, Abeokuta, Nigeria.

#### *2.8. Statistical Analysis*

Data were analysed by analysis of covariance (ANCOVA) procedures using R, (Version 3.6.3, R Core team, Vienna, Austria, 2020) and IBM SPSS Statistics, Version 25.0. (Armonk, NY, USA: IBM Corp) statistical packages with the least significant difference (LSD) at *p* = 0.05 and linear regression.

#### **3. Results**

#### *3.1. Comparison of the Storage Bag Materials and Wetting*

Loading bags with either dry cassava roots or roots that had been wetted with water did not result in any significant differences in root quality concerning PPD, fungal rot, starch content, or root moisture content (*p* < 0.05). Therefore, wet and non-wet scores were combined for the subsequent analyses. This lack of a significant effect of wetting is probably because the humidity within the sacks of cassava roots remained above 85% RH [1] regardless of whether the roots were wetted or not, as illustrated in Figure 3. It

is speculated that wetting the roots is probably important to maintain relative humidity above 85% in a small-scale laboratory context due to the small number of roots involved; where larger qualities of roots are involved (in this case 50 kg), the natural respiration is probably sufficient to keep the relative humidity above 85%, as demonstrated in this study.

**Figure 3.** Relative humidity (RH %) within the sacks of cassava roots during storage, either wetted by adding water or not wetted. On average, wetting increased the weight of roots by 0.7%.

Considering the bag type, polypropylene bags resulted in the least PPD compared to the control, but this difference was only significant for the 12 day duration (Figure 4a). Considering the fungal rotting, the bags made from tarpaulin resulted in a significant improvement in shelf-life compared to the control, such that for just perceptible rots (5%) the storage time was 8 days (Figure 4b). After eight days, the rotting was extensive in the roots, and the results for 10 and 12 days storage already exceeded the threshold rotting of 40% of roots and above. The other bag types (jute and tarpaulin) did not differ from the control. Regarding the roots' moisture content, bags made from tarpaulin resulted in the least reduction in moisture content during storage, followed by jute and lastly polypropylene (*p* < 0.05) (Figure 4c). Most of the decline in moisture content was seen after 6 to 8 days. Regarding the starch content, storage in bags led to a lesser loss in starch content than in the control. Although there were significant differences between the bags with respect to starch content change, in practice the differences were small and hence the polypropylene bag was used for other experiments, as this is already used by the industry to transport cassava roots. In general, the decline in starch content started to occur after 2 days, but the loss did not accelerate until eight days of storage (Figure 4d); the starch loss after two days when stored in bags was about 0.3%, and at eight days it was 2.4%. If the roots were not stored in a bag, the starch loss was higher, at 4.3%.

**Figure 4.** Variation in PPD (Postharvest physiological deterioration) score (**a**), fungal rot (**b**), moisture (**c**), and starch content (**d**) of fresh cassava roots with storage time in different types of bags.

#### *3.2. Effect of Root Damage on Shelf-Life*

Considering PPD measurements of the fresh cassava roots, after eight days (Figure 5) only roots with slight bruising or no bruising and no breaks had minimal discolouration. It should be noted that there was a slight increase in discolouration, but this was considered acceptable. However, roots that that were broken (either 1 or 2 breaks) or had extensive bruising resulted in significant discolouration. Regarding the extent of fungal rot, after eight days of storage roots with bruising (slight or extensive) and no breaks did not differ from the control sample (no breaks or bruising). However, the inclusion of extensive bruising was probably due to the wide variation between the replicates. However, any breaks (either 1 or 2) resulted in significant fungal rot. Considering the extent of starch loss after eight days of storage, where the roots were free of breaks or bruising (or had only slight bruising), the starch loss was 2.1% to 2.3%. However, if the roots had any breaks or extensive bruising, the starch loss was higher, at 4.4% to 5.8% on average.

**Figure 5.** Changes in postharvest physiological deterioration (PPD), fungal rot, and starch content of fresh cassava roots with varying degrees of damage after 8 days of storage.

How the root is disconnected from the stem during harvesting might have an impact on storage. Processors of starch prefer to harvest without the peduncle because this part of the root is more fibrous, but keeping the peduncle attached is thought to increase the shelf-life (unpublished). Harvesting the roots with the peduncle did not have a significant impact on the starch content. It is speculated that there may have been no significant loss in starch content due to cutting without the peduncle because the additional damage to the fresh root was relatively minor, and as shown in the previous section, minor root damage did not adversely affect the shelf-life when storing for up to eight days.

#### *3.3. Effect of Cassava Variety*

The cassava variety (TME 419, TMS 0581 and TMS 1632) did not have a significant effect on root quality (PPD, fungal rot, starch content, or weight loss). Previously, weight loss occurring during storage was reported to be about 10% after two weeks of storage [1], but this is the first time that comparisons between these varieties have been reported. The starch content of the three varieties did change during storage, but the initial starch content of the different varieties were also different (Figure 6). Previously, during the storage of fresh roots, the starch loss was observed to occur at a rate of about 1% per day [1]. This research agrees with the loss in the starch of about 1% per day, but also shows that the curve is not linear, with little loss during the first 48 h and then increasing losses after this point. Zainuddin et al. [2] reported that starch losses might vary with variety; in this study, variety did not have an effect. A gap in this research was that possible differences might exist in starch gel clarity and swelling power and gel viscosity between varieties [1]. This would require further investigation.

**Figure 6.** Variation in starch content of three varieties of cassava during storage of 50 kg in sacks.

#### *3.4. CO2 Concentration and Starch Losses*

Root damage is known to increase the rate of respiration in fresh cassava roots due to PPD and wound response along with a corresponding loss in starch [1,9]. This increase in respiration is associated with an increase in CO2 concentration, temperature, and RH % measured during storage, and could potentially be used to monitor changes in starch content during storage. While root damage did result in an increase in temperature and humidity in the stored fresh cassava roots, this did not significantly correlate with the extent of the starch loss. A significant correlation (*p* < 0.05) was found between starch loss (%) and maximum CO2 concentration measured at eight days (Figure 7). The maximum CO2 concentration was measured to account for the diurnal effect. While CO2 is known to be produced by the respiration of cassava roots, this is the first time that a relationship between CO2 concentration and starch loss in fresh cassava roots during storage has been reported. This research suggests that CO2 concentration in a cassava root store might be a suitable means to measure losses in starch, but would require further development.

**Figure 7.** Relationship between starch loss (%) and peak carbon dioxide concentration after eight days of storage.

#### **4. Discussion**

Previously, more costly and logistically challenging chemical treatments (oxidising agents such as calcium hypochlorite, disinfectants such as ethanol, and fungicides such as benomyl, dicloran, and thiabendazole) have been used to increase the shelf-life of cassava roots by reducing PPD. Most of these chemicals probably only provide relief from secondary deterioration, which is microbial (mostly fungal rotting). Wax coatings [17,18] have been reported to be successful in storing fresh cassava roots, which has been tested in Uganda. Edible surface coatings of fresh cassava roots are effective in preserving the quality of various perishable food products [19,20]. By using 1.5% xanthan guar/gum as an edible coating, cassava shelf life could be extended by up to 20 days at 25 ◦C. However, these approaches using chemical treatments were not included in this research because on a commercial scale, these methods would only be practical for high-value markets. Previous research has demonstrated that during the storage of fresh roots, starch loss occurs at a rate of about 1% per day [6] and might vary by variety and storage conditions [21,22]. Reported changes in the starch qualities during storage include a reduction in gel clarity and swelling power and an increase in gel viscosity; it is not known how these changes reflect the quality of the starch or its commercial value [4,23]. In this research, the effect of gel clarity and swelling power was not measured, and this would require further research.

The temperature, RH %, and CO2 in the sacks were similar regardless of the bag material, and this supports the findings that the bag material and addition of water had little effect. The average temperature varied between 27.1 and 29.1 ◦C and was within the range of 25 to 35 ◦C recommended by [12,24], while the average relative humidity varied between 92% and 98%, which is above the value of 85% for curing [25,26]. However, as the ambient conditions for humidity were also high at 74%, the higher humidities were probably acceptable. Previously, different bag materials have been compared for storing fresh cassava roots, such as open weave (manmade fibres), polyethylene (0.13 mm thick), and recycled rice/flour sacks made of tightly woven polyethylene. Of these, the polyethylene and recycled rice sacks were reported to retard deterioration the most [13]. In this research, 50 kg of cassava roots could be kept for up to eight days, irrespective of whether the roots were wet or dry and irrespective of the bag material. The loss in starch content, a key commercial product requirement, was reduced if the fresh roots were stored in bags, and was about 2.4% on average over eight days. This is less than the 8% over eight days (i.e., 1% per day of storage) reported by Sánchez et al. [6]. The difference in starch losses might be because we stored only a few roots and each was stored individually rather than in bags.

Root damage had an impact on the quality of the roots with respect to PPD, fungal rot, and starch content. Previous research has demonstrated that physical damage to the fresh roots (cuts, breaks, and bruising) increased PPD and reduced shelf-life [1,2,4,27]. After 11 days of storage, roots with slight damage lost 9.6% in weight compared to 18.2% for roots with severe damage [6]. In response to wounding, such as cuts and abrasions, cassava storage roots show early physiological changes, including increased respiratory rate and water loss. Increased respiration induces the conversion of starch to sugar [6,28]. During storage, the starch content of unwounded cassava storage roots gradually decreases. However, starch loss and sugar production during storage are significantly higher if the roots are wounded. This research is new in that the type of root damage (bruising or breaks) was considered. As the fresh cassava root remains biologically active, PPD is accompanied by an increase in respiration and the root can initiate a wound response, and discolouration of the roots (vascular streaking) occurs due to oxidation of secondary metabolites [1]. However, previous research was undertaken on only a few cassava roots and hence, little was known about the response with larger sample sizes or how much root damage was possible before PPD became unacceptable. This research, using larger sample sizes of 50 kg, shows that while root damage does reduce the shelf-life, slight bruising of the root is acceptable provided the roots are not cut or broken.

#### **5. Conclusions**

Currently, for the processing of cassava roots in the production of high-quality cassava flour or starch, a limitation is the rapid deterioration of the roots within 48 h after harvesting. It can be concluded that fresh cassava roots (50 kg) can be stored in a bag at ambient temperature in West Africa for up to eight days with minimal deterioration or loss in starch. The advantage would be reduced product handling and potentially lower costs when transporting the fresh cassava roots from the farm to the processing factory.

Several control measures would be required to support the management of the process to ensure that roots can be consistently stored. This includes minimizing root damage. Our results suggest that there is no need to wet the roots, harvest with the peduncle attached, or separate different varieties (TME 419, TMS 0581 and TMS 1632). Other varieties may have different storage characteristics, and this would need to be tested.

A new approach to monitoring starch losses in fresh cassava roots during storage using a simple carbon dioxide sensory is suggested, but would require further testing. In industrial applications, this could provide a potential way to continuously and remotely monitor large quantities of cassava roots, avoid postharvest losses, and maintain profitability.

**Author Contributions:** K.T., C.I.O., L.O.S., A.P., and B.B. conceived and planned the experiments. C.I.O., A.F.A., and A.-R.A.A. carried out the experiments. K.T. and A.P. took the lead in writing the manuscript. All authors provided critical feedback and helped shape the research, analysis, and manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Rockefeller Foundation, Cassava Challenge Fund 2018.

**Institutional Review Board Statement:** Not Applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** The authors would like to thank the Rockefeller Foundation for providing the financial support for these experiments. These laboratory studies would not have been possible without the support of numerous people in Nigeria. We particularly want to mention the staff of Federal University of Agriculture, Abeokuta (FUNAAB). Notwithstanding, the content of this report represents the views of the authors only.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

