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The Non-destructive Testing, In Situ Analysis, and Automated Sorting of...
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The Non-destructive Testing, In Situ Analysis, and Automated Sorting of Food/Agricultural Product Quality

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 3830

Special Issue Editors

Prof. Dr. Qiaohua Wang

E-Mail Website
Guest Editor
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Interests: non-destructive intelligent detection; origin traceability of agricultural and livestock products; agricultural electrification and automation; machine vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Meihu Ma

E-Mail Website
Guest Editor
National Research and Development Center for Egg Processing, College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
Interests: films; egg white protein emulsions; food hydrocolloids; egg
Dr. Wei Fan

E-Mail Website
Guest Editor
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Interests: intelligent detection and digital equipment; machine vision

Special Issue Information

Dear Colleagues,

The quality issues surrounding food/agricultural products have long been a focus of public attention, as their quality directly impacts both food safety and economic value. Conventional chemical analysis, while a common method for assessing the quality of food/agricultural products, can identify specific chemical and nutritional components, yet its operation is intricate, cumbersome and time-consuming. Non-destructive testing techniques, including spectroscopy, machine vision, vibration signal analysis, electromagnetic ultrasonic technology, electronic noses and electronic tongues, offer advantages such as non-invasiveness, effective detection, efficiency and simplicity of operation. These techniques are currently emerging as hot topics in the realm of food/agricultural product quality testing. Researchers have also shown considerable interest in furthering the in situ analysis and automation of food/agricultural product sorting. Therefore, this Special Issue aims to explore the application of various non-destructive testing techniques in quality assessment, enabling real-time analysis and automated sorting of food/agricultural products, thereby advancing the resolution of quality-related issues in this domain.

Prof. Dr. Qiaohua Wang
Prof. Dr. Meihu Ma
Dr. Wei Fan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • the quality of food/agricultural products
  • non-destructive inspection
  • in situ analysis
  • automated sorting
  • spectral technology
  • machine vision
  • vibration signal analysis
  • electromagnetic ultrasonic technology
  • electronic nose/electronic tongue

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Published Papers (4 papers)

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Research

25 pages, 6453 KiB  
Article
Comparison of Multiple NIR Instruments for the Quantitative Evaluation of Grape Seed and Other Polyphenolic Extracts with High Chemical Similarities
by Matyas Lukacs, Flora Vitalis, Adrienn Bardos, Judit Tormási, Krzysztof B. Bec, Justyna Grabska, Zoltan Gillay, Rita A. Tömösközi-Farkas, László Abrankó, Donatella Albanese, Francesca Malvano, Christian W. Huck and Zoltan Kovacs
Foods 2024, 13(24), 4164; https://doi.org/10.3390/foods13244164 - 23 Dec 2024
Abstract
Grape seed extract (GSE), one of the world’s bestselling dietary supplements, is prone to frequent adulteration with chemically similar compounds. These frauds can go unnoticed within the supply chain due to the use of unspecific standard analytical methods for quality control. This research [...] Read more.
Grape seed extract (GSE), one of the world’s bestselling dietary supplements, is prone to frequent adulteration with chemically similar compounds. These frauds can go unnoticed within the supply chain due to the use of unspecific standard analytical methods for quality control. This research aims to develop a near-infrared spectroscopy (NIRS) method for the rapid and non-destructive quantitative evaluation of GSE powder in the presence of multiple additives. Samples were prepared by mixing GSE with pine bark extract (PBE) and green tea extract (GTE) on different levels between 0.5 and 13% in singular and dual combinations. Measurements were performed with a desktop and three different handheld devices for performance comparison. Following spectral pretreatment, partial least squares regression (PLSR) and support vector regression (SVR)-based quantitative models were built to predict extract concentrations and various chemical parameters. Cross- and external-validated models could reach a minimum R2p value of 0.99 and maximum RMSEP of 0.27% for the prediction of extract concentrations using benchtop data, while models based on handheld data could reach comparably good results, especially for GTE, caffeic acid and procyanidin content prediction. This research shows the potential applicability of NIRS coupled with chemometrics as an alternate, rapid and accurate quality evaluation tool for GSE-based supplement mixtures. Full article
(This article belongs to the Special Issue The Non-destructive Testing, In Situ Analysis, and Automated Sorting of Food/Agricultural Product Quality)
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19 pages, 3938 KiB  
Article
Rapid Identification of the Geographical Origin of the Chinese Mitten Crab (Eriocheir sinensis) Using Near-Infrared Spectroscopy
by Renhao Liu, Qingxu Li and Hongzhou Zhang
Foods 2024, 13(20), 3226; https://doi.org/10.3390/foods13203226 - 10 Oct 2024
Viewed by 937
Abstract
The Chinese mitten crab (Eriocheir sinensis) is highly valued by consumers for its delicious taste and high nutritional content, including proteins and trace elements, giving it significant economic value. However, variations in taste and nutritional value among crabs from different regions [...] Read more.
The Chinese mitten crab (Eriocheir sinensis) is highly valued by consumers for its delicious taste and high nutritional content, including proteins and trace elements, giving it significant economic value. However, variations in taste and nutritional value among crabs from different regions lead to considerable price differences, fueling the prevalence of counterfeit crabs in the market. Currently, there are no rapid detection methods to verify the origin of Chinese mitten crabs, making it crucial to develop fast and accurate detection techniques to protect consumer rights. This study focused on Chinese mitten crabs from different regions, specifically Hongze Lake, Tuo Lake, and Weishan Lake, by collecting near-infrared (NIR) diffuse reflectance spectral data from both the abdomen and carapace regions of the crabs. To eliminate noise from the spectral data, pretreatment was performed using Savitzky–Golay (SG) smoothing, Standard Normal Variate (SNV) transformation, and Multiplicative Scatter Correction (MSC). Key wavelengths reflecting the origin of Chinese mitten crabs were selected using Competitive Adaptive Reweighted Sampling (CARS), Bootstrap Soft Shrinkage (BOSS), and Uninformative Variable Elimination (UVE) algorithms. Finally, Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Back Propagation Neural Network (BP) models were developed for rapid detection of crab origin. The results demonstrated that MSC provided the best preprocessing performance for NIR spectral data from both the abdomen and back of the crabs. For abdomen data, the SVM model developed using feature wavelengths selected by the CARS algorithm after MSC preprocessing achieved the highest accuracy (Acc) of 90.00%, with precision (P), recall (R), and F1-score for crabs from Weishan Lake at 89.29%, 86.21%, and 87.72%, respectively; for crabs from Tuo Lake at 86.96%, 95.24%, and 90.91%; and for crabs from Hongze Lake at 90.00%, 93.10%, and 91.53%. For carapace data, the SVM model based on wavelengths selected by the BOSS algorithm after MSC pretreatment achieved the best performance, with an Acc of 87.50%, and P, R, and F1 for crabs from Weishan Lake at 77.14%, 93.10%, and 84.38%; for Tuo Lake crabs at 100%, 90.47%, and 95.00%; and for Hongze Lake crabs at 92.31%, 80.00%, and 85.71%. In conclusion, NIR spectroscopy can effectively detect the origin of Chinese mitten crabs, providing technical support for developing rapid detection instruments and thereby safeguarding consumer rights. Full article
(This article belongs to the Special Issue The Non-destructive Testing, In Situ Analysis, and Automated Sorting of Food/Agricultural Product Quality)
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21 pages, 7493 KiB  
Article
Non-Destructive Inspection of Physicochemical Indicators of Lettuce at Rosette Stage Based on Visible/Near-Infrared Spectroscopy
by Wei Li, Qiaohua Wang and Yingli Wang
Foods 2024, 13(12), 1863; https://doi.org/10.3390/foods13121863 - 13 Jun 2024
Viewed by 786
Abstract
Lettuce is a globally important cash crop, valued by consumers for its nutritional content and pleasant taste. However, there is limited research on the changes in the growth indicators of lettuce during its growth period in domestic settings. Quality assessment primarily relies on [...] Read more.
Lettuce is a globally important cash crop, valued by consumers for its nutritional content and pleasant taste. However, there is limited research on the changes in the growth indicators of lettuce during its growth period in domestic settings. Quality assessment primarily relies on subjective evaluations, resulting in significant variability. This study focused on hydroponically grown lettuce during the rosette stage and investigated the patterns of changes in the indicators and spectral curves over time. By employing spectral preprocessing and selecting characteristic wavelengths, three models were developed to predict the indicators. The results showed that the optimal model structures were S_G-UVE-PLSR (SSC and vitamin C) and Nor-CARS-PLSR (moisture content). The PLSR models achieved prediction set correlation coefficients of 0.8648, 0.8578, and 0.8047, with residual prediction deviations of 1.9685, 1.9568, and 1.6689, respectively. The optimal models were integrated into a portable device, using real-time analysis software written in Matlab2021a, for the prediction of the physicochemical indicators of lettuce during the rosette stage. The results demonstrated prediction set correlation coefficients of 0.8215, 0.8472, and 0.7671, with root mean square errors of prediction of 0.5348, 1.5813, and 2.3347 for a sample size of 180. The small discrepancies between the predicted and actual values indicate that the developed device can meet the requirements for real-time detection. Full article
(This article belongs to the Special Issue The Non-destructive Testing, In Situ Analysis, and Automated Sorting of Food/Agricultural Product Quality)
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14 pages, 3585 KiB  
Article
Development and Validation of Near-Infrared Reflectance Spectroscopy Prediction Modeling for the Rapid Estimation of Biochemical Traits in Potato
by Paresh Chaukhande, Satish Kumar Luthra, R. N. Patel, Siddhant Ranjan Padhi, Pooja Mankar, Manisha Mangal, Jeetendra Kumar Ranjan, Amolkumar U. Solanke, Gyan Prakash Mishra, Dwijesh Chandra Mishra, Brajesh Singh, Rakesh Bhardwaj, Bhoopal Singh Tomar and Amritbir Singh Riar
Foods 2024, 13(11), 1655; https://doi.org/10.3390/foods13111655 - 25 May 2024
Cited by 1 | Viewed by 1323
Abstract
Potato is a globally significant crop, crucial for food security and nutrition. Assessing vital nutritional traits is pivotal for enhancing nutritional value. However, traditional wet lab methods for the screening of large germplasms are time- and resource-intensive. To address this challenge, we used [...] Read more.
Potato is a globally significant crop, crucial for food security and nutrition. Assessing vital nutritional traits is pivotal for enhancing nutritional value. However, traditional wet lab methods for the screening of large germplasms are time- and resource-intensive. To address this challenge, we used near-infrared reflectance spectroscopy (NIRS) for rapid trait estimation in diverse potato germplasms. It employs molecular absorption principles that use near-infrared sections of the electromagnetic spectrum for the precise and rapid determination of biochemical parameters and is non-destructive, enabling trait monitoring without sample compromise. We focused on modified partial least squares (MPLS)-based NIRS prediction models to assess eight key nutritional traits. Various mathematical treatments were executed by permutation and combinations for model calibration. The external validation prediction accuracy was based on the coefficient of determination (RSQexternal), the ratio of performance to deviation (RPD), and the low standard error of performance (SEP). Higher RSQexternal values of 0.937, 0.892, and 0.759 were obtained for protein, dry matter, and total phenols, respectively. Higher RPD values were found for protein (3.982), followed by dry matter (3.041) and total phenolics (2.000), which indicates the excellent predictability of the models. A paired t-test confirmed that the differences between laboratory and predicted values are non-significant. This study presents the first multi-trait NIRS prediction model for Indian potato germplasm. The developed NIRS model effectively predicted the remaining genotypes in this study, demonstrating its broad applicability. This work highlights the rapid screening potential of NIRS for potato germplasm, a valuable tool for identifying trait variations and refining breeding strategies, to ensure sustainable potato production in the face of climate change. Full article
(This article belongs to the Special Issue The Non-destructive Testing, In Situ Analysis, and Automated Sorting of Food/Agricultural Product Quality)
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