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Processes
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Drying Kinetics and Quality Control in Food Processing, 2nd Edition
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Drying Kinetics and Quality Control in Food Processing, 2nd Edition

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Food Process Engineering".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 4502

Special Issue Editors

Prof. Dr. Won Byong Yoon

E-Mail Website
Guest Editor
Food Science and Biotechnology, Kangwon National University, Chuncheon 24341, Republic of Korea
Interests: mathematical modelling in food and bioprocesses; computer simulations; biopolymer rheology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Meng-I Kuo (Marie)

E-Mail Website
Guest Editor
Department of Food Science, Fu Jen Catholic University, New Taipei City 24205, Taiwan
Interests: novel food ingredients; innovative food processing technology; texture tailoring; sustainable upcycling
Special Issues, Collections and Topics in MDPI journals
Dr. Huai-Wen Yang

E-Mail Website
Guest Editor
Department of Food Science, National Chiayi University, Chiayi City 60004, Taiwan
Interests: food engineering simulation; food texture modification; dysphagia-friendly formulation; sustainable processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The kinetic characteristics of food during dehydration play an important role in the final product quality, as well as the cost of the operation. The physico-chemical changes that occur during drying are highly related to the drying temperature and time, as well as the food compositions and matrix. The unstable properties of food could be improved under various drying conditions. Previous drying-related research has focused on characterizing the relationship between the moisture content changes and drying conditions, such as the drying temperature and time. The analytical, semi-empirical, and numerical models used to predict the moisture contents during drying have been well developed. However, relatively few studies on the mathematical or empirical analysis of the kinetic characteristics of food during rehydration have been reported. The kinetics characteristics of food are connected to the drying operations and the final quality of the dried food.

This Special Issue on "Drying Kinetics and Quality Control in Food Processing, 2nd Edition" aims to integrate novel advances in the mathematical modeling of the drying operations and kinetic changes in food. The topics include, but are not limited to, the following:

  • Integrated (or multidisciplinary) studies on quality changes in food or bioresource product during drying;
  • Novel technologies to control food quality during drying;
  • The optimization of the quality parameters involved in drying operations;
  • Fundamental and applied aspects of drying and dryers;
  • Transport phenomena in food- or bio-porous media;
  • The design, scale-up, and control of dryers in food or bioproduct processing.

Prof. Dr. Won Byong Yoon
Prof. Dr. Meng-I Kuo (Marie)
Dr. Huai-Wen Yang
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. Processes is an international peer-reviewed open access monthly 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 2400 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

  • drying
  • kinetics
  • dehydration
  • rehydration
  • modeling
  • simulation
  • texture
  • shelf life
  • mass transfer
  • transport phenomena

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Published Papers (5 papers)

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Research

18 pages, 11416 KiB  
Article
Monitoring Agitation Intensity in Fluidized Beds Containing Inert Particles via Acoustic Emissions and Neural Networks
by Willian Velloso Metzner and Gustavo Cesar Dacanal
Processes 2024, 12(12), 2691; https://doi.org/10.3390/pr12122691 - 29 Nov 2024
Viewed by 657
Abstract
This study utilized passive acoustic emissions from a fluidized bed containing spherical inert ABS particles, captured by an external piezoelectric microphone, to monitor fluidization agitation intensity. Acoustic signals were recorded during fluidization profiles achieved under air velocities ranging from 0.5 to 3.0 m/s [...] Read more.
This study utilized passive acoustic emissions from a fluidized bed containing spherical inert ABS particles, captured by an external piezoelectric microphone, to monitor fluidization agitation intensity. Acoustic signals were recorded during fluidization profiles achieved under air velocities ranging from 0.5 to 3.0 m/s and during the drying of water or maltodextrin aqueous solution (1:5 w/w) introduced as droplets. Analyzing audio features like waveforms, the Discrete Fourier Transform (DFT), and Mel Frequency Cepstral Coefficients (MFCCs) revealed changes corresponding to the agitation intensity of the particles. The MFCC coefficients were input into a three-layer artificial neural network (ANN) to predict fluidization dynamics based on air velocity, liquid flow rate, and drying time. The ANN efficiently learned from the data, achieving high predictive accuracy (R2 > 0.8) after 15 epochs of training, showcasing the robustness of MFCC coefficients for modeling. This approach highlights that the application of passive acoustic signals and neural networks allows for real-time monitoring of fluidization behavior during drying processes. Full article
(This article belongs to the Special Issue Drying Kinetics and Quality Control in Food Processing, 2nd Edition)
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16 pages, 3875 KiB  
Article
Temperature Effect of Cocoa (Theobroma cacao L.) Drying on Energy Consumption, Bioactive Composition and Vibrational Changes
by David J. Jiménez-Rodríguez, Pedro García-Alamilla, Facundo J. Márquez-Rocha, Rubén Vázquez-Medina, Areli Carrera-Lanestosa, Fanny A. González-Alejo, Carlos A. Sánchez-Ramos and Franco L. Ruiz-Santiago
Processes 2024, 12(11), 2523; https://doi.org/10.3390/pr12112523 - 12 Nov 2024
Viewed by 1008
Abstract
Cocoa drying is the post-harvest thermal process used to condition the beans to a moisture content between 6.5 and 7% for storage and further processing. Convective drying is an energy-intensive process where time and temperature are considered critical factors for the degradation of [...] Read more.
Cocoa drying is the post-harvest thermal process used to condition the beans to a moisture content between 6.5 and 7% for storage and further processing. Convective drying is an energy-intensive process where time and temperature are considered critical factors for the degradation of bioactive compounds in edible products. In the present study, the energy parameters, vibrational spectroscopy, and changes in bioactive compounds of cocoa beans were studied during thin-layer hot air drying at 50 °C, 60 °C, and 70 °C. Moisture loss, specific energy consumption (SEC), energy efficiency, total phenolics (TPs), total flavonoids (TFs), and antioxidant activity (DPPH) were determined. Fourier transform infrared (FT-IR) spectroscopy with attenuated total reflectance (ATR) was used to characterize the samples, and a multivariate analysis was applied to find interactions among the components. The obtained SEC was 18,947.30–24,469.51 kJ/kg, and the energy efficiency was 9.73–12.31%. When the temperature was 70 °C, the best values for SEC and energy efficiency were obtained. The results also showed that the convective drying generated changes in the TP levels for the three temperatures, mainly after 300 min, with maximum levels between 360 and 600 min, at 70 °C; however, it does not have a clear relationship with the TFs and the antioxidant activity. The FT-IR and the multivariate analysis revealed changes in several signals in the 1800 to 400 cm−1 range, confirming the variation in the associated signal with phenolic compounds. Full article
(This article belongs to the Special Issue Drying Kinetics and Quality Control in Food Processing, 2nd Edition)
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Graphical abstract

12 pages, 2367 KiB  
Article
Optimization of Steaming Conditions for Bellflower Root (Platycodon grandiflorus) Using K-Means Clustering-Based Morphological Grading System
by Timilehin Martins Oyinloye, Seohee An, Chang-Won Cho and Won Byong Yoon
Processes 2024, 12(11), 2347; https://doi.org/10.3390/pr12112347 - 25 Oct 2024
Viewed by 512
Abstract
Bellflower roots were categorized into three clusters (class 0, class 1, and class 2) using K-means clustering based on their morphological factors: length (282.8 ± 29.53, 138.75 ± 26.8, and 209.89 ± 20.49 mm), thickness (16.25 ± 2.82, 16.77 ± 3.35, and 16.52 [...] Read more.
Bellflower roots were categorized into three clusters (class 0, class 1, and class 2) using K-means clustering based on their morphological factors: length (282.8 ± 29.53, 138.75 ± 26.8, and 209.89 ± 20.49 mm), thickness (16.25 ± 2.82, 16.77 ± 3.35, and 16.52 ± 3.05 mm), and body shape coefficient (5.80 ± 1.15, 12.73 ± 4.82, and 7.95 ± 1.71). Internal void formation, a key quality factor for bellflower root, was analyzed under pre-steaming conditions, identifying temperatures between 20 and 25 °C as optimal for storage. Within the clustered class, steaming for a prolonged duration increased the formation of internal voids and caused a decrease in normal stress values, total dissolved solids (TDS), and pectin content. Class 0, with larger and thicker roots, exhibited higher internal voids (57% void rate) due to uneven heat distribution and incomplete starch gelatinization. Class 2 roots demonstrated better structural integrity, with a void rate of 26% and a stress value of 48 kN/m2. These findings highlight the importance of morphological classification and optimal storage temperatures to improve the quality of steamed bellflower roots. Full article
(This article belongs to the Special Issue Drying Kinetics and Quality Control in Food Processing, 2nd Edition)
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18 pages, 7952 KiB  
Article
Deep Learning Prediction of Moisture and Color Kinetics of Apple Slices by Long Short-Term Memory as Affected by Blanching and Hot-Air Drying Conditions
by Zehui Jia, Yanhong Liu and Hongwei Xiao
Processes 2024, 12(8), 1724; https://doi.org/10.3390/pr12081724 - 16 Aug 2024
Viewed by 899
Abstract
This study aimed to improve apple slices’ color and drying kinetics by optimizing the hot-air drying process, utilizing machine and deep learning models. Different steam blanching times (30, 60, 90, and 120 s), drying temperatures (50, 55, 60, 65, and 70 °C), and [...] Read more.
This study aimed to improve apple slices’ color and drying kinetics by optimizing the hot-air drying process, utilizing machine and deep learning models. Different steam blanching times (30, 60, 90, and 120 s), drying temperatures (50, 55, 60, 65, and 70 °C), and humidity control methods (full humidity removal or temperature–humidity control) were examined. These factors significantly affected the quality of apple slices. 60 s blanching, 60 °C temperature, and full dehumidification represented the optimal drying conditions for apple slices’ dehydration, achieving better drying kinetics and the best color quality. However, the fastest drying process (40 min) was obtained at a 60 °C drying temperature combined with complete dehumidification after 90 s blanching. Furthermore, machine and deep learning models, including backpropagation (BP), convolutional neural network–long short-term memory (CNN-LSTM), temporal convolutional network (TCN), and long short-term memory (LSTM) networks, effectively predicted the moisture content and color variation in apple slices. Among these, LSTM networks demonstrated exceptional predictive performance with an R2 value exceeding 0.98, indicating superior accuracy. This study provides a scientific foundation for optimizing the drying process of apple slices and illustrates the potential application of deep learning in the agricultural processing and engineering fields. Full article
(This article belongs to the Special Issue Drying Kinetics and Quality Control in Food Processing, 2nd Edition)
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15 pages, 770 KiB  
Article
Modifications on the Processing Parameters of Traditional Pineapple Slices by Stabilized Sound Pressure of Multiple Frequency Ultrasonic-Assisted Osmotic Dehydration
by Yu-Wen Lin, Yueh-An Yao, Da-Wei Huang, Chung-Jen Chen and Ping-Hsiu Huang
Processes 2024, 12(6), 1109; https://doi.org/10.3390/pr12061109 - 28 May 2024
Cited by 1 | Viewed by 911
Abstract
This study investigated the practical feasibility of synergistically and optimally applying ultrasound-assisted osmotic dehydration (UAOD) practices for the pineapple slice picking process (in sugar osmotic solution), with potential implications for improving current practices. This study was carried out to evaluate the effects of [...] Read more.
This study investigated the practical feasibility of synergistically and optimally applying ultrasound-assisted osmotic dehydration (UAOD) practices for the pineapple slice picking process (in sugar osmotic solution), with potential implications for improving current practices. This study was carried out to evaluate the effects of different treatment conditions of single (40 and 80 kHz)/multiple (40/80 kHz) frequencies, output powers (300, 450, and 600 W), and treatment time (5–40 min) at 30, 45, and 60 °Brix applied, respectively, on the pineapple slices picking process. The sound pressure of the UA was also measured to confirm that it provided the corresponding effect stably under different conditions. The ideal UAOD operating condition for pineapple slices is a 45 °Brix sugar osmotic solution, with frequency multiplexing at 40/80 kHz and an output power of 450 W for 25 min, which yields the optimal solids gain (SG) rate of 7.58%. The above results of this study indicated that UAOD could improve the accelerated quality transfer of pineapple slices and enhance the final product quality, thereby increasing the efficiency of the dehydration process and saving processing costs and time. Full article
(This article belongs to the Special Issue Drying Kinetics and Quality Control in Food Processing, 2nd Edition)
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