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AgriEngineering
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Latest Research on Post-Harvest Technology to Reduce Food Loss
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Latest Research on Post-Harvest Technology to Reduce Food Loss

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Pre and Post-Harvest Engineering in Agriculture".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 5284

Special Issue Editor

Dr. Ayesha Sarker

E-Mail Website
Guest Editor
Agricultural and Environmental Research Station, West Virginia State University, Institute, WV 25112, USA
Interests: biodegradable films; edible coatings; postharvest processing; shelf life extension of fruits and vegetables; functional foods; underutilized food sources; artificial intelligence

Special Issue Information

Dear Colleagues,

Do we need to grow more food to feed the estimated 9.8 billion people by 2050? We may not need to; about one-third of all foods, roughly 1.3 billion tons we produce, are never consumed. These are lost or wasted during harvesting, post-harvest handling, storage, processing, distribution, and consumption. Reductions in this postharvest loss would help ensure that everyone has enough to eat. Adopting suitable harvest practices, post-harvest processing and packaging, transportation, and storage practices could mitigate postharvest loss of fresh produce.

The quality of Fresh fruits and vegetables deteriorates rapidly after harvest due to the ripening process, weight loss, mechanical damage, and microbial contamination. Various strategies have been tested to slow down the degradation process or extend the shelf life, such as thermal, plasma, radiation, chemical, and biological treatments, nano-technology, modified and controlled atmosphere packaging, active biodegradable packaging, or edible coatings. Moreover, combining two or more technologies has been found to be beneficial. Artificial intelligence (AI) technology is an effective new means of achieving quality assessment, cold-chain monitoring, shelf life prediction, and optimizing supply chain management.

This Special Issue of AgriEngineering aims to present the latest research and innovation in postharvest technology targeting reduced food loss. This Special Issue welcomes various manuscript types, including original research papers, full-length review papers, mini-review papers, etc.

Dr. Ayesha Sarker
Guest Editor

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. AgriEngineering 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 1600 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

  • postharvest treatments
  • postharvest processing
  • sustainable packaging
  • storage conditions
  • edible films/coatings
  • active food packaging
  • intelligent packaging
  • nano-technology
  • transportation
  • artificial intelligence

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

19 pages, 4004 KB  
Article
Spectral-Spatial Fusion for Soybean Quality Evaluation Using Hyperspectral Imaging
by Md Bayazid Rahman, Ahmad Tulsi and Abdul Momin
AgriEngineering 2025, 7(9), 274; https://doi.org/10.3390/agriengineering7090274 - 25 Aug 2025
Viewed by 547
Abstract
Accurate postharvest quality evaluation of soybeans is essential for preserving product value and meeting industry standards. Traditional inspection methods are often inconsistent, labor-intensive, and unsuitable for high-throughput operations. This study presents a non-destructive soybean classification approach using a simplified reflectance-mode hyperspectral imaging system [...] Read more.
Accurate postharvest quality evaluation of soybeans is essential for preserving product value and meeting industry standards. Traditional inspection methods are often inconsistent, labor-intensive, and unsuitable for high-throughput operations. This study presents a non-destructive soybean classification approach using a simplified reflectance-mode hyperspectral imaging system equipped with a single light source, eliminating the complexity and maintenance demands of dual-light configurations used in prior studies. A spectral–spatial data fusion strategy was developed to classify harvested soybeans into four categories: normal, split, diseased, and foreign materials such as stems and pods. The dataset consisted of 1140 soybean samples distributed across these four categories, with spectral reflectance features and spatial texture attributes extracted from each sample. These features were combined to form a unified feature representation for use in classification. Among multiple machine learning classifiers evaluated, Linear Discriminant Analysis (LDA) achieved the highest performance, with approximately 99% accuracy, 99.05% precision, 99.03% recall and 99.03% F1-score. When evaluated independently, spectral features alone resulted in 98.93% accuracy, while spatial features achieved 78.81%, highlighting the benefit of the fusion strategy. Overall, this study demonstrates that a single-illumination HSI system, combined with spectral–spatial fusion and machine learning, offers a practical and potentially scalable approach for non-destructive soybean quality evaluation, with applicability in automated industrial processing environments. Full article
(This article belongs to the Special Issue Latest Research on Post-Harvest Technology to Reduce Food Loss)
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15 pages, 1762 KB  
Article
Long-Term Blueberry Storage by Ozonation or UV Irradiation Using Excimer Lamp
by Yujiro Takano, Daichi Hojo, Kosuke Sato, Noe Inubushi, Chieto Miyashita, Eiichi Inoue and Yuya Mochizuki
AgriEngineering 2025, 7(8), 269; https://doi.org/10.3390/agriengineering7080269 - 21 Aug 2025
Viewed by 661
Abstract
Blueberries are in high demand worldwide because of their taste and functional components. However, the shelf life of blueberries is short owing to their perishability and rapid quality deterioration. Therefore, a sterilization technology must be developed that can extend the shelf life of [...] Read more.
Blueberries are in high demand worldwide because of their taste and functional components. However, the shelf life of blueberries is short owing to their perishability and rapid quality deterioration. Therefore, a sterilization technology must be developed that can extend the shelf life of blueberries while maintaining their appearance and taste. As such, we verified the effectiveness of three pre-storage sterilization treatments (UV-C, ozone gas, and ozone water) using mercury-free excimer UV lamps that did not adversely affect the environment. We then created a device that continuously treated blueberries with approximately 2.57 ppm of ozone gas to ensure sterilization during the storage period, and we verified the effectiveness of the device. We found that the pre-storage ozone treatment reduced the number of fungi on the blueberry surface without adversely affecting fruit quality. The continuous ozone treatment suppressed the decrease in anthocyanin content, further reduced the number of fungi on the fruit surface and maintained fruit appearance for a longer period compared with the control. This suggests that continuous low-concentration ozone treatment suppresses the decay and extends the storage period of blueberries intended for raw consumption. Full article
(This article belongs to the Special Issue Latest Research on Post-Harvest Technology to Reduce Food Loss)
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24 pages, 3485 KB  
Article
Effect of Natural Edible Oil Coatings and Storage Conditions on the Postharvest Quality of Bananas
by Laila Al-Yahyai, Rashid Al-Yahyai, Rhonda Janke, Mai Al-Dairi and Pankaj B. Pathare
AgriEngineering 2025, 7(7), 234; https://doi.org/10.3390/agriengineering7070234 - 12 Jul 2025
Cited by 1 | Viewed by 1826
Abstract
Increasing the shelf-life of fruits and vegetables using edible natural substances after harvest is economically important and can be useful for human health. Postharvest techniques help maintain the quality of edible tissues resulting in extended marketing periods and reduced food waste. The edible [...] Read more.
Increasing the shelf-life of fruits and vegetables using edible natural substances after harvest is economically important and can be useful for human health. Postharvest techniques help maintain the quality of edible tissues resulting in extended marketing periods and reduced food waste. The edible coating on perishable commodities is a common technique used by the food industry during the postharvest supply chain. The objective of this research was to study the effect of edible oil to minimize the loss of postharvest physio-chemical and nutritional attributes of bananas. The study selected two banana cultivars (Musa, ‘Cavendish’ and ‘Milk’) to conduct this experiment, and two edible oils (olive oil (Olea europaea) and moringa oil (Moringa peregrina)) were applied as an edible coating under two different storage conditions (15 and 25 °C). The fruit’s physio-chemical properties including weight loss, firmness, color, total soluble solids (TSS), pH, titratable acidity (TA), TSS: TA ratio, and mineral content were assessed. The experiment lasted for 12 days. The physicochemical properties of the banana coated with olive and moringa oils were more controlled than the non-coated (control) banana under both storage temperatures (15 °C and 25 °C). Coated bananas with olive and moringa oils stored at 15 °C resulted in further inhibition in the ripening process. There was a decrease in weight loss, retained color, and firmness, and the changes in chemical parameters were slower in banana fruits during storage in the olive and moringa oil-coated bananas. Minerals were highly retained in coated Cavendish bananas. Overall, the coated samples visually maintained acceptable quality until the final day of storage. Our results indicated that olive and moringa oils in this study have the potential to extend the shelf-life and improve the physico-chemical quality of banana fruits. Full article
(This article belongs to the Special Issue Latest Research on Post-Harvest Technology to Reduce Food Loss)
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14 pages, 978 KB  
Article
Physical Classification of Soybean Grains Based on Physicochemical Characterization Using Near-Infrared Spectroscopy
by Marisa Menezes Leal, Nairiane dos Santos Bilhalva, Rosana Santos de Moraes and Paulo Carteri Coradi
AgriEngineering 2025, 7(6), 194; https://doi.org/10.3390/agriengineering7060194 - 17 Jun 2025
Cited by 1 | Viewed by 772
Abstract
The study aimed to determine the physical and physicochemical properties of soybean grains using NIR spectroscopy coupled with multivariate data analysis. The experiment was carried out in two stages: first, individual characterization of defects and healthy grains; then, analyses of samples classified into [...] Read more.
The study aimed to determine the physical and physicochemical properties of soybean grains using NIR spectroscopy coupled with multivariate data analysis. The experiment was carried out in two stages: first, individual characterization of defects and healthy grains; then, analyses of samples classified into different types (type I, type II, basic standard, and out of type). The centesimal composition of the grains (crude protein, lipids, water content, crude fiber, starch, and ash) was determined by NIR spectroscopy, and the data were analyzed by ANOVA, Scott-Knott test, principal component analysis (PCA), k-means clustering, and Pearson correlation. The results showed significant variations between defects and commercial types in all the variables evaluated (p < 0.05), with an emphasis on germinated grains (higher protein content) and broken grains (higher fiber content). The PCA explained 66.6% of the total variance in the defect sets and 52.2% of the types, allowing the formation of groups defined by the clustering algorithms. Pearson correlations indicated important interactions between the chemical variables, such as the negative correlation between protein and crude fiber (r = −0.73) and between lipids and water content (r = −0.66). It is concluded that the NIR method combined with multivariate modeling allows for the rapid assessment of soybean grain quality in real time, optimizing, reducing waste in, and increasing the efficiency of post-harvest processes. Full article
(This article belongs to the Special Issue Latest Research on Post-Harvest Technology to Reduce Food Loss)
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20 pages, 2727 KB  
Article
Mechanochemical Effects of High-Intensity Ultrasound on Dual Starch Modification of Mango Cotyledons
by Ramiro Torres-Gallo, Ricardo Andrade-Pizarro, Diego F. Tirado, Andrés Chávez-Salazar and Francisco J. Castellanos-Galeano
AgriEngineering 2025, 7(6), 190; https://doi.org/10.3390/agriengineering7060190 - 13 Jun 2025
Viewed by 803
Abstract
The starch modification of mango cotyledons with both single ultrasound (US) and dual (US followed by octenyl succinic anhydride, OSA) was optimized by response surface methodology (RSM). The mechanochemical effects of ultrasound on amylose content, particle size, and dual modification efficiency were assessed. [...] Read more.
The starch modification of mango cotyledons with both single ultrasound (US) and dual (US followed by octenyl succinic anhydride, OSA) was optimized by response surface methodology (RSM). The mechanochemical effects of ultrasound on amylose content, particle size, and dual modification efficiency were assessed. In addition, the structural, thermal, morphological, and functional properties were evaluated. After optimization with single US (41 min and 91% sonication intensity), sonication induced starch granule fragmentation, altering amorphous and partially crystalline regions, which increased amylose content (34%), reduced particle size (Dx50 = 12 μm), and modified granule surface morphology. The dual modification (the subsequent OSA reaction lasted 4.6 h under the same conditions) reached a degree of substitution of 0.02 and 81% efficiency, imparting amphiphilic properties to the starch. OSA groups were mainly incorporated into amorphous and surface regions, which decreased crystallinity, gelatinization temperature, and enthalpy. The synergistic effect of the modification with US and OSA in the dual modification significantly improved the solubility and swelling power of starch, resulting in better dispersion, functionality in aqueous systems, and chemical reactivity. These findings highlight the potential of dual modification to transform mango cotyledon starch into a versatile ingredient in the food industry as a thickener, a stabilizer in soups and sauces, an emulsifier, a carrier of bioactive and edible films; in the cosmetic industry as a gelling and absorbent agent; and in the pharmaceutical industry for the controlled release of drugs. Furthermore, valorizing mango cotyledons supports circular economy principles, promoting sustainable and value-added food product development. Full article
(This article belongs to the Special Issue Latest Research on Post-Harvest Technology to Reduce Food Loss)
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