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Applied Sciences
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Technical Advances in Food and Agricultural Product Quality Detection
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Technical Advances in Food and Agricultural Product Quality Detection

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Food Science and Technology".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 5870

Special Issue Editors

Dr. Didem Aykas

E-Mail Website
Guest Editor
Department of Food Engineering, Faculty of Engineering, Adnan Menderes University, 09010 Aydin, Turkey
Interests: vibrational spectroscopy; chemometrics; chromatography; portable and handheld sensors; food characterization; authentication
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luis E Rodriguez-Saona

E-Mail Website
Guest Editor
College of Food, Agricultural, and Environmental Sciences, Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA
Interests: NIR; FTIR Raman; miniaturized devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing demand for swift and precise quality assessment of food and agricultural products stems from the necessity to meet quality standards, uphold safety and hygiene protocols, and address concerns regarding food adulteration within the food supply chain. Regrettably, evaluating agricultural and food product quality still heavily relies on subjective measures, including visual inspection, consumer preference, and traditional wet chemistry methods, which can be labor-intensive, time-consuming, frequently unreliable, and require hazardous chemicals. Recent advancements in hardware design and data mining practices have unlocked the potential of new techniques and sensors as promising tools for routine analyses of agricultural and food products.

This Special Issue aims to explore cutting-edge technological advancements that ensure quality and food safety in food and agricultural products. We invite original research articles, reviews, and short communications that delve into emerging device technologies, emphasizing miniaturization or their application for characterizing food products. Your contributions in this area are highly encouraged and welcomed.

Dr. Didem Aykas
Prof. Dr. Luis E Rodriguez-Saona
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. Applied Sciences 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 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

  • in-field applications
  • high-throughput screening
  • miniaturization
  • artificial intelligence
  • chromatography
  • mass spectroscopy
  • FT-IR
  • NIR
  • hyperspectral Imaging
  • Raman
  • NIR

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Published Papers (4 papers)

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Research

18 pages, 1311 KiB  
Article
Comparison of Miniaturized and Benchtop NIR Spectrophotometers for Quantifying the Fatty Acid Profile of Iberian Ham
by Miriam Hernández-Jiménez, Isabel Revilla, Ana M. Vivar-Quintana, Justyna Grabska, Krzysztof B. Beć and Christian W. Huck
Appl. Sci. 2024, 14(22), 10680; https://doi.org/10.3390/app142210680 - 19 Nov 2024
Viewed by 307
Abstract
Iberian ham is a highly valued product, and considerable efforts have been made to characterize it quickly and accurately. In this scenario, portable NIR devices could provide an effective solution for the assessment of its attributes. However, the calibration quality of NIR equipment [...] Read more.
Iberian ham is a highly valued product, and considerable efforts have been made to characterize it quickly and accurately. In this scenario, portable NIR devices could provide an effective solution for the assessment of its attributes. However, the calibration quality of NIR equipment is directly influenced by the relevance of the used spectral region. Therefore, this study aims to evaluate the suitability of different NIR spectrometers, including four portable and one benchtop instrument, with varying spectral working ranges for quantifying the fatty acid composition of Iberian ham. Spectral measurements were carried out on both the muscle and the fat of the ham slices. The results showed that 24 equations with an RSQ > 0.5 were obtained for both the muscle and fat for the NIRFlex N-500 benchtop instrument, while 19 and 14 equations were obtained in the muscle and 16 and 10 equations in the fat for the Enterprise Sensor and MicroNIR, respectively. In general, more fatty acids could be calibrated when the spectra were taken from lean meat, except with the SCiO Sensor. Measurements performed in the lean and fat zones delivered complementary information. These initial findings indicate the suitability of using miniaturized NIR sensors, which are faster, are less expensive, and enable on-site measurements, for analyzing fatty acids in Iberian ham. Full article
(This article belongs to the Special Issue Technical Advances in Food and Agricultural Product Quality Detection)
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14 pages, 4591 KiB  
Article
A Deep Learning-Based Crop Disease Diagnosis Method Using Multimodal Mixup Augmentation
by Hyunseok Lee, Young-Sang Park, Songho Yang, Hoyul Lee, Tae-Jin Park and Doyeob Yeo
Appl. Sci. 2024, 14(10), 4322; https://doi.org/10.3390/app14104322 - 20 May 2024
Cited by 1 | Viewed by 1338
Abstract
With the widespread adoption of smart farms and continuous advancements in IoT (Internet of Things) technology, acquiring diverse additional data has become increasingly convenient. Consequently, studies relevant to deep learning models that leverage multimodal data for crop disease diagnosis and associated data augmentation [...] Read more.
With the widespread adoption of smart farms and continuous advancements in IoT (Internet of Things) technology, acquiring diverse additional data has become increasingly convenient. Consequently, studies relevant to deep learning models that leverage multimodal data for crop disease diagnosis and associated data augmentation methods are significantly growing. We propose a comprehensive deep learning model that predicts crop type, detects disease presence, and assesses disease severity at the same time. We utilize multimodal data comprising crop images and environmental variables such as temperature, humidity, and dew points. We confirmed that the results of diagnosing crop diseases using multimodal data improved 2.58%p performance compared to using crop images only. We also propose a multimodal-based mixup augmentation method capable of utilizing both image and environmental data. In this study, multimodal data refer to data from multiple sources, and multimodal mixup is a data augmentation technique that combines multimodal data for training. This expands the conventional mixup technique that was originally applied solely to image data. Our multimodal mixup augmentation method showcases a performance improvement of 1.33%p compared to the original mixup method. Full article
(This article belongs to the Special Issue Technical Advances in Food and Agricultural Product Quality Detection)
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11 pages, 1821 KiB  
Article
Discrimination of Cheese Products Regarding Milk Species’ Origin Using FTIR, 1H-NMR, and Chemometrics
by Maria Tarapoulouzi, Ioannis Pashalidis and Charis R. Theocharis
Appl. Sci. 2024, 14(6), 2584; https://doi.org/10.3390/app14062584 - 19 Mar 2024
Viewed by 1727
Abstract
The present study deals with the discrimination of various European cheese products based on spectroscopic data and chemometric analysis. It is the first study that includes cheese products from Cyprus along with cheese samples from abroad and several different cheese types. Therefore, forty-nine [...] Read more.
The present study deals with the discrimination of various European cheese products based on spectroscopic data and chemometric analysis. It is the first study that includes cheese products from Cyprus along with cheese samples from abroad and several different cheese types. Therefore, forty-nine samples were collected, freeze-dried, and measured by using spectroscopic techniques, such as FTIR (Fourier-Transform Infrared Spectroscopy) and 1H-NMR (proton nuclear magnetic resonance). Discriminant analysis was applied, particularly OPLS-DA. All data obtained from 1H-NMR were included, whereas, regarding the FTIR data, only the spectral subregion between 1900 and 400 cm−1 was used in the extracted model. The cheese samples were classified according to the milk species’ origin. In the future, the samples of this study will be enriched for further testing with spectroscopic techniques and chemometrics. Full article
(This article belongs to the Special Issue Technical Advances in Food and Agricultural Product Quality Detection)
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14 pages, 1370 KiB  
Article
What’s in Your Fruit Juice?—Rapid Quality Screening Based on Infrared (FT-IR) Spectroscopy
by Didem P. Aykas and Luis Rodriguez-Saona
Appl. Sci. 2024, 14(4), 1654; https://doi.org/10.3390/app14041654 - 19 Feb 2024
Viewed by 1678
Abstract
Fruit juices (FJ) have gained widespread global consumption, driven by their perceived health benefits. The accuracy of nutrition information is essential for consumers assessing FJ quality, especially with increasing concerns about added sugars and obesity risk. Conversely, ascorbic acid (Vitamin C), found in [...] Read more.
Fruit juices (FJ) have gained widespread global consumption, driven by their perceived health benefits. The accuracy of nutrition information is essential for consumers assessing FJ quality, especially with increasing concerns about added sugars and obesity risk. Conversely, ascorbic acid (Vitamin C), found in nature in many fruits and vegetables, is often lost due to its susceptibility to light, air, and heat, and it undergoes fortification during FJ production. Current analytical methods for determining FJ components are time-consuming and labor-intensive, prompting the need for rapid analytical tools. This study employed a field-deployable portable FT-IR device, requiring no sample preparation, to simultaneously predict multiple quality traits in 68 FJ samples from US markets. Using partial least square regression (PLSR) models, a strong correlation (RCV ≥ 0.93) between FT-IR predictions and reference values was obtained, with a low standard error of prediction. Remarkably, 21% and 37% of FJs deviated from nutrition label values for sugars and ascorbic acid, respectively. Portable FT-IR devices offer non-destructive, simultaneous, simple, and high-throughput approaches for chemical profiling and real-time prediction of sugars and acid levels in FJs. Their handiness and ruggedness can provide food processors with a valuable “out-of-the-laboratory” analytical tool. Full article
(This article belongs to the Special Issue Technical Advances in Food and Agricultural Product Quality Detection)
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