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Detecting the Quality and Geographic Origin of Agri-Food Products by...
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Detecting the Quality and Geographic Origin of Agri-Food Products by Using Spectroscopic Methods

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Quality and Safety".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 2321

Special Issue Editor

Dr. Yue Huang

E-Mail Website
Guest Editor
College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China
Interests: food quality assessment; agrofood process analysis; food sensory; sensors; chemometrics; spectroscopy modeling; imaging analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the continuous development of spectroscopic technology, the issue of hard-to-discern information of quality level and geographical origin traceability in agri-food products is being resolved by the emerging stage of fast, convenient and scenario-based detection. These spectroscopic means include ultraviolet, infrared, Raman, hyperspectral, and microscopic spectral imaging and machine vision methods. Importantly, computational tools and chemometrics such as artificial intelligence, pattern recognition, data mining, and machine learning are powerfully aiding these spectroscopic methods to better acquire information of adulterant ingredients, grading, and geographical origin identification of agri-foods. Therefore, this Special Issue will focus on, but will not be limited to, original research and reviews on the explorations of atomic spectroscopy, molecular spectroscopy, nuclear magnetic resonance (NMR), and isotope tracing techniques for the detection of agri-food quality, safety, and geographical origins. It is sincerely believed that this Special Issue would greatly popularize the knowledge and application of spectroscopic techniques in the field of agri-food analysis, improving food processing and protecting the economic interests of consumers.

Dr. Yue Huang
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. 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

  • agri-food products
  • spectroscopies
  • quality
  • geographical origin
  • chemometrics

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Published Papers (4 papers)

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Research

18 pages, 3626 KiB  
Article
A Dual-Technology Approach: Handheld NIR Spectrometer and CNN for Fritillaria spp. Quality Control
by Fengling Li, Wen Lei, Juan Li, Xiaoting Wang, Jingyu Su, Tangnuer Sahati, Xiahenazi Aierkenjiang, Ruyi Tian, Weihong Zhou, Jixiong Zhang and Jingjing Xia
Foods 2025, 14(11), 1907; https://doi.org/10.3390/foods14111907 - 28 May 2025
Viewed by 87
Abstract
Fritillaria spp. has an extremely high edible and medicinal value. Different parts of it exhibit significant variations in medicinal efficacy. To rapidly and accurately identify the origin and adulteration of Fritillaria spp., a handheld near-infrared spectrometer was combined with a convolutional neural network [...] Read more.
Fritillaria spp. has an extremely high edible and medicinal value. Different parts of it exhibit significant variations in medicinal efficacy. To rapidly and accurately identify the origin and adulteration of Fritillaria spp., a handheld near-infrared spectrometer was combined with a convolutional neural network (CNN) to establish an efficient and convenient quality assessment method. First, for the origin of Fritillaria spp., the CNN could achieve high accuracy, with 100 ± 0%. The features contributing to the origin of Fritillaria spp. were visualized using gradient-weighted class activation mapping (Grad-CAM). For the adulteration of Fritillaria spp., compared with partial least squares regression (PLSR), the CNN yielded the best performance, with the R2 of the test set being 0.9897. Additionally, to improve the interpretability of the adulteration model, a CNN model was established using data whose dimensions had been reduced by PCA (PCA–CNN), which also achieved an R2 of 0.9876. The features extracted by PCA focused on 1400–1500 nm, which was consistent with Grad-CAM. The visualization of Grad-CAM and the adulteration detection model achieved mutual validation, showing the effectiveness of both methods in analyzing the samples. The experimental results demonstrated that the integration of a handheld near-infrared spectrometer with a CNN enabled both reliable authentication of Fritillaria spp. geographical origins and quantitative determination of adulteration levels, establishing a novel analytical framework for rapid quality evaluation of Fritillaria spp. materials. Full article
(This article belongs to the Special Issue Detecting the Quality and Geographic Origin of Agri-Food Products by Using Spectroscopic Methods)
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13 pages, 5911 KiB  
Article
Research on Beef Marbling Grading Algorithm Based on Improved YOLOv8x
by Jun Liu, Lian Wang, Huafu Xu, Jie Pi and Daoying Wang
Foods 2025, 14(10), 1664; https://doi.org/10.3390/foods14101664 - 8 May 2025
Viewed by 339
Abstract
Marbling is a crucial indicator that significantly impacts beef quality grading. Currently, Chinese beef processing enterprises rely on professional graders who visually assess marbling using national standard atlases. However, this manual evaluation method is highly subjective and time consuming. This study proposes a [...] Read more.
Marbling is a crucial indicator that significantly impacts beef quality grading. Currently, Chinese beef processing enterprises rely on professional graders who visually assess marbling using national standard atlases. However, this manual evaluation method is highly subjective and time consuming. This study proposes a beef marbling grading algorithm based on an enhanced YOLOv8x model to address these challenges. The model integrates a convolutional neural network (CNN) augmented with an improved attention mechanism and loss function, along with a Region-of-Interest (ROI) preprocessing algorithm to automate the marbling grading process. A dataset comprising 1300 beef sample images was collected and split into training and test sets at an 8:2 ratio. Comparative experiments were conducted with other deep learning models as well as ablation tests to validate the proposed model’s effectiveness. The experimental results demonstrate that the improved YOLOv8x achieves a validation accuracy of 99.93%, a practical grading accuracy of 97.82%, and a detection time of less than 0.5 s per image. The proposed algorithm enhances grading efficiency and contributes to intelligent agricultural practices and livestock product quality assessment. Full article
(This article belongs to the Special Issue Detecting the Quality and Geographic Origin of Agri-Food Products by Using Spectroscopic Methods)
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18 pages, 3601 KiB  
Article
The Application of Near-Infrared Spectroscopy Combined with Chemometrics in the Determination of the Nutrient Composition in Chinese Cyperus esculentus L.
by Xiaobo Jiao, Dongliang Guo, Xinjun Zhang, Yunpeng Su, Rong Ma, Lewen Chen, Kun Tian, Jingyu Su, Tangnuer Sahati, Xiahenazi Aierkenjiang, Jingjing Xia and Liqiong Xie
Foods 2025, 14(3), 366; https://doi.org/10.3390/foods14030366 - 23 Jan 2025
Cited by 1 | Viewed by 795
Abstract
The nutritional content of tiger nut (Cyperus esculentus L.) is abundant, rich in oil, protein, and starch. Conventional methods for assessing the nutrient composition of tiger nuts (TNs) are time-consuming and labor-intensive. Near-infrared spectroscopy (NIR) combined with chemometrics has been widely applied [...] Read more.
The nutritional content of tiger nut (Cyperus esculentus L.) is abundant, rich in oil, protein, and starch. Conventional methods for assessing the nutrient composition of tiger nuts (TNs) are time-consuming and labor-intensive. Near-infrared spectroscopy (NIR) combined with chemometrics has been widely applied in rapidly predicting the nutritional content of various crops, but its application to TNs is rare. In order to enhance the practicality of the method, this study employed a portable NIR in conjunction with chemometrics to rapidly predict the contents of crude oil (CO), crude protein (CP), and total starch (TS) from TNs. In the period from 2022 to 2023, we collected a total of 75 TN tuber samples of 28 varieties from Xinjiang Uyghur Autonomous Region and Henan Province. The three main components were measured using common chemical analysis methods. Partial least squares regression (PLSR) was utilized to establish prediction models between NIR and chemical indicators. In addition, to further enhance the prediction performance of the models, various preprocessing and variable selection algorithms were utilized to optimize the prediction models. The optimal models for CO, CP, and TS exhibited coefficient of determination (R2) values of 0.8946, 0.8525, and 0.8778, with root mean square error of prediction (RMSEP) values of 1.1764, 0.7470, and 1.4601, respectively. The absolute errors between the predicted and actual values for the three-indicator spectral measurements were 0.80, 0.59, and 0.99. The results demonstrated that the portable NIR combined with chemometrics could be effectively utilized for the rapid analysis of quality-related components in TNs. With further refinements, this approach could revolutionize TN quality assessment and be used to determine optimal harvest times, as well as facilitate the graded marketing of TNs. Full article
(This article belongs to the Special Issue Detecting the Quality and Geographic Origin of Agri-Food Products by Using Spectroscopic Methods)
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12 pages, 22206 KiB  
Article
Accurate Discrimination of Mold-Damaged Citri Reticulatae Pericarpium Using Partial Least-Squares Discriminant Analysis and Selected Wavelengths
by Huizhen Tan, Yang Liu, Hui Tang, Wei Fan, Liwen Jiang and Pao Li
Foods 2024, 13(23), 3856; https://doi.org/10.3390/foods13233856 - 29 Nov 2024
Viewed by 678
Abstract
Unscrupulous merchants sell the mold-damaged Citri Reticulatae Pericarpium (CRP) after removing the mold. In this study, an accurate and non-destructive strategy was developed for the discrimination of mold-damaged CRPs using portable near-infrared (NIR) spectroscopy and chemometrics. The outer surface and inner surface spectra [...] Read more.
Unscrupulous merchants sell the mold-damaged Citri Reticulatae Pericarpium (CRP) after removing the mold. In this study, an accurate and non-destructive strategy was developed for the discrimination of mold-damaged CRPs using portable near-infrared (NIR) spectroscopy and chemometrics. The outer surface and inner surface spectra were obtained without destroying CRPs. The discrimination models were established using partial least squares-discriminant analysis (PLS-DA) and wavelength selection strategy was used to further improve the discrimination ability. The predictive ability of models was assessed using the test set and an independent test set obtained one month later. The results demonstrate that the models of the outer surface outperform those of the inner surface. With multiplicative scatter correction (MSC)-PLS-DA, 100% accuracies were obtained in test and independent test sets. Furthermore, the wavelength selection strategy simplified the models with 100% discrimination accuracy. In addition, the randomization test (RT)-PLS-DA model developed in this study combines both the benefits of high accuracy and robustness, which can be applied for the accurate discrimination of mold-damaged CRPs. Full article
(This article belongs to the Special Issue Detecting the Quality and Geographic Origin of Agri-Food Products by Using Spectroscopic Methods)
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