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Foods
Special Issues
Application of Analytical Techniques for Food Origin Traceability and Authenticity
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Application of Analytical Techniques for Food Origin Traceability and Authenticity

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

Deadline for manuscript submissions: 4 August 2025 | Viewed by 3773

Special Issue Editor

Dr. Yuwei Yuan

E-Mail Website
Guest Editor
Agricultural Products Quality and Nutrition Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
Interests: food analysis; risk assessment; stable isotope; trace element; food traceability and authenticity; IRMS; ICP-MS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sophisticated analytical methods, such as stable isotope, molecular profiling, and spectroscopic techniques, have enabled the accurate identification of the geographic origin and composition of food products, such as PGIs and special-character foods. The rigorous implementation of these techniques has become increasingly crucial in safeguarding consumer trust and upholding industry standards in the food industry.

This Special Issue will include both well-drafted manuscripts providing an overview of the current knowledge of food origin traceability and authenticity, and experimental investigations utilizing advanced analytical techniques to address specific problems in food adulteration or origin mislabeling.

The aim of this Special Issue is not only to provide a general overview of the analytical methods used to identify various food adulteration and origin mislabeling, but also to outline the current research trends in these methods, and to acquaint the research field of food origin traceability and authenticity with effective theoretical approaches and practical applications.

Dr. Yuwei Yuan
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

  • food fraud
  • food authenticity
  • origin mislabeling
  • PGI
  • origin traceability
  • analytical techniques

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Published Papers (5 papers)

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Research

22 pages, 7150 KiB  
Article
Geographical Origin Traceability of Navel Oranges Based on Near-Infrared Spectroscopy Combined with Deep Learning
by Yue Li, Zhong Ren, Chunyan Zhao and Gaoqiang Liang
Foods 2025, 14(3), 484; https://doi.org/10.3390/foods14030484 (registering DOI) - 3 Feb 2025
Viewed by 1
Abstract
The quality and price of navel oranges vary depending on their geographical origin, thus providing a financial incentive for origin fraud. To prevent this phenomenon, it is necessary to explore a fast, non-destructive, and precise method for tracing the origin of navel oranges. [...] Read more.
The quality and price of navel oranges vary depending on their geographical origin, thus providing a financial incentive for origin fraud. To prevent this phenomenon, it is necessary to explore a fast, non-destructive, and precise method for tracing the origin of navel oranges. In this study, a total of 490 Newhall navel oranges were selected from five major production regions in China, and the diffuse reflectance near-infrared spectrum in 4000–10,000 cm−1 were non-invasively collected. We examined seven preprocessing techniques for the spectra, including Savitzky–Golay (SG) smoothing, first derivative (FD), multiplicative scattering correction (MSC), combinations of SG with MSC (SG+MSC), SG with FD (SG+FD), MSC with FD (MSC+FD), and three combined (SG+MSC+FD). A one-dimensional convolutional neural network (1DCNN) deep learning model for geographical origin tracing of navel orange was established, and five machine learning algorithms, i.e., partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), and back-propagation neural network (BPNN), were compared with 1DCNN. The results show that the 1DCNN model based on the SG+FD preprocessing method achieved the optimal performance for the testing set, with prediction accuracy, precision, recall, and F1-score of 97.92%, 98%, 97.95%, and 97.90%, respectively. Therefore, NIRS combined with deep learning has a significant research and application value in the rapid, nondestructive, and accurate geographical origin traceability of agricultural products. Full article
(This article belongs to the Special Issue Application of Analytical Techniques for Food Origin Traceability and Authenticity)
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17 pages, 6437 KiB  
Article
Application of Infrared Thermography in Identifying Plant Oils
by Maria Marudova, Sotir Sotirov, Nadezhda Kafadarova and Ginka Antova
Foods 2024, 13(24), 4090; https://doi.org/10.3390/foods13244090 - 17 Dec 2024
Viewed by 561
Abstract
In this article, we present a unique system for identifying edible oils through the analysis of their thermophysical properties. The method is based on the use of active infrared thermography. The heating of the oils results from the optical absorption of laser radiation [...] Read more.
In this article, we present a unique system for identifying edible oils through the analysis of their thermophysical properties. The method is based on the use of active infrared thermography. The heating of the oils results from the optical absorption of laser radiation at a specified wavelength. This approach enables greater selectivity in differentiating between various types of edible oils, as the results depend not only on the thermal properties of the specific oils but also on their optical properties, which are uniquely characteristic of each oil. Additionally, the developed system provides a detailed visualization of spatial temperature gradients within the sample’s volume, as well as their changes over time. It overcomes the limitations of other methods that determine only the thermal conductivity coefficients of oils through resistive heating of the sample. In this article, four types of vegetable oils (extra virgin olive oil, sesame oil, sunflower oil, and rapeseed oil) have been studied. Fatty acid analysis, differential scanning calorimetry, and UV-VIS spectroscopy have been used to determine the authenticity, moisture content, and optical properties of the studied samples. The developed system allows for the visualization and determination of the emerging temperature gradients in the sample volume. Full article
(This article belongs to the Special Issue Application of Analytical Techniques for Food Origin Traceability and Authenticity)
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13 pages, 2336 KiB  
Article
Authenticating the Geographical Origin of Jingbai Pear in Northern China by Multiple Stable Isotope and Elemental Analysis
by An Li, Duoyong Zhao, Jiali Li, Jianping Qian, Qiusheng Chen, Xun Qian, Xusheng Yang and Jie Zhao
Foods 2024, 13(21), 3417; https://doi.org/10.3390/foods13213417 - 26 Oct 2024
Viewed by 950
Abstract
The Jingbai pear is one of the best pear species in China with high quality and nutrition values which are closely linked to its geographical origin. With the purpose of discriminating the PGI Mentougou Jingbai pear from three other producing regions, the stable [...] Read more.
The Jingbai pear is one of the best pear species in China with high quality and nutrition values which are closely linked to its geographical origin. With the purpose of discriminating the PGI Mentougou Jingbai pear from three other producing regions, the stable isotope ratios and elemental profiles of the pears (n = 52) and the corresponding soils and groundwater were determined using isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry (ICP-MS), respectively. The results revealed that δ15N, δ18OJ, and Li were significantly different (p < 0.05) in samples from different regions, which indicated their potential to be used in the geographical origin classification of the Jingbai pear. The nitrogen isotopic values of the pear pulp were positively correlated with the δ15N value and nitrogen content of the corresponding soils, whilst the B, Na, K, Cr, and Cd contents of the pear pulps were positively correlated with their corresponding soils. Orthogonal partial least squares discriminant analysis (OPLS-DA) was performed in combination with analysis of the stable isotopes and elemental profiles, making it possible to distinguish the cultivation regions from each other with a high prediction accuracy (a correct classification rate of 92.3%). The results of this study highlight the potential of stable isotope ratios and elemental profiles to trace the geographical origin of pears at a small spatial scale. Full article
(This article belongs to the Special Issue Application of Analytical Techniques for Food Origin Traceability and Authenticity)
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13 pages, 1674 KiB  
Article
Chemometric Discrimination of the Geographical Origin of Rheum tanguticum by Stable Isotope Analysis
by Bayan Nuralykyzy, Jing Nie, Guoying Zhou, Hanyi Mei, Shuo Zhao, Chunlin Li, Karyne M. Rogers, Yongzhi Zhang and Yuwei Yuan
Foods 2024, 13(19), 3176; https://doi.org/10.3390/foods13193176 - 6 Oct 2024
Viewed by 908
Abstract
Rheum tanguticum is one of the primary rhubarb species used for food and medicinal purposes, and it has recently been gaining more attention and recognition. This research represents the first attempt to use stable isotopes and elemental analysis via IRMS to identify the [...] Read more.
Rheum tanguticum is one of the primary rhubarb species used for food and medicinal purposes, and it has recently been gaining more attention and recognition. This research represents the first attempt to use stable isotopes and elemental analysis via IRMS to identify the geographical origin of Rheum tanguticum. A grand total of 190 rhubarb samples were gathered from 38 locations spread throughout the provinces of Gansu, Sichuan, and Qinghai in China. The carbon content showed a decreasing trend in the order of Qinghai, followed by Sichuan, and then Gansu. Nitrogen content was notably higher, with Qinghai and Sichuan displaying similar levels, while Gansu had the lowest nitrogen levels. Significant differences were noted in the δ13C (−28.9 to −26.5‰), δ15N (2.6 to 5.6‰), δ2H (−120.0 to −89.3‰), and δ18O (16.0‰ to 18.8‰) isotopes among the various rhubarb cultivation areas. A significant negative correlation was found between %C and both longitude and humidity. Additionally, δ13C and δ15N isotopes were negatively correlated with longitude, and δ15N showed a negative correlation with humidity as well. δ2H and δ18O isotopes exhibited a strong positive correlation with latitude, while significant negative correlations were observed between δ2H and δ18O isotopes and temperature, precipitation, and humidity. The LDA, PLS-DA, and k-NN models all exhibited strong classification performance in both the training and validation sets, achieving accuracy rates between 82.1% and 91.7%. The combination of stable isotopes, elemental analysis, and chemometrics provides a reliable and efficient discriminant model for accurately determining the geographical origin of R. tanguticum in different regions. In the future, the approach will aid in identifying the geographical origin and efficacy of rhubarb in other studies. Full article
(This article belongs to the Special Issue Application of Analytical Techniques for Food Origin Traceability and Authenticity)
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18 pages, 3452 KiB  
Article
Differentiating Pond-Intensive, Paddy-Ecologically, and Free-Range Cultured Crayfish (Procambarus clarkii) Using Stable Isotope and Multi-Element Analysis Coupled with Chemometrics
by Zhenzhen Xia, Zhi Liu, Yan Liu, Wenwen Cui, Dan Zheng, Mingfang Tao, Youxiang Zhou and Xitian Peng
Foods 2024, 13(18), 2947; https://doi.org/10.3390/foods13182947 - 18 Sep 2024
Viewed by 763
Abstract
The farming pattern of crayfish significantly impacts their quality, safety, and nutrition. Typically, green and ecologically friendly products command higher economic value and market competitiveness. Consequently, intensive farming methods are frequently employed in an attempt to replace these environmentally friendly products, leading to [...] Read more.
The farming pattern of crayfish significantly impacts their quality, safety, and nutrition. Typically, green and ecologically friendly products command higher economic value and market competitiveness. Consequently, intensive farming methods are frequently employed in an attempt to replace these environmentally friendly products, leading to potential instances of commercial fraud. In this study, stable isotope and multi-element analysis were utilized in conjunction with multivariate modeling to differentiate between pond-intensive, paddy-ecologically, and free-range cultured crayfish. The four stable isotope ratios of carbon, nitrogen, hydrogen, and oxygen (δ13C, δ15N, δ2H, δ18O) and 20 elements from 88 crayfish samples and their feeds were determined for variance analysis and correlation analysis. To identify and differentiate three different farming pattern crayfish, unsupervised methods such as hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used, as well as supervised multivariate modeling, specifically partial least squares discriminant analysis (PLS-DA). The HCA and PCA exhibited limited effectiveness in classifying the farming pattern of crayfish, whereas the PLS-DA demonstrated a more robust performance with a predictive accuracy of 90.8%. Additionally, variables such as δ13C, δ15N, δ2H, Mn, and Co exhibited relatively higher contributions in the PLS-DA model, with a variable influence on projection (VIP) greater than 1. This study is the first attempt to use stable isotope and multi-element analysis to distinguish crayfish under three farming patterns. It holds promising potential as an effective strategy for crayfish authentication. Full article
(This article belongs to the Special Issue Application of Analytical Techniques for Food Origin Traceability and Authenticity)
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