Advanced Analytical Methods for Determining the Origin of Foods

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

Deadline for manuscript submissions: closed (25 April 2023) | Viewed by 8869

Special Issue Editors

Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: food analysis; food authenticity; food traceability; mineral element; stable isotope; omics
Special Issues, Collections and Topics in MDPI journals
Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China
Interests: chemoinformatics and artificial intelligence; food quality and safety testing
Chinese Academy of Inspection and Quarantine, Beijing 100176, China
Interests: food authentication; metabolomics; LC-MS; GC-MS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the market economy driving the development of the food industry, consumers' focus on food has gradually shifted from food safety issues to food quality and brand aspects. Especially in recent years, driven by economic interests, the problems of "food authenticity" such as species adulteration, origin fraud and shoddy food occur frequently, which has hindered the development of food production, consumption and management. With the innovation and development of food traceability technology, emerging technologies such as spectroscopy, mass spectroscopy, DNA technology, senses and omics technology have been applied in the research of food traceability and authenticity. This Special Issue aims to contribute to filling the gap in the knowledge about novel methodologies including instrumental methods, chemometrics and other advanced means for determining the authenticity and geographical origin of foods. This Special Issue will focus on the advanced technology of food authenticity and traceability, aiming to promote the development of the origin traceability and authenticity identification technology of agricultural and food products, and further safeguard the rights and interests of consumers and the market order of the food industry.

Dr. Yan Zhao
Dr. Liangxiao Zhang
Dr. Jiukai Zhang
Guest Editors

Manuscript Submission Information

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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

  • origin of food
  • traceability
  • authenticity
  • chromatograph
  • spectroscopy
  • mass spectroscopy
  • DNA technology
  • sensory
  • omics technology
  • chemometrics

Published Papers (5 papers)

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Research

11 pages, 1177 KiB  
Article
A New and Effective Method to Trace Tibetan Chicken by Amino Acid Profiling
Foods 2023, 12(4), 876; https://doi.org/10.3390/foods12040876 - 18 Feb 2023
Cited by 1 | Viewed by 1172
Abstract
As a “rare bird on the plateau”, the Tibetan chicken is rich in nutrition and has high medicinal value. In order to quickly and effectively identify the source of food safety problems and to label fraud regarding this animal, it is necessary to [...] Read more.
As a “rare bird on the plateau”, the Tibetan chicken is rich in nutrition and has high medicinal value. In order to quickly and effectively identify the source of food safety problems and to label fraud regarding this animal, it is necessary to identify the geographical traceability of the Tibetan chicken. In this study, Tibetan chicken samples from four different cities in Tibet, China were analyzed. The amino acid profiles of Tibetan chicken samples were characterized and further subjected to chemometric analyses, including orthogonal least squares discriminant analysis, hierarchical cluster analysis, and linear discriminant analysis. The original discrimination rate was 94.4%, and the cross-validation rate was 93.3%. Moreover, the correlation between amino acid concentrations and altitudes in Tibetan chicken was studied. With the increase in altitude, all amino acid contents showed a normal distribution. For the first time, amino acid profiling has been comprehensively applied to trace the origin of plateau animal food with satisfactory accuracy. Full article
(This article belongs to the Special Issue Advanced Analytical Methods for Determining the Origin of Foods)
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12 pages, 3402 KiB  
Article
Precise Authenticity of Quinoa, Coix Seed, Wild Rice and Chickpea Components Using Optimized TaqMan Real-Time PCR
Foods 2023, 12(4), 852; https://doi.org/10.3390/foods12040852 - 16 Feb 2023
Cited by 1 | Viewed by 1350
Abstract
Functional food such as, quinoa, coix seed, wild rice and chickpea have experienced rapidly increasing demand globally and exhibit high economic values. Nevertheless, a method for rapid yet accurate detection of these source components is absent, making it difficult to identify commercially available [...] Read more.
Functional food such as, quinoa, coix seed, wild rice and chickpea have experienced rapidly increasing demand globally and exhibit high economic values. Nevertheless, a method for rapid yet accurate detection of these source components is absent, making it difficult to identify commercially available food with labels indicating the presence of relevant components. In this study, we constructed a real-time quantitative polymerase chain reaction (qPCR) method for rapid detection of quinoa, coix seed, wild rice and chickpea in food to identify the authenticity of such food. Specific primers and probes were designed with 2S albumin genes of quinoa, SAD genes of coix seed, ITS genes of wild rice and CIA-2 genes of chickpea as the target genes. The qPCR method could specifically identify the four wild rice strains, yielding, LODs of 0.96, 1.14, 1.04 and 0.97 pg/µL quinoa, coix seed, wild rice and chickpea source components, respectively. Particularly, the method allowed the identification of the target component with content below 0.01%. A total of 24 commercially available food samples of different types were detected by using the method and the results indicate that the developed method is applicable to the detection of different food matrices, as well as authenticity verification in deeply processed food. Full article
(This article belongs to the Special Issue Advanced Analytical Methods for Determining the Origin of Foods)
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24 pages, 2574 KiB  
Article
A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms
Foods 2023, 12(3), 499; https://doi.org/10.3390/foods12030499 - 21 Jan 2023
Cited by 8 | Viewed by 2175
Abstract
Identifying the geographical origins of white tea is of significance because the quality and price of white tea from different production areas vary largely from different growing environment and climatic conditions. In this study, we used near-infrared spectroscopy (NIRS) with white tea ( [...] Read more.
Identifying the geographical origins of white tea is of significance because the quality and price of white tea from different production areas vary largely from different growing environment and climatic conditions. In this study, we used near-infrared spectroscopy (NIRS) with white tea (n = 579) to produce models to discriminate these origins under different conditions. Continuous wavelet transform (CWT), min-max normalization (Minmax), multiplicative scattering correction (MSC) and standard normal variables (SNV) were used to preprocess the original spectra (OS). The approaches of principal component analysis (PCA), linear discriminant analysis (LDA) and successive projection algorithm (SPA) were used for features extraction. Subsequently, identification models of white tea from different provinces of China (DPC), different districts of Fujian Province (DDFP) and authenticity of Fuding white tea (AFWT) were established by K-nearest neighbors (KNN), random forest (RF) and support vector machine (SVM) algorithms. Among the established models, DPC-CWT-LDA-KNN, DDFP-OS-LDA-KNN and AFWT-OS-LDA-KNN have the best performances, with recognition accuracies of 88.97%, 93.88% and 97.96%, respectively; the area under curve (AUC) values were 0.85, 0.93 and 0.98, respectively. The research revealed that NIRS with machine learning algorithms can be an effective tool for the geographical origin traceability of white tea. Full article
(This article belongs to the Special Issue Advanced Analytical Methods for Determining the Origin of Foods)
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14 pages, 2934 KiB  
Article
An Accurate and Rapid Way for Identifying Food Geographical Origin and Authenticity: Editable DNA-Traceable Barcode
Foods 2023, 12(1), 17; https://doi.org/10.3390/foods12010017 - 21 Dec 2022
Cited by 3 | Viewed by 1835
Abstract
DNA offers significant advantages in information density, durability, and replication efficiency compared with information labeling solutions using electronic, magnetic, or optical devices. Synthetic DNA containing specific information via gene editing techniques is a promising identifying approach. We developed a new traceability approach to [...] Read more.
DNA offers significant advantages in information density, durability, and replication efficiency compared with information labeling solutions using electronic, magnetic, or optical devices. Synthetic DNA containing specific information via gene editing techniques is a promising identifying approach. We developed a new traceability approach to convert traditional digitized information into DNA sequence information. We used encapsulation to make it stable for storage and to enable reading and detection by DNA sequencing and PCR-capillary electrophoresis (PCR-CE). The synthesized fragment consisted of a short fragment of the mitochondrial cytochrome oxidase subunit I (COI) gene from the Holothuria fuscogilva (ID: LC593268.1), inserted geographical origin information (18 bp), and authenticity information from Citrus sinensis (20 bp). The obtained DNA-traceable barcodes were cloned into vector PMD19-T. Sanger sequencing of the DNA-traceable barcode vector was 100% accurate and provided a complete readout of the traceability information. Using selected recognition primers CAI-B, DNA-traceable barcodes were identified rapidly by PCR amplification. We encapsulated the DNA-traceable barcodes into amorphous silica spheres and improved the encapsulation procedure to ensure the durability of the DNA-traceable barcodes. To demonstrate the applicability of DNA-traceable barcodes as product labels, we selected Citrus sinensis as an example. We found that the recovered and purified DNA-traceable barcode can be analyzed by standard techniques (PCR-CE for DNA-traceable barcode identification and DNA sequencing for readout). This study provides an accurate and rapid approach to identifying and certifying products’ authenticity and traceability. Full article
(This article belongs to the Special Issue Advanced Analytical Methods for Determining the Origin of Foods)
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17 pages, 3875 KiB  
Article
Bayesian Fusion Model Enhanced Codfish Classification Using Near Infrared and Raman Spectrum
Foods 2022, 11(24), 4100; https://doi.org/10.3390/foods11244100 - 19 Dec 2022
Cited by 1 | Viewed by 1816
Abstract
In this study, a Bayesian-based decision fusion technique was developed for the first time to quickly and non-destructively identify codfish using near infrared (NIRS) and Raman spectroscopy (RS). NIRS and RS spectra from 320 codfish samples were collected, and separate partial least squares [...] Read more.
In this study, a Bayesian-based decision fusion technique was developed for the first time to quickly and non-destructively identify codfish using near infrared (NIRS) and Raman spectroscopy (RS). NIRS and RS spectra from 320 codfish samples were collected, and separate partial least squares discriminant analysis (PLS-DA) models were developed to establish the relationship between the raw data and cod identity for each spectral technique. Three decision fusion methods: decision fusion, data layer or feature layer, were tested and compared. The decision fusion model based on the Bayesian algorithm (NIRS-RS-B) was developed on the optimal discrimination features of NIRS and RS data (NIRS-RS) extracted by the PLS-DA method whereas the other fusion models followed conventional, non-Bayesian approaches. The Bayesian model showed enhanced classification metrics (92% sensitivity, 98% specificity, 98% accuracy) that were significantly superior to those demonstrated by any of other two spectroscopic methods (NIRS, RS) and the two data fusion methods (data layer fused, NIRS-RS-D, or feature layer fused, NIRS-RS-F). This novel proposed approach can provide an alternative classification for codfish and potentially other food speciation cases. Full article
(This article belongs to the Special Issue Advanced Analytical Methods for Determining the Origin of Foods)
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