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The Application of Chemometrics-Assisted Spectroscopy in Authentication of Foods and...
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The Application of Chemometrics-Assisted Spectroscopy in Authentication of Foods and Beverages

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Drinks and Liquid Nutrition".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 7406

Special Issue Editors

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
Dr. Zhanming Li

E-Mail
Guest Editor
School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang, China
Interests: food safety; food analysis and quality control; food hazardous control; chemometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the ultimate convenience of molecular spectroscopy, it has become an increasingly popular tool for food safety evaluation. There are multiple advantages of molecular spectroscopy detection, such as non-destruction, free-reagent, rapidness, and on-site possibilities. Without doubt, all new spectroscopy with chemometrics-assisted algorithms, technologies, and equipment will make detection in food authentication more convenient and sustainable. Based on the above research or methodologies, the evaluation standard of food quality could be greatly improved. As a result, this Research Topic focuses on food or beverage authentication. We welcome review submissions, innovative methods, or perspective articles on the advanced utilization of vibrational spectroscopy or innovative spectral approaches. Additionally, we set no limit on food fraud identification involving new chemometrics development, mobility or miniature implementation, selection of high-throughput equipment, statistical modeling, spectral signal processing, pattern recognition, 5G data transmission, and hyperdata mining.

Dr. Yue Huang
Dr. Zhanming Li
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. 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 authentication
  • food safety
  • chemometrics
  • spectroscopy
  • intelligent detection

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Published Papers (4 papers)

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Research

16 pages, 1864 KiB  
Article
Quantitative Analysis of High-Price Rice Adulteration Based on Near-Infrared Spectroscopy Combined with Chemometrics
by Mengting Chen, Jiahui Song, Haiyan He, Yue Yu, Ruoni Wang, Yue Huang and Zhanming Li
Foods 2024, 13(20), 3241; https://doi.org/10.3390/foods13203241 - 11 Oct 2024
Cited by 1 | Viewed by 1649
Abstract
Near-infrared spectroscopy (NIRS) holds significant promise in detecting food adulteration due to its non-destructive, simple, and user-friendly properties. This study employed NIRS in conjunction with chemometrics to estimate the content of low-price rice flours (Nanjing, Songjing, Jiangxi silk, Yunhui) blended with high-price rice [...] Read more.
Near-infrared spectroscopy (NIRS) holds significant promise in detecting food adulteration due to its non-destructive, simple, and user-friendly properties. This study employed NIRS in conjunction with chemometrics to estimate the content of low-price rice flours (Nanjing, Songjing, Jiangxi silk, Yunhui) blended with high-price rice (Wuchang and Thai fragrant). Partial least squares regression (PLSR), support vector regression (SVR), and back-propagation neural network (BPNN) models were deployed to analyze the spectral data of adulterated samples and assess the degree of contamination. Various preprocessing techniques, parameter optimization strategies, and wavelength selection methods were employed to enhance model accuracy. With correlation coefficients exceeding 87%, the BPNN models exhibited high accuracy in estimating adulteration levels in high-price rice. The SPXY-SG-BPNN, SPXY-MMN-BPNN, KS-SNV-BPNN, and SPXY-SG-BPNN models showcased exceptional performance in discerning mixed Wuchang japonica, Thai fragrant indica, and Thai fragrant Yunhui rice. As shown above, NIRS demonstrated its potential as a rapid, non-destructive method for detecting low-price rice in premium rice blends. Future studies should be performed to concentrate on enhancing the models’ versatility and practical applicability. Full article
(This article belongs to the Special Issue The Application of Chemometrics-Assisted Spectroscopy in Authentication of Foods and Beverages)
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17 pages, 6260 KiB  
Article
Glycogen Quantification and Gender Identification in Di-, Tri-, and Tetraploid Crassostrea gigas Using Portable Near-Infrared Spectroscopy
by Jingjing Fu, Weijun Wang, Youmei Sun, Yousen Zhang, Qihao Luo, Zhongping Wang, Degang Wang, Yanwei Feng, Xiaohui Xu, Cuiju Cui, Guohua Sun, Zan Li and Jianmin Yang
Foods 2024, 13(14), 2191; https://doi.org/10.3390/foods13142191 - 11 Jul 2024
Viewed by 1001
Abstract
Near-infrared spectroscopy (NIR) has become an essential tool for non-destructive analysis in various fields, including aquaculture. This study presents a pioneering application of portable NIR spectrometers to analyze glycogen content in the gonadal tissues of the Pacific oyster (Crassostrea gigas), marking [...] Read more.
Near-infrared spectroscopy (NIR) has become an essential tool for non-destructive analysis in various fields, including aquaculture. This study presents a pioneering application of portable NIR spectrometers to analyze glycogen content in the gonadal tissues of the Pacific oyster (Crassostrea gigas), marking the first instance of developing quantitative models for glycogen in tetraploid C. gigas. The research also provides a comparative analysis with models for diploid and triploid oysters, underscoring the innovative use of portable NIR technology in aquaculture. Two portable NIR spectrometers were employed: the Micro NIR 1700 (908–1676 nm) and the Micro PHAZIR RX (1624–2460 nm). Near-infrared spectra were acquired from the gonadal tissues of diploid, triploid, and tetraploid C. gigas. Quantitative models for glycogen content were developed and validated using cross-validation methods. Additionally, qualitative models for different ploidies and genders were established. For the Micro NIR 1700, the cross-validation correlation coefficients (Rcv) and cross-validation relative predictive errors (RPDcv) for glycogen were 0.949 and 3.191 for diploids, 0.915 and 2.498 for triploids, and 0.902 and 2.310 for tetraploids. The Micro PHAZIR RX achieved Rcv and RPDcv values of 0.781 and 2.240 for diploids, 0.839 and 2.504 for triploids, and 0.717 and 1.851 for tetraploids. The Micro NIR 1700 demonstrated superior quantitative performance, with RPD values exceeding 2, indicating its effectiveness in predicting glycogen content across different ploidy levels. Qualitative models showed a performance index of 91.6 for diploid and 95 for tetraploid genders using the Micro NIR 1700, while the Micro PHAZIR RX achieved correct identification rates of 99.79% and 100% for diploid and tetraploid genders, respectively. However, differentiation of ploidies was less successful with both instruments. This study’s originality lies in establishing the first quantitative models for glycogen content in tetraploid C. gigas using portable NIR spectrometers, highlighting the significant advancements in non-destructive glycogen analysis. The applicability of these findings is substantial for oyster breeding programs focused on enhancing meat quality traits. These models provide a valuable phenotyping tool for selecting oysters with optimal glycogen content, demonstrating the practical utility of portable NIR technology in aquaculture. Full article
(This article belongs to the Special Issue The Application of Chemometrics-Assisted Spectroscopy in Authentication of Foods and Beverages)
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16 pages, 3071 KiB  
Article
Discrimination among Fresh, Frozen–Stored and Frozen–Thawed Beef Cuts by Hyperspectral Imaging
by Yuewen Yu, Wenliang Chen, Hanwen Zhang, Rong Liu and Chenxi Li
Foods 2024, 13(7), 973; https://doi.org/10.3390/foods13070973 - 22 Mar 2024
Cited by 6 | Viewed by 1886
Abstract
The detection of the storage state of frozen meat, especially meat frozen–thawed several times, has always been important for food safety inspections. Hyperspectral imaging (HSI) is widely applied to detect the freshness and quality of meat or meat products. This study investigated the [...] Read more.
The detection of the storage state of frozen meat, especially meat frozen–thawed several times, has always been important for food safety inspections. Hyperspectral imaging (HSI) is widely applied to detect the freshness and quality of meat or meat products. This study investigated the feasibility of the low-cost HSI system, combined with the chemometrics method, to classify beef cuts among fresh (F), frozen–stored (F–S), frozen–thawed three times (F–T–3) and frozen–thawed five times (F–T–5). A compact, low-cost HSI system was designed and calibrated for beef sample measurement. The classification model was developed for meat analysis with a method to distinguish fat and muscle, a CARS algorithm to extract the optimal wavelength subset and three classifiers to identify each beef cut among different freezing processes. The results demonstrated that classification models based on feature variables extracted from differentiated tissue spectra achieved better performances, with ACCs of 92.75% for PLS-DA, 97.83% for SVM and 95.03% for BP-ANN. A visualization map was proposed to provide detailed information about the changes in freshness of beef cuts after freeze–thawing. Furthermore, this study demonstrated the potential of implementing a reasonably priced HSI system in the food industry. Full article
(This article belongs to the Special Issue The Application of Chemometrics-Assisted Spectroscopy in Authentication of Foods and Beverages)
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17 pages, 7440 KiB  
Article
Rapid Indentification of Auramine O Dyeing Adulteration in Dendrobium officinale, Saffron and Curcuma by SERS Raman Spectroscopy Combined with SSA-BP Neural Networks Model
by Leilei Zhang, Caihong Zhang, Wenxuan Li, Liang Li, Peng Zhang, Cheng Zhu, Yanfei Ding and Hongwei Sun
Foods 2023, 12(22), 4124; https://doi.org/10.3390/foods12224124 - 14 Nov 2023
Cited by 6 | Viewed by 2299
Abstract
(1) Background: Rapid and accurate determination of the content of the chemical dye Auramine O(AO) in traditional Chinese medicines (TCMs) is critical for controlling the quality of TCMs. (2) Methods: Firstly, various models were developed to detect AO content in Dendrobium officinale ( [...] Read more.
(1) Background: Rapid and accurate determination of the content of the chemical dye Auramine O(AO) in traditional Chinese medicines (TCMs) is critical for controlling the quality of TCMs. (2) Methods: Firstly, various models were developed to detect AO content in Dendrobium officinale (D. officinale). Then, the detection of AO content in Saffron and Curcuma using the D. officinale training set as a calibration model. Finally, Saffron and Curcuma samples were added to the training set of D. officinale to predict the AO content in Saffron and Curcuma using secondary wavelength screening. (3) Results: The results show that the sparrow search algorithm (SSA)-backpropagation (BP) neural network (SSA-BP) model can accurately predict AO content in D. officinale, with Rp2 = 0.962, and RMSEP = 0.080 mg/mL. Some Curcuma samples and Saffron samples were added to the training set and after the secondary feature wavelength screening: The Support Vector Machines (SVM) quantitative model predicted Rp2 fluctuated in the range of 0.780 ± 0.035 for the content of AO in Saffron when 579, 781, 1195, 1363, 1440, 1553 and 1657 cm−1 were selected as characteristic wavelengths; the Partial Least Squares Regression (PLSR) model predicted Rp2 fluctuated in the range of 0.500 ± 0.035 for the content of AO in Curcuma when 579, 811, 1195, 1353, 1440, 1553 and 1635 cm−1 were selected as the characteristic wavelengths. The robustness and generalization performance of the model were improved. (4) Conclusion: In this study, it has been discovered that the combination of surface-enhanced Raman spectroscopy (SERS) and machine learning algorithms can effectively and promptly detect the content of AO in various types of TCMs. Full article
(This article belongs to the Special Issue The Application of Chemometrics-Assisted Spectroscopy in Authentication of Foods and Beverages)
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