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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 2063

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 Website
Guest Editor
School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang, China
Interests: agrofood safety; agricultural intelligent; food analysis and quality control; environmental hazardous control; chemometrics; spectroscopy modeling
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

Published Papers (2 papers)

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Research

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
Viewed by 667
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
Viewed by 1100
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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