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Foods
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Spectroscopy and Chemometrics Applied in Food Authentication and Quality Evaluation
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Spectroscopy and Chemometrics Applied in Food Authentication and Quality Evaluation

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

Deadline for manuscript submissions: closed (15 May 2025) | Viewed by 8813

Special Issue Editors

Dr. Caterina Durante

E-Mail Website
Guest Editor
Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Modena, Italy
Interests: food authenticity; food traceability; VOCs; heavy isotopes; data analysis; chemometrics; analytical chemistry
Special Issues, Collections and Topics in MDPI journals
Dr. Lorenzo Strani

E-Mail Website
Guest Editor Assistant
Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Modena, Italy
Interests: food process monitoring; VOCs; chemometrics; process analytical technology (PAT); sensing techniques; multivariate statistical process control (MSPC)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, the importance of analytical tools that can objectively guarantee the quality and authenticity of food products cannot be underestimated. In this context, the use of chemometrics and spectroscopy plays a fundamental role. While spectroscopic techniques allow for obtaining signals rich in information on the chemical composition of a food product, and therefore related to its quality, they also provide a real digital fingerprint, which can be used to univocally identify a sample. However, the use of chemometrics in processing spectroscopic data cannot be overlooked. Indeed, handling, fusing, and interpreting data is difficult, as it is not possible to rapidly extract useful information from spectra without proper statistical tools. Furthermore, authenticity models require optimized analytical methodologies, significant sampling, and validation of the built model. All these aspects can only be correctly handled with the use of chemometrics techniques.

We are pleased to invite you to contribute your valuable work to this Special Issue on “Spectroscopy and Chemometrics Applied in Food Authentication and Quality Evaluation”.

This Special Issue aims to collect papers focused on developing novel analytical methodologies able to guarantee the quality and authenticity of food. In this context, the synergistic use of the spectroscopic characterization of food and chemometrics analysis could provide significant support for the development of these methodologies, given the multivariate nature of spectroscopic fingerprints. In this Special Issue, original research articles and reviews are welcome. In the field of food analysis, research areas may include spectroscopy, chemometrics, experimental design, hyphenated methods, food quality, fraud detection, authentication/characterization, and deep learning.

We look forward to receiving your contributions.

Dr. Caterina Durante
Guest Editor

Dr. Lorenzo Strani
Guest Editor Assistant

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

  • spectroscopy
  • chemometrics
  • experimental design
  • hyphenated methods
  • food quality
  • fraud detection
  • authentication/characterization
  • deep learning

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Published Papers (6 papers)

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Research

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13 pages, 5902 KiB  
Article
Quantitatively Detecting Camellia Oil Products Adulterated by Rice Bran Oil and Corn Oil Using Raman Spectroscopy: A Comparative Study Between Models Utilizing Machine Learning Algorithms and Chemometric Algorithms
by Henan Liu, Sijia Ma, Ni Liang and Xin Wang
Foods 2024, 13(24), 4182; https://doi.org/10.3390/foods13244182 - 23 Dec 2024
Viewed by 910
Abstract
The fast and accurate quantitative detection of camellia oil products is significant for multiple reasons. In this study, rice bran oil and corn oil, whose Raman spectra both hold great similarities with camellia oil, are blended with camellia oil, and the concentration of [...] Read more.
The fast and accurate quantitative detection of camellia oil products is significant for multiple reasons. In this study, rice bran oil and corn oil, whose Raman spectra both hold great similarities with camellia oil, are blended with camellia oil, and the concentration of each composition is predicted by models with varying feature extraction methods and regression algorithms. Back propagation neural network (BPNN), which has been rarely investigated in previous work, is used to construct regression models, the performances of which are compared with models using random forest (RF) and partial least squares regression (PLSR). Independent component analysis (ICA), competitive adaptive reweighing sampling (CARS), and their dual combinations served to extract spectral features. In camellia oil adulteration with rice bran oil, both the ICA-BPNN and ICA-PLSR models are found to achieve satisfactory performances. For camellia oil adulteration with rice bran oil and corn oil, on the other hand, the performances of BPNN-based models are substantially deteriorated, and the best prediction accuracy is achieved by a PLSR model coupled with CARS-ICA. In addition to performance fluctuations with varying regression algorithms, the output for feature extraction method also played a vital role in ultimate prediction performance. Full article
(This article belongs to the Special Issue Spectroscopy and Chemometrics Applied in Food Authentication and Quality Evaluation)
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19 pages, 6929 KiB  
Article
Combining Metal(loid) and Secondary Metabolite Levels in Olea europaea L. Samples for Geographical Identification
by Raffaello Nardin, Gabriella Tamasi, Michele Baglioni, Giacomo Fattori, Amedeo Boldrini, Rodolfo Esposito and Claudio Rossi
Foods 2024, 13(24), 4017; https://doi.org/10.3390/foods13244017 - 12 Dec 2024
Viewed by 757
Abstract
To fight counterfeits, and to protect the consumer, the interest in certifying the origin of agricultural goods has been growing in recent years. In this context and to increase the accuracy of zoning models, multiple analytical techniques must be combined via a multivariate [...] Read more.
To fight counterfeits, and to protect the consumer, the interest in certifying the origin of agricultural goods has been growing in recent years. In this context and to increase the accuracy of zoning models, multiple analytical techniques must be combined via a multivariate approach. During the sampling campaign, leaves and fruits (olives or drupes) were collected from multiple orchards and farms. By means of HPLC-DAD, metabolite levels were evaluated and combined with the trace and ultra-trace metal/metalloid levels evaluated by ICP-MS (QqQ). The combined dataset was then used to develop a model for geographical traceability. Furthermore, the mineral content of the soil, evaluated by means of ICP-MS, was correlated with both the mineral content in the leaves and drupes and the metabolomic profiles to further investigate the connection between the orchard’s location and characteristics of the final products. Full article
(This article belongs to the Special Issue Spectroscopy and Chemometrics Applied in Food Authentication and Quality Evaluation)
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19 pages, 3077 KiB  
Article
Chemical Characterization and Temporal Variability of Pasta Condiment By-Products for Sustainable Waste Management
by Lorenzo Strani, Giulia Farioli, Marina Cocchi, Caterina Durante, Alessandra Olarini and Samuele Pellacani
Foods 2024, 13(18), 3018; https://doi.org/10.3390/foods13183018 - 23 Sep 2024
Viewed by 1183
Abstract
Sustainable waste management is an extremely important issue due to its environmental, economic, and social impacts. Knowledge of the chemical composition of the waste produced is a starting point for its valorization. This research focuses, for the first time, on the by-products of [...] Read more.
Sustainable waste management is an extremely important issue due to its environmental, economic, and social impacts. Knowledge of the chemical composition of the waste produced is a starting point for its valorization. This research focuses, for the first time, on the by-products of pasta condiment production, starting with their characterization. In particular, the presence of potential bioactive compounds and their variability over time have been studied. The latter aspect is crucial for the subsequent valorization of these by-products. In addition to acidity and total phenolic content, an untargeted strategy was adopted, using spectroscopic and chromatographic techniques coupled with chemometrics, to study waste samples coming from four single condiment production lines, i.e., Genoese pesto, tomato, ricotta, and ragù sauces. The presence of lycopene, polyphenols, and several valuable volatile compounds was highlighted. Their presence and relative amounts vary mainly according to the presence of tomatoes in the sauce. The results obtained at different storage times (after 0, 7, 10, and 15 days) showed that the samples studied, despite having similar chemical characteristics, underwent changes after one week of storage and then presented a relatively stable chemical profile. A general decrease is observed after 7 days for almost all the chemical variables monitored, so careful planning within the first days is required to obtain a high recovery. Full article
(This article belongs to the Special Issue Spectroscopy and Chemometrics Applied in Food Authentication and Quality Evaluation)
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22 pages, 3668 KiB  
Article
The Potential Use of Near Infrared Spectroscopy (NIRS) to Determine the Heavy Metals and the Percentage of Blends in Tea
by Isabel Revilla, Miriam Hernández Jiménez, Iván Martínez-Martín, Patricia Valderrama, Marta Rodríguez-Fernández and Ana M. Vivar-Quintana
Foods 2024, 13(3), 450; https://doi.org/10.3390/foods13030450 - 31 Jan 2024
Cited by 2 | Viewed by 2073
Abstract
The following study analyzed the potential of Near Infrared Spectroscopy (NIRS) to predict the metal composition (Al, Pb, As, Hg and Cu) of tea and for establishing discriminant models for pure teas (green, red, and black) and their different blends. A total of [...] Read more.
The following study analyzed the potential of Near Infrared Spectroscopy (NIRS) to predict the metal composition (Al, Pb, As, Hg and Cu) of tea and for establishing discriminant models for pure teas (green, red, and black) and their different blends. A total of 322 samples of pure black, red, and green teas and binary blends were analyzed. The results showed that pure red teas had the highest content of As and Pb, green teas were the only ones containing Hg, and black teas showed higher levels of Cu. NIRS allowed to predict the content of Al, Pb, As, Hg, and Cu with ratio performance deviation values > 3 for all of them. Additionally, it was possible to discriminate pure samples from their respective blends with an accuracy of 98.3% in calibration and 92.3% in validation. However, when the samples were discriminated according to the percentage of blending (>95%, 95–85%, 85–75%, or 75–50% of pure tea) 100% of the samples of 10 out of 12 groups were correctly classified in calibration, but only the groups with a level of pure tea of >95% showed 100% of the samples as being correctly classified as to validation. Full article
(This article belongs to the Special Issue Spectroscopy and Chemometrics Applied in Food Authentication and Quality Evaluation)
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15 pages, 1826 KiB  
Article
Varietal Authenticity Assessment of QTMJ Tea Using Non-Targeted Metabolomics and Multi-Elemental Analysis with Chemometrics
by Huahong Liu, Yuxin Wu, Ziwei Zhao, Zhi Liu, Renjun Liu, Yuelan Pang, Chun Yang, Yun Zhang and Jinfang Nie
Foods 2023, 12(22), 4114; https://doi.org/10.3390/foods12224114 - 13 Nov 2023
Cited by 3 | Viewed by 1859
Abstract
In this paper, a combination of non-targeted metabolomics and multi-element analysis was used to investigate the impact of five different cultivars on the sensory quality of QTMJ tea and identify candidate markers for varietal authenticity assessment. With chemometric analysis, a total of 54 [...] Read more.
In this paper, a combination of non-targeted metabolomics and multi-element analysis was used to investigate the impact of five different cultivars on the sensory quality of QTMJ tea and identify candidate markers for varietal authenticity assessment. With chemometric analysis, a total of 54 differential metabolites were screened, with the abundances significantly varied in the tea cultivars. By contrast, the QTMJ tea from the Yaoshan Xiulv (XL) monovariety presents a much better sensory quality as result of the relatively more abundant anthocyanin glycosides and the lower levels of 2′-o-methyladenosine, denudatine, kynurenic acid and L-pipecolic acid. In addition, multi-elemental analysis found 14 significantly differential elements among the cultivars (VIP > 1 and p < 0.05). The differences and correlations of metabolites and elemental signatures of QTMJ tea between five cultivars were discussed using a Pearson correlation analysis. Element characteristics can be used as the best discriminant index for different cultivars of QTMJT, with a predictive accuracy of 100%. Full article
(This article belongs to the Special Issue Spectroscopy and Chemometrics Applied in Food Authentication and Quality Evaluation)
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Review

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34 pages, 2503 KiB  
Review
Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of Tomatoes
by Yuping Huang, Ziang Li, Zhouchen Bian, Haojun Jin, Guoqing Zheng, Dong Hu, Ye Sun, Chenlong Fan, Weijun Xie and Huimin Fang
Foods 2025, 14(2), 286; https://doi.org/10.3390/foods14020286 - 16 Jan 2025
Cited by 1 | Viewed by 1029
Abstract
Tomato, as the vegetable queen, is cultivated worldwide due to its rich nutrient content and unique flavor. Nondestructive technology provides efficient and noninvasive solutions for the quality assessment of tomatoes. However, processing the substantial datasets to achieve a robust model and enhance detection [...] Read more.
Tomato, as the vegetable queen, is cultivated worldwide due to its rich nutrient content and unique flavor. Nondestructive technology provides efficient and noninvasive solutions for the quality assessment of tomatoes. However, processing the substantial datasets to achieve a robust model and enhance detection performance for nondestructive technology is a great challenge until deep learning is developed. The aim of this paper is to provide a systematical overview of the principles and application for three categories of nondestructive detection techniques based on mechanical characterization, electromagnetic characterization, as well as electrochemical sensors. Tomato quality assessment is analyzed, and the characteristics of different nondestructive techniques are compared. Various data analysis methods based on deep learning are explored and the applications in tomato assessment using nondestructive techniques with deep learning are also summarized. Limitations and future expectations for the quality assessment of the tomato industry by nondestructive techniques along with deep learning are discussed. The ongoing advancements in optical equipment and deep learning methods lead to a promising outlook for the application in the tomato industry and agricultural engineering. Full article
(This article belongs to the Special Issue Spectroscopy and Chemometrics Applied in Food Authentication and Quality Evaluation)
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