Special Issue "Foods Quality Assessed by Chemometrics"

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

Deadline for manuscript submissions: 31 January 2020.

Special Issue Editors

Dr. Christos Soukoulis
E-Mail Website
Guest Editor
Luxembourg Institute of Science and Technology, Environmental Research and Innovation Department, Belvaux, Luxembourg
Interests: functional foods; biopolymers; probiotics; food quality; bioprocess engineering
Dr. Christelle Andre
E-Mail Website
Guest Editor
Luxembourg Institute of Science and Technology, Belvaux, Luxembourg
Interests: plant natural products; food chemistry; nutrition; bioactive compound

Special Issue Information

Dear Colleagues,

Food market globalisation, food security, as well as increasing consumer demand for safe, minimally-processed and wholesome food, impose the need to establish new approaches to identify and assess food quality markers. Nowadays, food industry stakeholders are challenged to promote food quality meeting several prerequisites encompassing sustainable and eco-green processing, increased shelf-life without safety, sensory satisfaction and nutritional value compromises. In addition, food fraud related to deliberate product mislabelling or economically intended adulteration, is of major concern for both industry and regulatory authorities due to cost and public health implications. Notwithstanding a great number of state-of-the-art analytical tools available for food quality fingerprinting their use in most of the cases results in highly complex and big dataset. In this context, chemometrics tools, such as optimisation designs, supervised and unsupervised exploratory analyses and multivariate regression modelling, are commonly implemented as part of food quality assessment.

In this Special Issue, we aim at publishing innovative research and review papers on: Food authenticity and adulteration case studies, foodomics, mathematical modelling and optimisation of food industry relevant unit operations including bioprocessing and food waste valorisation, optimisation of food composition, nutritional value, storage stability and consumers’ acceptability, as well as prediction of food shelf-life, adopting chemometrics assisted instrumental and sensory data analysis approaches.

Dr. Christos Soukoulis
Dr. Christelle Andre
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 papers will be 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 monthly 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 1200 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 quality
  • Food authenticity
  • Food safety
  • Foodomics
  • Sensory quality and consumers’ preference
  • Food product development
  • Process optimization and modelling
  • Chemometrics
  • Data mining
  • Bioactive compounds

Published Papers (4 papers)

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Open AccessArticle
Estimation of Minced Pork Microbiological Spoilage through Fourier Transform Infrared and Visible Spectroscopy and Multispectral Vision Technology
Foods 2019, 8(7), 238; https://doi.org/10.3390/foods8070238 - 01 Jul 2019
Abstract
Spectroscopic and imaging methods coupled with multivariate data analysis have been increasingly studied for the assessment of food quality. The objective of this work was the estimation of microbiological quality of minced pork using non-invasive spectroscopy-based sensors. For this purpose, minced pork patties [...] Read more.
Spectroscopic and imaging methods coupled with multivariate data analysis have been increasingly studied for the assessment of food quality. The objective of this work was the estimation of microbiological quality of minced pork using non-invasive spectroscopy-based sensors. For this purpose, minced pork patties were stored aerobically at different isothermal (4, 8, and 12 °C) and dynamic temperature conditions, and at regular time intervals duplicate samples were subjected to (i) microbiological analyses, (ii) Fourier transform infrared (FTIR) and visible (VIS) spectroscopy measurements, and (iii) multispectral image (MSI) acquisition. Partial-least squares regression models were trained and externally validated using the microbiological/spectral data collected at the isothermal and dynamic temperature storage conditions, respectively. The root mean squared error (RMSE, log CFU/g) for the prediction of the test (external validation) dataset for the FTIR, MSI, and VIS models was 0.915, 1.173, and 1.034, respectively, while the corresponding values of the coefficient of determination (R2) were 0.834, 0.727, and 0.788. Overall, all three tested sensors exhibited a considerable potential for the prediction of the microbiological quality of minced pork. Full article
(This article belongs to the Special Issue Foods Quality Assessed by Chemometrics)
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Open AccessArticle
Nondestructive Classification Analysis of Green Coffee Beans by Using Near-Infrared Spectroscopy
Foods 2019, 8(2), 82; https://doi.org/10.3390/foods8020082 - 22 Feb 2019
Cited by 1
Abstract
Near-infrared spectroscopy (NIRS) is a powerful tool for the nondestructive evaluation of organic materials, and it has found widespread use in a variety of industries. In the food industry, it is important to know the district in which a particular food was produced. [...] Read more.
Near-infrared spectroscopy (NIRS) is a powerful tool for the nondestructive evaluation of organic materials, and it has found widespread use in a variety of industries. In the food industry, it is important to know the district in which a particular food was produced. Therefore, in this study, we focused on determining the production area (five areas and three districts) of green coffee beans using classification analysis and NIRS. Soft independent modeling of class analogy (SIMCA) was applied as the classification method. Samples of green coffee beans produced in seven locations—Cuba, Ethiopia, Indonesia (Bari, Java, and Sumatra), Tanzania, and Yemen—were analyzed. These regions were selected since green coffee beans from these locations are commonly sold in Japan supermarkets. A good classification result was obtained with SIMCA for the seven green bean samples, although some samples were partly classified into several categories. Then, the model distance values of SIMCA were calculated and compared. A few model distance values were ~10; such small values may be the reason for misclassification. However, over a 73% correct classification rate could be achieved for the different kinds of green coffee beans using NIRS. Full article
(This article belongs to the Special Issue Foods Quality Assessed by Chemometrics)
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Open AccessArticle
Applying Fourier Transform Mid Infrared Spectroscopy to Detect the Adulteration of Salmo salar with Oncorhynchus mykiss
Foods 2018, 7(4), 55; https://doi.org/10.3390/foods7040055 - 05 Apr 2018
Cited by 2
Abstract
The aim of this study was to evaluate the potential of Fourier transform infrared (FTIR) spectroscopy coupled with chemometric methods to detect fish adulteration. Muscles of Atlantic salmon (Salmo salar) (SS) and Salmon trout (Onconrhynchus mykiss) (OM) muscles were [...] Read more.
The aim of this study was to evaluate the potential of Fourier transform infrared (FTIR) spectroscopy coupled with chemometric methods to detect fish adulteration. Muscles of Atlantic salmon (Salmo salar) (SS) and Salmon trout (Onconrhynchus mykiss) (OM) muscles were mixed in different percentages and transformed into mini-burgers. These were stored at 3 °C, then examined at 0, 72, 160, and 240 h for deteriorative microorganisms. Mini-burgers was submitted to Soxhlet extraction, following which lipid extracts were analyzed by FTIR. The principal component analysis (PCA) described the studied adulteration using four principal components with an explained variance of 95.60%. PCA showed that the absorbance in the spectral region from 721, 1097, 1370, 1464, 1655, 2805, to 2935, 3009 cm−1 may be attributed to biochemical fingerprints related to differences between SS and OM. The partial least squares regression (PLS-R) predicted the presence/absence of adulteration in fish samples of an external set with high accuracy. The proposed methods have the advantage of allowing quick measurements, despite the storage time of the adulterated fish. FTIR combined with chemometrics showed that a methodology to identify the adulteration of SS with OM can be established, even when stored for different periods of time. Full article
(This article belongs to the Special Issue Foods Quality Assessed by Chemometrics)
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Other

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Open AccessPerspective
From Academia to Reality Check: A Theoretical Framework on the Use of Chemometric in Food Sciences
Foods 2019, 8(5), 164; https://doi.org/10.3390/foods8050164 - 14 May 2019
Cited by 1
Abstract
There is no doubt that the current knowledge in chemistry, biochemistry, biology, and mathematics have led to advances in our understanding about food and food systems. However, the so-called reductionist approach has dominated food research, hindering new developments and innovation in the field. [...] Read more.
There is no doubt that the current knowledge in chemistry, biochemistry, biology, and mathematics have led to advances in our understanding about food and food systems. However, the so-called reductionist approach has dominated food research, hindering new developments and innovation in the field. In the last three decades, food science has moved into the digital and technological era, inducing several challenges resulting from the use of modern instrumental techniques, computing and algorithms incorporated to the exploration, mining, and description of data derived from this complexity. In this environment, food scientists need to be mindful of the issues (advantages and disadvantages) involved in the routine applications of chemometrics. The objective of this opinion paper is to give an overview of the key issues associated with the implementation of chemometrics in food research and development. Please note that specifics about the different methodologies and techniques are beyond the scope of this review. Full article
(This article belongs to the Special Issue Foods Quality Assessed by Chemometrics)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Estimation of minced pork microbiological spoilage through Fourier-transform infrared and visible spectroscopies and multispectral vision technology

Authors: Lemonia-Christina Fengou, Evgenia Spyrelli, Alexandra Lianou, Panagiotis Tsakanikas, Efstathios Z. Panagou, George-John E. Nychas

Affiliation: Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food, Biotechnology and Development, Agricultural University of Athens, Iera Odos 75, Athens 11855, Greece

Abstract: The aim of this work was the evaluation of the microbiological quality of minced pork by means of spectroscopy-based approaches in tandem with multivariate data analysis. Minced pork patties were stored aerobically at different isothermal (0, 4, 8 and 12 °C) and dynamic temperature conditions, and at regular time intervals duplicate samples were subjected to (i) microbiological analyses, (ii) Fourier-transform infrared (FTIR) and visible (VIS) spectroscopy measurements, (iii) and multispectral image (MSI) acquisition. Partial-least squares regression models were calibrated and externally validated using the microbiological/spectral data collected at the isothermal and dynamic temperature storage conditions, respectively. All three tested sensors exhibited a considerable potential for the prediction of the total mesophilic microbial populations of minced pork, with the FTIR and MSI models, however, outperforming the one based on VIS data. The RMSE of prediction of the developed FTIR, MSI and VIS models was 0.853, 0.965 and 1.034, respectively, while the corresponding R2 values were 0.856, 0.815 and 0.788.

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