Applications of Molecular Spectroscopy in Agri-Food Science and Manufacturing Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: closed (10 May 2023) | Viewed by 11090

Special Issue Editors


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Guest Editor
Department of Food and Analytical Chemistry, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary
Interests: food analysis; chemometrics; UV/Vis spectroscopy; NIR spectroscopy

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Guest Editor
Department of Measurements and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences (MATE), Gödöllő/Budapest, Hungary
Interests: instrumental taste and aroma sensing; near-infrared spectroscopy; aquaphotomics; chemometrics
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Guest Editor
Department of Food Science & Technology, Kwame Nkrumah University of Science & Technology, Kumasi-Ghana 00000, Ghana
Interests: electronic tongue; near-infrared spectroscopy; multivariate data analysis; aquaphotomics; chemometrics; fermentation; food quality

Special Issue Information

Dear Colleagues,

The various methods of molecular spectroscopy (UV-VIS, NIR, hyperspectral techniques, Raman, etc.) offer a wide range of possibilities, from the quantitative study of physicochemical and chemical properties to the structural investigation of molecules.

This broad applicability also means that spectroscopy used in almost all scientific fields (agriculture, food, medicine, diagnostics, etc.), complemented by the science of chemometrics, which is essential for data processing.

This Special Issue on “Application of Molecular Spectroscopy in Agricultural and Food Sciences” seeks high-quality works focusing on the field of agriculture and food science.

Topics include, but are not limited to:

  • Applications of molecular spectroscopy in quality assurance;
  • Advanced analytical techniques for monitoring agricultural products, food raw materials, and finished food products;
  • New food testing advances (e.g., insect fortified foods);
  • Relationship between UV/VIS, NIR, etc. techniques and chemometrics;
  • Recent applications of molecular spectroscopy;
  • Monitoring manufacturing processes using advanced molecular spectroscopy techniques;
  • Adulteration and origin identification.

Dr. Marietta Fodor
Dr. Zoltan Kovacs
Dr. John-Lewis Zinia Zaukuu
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. Processes 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 2000 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

  • molecular spectroscopy
  • food
  • agriculture
  • quality assurance
  • adulteration

Published Papers (4 papers)

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Research

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12 pages, 2515 KiB  
Article
Identification of Four Chicken Breeds by Hyperspectral Imaging Combined with Chemometrics
by Tiande Cheng, Peng Li, Junchao Ma, Xingguo Tian and Nan Zhong
Processes 2022, 10(8), 1484; https://doi.org/10.3390/pr10081484 - 28 Jul 2022
Cited by 3 | Viewed by 1610
Abstract
The current study aims to explore the potential of the combination of hyperspectral imaging and chemometrics in the rapid identification of four chicken breeds. The hyperspectral data of four chicken breeds were collected in the range of 400–900 nm. Five pretreatment methods were [...] Read more.
The current study aims to explore the potential of the combination of hyperspectral imaging and chemometrics in the rapid identification of four chicken breeds. The hyperspectral data of four chicken breeds were collected in the range of 400–900 nm. Five pretreatment methods were used to pretreat the original spectra. The important characteristic wavelength variables were extracted by random frog (RF), successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS) algorithms. The classification models were established by using support vector machine (SVM), k-nearest neighbor (KNN), and partial least squares-discriminant analysis (PLS-DA). The results showed that the mean normalization pretreatment method was preferable, and overall classification accuracy of SVM-based models was higher than that of KNN-based and PLS-DA-based models. The correct classification rate (CCR) of the full-spectrum SVM model (Full-SVM) could reach 96.25%. The SPA method extracted 13 important wavelengths, and the SVM model based on SPA (SPA-SVM) achieved 90% CCR. This study can provide a theoretical reference for the discriminant analysis of chicken breeds. Full article
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11 pages, 1497 KiB  
Article
Evaluation of Portable Vibrational Spectroscopy Sensors as a Tool to Detect Black Cumin Oil Adulteration
by Ahmed Menevseoglu
Processes 2022, 10(3), 503; https://doi.org/10.3390/pr10030503 - 2 Mar 2022
Cited by 6 | Viewed by 1883
Abstract
Black cumin oil adulteration has become a concern because it has numerous health benefits and a high price. Therefore, a simple, non-destructive, and rapid method to identify adulterations in black seed oil is necessary to protect the quality of the oils. This study [...] Read more.
Black cumin oil adulteration has become a concern because it has numerous health benefits and a high price. Therefore, a simple, non-destructive, and rapid method to identify adulterations in black seed oil is necessary to protect the quality of the oils. This study aimed to perform a non-invasive method to authenticate black cumin oil by portable FT-NIR, FT-MIR, and Raman spectrometers. Spectra were collected with portable devices and analyzed using Soft Independent Modelling of Class Analogy (SIMCA) to generate a classification model to identify pure black cumin oil and partial least squares regression (PLSR) to predict the adulterant levels. For confirmation, the fatty acid profile of the oils was determined by gas chromatography (GC). SIMCA and PLSR models provided a very high performance in detecting adulterated samples in all portable units. These portable units showed great potential for rapid and non-destructive monitoring to identify adulterated black cumin oils. Full article
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10 pages, 1833 KiB  
Article
Assessment of Wine Adulteration Using Near Infrared Spectroscopy and Laser Backscattering Imaging
by Anita Hencz, Lien Le Phuong Nguyen, László Baranyai and Donatella Albanese
Processes 2022, 10(1), 95; https://doi.org/10.3390/pr10010095 - 4 Jan 2022
Cited by 8 | Viewed by 2633
Abstract
Food adulteration is in the focus of research due to its negative effect on safety and nutritional value and because of the demand for the protection of brands and regional origins. Portugieser and Sauvignon Blanc wines were selected for experiments. Samples were made [...] Read more.
Food adulteration is in the focus of research due to its negative effect on safety and nutritional value and because of the demand for the protection of brands and regional origins. Portugieser and Sauvignon Blanc wines were selected for experiments. Samples were made by water dilution, the addition of sugar and then a combination of both. Near infrared (NIR) spectra were acquired in the range of 900–1700 nm. Partial least squares regression was performed to predict the adulteration level. The model including all wines and adulterations achieved a prediction error of 0.59% added sugar and 6.85% water dilution. Low-power laser modules were used to collect diffuse reflectance signals at wavelengths of 532, 635, 780, 808, 850, 1064 nm. The general linear model resulted in a higher prediction error of 3.06% added sugar and 20.39% water dilution. Instead of classification, the present study investigated the feasibility of non-destructive methods in the prediction of adulteration level. Laser scattering successfully detected the added sugar with linear discriminant analysis (LDA), but its prediction accuracy was low. NIR spectroscopy might be suitable for rapid non-destructive estimation of wine adulteration. Full article
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Review

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42 pages, 8546 KiB  
Review
Agricultural Potentials of Molecular Spectroscopy and Advances for Food Authentication: An Overview
by John-Lewis Zinia Zaukuu, Eszter Benes, György Bázár, Zoltán Kovács and Marietta Fodor
Processes 2022, 10(2), 214; https://doi.org/10.3390/pr10020214 - 24 Jan 2022
Cited by 15 | Viewed by 4025
Abstract
Meat, fish, coffee, tea, mushroom, and spices are foods that have been acknowledged for their nutritional benefits but are also reportedly targets of fraud and tampering due to their economic value. Conventional methods often take precedence for monitoring these foods, but rapid advanced [...] Read more.
Meat, fish, coffee, tea, mushroom, and spices are foods that have been acknowledged for their nutritional benefits but are also reportedly targets of fraud and tampering due to their economic value. Conventional methods often take precedence for monitoring these foods, but rapid advanced instruments employing molecular spectroscopic techniques are gradually claiming dominance due to their numerous advantages such as low cost, little to no sample preparation, and, above all, their ability to fingerprint and detect a deviation from quality. This review aims to provide a detailed overview of common molecular spectroscopic techniques and their use for agricultural and food quality management. Using multiple databases including ScienceDirect, Scopus, Web of Science, and Google Scholar, 171 research publications including research articles, review papers, and book chapters were thoroughly reviewed and discussed to highlight new trends, accomplishments, challenges, and benefits of using molecular spectroscopic methods for studying food matrices. It was observed that Near infrared spectroscopy (NIRS), Infrared spectroscopy (IR), Hyperspectral imaging (his), and Nuclear magnetic resonance spectroscopy (NMR) stand out in particular for the identification of geographical origin, compositional analysis, authentication, and the detection of adulteration of meat, fish, coffee, tea, mushroom, and spices; however, the potential of UV/Vis, 1H-NMR, and Raman spectroscopy (RS) for similar purposes is not negligible. The methods rely heavily on preprocessing and chemometric methods, but their reliance on conventional reference data which can sometimes be unreliable, for quantitative analysis, is perhaps one of their dominant challenges. Nonetheless, the emergence of handheld versions of these techniques is an area that is continuously being explored for digitalized remote analysis. Full article
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