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Foods
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Spectroscopy in Grape and Wine Chemistry and Colour
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Spectroscopy in Grape and Wine Chemistry and Colour

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

Deadline for manuscript submissions: closed (20 April 2021) | Viewed by 15016

Special Issue Editors

Prof. Dr. Jose Miguel Hernandez-Hierro

E-Mail Website
Guest Editor
Food Colour and Quality Laboratory (A. Nutrición y Bromatología), Facultad de Farmacia, Universidad de Sevilla, Sevilla, Spain
Interests: spectroscopy of agricultural products (especially oneological products); grape and wine chemistry and colour; food chemistry; polyphenols; food colour; oenology; wine technology; applied chemometrics
Dr. Raul Ferrer-Gallego

E-Mail Website
Guest Editor
Department of Ecology, Desertification Research Centre (CIDE-CSIC-UV-GV), Moncada, 46113 Valencia, Spain
Interests: viticulture; wine; sustainability; polyphenols; Mediterranean diet; circular economy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the wine industry, it is really important to know critical chemical parameters and attributes of grapes and wines, and it is necessary to do so quickly and precisely. The color of red wine is an important quality parameter, because it is the first characteristic perceived by consumers, and therefore, great attention should be paid to it.

A number of different non-destructive techniques could be investigated as possible instrumental methods for the quality evaluation of oenological and viticultural products. Among them, spectroscopic techniques have gained attention as candidates to efficiently face this task.

This Special Issue is intended to present and discuss thoroughly the use of spectroscopic tools to evaluate several important parameters in grapes and wine samples. This Special Issue should pave the way for the efficient use of these techniques in the oenological sector.

We thank you in advance for your interest and cooperation and look forward to hearing from you.

Kindest regards,

Assoc. Prof. Jose Miguel Hernandez-Hierro
Dr. Raul Ferrer-Gallego
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

  • Spectroscopy
  • Grape and wine chemistry
  • Color
  • Chemometrics
  • Food analysis

Published Papers (5 papers)

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Research

10 pages, 1601 KiB  
Article
A Study of Overripe Seed Byproducts from Sun-Dried Grapes by Dispersive Raman Spectroscopy
by Francisco J. Rivero, Leonardo Ciaccheri, M. Lourdes González-Miret, Francisco J. Rodríguez-Pulido, Andrea A. Mencaglia, Francisco J. Heredia, Anna G. Mignani and Belén Gordillo
Foods 2021, 10(3), 483; https://doi.org/10.3390/foods10030483 - 24 Feb 2021
Cited by 1 | Viewed by 2695
Abstract
Overripe seeds from sun-dried grapes submitted to postharvest dehydration constitute a scarcely investigated class of vinification byproduct with limited reports on their phenolic composition and industrial applications. In this study, Raman spectroscopy was applied to characterize a selection of overripe seed byproducts from [...] Read more.
Overripe seeds from sun-dried grapes submitted to postharvest dehydration constitute a scarcely investigated class of vinification byproduct with limited reports on their phenolic composition and industrial applications. In this study, Raman spectroscopy was applied to characterize a selection of overripe seed byproducts from different white grapes (cv. Moscatel, cv. Pedro Ximénez and cv. Zalema) submitted to postharvest sun drying. The Raman measurements were taken using a 1064 nm excitation laser in order to mitigate the fluorescent effect and the dispersive detection scheme allowed a compactness of the optical system. Spectroscopic data were processed by a principal component analysis to reduce the dimensionality and partner recognition. The evolution of the Raman spectrum during the overripening process was compared with the phenolic composition of grape seeds, which was determined by rapid resolution liquid chromatography/mass spectrometry (RRLC/MS). A multivariate processing of the spectroscopic data allowed the classification of overripe seeds according to the grape variety and the monitoring of stages of the postharvest sun drying process. Full article
(This article belongs to the Special Issue Spectroscopy in Grape and Wine Chemistry and Colour)
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13 pages, 2636 KiB  
Article
Reduction of the Number of Samples for Cost-Effective Hyperspectral Grape Quality Predictive Models
by Julio Nogales-Bueno, Francisco José Rodríguez-Pulido, Berta Baca-Bocanegra, Dolores Pérez-Marin, Francisco José Heredia, Ana Garrido-Varo and José Miguel Hernández-Hierro
Foods 2021, 10(2), 233; https://doi.org/10.3390/foods10020233 - 23 Jan 2021
Cited by 2 | Viewed by 2332
Abstract
Developing chemometric models from near-infrared (NIR) spectra requires the use of a representative calibration set of the entire population. Therefore, generally, the calibration procedure requires a large number of resources. For that reason, there is a great interest in identifying the most spectrally [...] Read more.
Developing chemometric models from near-infrared (NIR) spectra requires the use of a representative calibration set of the entire population. Therefore, generally, the calibration procedure requires a large number of resources. For that reason, there is a great interest in identifying the most spectrally representative samples within a large population set. In this study, principal component and hierarchical clustering analyses have been compared for their ability to provide different representative calibration sets. The calibration sets generated have been used to control the technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars. Finally, the accuracy and precision of the models obtained with these calibration sets resulted from the application of the selection algorithms studied have been compared with each other and with the whole set of samples using an external validation set. Most of the standard errors of prediction (SEP) in external validation obtained from the reduced data sets were not significantly different from those obtained using the whole data set. Moreover, sample subsets resulting from hierarchical clustering analysis appear to produce slightly better results. Full article
(This article belongs to the Special Issue Spectroscopy in Grape and Wine Chemistry and Colour)
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22 pages, 7385 KiB  
Article
NIR Analysis of Intact Grape Berries: Chemical and Physical Properties Prediction Using Multivariate Analysis
by Teodora Basile, Antonio Domenico Marsico and Rocco Perniola
Foods 2021, 10(1), 113; https://doi.org/10.3390/foods10010113 - 07 Jan 2021
Cited by 20 | Viewed by 3025
Abstract
Texture characteristics are valuable parameters in the perceived quality and overall acceptability of fresh fruit. The characterization of grape texture attributes, such as firmness and crunchiness, is usually performed by sensory analysis or instrumental texture analysis. Both methodologies are destructive. Hence, it is [...] Read more.
Texture characteristics are valuable parameters in the perceived quality and overall acceptability of fresh fruit. The characterization of grape texture attributes, such as firmness and crunchiness, is usually performed by sensory analysis or instrumental texture analysis. Both methodologies are destructive. Hence, it is not possible to test multiple times or perform any other analysis on the same sample. In this article, near-infrared (NIR) spectroscopy was applied to intact berries of table grape cv. Regal Seedless. NIR spectra were employed to predict both the physical parameter “hardness”, which is correlated with the crunchiness of berry flesh and the sweetness, which is correlated with the total soluble solids content (TSS, as °Brix). The chemometric analysis was carried out exclusively based on an open-source software environment, producing results readily usable for any operator, besides the specific level of experience with NIR spectroscopy. Full article
(This article belongs to the Special Issue Spectroscopy in Grape and Wine Chemistry and Colour)
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9 pages, 1156 KiB  
Article
Comprehensive Classification and Regression Modeling of Wine Samples Using 1H NMR Spectra
by Gábor Barátossy, Mária Berinkeiné Donkó, Helga Csikorné Vásárhelyi, Károly Héberger and Anita Rácz
Foods 2021, 10(1), 64; https://doi.org/10.3390/foods10010064 - 30 Dec 2020
Cited by 5 | Viewed by 3295
Abstract
Recently, 1H NMR (nuclear magnetic resonance) spectroscopy was presented as a viable option for the quality assurance of foods and beverages, such as wine products. Here, a complex chemometric analysis of red and white wine samples was carried out based on their [...] Read more.
Recently, 1H NMR (nuclear magnetic resonance) spectroscopy was presented as a viable option for the quality assurance of foods and beverages, such as wine products. Here, a complex chemometric analysis of red and white wine samples was carried out based on their 1H NMR spectra. Extreme gradient boosting (XGBoost) machine learning algorithm was applied for the wine variety classification with an iterative double cross-validation loop, developed during the present work. In the case of red wines, Cabernet Franc, Merlot and Blue Frankish samples were successfully classified. Three very common white wine varieties were selected and classified: Chardonnay, Sauvignon Blanc and Riesling. The models were robust and were validated against overfitting with iterative randomization tests. Moreover, four novel partial least-squares (PLS) regression models were constructed to predict the major quantitative parameters of the wines: density, total alcohol, total sugar and total SO2 concentrations. All the models performed successfully, with R2 values above 0.80 in almost every case, providing additional information about the wine samples for the quality control of the products. 1H NMR spectra combined with chemometric modeling can be a good and reliable candidate for the replacement of the time-consuming traditional standards, not just in wine analysis, but also in other aspects of food science. Full article
(This article belongs to the Special Issue Spectroscopy in Grape and Wine Chemistry and Colour)
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14 pages, 2111 KiB  
Article
Discrimination of Grape Seeds Using Laser-Induced Breakdown Spectroscopy in Combination with Region Selection and Supervised Classification Methods
by Yong He, Yiying Zhao, Chu Zhang, Yijian Li, Yidan Bao and Fei Liu
Foods 2020, 9(2), 199; https://doi.org/10.3390/foods9020199 - 15 Feb 2020
Cited by 21 | Viewed by 2967
Abstract
The wine-making industry generates a considerable amount of grape pomace. Grape seeds, as an important part of pomace, are rich in bioactive compounds and can be reutilized to produce useful derivatives. The nutritional properties of grape seeds are largely influenced by the cultivar, [...] Read more.
The wine-making industry generates a considerable amount of grape pomace. Grape seeds, as an important part of pomace, are rich in bioactive compounds and can be reutilized to produce useful derivatives. The nutritional properties of grape seeds are largely influenced by the cultivar, which calls for effective identification. In the present work, the spectral profiles of grape seeds belonging to three different cultivars were collected by laser-induced breakdown spectroscopy (LIBS). Three conventional supervised classification methods and a deep learning method, a one-dimensional convolutional neural network (CNN), were applied to establish discriminant models to explore the relationship between spectral responses and cultivar information. Interval partial least squares (iPLS) algorithm was successfully used to extract the spectral region (402.74–426.87 nm) relevant for elemental composition in grape seeds. By comparing the discriminant models based on the full spectra and the selected spectral regions, the CNN model based on the full spectra achieved the optimal overall performance, with classification accuracy of 100% and 96.7% for the calibration and prediction sets, respectively. This work demonstrated the reliability of LIBS as a rapid and accurate approach for identifying grape seeds and will assist in the utilization of certain genotypes with desirable nutritional properties essential for production rather than their being discarded as waste. Full article
(This article belongs to the Special Issue Spectroscopy in Grape and Wine Chemistry and Colour)
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