Classification of Wine Grape Based on Different Phytosanitary Status by Using Visible/Near Infrared Spectroscopy

The grape selection at the consignment is crucial for big cooperative companies with thousands of members, and therefore grapes may have different quality characteristics and health status. The quantification of diseases on wine grapes is commonly performed by a visual evaluation of the infection s...

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Main Authors: V. Giovenzana, R. Beghi, L. Brancadoro, R. Guidetti
Format: Article
Language:English
Published: AIDIC Servizi S.r.l. 2017-06-01
Series:Chemical Engineering Transactions
Online Access:https://www.cetjournal.it/index.php/cet/article/view/1320
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spelling doaj-59556555108d40bcabdc90657b22906d2021-02-18T21:01:19ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162017-06-015810.3303/CET1758056Classification of Wine Grape Based on Different Phytosanitary Status by Using Visible/Near Infrared Spectroscopy V. GiovenzanaR. BeghiL. BrancadoroR. GuidettiThe grape selection at the consignment is crucial for big cooperative companies with thousands of members, and therefore grapes may have different quality characteristics and health status. The quantification of diseases on wine grapes is commonly performed by a visual evaluation of the infection symptoms in grape bunches. Based on grapes quality, wineries often enforce a price penalty downgrading the grapes value imposing a severe reduction of vine growers’ income. The application of optical techniques could minimize contentious between members and winery and could improve the standardization of the grape quality and therefore of the wine. The aim of this work was to investigate the applicability of vis/NIR spectroscopy for a rapid assessment of phytosanitary status of grape bunches directly at the check point at the grape consignment. This preliminary experimental step was conducted in lab-scale condition using a vis/NIR device built specially for analysis of non-homogeneous product in the spectral range 400-1650 nm. Spectral measurements were carried out on healthy (1235 samples) and diseased bunches (1324) from white and red grapes for a total of 2559 spectra. Quantitative (Partial Least Squares – Discriminant Analysis, PLS-DA) analyses were applied on grape spectra in order to test the performance of vis/NIR spectroscopy to classify healthy and infected bunches. The results obtained from PLS-DA models, in cross-validation, gave positive predictive values of classification between 89.8 % and 91.1 %. Results demonstrated that vis/NIR spectroscopy is capable to provide useful information about wine grape phytosanitary status. However, further studies and real scale measurements are needed to determine the best operating conditions for a future engineering phases to perform the measurements directly at the consignment check point station. https://www.cetjournal.it/index.php/cet/article/view/1320
collection DOAJ
language English
format Article
sources DOAJ
author V. Giovenzana
R. Beghi
L. Brancadoro
R. Guidetti
spellingShingle V. Giovenzana
R. Beghi
L. Brancadoro
R. Guidetti
Classification of Wine Grape Based on Different Phytosanitary Status by Using Visible/Near Infrared Spectroscopy
Chemical Engineering Transactions
author_facet V. Giovenzana
R. Beghi
L. Brancadoro
R. Guidetti
author_sort V. Giovenzana
title Classification of Wine Grape Based on Different Phytosanitary Status by Using Visible/Near Infrared Spectroscopy
title_short Classification of Wine Grape Based on Different Phytosanitary Status by Using Visible/Near Infrared Spectroscopy
title_full Classification of Wine Grape Based on Different Phytosanitary Status by Using Visible/Near Infrared Spectroscopy
title_fullStr Classification of Wine Grape Based on Different Phytosanitary Status by Using Visible/Near Infrared Spectroscopy
title_full_unstemmed Classification of Wine Grape Based on Different Phytosanitary Status by Using Visible/Near Infrared Spectroscopy
title_sort classification of wine grape based on different phytosanitary status by using visible/near infrared spectroscopy
publisher AIDIC Servizi S.r.l.
series Chemical Engineering Transactions
issn 2283-9216
publishDate 2017-06-01
description The grape selection at the consignment is crucial for big cooperative companies with thousands of members, and therefore grapes may have different quality characteristics and health status. The quantification of diseases on wine grapes is commonly performed by a visual evaluation of the infection symptoms in grape bunches. Based on grapes quality, wineries often enforce a price penalty downgrading the grapes value imposing a severe reduction of vine growers’ income. The application of optical techniques could minimize contentious between members and winery and could improve the standardization of the grape quality and therefore of the wine. The aim of this work was to investigate the applicability of vis/NIR spectroscopy for a rapid assessment of phytosanitary status of grape bunches directly at the check point at the grape consignment. This preliminary experimental step was conducted in lab-scale condition using a vis/NIR device built specially for analysis of non-homogeneous product in the spectral range 400-1650 nm. Spectral measurements were carried out on healthy (1235 samples) and diseased bunches (1324) from white and red grapes for a total of 2559 spectra. Quantitative (Partial Least Squares – Discriminant Analysis, PLS-DA) analyses were applied on grape spectra in order to test the performance of vis/NIR spectroscopy to classify healthy and infected bunches. The results obtained from PLS-DA models, in cross-validation, gave positive predictive values of classification between 89.8 % and 91.1 %. Results demonstrated that vis/NIR spectroscopy is capable to provide useful information about wine grape phytosanitary status. However, further studies and real scale measurements are needed to determine the best operating conditions for a future engineering phases to perform the measurements directly at the consignment check point station.
url https://www.cetjournal.it/index.php/cet/article/view/1320
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