Berry Cell Vitality Assessment and the Effect on Wine Sensory Traits Based on Chemical Fingerprinting, Canopy Architecture and Machine Learning Modelling

Berry cell death assessment can become one of the most objective parameters to assess important berry quality traits, such as aroma profiles that can be passed to the wine in the winemaking process. At the moment, the only practical tool to assess berry cell death in the field is using portable near...

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Autores principales:	Sigfredo Fuentes, Claudia Gonzalez Viejo, Chelsea Hall, Yidan Tang, Eden Tongson
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	near-infrared spectroscopy computer vision sensory analysis machine learning berry cell death Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/f4717ac867d1427fb71fa20e5ae37e3f
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spelling	oai:doaj.org-article:f4717ac867d1427fb71fa20e5ae37e3f2021-11-11T19:15:43ZBerry Cell Vitality Assessment and the Effect on Wine Sensory Traits Based on Chemical Fingerprinting, Canopy Architecture and Machine Learning Modelling10.3390/s212173121424-8220https://doaj.org/article/f4717ac867d1427fb71fa20e5ae37e3f2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7312https://doaj.org/toc/1424-8220Berry cell death assessment can become one of the most objective parameters to assess important berry quality traits, such as aroma profiles that can be passed to the wine in the winemaking process. At the moment, the only practical tool to assess berry cell death in the field is using portable near-infrared spectroscopy (NIR) and machine learning (ML) models. This research tested the NIR and ML approach and developed supervised regression ML models using Shiraz and Chardonnay berries and wines from a vineyard located in Yarra Valley, Victoria, Australia. An ML model was developed using NIR measurements from intact berries as inputs to estimate berry cell death (BCD), living tissue (LT) (Model 1). Furthermore, canopy architecture parameters obtained from cover photography of grapevine canopies and computer vision analysis were also tested as inputs to develop ML models to assess BCD and LT (Model 2) and the intensity of sensory descriptors based on visual and aroma profiles of wines for Chardonnay (Model 3) and Shiraz (Model 4). The results showed high accuracy and performance of models developed based on correlation coefficient (R) and slope (b) (M1: R = 0.87; b = 0.82; M2: R = 0.98; b = 0.93; M3: R = 0.99; b = 0.99; M4: R = 0.99; b = 1.00). Models developed based on canopy architecture, and computer vision can be used to automatically estimate the vigor and berry and wine quality traits using proximal remote sensing and with visible cameras as the payload of unmanned aerial vehicles (UAV).Sigfredo FuentesClaudia Gonzalez ViejoChelsea HallYidan TangEden TongsonMDPI AGarticlenear-infrared spectroscopycomputer visionsensory analysismachine learningberry cell deathChemical technologyTP1-1185ENSensors, Vol 21, Iss 7312, p 7312 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	near-infrared spectroscopy computer vision sensory analysis machine learning berry cell death Chemical technology TP1-1185
spellingShingle	near-infrared spectroscopy computer vision sensory analysis machine learning berry cell death Chemical technology TP1-1185 Sigfredo Fuentes Claudia Gonzalez Viejo Chelsea Hall Yidan Tang Eden Tongson Berry Cell Vitality Assessment and the Effect on Wine Sensory Traits Based on Chemical Fingerprinting, Canopy Architecture and Machine Learning Modelling
description	Berry cell death assessment can become one of the most objective parameters to assess important berry quality traits, such as aroma profiles that can be passed to the wine in the winemaking process. At the moment, the only practical tool to assess berry cell death in the field is using portable near-infrared spectroscopy (NIR) and machine learning (ML) models. This research tested the NIR and ML approach and developed supervised regression ML models using Shiraz and Chardonnay berries and wines from a vineyard located in Yarra Valley, Victoria, Australia. An ML model was developed using NIR measurements from intact berries as inputs to estimate berry cell death (BCD), living tissue (LT) (Model 1). Furthermore, canopy architecture parameters obtained from cover photography of grapevine canopies and computer vision analysis were also tested as inputs to develop ML models to assess BCD and LT (Model 2) and the intensity of sensory descriptors based on visual and aroma profiles of wines for Chardonnay (Model 3) and Shiraz (Model 4). The results showed high accuracy and performance of models developed based on correlation coefficient (R) and slope (b) (M1: R = 0.87; b = 0.82; M2: R = 0.98; b = 0.93; M3: R = 0.99; b = 0.99; M4: R = 0.99; b = 1.00). Models developed based on canopy architecture, and computer vision can be used to automatically estimate the vigor and berry and wine quality traits using proximal remote sensing and with visible cameras as the payload of unmanned aerial vehicles (UAV).
format	article
author	Sigfredo Fuentes Claudia Gonzalez Viejo Chelsea Hall Yidan Tang Eden Tongson
author_facet	Sigfredo Fuentes Claudia Gonzalez Viejo Chelsea Hall Yidan Tang Eden Tongson
author_sort	Sigfredo Fuentes
title	Berry Cell Vitality Assessment and the Effect on Wine Sensory Traits Based on Chemical Fingerprinting, Canopy Architecture and Machine Learning Modelling
title_short	Berry Cell Vitality Assessment and the Effect on Wine Sensory Traits Based on Chemical Fingerprinting, Canopy Architecture and Machine Learning Modelling
title_full	Berry Cell Vitality Assessment and the Effect on Wine Sensory Traits Based on Chemical Fingerprinting, Canopy Architecture and Machine Learning Modelling
title_fullStr	Berry Cell Vitality Assessment and the Effect on Wine Sensory Traits Based on Chemical Fingerprinting, Canopy Architecture and Machine Learning Modelling
title_full_unstemmed	Berry Cell Vitality Assessment and the Effect on Wine Sensory Traits Based on Chemical Fingerprinting, Canopy Architecture and Machine Learning Modelling
title_sort	berry cell vitality assessment and the effect on wine sensory traits based on chemical fingerprinting, canopy architecture and machine learning modelling
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f4717ac867d1427fb71fa20e5ae37e3f
work_keys_str_mv	AT sigfredofuentes berrycellvitalityassessmentandtheeffectonwinesensorytraitsbasedonchemicalfingerprintingcanopyarchitectureandmachinelearningmodelling AT claudiagonzalezviejo berrycellvitalityassessmentandtheeffectonwinesensorytraitsbasedonchemicalfingerprintingcanopyarchitectureandmachinelearningmodelling AT chelseahall berrycellvitalityassessmentandtheeffectonwinesensorytraitsbasedonchemicalfingerprintingcanopyarchitectureandmachinelearningmodelling AT yidantang berrycellvitalityassessmentandtheeffectonwinesensorytraitsbasedonchemicalfingerprintingcanopyarchitectureandmachinelearningmodelling AT edentongson berrycellvitalityassessmentandtheeffectonwinesensorytraitsbasedonchemicalfingerprintingcanopyarchitectureandmachinelearningmodelling
_version_	1718431620728356864

Berry Cell Vitality Assessment and the Effect on Wine Sensory Traits Based on Chemical Fingerprinting, Canopy Architecture and Machine Learning Modelling

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