Grape composition is of high interest for producing quality wines. For that, grapeanalysesarenecessary,and requiresamplepreparation,whetherwithclassicalanalysesorwith NIR analyses. The aim of the study was to test the ability of hyperspectral imaging (HSI), a nondestructive analysis to assess their composition. For that, seven grape varieties were analyzed for twovintages. PLS-DA and PLS-R were realized respectively in order to classify the berries,to validate the data sets, and to provide models to assess grape composition after a 1st derivative data pretreatment. Results: HSI allowed a 100% good classification of the grape varieties. It showed good results to assess technological ripening parameters (sugar and acid contents) as well as phenolic content (TPI, Total Phenolics, Total Anthocyanins, Total Flavonoids and their extractable equivalents) (globally R2 > 0.81). However, itwasnotpossibletoreachthecolorintensityofgrapes.Conclusion: Hyperspectralimaging led to generate good models to assess wine grape composition. The quality of the generated models was dependent onthecolorofgrapesandtheparameterconsidered..thesefirstresultsshowedthat.
Gabrielli, M., Ounaissi, D., Lançon‐verdier, V., Julien, S., Le Meurlay, D., Maury, C., Hyperspectral imaging to assess wine grape quality, <<JSFA REPORTS>>, 2023; 3 (10): 452-462. [doi:10.1002/jsf2.150] [https://hdl.handle.net/10807/258187]
Hyperspectral imaging to assess wine grape quality
Gabrielli, MarioPrimo
Formal Analysis
;
2023
Abstract
Grape composition is of high interest for producing quality wines. For that, grapeanalysesarenecessary,and requiresamplepreparation,whetherwithclassicalanalysesorwith NIR analyses. The aim of the study was to test the ability of hyperspectral imaging (HSI), a nondestructive analysis to assess their composition. For that, seven grape varieties were analyzed for twovintages. PLS-DA and PLS-R were realized respectively in order to classify the berries,to validate the data sets, and to provide models to assess grape composition after a 1st derivative data pretreatment. Results: HSI allowed a 100% good classification of the grape varieties. It showed good results to assess technological ripening parameters (sugar and acid contents) as well as phenolic content (TPI, Total Phenolics, Total Anthocyanins, Total Flavonoids and their extractable equivalents) (globally R2 > 0.81). However, itwasnotpossibletoreachthecolorintensityofgrapes.Conclusion: Hyperspectralimaging led to generate good models to assess wine grape composition. The quality of the generated models was dependent onthecolorofgrapesandtheparameterconsidered..thesefirstresultsshowedthat.
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