Hyperspectral imaging to assess wine grape quality

Abstract

Background

Grape composition is of high interest for producing quality wines. For that, grape analyses are necessary, and require sample preparation, whether with classical analyses or with 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 two vintages. Partial least squares (PLS) and discriminate (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, it was not possible to reach the color intensity of grapes.

Conclusion

HSI led to generate good models to assess wine grape composition. The quality of the generated models was dependent on the color of grapes and the parameter considered.