Use of Artificial Neural Networks and NIR Spectroscopy for Non-Destructive Grape Texture Prediction

Teodora Basile; Antonio Domenico Marsico; Rocco Perniola

doi:10.3390/foods11030281

Use of Artificial Neural Networks and NIR Spectroscopy for Non-Destructive Grape Texture Prediction

Foods. 2022 Jan 20;11(3):281. doi: 10.3390/foods11030281.

Authors

Teodora Basile¹, Antonio Domenico Marsico¹, Rocco Perniola¹

Affiliation

¹ Centro di Ricerca Viticoltura ed Enologia (CREA-VE), Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria, Via Casamassima 148, 70010 Turi, BA, Italy.

Abstract

In this article, a combination of non-destructive NIR spectroscopy and machine learning techniques was applied to predict the texture parameters and the total soluble solids content (TSS) in intact berries. The multivariate models obtained by building artificial neural networks (ANNs) and applying partial least squares (PLS) regressions showed a better prediction ability after the elimination of uninformative spectral ranges. A very good prediction was obtained for TSS and springiness (R² 0.82 and 0.72). Qualitative models were obtained for hardness and chewiness (R² 0.50 and 0.53). No satisfactory calibration model could be established between the NIR spectra and cohesiveness. Textural parameters of grape are strictly related to the berry size. Before any grape textural measurement, a time-consuming berry-sorting step is compulsory. This is the first time a complete textural analysis of intact grape berries has been performed by NIR spectroscopy without any a priori knowledge of the berry density class.

Keywords: ANN; MC-UVE; PCA; PLS; R statistics; table grape; β coefficients.