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Predicting soluble solid content in intact jaboticaba [Myrciaria jaboticaba (Vell.) O. Berg] fruit using

Nathália Cristina Torres Mariani¹, Rosangela Câmara da Costa², Kássio Michell Gomes de Lima²

¹Universidade de São Paulo (USP), Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Departamento de Análises Clínicas, Toxicológicas e Bromatológicas, Av. do Café, s/n, Campus Universitário da USP, Ribeirão Preto, CEP 14040-903 São Paulo, Brazil.

|April 29, 2014

Abstract:

The aim of this study was to evaluate the potential of near-infrared reflectance spectroscopy (NIR) as a rapid and non-destructive method to determine soluble solid content (SSC) in intact jaboticaba [Myrciaria jaboticaba (Vell.) O. Berg] fruit. Multivariate calibration techniques were compared with pre-processed data and variable selection algorithms, such as partial least squares (PLS), interval partial least squares (iPLS), a genetic algorithm (GA), a successive projections algorithm (SPA) and nonlinear techniques (BP-ANN, back propagation of artificial neural networks; LS-SVM, least squares support vector machine) were applied to building the calibration models. The PLS model produced prediction accuracy (R(2)=0.71, RMSEP=1.33 °Brix, and RPD=1.65) while the BP-ANN model (R(2)=0.68, RMSEM=1.20 °Brix, and RPD=1.83) and LS-SVM models achieved lower performance metrics (R(2)=0.44, RMSEP=1.89 °Brix, and RPD=1.16). This study was the first attempt to use NIR spectroscopy as a non-destructive method to determine SSC jaboticaba fruit.

Keywords:

BP-ANN LS-SVM NIR spectroscopy PLS Variables selection

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