Predicting soluble solid content in intact jaboticaba [Myrciaria jaboticaba (Vell.) O. Berg] fruit using near-infrared spectroscopy and chemometrics

Author(s)

Torres Mariani, Nathalia Cristina ^[1] ; da Costa, Rosangela Camara ^[2] ; Gomes de Lima, Kassio Michell ^[2] ; Nardini, Viviani ^[1] ; Cunha Junior, Luis Carlos ^[1] ; de Almeida Teixeira, Gustavo Henrique ^[3]

Affiliation

^[1] Univ Sao Paulo, Fac Ciencias Farmacent Ribeirao Preto, Dept Anal Clin Toxicol & Bromatol, BR-14040903 Ribeirao Preto, SP - Brazil

^[2] Univ Fed Rio Grande do Norte, Grp Pesquisa Quim Biol & Quimiometria, Programa Posgrad Quim, BR-59072970 Natal, RN - Brazil

^[3] Univ Estadual Paulista UNESP, Fac Ciencias Agrorias & Vet, Dept Prod Vegetal, BR-14884900 Jaboticabal, SP - Brazil

• Document type: Journal article
• Source: Food Chemistry; v. 159, p. 458-462, SEP 15 2014.
• Web of Science Citations: 17

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 (Veil.) 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 degrees Brix, and RPD = 1.65) while the BP-ANN model (R-2 = 0.68, RMSEM = 1.20 degrees Brix, and RPD = 1.83) and LS-SVM models achieved lower performance metrics (R-2 = 0.44, RMSEP = 1.89 degrees 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. (C) 2014 Elsevier Ltd. All rights reserved. (AU)