Predicting soluble solid content in intact jaboticaba [Myrciaria jaboticaba (Vell.) O. Berg] fruit using near-infrared spectroscopy and chemometrics.
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.
Universidade 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.
Nathália Cristina Torres
NC
Mariani
Universidade Federal do Rio Grande do Norte, Programa de Pós-Graduação em Química, Grupo de Pesquisa em Química Biológica e Quimiometria, Natal, CEP 59072-970 RN, Brazil.
Rosangela Câmara
RC
da Costa
Universidade Federal do Rio Grande do Norte, Programa de Pós-Graduação em Química, Grupo de Pesquisa em Química Biológica e Quimiometria, Natal, CEP 59072-970 RN, Brazil. Electronic address: kassio@ufrnet.br.
Kássio Michell Gomes
KM
de Lima
Universidade 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.
Viviani
V
Nardini
Universidade 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.
Luís Carlos
LC
Cunha Júnior
Universidade Estadual Paulista (UNESP), Faculdade de Ciências Agrárias e Veterinárias, Departamento de Produção Vegetal, Via de Acesso Prof. Paulo Donato Castellane, s/n, Jaboticabal, CEP 14884-900 São Paulo, Brazil.
Gustavo Henrique de Almeida
GH
Teixeira
D000465:Algorithms; D002138:Calibration; D005638:Fruit / Q000737:chemistry*; D016018:Least-Squares Analysis; D015999:Multivariate Analysis; D027822:Myrtaceae / Q000737:chemistry*; D016571:Neural Networks, Computer; D010936:Plant Extracts / Q000737:chemistry*; D015203:Reproducibility of Results; D019265:Spectroscopy, Near-Infrared; D060388:Support Vector Machine
D010936:Plant Extracts
BP-ANN; LS-SVM; NIR spectroscopy; PLS; Variables selection