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

•NIRS was applied to determine soluble solid content (SSC) of intact jaboticaba fruit.•PLS, iPLS, GA, SPA, BP-ANN, and LS-SVM were applied to building calibration models.•The PLS model produced prediction accuracy (R2=0.71, RMSEP=1.33°Brix, and RPD=1.65). The aim of this study was to evaluate the po...

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Bibliographic Details
Published in:Food chemistry 2014-09, Vol.159, p.458-462
Main Authors: Mariani, Nathália Cristina Torres, da Costa, Rosangela Câmara, de Lima, Kássio Michell Gomes, Nardini, Viviani, Cunha Júnior, Luís Carlos, Teixeira, Gustavo Henrique de Almeida
Format: Article
Language:English
Subjects:
Algorithms
Biological and medical sciences
BP-ANN
Calibration
Food toxicology
Fruit - chemistry
Least-Squares Analysis
LS-SVM
Medical sciences
Multivariate Analysis
Myrtaceae - chemistry
Neural Networks (Computer)
NIR spectroscopy
Plant Extracts - chemistry
PLS
Reproducibility of Results
Spectroscopy, Near-Infrared
Support Vector Machine
Toxicology
Variables selection
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Summary:•NIRS was applied to determine soluble solid content (SSC) of intact jaboticaba fruit.•PLS, iPLS, GA, SPA, BP-ANN, and LS-SVM were applied to building calibration models.•The PLS model produced prediction accuracy (R2=0.71, RMSEP=1.33°Brix, and RPD=1.65). 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 (R2=0.71, RMSEP=1.33°Brix, and RPD=1.65) while the BP-ANN model (R2=0.68, RMSEM=1.20°Brix, and RPD=1.83) and LS-SVM models achieved lower performance metrics (R2=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.
ISSN:0308-8146
1873-7072
DOI:10.1016/j.foodchem.2014.03.066