Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/326406
Type: Artigo
Title: Comparing The Analytical Performances Of Micro-nir And Ft-nir Spectrometers In The Evaluation Of Acerola Fruit Quality, Using Pls And Svm Regression Algorithms
Author: Malegori
Cristina; Nascimento Marques
Emanuel Jose; de Freitas
Sergio Tonetto; Pimentel
Maria Fernanda; Pasquini
Celio; Casiraghi
Ernestina
Abstract: The main goal of this study was to investigate the analytical performances of a state-of-the-art device, one of the smallest dispersion NIR spectrometers on the market (MicroNIR 1700), making a critical comparison with a benchtop FT-NIR spectrometer in the evaluation of the prediction accuracy. In particular, the aim of this study was to estimate in a non-destructive manner, titratable acidity and ascorbic acid content in acerola fruit during ripening, in a view of direct applicability in field of this new miniaturised handheld device. Acerola (Malpighia emarginata DC.) is a super-fruit characterised by a considerable amount of ascorbic acid, ranging from 1.0% to 4.5%. However, during ripening, acerola colour changes and the fruit may lose as much as half of its ascorbic acid content. Because the variability of chemical parameters followed a non-strictly linear profile, two different regression algorithms were compared: PLS and SVM. Regression models obtained with Micro-MR spectra give better results using SVM algorithm, for both ascorbic acid and titratable acidity estimation. FT-MR data give comparable results using both SVM and PLS algorithms, with lower errors for SVM regression. The prediction ability of the two instruments was statistically compared using the Passing-Bablok regression algorithm; the outcomes are critically discussed together with the regression models, showing the suitability of the portable Micro-NIR for in field monitoring of chemical parameters of interest in acerola fruits.
Subject: Acerola
Malpighia Emarginata Dc.
Micronir
Partial Least Squares (pls)
Support Vector Machines (svm)
Passing-bablok Regression
Editor: Elsevier Science BV
Amsterdam
Rights: fechado
Identifier DOI: 10.1016/j.talanta.2016.12.035
Address: http://www-sciencedirect-com.ez88.periodicos.capes.gov.br/science/article/pii/S0039914016309754
Date Issue: 2017
Appears in Collections:Unicamp - Artigos e Outros Documentos

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