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Issue 2, 2017
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Quantification of quality parameters in castanhola fruits by NIRS for the development of prediction models using PLS and variable selection algorithms on a laboratory scale

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Abstract

This paper proposes a novel methodology for the quantification of total phenolic compounds (TPCs) and total anthocyanin compounds (TACs) in castanhola fruits (Terminalia catappa Linn), using near infrared spectroscopy (NIRS) coupled with variable selection algorithms, such as interval partial least squares (iPLS) and genetic algorithm-partial least squares (GA-PLS). GA-PLS showed the best results in the prediction of both parameters. TPC parameters, Rp2 = 0.82, RMSEP = 11.3 mg GAE g−1 (mg gallic acid equivalents (GAE) per g sample), SEL 16.70 mg GAE g−1, RPD = 2.89, sensibility 2.19 × 10−7, and selectivity 0.048 were also obtained using first derivative (5 points) and MSC pretreatment. TAC parameters, Rp2 = 0.80, RMSEP = 8.70 mg L−1, SEL 6.93 mg L−1, RPD = 1.90, sensibility 6.73 × 10−6 and selectivity 0.07 were attained using second derivative (11 points) pre-treatment. From these findings, it can be concluded that NIRS coupled GA-PLS can be used as a non-destructive technique for determining TACs and TPCs in intact castanhola fruits.

Graphical abstract: Quantification of quality parameters in castanhola fruits by NIRS for the development of prediction models using PLS and variable selection algorithms on a laboratory scale

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Article information


Submitted
01 Sep 2016
Accepted
29 Nov 2016
First published
30 Nov 2016

Anal. Methods, 2017,9, 352-357
Article type
Technical Note

Quantification of quality parameters in castanhola fruits by NIRS for the development of prediction models using PLS and variable selection algorithms on a laboratory scale

R. C. Costa, V. H. Uchida, T. B. Veríssimo Miguel, M. M. L. Duarte and K. M. G. Lima, Anal. Methods, 2017, 9, 352
DOI: 10.1039/C6AY02454H

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