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Talanta. 2017 Apr 1;165:112-116. doi: 10.1016/j.talanta.2016.12.035. Epub 2016 Dec 21.

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 information

1
DeFENS Department of Food, Environmental and Nutritional Sciences, Università degli Studi di Milano, Milano, Italy. Electronic address: cristina.malegori@unimi.it.
2
Department of Fundamental Chemistry, Federal University of Pernambuco, Recife (PE), Brazil.
3
Embrapa Tropical Semiarid, Brazilian Agricultural Research Corporation, Petrolina (PE), Brazil.
4
Department of Chemical Engineering, Federal University of Pernambuco, Recife (PE), Brazil.
5
Institute of Chemistry, University of Campinas, Campinas (SP), Brazil.
6
DeFENS Department of Food, Environmental and Nutritional Sciences, Università degli Studi di Milano, Milano, Italy.

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-NIR spectra give better results using SVM algorithm, for both ascorbic acid and titratable acidity estimation. FT-NIR 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.

KEYWORDS:

Acerola; Malpighia emarginata DC.; MicroNIR; Partial Least Squares (PLS); Passing-Bablok regression; Support Vector Machines (SVM)

PMID:
28153229
DOI:
10.1016/j.talanta.2016.12.035
[Indexed for MEDLINE]
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