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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.

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DeFENS Department of Food, Environmental and Nutritional Sciences, Università degli Studi di Milano, Milano, Italy. Electronic address:
Department of Fundamental Chemistry, Federal University of Pernambuco, Recife (PE), Brazil.
Embrapa Tropical Semiarid, Brazilian Agricultural Research Corporation, Petrolina (PE), Brazil.
Department of Chemical Engineering, Federal University of Pernambuco, Recife (PE), Brazil.
Institute of Chemistry, University of Campinas, Campinas (SP), Brazil.
DeFENS Department of Food, Environmental and Nutritional Sciences, Università degli Studi di Milano, Milano, Italy.


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.


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

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