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Classification of sugarcane varieties using visible/near infrared spectral reflectance of stalks and multivariate methods

Published online by Cambridge University Press:  26 July 2018

A. J. Steidle Neto ,
D. C. Lopes Open the ORCID record for D. C. Lopes [Opens in a new window] ,
J. V. Toledo ,
S. Zolnier  and
T. G. F. Silva
A. J. Steidle Neto
Affiliation:
Federal University of São João del-Rei, Campus Sete Lagoas, Rodovia MG 424, km 47, Sete Lagoas, 35701-970, Minas Gerais, Brazil
D. C. Lopes*
Affiliation:
Federal University of São João del-Rei, Campus Sete Lagoas, Rodovia MG 424, km 47, Sete Lagoas, 35701-970, Minas Gerais, Brazil
J. V. Toledo
Affiliation:
Federal University of Viçosa, Av. Peter Henry Rolfs, s/n, Viçosa, 36570-000, Minas Gerais, Brazil
S. Zolnier
Affiliation:
Federal University of Viçosa, Av. Peter Henry Rolfs, s/n, Viçosa, 36570-000, Minas Gerais, Brazil
T. G. F. Silva
Affiliation:
Federal Rural University of Pernambuco, Unidade Acadêmica de Serra Talhada, Serra Talhada, 56900-000, Pernambuco, Brazil
*
Author for correspondence: D. C. Lopes, E-mail: danielalopes@ufsj.edu.br
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Abstract

The use of fast and non-destructive techniques for identifying sugarcane varieties enables the development of automatic sorting systems, contributing towards improving pre-processing steps in the alcohol and sugar industries. In this context, principal component analysis (PCA), factorial discriminant analysis (FDA), stepwise forward discriminant analysis (SFDA) and partial least-squares discriminant analysis (PLS-DA) were used to classify four Brazilian sugarcane varieties based on visible/near infrared (Vis/NIR) spectral reflectance measurements (450–1000 nm range) of stalks. All wavelengths contributed towards discriminating the sugarcane varieties, but the 600–750 nm range was most relevant. When evaluating PCA results considering the four sugarcane varieties, two of them overlapped and it was only possible to use classifiers of three varieties. Factorial discriminant analysis, PLS-DA and SFDA reached correct classifications of 0.81, 0.82 and 0.74, respectively, when considering the external validation data and the four sugarcane varieties evaluated. Results showed that Vis/NIR spectroscopy combined with discriminating methods is a promising tool for non-destructive and fast sugarcane variety classification, which can be used in the agro-food industry or directly in the field.

Keywords

ChemometricsSaccharum officinarum L.spectroscopy
Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 156 , Issue 4 , May 2018 , pp. 537 - 546
Copyright
Copyright © Cambridge University Press 2018 

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