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

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 discriminan...

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Bibliographic Details
Published in:The Journal of agricultural science 2018-05, Vol.156 (4), p.537-546
Main Authors: Steidle Neto, A. J., Lopes, D. C., Toledo, J. V., Zolnier, S., Silva, T. G. F.
Format: Article
Language:English
Subjects:
Alcohol
Alcohols
Alternative energy sources
Classification
Crops and Soils Research Paper
Discriminant analysis
Ethanol
Food industry
Food processing industry
Identification methods
Morphology
Near infrared radiation
Nondestructive testing
Polyethylene
Principal components analysis
Reflectance
Renewable resources
Saccharum
Spectral reflectance
Spectroscopy
Spectrum analysis
Statistical methods
Sugar
Sugar industry
Sugarcane
Sun
Wavelengths
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Summary: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.
ISSN:0021-8596
1469-5146
DOI:10.1017/S0021859618000539