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Identification of waxy cassava genotypes using fourier‐transform near‐infrared spectroscopy
Author(s) -
do Carmo Cátia Dias,
Bandeira e Sousa Massaine,
Santos Pereira Jocilene dos,
Ceballos Hernán,
Oliveira Eder Jorge
Publication year - 2020
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.1002/csc2.20102
Subject(s) - principal component analysis , linear discriminant analysis , support vector machine , pattern recognition (psychology) , artificial intelligence , biology , partial least squares regression , feature selection , cross validation , kernel (algebra) , mathematics , statistics , biological system , computer science , combinatorics
High‐throughput phenotyping tools that allow the early and accurate evaluation of important agronomic traits have gained space in current breeding programs. The aim of this study was to evaluate the potential of Fourier‐transform near‐infrared spectroscopy (FT‐NIRS) to identify cassava ( Manihot esculenta Crantz) clones with waxy starch (i.e., amylose‐free) by screening leaves rather than roots, and to validate prediction models for classifying these phenotypes. We analyzed the spectra of 162 waxy and 180 nonwaxy genotypes from five different growing environments. The mean FT‐NIRS spectra and principal component analysis (PCA) were used to investigate the potential for grouping the data. For classification, five supervised pattern recognition techniques were tested: Bayesian generalized linear model (BGLM), high‐dimensional discriminant analysis (HDDA), partial least squares‐discriminant analysis (PLS‐DA), parallel random forest (PRANDF), and support vector machines with linear kernel (SVM). The mean spectra and the PCA did not allow discrimination of the genotypes based on starch classification. The SVM and BGLM showed the highest classification accuracy in cross‐validation (.86–.87), with higher concordance rates (.88–.83), sensitivity (.87–.85) and specificity (.88). The BGLM and SVM models also obtained better indices in the external validation, with high accuracy (.85) and correct classification of 93% of the waxy genotypes. Thus, performing early selection of root characteristics based on the indirect selection of variables extracted from leaf spectra is a good potential strategy for more efficient breeding of the waxy phenotype.