Near-infrared spectroscopy for early selection of waxy cassava clones via seed analysis.

Abstract

Cassava ( Manihot esculenta Crantz) starch consists of amylopectin and amylose, with its properties determined by the proportion of these two polymers. Waxy starches contain at least 95% amylopectin. In the food industry, waxy starches are advantageous, with pastes that are more stable towards retrogradation, while high-amylose starches are used as resistant starches. This study aimed to associate near-infrared spectrophotometry (NIRS) spectra with the waxy phenotype in cassava seeds and develop an accurate classification model for indirect selection of plants. A total of 1127 F 2 seeds were obtained from controlled crosses performed between 77 F 1 genotypes (wild-type, Wx _). Seeds were individually identified, and spectral data were obtained via NIRS using a benchtop NIRFlex N-500 and a portable SCiO device spectrometer. Four classification models were assessed for waxy cassava genotype identification: k-nearest neighbor algorithm (KNN), C5.0 decision tree (CDT), parallel random forest (parRF), and eXtreme Gradient Boosting (XGB). Spectral data were divided between a training set (80%) and a testing set (20%). The accuracy, based on NIRFlex N-500 spectral data, ranged from 0.86 (parRF) to 0.92 (XGB). The Kappa index displayed a similar trend as the accuracy, considering the lowest value for the parRF method (0.39) and the highest value for XGB (0.71). For the SCiO device, the accuracy (0.88-0.89) was similar among the four models evaluated. However, the Kappa index was lower than that of the NIRFlex N-500, and this index ranged from 0 (parRF) to 0.16 (KNN and CDT). Therefore, despite the high accuracy these last models are incapable of correctly classifying waxy and non-waxy clones based on the SCiO device spectra. A confusion matrix was performed to demonstrate the classification model results in the testing set. For both NIRS, the models were efficient in classifying non-waxy clones, with values ranging from 96-100%. However, the NIRS differed in the potential to predict waxy genotype class. For the NIRFlex N-500, the percentage ranged from 30% (parRF) to 70% (XGB). In general, the models tended to classify waxy genotypes as non-waxy, mainly SCiO. Therefore, the use of NIRS can perform early selection of cassava seeds with a waxy phenotype.

Code and data availability

The article reports NIRS spectra of 1127 cassava F2 seeds and classification models (KNN, CDT, parRF, XGB) built in R/caret, and its data availability statement points to a figshare deposit (dx.doi.org/10.6084/m9.figshare.21071257). However, that figshare URL/DOI is not among the allowed_urls, so no paper-specific, ver

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