Hyperspectral imaging for the determination of relevant cooking quality traits of boiled cassava.

Abstract

The purpose of this study was to investigate the potential of hyperspectral imaging (HSI) for the characterization of cooking quality parameters, dry matter content (DMC), water absorption (WAB) and texture in cassava genotypes contrasting for their cooking quality. Hyperspectral images were acquired on cooked and fresh intact longitudinal and transversal slices from 31 cassava genotypes harvested in March 2022 in Colombia. Different chemometric methods were tested for the quantification of DMC, WAB and texture parameters. Data analysis was conducted through Partial Least Square Regression (PLSR), K Nearest Neighbors Regression (KNNR), Support Vector Machine Regression (SVM) and CovSel Multiple Linear regression (CovSel_MLR). Efficient performances were obtained for DMC using CovSel_MLR with, R2 p = 0.94, RMSEP = 0.96 g/100g and RPD = 3.60. High heterogeneity was observed between contrasting genotypes. The predicted distribution of DMC within the root can be homogeneous or heterogeneous depending on the genotype. Weak predictions were obtained for WAB and texture parameters. This study showed that HSI could be used as a high throughput phenotyping tool for the visualization of DMC in contrasting cooking quality genotypes. Further improvement of protocols and larger datasets are required for WAB and texture quality traits. This article is protected by copyright. All rights reserved.