Achieving robustness across different ages and cultivars for an NIRS-PLSR model of fresh cassava root starch and dry matter content

Abstract

• Starch content and dry matter content models for fresh cassava tubers were optimized. • NIR spectroscopy was used to predict starch and dry matter content in fresh cassava roots under field conditions. • Spectral variable selection was carried out using SPA and GA methods. • The starch and dry matter content was evaluated under field conditions for breeding purposes. • New spectral processing methods improved the model’s accuracy. This study used a portable near-infrared (NIR) spectrometer at wavelengths of 570–1031 nm to evaluate starch content (SC) and dry matter content (DMC) in fresh cassava tubers. An improved model was developed for the prediction of cassava tuber quality. The cassava samples were taken from four main varieties: CMR38-125-77, KU50, RY11, and RY9. The samples were obtained 4–12 months after planting (MAP). Partial least squares (PLR) regression was combined with different variable selection methods and spectral pre-treatment. Their accuracies were then compared. Variable selection methods included the successive projections algorithm (SPA) and the genetic algorithm (GA). The NIR spectra were obtained in the interactance mode under field conditions. The GA wavelengths combined with sequential pre-processing by orthogonalization (SPORT) pre-processing provided the optimum model for predicting both the SC and the DMC of cassava. The R 2 p, RMSEp, and RPD of SC were 0.91, 1.76%, and 3.26, respectively, and those of DMC were 0.75, 2.00%, and 2.00, respectively. The most effective model was tested against unknown samples of newly developed varieties obtained from different harvest seasons, yielding RMSEp and bias values of 2.37% and −9.178 × 10 -6 %, respectively, for SC. For DMC, the RMSEp and bias values were 2.67% and 4.16 × 10 -14 %, respectively. The results suggest that the calibration model could be used to monitor the internal quality of cassava tubers in the field. The variety, age, position, and section of the tubers had a slight influence on the prediction performance; however, the prediction accuracy was acceptable for in-field applications. The in-field portable NIR spectrometer could become a new tool for breeders, saving time and costs. Breeders could evaluate SC without destroying the cassava roots or stalks and could correct and inspect the behaviour of the SC and DMC accumulation.