The potential of near–infrared spectroscopy as a rapid method for quality evaluation of cassava leaves and roots

Abstract

In this study, near–infrared spectroscopy (NIRS) was used to predict quality of cassava leaves and roots based on their own spectra and also to predict root quality based on the spectral data of the leaves. 990 cassava leaves and 330 roots were collected from 110 plants. The calibration and validation models were constructed by partial least square models with test set validation. High prediction performance models were found for predicting leaf dry matter (DM) and total cyanide (HCN) content using leaf spectra. The coefficient of determination of the validation set (R2val), root means square error of prediction (RMSEP), and the ratio performance deviation (RPDval) of leaf DM were 0.891, 1.04%, and 3.03, respectively, and those of leaf HCN were 0.909, 60.5 ppm, and 3.32, respectively. The prediction model developed from root spectra showed an excellent prediction result for root DM (R2val = 0.979, RMSEP = 0.677%, RPDval = 6.92), while good predicting ability models were obtained for HCN, starch, and TSS in root. Moreover, prediction models developed from leaf spectra could be used for qualitative screening of root quality. This study showed the potential of using NIRS as a rapid and reliable method to predict cassava quality at harvest or before processing.