Use of Low Cost Near-Infrared Spectroscopy, to Predict Pasting Properties of High Quality Cassava Flour

Abstract

Mobile near infrared spectroscopy (SCiO™) can offer quick, in-field phenotyping of cassava roots for pasting properties. However, validation is necessary to verify that reasonable expectations are established for the accuracy of a prediction model. In the context of an ongoing breeding effort, we investigated the use of an inexpensive, portable spectrometer that only records a portion (740–1070 nm) of the whole NIR spectrum to predict cassava pasting properties. Three machine-learning models, namely glmnet, lm, and gbm, implemented in the Caret package in R statistical program, were solely evaluated to approve one or two best models to move on with calibration and optimization. Based on calibration statistics (R2, RMSE and MAE), the best model was identified and further optimized. We found that model calibrations using glmnet provided the best model for breakdown viscosity, peak viscosity and pasting temperature. The glmnet model using the first derivative, peak viscosity had calibration and validation accuracy of R2 = 0.56 and R2 = 0.51 respectively while breakdown had calibration and validation accuracy of R2 = 0.66 and R2 = 0.66 respectively. We also found out that stacking of pre-treatments with Moving Average, Savitzky Golay, First Derivative, Second derivative and Standard Normal variate using glmnet model resulted in calibration and validation accuracy of R2 = 0.65 and R2 = 0.64 respectively for pasting temperature. The developed calibration model predicted the pasting properties of HQCF with sufficient accuracy. Therefore, SCiO™ can be reliably deployed in screening early-generation breeding materials for pasting properties.