Use of low cost near-infrared spectroscopy, to predict pasting properties of high quality cassava flour.

Abstract

Abstract Determination of pasting properties of high quality cassava flour using rapid visco analyzer is expensive and time consuming. The use of mobile near infrared spectroscopy (SCiO™) is an alternative high throughput phenotyping technology for predicting pasting properties of high quality cassava flour traits. However, model development and validation are 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. Based on calibration statistics (R 2 , RMSE and MAE), 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 R 2 = 0.56 and R 2 = 0.51 respectively while breakdown had calibration and validation accuracy of R 2 = 0.66 and R 2 = 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 R 2 = 0.65 and R 2 = 0.64 respectively for pasting temperature. The developed calibration model predicted the pasting properties of HQCF with sufficient accuracy for screening purposes. Therefore, SCiO™ can be reliably deployed in screening early-generation breeding materials for pasting properties.

Plant phenotyping relevance

携帯型近赤外分光法と機械学習モデルを用いて、カッサバ育種材料のペースト特性を推定するモデルを開発・検証しており、形質取得法が研究の中心である。

Code and data availability

The paper's Data Availability statement points to a public GitHub repository containing the SCiO calibration data (spectra and pasting-property reference values) used in this study. The URL matches an allowed URL verbatim. No separate analysis code or trained model deposit is stated.