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

Abstract

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 (R 2 , 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 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. Therefore, SCiO™ can be reliably deployed in screening early-generation breeding materials for pasting properties.

Plant phenotyping relevance

携帯型近赤外分光計と機械学習モデルを用いて、キャッサバ根の加工特性を推定する表現型取得・予測手法を開発および検証しており、方法が研究の中心である。

Code and data availability

The preprint describes SCiO NIR spectra of cassava flour and glmnet/gbm calibration models, but contains no data availability statement, no public deposit of spectra/reference data, and no author code or model release. All URLs in the text are citations to prior work, not paper-specific assets.

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