Rapid analysis of starch, sugar, and amylose in fresh yam tubers and boiled yam texture using near-infrared hyperspectral imaging and chemometrics

Abstract

The study investigated the use of the near-infrared hyperspectral imaging (NIR-HSI) technique (932 – 1721 nm) to rapidly evaluate the starch, sugar, and amylose content of fresh, intact yam tubers and the textural qualities of boiled yam. These quality characteristics often influence consumers’ and farmers’ acceptance of new yam varieties. Traditional methods for their determination are expensive, time-consuming, and sometimes subjective. The NIR-HSI system combined with three Effective Wavelengths (EWs) selection algorithms, including Successive Projections Algorithms (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Boruta Algorithm (BA), was used to extract the important spectral features. The PLSR-SPA-CARS gave the best prediction models in most cases, with a coefficient of determination in prediction (R²pre) of 0.952 for starch, 0.935 for sugar, and 0.978 for amylose content, respectively. The spatial distribution of starch, sugar, and amylose was visualized using the optimized PLSR model. Additionally, PLSR-SNV-SG (Standard Normal Variate and Savitzsky-Golay) showed the best R² pred of 0.846 for peak force (hardness) and 0.538 for the area under the curve (chewiness) of boiled yam. This study has demonstrated the potential of NIR-HSI techniques to rapidly predict the quality of fresh yam and its boiled food product.