Hyperspectral imaging for the determination of relevant cooking quality traits of boiled cassava

Abstract

Background The purpose of this study was to investigate the potential of hyperspectral imaging for the characterization of cooking quality parameters, dry matter content (DMC), water absorption (WAB), and texture in cassava genotypes contrasting for their cooking quality. Results Hyperspectral images were acquired on cooked and fresh intact longitudinal and transversal slices from 31 cassava genotypes harvested in March 2022 in Colombia. Different chemometric methods were tested for the quantification of DMC, WAB, and texture parameters. Data analysis was conducted through partial least squares regression, K nearest neighbors regression, support vector machine regression and CovSel multiple linear regression (CovSel_MLR). Efficient performances were obtained for DMC using CovSel_MLR with, coefficient of multiple determination Rp2=0.94 , root-mean-square error of prediction RMSEP = 0.96 g/100 g, and ratio of the standard deviation values RPD = 3.60. High heterogeneity was observed between contrasting genotypes. The predicted distribution of DMC within the root can be homogeneous or heterogeneous depending on the genotype. Weak predictions were obtained for WAB and texture parameters. Conclusions This study showed that hyperspectral imaging could be used as a high-throughput phenotyping tool for the visualization of DMC in contrasting cooking quality genotypes. Further improvement of protocols and larger datasets are required for WAB and texture quality traits. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Plant phenotyping relevance

キャッサバ塊根の乾物含量をハイパースペクトル画像とケモメトリクスで推定する高スループット表現型解析手法が中心であり、調理品質の単なるルーチン測定ではない。

Code and data availability

The paper's data availability statement explicitly says the HSI/DMC/WAB/texture data are not public ('Data not yet avalable in public'). No author analysis code, models, or datasets with public URLs are provided. The referenced Agritrop SOPs are measurement protocols (not this paper's data or code), and rchemo/prospect

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