Application of hyperspectral imaging for spatial prediction of soluble solid content in sweet potato.

Abstract

Visible and near infrared (Vis-NIR) hyperspectral imaging was used for fast detection and visualization of soluble solid content (SSC) in 'Beijing 553' and 'Red Banana' sweet potatoes. Hyperspectral images were acquired from 420 ROIs of each cultivar of sliced sweet potatoes. There were 8 and 10 outliers removed from 'Beijing 553' and 'Red Banana' sweet potatoes by Monte Carlo partial least squares (MCPLS). The optimal spectral pretreatments were determined to enhance the performance of the prediction model. Successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were employed to select characteristic wavelengths. SSC prediction models were developed using partial least squares regression (PLSR), support vector regression (SVR) and multivariate linear regression (MLR). The more effective prediction performances emerged from the SPA-SVR model with R p 2 of 0.8581, RMSEP of 0.2951 and RPD p of 2.56 for 'Beijing 553' sweet potato, and the CARS-MLR model with R p 2 of 0.8153, RMSEP of 0.2744 and RPD p of 2.09 for 'Red Banana' sweet potato. Spatial distribution maps of SSC were obtained in a pixel-wise manner using SPA-SVR and CARS-MLR models for quantifying the SSC level in a simple way. The overall results illustrated that Vis-NIR hyperspectral imaging was a powerful tool for spatial prediction of SSC in sweet potatoes.

Code and data availability

The article describes hyperspectral imaging of sweet potato slices and SSC prediction models, but contains no data availability statement, no public dataset or image deposit, and no author code release. Software mentioned (MATLAB, The Unscrambler, ENVI, Origin) is generic tooling, not paper-specific public assets. No E

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