Predicting the spatial distribution of reducing sugars using near-infrared hyperspectral imaging and chemometrics: a study in multiple potato genotypes

Abstract

The determination of reducing sugars in potatoes is important due to their impact on product quality during industrial processing. The significant variability of these compounds between genotypes presents a challenge to the development of accurate predictive models. This study evaluated the potential of near-infrared hyperspectral imaging (NIR-HSI) for the prediction of reducing sugars in potatoes. For this, a wide range of genotypes (n = 92) from two seasons (2020–2021) was selected. Partial Least Squares Regression (PLSR) and Support Vector Machine Regression (SVMR) methods were used to build the prediction models. Furthermore, interval PLS (iPLS), recursive weighted PLS (rPLS), Genetic Algorithm (GA) and Competitive Adaptive Reweighted Sampling (CARS) were used for relevant wavelength identification to develop less computationally complex models. The best full spectrum model (SNV-PLSR) achieved coefficient of determination and root mean square error values of 0.88 and 0.053 % and 0.86 and 0.057 %, for calibration and external validation, respectively. Variable selection algorithms successfully reduced the dimensionality of the data without compromising the performance of the models. Robust predicted models were built with only 2.65 % (CARS-PLSR) and 3.57 % (iPLS-SVMR) of the total wavelengths. Finally, a pixel-wise prediction was performed on the validation set and chemical images were built to visualise the spatial distribution of reducing sugars. This study demonstrated that NIR-HSI is a feasible technique for predicting reducing sugars in several potato genotypes.