Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible-Near-Infrared Hyperspectral Imaging

Excessive addition of food waste fertilizer to organic fertilizer (OF) is forbidden in the Republic of Korea because of high sodium chloride and capsaicin concentrations in Korean food. Thus, rapid and nondestructive evaluation techniques are required. The objective of this study is to quantitatively evaluate food-waste components (FWCs) using hyperspectral imaging (HSI) in the visible-near-infrared (Vis/NIR) region. A HSI system for evaluating fertilizer components and prediction algorithms based on partial least squares (PLS) analysis and least squares support vector machines (LS-SVM) are developed. PLS and LS-SVM preprocessing methods are employed and compared to select the optimal of two chemometrics methods. Finally, distribution maps visualized using the LS-SVM model are created to interpret the dynamic changes in the OF FWCs with increasing FWC concentration. The developed model quantitively evaluates the OF FWCs with a coefficient of determination of 0.83 between the predicted and actual values. The developed Vis/NIR HIS system and optimized model exhibit high potential for OF FWC discrimination and quantitative evaluation.

Automated summary

This study aims to quantitatively evaluate food-waste components (FWCs) using hyperspectral imaging (HSI) in the visible-near-infrared region. The developed model shows the potential for discrimination and quantitative evaluation of organic fertilizer (OF) FWCs, with a coefficient of determination of 0.83 between predicted and actual values.