Rapid and Nondestructive Determination of Oil Content and Distribution of Potato Chips Using Hyperspectral Imaging and Chemometrics

Abstract

Conventional techniques used to measure oil content in the food are laborious, rely on chemical agents, and have a negative environmental impact. In this study, near-infrared hyperspectral imaging was used as a rapid and nondestructive tool to determine the oil content and its distribution in commercial flat-cooked and batch-cooked potato chips. By evaluating various algorithmic models, such as partial least-squares regression (PLSR), ridge regression, random forest, gradient boosting, and support vector regression, in combination with preprocessing methods like multiplicative scattering correction, standard normal variable (SNV) transform, Savitzky-Golay filtering, normalization, and baseline correction, the most effective preprocessing method and model combination was determined to be SNV-PLSR. Moreover, by employing the optimized PLSR model, a highly accurate oil content prediction model was developed, achieving a coefficient of determination (R2) of 0.95. To identify the wavelengths that contributed most significantly to the model's predictive power, variable importance in projection (VIP) analysis was utilized. A dimensionally reduced PLSR model using only 68 selected wavelengths was developed based on the VIP analysis. This simplified model maintained similar performance to that of the full-spectrum model while using a smaller data set. The model was also used to apply the hyperspectral images of potato chips at the pixel level to visualize the oil distribution in potato chips with the intent to provide a real-time approach to quality control for the potato chip industry.