HYPERSPECTRAL IMAGING FOR QUALITY ASSESSMENT OF PROCESSED FOODS: A CASE STUDY ON SUGAR CONTENT IN APPLE JAM

Abstract

Ensuring the quality of apple jam through accurate sugar content measurement is crucial for maintaining taste and shelf life. Traditional methods such as refractometry and chromatography, while reliable, are time-consuming, destructive(the original product is altered or destroyed during testing), and require extensive sample preparation. This research introduces a noninvasive approach using Hyperspectral Imaging (HSI) to analyze sugar content in apple jam. The study aims to collect hyperspectral images of apple jam samples with varying sugar concentrations and apply advanced machine-learning techniques to predict sugar levels. Using HSI, we strive to differentiate between samples and batches made using different apple types and processing methods.