# Hyperspectral Imaging for Quality Assessment of Processed Foods: A Case Study on Sugar Content in Apple Jam

## TL;DR

This study explores using hyperspectral imaging and machine learning to non-invasively measure sugar content in apple jam, offering a faster and safer alternative to traditional methods.

## Contribution

The novel use of hyperspectral imaging combined with a 1D ResNet model for non-destructive sugar content assessment in processed foods.

## Key findings

- The 1D ResNet model achieved the highest prediction accuracy (R2 = 0.948) for sugar content estimation.
- Hyperspectral imaging combined with machine learning proved to be a fast and non-invasive method for quality assessment of apple jam.
- The method was tested on 88 jam samples with sugar concentrations ranging from 25% to 75%.

## Abstract

Apple jam is a widely used all-season product. The quality of the jam is closely related to its sugar concentration, which affects its taste, texture, shelf life, and legal compliance with production requirements. Although traditional methods for measuring sugar, such as titration, enzymatic methods, and chromatography, are accurate, they are also invasive, destructive, and unsuitable for rapid screening. This study investigates a non-destructive and non-invasive alternative method that uses hyperspectral imaging (HSI) in combination with machine learning to estimate the sugar content in processed apple products. Eight cultivars were selected from the Central Asian region, recognized as the origin of apples and known for its rich diversity of apple cultivars. A total of 88 jam samples were prepared with sugar concentrations ranging from 25% to 75%. For each sample, several hyperspectral images were obtained using a visible-to-near-infrared (VNIR) camera. The acquired spectral data were then processed and analyzed using regression models, including the support vector machine (SVM), eXtreme gradient boosting (XGBoost), and a one-dimensional residual network (1D ResNet). Among them, ResNet achieved the highest prediction accuracy of R2 = 0.948. The results highlight the potential of HSI and machine learning for a fast, accurate, and non-invasive assessment of the sugar content in processed foods.