# Application of Image Computing in Non-Destructive Detection of Chinese Cuisine

## TL;DR

This paper introduces a new hyperspectral imaging method to detect the quality and authenticity of Chinese cuisine non-destructively.

## Contribution

The study introduces a novel hyperspectral imaging framework with deep learning for non-destructive detection of Chinese cuisine ingredients and quality.

## Key findings

- The model achieved 97.8% average classification accuracy across 15 Chinese dish categories.
- It quantified chili oil in Mapo Tofu with 0.43% w/w MAE and assessed dim sum freshness with 95.2% accuracy.
- The method improved classification accuracy by over 15 percentage points compared to traditional RGB methods.

## Abstract

Food quality and safety are paramount in preserving the culinary authenticity and cultural integrity of Chinese cuisine, characterized by intricate ingredient combinations, diverse cooking techniques (e.g., stir-frying, steaming, and braising), and region-specific flavor profiles. Traditional non-destructive detection methods often struggle with the unique challenges posed by Chinese dishes, including complex textural variations in staple foods (e.g., noodles, dumplings), layered seasoning compositions (e.g., soy sauce, Sichuan peppercorns), and oil-rich cooking media. This study pioneers a hyperspectral imaging framework enhanced with domain-specific deep learning algorithms (spatial–spectral convolutional networks with attention mechanisms) to address these challenges. Our approach effectively deciphers the subtle spectral fingerprints of Chinese-specific ingredients (e.g., fermented black beans, lotus root) and quantifies critical quality indicators, achieving an average classification accuracy of 97.8% across 15 major Chinese dish categories. Specifically, the model demonstrates high precision in quantifying chili oil content in Mapo Tofu with a Mean Absolute Error (MAE) of 0.43% w/w and assessing freshness gradients in Cantonese dim sum (Shrimp Har Gow) with a classification accuracy of 95.2% for three distinct freshness levels. This approach leverages the detailed spectral information provided by hyperspectral imaging to automate the classification and detection of Chinese dishes, significantly improving both the accuracy of image-based food classification by >15 percentage points compared to traditional RGB methods and enhancing food quality safety assessment.