Freshness prediction of modified atmosphere packaging lamb meat based on digital images from mobile portable devices
Peilin Jin, Zhigang Li, Xiaoshuan Zhang
Abstract
Abstract This study aimed to quickly assess lamb meat freshness using smartphones under various modified atmosphere packaging and storage times (0–10 days). Lamb meat images were collected across different periods and packaging types (50% O 2 + 40% CO 2 + 10% N 2 ; 70% O 2 + 20% CO 2 + 10% N 2 ; air). Key color features were extracted, and significant differences in five color features were identified. Three distinct color feature combinations were chosen for models: support vector regression (SVR), genetic algorithm‐back propagation (GA‐BP) neural network, and convolutional neural network (CNN). Results showed SVR models with varied color feature inputs outperformed BP and CNN models. The SVR model with 12 input features yielded the best results, enabling effective spoilage level analysis of lamb meat under different storage times. This work establishes a foundation for future smartphone app development, utilizing Raspberry Pi hardware to evaluate lamb meat freshness under diverse storage conditions. Practical applications This study introduces a cell phone‐based method to rapidly determine lamb spoilage in air conditioning packages during different storage times. Using cell phone images, we collected data on lamb samples stored for 0–10 days under three packaging conditions. We extracted significant color features and selected three combinations as inputs for prediction models: SVR, GA‐BP neural network, and CNN. The SVR model with 12 feature values demonstrated the best performance. This research lays the foundation for a cell phone application based on Raspberry Pi hardware, enabling users to assess lamb freshness in various storage conditions. The potential benefits include efficient quality control in the food industry, increased consumer confidence, and improved preservation of lamb products through optimized storage and packaging. Overall, this study contributes to food quality evaluation and provides practical applications for professionals and consumers.