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Deep Learning-Based Prediction and Regulation for Remaining Shelf Life of Blueberries
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DOI：10.1111/1750-3841.71266.png)
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Abstract
En 中文
The rapid quality deterioration of blueberries during storage underscores the need for precise prediction and regulation of remaining shelf life. This study proposes a novel closed-loop framework integrating an analytic hierarchy process-principal component analysis-long short-term memory (AHP-PCA-LSTM) architecture to estimate remaining shelf life, coupled with Gaussian process regression (GPR) for temperature-based regulation. Utilizing a primary dataset of 72 observations (6 temperatures × 12 sampling points) expanded to 600 via third-order spline interpolation to capture nonlinear kinetics, blueberries were individually packaged in breathable biodegradable films and stored across diverse temperatures (4–25°C) at 50% ± 2% relative humidity. The model employs an autoencoder to construct a “freshness” index (0–100), where a failure threshold of 30 was empirically validated against a 10% fruit decay rate and sensory rejection points. The results indicate that the AHP-PCA-LSTM model effectively reduces data redundancy, with catalase identified as the primary quality weight (0.34), and achieves high-precision remaining shelf life forecasting with a 2.5% error rate, significantly outperforming traditional Arrhenius kinetic models. Furthermore, GPR with Bayesian optimization successfully mapped the nonlinear freshness-temperature dependency, enabling the inverse deduction of optimal storage conditions to prolong freshness. This dynamic system offers a robust paradigm for advanced blueberry supply chain management by quantifying predictive uncertainty through 95% confidence intervals.
Keywords:
blueberry preservation
deep learning
neural network
prediction and regulation
remaining shelf life
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