Full
Outline
To develop an intelligent method for determining the storage time of chicken patties that balances prediction accuracy and model interpretability.
Cooked chicken patties stored at 4 °C were selected as the research object. Multi-dimensional quality data were collected across storage 0, 1, 3, 5 and 7 d, including moisture-related properties, color difference, texture characteristics, electronic nose response signals and sensory evaluation scores. Principal component analysis was employed for data dimensionality reduction and visualization. A back propagation artificial neural network (BP-ANN) classification model was then constructed to predict storage time.To enhance model transparency, the Shapley additive explanations (SHAP) framework was introduced to quantify the contribution of each input feature to the model’s predictive outcomes.
During storage, the moisture content of chicken patties decreased significantly (P<0.05), while water activity showed an upward trend. The lightness value (L*)increased significantly and the redness value (a*) decreased from –0.09 to –0.25. Texture properties exhibited an overall significant decline: hardness differed significantly among samples with different storage durations (P<0.05), and chewiness decreased by 59.62%. Sensory scores generally decreased with extended storage but showed limited discriminative power for samples from adjacent storage days. The BP-ANN model achieved an overall prediction accuracy of 0.985, a recall of 0.975, and a mean absolute error of 0.246 d. SHAP analysis revealed that the redness value(a*), E-nose sensor signal S3 and adhesiveness were the 3 core features driving the model’s decisions.
The integration of the BP-ANN model and SHAP method not only enables accurate discrimination of chicken patties across different storage times but also elucidates the underlying decision-making logic. This approach provides a reliable and interpretable technical tool for shelf-life prediction and quality analysis of meat products.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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