Full
Outline
To achieve rapid and non-destructive detection of thiobarbituric acid reactive substances(TBARS) content in beef by leveraging deep learning and explainable artificial intelligence-driven visible-near infrared (Vis-NIR) spectroscopy.
Vis-NIR spectra were collected from 420 beef samples, and the TBARS values were determined experimentally. Different spectral pretreatment methods, feature selection methods and modeling algorithms were compared to realize the non-destructive and rapid detection of TBARS in beef.
It demonstrated that the standard normal variate preprocessing-successive projections algorithm for feature selection-long short-term memory network model achieved the best performance [R-square of prediction set(R2P)=0.8378, root mean square error of prediction set (RMSEP)=0.0679 mg/kg, residual prediction deviation(RPD)=2.4833], outperforming partial least squares regression (PLSR) and extreme gradient boosting(XGBoost). Furthermore, through the interpretability analysis using Shapley additive explanations (SHAP)revealed the contribution mechanisms of critical wavelengths, such as 1080 nm and 956 nm, to TBARS prediction.
The proposed method offers an innovative strategy for rapid, non-destructive, accurate and explainable detection of TBARS in beef.
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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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