Full
Outline
To explore the feasibility of constructing a rapid and nondestructive detection model for fumonisin (FUMs) in corn residue using hyperspectral imaging (HSI, spectral range 900–1700 nm) combined with deep learning methods.
Corn bran samples containing a concentration gradient of FUMs (0–6 mg/kg)were artificially prepared (total of 175 samples). After removing interference through multiplicative scatter correction(MSC) preprocessing, the univariate variable elimination (UVE) and variable combination population analysis (VCPA)methods were used to identify key feature wavelengths for FUMs (26 wavelengths), respectively.
After comparing models including partial least squares regression (PLSR), support vector machine (SVM), convolutional neural network (CNN), long short-term memory (LSTM), gated recurrent unit (GRU) and Attention, the CNN-GRU-Attention fusion model demonstrated optimal performance: For FUMs detection, the prediction coefficient of determination reached 0.9328, the root mean square prediction error was 0.3931, and the prediction deviation ratio was 3.8247.
The established detection method enables rapid, precise and nondestructive simultaneous detection of FUMs, providing a novel technical pathway for grain mycotoxin detection and offering reference for the application of interpretable deep learning in spectral analysis.
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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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