Full
Outline
To construct an intelligent detection system based on portable near infrared spectroscopy(1000–1800 nm) and ensemble learning algorithms for rapid identification of 8 kinds of edible starch.
A total of 239 commercial starch samples from 8 botanical sources—corn, potato, cassava, wheat, sweet potato, mung bean, lotus root and pea—were collected, yielding 2392 near infrared spectroscopy spectra. The spectra were preprocessed by standard normal variate (SNV) and Savitzky-Golay first derivative (window length=11, polynomial order=2). And it was directly modeled across the full spectral range without conducting characteristic wavelength screening.
The 5 kinds of classification models—K-nearest neighbors (KNN), random forest (RF), partial least squares discriminant analysis (PLS-DA), eXtreme gradient boosting (XGBoost), and a weighted soft-voting ensemble model—were trained and compared. In validation using an independent test set of 59 samples, the ensemble model achieved an accuracy of 97.0% with a Brier score as low as 0.008.
The system takes only 0.8 seconds for a single prediction, providing an efficient and reliable on-site detection technical solution for starch adulteration monitoring and species traceability.
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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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