Full
Outline
The carotenoid content in wheat kernels serves as a crucial determinant of color appearance and commercial value, representing an important indicator in wheat breeding. While conventional detection methods (ultraviolet spectrophotometry, thin-layer chromatography, and high-performance liquid chromatography) demonstrate accuracy, their operational costs and time requirements prove prohibitive for large-scale applications. This study provided a rapid non-destructive prediction method through the integration of near-infrared spectroscopy (NIRS) with chemometric techniques. We systematically evaluated: (1) three dataset partitioning methods hold-out method (HOM), K-fold cross-validation (KFCV), time series split (TSS); (2) four spectral preprocessing techniques [Savitzky-Golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), trend correction (TC)], three feature selection methods were obtained by fusing variance threshold feature selection (VTFS), SelectKBest (SKB), and recursive feature elimination (RFE) with principal component analysis (PCA), respectively. Subsequent modeling employed partial least squares regression (PLSR), support vector machine regression (SVR), and gradient boosting regression (GBR) were used to build different prediction models. The results showed that HOM achieved optimal dataset partitioning, SG preprocessing provided superior spectral enhancement, and PCA-SKB feature fusion delivered maximum information retention. The optimized was PCA-SKB-GBR. In the validation set and prediction set, R2 were 0.99 and 0.89 with RMSE of 0.04 and 0.34 μg·g-1, respectively. The residual prediction deviation (RPD) reached 3.01. Therefore, based on the hold-out method of sample set, SG spectral pretreatment and PCA-SKB feature selection method, the PCA-SKB-GBR model can be used to predict the carotenoid content of wheat grain quickly and efficiently.
115
24
| 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 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
