文献类型： 外文期刊

作者： Chen, Runfeng ¹ ; Yan, Qingqing ² ; Tuoheti, Tuhanguli ² ; Xu, Lin ² ; Gao, Qiang ² ; Zhang, Yan ² ; Ren, Hailong ⁴ ; Zheng, Lipeng ¹ ; Wang, Feng ⁵ ; Liu, Ya ⁶ ;

作者机构： 1.Xinjiang Agr Univ, Agr Coll, Urumqi 830052, Peoples R China

2.Xinjiang Acad Agr Sci, Inst Crop Germplasm Resource, Urumqi 830091, Peoples R China

3.Natl Cent Asian Characterist Crop Germplasm Resour, Urumqi 830091, Peoples R China

4.Guangdong Acad Agr Sci, Crops Res Inst, Guangdong Prov Key Lab Crop Genet Improvement, Guangzhou 510308, Peoples R China

5.Beijing Linglong Tyre Co Ltd, Beijing 101102, Peoples R China

6.Xinjiang Acad Agr Sci, Comprehens Testing Ground, Urumqi 830013, Peoples R China

关键词： Near-infrared spectroscopy; Taraxacum kok-saghyz; Natural rubber; Rapid detection; PLS; RF; LightGBM; CNN

期刊名称：PLANT METHODS （ 影响因子：5.1； 五年影响因子：6.1 ）

ISSN：

年卷期： 2024 年 20 卷 1 期

页码：

收录情况： SCI

摘要： Background Taraxacum kok-saghyz Rodin (TKS) is a highly potential source of natural rubber (NR) due to its wide range of suitable planting areas, strong adaptability, and suitability for mechanized planting and harvesting. However, current methods for detecting NR content are relatively cumbersome, necessitating the development of a rapid detection model. This study used near-infrared spectroscopy technology to establish a rapid detection model for NR content in TKS root segments and powder samples. The K445 strain at different growth stages within a year and 129 TKS samples hybridized with dandelion were used to obtain their near-infrared spectral data. The rubber content in the root of the samples was detected using the alkaline boiling method. The Monte Carlo sampling method (MCS) was used to filter abnormal data from the root segments of TKS and powder samples, respectively. The SPXY algorithm was used to divide the training set and validation set in a 3:1 ratio. The original spectrum was preprocessed using moving window smoothing (MWS), standard normalized variate (SNV), multiplicative scatter correction (MSC), and first derivative (FD) algorithms. The competitive adaptive reweighted sampling (CARS) algorithm and the corresponding chemical characteristic bands of NR were used to screen the bands. Partial least squares (PLS), random forest (RF), Lightweight gradient augmentation machine (LightGBM), and convolutional neural network (CNN) algorithms were employed to establish a model using the optimal spectral processing method for three different bands: full band, CARS algorithm, and chemical characteristic bands corresponding to NR. The model with the best predictive performance for high rubber content intervals (rubber content > 15%) was identified. Result The results indicated that the optimal rubber content prediction models for TKS root segments and powder samples were MWS-FD CASR-RF and MWS-FD chemical characteristic band RF, respectively. Their respective R-P(2), RMSEP, and RPDP values were 0.951, 0.979, 1.814, 1.133, 4.498, and 6.845. In the high rubber content range, the model based on the LightGBM algorithm had the best prediction performance, with the RMSEP of the root segments and powder samples being 0.752 and 0.918, respectively. Conclusions This research indicates that dried TKS root powder samples are more appropriate for constructing a rubber content prediction model than segmented samples, and the predictive capability of root powder samples is superior to that of root segmented samples. Especially in the elevated rubber content range, the model formulated using the LightGBM algorithm has superior predictive performance, which could offer a theoretical basis for the rapid detection technology of TKS content in the future.