Shengxin Li,Ziyan Zhang,Zhiran Zhang,Sen Zhou,Mengkai Liu,Xichao Li,Zheng Zheng,Jie Sun
The moisture, oil, protein, and nervonic acid contents are crucial criteria for assessing the nutritional value and processing quality of Xanthoceras sorbifolia Bunge seed kernels (XSKs). Near-infrared spectroscopy (NIRS) offers a viable alternative to conventional chemical methods for rapid and non-destructive sample analysis. This study employed a highly adaptive method known as competitive adaptive reweighted sampling (CARS), which facilitates variable selection for different components and streamlines the feature-variable screening process. Using this method, various algorithms were applied to establish NIRS detection models for the four components of XSKs, and accuracy evaluations were conducted using the prediction set determination coefficient ( R P 2 ), root-mean-square error (RMSEP), and other parameters. The results indicated that CARS effectively extracted key information on various components in NIRS. Based on the feature variables selected by CARS, the partial least squares regression (PLSR) model exhibited optimal performance regarding moisture ( R P 2 = 0.978, RMSEP = 0.117 %) and nervonic acid content ( R P 2 = 0.988, RMSEP = 0.072 %). Meanwhile, the particle swarm optimization-enhanced support vector regression (PSO-SVR) model and back-propagation neural networks (BPNN) achieved superior performance for protein ( R P 2 = 0.989, RMSEP = 0.243 %) and oil content ( R P 2 = 0.978, RMSEP = 0.467 %), respectively. These models enable rapid and non-destructive detection of multi-component content, thereby facilitating the expeditious identification and selection of XSKs with high nutritional value and superior quality.
Zhenzhen Xia,Jie Yang,Jing Wang,Shengpeng Wang,Yan Liu
Developing a rapid and stable method for analyzing the quality parameters of rice is important. Near-infrared (NIR) spectroscopy combined with chemometric techniques have been used to predict the critical contents of rice and shown its accuracy and stability. To further improve the predictive ability, we combine the derivative method of fractional order Savitzky–Golay derivation (FOSGD) with the wavelength selection method of competitive adaptive reweighted sampling (CARS). Compared with the traditional integer order Savitzky–Golay derivation (IOSGD), the FOSGD could improve the resolution ratio of the raw spectra more effectively. The wavelength selection method, CARS, could further extract the informative variables from the processed spectra. Four key contents of rice samples, including moisture, amylose, chalkiness degree, and gel consistency, were utilized to validate this method. The prediction results indicated that partial least squares (PLS) models optimized with FOSGD-CARS own higher accuracy and stability with smaller the root mean squared error of cross validations (RMSECVs) and root mean squared error of predictions (RMSEPs). The proposed method is convenient and provides a practical alternative for rice analysis.
危险品化学品经营许可证（带存储）