Optimizing Rice Near-Infrared Models Using Fractional Order Savitzky-Golay Derivation (FOSGD) Combined with Competitive Adaptive Reweighted Sampling (CARS)

文献类型： 外文期刊

第一作者： Xia, Zhenzhen

作者： Xia, Zhenzhen;Yang, Jie;Wang, Jing;Wang, Shengpeng;Liu, Yan

作者机构：

关键词： Rice quality; near-infrared spectroscopy; NIR; partial least squares; PLS; fractional order Savitzky-Golay derivation; competitive adaptive reweighted sampling

期刊名称：APPLIED SPECTROSCOPY （ 影响因子：2.388； 五年影响因子：2.296 ）

ISSN： 0003-7028

年卷期： 2020 年 74 卷 4 期

页码：

收录情况： SCI

摘要： 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.

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