Institute of Agricultural Quality Standards and Testing Technology Research, Hubei Academy of Agricultural Science/Laboratory of Quality & Safety Risk Assessment for Agro-Products (Wuhan), Ministry of Agriculture, Wuhan, China.
Institute of Fruit & Tea, Hubei Academy of Agricultural Science, Wuhan, China.
Appl Spectrosc. 2020 Apr;74(4):417-426. doi: 10.1177/0003702819895799. Epub 2020 Jan 21.
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.
发展一种快速且稳定的分析稻米质量参数的方法很重要。近红外（NIR）光谱结合化学计量学技术已被用于预测稻米的关键含量，并显示了其准确性和稳定性。为了进一步提高预测能力，我们将分数阶 Savitzky-Golay 导数（FOSGD）的导数方法与竞争自适应重加权采样（CARS）的波长选择方法相结合。与传统的整数阶 Savitzky-Golay 导数（IOSGD）相比，FOSGD 可以更有效地提高原始光谱的分辨率。波长选择方法 CARS 可以进一步从处理后的光谱中提取信息变量。利用稻米样本的四个关键含量（水分、直链淀粉含量、垩白度和胶稠度）对该方法进行验证。预测结果表明，经过 FOSGD-CARS 优化的偏最小二乘（PLS）模型具有更高的准确性和稳定性，其交叉验证均方根误差（RMSECV）和预测均方根误差（RMSEP）更小。该方法方便快捷，为稻米分析提供了一种实用的替代方法。
