Miao Xuexue, Miao Ying, Gong Haoru, Tao Shuhua, Chen Zuwu, Wang Jiemin, Chen Yingzi, Chen Yancheng
Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture, Changsha 410125, China.
College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Aug 5;257:119700. doi: 10.1016/j.saa.2021.119700. Epub 2021 Mar 17.
Fast determination of heavy metals is necessary and important to ensure the safety of crops. The potential of near-infrared spectroscopy coupled with chemometric technology for quantitative analysis of cadmium in rice was investigated. A total of 825 rice samples were collected and scanned by NIRS. The Kennard-Stone method was applied to divide the samples into calibration and validation sets. Before modeling, the spectrum was preprocessed using first derivation to reduce the baseline shift. Different chemometric tools such as interval partial least squares, moving window partial least squares, synergy interval partial least squares, and backward interval partial least squares were proposed to extract and optimize spectral interval from full-spectrum data. The performance of the calibration models generated on the basis of different regression algorithms was compared and evaluated. Results showed that the PLS models based on four chemometric algorithms outperformed the full-spectrum PLS model. Among the tools, biPLS performed better with the optimal subinterval selection. The root-mean-square error of prediction and correlation coefficient (R) of the biPLS model were 0.2133 and 0.9020, respectively. In addition, the low root-mean-square error of cross-validation was obtained in biPLS, which was 0.1756. NIRS technology combined with biPLS could be considered as an effective and convenient tool for primary screening and measuring of cadmium content in rice. In comparison with classical methodologies, this new technology was beneficial because of its eco-friendliness, fast analysis, and virtually no sample preparation required.
快速测定重金属对于确保作物安全至关重要。研究了近红外光谱结合化学计量技术对水稻中镉进行定量分析的潜力。共收集了825个水稻样品并用近红外光谱仪进行扫描。采用肯纳德-斯通法将样品分为校正集和验证集。在建模前，对光谱进行一阶导数预处理以减少基线漂移。提出了不同的化学计量工具，如间隔偏最小二乘法、移动窗口偏最小二乘法、协同间隔偏最小二乘法和后向间隔偏最小二乘法，从全光谱数据中提取和优化光谱区间。比较和评估了基于不同回归算法生成的校正模型的性能。结果表明，基于四种化学计量算法的偏最小二乘模型优于全光谱偏最小二乘模型。在这些工具中，双偏最小二乘法在最优子区间选择方面表现更好。双偏最小二乘模型的预测均方根误差和相关系数（R）分别为0.2133和0.9020。此外，双偏最小二乘法获得了较低的交叉验证均方根误差，为0.1756。近红外光谱技术结合双偏最小二乘法可被视为水稻镉含量初步筛选和测定的有效便捷工具。与传统方法相比，这项新技术具有环保、分析快速且几乎无需样品制备等优点。
