化学计量学
主成分分析
偏最小二乘回归
水稻
数学
平滑的
模式识别(心理学)
支持向量机
近红外光谱
二阶导数
人工智能
生物系统
分析化学(期刊)
计算机科学
化学
统计
光学
物理
生物
色谱法
机器学习
数学分析
生物化学
基因
作者
Liusan Wang,Weisheng Wang,Zhigang Huang,Shijian Zhen,Rujing Wang
标识
DOI:10.1016/j.saa.2024.124578
摘要
It is an important thing to identify internal crack in seeds from normal seeds for evaluating the quality of rice seeds (Oryza sativa L.). In this study, non-destructive discrimination of internal crack in rice seeds using near infrared spectroscopy and chemometrics is proposed. Principal component analysis (PCA) was used to analyze the rice seeds spectra. Four supervised classification techniques(partial least squares discriminate analysis (PLS-DA), support vector machines (SVM), k-nearest neighbors (KNN) and random forest (RF)) with four different pre-processing techniques (standard normal variate (SNV), multiplicative scatter correction (MSC), first and second derivative with Savitzky-Golay (SG) smoothing) were applied. The best results (Sn = 0.8824, Sp = 0.9429, Acc = 0.913) were achieved by PLS-DA with the raw spectral data. The performance of the best SVM model was inferior to that of PLS-DA, but superior to that of RF and KNN. Except for PLS-DA, four different preprocessing techniques were improved the performance of the developed models. The important variables for discriminating internal cracks in rice seeds were related to the amylose. Overall, the all results demonstrated the feasibility of non-destructive discrimination of internal crack for rice seeds (Oryza sativa L.) using near infrared spectroscopy and chemometrics.
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