Rapid spectral analysis of agro-products using an optimal strategy: dynamic backward interval PLS–competitive adaptive reweighted sampling

Analytical and Bioanalytical Chemistry(2020)

引用 18|浏览7
暂无评分
摘要
A novel strategy of variable selection approach named dynamic backward interval partial least squares–competitive adaptive reweighted sampling (DBiPLS-CARS) was proposed in this study. Near-infrared data sets of three different agro-products, namely corn, crop processing lamina, and plant leaf samples, were collected to investigate the performance of the proposed method. Weak relevant variables were first removed by DBiPLS and a refined selection of the remaining variables was then conducted by CARS. The Monte Carlo uninformative variable elimination (MCUVE) was used as a classical beforehand uninformative variable elimination method for comparison. Results showed that DBiPLS can select informative variables more continuously than MCUVE. Some synergistic variables which may be omitted by MCUVE can be retained by DBiPLS. By contrast, MCUVE can hardly avoid the disturbance of certain weak relevant variables as a result of its calculation based on the full spectrum regression. Therefore, DBiPLS exhibited the advantage of removing the weak relevant variables before CARS, and simultaneously improved the prediction performance of CARS.
更多
查看译文
关键词
Variable selection,Dynamic backward interval partial least squares (DBiPLS),Competitive adaptive reweighted sampling (CARS),Monte Carlo uninformative variable elimination (MCUVE),Agro-products
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要