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.

中文翻译：

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使用最佳策略对农产品进行快速光谱分析：动态后向间隔PLS-竞争性自适应重加权采样。

提出了一种新的变量选择方法，即动态后向区间偏最小二乘竞争自适应重加权采样（DBiPLS-CARS）。收集了三种不同农产品的近红外数据集，即玉米，农作物加工叶片和植物叶片样品，以研究该方法的性能。DBiPLS首先删除了弱相关变量，然后由CARS对剩余变量进行了精确选择。蒙特卡洛无信息变量消除（MCUVE）被用作经典的事先无信息变量消除方法进行比较。结果表明，DBiPLS可以比MCUVE更连续地选择信息变量。DBiPLS可以保留某些MCUVE可能忽略的协同变量。相比之下，基于全频谱回归的计算结果，MCUVE几乎无法避免对某些弱相关变量的干扰。因此，DBiPLS具有消除CARS之前弱相关变量的优势，同时提高了CARS的预测性能。