Wu Xiaohong, Zeng Shupeng, Fu Haijun, Wu Bin, Zhou Haoxiang, Dai Chunxia
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China.
Food Chem X. 2023 Mar 30;18:100666. doi: 10.1016/j.fochx.2023.100666. eCollection 2023 Jun 30.
In order to quickly and accurately determine the protein content of corn, a new characteristic wavelength selection algorithm called anchor competitive adaptive reweighted sampling (A-CARS) was proposed in this paper. This method first lets Monte Carlo synergy interval PLS (MC-siPLS) to select the sub-intervals where the characteristic variables exist and then uses CARS to screen the variables further. A-CARS-PLS was compared with 6 methods, including 3 feature variable selection methods (GA-PLS, random frog PLS, and CARS-PLS) and 2 interval partial least squares methods (siPLS and MWPLS). The results showed that A-CARS-PLS was significantly better than other methods with the results: RMSECV = 0.0336, R = 0.9951 in the calibration set; RMSEP = 0.0688, R = 0.9820 in the prediction set. Furthermore, A-CARS reduced the original 700-dimensional variable to 23 variables. The results showed that A-CARS-PLS was better than some wavelength selection methods, and it has great application potential in the non-destructive detection of protein content in corn.
为了快速准确地测定玉米的蛋白质含量，本文提出了一种新的特征波长选择算法——锚定竞争自适应重加权采样（A-CARS）。该方法首先让蒙特卡罗协同区间偏最小二乘法（MC-siPLS）选择存在特征变量的子区间，然后使用CARS进一步筛选变量。将A-CARS-PLS与6种方法进行了比较，包括3种特征变量选择方法（GA-PLS、随机蛙跳PLS和CARS-PLS）和2种区间偏最小二乘法（siPLS和MWPLS）。结果表明，A-CARS-PLS明显优于其他方法，在校准集中的结果为：RMSECV = 0.0336，R = 0.9951；在预测集中的结果为：RMSEP = 0.0688，R = 0.9820。此外，A-CARS将原始的700维变量减少到了23个变量。结果表明，A-CARS-PLS优于一些波长选择方法，在玉米蛋白质含量无损检测中具有很大的应用潜力。
