Wild Horse Optimizer for Variable Selection in Partial Least Squares Spectral Quantification of Complex Samples

Spectral analysis technology has emerged as a vital tool for quantifying complex samples because of its simplicity and high efficiency. However, spectral data has a high-dimensional characteristic and traditional variable selection methods struggle to balance computational efficiency and prediction accuracy. Hence, a discretized wild horse optimizer (WHO) algorithm was introduced in this study. Firstly, transfer functions were introduced to solve the discrete optimization in spectral variable selection. The optimal number of latent variables (LVs) in partial least squares (PLS), DWHO iterations and the population size were determined to establish the DWHO-PLS quantitative analysis model. Then, three spectral datasets of pork, marzipan and DOSY samples were used to assess the effectiveness of the method. Finally, the DWHO-PLS model was compared with full-spectrum PLS, uninformative variable elimination-PLS (UVE-PLS), Monte Carlo-UVE-PLS (MC-UVE-PLS), randomization test-PLS (RT-PLS), gray wolf optimizer-PLS (GWO-PLS) and whale optimization algorithm-PLS (WOA-PLS). Results show that the number of variables selected by DWHO-PLS was the smallest and the root mean squared error of prediction (RMSEP) had the lowest value compared with other methods for the three datasets. The research indicates that DWHO can effectively simplify the PLS model while enhancing its accuracy and stability.