## 1 Introduction
Compared with models based on neural networks, the principal component regression model's main advantage is its ability to effectively eliminate the multi-collinearity that influences the relationships between variables, to utilize redundant information among the initial variables, and to maintain interpretable and operable features, effectively handling multidimensional data in large-scale datasets. Partial least squares regression (PLSR) can be regarded as an improved version of PCR, as it ensures that the regression model takes into account the essential correlations between the X and Y datasets through cross-projection methods, achieving more accurate prediction results when using a small number of principal components compared to PCR. Because of numerous benefits, PCR and PLSR are widely applied in various fields. [PERSON], [PERSON], et al. compared the accuracy of principal component regression (PCR) and partial least squares regression (PLSR) in predicting neurochemical concentrations from fast-scan cyclic voltammetry (FSCV) measurements and found that PLSR showed higher accuracy and selectivity than PCR, making it a more reliable tool for FSCV data analysis [13]. [PERSON], [PERSON], et al. [14] compared the performance of partial least squares regression (PLSR) and improved principal component regression (PCR) in an ultraviolet spectral analysis of water quality detection. The results show that while PLSR is generally the predominant technology, the improved PCR method outperforms PLSR for data near the detection limit. Both methods rely on principal component analysis (PCA) to reduce the dimensionality of the spectral data and linear regression analysis to obtain the coefficients and relationships of the principal components. [PERSON], [PERSON], et al. [15] explore the use of latent variables modeling and Kalman filtering theory to optimize PLSR and PCR predictors, based on prior X noise covariance estimates. The result is a new PLSR optimization method, while the PCR optimization is identical to a previously known method. The study includes a simulation example and two real-world data examples and is limited to cases with only one response variable. [PERSON], [PERSON], et al. [16] compare robust and classical versionsof principal component regression (PCR) and partial least squares regression (PLSR) in terms of efficiency, goodness-of-fit, predictive power, and robustness, as these are the two most popular regression techniques in chemometrics that fit a linear relationship between two sets of variables, with responses typically being low-dimensional and regressors being numerous. [PERSON], [PERSON], et al. [17] compare the performance of principal component regression (PCR) and partial least squares regression (PLSR) in analyzing the laser-induced breakdown spectroscopy (LIBS) data of stainless steel samples with multi-elemental concentration data and propose guidelines for selecting the appropriate approach based on the analytical situation. [PERSON], [PERSON], et al. compared the performance of PLSR and PCR models in predicting soil salinity using soil spectra data and found that PLSR was superior to PCR when using the original spectral bands, but after smoothing the data, PCR performed better [18]. [PERSON], [PERSON], et al. compare the efficiency, goodness-of-fit, and predictive power of robust and classical versions of principal component regression (PCR) and partial least squares regression (PLSR) methods through a simulation study [19]. In summary, PCR and PLSR have a wide range of applications in many fields due to their many advantages. In this paper, the flight load data are used as sample data to establish principal component regression (PCR) model and partial least squares (PLSR) model for predicting wing loads under different flight conditions using altitude, Mach number, and load factor as input parameters. Evaluations confirm the high prediction accuracy of two models, with PLSR being the most accurate.