## 1 Introduction  
In the field of Chemometrics, spectral analysis has been a cornerstone for both qualitative and quantitative assessments of various compounds. Among the various spectroscopic techniques, infrared (IR) spectroscopy stands out due to its non-destructive nature, rapid analysis speed, and its ability to provide detailed chemical information. However, the direct interpretation of IR spectra can be challenging due to the complex overlap of absorption bands and the presence of noise. To address this issue, Partial Least Squares (PLS) regression has been widely adopted for spectral data analysis, aiming to establish a linear relationship between predictors and the response [1]. Despite its popularity, PLS regression may suffer from overfitting and reduced predictive performance when dealing with noisy or highly correlated spectral data. This limitation underscores the need to develop more robust and adaptive models that can enhance the accuracy and reliability of spectral-based predictions. The consequences of inaccurate predictions in fields such as food quality control, environmental monitoring, and pharmaceutical analysis can be significant. Therefore, refining spectral analysis techniques is crucial for advancing the precision and applicability of Chemometrics models. Filtering techniques have traditionally been employed for preprocessing spectral data, aiming to remove noise while enhancing the signal-to-noise ratio [2; 3]. However, the application of these filters is often conducted in an unsupervised manner, meaning that they do not account for the ultimate goal of the analysis, which is to establish accurate predictive models. This disconnect between the filtering process and the subsequent regression analysis can potentially compromise the overall performance of the model. To address this gap, this paper introduces a novel filter-based PLS regression model that bridges the divide between data preprocessing and regression analysis. The proposed model integrates the learning of filter parameters and PLS regression into a unified framework, enabling a more efficient and effective analysis pipeline. By leveraging supervised learning on samples, the model simultaneously optimizes both the filter parameters and the PLS regression model, ensuring that the filtering process is aligned with the ultimate goal of accurate prediction. Previous research in this area has explored various modifications and extensions of PLS regression to enhance its performance. Techniques such as Orthogonal Signal Correction PLS (OSCPLS), Variance Constrained PLS (VCPLS), Powered PLS (PoPLS), Low-pass Filter PLS (LoPLS), Direct Orthogonal Signal Correction (DOSC), Orthogonal Projection to Latent Structures (OPLS), Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV) transformation, and Savitzky-Golay Filtering (SGFilter) have been proposed to improve PLS's performance. These methods can be divided into four categories: Orthogonal Signal Correction (OSC) Methods, Scatter Correction and Baseline Methods, Smoothing and Filtering Methods, and PLS Variants. We summarize the pros and cons for these methods and their areas of usage as follows:  
1. Orthogonal Signal Correction (OSC) Methods: OSC-PLS removes spectral variance orthogonal to the response variable Y before PLS modeling, enhancing model interpretability by isolating Y-correlated signals and improving prediction accuracy in multivariate calibration. Its iterative computation risks overfitting if Y-relevant information is inadvertently discarded, making it suitable for NIR datasets dominated by scatter effects but less ideal for resource-constrained applications. DOSC directly computes Y-orthogonal components via least squares, offering simpler implementation than OSC-PLS but potentially retaining residual noise correlated with Y. This approach suits rapid preprocessing in high-throughput industrial screening.