## 1 Introduction  
Therapeutic proteins, such as monoclonal antibodies, play an increasingly important role in the field of life sciences and health. Mammalian cells, particularly Chinese hamster ovary (CHO) cells, are the main force in the production of therapeutic proteins. Mammalian cell lines are capable of adding complexity to biologics through the post-translational modification of proteins [1]. However, these products are produced in relatively small quantities due to the highly specialized culture conditions and their susceptibility to reduced productivity or cell death from even slight deviations in these conditions [2]. The cell culture process seriously affects the yield and quality of therapeutic proteins. Conventionally, sampling the culture solution in the bioreactor for measuring metabolite concentrations and cell density introduces risks of contamination and analytical delays. For commercial production, real-time monitoring of primary metabolites and biomass is pivotal to ensure successful cell culture [3]. To circumvent the need for bioreactor sampling and attain real-time component information, Raman spectroscopy, as a potent process analytical technology (PAT) tool, has been extensively explored in upstream bioprocess manufacturing [4]. Its applicability stems from the weak scattering of water, which allows it to operate in aqueous culture media. Leveraging the multivariate data analysis, a multitude of studies have demonstrated Raman spectroscopy's capability to monitor key components in the CHO cell culture. For instance, [PERSON] et al. [5] developed generic Raman models for glucose (Gluc), lactate (Lac), ammonium ion (NH4\({}^{+}\)), viable cell density (VCD), and total cell density for a GS-KO\({}^{\text{TM}}\) CHO Platform Process using partial least squares (PLS) regression. [PERSON] et al. [6] proposed a novel machine-learning procedure based on just-in-time learning (JITL) to calibrate generic Raman models for the real-time monitoring of critical cell culture performance parameters. Additionally, some studies have focused on enhancing cell growth by integrating Raman spectroscopy with controllers to manage cell culture processes. For example, Raman spectroscopy has been employed to control glucose concentration [7], tune monoclonal antibody galactosylation [8], or maintain viable cell density at target levels [9]. Complications arise from the complex sample matrix and interactions between multiple components, leading to significant spectral shifts and weaker characteristic peak of the molecules of interest [10]. Some spectral regions are redundant, noisy, or irrelevant, undermining the reliability of key component predictions [11; 12; 13]. Limited implementation of Raman spectroscopy has been observed in commercial production [14], so it is necessary to study how to improve the prediction accuracy of Raman-based models. In addition, the number of Raman data collected during cell culture is substantial. Each Raman spectrum contains thousands of data points, and each batch of cell cultures produces thousands of spectra, posing a challenge for computer performance. Therefore, reducing spectral variables and simplifying Raman-based models are more conducive to the application of Raman spectroscopy in the monitoring and controlling of cell culture processes. Variable selection could reduce spectral variables used for modeling and improve model performance through excluding interfering bands and irrelevant regions. The effectiveness of enhancing model performance by variable selection has been proven [15]. During the development of Raman-based models, certain spectral regions have been employed, encompassing the Raman fingerprint and C-H stretching regions. These Raman spectral regions involve structured chemical variations and biochemical information, corresponding to the fingerprint region of organic compounds and the C-H contributions. However, there is no universal approach for the selection of these spectral regions.