## 1 Introduction
Different researchers have selected different spectral ranges, adhering to certain guiding principles, such as experiential insights [6], capturing the organic compounds' fingerprint region [9], circumventing the lambda region [16], omitting regions with low signal-to-noise ratios [17], or excluding the Rayleigh scattering of the excitation source [18]. Table 1 presents different spectral ranges used for modeling in cell culture, and the numbers of selected spectral variables vary from 1001 to 2801. These studies all employed a Kaiser Raman spectrometer to collect spectra with the same resolution and similar raw spectral range, but different spectral regions were selected to represent the Raman fingerprint regions and C-H stretching regions, potentially leading to confusion among researchers. These spectral regions were used to build models for various components, such as Gluc, Lac, VCD, NH4\({}^{+}\), and protein titer, but explanations were not provided as to why the specific region was chosen for a certain component. Considering the specificity of Raman-based models, different spectral regions should be assigned to different analyzed components. Considering that it is difficult to manually assign specific spectral variables to each analyzed component in a complex system, variable selection algorithms can be useful for choosing spectral variables that are sensitive to chemical changes in the matrix [21]. According to the literature [27], variable selection algorithms can be categorized into three main groups: filter methods, wrapper methods, and embedded methods. Filter methods involve variable selection based on a defined threshold; examples include [PERSON]'s correlation coefficient method (PCC) and variable importance in projection (VIP). Wrapper methods employ an iterative process to filter variables and include techniques like uninformative variable elimination method (UVE) [28], moving-window partial least square (MWPLS), and genetic algorithm (GA). Within the wrapper methods' category, UVE and MWPLSare classified as dynamic wrapper methods, while GA is considered a randomized wrapper method. Lastly, embedded methods are integrated into modified algorithms, such as random forest (RF). [PERSON] et al. [29] improved the predictive performance of PLS models through variable selection algorithms, including VIP and forward-interval partial least squares (iPLS), compared to models based on whole spectra or those driven by chemical information alone. In addition, combining different variable selection algorithms takes advantage of the complementarity between them. Firstly, spectral variables are selected coarsely, and then these selected spectral variables are chosen finely. Such a variable selection strategy is usually better than using a single variable selection algorithm. [PERSON] et al. [30] used the UVE-CARS combination method to select spectral variables, which effectively predicted and visualized the concentration of total flavonoids in fruits. [PERSON] et al. [31] studied four combination methods, IPLS-VIP-GA, IPLS-VIP-IRIV, IPLS-MVCPA-GA, and IPLS-MVCPA-IRIV, and the results showed that these methods had a better performance than a single variable selection algorithm. Different variable selection algorithms and their combinations have been used in various studies, with varying results [15; 32; 33]. Using only Raman characteristic peaks or a single variable selection algorithm to construct the monitoring model may yield one-sided results [13]. It is meaningful to investigate different variable selection algorithms and their combinations to effectively improve the Raman model performance during cell culture. In this study, we provided a comprehensive assessment of various variable selection strategies, including selection from different spectral regions, the use of different variable selection algorithms, and combination methods. It is important to determine which variable selection strategy is optimal and how many variables should be chosen to simplify the model and enhance its performance.