Raman spectroscopy and machine learning have become popular in in-line monitoring of bioreactors. However, traditional modeling processes typically entail extensive fermentation batches to collect learning datasets, which are significantly time–consuming and laborious. In addition, these models are limited to configurations with the same conditions as the training batches. The present work proposes a reproducible and adaptable modeling approach by combining standard spectra as a training dataset, with a simple means of correcting for cell scattering. Alcoholic fermentation by Saccharomyces cerevisiae is used as a benchmark. Initially, a partial least squares (PLS) regression model was developed based on the spectra of pure solutions of glucose and ethanol. Then, a mathematical expression was defined to estimate yeast concentration, allowing the correction of Raman intensity attenuated by cell scattering. The corrected spectra demonstrate close alignment with reference spectra in both shape and intensity. Validation of the methodology was conducted across numerous batches and one fed–batch bioreactor. As a result, the developed method enables the simultaneous monitoring of glucose, ethanol, and yeast concentrations, effectively addressing the challenge of implementing an independent standards based PLS model to manage the intricate compositional dynamics in bio–processes. The conclusion underscores the effectiveness of the proposed method and offers new prospects in biotechnological industries.