Machine learning aided UV absorbance spectroscopy for microbial contamination in cell therapy products.

ABSTRACT: We demonstrate the feasibility of machine-learning aided UV absorbance spectroscopy for in-process microbial contamination detection during cell therapy product (CTP) manufacturing. This method leverages a one-class support vector machine to analyse the absorbance spectra of cell cultures and predict if a sample is sterile or contaminated. This label-free technique provides a rapid output (< 30 minutes) with minimal sample preparation and volume (< 1 mL). Spiking of 7 microbial organisms into mesenchymal stromal cells supernatant aliquots from 6 commercial donors showed that contamination events could be detected at low inoculums of 10 CFUs with mean true positive and negative rates of 92.7% and 77.7% respectively. The true negative rate further improved to 92% after excluding samples from

SUBMITTER: Pandi Chelvam S 

PROVIDER: S-EPMC11880552 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature