Single-cell Growth Detection Based on Raman Technology

Objective Single-cell growth detection can more scientifically reveal the rules of microbial metabolic changes and guide later microbial engineering applications. To study the accurate detection of microbial growth during the food safety period and optimal edible period, a single-cell growth detection method based on Raman technology is proposed in this paper. Methods First, a total of 900 single-cell Raman spectroscopy (SCRS) data were collected from two batches of Bacillus subtilis through a simultaneous culture experiment, of which 600 were used for training and testing and the other 300 for model validation. Secondly, based on the feature relationship matrix of principal component analysis, CP-SP feature evaluation method was proposed to screen SCRS features for model detection. Then, a detection model based on XGBoost was built, and grid search and cross-validation were applied to optimize the detection model. Finally, confusion matrix and ROC curve were used to evaluate the detection accuracy, sensitivity and specificity of the model for cell lag phase, log phase and stationary phase. Results The experiment found that the classification performance of the first, second, and fourth principal components screened by CP-SP was improved by 3.1% compared with the first three principal components of the feature contribution rate. The test accuracy of the optimized cell growth detection model was 96.0%, and the verification accuracy was 92.3%. Conclusion The results show that the single-cell growth detection method based on Raman technology can accurately identify the single-cell growth state and has a high generalization ability, which can provide scientific guidance for the formulation of precise regulatory mechanisms for food safety and preservation.