Microbial Classifier Using Supervised Machine Learning

The patent applies preliminary action by pre-training machine learning classifiers with extensive flow cytometry data from pure microbial cultures before deploying them for actual sample analysis. This preprocessing step creates ready-to-use classification models that can rapidly identify target microbes in complex samples without requiring complex real-time processing during analysis.

The patent introduces flow cytometry as an intermediary measurement technique that converts complex microbial identification problems into quantifiable optical signals. By using flow cytometry parameters as intermediate features, the system bridges the gap between simple measurements and complex microbial identification, enabling accurate classification without directly analyzing complex microbial structures.

The patent implements feedback by using machine learning classifiers that learn from labeled training data and continuously improve their classification accuracy. The system incorporates feedback loops where classification results are validated against known standards, and the models are refined iteratively to improve species identification precision while maintaining high throughput flow cytometry analysis.

The patent applies parameter changes by transforming raw flow cytometry measurements into standardized feature vectors that capture essential microbial characteristics. By changing the parameter representation from raw optical signals to normalized, scaled features, the system maintains the high throughput advantage of flow cytometry while improving the precision of microbial identification through standardized parameter comparison.