Improving LIBS machine learning bacteria classification performance based on self-absorption correction

doi:10.1039/D6JA00122J

Improving LIBS machine learning bacteria classification performance based on self-absorption correction

2026-06-04

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DOI：10.1039/D6JA00122J

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Rapid and accurate bacterial detection is vital for the effective management and mitigation of risks associated with bacterial contamination. Recently; the integration of laser-induced breakdown spectroscopy (LIBS) and machine learning provides an effective pathway for the rapid identification of bacteria. However; the ubiquitous self-absorption effect in LIBS results in non-linear attenuation of spectral intensity and distortion of peak profiles; which introduces systematic errors; diminishes the physical consistency of spectral features; and limits the discriminative performance of machine learning classifiers for high-concentration samples. To address these issues; this study presents a self-absorption correction method based on the ratio of spectral line intensities and self-absorption parameters to improve the classification performance of bacterial LIBS spectra. The method utilizes elemental doublets with similar upper and lower energy level structures to retrieve self-absorption coefficients through the relationship between experimental intensity ratios and theoretical parameter ratios; which achieves effective intensity correction without direct measurement of complex plasma parameters. To systematically evaluate the effectiveness of this correction; spectral data before and after correction are processed using four standard machine learning models; specifically partial least squares discriminant analysis (PLS-DA); principal component analysis combined with k-nearest neighbor (PCA-KNN); support vector machine (SVM); and random forest (RF); to classify seven bacterial species. Results indicate that self-absorption correction significantly enhances classification performance across all models; with the overall accuracies of PLS-DA; PCA-KNN; SVM; and RF increasing from 76.8%; 92.7%; 95.9%; and 95.9% to 87.0%; 99.2%; 98.0%; and 98.8%; respectively. Key evaluation metrics; including area under the curve (AUC); average precision (AP); Precision; Recall; and F1-score; also show systematic improvements; where PCA-KNN and RF approach ideal classification levels across multiple indicators. In conclusion; the proposed dual-line intensity ratio correction method effectively suppresses self-absorption in LIBS spectra and substantially improves the accuracy and robustness of machine learning models for bacterial classification. This approach provides technical support for constructing high-precision LIBS systems and possesses potential applications in food safety; clinical diagnosis; and biosafety monitoring.

Improving LIBS machine learning bacteria classification performance based on self-absorption correction

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