Establishing a competence model in discriminating Mycobacterium species and Mycobacterium abscessus subspecies by using surface-enhanced Raman spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) is a powerful laser-based technique with high sensitivity and rapid detection. The present study aimed to establish a model for distinguishing Mycobacterium abscessus (nontuberculous mycobacteria; NTM) from M. tuberculosis H37Ra (MTB-H37Ra; a nonvirulent strain) based on genomic DNA detection, as well as distinguishing M. abscessus subspecies abscessus (MAB) from M. abscessus subspecies massiliense (MMAS). OnSpec-Prime SERS chips and a portable Raman spectrometer device were used. Machine learning approaches, including linear discriminant analysis (LDA), random forest, extreme gradient boosting (XGB), and logistic regression (LR), as well as the receiver operating characteristic (ROC) curve and area under the curve (AUC) were analyzed. A competence model to distinguish NTM from MTB-H37Ra was established; MAB and MMAS were potentially differentiated. At 15 and 50 ng/µl of genomic DNA, respectively, LR demonstrated 99.74 and 99.73% accuracy in differentiating NTM from MTB-H37Ra; XGB displayed 96.25 and 92.97% accuracy in differentiating between MAB and MMAS. LDA revealed clear clustering in each group. The ROC curves showed strong performance of the XGB model across various DNA concentrations. All models achieved an excellent to perfect AUC of 0.96 to 1.00. The present study established a competence model using SERS which may represent a rapid and high-accuracy detection approach, especially in M. abscessus subspecies-level discrimination in clinical specimens.