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Article Open Access

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

  • Authors:
    • Apisith Pichitphorn
    • Kiatichai Faksri
    • Benjawan Kaewseekhao
    • Chotima Potisap
    • Tran Duong Thai
    • Jukgarin Eisiri
    • Chadatan Juntagran
    • Wises Namwat
    • Priyakorn Khwansirikul
    • Kanin Salao
    • Suwit Chaisri
    • Wisitsak Phoksawat
  • View Affiliations / Copyright

    Affiliations: Research and Diagnostic Center for Emerging Infectious Diseases, Khon Kaen University, Khon Kaen 40002, Thailand, Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore, Chulabhorn International College of Medicine (CICM), Thammasat University, Pathum Thani 12120, Thailand
    Copyright: © Pichitphorn et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 81
    |
    Published online on: May 5, 2026
       https://doi.org/10.3892/br.2026.2154
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Abstract

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.
View Figures

Figure 1

Study workflow (experimental design
and analysis). The design of the present study comprised two
phases. Phase I (blue background; left hand): Bacterial cultivation
and data acquisition; collected bacterial samples maintained in the
biobank stock were re-cultured. Bacterial DNA were then extracted,
dropped on SERS substrates, and air-dried. DNA signals were
measured using a portable Raman spectrometer. Phase II (peach-pink
background; right hand): Data processing and computational
analyses; data were processed using the Python programming
languages using the LOOCV function to distinguish the NTM
subspecies (MAB and MMAS) and MTB-H37Ra. For validation, SERS
spectra were evaluated based on the peak assignments. The ML
approaches (PCA, UMAP, LDA, RF, XGB, and LR) were analyzed.
Performance of the constructed classification models was evaluated
using ROC curves and the AUC to quantify discriminative ability.
LOOCV, leave-one-out cross-validation; NTM, nontuberculous
mycobacteria; MAB, M. abscessus subspecies abscessus;
MMAS, M. abscessus subspecies massiliense; MTB-H37Ra,
M. tuberculosis H37Ra; SERS, surface-enhanced Raman
spectroscopy; ML, machine learning; PCA, principal component
analysis; UMAP, uniform manifold approximation and projection; LDA,
linear discriminant analysis; RF, random forest; XGB, extreme
gradient boosting; LR, logistic regression; ROC, receiver operating
characteristic; AUC, area under the curve.

Figure 2

Raman spectrum patterns obtained from
SERS analysis. (A) Raman fingerprinting of each SERS chip, analyzed
at 785 nm. Pre-analysis was conducted before the chips were used to
investigate sample specimens. (B) Raman fingerprinting of
Mycobacterium abscessus at specific concentrations (15 and
50 ng/µl) quantified via Nanodrop. Prominent peak positions are
indicated above each peak, allowing for a clear comparison related
to DNA content. Raman spectra are displayed as plots of Raman
intensity vs. Raman shift (cm-¹). SERS, surface-enhanced
Raman spectroscopy; a.u., arbitrary unit.

Figure 3

SERS spectrum results for three
bacteria: MAB, MMAS and MTB-H37Ra. (A) Raman spectra from DNA at 15
ng/µl. (B) Raman spectra from DNA at 50 ng/µl. The solid lines
indicate the mean spectra; the shaded regions represent the
standard deviation. Numbers above the peaks correspond to the
strong peak positions, with dotted lines marking the precise
location of each peak. All Raman spectra are plotted as Raman
intensity vs. Raman shift (cm-¹). SERS, surface-enhanced
Raman spectroscopy; MAB, M. abscessus subspecies
abscessus; MMAS, M. abscessus subspecies
massiliense; MTB-H37Ra, M. tuberculosis H37Ra; a.u.,
arbitrary unit.

Figure 4

Box plot comparisons of peak
intensities for eight candidate peaks for bacterial DNA content at
15 ng/µl. The specific Raman intensities examined were at 726, 755,
781, 1,097, 1,317, 1,331, 1,486 and 1,577 cm-¹. The
y-axis represents the relative intensity of the peaks; the x-axis
indicates the bacterial DNA species. Multiple comparisons were
analyzed by Kruskal-Wallis test prior to examining pairwise
differences between groups by using Dunn-Bonferroni post-hoc
analysis. *P<0.05, **P<0.01, and
****P<0.0001; n.s., non-significant. H37Ra, M.
tuberculosis H37Ra; MMAS, M. abscessus subspecies
massiliense; MAB, M. abscessus subspecies
abscessus; a.u., arbitrary unit.

Figure 5

Box plot comparisons of peak
intensities for eight candidate peaks for bacterial DNA content at
50 ng/µl. The specific Raman intensities examined were at 726, 755,
781, 1,097, 1,317, 1,331, 1,486, and 1,577 cm-¹. The
y-axis represents the relative intensity of the peaks; the x-axis
indicates the bacterial DNA species. Multiple comparisons were
analyzed by Kruskal-Wallis test prior to examining pairwise
differences between groups by using Dunn-Bonferroni post-hoc
analysis. **P<0.01, ***P<0.001 and
****P<0.0001; n.s., non-significant. H37Ra, M.
tuberculosis H37Ra; MMAS, M. abscessus subspecies
massiliense; MAB, M. abscessus subspecies
abscessus; a.u., arbitrary unit.

Figure 6

Plot analysis of PCA, UMAP and LDA.
Plots compare three bacteria at genomic DNA concentrations of 15
(left hand) and 50 ng/µl (right hand). (A) PCA, (B) UMAP, and (C)
LDA were analysed and represented in dot plot. Blue dots represent
MAB, green dots indicate MMAS, and red dots represent MTB-H37Ra.
PCA, principal component analysis; UMAP, uniform manifold
approximation and projection; LDA, linear discriminant analysis;
MAB, M. abscessus subspecies abscessus; MMAS, M.
abscessus subspecies massiliense; MTB-H37Ra, M.
tuberculosis H37Ra.

Figure 7

The LDA, XGB, RF and LR analyses of
the ROC curve. (A) MAB vs MMAS. (B) NTM group vs. MTB-H37Ra. LDA,
linear discriminant analysis; XGB, extreme gradient boosting; RF,
random forest; LR, logistic regression; ROC, receiver operating
characteristic; MAB, M. abscessus subspecies
abscessus; MMAS, M. abscessus subspecies
massiliense; NTM, nontuberculous mycobacteria; MTB-H37Ra,
M. tuberculosis H37Ra.
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Copy and paste a formatted citation
Spandidos Publications style
Pichitphorn A, Faksri K, Kaewseekhao B, Potisap C, Duong Thai T, Eisiri J, Juntagran C, Namwat W, Khwansirikul P, Salao K, Salao K, et al: Establishing a competence model in discriminating <em>Mycobacterium</em> species and <em>Mycobacterium abscessus</em> subspecies by using surface‑enhanced Raman spectroscopy. Biomed Rep 25: 81, 2026.
APA
Pichitphorn, A., Faksri, K., Kaewseekhao, B., Potisap, C., Duong Thai, T., Eisiri, J. ... Phoksawat, W. (2026). Establishing a competence model in discriminating <em>Mycobacterium</em> species and <em>Mycobacterium abscessus</em> subspecies by using surface‑enhanced Raman spectroscopy. Biomedical Reports, 25, 81. https://doi.org/10.3892/br.2026.2154
MLA
Pichitphorn, A., Faksri, K., Kaewseekhao, B., Potisap, C., Duong Thai, T., Eisiri, J., Juntagran, C., Namwat, W., Khwansirikul, P., Salao, K., Chaisri, S., Phoksawat, W."Establishing a competence model in discriminating <em>Mycobacterium</em> species and <em>Mycobacterium abscessus</em> subspecies by using surface‑enhanced Raman spectroscopy". Biomedical Reports 25.1 (2026): 81.
Chicago
Pichitphorn, A., Faksri, K., Kaewseekhao, B., Potisap, C., Duong Thai, T., Eisiri, J., Juntagran, C., Namwat, W., Khwansirikul, P., Salao, K., Chaisri, S., Phoksawat, W."Establishing a competence model in discriminating <em>Mycobacterium</em> species and <em>Mycobacterium abscessus</em> subspecies by using surface‑enhanced Raman spectroscopy". Biomedical Reports 25, no. 1 (2026): 81. https://doi.org/10.3892/br.2026.2154
Copy and paste a formatted citation
x
Spandidos Publications style
Pichitphorn A, Faksri K, Kaewseekhao B, Potisap C, Duong Thai T, Eisiri J, Juntagran C, Namwat W, Khwansirikul P, Salao K, Salao K, et al: Establishing a competence model in discriminating <em>Mycobacterium</em> species and <em>Mycobacterium abscessus</em> subspecies by using surface‑enhanced Raman spectroscopy. Biomed Rep 25: 81, 2026.
APA
Pichitphorn, A., Faksri, K., Kaewseekhao, B., Potisap, C., Duong Thai, T., Eisiri, J. ... Phoksawat, W. (2026). Establishing a competence model in discriminating <em>Mycobacterium</em> species and <em>Mycobacterium abscessus</em> subspecies by using surface‑enhanced Raman spectroscopy. Biomedical Reports, 25, 81. https://doi.org/10.3892/br.2026.2154
MLA
Pichitphorn, A., Faksri, K., Kaewseekhao, B., Potisap, C., Duong Thai, T., Eisiri, J., Juntagran, C., Namwat, W., Khwansirikul, P., Salao, K., Chaisri, S., Phoksawat, W."Establishing a competence model in discriminating <em>Mycobacterium</em> species and <em>Mycobacterium abscessus</em> subspecies by using surface‑enhanced Raman spectroscopy". Biomedical Reports 25.1 (2026): 81.
Chicago
Pichitphorn, A., Faksri, K., Kaewseekhao, B., Potisap, C., Duong Thai, T., Eisiri, J., Juntagran, C., Namwat, W., Khwansirikul, P., Salao, K., Chaisri, S., Phoksawat, W."Establishing a competence model in discriminating <em>Mycobacterium</em> species and <em>Mycobacterium abscessus</em> subspecies by using surface‑enhanced Raman spectroscopy". Biomedical Reports 25, no. 1 (2026): 81. https://doi.org/10.3892/br.2026.2154
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