Continuous wavelet-based feature extraction for laser-induced breakdown spectroscopy classification of human plasma samples

Continuous wavelet-based feature extraction for laser-induced breakdown spectroscopy classification of human plasma samples Md Mahmudur Rahman1 , Atqiya Munawara Mahi1 , Nazim Ahmed Belabbaci1 , and Mohammad Arif Ul Alam1,2,*

arXiv:2611.09746v1 [cs.AI] 6 Nov 2026

1 Miner School of Computer and Information Sciences, Kennedy College of Sciences, University of Massachusetts,

Lowell, USA 2 Department of Computer Systems Technology, North Carolina Agriculture and Technical University * malam7@ncat.edu

ABSTRACT Extracting discriminative features from laser-induced breakdown spectroscopy (LIBS) spectra is important for the classification of complex biological samples. This study evaluates continuous wavelet transform (CWT)-based spectral peak extraction for distinguishing human plasma samples labeled as SARS-CoV-2-positive or negative. Plasma specimens from 181 individuals, comprising 53 positive samples and 128 negative controls, were deposited as 5 µL droplets on silicon substrates and dried before analysis. Spectra were acquired using a nanosecond Nd:YAG laser operating at 1064 nm, with 100 single-shot measurements collected per specimen and averaged after intensity-based outlier rejection. CWT-based peak detection was coupled with a support vector machine (SVM) classifier. The reported leave-one-out evaluation yielded an accuracy of 93.00%, sensitivity of 94.67%, and specificity of 91.61%; these estimates remain provisional pending reconciliation with specimen-level predictions. Features identified by the CWT pipeline included emission lines assigned to Ca II, K I, and N I. The workflow provides a basis for examining spectral features associated with the studied plasma groups. Independent validation and assessment of specimen, substrate, and acquisition-related variability are required to establish generalizability and diagnostic relevance.

1 Introduction Laser-induced breakdown spectroscopy (LIBS) offers a multielemental approach to characterizing biological specimens through their atomic and ionic emission spectra. A focused laser pulse ablates a small amount of material and generates a transient plasma, whose emission provides information about the elemental composition of the interrogated sample1 . The ability to acquire multiple elemental signatures from small sample volumes, with comparatively limited chemical preparation, has motivated applications in biological and biomedical analysis2 . For complex biofluids, however, the analytical value of LIBS depends on more than detecting emission lines: it also requires spectral representations that preserve useful sample differences while limiting the influence of background emission, measurement variability, and matrix effects. Blood plasma is a particularly challenging matrix because its spectra reflect contributions from endogenous constituents, specimen preparation, and the conditions under which the laser-induced plasma is formed. Emission intensity cannot generally be interpreted as elemental concentration without accounting for excitation conditions, ablation behavior, and matrix-dependent effects1, 2 . Drying a specimen on a solid substrate further introduces potential variability associated with deposition morphology and substrate emission. Consequently, identifying spectral features associated with sample labels requires careful distinction between biological variation and variation introduced during preparation or acquisition. This distinction is especially relevant when classification models are developed from relatively small collections of biological specimens. Previous studies have established the feasibility of combining LIBS with statistical learning for biological sample discrimination. Applications include the classification of protein biomarker solutions3 , discrimination of plasma samples from healthy and ovarian cancer-prone mice4 , and investigations of Alzheimer’s disease using LIBS and machine learning5 . These studies motivate the use of multivariate spectral patterns in biomedical analysis, while also emphasizing the need to establish the analytical basis and generalizability of observed discrimination. A spectral feature associated with a disease-labeled group is not, by itself, evidence of a disease-specific biomarker. SARS-CoV-2-associated plasma samples provide a relevant setting for investigating these methodological questions. Molecular and antigen tests assess viral targets, whereas LIBS-based analysis of plasma examines spectral patterns that may reflect host-associated compositional differences. These approaches therefore address different analytical signals. Berlo et al.

demonstrated that LIBS combined with machine learning could distinguish plasma from donors who had previously tested positive for SARS-CoV-2 by reverse-transcription polymerase chain reaction from control plasma6 . Their findings establish an important precedent for the present application. Further methodological evaluation should therefore address how spectral information is represented and used for classification, rather than assume that classification performance alone establishes direct viral detection or clinical diagnostic validity. Feature extraction is central to this problem. Full LIBS spectra contain numerous correlated variables, while potentially informative differences may be concentrated within a smaller set of emission features. With limited numbers of independent specimens, the choice of spectral representation can influence both classification performance and the interpretation of the resulting model. Continuous wavelet transform (CWT)-based peak detection uses localized responses across spectral scales to identify candidate peaks; wavelet-based methods have previously been investigated for analytical peak detection7 . The resulting spectral representation depends on peak structure, background variation, and peak-detection settings. In this study, we evaluate CWT-based peak detection for the classification of human plasma samples labeled as SARSCoV-2-positive or negative. The dataset comprises 181 individual specimens, including 53 positive samples and 128 negative controls. Spectra were acquired from dried plasma droplets on silicon substrates, and the extracted peak features were used to train an SVM classifier. The evaluation focuses on classification performance under the reported leave-one-out cross-validation procedure, the emission features retained by the peak-based representation, and the effects of selected wavelength exclusions and wavelet settings. The contribution of this work is an evaluation of CWT-based spectral peak extraction within a specimen-level LIBS classification workflow. Using plasma specimens labeled as SARS-CoV-2-positive or negative, we examine the resulting classification performance, the sensitivity of peak selection to wavelet settings, and the effects of selected wavelength exclusions. The analysis is intended to characterize the behavior of the spectral representation within the available specimen collection; independent validation is required to establish its generalizability. Plasma specimen collection Round 1: 118 specimens (24 positive, 94 negative) Round 2: 63 specimens (29 positive, 34 negative) ↓ Combined LIBS cohort 181 individuals: 53 positive, 128 negative controls ↓ Specimen preparation 5 µL plasma on silicon; infrared drying for 10 min ↓ LIBS acquisition 1064 nm Nd:YAG laser; 100 single-shot spectra per specimen ↓ Within-specimen preprocessing Reject spectra outside mean ± 1 SD of total emission intensity Average retained spectra ↓ Specimen-level LIBS dataset CWT–SVM analysis

Figure 1. Plasma specimen collection, preparation, and LIBS data acquisition. Two collection rounds yielded 181 specimens, comprising 53 SARS-CoV-2-positive samples and 128 negative controls. Following deposition and drying on silicon substrates, 100 single-shot spectra were acquired per specimen. Spectra with total emission intensities outside the within-specimen mean ± one standard deviation were excluded, and retained spectra were averaged for subsequent CWT-SVM analysis. Sample counts refer to individual specimens rather than laser shots.

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2 Datasets 2.1 Blood Plasma LIBS Spectra Sample Collection and Initial Signal Processing A total of 181 human plasma specimens were collected in sodium citrate anticoagulant from donors drawn by MRN Diagnostics both before (pre 11/2019) and after the appearance of SARS-CoV-2 in the United States. Informed consent was obtained from all subjects and studies were performed in accordance with FDA guidelines and regulations including IRB approval. Specimens were collected in two rounds: in the first round, 118 samples were collected (24 SARS-CoV-2 ‘positive’ and 94 ‘negative’ control) and in the second round, 63 samples were collected (29 SARS-CoV-2 ‘positive’ and 34 ‘negative’ control) samples. The 181 samples provided by MRN Diagnostics were analyzed by University of Massachusetts using LIBS technology. Prior to performing LIBS measurements of the blood plasma samples, 5 µL of each individual blood plasma specimen were deposited on the unpolished side of pure Si wafers. These wafers were previously rinsed in 2-propanol. The blood plasma samples deposited on the Si wafers were then dried for 10 min using a Tungsten infrared lamp. During the laser ablation process, separate single-shot spectra were captured from adjacent spots within the deposited droplet. The experimental setup for LIBS involved focusing 7 ns Nd:YAG laser pulses (Surelite II, Continuum) with a wavelength of 1064 nm onto the samples using an air-spaced doublet lens with a focal length of 30 mm. The blood plasma specimens were loaded onto a computer-controlled translation stage within a SciTrace chamber (AtomTrace), and the emission from the laser-induced plasma was collected using a 50 µm core-diameter optical fiber positioned at a 45◦ angle relative to the laser beam. The optical fiber was connected to an Echelle spectrograph (Andor Technology, ME 5000) and a thermoelectrically cooled iStar Intensified Charge Coupled Device (ICCD) camera (Andor Technology, DH734-18F-03). The time parameters utilized for this work were as follows: a gate delay of 1 microsecond and a gate width of 5 microseconds. The focused laser spot had a diameter of approximately 100 µm, operating at a repetition rate of 1 Hz with a laser energy of 130±2 mJ. All measurements were conducted in ambient air pressure. For each blood plasma sample, a total of 100 single-shot spectra were collected. Each spectrum was obtained from a new location on the dried blood plasma drop by using the 3-D translation stage after each laser shot. During analysis, the 100 individual spectra obtained for each sample were averaged, with any outlier spectra - those falling outside the range of mean ±1 standard deviation of total emission intensity - being excluded from the average process.

Figure 2. Median specimen-level LIBS intensities in the positive (green) and negative (red) groups at selected wavelengths. Error bars indicate the first and third quartiles. The horizontal axis shows wavelength in nanometers.

3 Methods 3.1 Analytical workflow and unit of analysis The analysis combined continuous wavelet transform (CWT)-based spectral peak detection with support vector machine (SVM) classification. Specimen preparation, LIBS acquisition, and intensity-based rejection of individual laser-shot spectra are described in the preceding section. Following these procedures, the retained spectra were averaged within each specimen to obtain one representative spectrum per individual. The individual specimen, rather than the laser shot, was the unit of analysis. The resulting dataset comprised 181 specimen-level spectra, including 53 specimens labeled as SARS-CoV-2-positive and 128 negative controls. The analytical workflow consisted of detecting candidate emission peaks, aligning their wavelength posi3/10

tions across specimens, constructing peak-intensity feature vectors, and classifying the specimens using an SVM. Classification was evaluated against the supplied specimen labels. 3.2 Continuous wavelet transform for spectral peak detection CWT-based peak detection was used to identify localized features in the specimen-level LIBS spectra. Wavelet methods characterize spectral structure by comparing the measured signal with translated and scaled versions of a reference function, providing a basis for detecting peaks over a range of characteristic widths7, 8 . Let fi (t) denote the averaged emission intensity of specimen i at spectral coordinate t. Its continuous wavelet transform is expressed as   Z 1 +∞ ∗ t −u √ fi (t)ψ dt, s > 0, (1) Wi (u, s) = s s −∞ where u is the translation parameter, s is the scale parameter, ψ is the mother wavelet, and the asterisk denotes complex conjugation. The transform was applied to the discretely sampled spectra. Spectral coordinates were associated with their measured wavelengths for subsequent peak alignment and interpretation. A Mexican-hat wavelet was used because its central positive lobe and flanking negative lobes provide a localized representation of peak-like structure. A normalized form is  2
t 2 ψ(t) = √ 1 − t 2 exp − . (2) 2 3 π 1/4 This real-valued wavelet has zero mean and is proportional to the negative second derivative of a Gaussian. Translation determines the position at which the spectrum is examined, whereas scale controls the characteristic width of the structure to which the wavelet responds. Candidate peaks were identified from the wavelet response and screened using a signal-to-noise criterion. The retained peak positions were then associated with their intensities in the specimen-level spectrum. Figure 3 illustrates the resulting peak detections.

Figure 3. Illustration of CWT-based peak detection in a specimen-level LIBS spectrum. The blue curve represents the measured spectrum, and red markers indicate retained peak positions.

3.3 Peak alignment and feature construction Detected peaks were aligned by wavelength to establish corresponding spectral features across specimens. This alignment provided a common feature representation in which each column corresponded to a matched peak position and each row 4/10

corresponded to an individual specimen. For p aligned features, the feature vector for specimen i was xi = (xi1 , xi2 , . . . , xip )T ,

(3)

where xi j denotes the spectral peak height assigned to feature j in specimen i. The complete feature matrix was X ∈ RN×p , with N = 181. The response variable was yi = 1 for a positive specimen and yi = 0 for a negative control. Peak heights were treated as empirical spectral descriptors. They were not converted to elemental concentrations, and their association with specimen labels was not interpreted as evidence of disease-specific elemental biomarkers. 3.4 Support vector machine classification An SVM was used to classify specimens from their aligned peak-intensity vectors9, 10 . The reported analysis examined linear and nonlinear kernels and selected a radial basis function (RBF) kernel. For feature vectors xi and x j , the RBF kernel is   2 K(xi , x j ) = exp −γ xi − x j 2 ,

(4)

where γ > 0 determines the kernel width. The regularization parameter C controls the trade-off between the margin penalty and classification errors. The reported CWT-SVM configuration used C = 10−3 . 3.5 Cross-validation and performance measures The reported classification analysis used leave-one-out cross-validation at the specimen level. In each iteration, one specimen was withheld for evaluation and the remaining 180 specimens were used for classifier training. This procedure was repeated until every specimen had served once as the evaluation case. The procedure therefore assessed internal predictive performance within the available specimen collection. For evaluation, positive specimens were designated as the positive class. Classification performance is defined from the pooled out-of-fold class predictions as TP+TN , T P + T N + FP + FN TP , Sensitivity = T P + FN TN Specificity = , T N + FP Accuracy =

(5) (6) (7)

where T P, T N, FP, and FN denote the numbers of true-positive, true-negative, false-positive, and false-negative predictions, respectively. Values are expressed as percentages in the results. 3.6 Peak-detection settings and wavelength-exclusion analysis The influence of peak-detection settings was examined by varying the signal-to-noise criterion and wavelet width. The resulting peak selections were compared to assess how these parameters affected the retention of weak features and the separation of neighboring peaks. Figure 4 illustrates the effect of the signal-to-noise setting, and Figure 5 illustrates the effect of wavelet width. The reported analysis used a wavelet-width parameter of 20; this value denotes an algorithm parameter rather than a physical emission-line width unless a conversion to wavelength units is specified. A wavelength-exclusion analysis was also conducted for the CWT-SVM pipeline. Features corresponding to selected wavelengths were omitted individually, and classification performance was compared with that obtained using the complete feature set. The reported exclusions concerned features near 396.880, 288.179, 413.085, and 393.398 nm. These comparisons assess the dependence of the classifier on particular spectral features; they do not establish a causal relationship between an element and the specimen label.

4 Results 4.1 Specimen-level classification The analytical dataset comprised 181 plasma specimens, including 53 SARS-CoV-2-positive specimens and 128 negative controls. The retained classification pipeline used CWT-derived spectral peak intensities as inputs to an SVM with an RBF kernel. The existing evaluation reported an accuracy of 93.00%, sensitivity of 94.67%, and specificity of 91.61% (Table 1). These estimates describe the reported internal evaluation of the available specimen collection. 5/10

Figure 4. Effect of the signal-to-noise setting on CWT-based peak detection in a LIBS spectrum. The comparison illustrates changes in the set of retained spectral peaks.

Table 1. Reported classification performance of the CWT-SVM pipeline. Values are reproduced from the existing analysis and remain subject to verification against the pooled out-of-fold predictions. Method CWT-SVM

Accuracy (%) 93.00

Sensitivity (%) 94.67

Specificity (%) 91.61

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4.2 Spectral features and peak-detection behavior The reported CWT analysis highlighted spectral features at 393.398, 396.880, 742.512, and 766.49 nm. The corresponding assignments in the original analysis were Ca II, Ca II, N I, and K I, respectively. These assignments describe candidate emission features and do not establish their specificity to SARS-CoV-2-associated specimens. The peak-detection examples illustrate the dependence of the extracted representation on the wavelet settings. The original qualitative comparison indicated that smaller wavelet widths retained more local spectral features, whereas larger widths omitted some narrower features (Figure 5). The reported analysis adopted a width parameter of 20. The signal-to-noise comparison illustrates how the retained peak set changed with the detection criterion (Figure 4). These comparisons characterize peak-selection behavior; quantitative performance differences between the tested settings were not reported. 4.3 Wavelength-exclusion analysis The reported wavelength-exclusion results are presented in Table 2. Relative to the reported complete-feature accuracy of 93.00%, exclusion of the feature at 396.880 nm yielded an accuracy of 92.42%, a decrease of 0.58 percentage points. Exclusion of the features at 288.179 and 413.085 nm yielded accuracies of 87.45% and 88.23%, corresponding to decreases of 5.55 and 4.77 percentage points, respectively. The largest reported accuracy decrease occurred after exclusion of the feature at 393.398 nm, for which accuracy was 84.65%, or 8.35 percentage points below the complete-feature value. Table 2. Reported wavelength-exclusion results for the CWT-SVM pipeline. The accuracy decrease is calculated relative to the reported complete-feature accuracy of 93.00% and is expressed in percentage points (pp). All performance values remain provisional pending verification of their calculation and evaluation denominators. Excluded wavelength (nm) None 396.880 288.179 413.085 393.398

Accuracy (%) 93.00 92.42 87.45 88.23 84.65

Sensitivity (%) 94.67 91.65 82.34 84.67 82.13

Specificity (%) 91.61 89.46 84.87 83.65 80.91

Accuracy decrease (pp) 0.00 0.58 5.55 4.77 8.35

These numerical differences summarize the existing analysis; their statistical significance has not been established. In particular, the reported decrease following exclusion of 288.179 nm suggests a possible dependence on this feature, subject to verification of the performance calculations. Interpretation of the exclusion results requires verification of the performance estimates and confirmation of the corresponding spectral assignments.

5 Discussion 5.1 Analytical significance of the CWT-SVM workflow This study examined CWT-based spectral feature extraction coupled with SVM classification for the analysis of 181 human plasma specimens. The analytical contribution is the use of localized spectral peaks to represent complex LIBS measurements in a specimen-level classification workflow. This representation connects the classifier inputs to identifiable spectral positions, facilitating examination of the effects of peak-detection settings and individual wavelength exclusions. The present results should, however, be interpreted as an exploratory evaluation within the available specimen collection. The reported classification estimates suggest that the extracted features contain information associated with the supplied specimen labels. Nevertheless, the inconsistencies identified in the performance summaries prevent firm conclusions about the magnitude of predictive performance. Verification against the original out-of-fold predictions is necessary before the reported accuracy, sensitivity, specificity, or wavelength-exclusion differences can be interpreted quantitatively. In particular, these estimates cannot currently establish clinical diagnostic validity or generalizability to independently collected specimens. Previous investigations have demonstrated the feasibility of combining LIBS with statistical learning for protein-solution classification and biomedical plasma analysis3, 5 . Berlo et al. also reported discrimination of plasma associated with prior SARS-CoV-2 positivity using LIBS and machine learning6 . Accordingly, the present study addresses spectral representation and feature dependence within this application rather than establishing the first demonstration of SARS-CoV-2-associated plasma discrimination. Differences in specimen composition, acquisition conditions, and validation procedures preclude direct comparisons of classification performance across studies. 7/10

Figure 5. Effect of the wavelet-width parameter on CWT-based peak detection in a representative LIBS spectrum. Different settings alter the number and positions of retained candidate peaks. Width values denote algorithm parameters.

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5.2 Spectral interpretation and substrate effects The CWT analysis highlighted features assigned in the original analysis to Ca II, K I, and N I. These assignments provide candidate spectral locations for further examination, but their biological interpretation remains conditional on wavelength verification, assessment of spectral overlap, and characterization of measurement variability. Atomic and ionic emission intensities depend on both specimen composition and the conditions of laser ablation and plasma excitation1, 2 . Consequently, differences in peak intensity cannot be interpreted directly as differences in circulating elemental concentrations. The wavelength-exclusion analysis offers a way to examine the classifier’s dependence on selected features. Conditional on verification of the reported metrics, the larger decrease associated with exclusion of the feature near 393.398 nm would motivate further investigation of that spectral region. It would not establish a causal contribution of calcium to SARS-CoV-2 infection or demonstrate that this line is a disease-specific biomarker. Correlation among spectral features also limits the interpretation of single-feature exclusions: other variables may retain related information, while removal of one feature can alter the fitted decision boundary. The feature near 288.179 nm requires particular attention because it was assigned to Si I in the original analysis and the specimens were deposited on silicon substrates. The reported exclusion results suggest a possible dependence on this feature, subject to verification of the performance calculations. A substrate-associated feature could reflect differences in specimen coverage, dried-droplet morphology, or laser interaction with the underlying surface. These possibilities remain hypotheses rather than demonstrated mechanisms in the present study. Blank-substrate measurements, replicate deposits, spatially resolved acquisition, and comparisons across substrate batches would help distinguish specimen-related information from preparation-dependent effects. 5.3 Influence of wavelet settings Figure 5 illustrates the dependence of the extracted peak set on wavelet width. Smaller widths respond to finer spectral structure and may retain both narrow emission features and local fluctuations. Larger widths emphasize broader structures and may fail to resolve some closely spaced or narrow peaks. The analytical objective is therefore to identify settings that preserve reproducible spectral information while limiting sensitivity to noise. The width parameter of 20 used in the reported analysis should be regarded as an implementation-specific choice. The qualitative comparison does not establish its statistical optimality, and its physical interpretation depends on the spectral sampling interval and the definition of width in the implementation. Similarly, the signal-to-noise criterion influences which weak features enter the classifier (Figure 4). Together, these observations support evaluating peak-detection settings as part of the complete classification pipeline, rather than selecting them solely from visual inspection of representative spectra. Future evaluation should quantify the stability of detected peaks across replicate measurements and training subsets. Parameter selection should occur within the training data, with performance assessed on specimens excluded from that selection. Such an analysis would clarify whether the observed discrimination persists across reasonable choices of wavelet width, signal-to-noise threshold, and wavelength-alignment tolerance. 5.4 Study limitations The specimen collection is limited in size and contains fewer positive specimens than negative controls. Although multiple laser shots were acquired from each deposit, these measurements are technical replicates and do not increase the number of independent individuals. Leave-one-out cross-validation provides an internal evaluation, but it does not establish reproducibility across collection sites, instruments, or clinical populations. Its interpretation also depends on whether feature alignment, scaling, parameter selection, and any other data-dependent operations were performed independently within the training portion of each iteration. The two collection rounds and the inclusion of specimens obtained before the emergence of SARS-CoV-2 introduce possible temporal and batch-related confounding. Storage history, specimen handling, and acquisition conditions could differ between groups and contribute to apparent discrimination. The available analysis does not establish that these influences have been excluded. Evaluation across collection rounds, accompanied by appropriate specimen metadata and laboratory quality controls, is therefore needed to assess whether the classifier captures reproducible sample differences. Clinical interpretation is additionally constrained by the absence of demonstrated specificity against other infectious or inflammatory conditions. A classifier trained on the supplied positive and negative labels may respond to host-associated changes or other group differences without detecting the virus itself. The present analysis consequently does not establish active-infection detection, performance in asymptomatic individuals, or transferability across viral variants. Finally, the current performance summaries require reconciliation with the specimen-level predictions. Confidence intervals, a verified confusion matrix, and comparisons with conventional chemometric baselines would provide a stronger basis for assessing the CWT-SVM pipeline. The practical advantages of the workflow also remain to be measured: the use of small plasma volumes and limited chemical preparation does not by itself demonstrate short clinical turnaround time, low operating cost, or point-of-care suitability. 9/10

6 Conclusion and Future Works This study presents a CWT-based peak-extraction and SVM classification workflow for LIBS spectra acquired from 181 human plasma specimens. The workflow represents each specimen through aligned spectral peak intensities and provides a basis for examining the effects of peak-detection settings and selected wavelength exclusions. The current analysis identifies candidate spectral regions for further investigation, while verification of the performance calculations remains necessary before quantitative claims about classification accuracy can be finalized. Further work should first establish a reproducible evaluation using verified specimen-level predictions, complete reporting of preprocessing and model settings, and training-restricted parameter selection. Independent specimen collections and comparisons with established chemometric methods are needed to assess generalizability and the added value of CWT-based feature extraction. Analytical validation should include replicate deposits, substrate blanks, acquisition-day controls, and assessment of peak stability across instruments and measurement conditions. Subsequent clinical studies should include contemporaneous controls and specimens representing other infectious and inflammatory conditions, with documented collection and storage histories. Where feasible, paired elemental measurements would help evaluate the relationship between LIBS features and specimen composition. These steps would support a more rigorous assessment of the workflow as an analytical approach for plasma characterization and determine whether it warrants further development for a defined clinical application. Data Availability Statement The data and source code will be available upon reasonable request to the authors.

References 1. E. Tognoni, V. Palleschi, S. Corsi and G. Cristoforetti, Quantitative micro-analysis by laser-induced breakdown spectroscopy: a review of the experimental approaches, Spectrochimica Acta Part B: Atomic Spectroscopy 57 (2002) 1115–1130. doi:10.1016/S0584-8547(02)00053-8. 2. R. Gaudiuso et al., Laser-induced breakdown spectroscopy for human and animal health: a review, Spectrochimica Acta Part B: Atomic Spectroscopy 152 (2019) 123–148. doi:10.1016/j.sab.2018.11.006. 3. D. Pokrajac et al., Automatic classification of laser-induced breakdown spectroscopy (LIBS) data of protein biomarker solutions, Applied Spectroscopy 68 (2014) 1067–1075. 4. N. Melikechi et al., Age-specific discrimination of blood plasma samples of healthy and ovarian cancer prone mice using laser-induced breakdown spectroscopy, Spectrochimica Acta Part B: Atomic Spectroscopy 123 (2016) 33–41. 5. R. Gaudiuso, E. Ewusi-Annan, W. Xia and N. Melikechi, Diagnosis of Alzheimer’s disease using laser-induced breakdown spectroscopy and machine learning, Spectrochimica Acta Part B: Atomic Spectroscopy 171 (2020) 105931. doi:10.1016/j.sab.2020.105931. 6. K. Berlo et al., Laser induced breakdown spectroscopy for the rapid detection of SARS-CoV-2 immune response in plasma, Scientific Reports 12 (2022) 1614. doi:10.1038/s41598-022-05509-z. 7. P. Du, W. A. Kibbe and S. M. Lin, Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching, Bioinformatics 22 (2006) 2059–2065. 8. S. Mallat, A Wavelet Tour of Signal Processing (1999). 9. W. S. Noble, What is a support vector machine?, Nature Biotechnology 24 (2006) 1565–1567. 10. D. A. Pisner and D. M. Schnyer, Support vector machine, in: Machine Learning, Elsevier (2020), pp. 101–121.

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