Practical laboratory medicine · 2026
Capillary serum-based spectroscopic signature for multiple sclerosis: A novel diagnostic perspective.
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Santiago Raí S, Nascimento Marcia H C, Araújo Jaya M C, Castrillo Bruno B, Dias Paula Z M, Albano Regina E, Nascimento Carla C, Filgueiras Paulo R, Barauna Valerio G, Rodrigues Lívia C M
Аннотация
OBJECTIVE: To analyze the biochemical characteristics of capillary blood-derived serum using Fourier-transform infrared (FTIR) spectroscopy, aiming to differentiate individuals diagnosed with Multiple Sclerosis (MS) from a matched control group (CG).
METHODS: The study included 52 patients with confirmed MS and 52 healthy control participants, matched by sex and age. Serum samples were obtained via capillary blood collection and analyzed using mid-infrared ATR-FTIR spectroscopy. Chemometric analyses were performed on the full spectrum (3700-900 cm-1) and on the fingerprint (1800-900 cm-1) and high-wavenumber (3700-2800 cm-1) regions separately. Preprocessing included baseline correction and smoothing, followed by multivariate modeling using Partial Least Squares Discriminant Analysis (PLS-DA).
RESULTS: FTIR spectroscopy combined with supervised machine learning revealed metabolic alterations between groups, including increased concentrations of phospholipids/lipids, nucleic acids, and Amide I/II in MS patients. The high-wavenumber region showed enhanced differentiation of control individuals, whereas the fingerprint region provided better separation between MS and CG. The combination of both spectral regions (full spectrum) yielded balanced performance metrics, sensitivity, specificity, and accuracy of 87.5%, highlighting their complementary diagnostic value.
CONCLUSION: FTIR spectroscopy of capillary blood serum provides distinct and complementary biochemical signatures for MS diagnosis. When integrated with machine learning algorithms, this approach demonstrated high discriminatory power. This is the first study to employ serum for capillary blood for FTIR-based MS classification, supporting its potential as a non-invasive, rapid, and accessible tool to assist in clinical decision-making.
METHODS: The study included 52 patients with confirmed MS and 52 healthy control participants, matched by sex and age. Serum samples were obtained via capillary blood collection and analyzed using mid-infrared ATR-FTIR spectroscopy. Chemometric analyses were performed on the full spectrum (3700-900 cm-1) and on the fingerprint (1800-900 cm-1) and high-wavenumber (3700-2800 cm-1) regions separately. Preprocessing included baseline correction and smoothing, followed by multivariate modeling using Partial Least Squares Discriminant Analysis (PLS-DA).
RESULTS: FTIR spectroscopy combined with supervised machine learning revealed metabolic alterations between groups, including increased concentrations of phospholipids/lipids, nucleic acids, and Amide I/II in MS patients. The high-wavenumber region showed enhanced differentiation of control individuals, whereas the fingerprint region provided better separation between MS and CG. The combination of both spectral regions (full spectrum) yielded balanced performance metrics, sensitivity, specificity, and accuracy of 87.5%, highlighting their complementary diagnostic value.
CONCLUSION: FTIR spectroscopy of capillary blood serum provides distinct and complementary biochemical signatures for MS diagnosis. When integrated with machine learning algorithms, this approach demonstrated high discriminatory power. This is the first study to employ serum for capillary blood for FTIR-based MS classification, supporting its potential as a non-invasive, rapid, and accessible tool to assist in clinical decision-making.