Near-infrared spectroscopy as a tool for in vivo analysis of human muscles

Abstract

Recent advances in materials and fabrication techniques provided portable, performant, sensing optical spectrometers readily operated by user-friendly cabled or wireless systems. Such systems allow rapid, non-invasive, and not destructive quantitative analysis of human tissues. This proof-of-principle investigation tested whether infrared spectroscopy techniques, currently utilized in a variety of areas, could be applied in living humans to categorize muscles. Using an ASD FieldSpec® 4 Standard-Res Spectroradiometer with a spectral sampling capability of 1.4 nm at 350-1000 nm and 1.1 nm at 1001-2500 nm, we acquired reflectance spectra in visible short-wave infra-red regions (350-2500 nm) from the upper limb muscles (flexors and extensors) of 20 healthy subjects (age 25-89 years, 9 women). Spectra off-line analysis included preliminary preprocessing, Principal Component Analysis, and Partial Least-Squares Discriminant Analysis. Near-infrared (NIR) spectroscopy proved valuable for noninvasive assessment of tissue optical properties in vivo. In addition to the non-invasive detection of tissue oxygenation, NIR spectroscopy provided the spectral signatures (ie, "fingerprints") of upper limb flexors and extensors, which represent specific, accurate, and reproducible measures of the overall biological status of these muscles. Thus, non-invasive NIR spectroscopy enables more thorough evaluation of the muscular system and optimal monitoring of the effectiveness of therapeutic or rehabilitative interventions.

Figure 1

(a) Mean reflectance spectra of the ventral and dorsal arm for each subject. (b) Mean reflectance spectra of ventral arm/biceps (blue) and dorsal arm/triceps (yellow) averaged over all subjects. In the legend, the first letters identify the subject. D, dorsal arm; B, ventral arm.

Figure 2

Scores plot of PC 1 vs. PC 2 of ventral arm/biceps (blue diamonds) and dorsal arm/triceps (yellow squares) reflectance data without any distinction among the different examined individuals.

Figure 3

PLS regression results for the anthropometric variables, showing (a) Actual Age vs. Predicted Age and (b) Actual BMI vs. Predicted BMI. Each panel shows the squared correlation ($R_p^{\mathrm{2}}$) and the Root Mean Square Error in Prediction (RMSEP) of each Y. The fit line is shown in red. The 1:1 line is shown in green. Grey dots represent the calibration data; red diamonds are the data used to validate the model.

Figure 4

Mean reflectance spectra from ventral and dorsal arm for women and men.

Figure 5

Scores plot of PC 1 vs. PC 2 of women (blue diamonds) and men (yellow squares) reflectance data.

Figure 6

PLS-DA latent variables (LV) scores plot of LV 1 vs. LV 2 to discriminate sex from acquired spectra (a) and the position of the discrimination boundary for the two classes (“female” blue diamonds, and “male” yellow squares) as determined by PLS-DA model (b) for 450–2500 nm. Calibration and validation data are shown in both panels.

Figure 7

Subject vs. KNN Score Distance. Dots represent subjects according to the KNN score distance. Y values reflect the intra-subject variability of anthropometric variables.

Figure 8

Position of the discrimination boundary for ventral arm/biceps (blue diamonds) and dorsal arm/triceps (yellow squares) as determined by PLS-DA model for: (a) 450–2500 nm; (b) VNIR/450–1000 nm; (c) SWIR1/1001–1800 nm; (d) SWIR2/1801–2500 nm. Scores lying above the boundary line are classified as “biceps”, Scores lying below the boundary line are classified as “triceps”. All panels display both the calibration and the test set.

Figure 9

(a) Scores plot and (b) loadings plot of PC 1 vs. PC 2 values calculated on anthropometric variables. The positions of the anthropometric variables in the loadings plot indicate which variable weights the most in the score of each subject (shown in scores plot).