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. In this proof-of-principle investigation, we will test whether infrared spectroscopy techniques, currently utilized in areas as primary/secondary raw materials sector, cultural heritage, agricultural/food/pharmaceutical industry, environmental remote and proximal sensing, 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 will acquire muscles reflectance spectra in the visible short-wave infra-red regions (350-2500 nm), from the upper limb muscles (flexors and extensors) in healthy subjects. Spectra off-line analysis will include preliminary pre-processing for signal scattering reduction, Principal Component Analysis aimed to spectral grouping, and Partial Least-Squares Discriminant Analysis for implementing discrimination/prediction models.
Currently, we are unaware of any a priori knowledge about classification of NIR spectra acquired in vivo from human muscles, therefore this study provides essentially an exploratory approach. At this stage of the research, we are not interested to gain information on the intimate physical chemical composition of the living muscles, rather to obtain an objective indicator of the current state of the organ which could be measured accurately, and reproducibly observed from outside the organ itself. Therefore, in this study we will investigate the reliability and accuracy of the visible and near infrared spectroscopy-application for identification and authentication of muscle groups in the upper limb, without depending on the chemical information. We will acquire spectra to check whether they provide muscles "fingerprints", whether these "fingerprints" can be modeled and classified, and whether they change according to anthropometric or physiologic variables.
At present, a cheap, reliable, and widely applicable technique for non-invasive in vivoanalysis of human muscles is lacking, and we wish to determine whether NIRS of muscles can be adopted in clinical investigation without significant cost and time penalties.