Among several complications related to physiotherapy, osteosarcopenia is one of the most frequent in elderly patients. This condition is limiting and quite harmful to the patient’s health by disabling several basic musculoskeletal activities. Currently, the test to identify this health condition is complex. In this study, we use mid-infrared spectroscopy combined with chemometric techniques to identify osteosarcopenia based on blood serum samples. The purpose of this study was to evaluate the mid-infrared spectroscopy power to detect osteosarcopenia in community-dwelling older women (n= 62, 30 from patients with osteosarcopenia and 32 healthy controls). Feature reduction and selection techniques were employed in conjunction with discriminant analysis, where a principal component analysis with support vector machines (PCA–SVM) model achieved 89% accuracy to distinguish the samples from patients with osteosarcopenia. This study shows the potential of using infrared spectroscopy of blood samples to identify osteosarcopenia in a simple, fast and objective way.