Noninvasive glucometer model using partial least square regression technique for human blood matrix

Abstract

We have highlighted the partial least square regression (PLSR) model to predict the glucose level in human blood by considering only five variants. The PLSR model is experimentally validated for the 13 templates samples.The root mean square error analysis of design model and experimental sample is found to be satisfactory with the values of 3.459 and 5.543, respectively. In PLSR templates design is a critical issue for the number of variants participating in the model. Ensemble consisting of five major variants is simulated to replicate the signatures of these constituents in the human blood, i.e., alanine, urea, lactate, glucose, and ascorbate. Multivariate system using PLSR plays important role in understanding chemometrics of such ensemble. The resultant spectra of all these constituents are generated to create templates for the PLSR model. This model has potential scope in designing a near-infrared spectroscopy based noninvasive glucometer.