Abstract: Near Infrared Spectroscopy (NIS) is employed to non-invasively detect health-related conditions, such as blood glucose concentrations, and accounting for non-linear interference from human tissues, the differences among individuals, and multiple interfering compounds within blood. A multi-layered artificial neural network can be used to assess these relationships and accurately estimate blood glucose levels. Diffuse reflectance spectrum at six different wavelengths are analyzed with a neural network, resulting in a correlation coefficient as high as 0.9216 when compared to a standard electrochemical glucose analysis test.

Abstract: Near Infrared Spectroscopy is employed to non-invasively detect blood glucose concentrations, in a multi-sensing detection device. A multi-layered artificial neural network is used to assess these relationships of non-linear interference from human tissue, as well as differences among individuals, and accurately estimate blood glucose levels. Diffuse reflectance spectrum from the palm at six different wavelengths analyzed with a neural network, results in a correlation coefficient as high as 0.9216 when compared to a standard electrochemical glucose analysis test.

Abstract: Near Infrared Spectroscopy is employed to non-invasively detect blood glucose concentrations, in a multi-sensing detection device. A multi-layered artificial neural network is used to assess these relationships of non-linear interference from human tissue, as well as differences among individuals, and accurately estimate blood glucose levels. Diffuse reflectance spectrum from the palm at six different wavelengths analyzed with a neural network, results in a correlation coefficient as high as 0.9216 when compared to a standard electrochemical glucose analysis test.