Determination of glucose concentrations in an aqueous matrix from NIR spectra using optimal time-domain filtering and partial least-squares regression

Abstract

We have investigated the use of a time-domain optimal filtering method to simultaneously minimize both the baseline variation and high-frequency noise in near-infrared (NIR) spectrophotometric absorption data of glucose dissolved in a simple aqueous (deionized water) matrix. By coupling a third-order (six-pole) digital Butterworth bandpass filter with partial least-squares (PLS) regression modeling, glucose concentrations were determined for a set of test data with a standard error of prediction (SEP) of 10.53 mg/dl (mean percent error: 4.24%) using seven PLS factors. Compared to the unfiltered test data for six PLS factors and a SEP = 17.00 (mean percent error: 7.38%) this results shows more than a 38% decrease in the error. The glucose concentrations ranged from 51 mg/dl to 493 mg/dl, and the NIR spectral region between 2088 nm and 2354 nm (4789 cm-1 and 4248 cm-1) was used to develop the optimal PLS model. The optimal PLS model was determined from a sequence of three-dimensional performance response maps for different numbers of PLS factors (2-10). A total of 99 NIR spectra were generated for glucose dissolved in deionized water using a NIRsystems 5000 dispersive spectrophotometer. Nine of these spectra were generated for only water, which were averaged and subtracted from the remaining 90 spectra to generate the training and test data sets, thereby, removing the intrinsic high background absorption due to the water. The training set consisted of 57 spectra and associated glucose concentration target values, and the test set was comprised of the remaining 33 spectra and target values. Performance results were compared for three different digital Butterworth bandpass filters (four-poles, six-poles, and eight-poles), and a digital Gaussian filter design approach (i.e., Fourier filtering).

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Local linear embedded regression in the quantitative analysis of glucose in near infrared spectra

“…To our knowledge, this is the first time LLER is combined with digital bandpass filtering for NIR spectroscopy. In this work, the digital Gaussian and Chebyshev bandpass filters have been used to suppress the high frequency components as well as the baseline variations which dominate the low frequency components in the raw spectra [18,19]. The digital bandpass filters are defined by two parameters [20,21], the centre frequency and the bandwidth.…”

Noninvasive blood glucose monitoring with laser diode

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