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A spectral analysis of whole EDTA blood was undertaken by using attenuated total reflection and Fourier-transform infrared spectroscopy. The concentration of blood glucose was measured by an enzymatic method using glucose dehydrogenase and ranged between 40 and 290 mg/dL with an average concentration of 90.4 mg/dL. Multivariate calibration with the partial least-squares (PLS) algorithm was performed on spectral data between 1500 and 750 cm-1 showing a varying background from different unidentified interfering compounds. Cross validation was carried out for optimizing the PLS model. PRESS was 19.8 mg/dL, which was calculated on the basis of 127 standards, whereas the estimated standard deviation for the calibration fit was computed to be 11.9 mg/dL. Infrared spectroscopy can be used for monitoring glucose levels within the normal physiological range in a complex matrix like whole blood as an alternative to electrochemical sensors.

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The infrared (IR) spectra of whole blood EDTA samples, in the range between 1500 and 750 cm−1, obtained from the patient population of a general hospital, were used to compare different multivariate calibration techniques for quantitative glucose determination. Ninety-six spectra of whole undiluted blood samples with glucose concentration ranging between 44 and 291 mg/dL were used to create calibration models based on a combination of partial least-squares (PLS) and artificial neural network (ANN) methods. The prediction capabilities of these calibration models were evaluated by comparing their standard errors of prediction (SEP) with those obtained with the use of PLS and principal component regression (PCR) calibration models in an independent prediction set consisting of 31 blood samples. The optimal model based on the combined PLS-ANN produced smaller SEP values (15.6 mg/dL) compared with those produced with the use of either PLS (21.5 mg/dL) or PCR (24.0 mg/dL) methods. Our results revealed that the combined PLS-ANN models can better approximate the deviations from linearity in the relationship between spectral data and concentration, compared with either PLS or PCR models.