P. Bhandare et al., MULTIVARIATE DETERMINATION OF GLUCOSE IN WHOLE-BLOOD USING PARTIAL LEAST-SQUARES AND ARTIFICIAL NEURAL NETWORKS BASED ON MIDINFRARED SPECTROSCOPY, Applied spectroscopy, 47(8), 1993, pp. 1214-1221
The infrared (IR) spectra of whole blood EDTA samples, in the range be
tween 1500 and 750 cm-1, obtained from the patient population of a gen
eral 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 b
etween 44 and 291 mg/dL were used to create calibration models based o
n a combination of partial least-squares (PLS) and artificial neural n
etwork (ANN) methods. The prediction capabilities of these calibration
models were evaluated by comparing their standard errors of predictio
n (SEP) with those obtained with the use of PLS and principal componen
t regression (PCR) calibration models in an independent prediction set
consisting of 31 blood samples. The optimal model based on the combin
ed PLS-ANN produced smaller SEP values (15.6 mg/dL) compared with thos
e 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 be
tter approximate the deviations from linearity in the relationship bet
ween spectral data and concentration, compared with either PLS or PCR
models.