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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.

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Thermogravimetric analysis (TG) provides information regarding mass changes in the sample resulting from heat treatment under controlled environment. However, it does not provide any chemical information regarding the gases evolved during the thermal degradation. Using FT-IR spectrometry in combination with TG, it is often possible to identify the evolved gases, and also monitor their evolution profiles during thermal degradation. In this study, we present the TG/FT-IR combined analysis of incineration and pyrolysis of some common plastics such as high density polyethylene (HDPE), polyvinyl chloride (PVC), polyethylene terephthalate (PET), and polystyrene (PS). This study demonstrates the utility of such combined analysis in providing useful information regarding the use of thermal treatment for recycling or incineration.