The structure of water molecules in the pure liquid state has been subjected to extensive research for several decades. Questions still remain unanswered, however, and no single model has been found capable of explaining all the anomalies of water. In the present study, near-infrared spectra of water in the temperature region 6-80 degrees C have been analyzed by use of principal component analysis and two-dimensional correlation spectroscopy in order to study the dynamic behavior of a band centered around 1,450 nm at room temperature, which is due to the combination of symmetric and antisymmetric O-H stretching modes (first overtone) of water. It has been found that the wavelengths 1,412 and 1,491 nm account for more than 99% of the spectral variation, representing two major water species with weaker and stronger hydrogen bonds, respectively. A third species located at 1438 nm, whose concentration was relatively constant as a function of temperature, is also indicated. A somewhat distorted two-state structural model for water is suggested.

Near-infrared (NIR) reflectance spectra of five different food products were measured. The spectra were transformed by multiplicative scatter correction (MSC). Principal component regression (PCR) was performed, on both scatter-corrected and uncorrected spectra. Calibration and prediction were performed for four food constituents: protein, fat, water, and carbohydrates. All regressions gave lower prediction errors (7–68% improvement) by the use of MSC spectra than by the use of uncorrected absorbance spectra. One of these data sets was studied in more detail to clarify the effects of the MSC, by using PCR score, residual, and leverage plots. The improvement by using nonlinear regression methods is indicated.

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This paper presents a new approach for distance measurement in locally weighted regression (LWR2) by balancing the information in both chemical and spectral spaces. The new method (LWR2) is compared with the ordinary locally weighted regression method (LWR), another modified LWR method (LWR1), and the linear calibration methods, principal component regression (PCR) and partial least squares (PLS). A