The near-infrared transmission spectra of two organic liquid three-component systems of variable compositions were investigated in detail. To evaluate the interaction of the different components in the two systems the experimental spectra of the pure components were compared to mathematically constructed "pure component" spectra. Though usually the correlation coefficient (CC) and Manhattan distance (MD) are used to measure the similarity of spectra, in the present investigations principal component analysis (PCA) was found to be a more effective tool to investigate the difference between these spectra and derive parameters characterizing the interaction between the different components. Thus, PC scores for the two types of spectra established some distinct patterns which clearly expressed their differences. For a three-dimensional coordinate system of selected principal components, the Euclidean distances between the mathematically constructed and the experimental spectra of the pure components were calculated. Finally, the mean values of the distances for each component provided indices to rank the interaction of the components in the mixtures. Thus, the results offer a convenient approach that can quantitatively evaluate the molecular interactions of the individual components in organic liquid mixtures by various spectroscopies.
The band shapes and band positions of near-infrared (NIR) and Raman spectra change depending on the concentrations of specific chemical functionalities in a multicomponent system. To elucidate these effects in more detail and clarify their impact on the analytical measurement techniques and evaluation procedures, NIR transmission spectra and Raman spectra of two organic liquid three-component systems with variable compositions were analyzed by two different multivariate calibration procedures, partial least squares (PLS) and classical least-squares (CLS) regression. Furthermore, the effect of applying different concentration units (volume percent (%V) and weight percent (%W) on the performance of the two calibration procedures have been tested. While the mixtures of benzene/cyclohexane/ethylbenzene (system 1) can be regarded as a blended system with comparatively low molecular interactions, hydrogen bonding plays a dominant role in the blends of ethyl acetate/1-heptanol/1,4-dioxane (system 2). Whereas system 1 yielded equally good calibrations by PLS and CLS regression, for system 2 acceptable results were only obtained by PLS regression. Additionally, for both sample systems, Raman spectra generally led to lower calibration performance than NIR spectra. Finally, volume and weight percent concentration units yielded comparable results for both chemometric evaluation procedures.
The potential of near-infrared (NIR) spectroscopy coupled with partial least-squares (PLS) regression was used to determine the moisture content and basic density of poplar wood chips. NIR spectra collected from the surface of wood chips were used to develop calibration models for moisture content and basic density predication, and various spectral preprocessing techniques were applied to improve the accuracy and robustness of the prediction models. The models were tested using totally independent sample sets and exhibited acceptable predictive performance for moisture content (coefficient of determination for prediction [R2p] = 0.98 and standard error of prediction [SEP] = 2.51 percent) and basic density (R2p = 0.87 and SEP = 17.61 kg m–3). In addition, the effect of moisture variations on prediction of basic density was investigated based on NIR spectra from wood chips under various moisture levels. The results demonstrated that broad absorption bands from water molecules, especially when free water exists in the cell lumen, overlap with informative signals related to wood properties and weaken the calibration relation between spectral features and basic density. Thus, maintaining wood chips in a low and even moisture state would help achieve reliable estimates of wood density by NIR analysis models.
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