Beyond Beer’s Law: Quasi-Ideal Binary Liquid Mixtures

We have recorded attenuated total reflection infrared spectra of binary mixtures in the (quasi-)ideal systems benzene–toluene, benzene–carbon tetrachloride, and benzene–cyclohexane. We used two-dimensional correlation spectroscopy, principal component analysis, and multivariate curve resolution to analyze the data. The 2D correlation proves nonlinearities, also in spectral ranges with no obvious deviations from Beer’s approximation. The number of principal components is much higher than two and multivariate curve resolution carried out under the assumption of the presence of a third component, results in spectra which only show bands of the original components. The results negate the presence of third components, since any complex should have lower symmetry than the individual molecules and thus more and/or different infrared-active bands in the spectra. Based on Lorentz–Lorenz theory and literature values of the optical constants, we show that the nonlinearities and additional principal components are consequences of local field effects and the polarization of matter by light. Lorentz–Lorenz theory is, however, not able to explain, for example, the different blueshifts of the strong A2u band of benzene in the three mixtures. Obviously, infrared spectroscopy is sensitive to the short-range order around the molecules, which changes with content, their shapes, and their anisotropy.

Evaluation of the Miscibility of Novel Cocoa Butter Equivalents by Raman Mapping and Multivariate Curve Resolution–Alternating Least Squares

Cocoa butter (CB) is an ingredient traditionally used in the manufacturing of chocolates, but its availability is decreasing due to its scarcity and high cost. For this reason, other vegetable oils, known as cocoa butter equivalents (CBE), are used to replace CB partially or wholly. In the present work, two Peruvian vegetable oils, coconut oil (CNO) and sacha inchi oil (SIO), are proposed as novel CBEs. Confocal Raman microscopy (CRM) was used for the chemical differentiation and polymorphism of these oils with CB based on their Raman spectra. To analyze their miscibility, two types of blends were prepared: CB with CNO, and CB with SIO. Both were prepared at 5 different concentrations (5%, 15%, 25%, 35%, and 45%). Raman mapping was used to obtain the chemical maps of the blends and analyze their miscibility through distribution maps, histograms and relative standard deviation (RSD). These values were obtained with multivariate curve resolution–alternating least squares. The results show that both vegetable oils are miscible with CB at high concentrations: 45% for CNO and 35% for SIO. At low concentrations, their miscibility decreases. This shows that it is possible to consider these vegetable oils as novel CBEs in the manufacturing of chocolates.

Classification of human skin Raman spectra using multivariate curve resolution (MCR) and partial least squares discriminant analysis (PLS-DA)

The main purpose of the paper is classification of the human skin Raman spectra using partial least squares discriminant analysis (PLS-DA) into classes depending on the disease. In vivo Raman spectra of normal skin, basal cell carcinoma, malignant melanoma and pigmented nevus are considered. A feature of the approach is the analysis not of the Raman spectra themselves, but of the concentrations of the eight most significant spectra components identified using multivariate curve resolution (MCR). As a result, the ROC curve was calculated and the optimal classification threshold was chosen. The accuracy of the classification models ranged from 63.3 to 86.7%, depending on the model. The findings suggest that this approach could also be useful for classification of specific diseases.

UV-Vis Spectrophotometry Coupled to Chemometric Analysis for the Evaluation of Atrazine Photolysis and Photocatalysis

In this study, a spectrophotometric-chemometric (Spec-Chem) approach was applied as an alternative to chromatography to monitor ATZ and by-products after photolytic and photocatalytic oxidation aiming to unveil the ATZ degradation mechanism. Spec-Chem is an accessible, easy-to-operate, low-cost analytical approach to monitor atrazine (ATZ) and by-products and its applicability was validated by HPLC, the reference technique for the evaluation of pollutants degradation mechanisms. The chromatographic (DChro) and spectrophotometric (DSpec) data found 95% and 57% ATZ removal after 30 min, respectively, proving that the DSpec erroneously induces a 38% loss in removal efficiency. When DSpec was treated by Multivariate Curve Resolution (MCR) analysis for providing chemometric data (DChem), it found ATZ removal and hydroxyatrazine (HAT) formation statistically equal to DChro (t-test, p = 0.05). After unraveling the ATZ degradation mechanism using Spec-Chem, a new hypothesis for the kinetic calculation of ATZ degradation was presented, where the concentrations of ATZ and HAT were used to find k and R2 values representative for the ATZ degradation mechanism. The values found for k were compatible with the literature under similar conditions of ATZ degradation and the linear correlation coefficients (R2 = 0.99) showed an optimal fit for the proposed hypothesis. Thus, Spec-Chem was successfully applied to unravel the mechanism of photocatalytic degradation of ATZ in the presence of TiO2, while k was obtained by the new hypothesis proposed that considered ATZ and HAT concentration as parameters of kinect interest. Therefore, the importance of monitoring quantitatively ATZ and HAT were provided in this study, providing new information for the scientific community.