Evaluating the Molecular Interaction of Organic Liquid Mixtures Using Near-Infrared Spectroscopy

Portable NIR spectroscopy: the route to green analytical chemistry

There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

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 A_2u 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.

Identifying Chemical, Physical, and Instrumental Matrix Matched Samples by Leveraging Spectral Model Regression Vectors

Developing spectroscopic calibration models requires calibration samples that mimic as much as possible new sample compositions as well as measurement conditions. This requirement is known as matrix matching calibration samples to new samples, that is, samples are matrix matched chemically, physically, and instrumentally. To accomplish this task, calibration sets have large sample numbers to span the expected sample matrix variations. This large range of calibration variability can result in poor performance. Preferred is a calibration set distinctly matched to the new samples. However, assessing whether each sample in a particular calibration set is appropriately matched to new samples relative to the specific analyte content and all other constituents is not an easy task. It is well documented that even though calibration samples are spectral matches to new sample spectra (have similar measured spectra), the calibration set is usually not fully matrix matched to new sample compositions. For example, using a spectral similarity measure such as Euclidean distance, the same calibration samples are deemed spectral matches to new samples regardless of the analyte of interest. This work presents a process to assess underlying sample matrix interactions between calibration model regression vectors and new sample spectra allowing fully matrix matched samples to be identified. The process is general and applicable to other situations such as matching historical batch processing data where references values are not known for new samples (unlabeled). Two data sets are used to demonstrate the functionality of the process. One consists of nuclear magnetic resonance spectra for mixtures of three alcohols and the other is near-infrared corn spectra with four prediction properties measured on three instruments. General trends are reported for a few of the possible data situations. Calibration samples identified as matrix matched to new samples are shown to predict the new samples with the lowest prediction errors.

Quantitative Analysis of Organic Liquid Three-Component Systems: Near-Infrared Transmission versus Raman Spectroscopy, Partial Least Squares versus Classical Least Squares Regression Evaluation and Volume versus Weight Percent Concentration Units

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.

Identification Performance of Different Types of Handheld Near-Infrared (NIR) Spectrometers for the Recycling of Polymer Commodities

For sustainable utilization of raw materials and environmental protection, the recycling of the most common polymers-polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polystyrene (PS)-is an extremely important issue. In the present communication, the discrimination performance of the above polymer commodities based on their near-infrared (NIR) spectra measured with four real handheld (<200 g) spectrometers based on different monochromator principles were investigated. From a total of 43 polymer samples, the diffuse reflection spectra were measured with the handheld instruments. After the original spectra were pretreated by second derivative and standard normal variate (SNV), principal component analysis (PCA) was applied and unknown samples were tested by soft independent modeling of class analogies (SIMCA). The results show that the five polymer commodities cluster in the score plots of their first three principal components (PCs) and, furthermore, samples in calibration and test sets can be correctly identified by SICMA. Thus, it was concluded that on the basis of the NIR spectra measured with the handheld spectrometers the SIMCA analysis provides a suitable analytical tool for the correct assignment of the type of polymer. Because the mean distance between clusters in the score plot reflects the discrimination capability for each polymer pair the variation of this parameter for the spectra measured with the different handheld spectrometers was used to rank the identification performance of the five polymer commodities.

The Effect of Chain Length on Mid-Infrared and Near-Infrared Spectra of Aliphatic 1-Alcohols

Effect of the chain length on mid-infrared (MIR) and near-infrared (NIR) spectra of aliphatic 1-alcohols from methanol to 1-decanol was examined in detail. Of particular interest were the spectra-structure correlations in the NIR region and the correlation between MIR and NIR spectra of 1-alcohols. An application of two-dimensional correlation analysis (2D-COS) and chemometric methods provided comprehensive information on spectral changes in the data set. Principal component analysis (PCA) and cluster analysis evidenced that the spectra of methanol, ethanol, and 1-propanol are noticeably different from the spectra of higher 1-alcohols. The similarity between the spectra increases with an increase in the chain length. Hence, the most similar are the spectra of 1-nonanol and 1-decanol. Two-dimensional hetero-correlation analysis is very helpful for identification of the origin of bands and may guide selection of the best spectral ranges for the chemometric analysis. As shown, normalization of the spectra pronounces the intensity changes in various spectral regions and provides information not accessible from the raw data. The spectra of alcohols cannot be represented as a sum of the CH₃, CH₂, and OH group spectra since the OH group is involved in the hydrogen bonding. As a result, the spectral changes of this group are nonlinear and its spectral profile cannot be properly resolved. Finally, this work provides a lot of evidence that the degree of self-association of 1-alcohols decreases with the increase in chain length because of the growing meaning of the hydrophobic interactions. For butyl alcohol and higher 1-alcohols the hydrophobic interactions are more important than the OH OH interactions. Therefore, methanol, ethanol, and 1-propanol have unlimited miscibility with water, whereas 1-butanol and higher 1-alcohols have limited miscibility with water.