Moving-window two-dimensional heterospectral (MW2DHetero) correlation analysis and its application for the process monitoring of alcoholic fermentation

New insights into the interactions between humic acid and three neonicotinoid pesticides, with multiple spectroscopy technologies, two-dimensional correlation spectroscopy analysis and density functional theory

The widespread use of neonicotinoid pesticides in agricultural production has caused pressure on the environment. In the present work, the interactions between humic acid (HA) and three neonicotinoid insecticides, dinotefuran, clothianidin and nitenpyram, were investigated by using multiple spectroscopy techniques combined with two-dimensional correlation spectroscopy analysis and density functional theory (DFT). Dinotefuran, clothianidin and nitenpyram could quench the endogenous fluorescence of HA through a static quenching process dominated by hydrogen bonds and van der Waals forces. According to the revised Stern-Volmer equation and DFT calculation, the binding abilities of the three pesticides with HA were ranked as dinotefuran < clothianidin < nitenpyram. The results of dynamic light scattering showed that neutral conditions were more conducive to the combination of HA and dinotefuran, clothianidin and nitenpyram. Through Fourier transform infrared spectroscopy (FTIR) combined with two-dimensional correlation analysis (2D-COS), the functional group with the strongest binding ability in the HA-dinotefuran, HA-clothianidin and HA-nitenpyram system was CH, CO and CO, respectively. The work will help to further understand the behavior of neonicotinoid pesticides in the environment.

Use of Near-Infrared-Mid-Infrared Dual-Wavelength Spectrometry to Obtain Two-Dimensional Difference Spectra of Sesame Oil as Inactive Drug Ingredient

The present study has investigated the transformation of sesame oil kept at low temperature during a definite period of time for refinement (called winterization) as an inactive drug ingredient by using two-dimensional difference spectra (2D-DS) analysis of spectra collected using a near-infrared (NIR) and mid-infrared (MIR) dual-wavelength spectrometer (NIR-MIR-DWS). The NIR and MIR spectra were measured nearly simultaneously from samples of sesame oil before and after winterization. The difference spectrum analysis of the obtained NIR-MIR data elucidated that, after the winterization process, the absorbances at peaks attributed to C=O, C=C, and OH groups decrease while the absorbances arising from the main chain (CH₂) increase. The result indicated the removal of lignan and the fatty acids with relatively short main chains. Moreover, sesame oil unwinterized was cooled from room temperature to near 1 ℃ and subsequently warmed to room temperature. And the cycle was repeated two times. Real-time monitoring during the cooling and warming processes were carried out using the NIR-MIR-DWS. The prediction results obtained from partial least square calibration model for the temperature suggests that there are subtle differences in the oil composition between the first cooling process and after the warming and cooling cycle. For the more detailed analysis, the 2D-DS method is proposed. The results of the analyses using 2D-DS revealed that the starting point of the transformation is around 15 ℃. It can be estimated that sesame oil is mainly transformed by the first cooling down. Moreover, it was implied that the structure of methylene (CH₂) was significantly related to the modifications in sesame oil with temperature change. A series of experimental results elucidated that the winterization of sesame oil removed its impurities and stabilized its conditions. These results are probably the first report on the effect of the winterization process on sesame oil.

Chemometrics and Related Fields in Python

The Python programing language is becoming a promising tool for data analysis in various fields. However, little attention has been paid to using Python in the field of analytical chemistry, though recent advances in instrumental analysis require robust and reliable data analysis. In order to overcome the difficulty in accurate analysis, multivariate analysis, or chemometrics, has been widely applied to various kinds of data obtained by instrumental analysis. In the present work, the potential usefulness of Python for chemometrics and related fields in chemistry is reviewed. Many practical tools for chemometrics, e.g., principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), etc., are included in the scikit-learn machine learning (ML) library for Python. Other useful libraries such as pyMCR for multivariate curve resolution (MCR), 2Dpy for two-dimensional correlation spectroscopy (2D-COS), etc. can be obtained from GitHub. For these reasons, a computational environment for chemometrics is easily constructed in Python.

Least Squares Moving-Window Spectral Analysis

Least squares regression is proposed as a moving-windows method for analysis of a series of spectra acquired as a function of external perturbation. The least squares moving-window (LSMW) method can be considered an extended form of the Savitzky-Golay differentiation for nonuniform perturbation spacing. LSMW is characterized in terms of moving-window size, perturbation spacing type, and intensity noise. Simulation results from LSMW are compared with results from other numerical differentiation methods, such as single-interval differentiation, autocorrelation moving-window, and perturbation correlation moving-window methods. It is demonstrated that this simple LSMW method can be useful for quantitative analysis of nonuniformly spaced spectral data with high frequency noise.

Analytical and Numerical Characterization of Autocorrelation and Perturbation-Correlation Moving-Window Methods

Moving-window (MW) approaches to two-dimensional correlation spectroscopy (2D-COS) make it possible to characterize spectral changes occurring in a narrow range of perturbation variable (e.g., time, temperature, and concentration). Despite the wide range of application, the physical meanings of MW correlation intensities have been only qualitatively associated with the direction and curvature of spectral intensity change with regard to a perturbation variable. Here are full and simplified analytical expressions of autocorrelation moving-window (ACMW) and synchronous and asynchronous perturbation-correlation moving-window ( s-PCMW and as-PCMW) intensities. When the window is set sufficiently narrower than the bandwidth of spectral change, the square root of ACMW intensity and s-PCMW intensity becomes proportional to the first order derivative, and as-PCMW intensity becomes proportional to the negative of the second order derivative. This paper demonstrates that both ACMW and PCMW profiles can be significantly altered by non-uniform perturbation spacing. It is also found that intensity noise can cause ACMW to display a false offset drift. This analytical and numerical characterization of the two MW correlation intensities elucidates their physical meanings and ascertains the analysis conditions for reliable interpretation.