Spectral Characterization and Molecular Dynamics Simulation of Pesticides Based on Terahertz Time-Domain Spectra Analyses and Density Functional Theory (DFT) Calculations.

This work provides the experimental and theoretical fundamentals for detecting the molecular fingerprints of six kinds of pesticides by using terahertz (THz) time-domain spectroscopy (THz-TDS). The spectra of absorption coefficient and refractive index of the pesticides, chlorpyrifos, fipronil, carbofuran, dimethoate, methomyl, and thidiazuron are obtained in frequencies of 0.1⁻3.5 THz. To accurately describe the THz spectral characteristics of pesticides, the wavelet threshold de-noising (WTD) method with db 5 wavelet fucntion, 5-layer decomposition, and soft-threshold de-noising was used to eliminate the spectral noise. The spectral baseline correction (SBC) method based on asymmetric least squares smoothing was used to remove the baseline drift. Spectral results show that chlorpyrifo had three characteristic absorption peaks at 1.47, 1.93, and 2.73 THz. Fipronil showed three peaks at 0.76, 1.23, and 2.31 THz. Carbofuran showed two peaks at 2.72 and 3.06 THz. Dimethoate showed three peaks at 1.05, 1.89, and 2.92 THz. Methomyl showed five peaks at 1.01, 1.65, 1.91, 2.72, and 3.20 THz. Thidiazuron showed four peaks at 0.99, 1.57, 2.17, and 2.66 THz. The density functional theory (DFT) of B3LYP/6-31G+(d,p) was applied to simulate the molecular dynamics for peak analyzing of the pesticides based on isolated molecules. The theoretical spectra are in good agreement with the experimental spectra processed by WTD + SBC, which implies the validity of WTD + SBC spectral processing methods and the accuracy of DFT spectral peak analysis. These results support that the combination of THz-TDS and DFT is an effective tool for pesticide fingerprint analysis and the molecular dynamics simulations.

Parameterization of spectral baseline directly from short echo time full spectra in 1 H-MRS.

PURPOSE: To investigate the feasibility of parameterizing macromolecule (MM) resonances directly from short echo time (TE) spectra rather than pre-acquired, T1 -weighted, metabolite-nulled spectra in 1 H-MRS. METHODS: Initial line parameters for metabolites and MMs were set for rat brain spectra acquired at 9.4 Tesla upon a priori knowledge. Then, MM line parameters were optimized over several steps with fixed metabolite line parameters. The proposed method was tested by estimating metabolite T1 . The results were compared with those obtained with two existing methods. Furthermore, subject-specific, spin density-weighted, MM model spectra were generated according to the MM line parameters from the proposed method for metabolite quantification. The results were compared with those obtained with subject-specific, T1 -weighted, metabolite-nulled spectra. RESULTS: The metabolite T1 were largely in close agreement among the three methods. The spin density-weighted MM resonances from the proposed method were in good agreement with the T1 -weighted, metabolite-nulled spectra except for the MM resonance at ∼3.2 ppm. The metabolite concentrations estimated by incorporating these two different spectral baselines were also in good agreement except for several metabolites with resonances at ∼3.2 ppm. CONCLUSION: The MM parameterization directly from short-TE spectra is feasible. Further development of the method may allow for better representation of spectral baseline with negligible T1 -weighting. Magn Reson Med 78:836-847, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Baseline correction for the infrared spectra of exhaled breath.

Infrared spectroscopy appears to be a promising analytical method for the metabolic analysis of breath. However, due to the presence of trace amounts in exhaled breath, the absorption strength of the metabolites remains extremely low. In such low detection limits, the nonlinear detection sensitivity of the infrared detector and electronic noise strongly modify the baseline of the acquired infrared spectra of breath. Fitting the reference molecular spectra with the baseline-modified spectral features of breath metabolites does not provide accurate identification. Therefore, baseline correction of the acquired infrared spectra of breath is the primary requirement for the success of breath-based infrared diagnosis. A selective spectral region-based, simple baseline correction method is proposed for the infrared spectroscopy of breath.

Magnetic Field Dependence of Spectral Correlations between 31P-Containing Metabolites in Brain.

Spectral correlations between metabolites in 31P magnetic resonance spectroscopy (MRS) spectra of human brain were compared at 3 and 7 Tesla, the two commonly used magnetic field strengths for clinical research. It was found that at both field strengths, there are significant correlations between 31P-containing metabolites arising from spectral overlap, and their downfield correlations are markedly altered by the background spectral baseline. Overall, the spectral correlations between 31P-containing metabolites are markedly reduced at 7 Tesla with the increased chemical shift dispersion and the decreased membrane phospholipid signal. The findings provide the quantitative landscape of pre-existing correlations in 31P MRS spectra due to overlapping signals. Detailed procedures for quantifying the pre-existing correlations between 31P-containing metabolites are presented to facilitate incorporation of spectral correlations into statistical modeling in clinical correlation studies.