Density Functional Theory Calculation and Raman Spectroscopy Studies of Carbamate Pesticides.

In order to obtain the molecular structure vibration information of carbamate pesticide, three carbamate pesticides (carbaryl, carbofuran and aldicarb) were optimized and calculated with B3LYP hybrid functional and 6-31G(d,p) basis set, and their experimental spectra were collected with the Raman spectrometer. The theoretically calculated spectra were compared with the experimental spectra carefully. The results indicated that the theoretically calculated spectra have a very good match with the experimental spectra. The vibrational peaks of three carbamate pesticides were assigned between the range of 400～3 200 cm-1, and the characteristic peaks of carbamate pesticide were found at 874, 1 014, 1 162 and 1 716 cm-1. The characteristic peaks of three carbamate pesticides were found by the contrast of the experimental spectra. The results can provide a theoretical basis for the detection of carbamate pesticide, and will be applied to the identification of carbamate pesticide residues in agricultural products.

[Study on the Rapid Detection of Triazophos Residues in Flesh of Navel Orange by Using Surface-Enhanced Raman Scattering].

Surface enhanced Raman spectroscopy (SERS) and quick pre-treatment technology were used to detect triazophos residues in flesh of navel orange. Quantitative analysis model was developed by partial least squares (PLS) algorithm. SERS of different concentration (0.5 to 20 mg x L(-1)) triazophos juice solution with flesh extract as the matrix were collected by laser Raman spectrometer. Three preprocessing methods such as normalization, MSC and SNV were used to optimize Raman signals and PLS models were set up. The results showed that minimum detection concentration for triazophos in navel orange below 0.5 mg L(-1). The model built with normalization pre-processing gave the best result; the values of correlation (R(p)) and Root mean square error of prediction set (RMSEP) were 1.38 and 0.976 6, respectively. The predict recoveries were 95.97%-103.18%, and the absolute values of relative errors were below 5%. T-test (t = -0.018) showed that there was no significant difference between the true values and prediction values. This study demonstrates that this method is accurate and reliable.

Identification Study of Edible Oil Species with Laser Induced Fluorescence Technology Based on Liquid Core Optical Fiber.

A laser- induced fluorescence detection set based on liquid core optical fiber was established in this study. Eight edible oils were discriminated by using this detection set combined with chemometrics method. The effect of length of liquid core optical fiber on laser induced fluorescence spectrum was explored, and the differences between the spectra of different edible oils were analyzed. The fluorescence spectra of 320 samples covering 8 types of edible oil were measured in 1 meter liquid core optical fiber. Principal component analysis was used in fluorescence data dimensionality reduction process. Partial least squares discriminant analysis (PLS-DA) method was used to develop the identification model to distinguish edible oil species. The results showed that the oil fluorescence intensity is greatly enhanced when liquid core optical fiber was used. With the increase of liquid core optical fiber length，the peaks of laser induced edible oil fluorescence spectra increased and the fluorescence spectra will produce red shift. The red shift tended to a constant value when the fiber length was more than 80 cm. The fluorescence spectra of different edible oils were quite different, its can be used to distinguish different types of edible oil. Principal component scores chart were get using PC1 and PC2 of edible oils fluorescence data which result in a trend of certain gather of same type of edible oil. The recognition rates of PLS-DA model for the calibration set and prediction set were both 100%. The study shows that the developed device in this study has high accuracy for identifying the edible oil species.

[DFT and Raman Scattering Studies of Benzimidazole].

The Raman spectra of carbendazim, thiabendazole and benomyl were collected by laser Raman spectrometer. The molecules of the three pesticides were optimized and calculated by B3LYP hybrid functional and 6-31G(d, p) basis set. The results showed that the calculated value anastomosed preferably to measure value. Vibrational modes of pesticide molecules were assigned between 200 and 1 600 cm(-1) range, and found three characteristic peaks of benzimidazole about at 1 015, 1 265 and 1 595 cm(-1). The comparative analysis on the differences of normal Raman spectra, found different characteristic peaks in three pesticide molecules. The results can provide theoretical for analysis Raman spectra of benzimidazole pesticide. This work will promote the research of benzimidazole pesticide residue in food and agricultural products based on Raman spectra.

[Surface-enhanced Raman spectroscopy analysis of thiabendazole pesticide].

Surface-enhanced Raman spectroscopy (SERS) technique was used to analyze the Raman peaks of thiabendazole pesticides in the present paper. Surface enhanced substrates of silver nanoparticle were made based on microwave technology. Raman signals of thiabendazole were collected by laser Micro-Raman spectrometer with 514. 5 and 785 nm excitation wavelengths, respectively. The Raman peaks at different excitation wavelengths were analyzed and compared. The Raman peaks 782 and 1 012 at 785 nm excitation wavelength were stronger, which were C--H out-of-plane vibrations. While 1284, 1450 and 1592 cm(-1) at 514.5 nm excitation wavelength were stronger, which were vng and C==N stretching. The study results showed that the intensity of Raman peak and Raman shift at different excitation wavelengths were different And strong Raman signals were observed at 782, 1012, 1284, 1450 and 1592 cm(-1) at 514.5 and 785 nm excitation wavelengths. These characteristic vibrational modes are characteristic Raman peaks of carbendazim pesticide. The results can provide basis for the rapid screening of pesticide residue in agricultural products and food based on Raman spectrum.

[Laser Raman spectrum analysis of carbendazim pesticide].

Raman signal of solid and liquid carbendazim pesticide was collected by laser Raman spectrometer. The acquired Raman spectrum signal of solid carbendazim was preprocessed by wavelet analysis method, and the optimal combination of wavelet denoising parameter was selected through mixed orthogonal test. The results showed that the best effect was got with signal to noise ratio (SNR) being 62.483 when db2 wavelet function was used, decomposition level was 2, the threshold option scheme was 'rigisure' and reset mode was 'sln'. According to the vibration mode of different functional groups, the de-noised Raman bands could be divided into 3 areas: 1 400-2 000, 700-1 400 and 200-700 cm(-1). And the de-noised Raman bands were assigned with and analyzed. The characteristic vibrational modes were gained in different ranges of wavenumbers. Strong Raman signals were observed in the Raman spectrum at 619, 725, 964, 1 022, 1 265, 1 274 and 1 478 cm(-1), respectively. These characteristic vibrational modes are characteristic Raman peaks of solid carbendazim pesticide. Find characteristic Raman peaks at 629, 727, 1 001, 1 219, 1 258 and 1 365 cm(-1) in Raman spectrum signal of liquid carbendazim. These characteristic peaks were basically tallies with the solid carbendazim. The results can provide basis for the rapid screening of pesticide residue in food and agricultural products based on Raman spectrum.

[Determination of taste quality of green tea using FT-NIR spectroscopy and variable selection methods].

The present paper was attempted to study the feasibility to determine the taste quality of green tea using FT-NIR spectroscopy combined with variable selection methods. Chemistry evaluation, as the reference measurement, was used to measure the total taste scores of green tea infusion. First, synergy interval PLS (siPLS) was implemented to select efficient spectral regions from SNV preprocessed spectra; then, optimal variables were selected using genetic algorithm (GA) from these selected spectral regions by siPLS, and the optimal model was achieved with Rp = 0.8908, RMSEP = 4.66 in the prediction set when 38 variables and 6 PLS factors were included. Experimental results showed that the performance of siPLS-GA model was superior to those of others. This study demonstrated that NIR spectra could be used successfully to measure taste quality of green tea and siPLS-GA algorithm has superiority to other algorithm in developing NIR spectral regression model.