Terahertz fingerprint characterization of 2,4-dichlorophenoxyacetic acid and its enhanced detection in food matrices combined with spectral baseline correction.

Rapid and accurate detection of pesticide residues in food matrices are of great significance to food safety. This study aimed to characterize the fingerprint peaks of 2,4-dichlorophenoxyacetic acid (2,4-D) and to enhance its detection accuracy in food matrices by using terahertz (THz) time-domain spectroscopy. Density functional theory was used to simulate molecular dynamics of 2,4-D peaks (1.35, 1.60, 2.37 and 3.00 THz). Four baseline correction methods, including asymmetric least squares smoothing (AsLS), adaptive iteratively reweighted penalized least squares (AirPLS), background correction (Backcor), baseline estimation and denoising with sparsity (BEADS) were compared and used to eliminate spectral baselines of Zizania latifolia (ZIZLA), rice and maize containing 2,4-D residues, from 0.1 to 4 THz. Based on the peak information of 1.35 THz, the detection limit and accuracy of 2,4-D residues in these food matrices were significantly improved after THz spectral baseline correction, providing a new feasibility for food safety and agricultural applications.

Rapid Determination of Chlorogenic Acid, Luteoloside and 3,5-O-dicaffeoylquinic Acid in Chrysanthemum Using Near-Infrared Spectroscopy.

The feasibility of near-infrared spectroscopy (NIR) to detect chlorogenic acid, luteoloside and 3,5-O-dicaffeoylquinic acid in Chrysanthemum was investigated. An NIR spectroradiometer was applied for data acquisition. The reference values of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid of the samples were determined by high-performance liquid chromatography (HPLC) and were used for model calibration. The results of six preprocessing methods were compared. To reduce input variables and collinearity problems, three methods for variable selection were compared, including successive projections algorithm (SPA), genetic algorithm-partial least squares regression (GA-PLS), and competitive adaptive reweighted sampling (CARS). The selected variables were employed as the inputs of partial least square (PLS), back propagation-artificial neural networks (BP-ANN), and extreme learning machine (ELM) models. The best performance was achieved by BP-ANN models based on variables selected by CARS for all three chemical constituents. The values of rp2 (correlation coefficient of prediction) were 0.924, 0.927, 0.933, the values of RMSEP were 0.033, 0.018, 0.064 and the values of RPD were 3.667, 3.667, 2.891 for chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid, respectively. The results indicated that NIR spectroscopy combined with variables selection and multivariate calibration methods could be considered as a useful tool for rapid determination of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid in Chrysanthemum.

Predicting pork freshness using multi-index statistical information fusion method based on near infrared spectroscopy.

In this work, the near infrared spectroscopy (NIR) technology was applied to nondestructively evaluate the freshness of pork. The total volatile basic-nitrogen (TVB-N) and pH value of pork were detected as freshness evaluation indicators. A multi-index statistical information fusion (MSIF) modeling method based on variable selection was proposed to evaluate pork freshness. In the experiment, the proposed MSIF was compared with other state-of-the-art variable selection methods. Results showed that the proposed method achieved the best generalization performance and stability. The prediction correlation coefficient (Rval) and root mean square error (RMSEP) of MSIF were: Rval = 0.8618 and RMSEP = 3.910 for TVB-N content, Rval = 0.9379 and RMSEP = 0.1046 for pH value. The research demonstrated that NIR combined with MSIF has the potential for rapid and nondestructive determination of pork freshness, and so hopefully to provide a promising tool for monitoring meat quality and enriching the extracted information from food industry.

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.