[Fast Determination of Auramine â ¡, Basic Orange â ¡ and Metanil Yellow in Bean Products on Surface Enhanced Raman Spectroscopy and Use High Pergormance Liquid Chromatography-Tandem Mass Spectrometry to Verify]

This experiment adopts Surface Enhanced Raman Spectroscopy (SERS) to quickly detect auramine Ⅱ, basic orange Ⅱ and metanil yellow in bean products. It uses High Performance Liquid Chromatography (HPLC)-tandem mass spectrometry to verify. The best extraction solvent is methanol-water (Seven plus three) solution. Before classification, extracting the bean products withAccelerated Solvent Extaction (ASE) and purifying it with Gel Permeation Chromatography(GPC), which improves the extraction efficiency, improves the detection sensitivity, reduces the dosage of extraction solvent and effective in the matrix of macromolecular distractors.ASE and GPC conditions are optimized. This study of three types of pigment surface enhanced Raman spectra characteristic peak of the ownership certification. The characteristic peak of auramine Ⅱ, basic orange Ⅱ and metanil yellow is respectively 652, 995 and 983 cm-1; he method detection limit is 3.0, 1.0 and 4.0 mg·kg-1. Three quantitative characteristic peak of pigment had a good linear relationship with pigment concentration，Recovery of this experiment was 83.48%～92.59% range, relative standard deviation less than 7.2%. The method is characterized by simple pretreatment, short analysis period and high sensitivity, etc. The method provides a reliable reference for food pigment detection.

A sensitive fluorescence nanoplatform for monitoring benzoyl peroxide in food using carbon dots coupled with glutathione capped gold nanoparticles as FRET probe.

Herein, a sensitive fluorescence nanoplatform for benzoyl peroxide (BPO) detection is constructed from carbon dots (CDs) and glutathione capped gold nanoparticles (GSH-AuNPs). The fluorescence of CDs is first quenched due to the fluorescence resonance energy transfer (FRET) effect in the presence of GSH-AuNPs, and then effectively recovered when adding BPO. The detection mechanism lies in the aggregation of AuNPs in a high salt background due to oxidation of GSH caused by BPO, thus the amount of BPO is reflected by the variations of the recovered signals. The linear range and detection limit in this detection system is found to be 0.05-200 µM (R2 = 0.994) and 0.1 µg g-1 (3σ/K), respectively. Several possible interferents with high concentration show little influence on BPO detection. The proposed assay exhibits good performance for BPO determination in wheat flour and noodles, demonstrating its applicability for facile monitoring BPO additive amount in real foods.

Preparation of Micron-Sized TS-1 Spherical Membrane Catalysts and Their Performance in the Epoxidation of Chloropropene.

Titanium silica (TS-1) membrane catalysts grown on the surfaces of spherical substrates can both exploit the high catalytic performance and facilitate their separation from products after the reaction. In this work, a simple static crystallization method was used to perform the in situ construction of a TS-1 membrane on the surfaces of micron-sized spherical carriers. The shortcomings of the TS-1 membrane under static crystallization conditions were overcome by in situ dynamic crystallization, and the effect of rotation speed on the formation of the molecular sieve membrane was investigated. The results showed that the molecular sieve membrane was smooth and homogeneous, with a higher synthesis efficiency at a slow rotational speed. The micron TS-1 spherical membrane catalytic chloropropene epoxidation reaction was investigated in a fixed bed, and the conversion of hydrogen peroxide and selectivity of epichlorohydrin reached 99.4 and 96.8%, respectively. After being reused twice, the catalyst still maintained a stable catalytic performance.

Lanthanum ions assisted non-enzymatic ratiometric fluorescence probe for monitoring fenthion residues in agro-product samples.

In this work, a La3+ assisted glutathione-capped gold nanoclusters and carbon dots (GSH-Au NCs/CDs) nanoplatform for sensitive detection of fenthion (FEN) is fabricated. The fluorescence response of GSH-Au NCs significantly increases due to aggregation-induced emission enhancement (AIEE) effect induced by La3+, which is further enhanced with adding FEN due to the coordination between La3+ and FEN. Taking the fluorescence intensity of CDs as the signal background, the ratiometric fluorescence of GSH-Au NCs and CDs has a good linear relationship with the FEN concentration from 0.01 to 1.10 µg mL-1, and detecting FEN exhibits a good sensitivity at a low detection limit of 6.74 ng g-1. The La3+ assisted GSH-Au NCs/CDs nanoplatform demonstrates desirable selectivity and applicability for monitoring trace level of FEN in fruit and vegetable samples. The non-enzymatic strategy by taking advantage of successive AIEE of GSH-Au NCs has a great potential for facile screening organophosphate pesticides in agro-products.

A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.