An LC microfluidic chip (LC chip) with amperometric detection was developed. The LC chip employed a 7.5 cm long reversed-phase polymethacrylate monolithic column as a stationary phase and a "three-T" injection mode. A convenient interface was designed to conduct pump pressure into the microfluidic chip to drive solution, and a home-made device was used to control the distance between the working electrode and the LC chip accurately. The "three-T" sample injection mode completely avoided the problem of sample dilution and sample leakage during separation, which is usually observed in traditional and "T" type LC chip, without the using of valve and finally results in a better resolution, reproducibility and relatively high sensitivity. Using the proposed LC chip system, we have successfully separated two isomers, catechol and hydroquinone, within 12 min with a RSD (n=3) <3.0% for retention time and <2.4% for peak area. We have also successfully separated and determined 5-hydroxy-L-tryptophan, dopamine and 5-hydroxytryptamine within 25 min with a RSD (n=3) <5% (for peak area) and a detection limit of 0.16-0.51 μmol/L.
A simple, reliable and reproducible method for the separation and determination of five β-casomorphins (β-CMs, namely TPGN, PGPI, TPGI, TPGP and TPPG) based on glass microfluidic chip electrophoresis and laser-induced fluorescence detection is first described in here. The microfluidic chip electrophoresis and laser-induced fluorescence detection system consisted of a home-made glass "double-T" microchip and a simple LIF detector with excitation and emission wavelengths of 473 and 525 nm, respectively. Fluorescein isothiocyanate (FITC) was used as the precolumn derivatization reagent to label fluorophore on five β-CMs, and the optimum conditions of FITC-derivatization reaction and MCE separation were investigated in detail. Under optimum conditions, five β-CMs were completely separated and detected within 30 min with a detection limit of 18.7-75.1 nmol/L and an RSD (n=5) of 3.0-5.9%, respectively. The proposed method has been successfully used to detect β-CMs in real cheese sample with a recovery of 89-109%, suggesting that our method is sensitive and reliable. These features, as well as its low cost, operation convenience, stability and reusability, make it a promising alternative to β-CMs detection methods.
In recent years, Internet of Things has not only promoted the continuous development of e-commerce transaction but also brought loop-hole to the fraud gangs who always utilize mobile devices to commit fraud crimes. For example, fraud gangs are usually organized to purchase commodities at low prices in e-commerce promotions. They benefit from the price spread by reselling commodities at high prices. In the past few years, the transaction fraud caused serious financial losses to merchants in e-commerce platform. To detect the fraudulent user and behavior effectively, a multiview graph clustering-based abnormal detection model is developed in this paper. In the proposed model, two fraudulent behavior patterns are proposed by abstracting the e-commerce network as a heterogeneous information graph. On this basis, two user-similarity graphs are reorganized from the heterogeneous graph with the help of different metapaths. Subsequently, in order to capture the corresponding fraudulent behavior patterns, the above two graphs are encoded into user embeddings and assigned to specific clusters in respective views. Finally, the consensus detection result is produced by fusing the complementary information of different views in a joint multiview learning framework. As we know, our work is the first one that uses multiview graph clustering in e-commerce fraud detection, which will provide a new research perspective for fraud detection in e-commerce platform. Extensive experiments are conducted on real and semisynthetic datasets, and the results demonstrate the effectiveness and superiority of the proposed model.
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