Enzyme-induced Cu2+/Cu+ conversion as the electrochemical signal for sensitive detection of ethyl carbamate.

Traditional enzyme-linked immunosorbent assay (t-ELISA) method suffers from its relatively low sensitivity or accuracy in the detection of trace level of analyte in complicated samples. In this work, to extend the application of ELISA in practical samples, a newly electrochemical immunoassay (ECIA) was developed based on an enzyme-induced Cu2+/Cu+ conversion for the determination of ethyl carbamate (EC). Wherein, three rounds of signal transformation-the catalysis of ALP enzyme, the conversion of Cu2+/Cu+ and signal output of square wave voltammetry (SWV), can be realized to obtain higher sensitivity as compared to t-ELISA. The ECIA method combines the advantages of electrochemistry and ELISA, behaving superior detection performance, such as good selectivity, high sensitivity, and low background signal. For the wine samples, the method showed a linear detection range from 2.5 nM to 2.5 × 104 nM with a limit of detection of 2.28 nM (S/N = 3), which reveals that the ECIA sensor is a promising platform for the detection of trace level of EC in practical samples.

Nanozyme and aptamer- based immunosorbent assay for aflatoxin B1.

Traditional enzyme-linked immunosorbent assay (ELISA) suffers from the limitations of relatively low sensitivity and stability, and enzyme-labelled antibodies are hard to be prepared and purified. Based on a nanozyme, an aptamer and Fe3O4 magnetic nanoparticles (MNP), a nanozyme and aptamer-based immunosorbent assay (NAISA) was developed for aflatoxin B1 (AFB1) detection with simpler operation and separation. In this work, mesoporous SiO2/Au-Pt (m-SAP) were prepared to act as signal labels, which showed high catalase-like activity and was denoted as nanozyme. Aptamer was adopted to specifically recognize with AFB1, and MNP facilitated to realize magnetic separation. To verify the performance of NAISA, traditional ELISA (t-ELISA) and enhanced ELISA (e-ELISA) using MNP and m-SAP nanozyme were applied in AFB1 detection. The NAISA method showed the lowest limit of detection (LOD) with 5 pg mL-1 (n = 3, ±4.2 %), 600 and 12-fold lower than that of t-ELISA (3 ng mL-1) and e-ELISA (0.06 ng mL-1), respectively. In the interference tests, AFB1 can be identified among six different interfering substances. The NAISA method, thus, can be of great importance as it allows selective and sensitive AFB1 detection, while providing the simplicity of use and need for screening hazardous materials.

[Rapid Coal Classification Based on Confidence Machine and Near Infrared Spectroscopy].

Near-infrared reflectance spectroscopy (NIRS) is a simple, convenient and safe technology which is widely used in many industries. NIRS was employed to the rapid classification of coal in this study. The new method can be a replacement of the chemical analysis which is laborious and time consuming. Confidence machine was firstly applied to NIRS in this study which was used to evaluate the risk of the analysis. The near infrared reflectance spectrum of 199 coal samples including four types of coal (50 fat coal samples, 50 coking coal samples, 49 lean coal samples and 50 meager lean coal samples) from different mines in China were collected and classifiers based on the near infrared spectra of coal samples which were established by using machine learning methods to realize the rapid classification of coal samples. Confidence machine was introduced into the analysis technology based on NIRS in this paper. Confidence machine based on support vector machine (CM-SVM) was built and applied to the classification of coal samples via NIRS. Confidence machine is a probabilistic algorithm and instead of using hyper plane (SVM) to carry out the classification, using probability (CM-SVM) turned to be more effective which had 95.45% of the samples correctly grouped. Besides that, CM-SVM also estimated the confidence and credibility for each predicted sample. By setting different confidence levels, CM-SVM can perform region prediction whose error rate was predefined by the different confidence levels, which was very important for the control of product quality when NIRS was applied to the analysis of productions. Confidence machine is designed as an on-line learning method; new samples can be added to the training set one by one to improve the efficiency of the model and is very appropriate for industry on-line analysis. On-line CM-SVM models showed that the confidence of prediction would be raised as the samples increased, which was valuable for industry on-line analysis.