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A sensitive dual-readout immunosensor for fluorescence and electrochemiluminescence (ECL) detection of ricin was established, which was combined with a streptavidin-biotin signal amplification system. CdSe/ZnS quantum dots with fine fluorescence and ECL properties were used as the dual-signal function probes of the sandwich immunocomplex. Under the optimum experimental conditions, the dual signal intensity increased significantly with the rise in ricin concentration. The fluorescence intensity of the senor exhibited a good liner relationship toward the ricin concentrations with 0.1~100 ng/mL and the limit of detection (LOD) was 81.7 pg/mL; taking ECL as the detection signal, the sensor showed a linear relationship with the ricin concentrations ranging from 0.01 ng/mL to 100 ng/mL and the LOD was 5.5 pg/mL. The constructed sensor with high sensitivity had been successfully applied to the detection of ricin in complex matrices with satisfactory recoveries. The proposed immunosensor model can be extended to the analysis and detection of others target proteins.

Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky–Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices. [ABSTRACT FROM AUTHOR]

In this paper, a core-shell based on the Fe3O4@SiO2@Au nanoparticle amplification technique for a surface plasmon resonance (SPR) sensor is proposed. Fe3O4@SiO2@AuNPs were used not only to amplify SPR signals, but also to rapidly separate and enrich T-2 toxin via an external magnetic field. We detected T-2 toxin using the direct competition method in order to evaluate the amplification effect of Fe3O4@SiO2@AuNPs. A T-2 toxin–protein conjugate (T2-OVA) immobilized on the surface of 3-mercaptopropionic acid-modified sensing film competed with T-2 toxin to combine with the T-2 toxin antibody–Fe3O4@SiO2@AuNPs conjugates (mAb-Fe3O4@SiO2@AuNPs) as signal amplification elements. With the decrease in T-2 toxin concentration, the SPR signal gradually increased. In other words, the SPR response was inversely proportional to T-2 toxin. The results showed that there was a good linear relationship in the range of 1 ng/mL~100 ng/mL, and the limit of detection was 0.57 ng/mL. This work also provides a new possibility to improve the sensitivity of SPR biosensors in the detection of small molecules and in disease diagnosis. [ABSTRACT FROM AUTHOR]