A FRET aptasensor for sensitive detection of aflatoxin B1 based on a novel donor-acceptor pair between ZnS quantum dots and Ag nanocubes.

Aflatoxin B1 (AFB1) is one of the most carcinogenic chemicals. A novel fluorescence resonance energy transfer (FRET) sensor based on aptamer recognition technology is proposed for the sensitive detection of AFB1 in moldy peanuts using Ag nanocubes as energy acceptors and ZnS quantum dots (QDs) as energy donors. Compared to the traditional FRET system based on an Au quencher, Ag nanocubes can not only quench the fluorescence of aptamer modified ZnS QDs, but are also inexpensive. In addition, compared with heavy metal QDs, ZnS QDs are environmentally friendly, have excellent photochemical properties, and are ideal energy donors. Without Ag nanocubes, the aptamer modified ZnS QDs emits blue fluorescence under an ultraviolet lamp. Because the emission spectrum of ZnS and the absorption spectrum of Ag nanocubes meet the requirements of FRET, the fluorescence quenching of ZnS QDs is realized. Nevertheless, with AFB1, the specific binding of aptamer and complementary chain makes the ZnS QDs break away from the Ag nanocubes, which leads to the fluorescence recovery of the ZnS QDs. Under the optimized detection conditions, the linear range of AFB1 was 5 pg mL-1 to 300 ng mL-1, and there was no obvious reaction with other similar mycotoxins. According to S/N = 3, the detection limit of AFB1 was 2.67 pg mL-1. The detection of AFB1 in peanut samples shows that the new FRET system can successfully be applied in the future to agricultural products.

Intelligent evaluation of total polar compounds (TPC) content of frying oil based on fluorescence spectroscopy and low-field NMR.

The purpose of this study was to construct a fusion model using probe-based and non-probe-based fluorescence spectroscopy and low-field nuclear magnetic resonance spectroscopy (Low-field NMR) for rapid quality evaluation of frying oil. Iron tetraphenylporphyrin (FeTPP) was selected as the probe to detect polar compounds in frying oil samples. Non-probe-based fluorescence spectroscopy and low-field NMR were employed to determine the fluorescence changes of antioxidants, triglycerides and fatty acids in frying oil samples. Compared to the models constructed using non-fusion data, the fusion-data models achieved a better regression prediction performance and correlation coefficients with values of 0.9837 and 0.9823 for the training and test sets, respectively. This study suggested that the multiple data fusion method was capable to construct better regression models to rapidly evaluate the quality of frying oil and other food with high oil contents.

A semiconductor quantum dot-based ratiometric electrochemical aptasensor for the selective and reliable determination of aflatoxin B1.

In recent years, a ratiometric electrochemical method has been investigated due to its ability to effectively reduce the background electrical signals via the introduction of an internal calibration mechanism, which has great practical significance in the detection of mycotoxins in foods. Herein, we report a ratiometric electrochemical aptasensor based on two semiconductor quantum dots (i.e. CdTe and PbS QDs) for the detection of aflatoxin B1 (AFB1). The aptasensor was fabricated by immobilizing PbS QD-coated silica hybrid spheres (SiO2@PbS) onto CdTe QD-modified Fe3O4@SiO2 (Fe3O4@SiO2/CdTe) surface through biorecognition between the aptamer and complementary DNAs, where PbS QDs acted as external signal labels and CdTe QDs acted as internal reference labels. In the presence of AFB1, the aptamer connected to SiO2@PbS preferred to form an aptamer/AFB1 complex, which brought about the separation of SiO2@PbS linked with the CdTe QDs; with the addition of more AFB1 to the solution, the amount of SiO2@PbS present on the Fe3O4@SiO2/CdTe surface reduced. After several steps of endonuclease cleavage, magnetic separation, and dissolution with acid, the square wave voltammetry signals of Pb2+ and Cd2+ maintained an inverse relationship with the target content based on the SWV stripping measurements; the proposed method had the wide linear range of 5 pg mL-1-50 ng mL-1 and the determination limit of 4.5 pg mL-1 (S/N = 3) and was applied for the detection of AFB1 in peanuts. The proposed aptasensor has an important practical significance for the development of food safety.

Magneto-controlled aptasensor for simultaneous electrochemical detection of dual mycotoxins in maize using metal sulfide quantum dots coated silica as labels.

Currently there is an urgent need for multi-mycotoxin detection methods due to the co-occurrence of multiple mycotoxins in food raw materials and their augmented toxicity. Herein, a magneto-controlled aptasensor has been developed for simultaneous electrochemical detection of ochratoxin A (OTA) and fumonisin B1 (FB1), two typical mycotoxins found in food crops world-wide. This aptasensor was designed using the high specificity between the target and aptamer with heavy CdTe or PbS quantum dots (QDs) coated silica as labels and the complementary DNA functionalized magnetic beads as capture probes. In presence of targets, the aptamer preferred to form the target-aptamer binding which forced the partial release of the preloaded labels from the magnetic beads. After a one-step incubation and a simple magnetic separation, the electrochemical signals of Cd2+ and Pb2+ dissolved from the reserved labels which had negative correlation with targets contents, was measured based on the difference of peak potentials. This aptasensor provided a wide detection range of 10pgmL-1 to 10ngmL-1 for OTA and 50pgmL-1 to 50ngmL-1 for FB1, and succeeded in real maize samples. This method provides a new avenue for high throughput screen of mycotoxins due to the advantages of simple instrument, low sample consumption, short assay times, and lower detection costs per assay. Moreover, it could be readily expanded for the simultaneous detection of a large panel of mycotoxins by using different metal sulfide QDs when their specific aptamers are available.

Magnetic-fluorescent-targeting multifunctional aptasensorfor highly sensitive and one-step rapid detection of ochratoxin A.

A multifunctional aptasensor for highly sensitive and one-step rapid detection of ochratoxin A (OTA), has been developed using aptamer-conjugated magnetic beads (MBs) as the recognition and concentration element and a heavy CdTe quantum dots (QDs) as the label. Initially, the thiolated aptamer was conjugated on the Fe3O4@Au MBs through Au-S covalent binding. Subsequently, multiple CdTe QDs were loaded both in and on a versatile SiO2 nanocarrier to produce a large amplification factor of hybrid fluorescent nanoparticles (HFNPs) labeled complementary DNA (cDNA). The magnetic-fluorescent-targeting multifunctional aptasensor was thus fabricated by immobilizing the HFNPs onto MBs' surface through the hybrid reaction between the aptamer and cDNA. This aptasensor can be produced at large scale in a single run, and then can be conveniently used for rapid detection of OTA through a one-step incubation procedure. The presence of OTA would trigger aptamer-OTA binding, resulting in the partial release of the HFNPs into bulk solution. After a simple magnetic separation, the supernatant liquid of the above solution contained a great number of CdTe QDs produced an intense fluorescence emission. Under the optimal conditions, the fluorescence intensity of the released HFNPs was proportional to the concentration of OTA in a wide range of 15 pg mL(-1) -100 ng mL(-1) with a detection limit of 5.4 pg mL(-1) (S/N=3). This multifunctional aptasensor represents a promising path toward routine quality control of food safety, and also creates the opportunity to develop aptasensors for other targets using this strategy.

[Determination of taste quality of green tea using FT-NIR spectroscopy and variable selection methods].

The present paper was attempted to study the feasibility to determine the taste quality of green tea using FT-NIR spectroscopy combined with variable selection methods. Chemistry evaluation, as the reference measurement, was used to measure the total taste scores of green tea infusion. First, synergy interval PLS (siPLS) was implemented to select efficient spectral regions from SNV preprocessed spectra; then, optimal variables were selected using genetic algorithm (GA) from these selected spectral regions by siPLS, and the optimal model was achieved with Rp = 0.8908, RMSEP = 4.66 in the prediction set when 38 variables and 6 PLS factors were included. Experimental results showed that the performance of siPLS-GA model was superior to those of others. This study demonstrated that NIR spectra could be used successfully to measure taste quality of green tea and siPLS-GA algorithm has superiority to other algorithm in developing NIR spectral regression model.

Qualitative identification of tea categories by near infrared spectroscopy and support vector machine.

Near-infrared (NIR) spectroscopy has been successfully utilized for the rapid identification of green, black and Oolong tea. The spectral features of each tea category are reasonably differentiated in the NIR region, and the spectral differences provided enough qualitative spectral information for the identification of tea. Support vector machine (SVM) as the pattern recognition was applied to identify three tea categories in this study. The top five principal components (PCs) were extracted as the input of SVM classifiers by principal component analysis (PCA). The RBF SVM classifiers and the polynomial SVM classifiers were studied comparatively in this experiment. The best experimental results were obtained using the radial basis function (RBF) SVM classifier with sigma=0.5. The accuracies of identification were all more than 90% for three tea categories. Finally, compared with the back propagation artificial neural network (BP-ANN) approach, SVM algorithm showed its excellent generalization for identification results. The overall results show that NIR spectroscopy combined with SVM can be efficiently utilized for rapid and simple identification of the tea categories.