In this paper, a novel and sensitive electrochemical aptasensor for detecting tetracycline(TET)with prussian blue(PB) as the label-free signal was fabricated. A PB-chitosan-glutaraldehyde(PB-CS-GA)system acting as the...In this paper, a novel and sensitive electrochemical aptasensor for detecting tetracycline(TET)with prussian blue(PB) as the label-free signal was fabricated. A PB-chitosan-glutaraldehyde(PB-CS-GA)system acting as the signal indicator was developed to improve the sensitivity of the electrochemical aptasensor.Firstly, the PB-CS-GA was fixed onto the glass carbon electrode surface. Then, colloidal gold nanoparticles(Au NPs) were droped onto the electrode to immobilize the anti-TET aptamer for preparation of the aptasensor.The stepwise assembly process of the aptasensor was characterized by cyclic voltammetry(C-V) and scanning electron microscope(SEM). The target TET captured onto the electrode induced the current response of the electrode due to the non-conducting biomoleculars. Under the optimum operating conditions, the response of differential pulse voltammetry(DPV) was used for detecting the concentration of TET. The proposed aptasensor showed a high sensitivity and a wide linear range of 109-～ 105-M and 105-～ 102-M with the correlation coefficients of 0.994 and 0.992, respectively. The detection limit was 3.2×1010-M(RSD 4.12%). Due to its rapidity, sensitivity and low cost, the proposed aptasensor could be used as a pre-scanning method in TET determination for the analysis of livestock products.展开更多
Rice straw is a major kind of biomass that can be utilized as lignocellulosic materials and renewable energy.Rapid prediction of the lignocellulose(cellulose,hemicellulose,and lignin)and organic elements(carbon,hydrog...Rice straw is a major kind of biomass that can be utilized as lignocellulosic materials and renewable energy.Rapid prediction of the lignocellulose(cellulose,hemicellulose,and lignin)and organic elements(carbon,hydrogen,nitrogen,and sulfur)of rice straw would help to decipher its growth mechanisms and thereby improve its sustainable usages.In this study,364 rice straw samples featuring different rice subspecies(japonica and indica),growing seasons(early-,middle-,and late-season),and growing environments(irrigated and rainfed)were collected,the differences among which were examined by multivariate analysis of variance.Statistic results showed that the cellulose content exhibited significant differences among different growing seasons at a significant level(p<0.01),and the contents of cellulose and nitrogen had significant differences between different growing environments(p<0.01).Near infrared reflectance spectroscopy(NIRS)models for predicting the lignocellulosic and organic elements were developed based on two algorithms including partial least squares(PLS)and competitive adaptive reweighted sampling-partial least squares(CARS-PLS).Modeling results showed that most CARS-PLS models are of higher accuracy than the PLS models,possibly because the CARS-PLS models selected optimal combinations of wavenumbers,which might have enhanced the signal of chemical bonds and thereby improved the predictive efficiency.As a major contributor to the applications of rice straw,the nitrogen content was predicted precisely by the CARS-PLS model.Generally,the CARS-PLS models efficiently quantified the lignocellulose and organic elements of a wide variety of rice straw.The acceptable accuracy of the models allowed their practical applications.展开更多
基金supported by the National Natural Science Foundation of China (No. 30972055, 31101286)Agricultural Science and Technology Achievements Transformation Fund Projects of the Ministry of Science and Technology of China (No.2011GB2C60020)
文摘In this paper, a novel and sensitive electrochemical aptasensor for detecting tetracycline(TET)with prussian blue(PB) as the label-free signal was fabricated. A PB-chitosan-glutaraldehyde(PB-CS-GA)system acting as the signal indicator was developed to improve the sensitivity of the electrochemical aptasensor.Firstly, the PB-CS-GA was fixed onto the glass carbon electrode surface. Then, colloidal gold nanoparticles(Au NPs) were droped onto the electrode to immobilize the anti-TET aptamer for preparation of the aptasensor.The stepwise assembly process of the aptasensor was characterized by cyclic voltammetry(C-V) and scanning electron microscope(SEM). The target TET captured onto the electrode induced the current response of the electrode due to the non-conducting biomoleculars. Under the optimum operating conditions, the response of differential pulse voltammetry(DPV) was used for detecting the concentration of TET. The proposed aptasensor showed a high sensitivity and a wide linear range of 109-～ 105-M and 105-～ 102-M with the correlation coefficients of 0.994 and 0.992, respectively. The detection limit was 3.2×1010-M(RSD 4.12%). Due to its rapidity, sensitivity and low cost, the proposed aptasensor could be used as a pre-scanning method in TET determination for the analysis of livestock products.
基金We would like to acknowledge the support given by the Innovation Team Project of the Ministry of Education(IRT_17R105)the China Agriculture Research System(CARS-36)Program for Changjiang Scholars.
文摘Rice straw is a major kind of biomass that can be utilized as lignocellulosic materials and renewable energy.Rapid prediction of the lignocellulose(cellulose,hemicellulose,and lignin)and organic elements(carbon,hydrogen,nitrogen,and sulfur)of rice straw would help to decipher its growth mechanisms and thereby improve its sustainable usages.In this study,364 rice straw samples featuring different rice subspecies(japonica and indica),growing seasons(early-,middle-,and late-season),and growing environments(irrigated and rainfed)were collected,the differences among which were examined by multivariate analysis of variance.Statistic results showed that the cellulose content exhibited significant differences among different growing seasons at a significant level(p<0.01),and the contents of cellulose and nitrogen had significant differences between different growing environments(p<0.01).Near infrared reflectance spectroscopy(NIRS)models for predicting the lignocellulosic and organic elements were developed based on two algorithms including partial least squares(PLS)and competitive adaptive reweighted sampling-partial least squares(CARS-PLS).Modeling results showed that most CARS-PLS models are of higher accuracy than the PLS models,possibly because the CARS-PLS models selected optimal combinations of wavenumbers,which might have enhanced the signal of chemical bonds and thereby improved the predictive efficiency.As a major contributor to the applications of rice straw,the nitrogen content was predicted precisely by the CARS-PLS model.Generally,the CARS-PLS models efficiently quantified the lignocellulose and organic elements of a wide variety of rice straw.The acceptable accuracy of the models allowed their practical applications.
