Citronellol is a kind of terpene produced by plants in response to external stress;thus can be used as a gas biomarker to detect black spot Ceratocystis fimbriata infection in sweetpotato.However,many contemporary ana...Citronellol is a kind of terpene produced by plants in response to external stress;thus can be used as a gas biomarker to detect black spot Ceratocystis fimbriata infection in sweetpotato.However,many contemporary analytical methods,exemplified by gas chromatography-mass spectrometry,are technically demanding,time-consuming,and require complex sample preparation procedures.In this study,a quartz crystal microbalance(QCM)-based gas sensor fabricated via a surface molecular imprinting technique was modified with a Co/Zn-ZIF@MIP composite,in which cobalt-zinc bimetallic ZIF(Co/Zn-ZIF)served as the support material.A linear relationship was observed between the frequency shift and citronellol concentrations ranging from 0.88 to 22 mg/L,with a sensitivity of−6.08 Hz/(mg·L)and a limit of detection(LOD)of 1.35 mg/L.This result indicated that this sensor has excellent selectivity for citronellol and demonstrates high repeatability,as evidenced by R^(2)value of 0.97.In evaluations with real samples,the sensor reliably identified citronellol among the complex volatile organic compounds(VOCs)emitted from black spot-infected sweetpotato,indicating a high level of selectivity.Our research achieved the rapid characterization of sweetpotato black spot disease within 4 min and provided new insights into the development of QCM-based gas sensors for the rapid assessment of agricultural product quality and safety.展开更多
Objectives:The composition and content of fatty acids are critical indicators of vegetable oil quality.To overcome the drawbacks of traditional detection methods,Raman spectroscopy was investigated for the fast determ...Objectives:The composition and content of fatty acids are critical indicators of vegetable oil quality.To overcome the drawbacks of traditional detection methods,Raman spectroscopy was investigated for the fast determination of the fatty acids composition of oil.Materials and Methods:Rapeseed and soybean oil at different depths of the oil tank at different storage times were collected and an eighth-degree polynomial function was used to fit the Raman spectrum.Then,the multivariate scattering correction,standard normal variable transformation(SNV),and Savitzky–Golay convolution smoothing methods were compared.Results:Polynomial fitting combined with SNV was found to be the optimal pretreatment method.Characteristic wavelengths were selected by competitive adaptive reweighted sampling.For monounsaturated fatty acids(MUFAs),polyunsaturated fatty acids(PUFAs),and saturated fatty acids(SFAs),44,75,and 92 characteristic wavelengths of rapeseed oil,and 60,114,and 60 characteristic wavelengths of soybean oil were extracted.Support vector regression was used to establish the prediction model.The R^(2)values of the prediction results of MUFAs,PUFAs,and SFAs for rapeseed oil were 0.9670,0.9568,and 0.9553,and the root mean square error(RMSE)values were 0.0273,0.0326,and 0.0340,respectively.The R^(2)values of the prediction results of fatty acids for soybean oil were respectively 0.9414,0.9562,and 0.9422,and RMSE values were 0.0460,0.0378,and 0.0548,respectively.A good correlation coefficient and small RMSE value were obtained,indicating the results to be highly accurate and reliable.Conclusions:Raman spectroscopy,based on competitive adaptive reweighted sampling coupled with support vector regression,can rapidly and accurately analyze the fatty acid composition of vegetable oil.展开更多
基金support of the Earmarked Fund for CARS-10-Sweetpotato,China,the National Foundation of Nature and Science of China(Nos.32102083 and M2242001)the Natural Science Foundation of Shandong Province of China(Nos.ZR2021QC204 and ZR2022MC196).
文摘Citronellol is a kind of terpene produced by plants in response to external stress;thus can be used as a gas biomarker to detect black spot Ceratocystis fimbriata infection in sweetpotato.However,many contemporary analytical methods,exemplified by gas chromatography-mass spectrometry,are technically demanding,time-consuming,and require complex sample preparation procedures.In this study,a quartz crystal microbalance(QCM)-based gas sensor fabricated via a surface molecular imprinting technique was modified with a Co/Zn-ZIF@MIP composite,in which cobalt-zinc bimetallic ZIF(Co/Zn-ZIF)served as the support material.A linear relationship was observed between the frequency shift and citronellol concentrations ranging from 0.88 to 22 mg/L,with a sensitivity of−6.08 Hz/(mg·L)and a limit of detection(LOD)of 1.35 mg/L.This result indicated that this sensor has excellent selectivity for citronellol and demonstrates high repeatability,as evidenced by R^(2)value of 0.97.In evaluations with real samples,the sensor reliably identified citronellol among the complex volatile organic compounds(VOCs)emitted from black spot-infected sweetpotato,indicating a high level of selectivity.Our research achieved the rapid characterization of sweetpotato black spot disease within 4 min and provided new insights into the development of QCM-based gas sensors for the rapid assessment of agricultural product quality and safety.
基金funded by the Key Science and Technology Program of Henan Province under Grant No.212102110262Science and Technology Plan Project of Henan Provincial Market Supervision and Administration Bureau under Grant No.2021sj40+1 种基金the Key Research Program of Zhejiang Province under Grant No.2020C02018Scientific Research Projects for College Students under Grant No.2020KX0006,China.The authors acknowledge the support.
文摘Objectives:The composition and content of fatty acids are critical indicators of vegetable oil quality.To overcome the drawbacks of traditional detection methods,Raman spectroscopy was investigated for the fast determination of the fatty acids composition of oil.Materials and Methods:Rapeseed and soybean oil at different depths of the oil tank at different storage times were collected and an eighth-degree polynomial function was used to fit the Raman spectrum.Then,the multivariate scattering correction,standard normal variable transformation(SNV),and Savitzky–Golay convolution smoothing methods were compared.Results:Polynomial fitting combined with SNV was found to be the optimal pretreatment method.Characteristic wavelengths were selected by competitive adaptive reweighted sampling.For monounsaturated fatty acids(MUFAs),polyunsaturated fatty acids(PUFAs),and saturated fatty acids(SFAs),44,75,and 92 characteristic wavelengths of rapeseed oil,and 60,114,and 60 characteristic wavelengths of soybean oil were extracted.Support vector regression was used to establish the prediction model.The R^(2)values of the prediction results of MUFAs,PUFAs,and SFAs for rapeseed oil were 0.9670,0.9568,and 0.9553,and the root mean square error(RMSE)values were 0.0273,0.0326,and 0.0340,respectively.The R^(2)values of the prediction results of fatty acids for soybean oil were respectively 0.9414,0.9562,and 0.9422,and RMSE values were 0.0460,0.0378,and 0.0548,respectively.A good correlation coefficient and small RMSE value were obtained,indicating the results to be highly accurate and reliable.Conclusions:Raman spectroscopy,based on competitive adaptive reweighted sampling coupled with support vector regression,can rapidly and accurately analyze the fatty acid composition of vegetable oil.
