亚洲免费av电影一区二区三区,日韩爱爱视频,51精品视频一区二区三区,91视频爱爱,日韩欧美在线播放视频,中文字幕少妇AV,亚洲电影中文字幕,久久久久亚洲av成人网址,久久综合视频网站,国产在线不卡免费播放

        ?

        Titanium dioxide as an adsorbent to enhance the detection ability of near-infrared diffuse reflectance spectroscopy

        2019-06-20 12:35:26ShuyuWngJinZhngWenshengCiXuegungSho
        Chinese Chemical Letters 2019年5期

        Shuyu Wng,Jin Zhng,Wensheng Ci,Xuegung Sho,b,c,d,*

        a Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin 300071, China

        b Tianjin Key Laboratory of Biosensing and Molecular Recognition, Tianjin 300071, China

        c State Key Laboratory of Medicinal Chemical Biology, Tianjin 300071, China

        d Collaborative Innovation Center of Chemical Science and Engineering, Tianjin 300071, China

        Keywords:

        Near-infrared diffuse reflectance spectroscopy

        Quantitative model

        Detection ability

        Titanium dioxide

        Fish sperm DNA

        ABSTRACT

        A method for quantitative determination of fish sperm deoxyribonucleic acid(fsDNA)was developed by using titanium dioxide (TiO2) as an adsorbent and near-infrared diffuse reflectance spectroscopy(NIRDRS).The selective enrichment of fsDNA was proved by comparing the adsorption efficiency of bovine serum albumin, tyrosine and tryptophan, and the low adsorption background of TiO2 was illustrated by comparing the spectra of four commonly-used inorganic adsorbents(alkaline aluminium oxide, neutral aluminium oxide, nano-hydroxyapatite and silica).The spectral feature of fsDNA can be clearly observed in the spectrum of the sample.Partial least squares (PLS) model was built for quantitative determination of fsDNA using 28 solutions,and 13 solutions with interferences were used for validation of the model.The results showed that the correlation coefficient(R)between the predicted and the reference concentration is 0.9727 and the recoveries of the validation samples are in the range of 98.2%-100.7%

        Near-infrare d diffuse reflectance spectroscopy (NIRDRS) has been proved to be a convenient,cost-effective and nondestructive analytical method for analyzing the samples with complex matrix[1].For complex samples, the measured spectra contain not only the useful information,but also variant background,noise,and so on.These are known as the reason for the low detection limit of the method.Thus,chemometric methods were proposed for extracting the useful information [2-4]and eliminating the interference [5-7].However,for most of the cases,the detection limit is still hard to meet the demand of the applications.The combination of the chemometric and experimental strategies was considered as the way to further enhance the detection ability of NIRDRS [8-10].

        Enrichment of the target component from the analyzing samples is one of the commonly used strategies to improve the detection limit for the analysis of micro or trace component.Pimentel et al.developed a method for quantitative analysis of micro-aromatic hydrocarbons in water by employing a silicone sensing phase [11,12].Du et al.successfully detected the trace amounts of lead ion[13]and carbaryl[14,15]in aqueous solutions via an online enrichment device.In our works, enrichment was also used to determine organic acids [16], metal ions [17,18],phenols[19],etc.in solution samples.By these studies,enrichment was found to be an efficient way to improve the detection ability of NIRDRS.However, the spectral responses of the coexisting components and the absorption of the adsorbent(as a background)were still to be interferences to the quantitative models.Therefore,the selective adsorption was adopted for further improving the detection limit.In our work, β-cyclodextrin based adsorbent was tried to selectively adsorb bilirubin from aqueous solution via hydrophobic interaction and hydrogen bonding [8], and maleimide-functionalized silicon dioxide (SiO2) nanoparticles were prepared to enrich cysteine in aqueous solution and human serum via thiol-maleimide click reaction [20].Furthermore, the adsorbent materials with low background absorption were also employed.For examples,quantitative detection of micro pesticides was achieved via preconcentration by [Zn2-Al-Cl]layered double hydroxides(LDH)[21],a method for quantitative determination of bull serum albumin(BSA)in micro-volume samples was achieved by depositing BSA on the filter paper[22],and silver mirror,which has very low background absorbance,was adopted to enhance the spectral feature of the analyte [9,10].

        Titanium dioxide (TiO2) has been found to be an efficient material for absorbing the biological compounds such as DNAs[23,24].The affinity of the phosphate group to TiO2surface improves the specificity for the absorption [25,26].Due to abundance of phosphate group in DNA, the interaction of DNA and TiO2has been studied [27,28].On the other hand, TiO2was found to have a high near-infrared(NIR)solar reflectance[29,30].Therefore,if TiO2is employed as an adsorbent to selectively enrich the analytes such as DNAs, a spectrum with less spectral interference and low background may be obtained.

        In this work, a method utilizing TiO2as adsorbent to quantitative determination of fsDNA in solutions by NIRDRS was studied.Forty-one samples containing fish sperm deoxyribonucleic acid (fsDNA), including 28 samples in calibration set and 13 samples in validation set, in a concentration range of 0.680-0.795μg/mL were used in this study.To demonstrate the selective adsorption of fsDNA on TiO2,the samples without any interference were used for building the calibration model, however, the samples with different concentrations of sodium chloride,potassium chloride, D-glucose, bovine serum albumin (BSA),tyrosine and tryptophan were used for an external validation.Quantitative determination was achieved with the help of partial least squares (PLS) regression modeling.Signal processing technique continuous wavelet transform (CWT) [31-33]and standard normal variates (SNV) [34]were used to obtain an optimized PLS model.The details of the experiment are described in the Supporting information.

        To investigate the spectral properties of TiO2, the measured spectra of four commonly used inorganic adsorbents including AlO3(alkaline),AlO3(neutral),n-HAP and SiO2were compared.The spectra are shown in Fig.1a.Clearly,the intensities of the peaks in the wavenumber region of 4000-7500 cm-1in the spectra of n-HAP and TiO2are lower than that of the other three adsorbents.Compared with the spectrum of n-HAP, the adsorption of TiO2is lower in the region of 4800-5500 cm-1.Furthermore,the spectral profile of TiO2is smooth in the entire wavenumber region.Apparently,the background absorbance of TiO2is the lowest in all the compared adsorbents.This result indicates that TiO2is a good adsorbent with low background adsorption for NIRDRS.To further observe the spectral properties of TiO2before and after adsorption,the spectra of TiO2power, fsDNA power and the sample of TiO2adsorbed with fsDNA are shown in Fig.1b.The inset is a magnified figure of the spectra in the range of 4600-4250 cm-1.Compared with the spectra of TiO2power,the spectral features of fsDNA can be observed in the spectra of the sample.The peaks in the range of 5400-4700 cm-1may be related to the overlapped absorption of amide and hydroxyl in deoxyribonucleoside[35,36],and the peaks between 4450 cm-1and 4300 cm-1may be assigned as the vibration of the C-H groups in fsDNA[37,38].The result indicates that fsDNA is adsorbed in TiO2powder and a comparatively pure spectrum of the analyte can be obtained to offer useful information for quantitative analysis.However,the absorption bands of bonded OH appears in all the spectra of the adsorbents and the samples around the wavenumber 5000 cm-1and 7500 cm-1.The interference can be removed through the optimization of the models.

        Fig.1.NIRDRS spectra of (a) adsorbent materials, (b) fsDNA powder, fsDNA adsorbed on TiO2 sample and TiO2 powder.The inset is a magnified image of the spectra in the range of 4600-4250 cm-1.

        Fig.2.(a)Effect of adsorption time on the adsorption rate.(b)The percentage of the residual content of fsDNA, BSA, tyrosine and tryptophan in the solution after adsorption.

        To investigate the effect of time on the adsorption, the adsorption rate of fsDNA onto TiO2was measured.Fig.2a shows the variation of adsorption rate at different adsorption time for the experiment using 100 mL of 0.800μg/mL fsDNA aqueous solution and 400 mg TiO2powder.The adsorption rate was calculated by measuring the absorbance intensity of the supernatant after the adsorption at 260 nm.Clearly,the adsorption rate can be as high as 86.5%in 1 min adsorption.The result may be due to the abundant availability of active site on TiO2powder and the fastness of the electrostatic attraction.To ensure a high adsorption rate,5 min was used for the adsorption.To investigate the selective adsorption of fsDNA on TiO2, interfering substances of sodium chloride,potassium chloride, D-glucose,BSA,tyrosine and tryptophan were added to the solution for simulation of biological samples.As shown in Fig.2b, the percentages of the residual content of BSA,tyrosine and tryptophan after the adsorption are almost 100%.However, the percentage of the residual content of fsDNA is only 4.97%.The results clearly demonstrate that the presence of fivefold BSA,tyrosine and tryptophan(4.000μg/mL)does not have any interference to the adsorption of fsDNA when fsDNA concentration is only 0.800μg/mL.Therefore,because of the selective adsorption,the presence of interferences does not affect the accuracy of the determination.

        In the calculations, 28 samples containing fsDNA in a concentration range of 0.680-0.795μg/mL without any interference were used for building the calibration model.The wavenumber region 6000-4000 cm-1was used to build the model,since the spectral information of fsDNA is in this region according to the spectra in Fig.1b.To obtain an optimal PLS model, CWT and SNV were used to pre-process the spectra.CWT was used to eliminate the noise and variant background, and “sym2” wavelet and scale=20 were employed.SNV was adopted to correct the scattering effect in the spectra.The models were evaluated by three parameters of the correlation coefficient (Rcv), root mean square error of cross validation (RMSECV) and residual predictive deviation (RPD).RPD is defined as the ratio of standard deviation(SD) to the standard error of prediction (SEP) in cross validation and used to indicate the quality of the models.Generally,a model with RPD over 5.0 is considered suitable for accurate quantitative analysis [39].Apparently, a higher value of Rcv, a lower value of RMSECV and a bigger value of RPD indicate a better model.The parameter of correlation coefficient(R)and root mean square error of prediction(RMSEP) obtained with the validation samples were used to evaluate the practicability of the model.Besides, Monte Carlo cross validation (MCCV) combined with adjusted Wold’s R criterion [40]was utilized to determine the latent variable (LV)number in the modeling.To obtain an optimized PLS model,signal processing technique CWT and SNV were used.Table 1 shows the parameters obtained with cross validation.From the values of Rcv,RMSECV and RPD in the table, it is clear that the raw spectra (nopreprocessing) model is not qualified for quantitative prediction,CWT and SNV only improve the model a little,but the combination of SNV with CWT makes the model improved significantly.The RPD value is as high as 5.1 for the SNV-CWT model,indicating that the model can be used for accurate quantitative determination [39].Therefore, SNV-CWT was adopted for the signal preprocessing in this work.

        Table 1 Statistics for the calibration and validation performance of PLS models.

        Fig.3.Scatter plot between the reference and predicted contents.

        To investigate the predictability of the optimized model, the spectra of the 13 samples with interferences were used for an external validation.Among the validation samples, two samples with one duplicate and three samples with two duplicates were used to investigate the repeatability of the method.The same conditions were used for the adsorption and the spectral measurements.Fig.3 shows the relationship of the predicted values by the optimal model and reference concentrations of the analyte.The straight line is fitted by least squares regression, and the dot line is the diagonal of the plot.A good linearity is obtained,although there is a slight deviation between the two lines.It can be seen that all the predicted concentrations are reasonably distributed along the straight line.The values of R and RMSEP are 0.9727 and 6.06×10-3μg/mL, respectively.The recoveries of the 13 samples are calculated and the results are in a range of 98.2%-100.7%.For the two paired samples,the differences between the two predicted values are less than 0.013μg/mL.Moreover,relative standard deviations (RSDs), calculated from the three predicted values of the triplicate samples, are 0.96%, 0.62% and 0.14%,respectively.These results demonstrate that the repeatability of the method is acceptable,and the method is less interfered by interference substances in the system.

        In conclusion, a method for determination of fsDNA was developed by using NIRDRS.TiO2was used as an adsorbent for selective enrichment of fsDNA.Due to the low adsorption background in NIR spectra, the detection ability of NIRDRS was enhanced.With the help of SNV-CWT preprocessing techniques,an optimal PLS model was built for quantitative determination of fsDNA.The recoveries of the determination for validation samples in a concentration range of 0.685-0.755μg/mL are from 98.2% to 100.7% with a good repeatability in spite of the presence of interfering substances.A progress in improving the sensitivity of NIRDRS was achieved.Furthermore, the method may provide an efficient way for the determination of DNAs in low concentration solutions.

        Acknowledgments

        This work is supported by the National Natural Science Foundation of China(No.21775076),and the fundamental research funds for central universities (China).

        Appendix A.Supplementary data

        Supplementary data associated with this article can be found,in the online version, at https://doi.org/10.1016/j.cclet.2019.01.005.

        中文字幕无码家庭乱欲| 加勒比日韩视频在线观看 | 国产99视频一区二区三区 | 国产人妻熟女呻吟在线观看| 久久96国产精品久久久| 中文成人无字幕乱码精品区 | 国产在线h视频| 视频一区视频二区亚洲| 久久99天堂av亚洲av| 一本精品99久久精品77| 国产精品免费久久久久影院仙踪林| 国内精品国产三级国产av另类| 自拍偷拍一区二区三区四区| 国产亚洲午夜精品久久久| 久久久中文久久久无码| 久久久久久人妻一区二区三区| 久久国产精品99精品国产987| 日本在线观看一区二区视频| 日本av亚洲中文字幕| 中文天堂国产最新| 丰满爆乳一区二区三区| 国产桃色精品网站| 国产爽快片一区二区三区| 高清午夜福利电影在线| 熟女人妇交换俱乐部| 成人日韩av不卡在线观看| 中文字幕久久国产精品| 亚洲天堂丰满人妻av| 亚洲色大成网站www久久九九| 亚洲国产精品尤物yw在线观看| 人妻少妇精品视频一区二区三区| 国内自拍视频一区二区三区| 国自产拍偷拍精品啪啪一区二区| 使劲快高潮了国语对白在线| 男人深夜影院无码观看| 国产精品国产三级国产专区50| 国产av精品一区二区三| 少妇高清精品毛片在线视频| 九九在线精品视频xxx| 国产自拍成人在线免费视频| 色婷婷一区二区三区四区成人网 |