With the increasing abuse of antibiotics, more attention has been focused on the potential harm on ecological environment and human health. The aim of this study was quantitative analysis of ceftazidime using Surface Enhanced Raman spectroscopy (SERS). Flower-shaped silver nanoparticles adsorbed on silicon wafer were fabricated in an aqueous medium without heavy metal or organic wastes. The limits of detection (LOD) of Rhodamine 6G (R6G) could reached to 10-9 M, indicating that the substrates had high SERS activity. Meanwhile, the substrates showed excellent stability and uniformity. Flower-shaped silver nanoparticles substrates were selected as substrates for detecting the SERS spectra of ceftazidime in different concentrations. The information about the structure of ceftazidime molecule was reflect by Raman vibration assignments efficiently. Based on the Raman characteristic bands of ceftazidime, four quantitative analysis models using linear regression were compared. It was found that equation between log10C (C refers to the concentration of ceftazidime) and Iavg (average intensity) of Raman characteristic bands (749 cm-1 , 850 cm-1 and 1025 cm-1) was more suitable for quantitative analysis of ceftazidime, and correlation coefficient R2 was up to 0.96. The quantitative model was used to detect the concentration of ceftazidime in practical surface water sample (10-2 g/L), and the relative error between actual values and calculated values was 1.95%. This method is high sensitivity and rapid, which is potentially a powerful tool for quantitative analysis of other antibiotics.
The interaction between bovine serum albumin(BSA) and Ag-nanoparticles was studied under a pH 7.4 buffer system by time-resolved fluorescence technique combined with the steady-state absorption and fluorescence spectrum. With Ag-nanoparticles, the BSA showed blue shift of fluorescence from 335nm to 332.5nm, accompanied by the fluorescence intensity decreasing. When adding the Ag-nanoparticles to the three fluorescent amino acids tryptophan(Trp), tyrosine(Tyr)and phenylalanine(Phe), only Trp displayed peak shift which from 346.5nm to 341nm. Strong interaction between BSA and the Ag-nanoparticles may come from Trp residue. Time-resolved fluorescence gave that BSA had only one fluorescence lifetime around 6ns from 308 to 313K. When adding Ag-nanoparticles, two fluorescence lifetimes appeared. One is a little above than 6ns and the other is around 3ns. The two Trp residues in 134th and 212th position may give contribution to the changes of the fluorescence lifetime. The 134th Trp residue is probably protected by BSA molecule structure and basically don't contact with Ag-nanoparticles, which shows little change of fluorescence lifetime. The 212th Trp residue is likely the target of the Ag-nanoparticles. The Ag-nanoparticles changed the microenvironment of BSA around the 212th Trp residue and therefore increases the exposure of the 212th Trp and the 134th Trp .
A taper at one end of the fiber serving as the sensing element is widely used to enhance the performance of evanescentwave
(EW) fiber-optic sensors. However, almost all sensors of this type launch the excitation light into the core at the
opposite end and the entire taper has to be immersed into the bulky sample volume. This paper introduces a new way of
injecting excitation light: from the outside of the taper perpendicularly. As a result, several desirable features are
achieved simultaneously, including a dramatic increase of collectable EW signal, elimination of stray excitation light,
easy system construction and reduction of the sample volume to mere microliters.
Tea polyphenols (Tp) and free amino acids (Aa) are the most important quality materials in tea
drinks. Due to the high number of samples to be analyzed, new analytical techniques providing fast and
reliable data about the quality are essential. Therefore, a portable near-infrared spectroscopy (NIR)
analyzer was developed for real-time, continuous and quantitative determination of Tp and Aa in tea
drinks. The portable NIR tea drinks analyzer is composed of a lamphouse, a temperature-controlled
sample chamber, an optical fiber and an InGaAs array mini grating spectrometer. The analyzer is
compact, lightweight and robust with no movable elements. The software with the functions of
spectrum acquisition, model establishment, method selection and real-time analysis was also developed
for the analyzer. Using partial least squares (PLS) regression, the calibration models for the
quantification of Tp and Aa were established with reference to the GB methods (the national standard
methods). The values of root mean square error of cross validation (RMSECV) of the models for Tp
and Aa calibration were 0.059 mg/mL, 0.005 mg/mL, the values of the correlation coefficients (R2)
were 0.99 and 0.98 respectively. The relative standard deviation (RSD) of ten repetitive testing were
3.17% and 4.15%. It suggested that the portable NIR tea drinks analyzer could be a fast and reliable
alternative for tea drinks quality testing.
Calibration models of quantitative analysis of peanut oil content in ternary blended edible oil by near infrared
spectroscopy were built using partial least square (PLS) regression. A total of 92 samples blended with three kinds of
pure oil in different proportion (V/V) were prepared. Near infrared diffuse reflectance spectra of the samples were
collected over 4 000 cm-1-10 000 cm-1 spectral region with a FT-NIR spectrometer. A calibration model of prediction to
the peanut oil content was established with PLS using the original spectra and validated with leave-one-out cross
validation method. The correlation coefficient and the RMSEC of the model were 0.9926 and 2.91%, respectively. The
result showed that near infrared spectroscopy could be an ideal tool for fast determination to the peanut oil content in
blended edible oil.
Near infrared (NIR) reflectance spectroscopy was used to develop a fast determination method for total ginsenosides in Ginseng (Panax Ginseng) powder. The spectra were analyzed with multiplicative signal correction (MSC) correlation method. The best correlative spectra region with the total ginsenosides content was 1660 nm~1880 nm and 2230nm~2380 nm. The NIR calibration models of ginsenosides were built with multiple linear regression (MLR), principle component regression (PCR) and partial least squares (PLS) regression respectively. The results showed that the calibration model built with PLS combined with MSC and the optimal spectrum region was the best one. The correlation coefficient and the root mean square error of correction validation (RMSEC) of the best calibration model were 0.98 and 0.15% respectively. The optimal spectrum region for calibration was 1204nm~2014nm. The result suggested that using NIR to rapidly determinate the total ginsenosides content in ginseng powder were feasible.
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