Surface-enhanced Raman scattering based determination on sulfamethazine using molecularly imprinted polymers decorated with silver nanoparticles.

Molecularly imprinted polymers (MIPs) were combined with surface-enhanced Raman scattering (SERS) and AgNPs were prepared by in situ reduction within the MIP for selective and sensitive detection of sulfamethazine (SMZ). The MIP@AgNPs composites were characterized in detail by several analytical techniques, showing the generation of polymers and the formation of AgNPs hot spots. The specific affinity and rapid adsorption equilibrium rates of MIP@AgNPs composites were verified by static and kinetic adsorption studies. The MIP@AgNPs with high selectivity and excellent sensitivity were used as SERS substrates to detect SMZ. A good linear correlation (R2 = 0.996) in rang of 10-10-10-6 mol L-1 was observed between the Raman signal (1596 cm-1) and the concentration of SMZ. The limit of detection (LOD) was as low as 8.10 × 10-11 mol L-1 with relative standard deviations (RSD) of 6.32%. The good stability and reproducibility are also fully reflected in the SERS detection based on MIP@AgNPs. The method was successfully applied to the analysis of lake water samples, with recoveries in the range 85.1% to 102.5%. In summary, SERS detection based on MIP@AgNPs can be developed for a wider and broader range of practical applications. Schematic illustration of MIP@AgNPs sensor for the SERS detection of sulfamethazine.

[Relationship between different milk processing and near infrared spectra].

In the present paper, the effects of different milk processing (homogenizing process, pasteurization) on the milk NIR spectra were discussed. It was found that the raw milk and processed milk show significant difference in the 1 890 nm region, which can be used not only to identify the processed milk, but also to offer the basic theory for NIRS in the quality control researches of milk. The absorbance sharply reduced when the liquid milk was treated by a homogenizer, but the absorbance increased after pasteurization. Raw milks absorbance shows a downtrend in the whole region of spectra with increasing pressure. The changes in fat globules structure finally result in absorbance decline. The commercial milk including remade milk was taken for example to discuss the mechanism of detection. The discriminate analysis calibration was developed by SIMCA method and the accuracy of detection is 98.1% for identifying the reconstituted milk in pasteurized milk between 1 800 and 2 200 nm with the pretreatment method of second derivatived and Norris 5.5.

[Study on component detection of cheese wrapped by polyethylene film based on NIRS].

The possibility of direct determination of fat and protein of wrapped cheese by near infrared spectroscopy was studied. The influences of polyethylene film on the spectra were discussed in order to detect the components of wrapped cheese. And the influences were eliminated using Norris derivation filter pretreatment means. The models for fat and protein of wrapped cheese were calibrated by partial least squares regression (PLS) following eliminating outline, spectral pretreatment, and PLS factors optimization. The best models gave standard errors for calibration of 0.240 and 0.355, standard errors for prediction of 0.326 and 0.219, and correlation coefficients of 0.928 and 0.952 for fat and protein of wrapped cheese, respectively. The results showed no difference from those by non-wrapped cheese's models, and were better than wrapped cheese's models without Norris derivation filter pretreatment. Based on the results, it was concluded that near infrared spectroscopy is a reliable, accurate and fast method for non-invasive measurement of wrapped cheese fat and protein.

[Identification of reconstructed milk in raw milk using near infrared spectroscopy].

Feasibility of reconstituted-milk identification in raw milk was investigated using near infrared spectroscopy. And the applicability of MSC for reconstituted-milk identification was discussed. The discrimination analysis calibration was developed by SIMCA method, and the result indicated that the accuracy of detection is 100%, when the content of reconstructed Milk is above 20%, while for the 10% reconstituted milk, the accuracy of detection is 96.7%; On the other hand, the quantity models of reconstituted milk were calibrated by partial least squares regression (r = 0.971, RMSECV = 7.76%, RPD = 5.13), and there were no significant differences between actual value and reconstituted milk prediction value by t test (p = 0.01). All of these suggested that NIRS has good potential to detect adulteration of raw milk with reconstituted milk.