[Two-Dimensional Hetero-Spectral Near-Infrared and Mid-Infrared Correlation Spectroscopy for Discrimination Adulterated Milk].

New approach for discriminant analysis of adulterated milk is proposed based on combining hetero-spectral two-dimensional (2D) near-infrared (NIR) and mid-infrared (IR) correlation spectroscopy along with multi-way partial least squares discriminant analysis (NPLS-DA). Firstly, 36 pure milk samples were collected and 36 adulterated milk with starch samples (0.01 to 1 g · L(-1)) were prepared by adding appropriate mass of starch into pure milk. Then, one-dimensional NIR transmittance spectra and IR attenuated total reflection spectra of pure milk and adulterated milk with starch were measured at room temperature. And the synchronous 2D NIR-IR (4200~4800 vs. 900~1700 cm(-1)) correlation spectra of all samples were calculated. Due to the trace of adulterants, the synchronous 2D IR-NIR correlation spectral differences between adulterated milk with starch and pure milk are very subtle. Consequently, it was impossible to directly distinguish whether the sample was pure milk or adulterated milk. Finally, 2D IR-NIR correlation spectra were to build a discriminant model to classify adulterated milk and pure milk. The classification accuracy rates of samples in calibration set and in prediction set were 95.8% and 100% respectively. Also, the NPLS-DA models were built based on 2D NIR and 2D IR correlation spectra, respectively. The classification accuracy rates of samples in prediction set were 95.8%. Comparison results showed that the NPLS-DA model could provide better results using 2D NIR-IR correlation spectra than using 2D NIR, and 2D IR correlation spectra. The proposed method can not only effectively extract the feature information of adulterants in milk, but also explores a new perspective method for detection of adulterated food.

Three dimensional design of large-scale TiO(2) nanorods scaffold decorated by silver nanoparticles as SERS sensor for ultrasensitive malachite green detection.

We have designed a large-scale three-dimensional (3D) hybrid nanostructure as surface-enhanced Raman scattering (SERS) sensor by decorating silver nanoparticles on TiO2 nanorods scaffold (Ag/TiO2). Taking p-mercaptobenzoic acid (PMBA) as the probe molecule, the SERS signals collected by point-to-point and time mapping modes show that the relative standard deviation (RSD) in the intensity of the main Raman vibration modes (1079, 1586 cm(-1)) is less than 10%, demonstrating good spatial uniformity and time stability. This hybrid substrate also exhibits excellent SERS enhancement effect due to the formation of high-density hot spots among the AgNPs, which was proved by finite-difference time-domain (FDTD) simulations. The application of the new nanostructures as SERS sensors was demonstrated with the detection of malachite green (MG). The quantification of MG can be accomplished with the detection limit of 1 × 10(-12) M based on the Raman intensity. The results show that the Ag/TiO2 nanostructure can be a promising candidate for SERS sensor.

[Preparation of Au@SiO2 core-shell nanoparticles: controlling the optical properties].

The authros developed a new approach to preparing the Au@SiO2 core-shell nanostructure. The morphology and stability of the composite were characterized by the UV-Vis absorption spectroscopy and transmission electron microscopy (TEM) methods. The stable SERS spectra were obtained from the PMBA-functionalized Au@SiO2 composite. In addition, the authros succeeded in adjusting the thickness of SiO2 layer by controlling the precursor consumption. The stability of Au@SiO2 in basic solution was studied and the results showed that the SiO2 shell was facile to be etched. The present work may provide a reference and gist for research on the preparation, storage and application of Au@SiO2.

[Study on Raman linear model of human breast tissue].

A linear regression model for Raman spectra of human breast tissue was developed with 10 basis spectra which were derived from the spectra of fat, cell cytoplasm, collagen, beta-carotene, DNA and cholesterol etc. This model was tested statistically with more than 2 000 spectra collected from both normal and tumor breast tissue samples. The confidence of F-test for the model significance was 1 to all the spectra and the average of multiple determination coefficients measuring the fit goodness was 0.95. The normalized fit coefficients reflected, to a certain extent, the relative quantities of the Raman-active components in tissue represented by the basis spectra. In comparison with normal breast tissue, the coefficients of the cell cytoplasm and DNA basis spectra increased apparently for the tumor one, and the coefficient of fat basis spectrum changed reversely, which was easily understood with the known pathological changes in the tissue. Mapping spectra were also fitted with this model. This work is helpful to understanding the biochemical/morphological changes in the breast tumor tissues and to developing the Raman diagnostic method for breast tumor.

[Study of human tumor tissues by Raman imaging spectra].

The Raman mapping spectra were collected from the samples of fresh human breast tumor tissues with the excitation wavelength at 633 nm, their correlation coefficients with the reference spectra of chemical DNA, cytoplasm and extra-cellular matrix etc. were calculated, and the images were created based on these correlation coefficients. Examining these images, the authors found that they show approximately the distribution of the corresponding reference chemicals in human tissues and that the DNA spectrum correlation images reflect the DNA distribution quite well because of its much lower correlation with the spectra of other morphological chemicals. This work is helpful in understanding the chemical/morphological basis of the breast tissue Raman spectra and in developing the Raman diagnostic method for breast tumor.