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ABSTRACT: In this report, we present a novel approach to detect clenbuterol based on competitive surface-enhanced Raman scattering (SERS) immunoassay. Herein, a SERS nanoprobe that relies on gold nanoparticle (GNP) is labeled by 4,4'-dipyridyl (DP) and clenbuterol antibody, respectively. The detection of clenbuterol is carried out by competitive binding between free clenbuterol and clenbuterol-BSA fastened on the substrate with their antibody labeled on SERS nanoprobes. The present method allows us to detect clenbuterol over a much wider concentration range (0.1-100 pg mL(-1)) with a lower limit of detection (ca. 0.1 pg mL(-1)) than the conventional methods. Furthermore, by the use of this new competitive SERS immunoassay, the clenbuterol-BSA (antigen) is chosen to fasten on the substrate instead of the clenbuterol antibody, which could reduce the cost of the assay. Results demonstrate that the proposed method has the wide potential applications in food safety and agonist control.
Analytica chimica acta 07/2011; 697(1-2):61-6. · 4.31 Impact Factor
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ABSTRACT: First of all the surface enhanced Raman spectroscopy (SERS) and normal Raman spectra of caffeine aqueous solution were obtained at different pH values. In order to obtain the detailed vibrational assignments of the Raman spectroscopy, the geometry of caffeine molecule was optimized by density functional theory (DFT) calculation. By comparing the SERS of caffeine with its normal spectra at different pH values; it is concluded that pH value can dramatically affect the SERS of caffeine, but barely affect the normal Raman spectrum of caffeine aqueous solution. It can essentially affect the reorientation of caffeine molecule to the Ag colloid surface, but cannot impact the vibration of functional groups and chemical bonds in caffeine molecule.
Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy 02/2011; 78(2):757-62. · 2.10 Impact Factor
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ABSTRACT: Hyperspectral remote sensing plays an important role in earth observation on land, ocean and atmosphere. A key issue in hyperspectral data exploitation is to extract the spectra of the constituent materials (endmembers) as well as their proportions (fractional abundances) from each measured spectrum of mixed pixel in hyperspectral remote sensing image, called spectral un-mixing. Linear spectral mixture model (LSMM) provides an effective analytical model for spectral unmixing, which assumes that there is a linear relationship among the fractional abundances of the substances within a mixed pixel. To be physically meaningful, LSMM is subject to two constraints: the first constraint requires all abundances to be nonnegative and the second one requires all abundances to be summed to one. Independent component analysis (ICA) has been proposed as an advanced tool to un-mix hyperspectral image. However, ICA is based on the assumption of mutually independent sources, which violates the constraint conditions in LSMM. This embarrassment compromises ICA applicability to hyperspectral data. To overcome this problem, the present paper introduces a solution of minimization of total correlation of the components. Interestingly, with the minimization of total correlation of the components, the angle of the direction between each components is invariable. A Parallel oblique-ICA (Pob-ICA) algorithm is proposed to correct the angle of the searching direction between the components. Two novelties result from our proposed Pob-ICA algorithm. First, the algorithm completely satisfies the physical constraint conditions in LSMM and overcomes the limitation of statistical independency assumed by ICA. Second, the last component, which is missed in other existing ICA algorithms, can be estimated by our proposed algorithm. In experiments, Pob-ICA algorithm demonstrates excellent performance in the simulative and real hyperspectral images.
Guang pu xue yu guang pu fen xi = Guang pu 06/2010; 30(6):1628-33. · 0.84 Impact Factor
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ABSTRACT: Surface-enhanced Raman spectra of methylene blue (MB) at different concentrations in silver colloid were obtained. The results indicate that the physical adsorption is dominant at high concentration while the chemical adsorption is the main fashion at relatively low concentration; there are different adsorption orientations at different concentration: MB+ molecule is perpendicular to the surface of silver nanoparticle at high concentration and adopts a parallel orientation on the surface of nanoparticle at low concentration. The effect of adsorbing time of MB molecule in Ag colloid was investigated and the adsorption dynamics study shows that the parallel orientation at low concentration does not change with the adsorbing time increasing. Density functional theory (DFT) calculations at the level of B3LYP/6-311+G * (for C, S, N, H)/LANL2DZ (for Ag) were employed to optimize the structures and predict Raman frequencies of MB+ and various MB+ -Ag complexes. The results of experiments and calculations suggest that the silver atom prefers to be bound to N and S atoms in the aromatic ring, and thus two different complexes are formed, i.e., conformer N-Ag and conformer S-Ag. Moreover, the Mulliken charge population analysis indicates that N atom in the aromatic ring prefers to interact with Ag than S atom does. Finally, the Raman frequencies observed in the experiments and their vibrational modes were tentatively assigned and discussed.
Guang pu xue yu guang pu fen xi = Guang pu 01/2010; 30(1):90-4. · 0.84 Impact Factor
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ABSTRACT: Hyperspectral remote sensing is a new and fast growing remote sensing technology that is currently being investigated by researchers and scientists. A great challenge in hyperspectral image analysis is decomposing a mixed pixel into a collection of endmembers and their corresponding abundance fractions, namely spectral unmixing. This paper introduces null subspace to the process of spectral unmixing. Null subspace is the orthogonal complement space of the subspace spanned by some endmembers. Take advantage of null subspace, this paper presents a solution of obtaining the distance from a pixel to subspace in the null subspace form. By analysis on the null subspace, all endmembers in the hyperspectral image can be extracted by the maximal distance criterion and the abundance can be obtained by the way of distance proportion. In the experiment, it shows that null subspace provides a fast and effective way for spectral unmixing.
Image and Signal Processing, Congress on. 4:763-767.
