No abstract

A newly developed band-target entropy minimization (BTEM) algorithm was tested on experimental FTIR data of Rh 4 (CO) 12 /Rh 6 (CO) 16 mixtures in order to recover the pure component spectra of the constituent complexes. Bands in the nonoverlapping bridging carbonyl region as well as bands in the highly overlapping terminal carbonyl region were targeted for retention. The bands are identified in the vector-space decomposition of the observations, a crucial first step in untangling the superposition of the pure component spectra. In both cases, the targeted band was retained, and exceptionally accurate whole spectral estimates of Rh 4 (CO) 12 and Rh 6 (CO) 16 were obtained. Due to the constructs used in BTEM, enhanced signal-to-noise characteristics result, and spectral nonlinearities arising from changing band positions and changing band shapes are essentially eliminated. For the experimentalist, the utility of BTEM arises from its direct one-spectrum-at-a-time spectral reconstruction approachswhich is guided by the choice of the targeted region. As such, BTEM appears particularly applicable to spectroscopy possessing highly localized features: i.e., FTIR, Raman, etc. The BTEM algorithm is so useful that the spectral pattern from the minute presence of suspended particles of Rh 6 (CO) 16 could be reconstructed. Indeed, the integrated absorbance of Rh 4 (CO) 12 , Rh 6 (CO) 16 , and Rh 6 (CO) 16 solids account for only ca. 0.3, 0.09, and 0.04% of the experimental observations. The new BTEM algorithm was compared to other algorithms such as SIMPLISMA, IPCA, and OPA-ALS. The latter either fail with the present data set or are unable to produce reconstructed spectra of similar quality to BTEM. This new algorithm holds considerable promise for the analysis of in-situ spectroscopic reaction data such as those arising in complex organometallic and organic syntheses, where absolutely no a priori information about the constituents/intermediates is available.

A method established in the present study has proven to be effective in the synthesis of Mn(2)O(3) nanocrystals by the thermolysis of manganese(III) acetyl acetonate ([CH(3)COCH=C(O)CH(3)](3)-Mn) and Mn(3)O(4) nanocrystals by the thermolysis of manganese(II) acetyl acetonate ([CH(3)COCH=C(O)-CH(3)](2)Mn) on a mesoporous silica, SBA-15. In particular, Mn(2)O(3) nanocrystals are the first to be reported to be synthesized on SBA-15. The structure, texture, and electronic properties of nanocomposites were studied using various characterization techniques such as N2 physisorption, X-ray diffraction (XRD), laser Raman spectroscopy (LRS), temperature-programmed reduction (TPR), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). The results of powder XRD at low angles show that the framework of SBA-15 remains unaffected after generation of the manganese oxide (MnO(x)) nanoparticles, whereas the pore volume and the surface area of SBA-15 dramatically decreased as indicated by N2 adsorption-desorption. TEM images reveal that the pores of SBA-15 are progressively blocked with MnO(x) nanoparticles. The formation of the hausmannite Mn(3)O(4) and bixbyite Mn(2)O(3) structures was clearly confirmed by XRD. The surface structures of MnO(x) were also determined by LRS, XPS, and TPR. The crystalline phases of MnO(x) were identified by LRS with corresponding out-of-plane bending and symmetric stretching vibrations of bridging oxygen species (M-O-M) of both MnO(x) nanoparticles and bulk MnO(x). We also observed the terminal Mn=O bonds corresponding to vibrations at 940 and 974 cm-1 for Mn(3)O(4)/SBA-15 and Mn(2)O(3)/SBA-15, respectively. These results show that the MnO(x) species to be highly dispersed inside the channels of SBA-15. The nanostructure of the particles was further identified by the TPR profiles. Furthermore, the chemical states of the surface manganese (Mn) determined by XPS agreed well with the findings of LRS and XRD. These results suggest that the method developed in the present study resulted in the production of MnO(x) nanoparticles on mesoporous silica SBA-15 by controlling the crystalline phases precisely. The thus-prepared nanocomposites of MnO(x) showed significant catalytic activity toward CO oxidation below 523 K. In particular, the MnO(x) prepared from manganese acetyl acetonate showed a higher catalytic reactivity than that prepared from Mn(NO(3))2.

A newly developed self-modeling curve resolution method, band-target entropy minimization (BTEM), is described. This method starts with the data decomposition of a set of spectroscopic mixture data using singular value decomposition. It is followed by the transformation of the orthonormal basis vectors/loading vectors into individual pure component spectra one at a time. The transformation is based in part on some seminal ideas borrowed from information entropy theory with the desire to maximize the simplicity of the recovered pure component spectrum. Thus, the proper estimate is obtained via minimization of the proposed information entropy function or via minimization of derivative and area of the spectral estimate. Nonnegativity constraints are also imposed on the recovered pure component spectral estimate and its corresponding concentrations. As its name suggests, in this method, one targets a spectral feature readily observed in loading vectors to retain, and then combinations of the loading vectors are searched to achieve the global minimum value of an appropriate objective function. The major advantage of this method is its one spectrum at a time approach and its capability of recovering minor components having low spectroscopic signals. To illustrate the application of BTEM, spectral resolution was performed on FT-IR measurements of very highly overlapping mixture spectra containing six organic species with a two-component background interference (air). BTEM estimates were also compared with the estimates obtained using other self-modeling curve resolution techniques, i.e., SIMPLISMA, IPCA, OPA-ALS, and SIMPLISMA-ALS.

Abstract. The ability of combining near-infrared (NIR) Raman spectroscopy with support vector machines (SVM) for improving multi-class classification between different histopathological groups in tissues was evaluated in this study. A total of 105 colonic tissue specimens from 59 patients including 41 normal, 18 hyperplastic polyps and 46 adenocarcinomas were used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was utilized for tissue Raman spectroscopic measurements at 785-nm laser excitation. A total of 817 tissue Raman spectra were acquired and subjected to principal components analysis (PCA) for SVM-based multi-class classification, in which 324 Raman spectra were from normal, 184 from polyps and 309 from adenocarcinomatous colonic tissue. Two types of SVM (i.e., C-SVM and ν-SVM) with three different kernel functions (linear, polynomial and Gaussian radial basis function (RBF) in combination with PCA were used to develop effective diagnostic algorithms for classification of Raman spectra of different colonic tissues. The performance of various SVMbased algorithms was evaluated and compared using a leave-one-out, cross-validation method. The results showed that in the C-SVM classification, the maximum overall diagnostic accuracy of 99.3, 99.4 and 99.9% can be achieved using the linear, polynomial and RBF kernels, respectively; while in the ν-SVM classification, the maximum overall diagnostic accuracy of 98.4, 98.5 and 99.6% can be obtained using the linear, polynomial and RBF kernels, respectively. All the polyps can be identified from normal and adenocarcinomatous tissue using the C-SVM algorithms. The RBF C-SVM algorithm was proven to be the best classifier for providing the highest diagnostic accuracy (99.9%) for multi-class classification. This study demonstrates that NIR Raman spectroscopy in combination with a powerful SVM technique has great potential for providing an effective and accurate diagnostic schema for cancer diagnosis in the colon.