RESUMEN
The effect of temperature (25, 45, and 65 °C) on the gluten secondary structure was investigated by using Fourier transform infrared (FTIR) spectroscopy and modulation of disulfide and hydrogen bonds contributions (100 ppm ascorbic acid (AA), 0.6% diacetyl tartaric acid ester of monoglycerides (DATEM), and 0.25 mM dithiothreitol (DTT)). The results showed that additives heated at 65 °C altered most of the gluten matrix formation by changing structural secondary structures compared to the secondary structures of native gluten (control). The content of random coils, α-helices, and ß-sheet of gluten increased, while the extent of ß-turns and antiparallel ß-sheets decreased, which led to the transformation to a more stable secondary conformation. In addition, the rheological properties (%creep strain) revealed that gluten deformation increased during the heating process with all of the additives. The chemometric method could quantitate an overall alteration of gluten polymerization and gluten matrix formation during heating with additive treatments.
RESUMEN
A prototype library search engine has been further developed to search the infrared spectral libraries of the paint data query database to identify the line and model of a vehicle from the clear coat, surfacer-primer, and e-coat layers of an intact paint chip. For this study, search prefilters were developed from 1181 automotive paint systems spanning 3 manufacturers: General Motors, Chrysler, and Ford. The best match between each unknown and the spectra in the hit list generated by the search prefilters was identified using a cross-correlation library search algorithm that performed both a forward and backward search. In the forward search, spectra were divided into intervals and further subdivided into windows (which corresponds to the time lag for the comparison) within those intervals. The top five hits identified in each search window were compiled; a histogram was computed that summarized the frequency of occurrence for each library sample, with the IR spectra most similar to the unknown flagged. The backward search computed the frequency and occurrence of each line and model without regard to the identity of the individual spectra. Only those lines and models with a frequency of occurrence greater than or equal to 20% were included in the final hit list. If there was agreement between the forward and backward search results, the specific line and model common to both hit lists was always the correct assignment. Samples assigned to the same line and model by both searches are always well represented in the library and correlate well on an individual basis to specific library samples. For these samples, one can have confidence in the accuracy of the match. This was not the case for the results obtained using commercial library search algorithms, as the hit quality index scores for the top twenty hits were always greater than 99%.
RESUMEN
Pattern recognition techniques have been developed to search the infrared (IR) spectral libraries of the paint data query (PDQ) database to differentiate between similar but nonidentical IR clear coat paint spectra. The library search system consists of two separate but interrelated components: search prefilters to reduce the size of the IR library to a specific assembly plant or plants corresponding to the unknown paint sample and a cross-correlation searching algorithm to identify IR spectra most similar to the unknown in the subset of spectra identified by the prefilters. To develop search prefilters with the necessary degree of accuracy, IR spectra from the PDQ database were preprocessed using wavelets to enhance subtle but significant features in the data. Wavelet coefficients characteristic of the assembly plant of the vehicle were identified using a genetic algorithm for pattern recognition and feature selection. A search algorithm was then used to cross-correlate the unknown with each IR spectrum in the subset of library spectra identified by the search prefilters. Each cross-correlated IR spectrum was simultaneously compared to an autocorrelated IR spectrum of the unknown using several spectral windows that span different regions of the cross-correlated and autocorrelated data from the midpoint. The top five hits identified in each search window are compiled, and a histogram is computed that summarizes the frequency of occurrence for each selected library sample. The five library samples with the highest frequency of occurrence are selected as potential hits. Even in challenging trials where the clear coat paint samples evaluated were all the same make (e.g., General Motors) within a limited production year range, the model of the automobile from which the unknown paint sample was obtained could be identified from its IR spectrum.
RESUMEN
Clear coat searches of the infrared (IR) spectral library of the paint data query (PDQ) forensic database often generate an unusable number of hits that span multiple manufacturers, assembly plants, and years. To improve the accuracy of the hit list, pattern recognition methods have been used to develop search prefilters (i.e., principal component models) that differentiate between similar but non-identical IR spectra of clear coats on the basis of manufacturer (e.g., General Motors, Ford, Chrysler) or assembly plant. A two step procedure to develop these search prefilters was employed. First, the discrete wavelet transform was used to decompose each IR spectrum into wavelet coefficients to enhance subtle but significant features in the spectral data. Second, a genetic algorithm for IR spectral pattern recognition was employed to identify wavelet coefficients characteristic of the manufacturer or assembly plant of the vehicle. Even in challenging trials where the paint samples evaluated were all from the same manufacturer (General Motors) within a limited production year range (2000-2006), the respective assembly plant of the vehicle was correctly identified. Search prefilters to identify assembly plants were successfully validated using 10 blind samples provided by the Royal Canadian Mounted Police (RCMP) as part of a study to populate PDQ to current production years, whereas the search prefilter to discriminate among automobile manufacturers was successfully validated using IR spectra obtained directly from the PDQ database.
RESUMEN
Attenuated total reflection (ATR) is a widely used sampling technique in infrared (IR) spectroscopy because minimal sample preparation is required. Since the penetration depth of the ATR analysis beam is quite shallow, the outer layers of a laminate or multilayered paint sample can be preferentially analyzed with the entire sample intact. For this reason, forensic laboratories are taking advantage of ATR to collect IR spectra of automotive paint systems that may consist of three or more layers. However, the IR spectrum of a paint sample obtained by ATR will exhibit distortions, e.g., band broadening and lower relative intensities at higher wavenumbers, compared with its transmission counterpart. This hinders library searching because most library spectra are measured in transmission mode. Furthermore, the angle of incidence for the internal reflection element, the refractive index of the clear coat, and surface contamination due to inorganic contaminants can profoundly influence the quality of the ATR spectrum obtained for automotive paints. A correction algorithm to allow ATR spectra to be searched using IR transmission spectra of the paint data query (PDQ) automotive database is presented. The proposed correction algorithm to convert transmission spectra from the PDQ library to ATR spectra is able to address distortion issues such as the relative intensities and broadening of the bands, and the introduction of wavelength shifts at lower frequencies, which prevent library searching of ATR spectra using archived IR transmission data.
RESUMEN
Search prefilters developed from spectral data collected on two 6700 Thermo-Nicolet FTIR spectrometers were able to identify the respective manufacturing plant and the production line of an automotive vehicle from its clear coat paint smear using IR transmission spectra collected on a Bio-Rad 40A or Bio-Rad 60 FTIR spectrometer. All four spectrometers were equipped with DTGS detectors. An approach based on instrumental line functions was used to transfer the classification model between the Thermo-Nicolet and Bio-Rad instruments. In this study, 209 IR spectra of clear coat paint smears comprising the training set were collected using two Thermo-Nicolet 6700 IR spectrometers, whereas the validation set consisted of 242 IR spectra of clear coats obtained using two Bio-Rad FTIR instruments.
RESUMEN
Very small (<10 nm) monodisperse gold nanoparticles (AuNPs) coated with a monolayer of decanethiol were prepared and their surface-enhanced infrared absorption (SEIRA) spectra were measured in the transmission mode. The AuNPs were prepared by the borohydride reduction of HAuCl(4) inside reverse micelles that were made by adding water to a hexane solution of sodium bis(2-ethylhexyl)sulfosuccinate (AOT). The gold nanoparticles were then stabilized by the addition of decanethiol. Subsequent addition of p-nitrothiophenol both facilitated the removal of excess AOT and showed that the gold surface was completely covered by the decanethiol. SEIRA spectra of decanethiol on gold particles prepared in AOT microemulsions were about twelve times more intense than corresponding layers on gold produced by electroless deposition and gave a significantly less noisy spectrum compared to the corresponding surface-enhanced Raman spectrum. The surface-enhanced Raman scattering (SERS) spectra of the same samples showed that the most intense spectrum was obtained from gold nanoparticles with a mean diameter of 2.5 nm. This result is in contrast to previous statements that SERS spectra could only be obtained from particles larger than 10 nm.
RESUMEN
Copper nanoparticles (Cu NPs) were made by electroless deposition on Ge disks as substrates for surface-enhanced infrared absorption (SEIRA). Previous X-ray photoelectron spectra had shown that elemental copper is deposited on the Ge substrate and that the nanoparticulate film remains resistant to oxidation even after several days of air exposure at room temperature. SEIRA spectra of p-nitrothiophenol (p-NTP) adsorbed on the copper nanoparticles were measured. Freshly made substrates made by electroless deposition gave higher enhancements than both the 12-day-old oxidized substrates and substrates made by physical vapor deposition. The intensity of the antisymmetric NO(2) stretching band of p-NTP relative to that of the symmetric stretch was significantly higher for p-NTP adsorbed on copper than on silver nanofilms, indicating that the C(2) axis of the aromatic ring is tilted with respect to the copper surface.
RESUMEN
The catalytic activity of silver nanoparticles (AgNPs) on a germanium substrate is reported. Para-nitrothiophenol (pNTP) that had been adsorbed on this substrate is converted to p-aminothiophenol (pATP) under very mild reaction conditions, such as simply soaking in water. The AgNPs may be formed either by physical vapor deposition or by electroless deposition from a solution of silver nitrate. Analogous reactions were not observed on copper nanoparticles on germanium or AgNPs on silicon or zinc selenide even though very slow conversion of pNTP to pATP was observed with Au nanoparticles (AuNPs) on Ge under controlled reaction conditions. The effects of factors that could influence the catalytic reaction were examined; these included the particle size of the AgNPs, reaction temperature, concentration and chemical nature of other ions present in the solution, the pH of the water, and the nature of the substrate. The reaction rate was approximately independent of the particle size for AgNPs between 50 and 150 nm in diameter. Increasing the temperature accelerates the reaction significantly; at temperatures above 40 °C, the adsorbed pNTP is completely converted by water within five minutes. Not surprisingly, the reaction rate was increased as the pH of the solution was decreased, as the reduction of each nitro group to an amino group requires six protons. The presence of Br(-) and I(-) ions accelerated the reaction to the point that even at 4 °C, the conversion of the nitro group was still observable, while solutions containing chloride ions had to be heated to 40 °C before their effect became apparent. Apparently, Br(-) and I(-) ions remove the oxide layer from the surface of the germanium substrate, facilitating transfer of electrons from the germanium to the nitro group of the pNTP.