Fluorescence spectroscopy for rapid detection and classification of bacterial pathogens.

This study deals with the rapid detection and differentiation of Escherichia coli, Salmonella, and Campylobacter, which are the most commonly identified commensal and pathogenic bacteria in foods, using fluorescence spectroscopy and multivariate analysis. Each bacterial sample cultured under controlled conditions was diluted in physiologic saline for analysis. Fluorescence spectra were collected over a range of 200-700 nm with 0.5 nm intervals on the PerkinElmer Fluorescence Spectrometer. The synchronous scan technique was employed to find the optimum excitation (lambda(ex)) and emission (lambda(em)) wavelengths for individual bacteria with the wavelength interval (Deltalambda) being varied from 10 to 200 nm. The synchronous spectra and two-dimensional plots showed two maximum lambda(ex) values at 225 nm and 280 nm and one maximum lambda(em) at 335-345 nm (lambda(em) = lambda(ex) + Deltalambda), which correspond to the lambda(ex) = 225 nm, Deltalambda = 110-120 nm, and lambda(ex) = 280 nm, Deltalambda = 60-65 nm. For all three bacterial genera, the same synchronous scan results were obtained. The emission spectra from the three bacteria groups were very similar, creating difficulty in classification. However, the application of principal component analysis (PCA) to the fluorescence spectra resulted in successful classification of the bacteria by their genus as well as determining their concentration. The detection limit was approximately 10(3)-10(4) cells/mL for each bacterial sample. These results demonstrated that fluorescence spectroscopy, when coupled with PCA processing, has the potential to detect and to classify bacterial pathogens in liquids. The methodology is rapid (>10 min), inexpensive, and requires minimal sample preparation compared to standard analytical methods for bacterial detection.

Novel search algorithms for a mid-infrared spectral library of cotton contaminants.

During harvest, a variety of plant based contaminants are collected along with cotton lint. The USDA previously created a mid-infrared, attenuated total reflection (ATR), Fourier transform infrared (FT-IR) spectral library of cotton contaminants for contaminant identification as the contaminants have negative impacts on yarn quality. This library has shown impressive identification rates for extremely similar cellulose based contaminants in cases where the library was representative of the samples searched. When spectra of contaminant samples from crops grown in different geographic locations, seasons, and conditions and measured with a different spectrometer and accessories were searched, identification rates for standard search algorithms decreased significantly. Six standard algorithms were examined: dot product, correlation, sum of absolute values of differences, sum of the square root of the absolute values of differences, sum of absolute values of differences of derivatives, and sum of squared differences of derivatives. Four categories of contaminants derived from cotton plants were considered: leaf, stem, seed coat, and hull. Experiments revealed that the performance of the standard search algorithms depended upon the category of sample being searched and that different algorithms provided complementary information about sample identity. These results indicated that choosing a single standard algorithm to search the library was not possible. Three voting scheme algorithms based on result frequency, result rank, category frequency, or a combination of these factors for the results returned by the standard algorithms were developed and tested for their capability to overcome the unpredictability of the standard algorithms' performances. The group voting scheme search was based on the number of spectra from each category of samples represented in the library returned in the top ten results of the standard algorithms. This group algorithm was able to identify correctly as many test spectra as the best standard algorithm without relying on human choice to select a standard algorithm to perform the searches.

Preparation of interesterified plastic fats from fats and oils free of trans fatty acid.

Interesterified plastic fats were produced with trans-free substrates of fully hydrogenated soybean oil, extra virgin olive oil, and palm stearin in a weight ratio of 10:20:70, 10:40:50, and 10:50:40, respectively, by lipase catalysis. The major fatty acids of the products were palmitic (32.2-47.4%), stearic (12.0-12.4%), and oleic acid (33.6-49.5%). After storage at 5 degrees C (refrigerator temperature) or 24 degrees C (room temperature) for 16 h, the physical properties were evaluated for solid fat content, texture, melting, and crystallization behavior, viscoelastic properties, crystal polymorphism, and crystal microstructure. The interesterified fats contained desirable crystal polymorphs (beta' form) as determined by X-ray diffraction spectroscopy. They exhibited a wide plastic range of solid fat content of 52-58% at 10 degrees C and 15% at 40 degrees C. The physical properties were influenced by the ratio of palm stearin and olive oil. Harder and more brittle texture, crystallization and melting at higher temperature, higher solid fat contents, and more elastic (G') or viscous (G') characteristics were observed in the produced fats containing a higher content of palm stearin and lower content of olive oil. The produced fats stored at 5 degrees C consisted mostly of beta' form crystal together with a small content of beta form, while those at 24 degrees C had only beta' form. The produced fat with a higher amount of palm stearin appeared to have more beta' form crystal and small size crystal clusters. Thus, the physical properties of the produced plastic fats may be desirable for use in a bakery product.

Near-infrared analysis of whole kernel barley: comparison of three spectrometers.

This study was conducted to develop calibration models for determining quality parameters of whole kernel barley using a rapid and nondestructive near-infrared (NIR) spectroscopic method. Two hundred and five samples of whole barley grains of three winter-habit types (hulled, malt, and hull-less) produced over three growing seasons and from various locations in the United States were used in this study. Among these samples, 137 were used for calibration and 68 for validation. Three NIR instruments with different resolutions, one Fourier transform instrument (4 cm(-1) resolution), and two dispersive instruments (8 nm and 10 nm bandpass) were utilized to develop calibration models for six components (moisture, starch, beta-glucan, protein, oil, and ash) and the results were compared. Partial least squares regression was used to build models, and various methods for preprocessing of spectral data were used to find the best model. Our results reveal that the coefficient of determination for calibration models (NIR predicted versus reference values) ranged from 0.96 for moisture to 0.79 for beta-glucan. The level of precision of the model developed for each component was sufficient for screening or classification of whole kernel barley, except for beta-glucan. The higher resolution Fourier transform instrument gave better results than the lower resolution instrument for starch and beta-glucan analysis. The starch model was most improved by the increased resolution. There was no advantage of using a higher resolution instrument over a lower resolution instrument for other components. Most of the components were best predicted using first-derivative processing, except for beta-glucan, where second-derivative processing was more informative and precise.

Near-infrared analysis of ground barley for use as a feedstock for fuel ethanol production.

The objective of this study was to explore the potential of near-infrared spectroscopy for determining the compositional quality properties of barley as a feedstock for fuel ethanol production and to compare the prediction accuracy between calibration models obtained using a Fourier transform near-infrared system (FT-NIR) and a dispersive near-infrared system. The total sample set contained 206 samples of three types of barley, hull-less, malt, and hulled varieties, which were grown at various locations in the eastern U.S. from 2002 to 2005 years. A new hull-less barley variety, Doyce, which was specially bred for potential use in ethanol production, was included in the sample set. One hundred and thirty-eight barley samples were used for calibration and sixty-eight were used for validation. Ground barley samples were scanned on both a FTNIR spectrometer (10 000 to 4000 cm(-1) at 4 cm(-1) resolution) and a dispersive NIR spectrometer (400 to 2498 nm at 10 nm resolution), respectively. Six grain components, moisture, starch, beta-glucan, protein, oil, and ash content, were analyzed as parameters of barley quality. Principal component analysis showed that barley samples could be classified by their types: hull-less, malt, and hulled. Partial least squares regression indicated that both FT-NIR and dispersive NIR spectroscopy have the potential to determine quality properties of barley with an acceptable accuracy, except for beta-glucan content. There was no predictive advantage in using a high-resolution FT-NIR instrument over a dispersive system for most components of barley.

The PARVUS gene is expressed in cells undergoing secondary wall thickening and is essential for glucuronoxylan biosynthesis.

Xylan, cellulose and lignin are the three major components of secondary walls in wood, and elucidation of the biosynthetic pathway of xylan is of importance for potential modification of secondary wall composition to produce wood with improved properties. So far, three Arabidopsis glycosyltransferases, FRAGILE FIBER8, IRREGULAR XYLEM8 and IRREGULAR XYLEM9, have been implicated in glucuronoxylan (GX) biosynthesis. In this study, we demonstrate that PARVUS, which is a member of family GT8, is required for the biosynthesis of the tetrasaccharide primer sequence, beta-D-Xyl-(1 --> 3)-alpha-l-Rha-(1 --> 2)-alpha-D-GalA-(1 --> 4)-D-Xyl, located at the reducing end of GX. The PARVUS gene is expressed during secondary wall biosynthesis in fibers and vessels, and its encoded protein is predominantly localized in the endoplasmic reticulum. Mutation of the PARVUS gene leads to a drastic reduction in secondary wall thickening and GX content. Structural analysis of GX using (1)H-nuclear magnetic resonance (NMR) spectroscopy revealed that the parvus mutation causes a loss of the tetrasaccharide primer sequence at the reducing end of GX and an absence of glucuronic acid side chains in GX. Activity assay showed that the xylan xylosyltransferase and glucuronyltransferase activities were not affected in the parvus mutant. Together, these findings implicate a possible role for PARVUS in the initiation of biosynthesis of the GX tetrasaccharide primer sequence and provide novel insights into the mechanisms of GX biosynthesis.

ATR-Fourier transform mid-infrared spectroscopy for determination of trans fatty acids in ground cereal products without oil extraction.

Fourier transform mid-infrared (FT-IR) spectroscopy was investigated as a method of analysis for trans fatty acid content of cereal products without the need for prior oil extraction. Spectra were obtained, with an FT-IR spectrometer equipped with an attenuated total reflectance (ATR) device, of ground samples pressed onto the diamond ATR surface, and trans fatty acids were measured by a modification of AOAC Method 996.01. Partial least-squares (PLS) models were developed for the prediction of trans fatty acids in ground samples using several wavenumber selections on the basis of bands related to lipids. The models (n = 79) predicted trans fatty acids in ground samples with standard error of cross-validation (SECV) of 1.10-1.25 (range 0-12.4) % and R2 of 0.85-0.88 and in validation samples (n = 26) with standard error of performance (SEP) of 0.96-1.12 (range 0-12.2) % and r2 of 0.89-0.92, indicating sufficient accuracy for screening. Sample trans fatty acid % was predicted as accurately with the fingerprint region (1500-900 cm(-1)) as with the entire range (4000-650 cm(-1)) indicating, in concert with the regression coefficients, the importance of the isolated trans double bonds at 966 cm(-1) in development of the model. Data is also presented on prediction of trans fatty acids using the spectra of residual oil films on the ATR surface after removing the solid portion of the sample.

Transfer of near-infrared calibration model for determining fiber content in flax: effects of transfer samples and standardization procedure.

The transfer of a calibration model for determining fiber content in flax stem was accomplished between two near-infrared spectrometers, which are the same brand but which require a standardization. In this paper, three factors, including transfer sample set, spectral type, and standardization method, were investigated to obtain the best standardization result. Twelve standardization files were produced from two sets of the transfer sample (sealed reference standards and a subset of the prediction set), two types of the transfer sample spectra (raw and preprocessed spectra), and three standardization methods (direct standardization (DS), piecewise direct standardization (PDS), and double window piecewise direct standardization (DWPDS)). The efficacy of the model transfer was evaluated based on the root mean square error of prediction, calculated using the independent prediction samples. Results indicated that the standardization using the sealed reference standards was unacceptable, but the standardization using the prediction subset was adequate. The use of the preprocessed spectra of the transfer samples led to the calibration transfers that were successful, especially for the PDS and the DWPDS correction. Finally, standardization using the prediction subset and their preprocessed spectra with DWPDS correction proved to be the best method for transferring the model.

Molecular characterization of PoGT8D and PoGT43B, two secondary wall-associated glycosyltransferases in poplar.

Dicot wood is mainly composed of cellulose, lignin and glucuronoxylan (GX). Although the biosynthetic genes for cellulose and lignin have been studied intensively, little is known about the genes involved in the biosynthesis of GX during wood formation. Here, we report the molecular characterization of two genes, PoGT8D and PoGT43B, which encode putative glycosyltransferases, in the hybrid poplar Populus alba x tremula. The predicted amino acid sequences of PoGT8D and PoGT43B exhibit 89 and 75% similarity to the Arabidopsis thaliana IRREGULAR XYLEM8 (IRX8) and IRX9, respectively, both of which have been shown to be required for GX biosynthesis. The PoGT8D and PoGT43B genes were found to be expressed in cells undergoing secondary wall thickening, including the primary xylem, secondary xylem and phloem fibers in stems, and the secondary xylem in roots. Both PoGT8D and PoGT43B are predicted to be type II membrane proteins and shown to be targeted to Golgi. Overexpression of PoGT43B in the irx9 mutant was able to rescue the defects in plant size and secondary wall thickness and partially restore the xylose content. Taken together, our results demonstrate that PoGT8D and PoGT43B are Golgi-localized, secondary wall-associated proteins, and PoGT43B is a functional ortholog of IRX9 involved in GX biosynthesis during wood formation.

Development and use of an attenuated total reflectance/fourier transform infrared (ATR/FT-IR) spectral database to identify foreign matter in cotton.

The presence of foreign matter in cotton seriously affects the cotton grade and thus the price per bale paid by the spinner to the grower, the efficiency of the spinning and ginning operations, and the quality of the final woven product. Rapid identification of the nature of the extraneous matter in cotton at each stage of cleaning and processing is necessary to permit actions to eliminate or reduce its presence and improve efficiency and quality. Although several instruments are being successfully employed for the measurement of contamination in cotton fibers based on particle size/weight, no commercial instrument is capable of accurate qualitative identification of contaminants. To this end, ATR/FT-IR spectra of retrieved foreign matter were collected and subsequently rapidly matched to an authentic spectrum in a spectral database. The database includes contaminants typically classified as "trash", cotton plant parts (hull, shale, seed-coat fragments, bract, cacyx, leaf, bark, sticks, and stems) and grass plant parts (leaf and stem); "foreign objects and materials", synthetic materials (plastic bags, film, rubber, bale wrapping and strapping); organic materials (other fibers, yarns, paper, feathers, and leather); plus entomological and physiological sugars and inorganic materials (sand and rust). The spectral matching resulted in consistently high-score identification of the foreign matter based on chemical composition, irrespective of its particle size. The method is envisioned to be employed with stand-alone rugged infrared instrumentation to provide specific identification of extraneous materials in cotton as opposed to only general classification of the type by particle size or shape.

Corn stover fractions and bioenergy: chemical composition, structure, and response to enzyme pretreatment.

Information is presented on structure, composition, and response to enzymes of corn stover related to barriers for bioconversion to ethanol. Aromatic compounds occurred in most tissue cell walls. Ferulic acid esterase treatment before cellulase treatment significantly improved dry weight loss and release of phenolic acids and sugars in most fractions over cellulase alone. Leaf fractions were considerably higher in dry weight loss and released sugars with esterase treatment, but stem pith cells gave up the most phenolic acids. Results help identify plant fractions more appropriate for coproducts and bioconversion and those more suitable as residues for soil erosion control.

Partial least squares regression calibration for determining wax content in processed flax fiber by near-infrared spectroscopy.

The quality of flax fiber in the textile industry is closely related to the wax content remaining on the fiber after the cleaning process. Extraction by organic solvents, which is currently used for determining wax content, is very time consuming and produces chemical waste. In this study, near-infrared (NIR) spectroscopy was used as a rapid analytical technique to develop models for wax content associated with flax fiber. Calibration samples (n=11) were prepared by manually mixing dewaxed fiber and isolated wax to provide a range of wax content from 0 to 5%. A total of fourteen flax fiber samples obtained after a cleaning process were used for prediction. Principal component analysis demonstrated that one principal component is enough to separate the flax fibers by their wax content. The most highly correlated wavelengths were 2312, 2352, 1732, and 1766 nm, in order of significance. Partial least squares models were developed with various chemometric preprocessing approaches to obtain the best model performance. Two models, one using the entire region (1100-2498 nm) and the other using the selected wavelengths, were developed and the accuracies compared. For the model using the entire region, the correlation coefficient (R2) between actual and predicted values was 0.996 and the standard error of prediction (RMSEP) was 0.289%. For the selected-wavelengths model, the R2 was 0.997 and RMSEP was 0.272%. The results suggested that NIR spectroscopy can be used to determine wax content in very clean flax fiber and that development of a low-cost device, using few wavelengths, should be possible.