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.

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.

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.

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.

Application of partial least-squares regression to near-infrared reflectance spectroscopic determination of shive content in flax.

Shive, the nonfiberous core portion of the stem, in flax fiber after retting is related to fiber quality. The objective of this study is to develop a standard calibration model for determining shive content in retted flax by using near-infrared reflectance spectroscopy. Calibration samples were prepared by manually mixing pure, ground shive and pure, ground fiber from flax retted by three different methods (water, dew, and enzyme retting) to provide a wide range of shive content from 0 to 100%. Partial least-squares (PLS) regression was used to generate a calibration model, and spectral data were processed using various pretreatments such as a multiplicative scatter correction (MSC), normalization, derivatives, and Martens' Uncertainty option to improve the calibration model. The calibration model developed with a single sample set resulted in a standard error of 1.8% with one factor. The best algorithm was produced from first-derivative processing of the spectral data. MSC was not effective processing for this model. However, a big bias was observed when independent sample sets were applied to this calibration model to predict shive content in flax fiber. The calibration model developed using a combination sample set showed a slightly higher standard error and number of factors compared to the model for a single sample set, but this model was sufficiently accurate to apply to each sample set. The best algorithm for the combination sample set was generated from second derivatives followed by MSC processing of spectral data and from Martens' Uncertainty option; it resulted in a standard error of 2.3% with 2 factors. The value of the digital second derivative centered at 1674 nm for these spectral data was highly correlated to shive content of flax and could form the basis for a simple, low-cost sensor for the shive or fiber content in retted flax.