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.
To evaluate the feasibility of an intact product approach to the near-infrared (NIR) determination of fat content, a rapid acquisition spectrometer, with an InGaAs diode-array detector and custom built sampling device, was used to obtain reflectance spectra (1100-1700 nm) of diverse cereal food products. Fat content reference data were obtained gravimetrically by extraction with petroleum ether (AOAC Method 945.16). Using spectral and reference data, partial least-squares regression analysis was applied to calculate a NIR model (n = 89) to predict fat in intact cereal products; the model was adequate for rapid screening of samples, predicting the test samples (n = 44) with root mean square error of prediction (RMSEP) of 11.8 (range 1.4-204.8) g kg −1 and multiple coefficient of determination of 0.98. Repeated repacking and rescanning of the samples did not appreciably improve model performance. The model was expanded to include samples with a broad range of particle sizes and moisture contents without reduction in prediction accuracy for the untreated samples. The regression coefficients for the models calculated indicated that spectral features at 1165, 1215 and 1395 nm, associated with CH stretching in fats, were the most critical for model development.
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.
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