Search

Previous work showed total fat can be assessed rapidly and accurately by near infrared (NIR) reflectance spectroscopy in processed cereal food products. In this study, the potential of NIR spectroscopy for the rapid measurement of saturated, monounsaturated and polyunsaturated fat was investigated. Fatty acid composition was determined in ground cereal products using a modification of AOAC Method 996.01 and reflectance spectra obtained with a dispersive NIR instrument. Modified partial least squares models were calculated for the prediction of lipid classes using multivariate analysis software. Models predicted saturated, monounsaturated and polyunsaturated fatty acids in separate validation samples with sufficient accuracy for screening samples ( RPDs of 3.5–4.2).

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, β-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 β-glucan. The level of precision of the model developed for each component was sufficient for screening or classification of whole kernel barley, except for β-glucan. The higher resolution Fourier transform instrument gave better results than the lower resolution instrument for starch and β-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 β-glucan, where second-derivative processing was more informative and precise.

Flax fibers may be blended with cotton to provide an aesthetic property, improve performance and tailor fabric properties. The quality and cost of the woven fabric blends are affected by the amount of linen in the blend. Microscopic and chemical analyses are currently used to determine linen content in fabrics. This study describes a method to predict the linen percentage in linen/cotton blends using Fourier transform near-infrared (FT-NIR) spectroscopy, rapidly and non-invasively. A calibration model using partial least squares regression analysis was developed with gravimetrically measured ground flax-cotton fiber mixtures as reference samples versus NIR spectra. The best model occurred with a combination of multiplicative scatter correction and first derivative processing of the spectral data gave a standard error of validation of 2.20%, and only one factor was used for the model performance. Using this model, flax content was predicted in specific mixtures of flax and cotton fibers, blended flax-cotton yarns, and various non-scoured flax-cotton fabrics, giving standard errors of prediction less than 3%. Application of the calibration model to the scoured fabric, however, resulted in a higher error value. This result seemed to be due to loss in wax components and substantial changes in the NIR absorbance values of the fabric resulting from the scouring process. An alternative calibration model for scoured and dyed fabrics was developed, and using the model it was possible to predict flax contents in dyed fabrics with an error of 4–6%.

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. Published in 2005 for SCI by John Wiley & Sons, Ltd.

Flax must be retted, in which bast fibres are separated from non-fibre components, and then mechanically processed to clean the fibres before industrial application. In the USDA Flax Fiber Pilot Plant, flax is first cleaned through four separate modules and then passed through a Shirley Analyzer to further clean fibres for high-value applications such as textiles. Often, multiple passages through the Shirley Analyzer are employed to obtain higher quality fibres, but it is difficult to determine when the limit for cleanliness is reached by this method. Further, it is clear that materials other than the woody shive components are being removed by Shirley-cleaning, and a method is needed to assess cleanliness beyond the measure for shives. In this study, we attempted to establish an index to determine the degree of purity of flax fibre during the secondary cleaning stage for high quality fibre. Dew-retted (DR) flax and enzyme-retted (ER) flax, which had been first processed through the USDA Flax Fiber Pilot Plant and assessed for shive content, were processed with 10 repetitions of cleaning through the Shirley Analyzer. For both flax samples, absorbances at 1730, 1766, 2312 and 2350 nm decreased with successive Shirley-cleaning steps. These wavelengths appeared to originate from the epidermal layer (EL) that was associated with the flax fibre, an index was calculated using 11 training samples and validated using 10 independent test samples from the same flax samples. Index values gradually decreased with successive Shirley-cleaning steps for both retted flax samples; a lower index value indicated cleaner fibre. Different curves were apparent for the two flax samples, suggesting variations in the cleanliness of the starting material or perhaps differencess in fibre composition. The results suggest it is possible to determine the extent of cleaning of flax fibre using NIR spectroscopy beyond that for shive content based on the epidermal layer of the plant.

Shive is the main contaminant in flax fibre and affects fibre quality. In this study, we developed a calibration for determining shive content in flax using near infrared (NIR) spectroscopy and applied the model to pilot plant processed flax to predict shive content. The model based on “ground” mixtures performed best from multiplicative scatter correction after a second derivative treatment of the spectral data, giving a standard error of cross-validation of 0.35% using five factors. Prediction samples were Jordan enzyme-(ER) and Natasja dew-retted (DR) flax that was collected after various stages of processing. When the model was applied to the “ground” flax, a high correlation was obtained between the NIR predicted value and actual shive content, giving a correlation coefficient of > 0.98 for both retted flax samples. However, when the model was applied to the “as-is” flax, a slope and bias were observed. These deviations were corrected by a linear regression between predicted values of “ground” and “as-is” flax. For the NIR analysis of ER flax, the shive content decreased rapidly by the third processing step to 4 to 5% and almost 0% after the last step. For the DR flax, the shive content continuously decreased with processing to about 5% after the last step. The results indicate that it is possibile to measure shive in flax on a commercial processing line.

The use of the derivative method for near-infrared (NIR) calibration was investigated to determine protein and amylose content in rice flour. Samples for two years, 1996 and 1999, were combined to give a wide range of the constituents for development of the calibration model. The NIR spectral data were transformed with Savitzky-Golay derivative with multiplicative scatter correction. To develop the best derivative models, the polynomial fits (quadratic, cubic, and quartic), convolution intervals (3–11 points for protein, 3–17 points for amylose), and derivative orders (1st derivative D1; 2nd derivative D2) were investigated. For the protein analysis, all polynomial fits with 3–11 points were acceptable to develop both the D1 and D2 models. However, the three-point quadratic and five-point quartic fits were not acceptable for the D1 model, and the three-point quadratic fit was not acceptable for D2. For the amylose analysis, the D1 model produced generally better results than D2. Higher convolutio...

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.

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 (λex) and emission (λem) wavelengths for individual bacteria with the wavelength interval (Δλ) being varied from 10 to 200 nm. The synchronous spectra and two-dimensional plots showed two maximum λex values at 225 nm and 280 nm and one maximum λem at 335–345 nm (λem=λex + Δλ), which correspond to the λex=225 nm, Δλ=110–120 nm, and λex=280 nm, Δλ=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 103–104 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 methology is rapid (

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 FT-NIR spectrometer (10000 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, β-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 β-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 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.

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.

The classification of cereals using near-infrared Fourier transform Raman (NIR-FT/Raman) spectroscopy was accomplished. Cereal-based food samples (n = 120) were utilized in the study. Ground samples were scanned in low-iron NMR tubes with a 1064 nm (NIR) excitation laser using 500 mW of power. Raman scatter was collected using a Ge (LN(2)) detector over the Raman shift range of 202.45 approximately 3399.89 cm(-1). Samples were classified based on their primary nutritional components (total dietary fiber TDF, fat, protein, and sugar) using principle component analysis (PCA) to extract the main information. Samples were classified according to high and low content of each component using the spectral variables. Both soft independent modeling of class analogy (SIMCA) and partial least squares (PLS) regression based classification were investigated to determine which technique was the most appropriate. PCA results suggested that the classification of a target component is subject to interference by other components in cereal. The Raman shifts that were most responsible for classification of each component were 1600 approximately 1630 cm(-1) for TDF, 1440 and 2853 cm(-1) for fat, 2910 and 1660 cm(-1) for protein, and 401 and 848 cm(-1) for sugar. The use of the selected spectral region (frequency region) for each component produced better results than the use of the entire region in both SIMCA and PLS-based classifications. PLS-based classification performed better than SIMCA for all four components, resulting in correct classification of samples 85 approximately 95% of the time. NIR-FT/Raman spectroscopy represents a rapid and reliable method by which to classify cereal foods based on their nutritional components.