Miryeong Sohn

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 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.

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, 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.

A new approach for estimating purity of processed flax fibre by NIR spectroscopy

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.

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.

Fourier transform near-infrared spectroscopy for determining linen content in linen/cotton blend products

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%.

Prediction of shive content in pilot plant processed flax by near infrared reflectance spectroscopy

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.

NIR-FT/Raman spectroscopy for nutritional classification of cereal foods

ABSTRACT 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 (LN2) detector over the Raman shift range of 202.45~3399.89 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 ...

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.

Transfer of calibrations for barley quality from dispersive instrument to Fourier transform near-infrared instrument.

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