• KOREAN JOURNAL OF CROP SCIENCE
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• v.40 no.6
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• pp.716-722
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• 1995

Near Infrared Reflectance Spectroscopy (NIRS) has been used as a tool for the rapid, accurate and nondestructive assay of small grain and forage quality analysis. The objective of this study was to establish the rapid, easy and accurate analysis method for major components of covered barley using NIRS system. NIRS used in this study was filter type instrument, Neotec 102. To obtain a useful calibration equation, standard regression between the data was analyzed by chemical analysis and by NIRS method. Standard errors of prediction (SEP) and simple correlations for unknown samples were calculated using obtained equation. SEPs for starch, $\beta$-glucan, protein and ash contents were 2.75%, 0.64%, 0.26% and 0.19%, respectively. The simple correlations for starch, $\beta$-glucan, protein and ash contents were 0.932, 0.588, 0.984 and 0.867, respectively. It was concluded that the NIRS method would be applicabl for the rapid determination of starch, protein and ash contents in barley grains.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.5
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• pp.451-457
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• 2006

The purpose of this study is to measure fundamental data required for the prediction of rice quality and to develop regression models to predict protein, amylose, moisture and fatty acid contents, and Toyo taste meter value (TTMV) of brown and polished rice from hulled rice NIR spectra. NIR spectra of hulled rice measured in transmittance mode (850-1050 nm) and in reflectance mode (400-2500 nm) were used to predicted chemical compositions of brown rice and polished rice. For most chemicals, the transmittance spectra could provide better calibration results than the reflectance ones. Beside the Toyo taste meter value (TTMV), the hulled rice spectra could predict chemical contents with the determination coefficients higher than 0.8. Spectra of hulled rice measured in transmittance mode could be used for the prediction of chemical compositions in brown rice and polished rice precisely. However, taste value of polished rice was a constituent that was hardly to be predicted.

• Analytical Science and Technology
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• v.16 no.5
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• pp.1041-1051
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• 2003

Neutron induced prompt gamma activation analysis (PGAA) offers a nondestructive, sensitive and relatively rapid method for the determination of trace and major elements and is proven to be convenient for online analysis of minerals, metals, coal, cement, petrochemical, coating, paper as well as many other materials and products. The technique has found many uses in medicine, industry, research, security and the detection of contraband items. This report reviews the present status and future trends of the PGAA techniques. Requirements for the system are neutron source, high resolution HPGe detectors with a high-voltage power supply, an amplifier, analog-to-digital converter, and a multichannel analyzer for the detection and measurement of prompt ${\gamma}$-ray emit form the neutron capture elements. Introducing a ${\gamma}$-${\gamma}$ coincidence system also improves the quality of the ${\gamma}$-ray spectrum by suppressing the background created from the Compton scattering of high energy prompt ${\gamma}$-rays. A PGAA system using a $^{252}Cf$ neutron source is currently under construction for the on-line measurement of several elements in aqueous samples at KAERI. The system can be applied for the detection of chemical weapons and explosives as well as various narcotics.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1031-1031
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• 2001

The mammary gland is made up of remarkably sensitive tissue, which has the capability of producing a large volume of secretion, milk, under normal or healthy conditions. When bacteria enter the gland and establish an infection (mastitis), inflammation is initiated accompanied by an influx of white cells from the blood stream, by altered secretory function, and changes in the volume and composition of secretion. Cell numbers in milk are closely associated with inflammation and udder health. These somatic cell counts (SCC) are accepted as the international standard measurement of milk quality in dairy and for mastitis diagnosis. NIR Spectra of unhomogenized composite milk samples from 14 cows (healthy and mastitic), 7days after parturition and during the next 30 days of lactation were measured. Different multivariate analysis techniques were used to diagnose the disease at very early stage and determine how the spectral properties of milk vary with its composition and animal health. PLS model for prediction of somatic cell count (SCC) based on NIR milk spectra was made. The best accuracy of determination for the 1100-2500nm range was found using smoothed absorbance data and 10 PLS factors. The standard error of prediction for independent validation set of samples was 0.382, correlation coefficient 0.854 and the variation coefficient 7.63%. It has been found that SCC determination by NIR milk spectra was indirect and based on the related changes in milk composition. From the spectral changes, we learned that when mastitis occurred, the most significant factors that simultaneously influenced milk spectra were alteration of milk proteins and changes in ionic concentration of milk. It was consistent with the results we obtained further when applied 2DCOS. Two-dimensional correlation analysis of NIR milk spectra was done to assess the changes in milk composition, which occur when somatic cell count (SCC) levels vary. The synchronous correlation map revealed that when SCC increases, protein levels increase while water and lactose levels decrease. Results from the analysis of the asynchronous plot indicated that changes in water and fat absorptions occur before other milk components. In addition, the technique was used to assess the changes in milk during a period when SCC levels do not vary appreciably. Results indicated that milk components are in equilibrium and no appreciable change in a given component was seen with respect to another. This was found in both healthy and mastitic animals. However, milk components were found to vary with SCC content regardless of the range considered. This important finding demonstrates that 2-D correlation analysis may be used to track even subtle changes in milk composition in individual cows. To find out the right threshold for SCC when used for mastitis diagnosis at cow level, classification of milk samples was performed using soft independent modeling of class analogy (SIMCA) and different spectral data pretreatment. Two levels of SCC - 200 000 cells/$m\ell$ and 300 000 cells/$m\ell$, respectively, were set up and compared as thresholds to discriminate between healthy and mastitic cows. The best detection accuracy was found with 200 000 cells/$m\ell$ as threshold for mastitis and smoothed absorbance data: - 98% of the milk samples in the calibration set and 87% of the samples in the independent test set were correctly classified. When the spectral information was studied it was found that the successful mastitis diagnosis was based on reviling the spectral changes related to the corresponding changes in milk composition. NIRS combined with different ways of spectral data ruining can provide faster and nondestructive alternative to current methods for mastitis diagnosis and a new inside into disease understanding at molecular level.