• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1246-1246
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• 2001

The quality of meat is highly variable in many properties. This variability originates from both animal production and meat processing. At the pre-slaughter stage, animal factors such as breed, sex, age contribute to this variability. Environmental factors include feeding, rearing, transport and conditions just before slaughter (Hildrum et al., 1995). Meat can be presented in a variety of forms, each offering different opportunities for adulteration and contamination. This has imposed great pressure on the food manufacturing industry to guarantee the safety of meat. Tissue and muscle speciation of flesh foods, as well as speciation of animal derived by-products fed to all classes of domestic animals, are now perhaps the most important uncertainty which the food industry must resolve to allay consumer concern. Recently, there is a demand for rapid and low cost methods of direct quality measurements in both food and food ingredients (including high performance liquid chromatography (HPLC), thin layer chromatography (TLC), enzymatic and inmunological tests (e.g. ELISA test) and physical tests) to establish their authenticity and hence guarantee the quality of products manufactured for consumers (Holland et al., 1998). The use of Near Infrared Reflectance Spectroscopy (NIRS) for the rapid, precise and non-destructive analysis of a wide range of organic materials has been comprehensively documented (Osborne et at., 1993). Most of the established methods have involved the development of NIRS calibrations for the quantitative prediction of composition in meat (Ben-Gera and Norris, 1968; Lanza, 1983; Clark and Short, 1994). This was a rational strategy to pursue during the initial stages of its application, given the type of equipment available, the state of development of the emerging discipline of chemometrics and the overwhelming commercial interest in solving such problems (Downey, 1994). One of the advantages of NIRS technology is not only to assess chemical structures through the analysis of the molecular bonds in the near infrared spectrum, but also to build an optical model characteristic of the sample which behaves like the “finger print” of the sample. This opens the possibility of using spectra to determine complex attributes of organic structures, which are related to molecular chromophores, organoleptic scores and sensory characteristics (Hildrum et al., 1994, 1995; Park et al., 1998). In addition, the application of statistical packages like principal component or discriminant analysis provides the possibility to understand the optical properties of the sample and make a classification without the chemical information. The objectives of this present work were: (1) to examine two methods of sample presentation to the instrument (intact and minced) and (2) to explore the use of principal component analysis (PCA) and Soft Independent Modelling of class Analogy (SIMCA) to classify muscles by quality attributes. Seventy-eight (n: 78) beef muscles (m. longissimus dorsi) from Hereford breed of cattle were used. The samples were scanned in a NIRS monochromator instrument (NIR Systems 6500, Silver Spring, MD, USA) in reflectance mode (log 1/R). Both intact and minced presentation to the instrument were explored. Qualitative analysis of optical information through PCA and SIMCA analysis showed differences in muscles resulting from two different feeding systems.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1263-1263
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• 2001

The amounts of organic matter present in soil and the rate of soil organic matter (SOM) turnover are influenced by agricultural management practice, such as rotation, tillage, forage plow down direct seeding and manure application. The amount of nutrients released from SOM is highly dependent upon the state of the organic matter. If it contains a large proportion of light fractions (low-density) more nutrients will be available to the glowing crops. However, if it contains mostly heavy fractions (high-density) that are difficult to breakdown, then lesser amounts of nutrients will be available. The state of the SOM and subsequent release of nutrients into the soil can be predicted by NIRS as long as a robust regression equation is developed. The NIRS method is known for its rapidity, convenience, simplicity, accuracy and ability to analyze many constituents at the same time. Our hypothesis is that the NIRS technique allows researchers to investigate fully and in more detail each field for the status of SOM, available moisture and other soil properties in Alberta soils for precision farming in the near future. One hundred thirty one (131) Alberta soils with various levels (low 2-6%, medium 6-10%, and high >10%) of organic matter content and most of dry land soils, including some irrigated soils from Southern Alberta, under various management practices were collected throughout Northern, Central and Southern Alberta. Two depths (0- 15 cm and 15-30 cm) of soils from Northern Alberta were also collected. These air-dried soil samples were ground through 2 mm sieve and scanned using Foss NIR System 6500 with transport module and natural product cell. With particle size above 150 microns only, the “Ludox” method (Meijboom, Hassink and van Noorwijk, Soil Biol. Biochem.27: 1109-1111, 1995) which uses stable silica, was used to fractionate SOM into light, medium and heavy fractions with densities of <1.13, 1.13-1.37 and >1.37 respectively, The SOM fraction with the particle size below 150 microns was discarded because practically, this fraction with very fine particles can't be further separated by wet sieving based on density. Total organic matter content, mechanical texture, ash after 375$^{\circ}C$, and dry matter (DM) were also determined by “standard” soil analysis methods. The NIRS regression equations were developed using Infra-Soft-International (ISI) software, version 3.11.

• Journal of Science Education
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• v.46 no.3
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• pp.255-265
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• 2022

The purpose of this study is to analyze and compare brain activation that appears in the self-regulation process of biology major and non-major college students in life science learning. The self-regulation task implemented a life science learning situation with the concept of biological classification. The brain activation of college students was measured and analyzed by fNIRS. In the assimilation process, bilateral FP and left DLPFC show significant activation, and the two groups show a difference in the left OFC activation related to motivation and reward. In the conflict process, the left DLPFC shows significantly lower activation in common, and the two groups show a difference in activation between BA 46, which is related to recent memory, and BA 47, which is related to long-term memory. In the accommodation process, a significantly high activation was found in right DLPFC in common, and the two groups show a difference in activation between right DLPFC and right FP. These areas are in the right frontal lobe area and are related to the understanding of life science knowledge. As a result of this study, it can be seen that the brain activation patterns of biology major and non-major college students are different in the self-regulation process. In addition, we will propose additional neurological studies on self-regulation and present systems and learning strategies that can be constructed in school settings.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.08a
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• pp.100-100
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• 2020

Wheat is mainly composed of carbohydrate but it contains a moderate amount of protein, which gives a very useful characteristics to flour food such as the unique elasticity and stickiness of the dough. We developed a calibration equation for analyzing crude protein content using Near Infrared Spectroscopy to quick analyze the crude protein content of wheat germplasm stored in the National Agrobiodiversity Center, RDA, Korea. The 1,798 wheat germplasms were used to draw up the calibration formula. The crude protein's interval distribution of 1,798 wheat germplasms used for the calibration was 7.04-20.84%, the average content was 13.2%, and standard deviation was 2.6%. The germplasms distribution was composed of a suitable group for the preparation of the calibration formula because the content distribution was a normal, excluding the 13.0-15.5% content section. In order to verify the applicability of the NIRS prediction model, we measured the crude protein content of the 300 wheat germplasms that were not used for the calibration using both Kjeldahl analysis and NIR spectrum. The analysis value calculated using each method were statistically processed, and the test results and statistical indicators of the predictive model were compared. As a result, The R² value of the optimized NIRS prediction model was 0.997, and the Standard error of Calibration value(SEC) was 0.132, and slope value was 1.000. With prediction model selection, compared to Kjeldahl method, R² values were 0.994(Kjeldahl), 0.998(NIRS), and the SEC value were 0.191 and 0.132, respectively, comparing the statistical indices of the forecast model. And slope value were 1.013, 1.000, respectively. The analysis of crude protein content by the NIRS predictive model developed by each statistical index showing similar figures is judged to show a high degree of correlation with the Kjeldahl analysis. The proven calibration equation will be used to measure the crude protein content of wheat germplasms held by the National Agrobiodiversity Center, and by dividing the wheat germplasms by their use according to the crude protein content, it will provide useful information to relevant researchers.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1389-1399
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• 2023

KOMPSAT-3/3A (K3/K3A) absolute radiometric calibration study was conducted based on a Field Line of sight Automated Radiance Exposure (FLARE) system. FLARE is a system, which has been developed by Labsphere, Inc. adopted a SPecular Array Radiometric Calibration (SPARC) concept. The FLARE utilizes a specular mirror target resulting in a simplified radiometric calibration method by minimizing other sources of diffusive radiative energies. Several targeted measurements of K3/3A satellites over a FLARE site were acquired during a field campaign period (July 5-15, 2021). Due to bad weather situations, only two observations of K3 were identified as effective samples and they were employed for the study. Absolute radiometric calibration coefficients were computed using combined information from the FLARE and K3 satellite measurements. Comparison between the two FLARE measurements (taken on 7/7 and 7/13) showed very consistent results (less than 1% difference between them except the NIR channel). When additional data sets of K3/K3A taken on Aug 2021 were also analyzed and compared with gain coefficients from the metadata which are used by current K3/K3A, It showed a large discrepancy. It is assumed that more studies are needed to verify usefulness of the FLARE system for the K3/3A absolute radiometric calibration.

• Journal of the Korea Organic Resources Recycling Association
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• v.32 no.1
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• pp.39-47
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• 2024

In this study, LiDAR-detective black material is synthesized by recycling silicon sludge (SS) that is generated from semiconductor manufacturing process, and its recognition is confirmed using two types of LiDAR sensors (MEMS and Rotating LiDAR). In detail, metal impurities on the surface of SS is removed, followed by coating of titanium dioxide (TiO₂) and subsequent chemical reduction to obtain SS-derived black TiO₂ (SS/bTiO₂) material. As-prepared SS/bTiO₂ is mixed with transparent paint to prepare hydrophilic black paints and applied to a glass substrate using a spray gun. SS/bTiO₂-based paint shows similar blackness (L^*=15.7) compared to commercial carbon black-based paint, and remarkable NIR reflectance (26.5R%, 905nm). Furthermore, MEMS and Rotating LiDAR have successfully detected the SS/bTiO₂-based paint. This is attributed to the occurrence of high reflection of light at the interface between the black TiO₂ and the silicon sludge according to the Fresnel's reflection principle. Hence, the new application field to effectively recycle silicon sludge generated in the semiconductor manufacturing process has been presented.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.spc1
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• pp.283-291
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• 2006

To investigate seed non destructive and fast determination technique utilizing near infrared reflectance spectroscopy (NIRs) for screening ultra high oleic (C18:1) and linoleic (C18:2) fatty acid content sesame varieties among genetic resources and lines of pedigree generations of cross and mutation breeding were carried out in National Institute of Crop Science (NICS). 150 among 378 landraces and introduced cultivars were released to analyse fatty acids by NIRs and gas chromatography (GC). Average content of each fatty acid was 9.64% in palmitic acid (C16:0), 4.73% in stearic acid (C18:0), 42.26% in oleic acid and 43.38% in linoleic acid by GC. The content range of each fatty acid was from 7.29 to 12.27% in palmitic, 6.49% from 2.39 to 8.88% in stearic, 12.59% of wider range compared to that of stearic and palmitic from 37.36 to 49.95% in oleic and of the widest from 30.60 to 47.40% in linoleic acid. Spectrums analyzed by NIRs were distributed from 400 to 2,500 nm wavelengths and varietal distribution of fatty acids were appeared as regular distribution. Varietal differences of oleic acid content good for food processing and human health by NIRs was 14.08% of which 1.49% wider range than that of GC from 38.31 to 52.39%. Varietal differences of linoleic acid content by NIRs was 16.41% of which 0.39% narrower range than that of GC from 30.60 to 47.01%. Varietal differences of oleic and linoleic acid content in NIRs analysis were appeared relatively similar inclination compared with those of GC. Partial least square regression (PLSR) among multiple variant regression (MVR) in NIRs calibration statistics was carried out in spectrum characteristics on the wavelength from 700 to 2,500 nm with oleic and linoleic acids. Correlation coefficient of root square (RSQ) in oleic acid content was 0.724 of which 72.4 percent of sample varieties among all distributed in the range of 0.570 percent of standard error when calibrated (SEC) which were considerably acceptable in statistic confidence significantly for analysis between NIRs and GC. Standard error of cross validation (SECV) of oleic acid was 0.725 of which distributed in the range of 0.725 percent standard error among the samples of mother population between analyzed value by NIRs analysis and analyzed value by GC. RSQ of linoleic acid content was 0.735 of which 73.5 percent of sample varieties among all distributed in the range of 0.643 percent of SEC. SECV of linoleic acid was 0.711 of which distributed in the range of 0.711 percent standard error among the samples of mother population between NIRs analysis and GC analysis. Consequently, adoption NIR analysis for fatty acids of oleic and linoleic instead that of GC was recognized statistically significant between NIRs and GC analysis through not only majority of samples distributed in the range of negligible SEC but also SECV. For enlarging and increasing statistic significance of NIRs analysis, wider range of fatty acids contented sesame germplasm should be kept on releasing additionally for increasing correlation coefficient of RSQ and reducing SEC and SECV in the future.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1153-1153
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• 2001

Directive 79/373/EEC on the marketing of compound feeding stuffs, provided far a flexible declaration arrangement confined to the indication of the feed materials without stating their quantity and the possibility was retained to declare categories of feed materials instead of declaring the feed materials themselves. However, the BSE (Bovine Spongiform Encephalopathy) and the dioxin crisis have demonstrated the inadequacy of the current provisions and the need of detailed qualitative and quantitative information. On 10 January 2000 the Commission submitted to the Council a proposal for a Directive related to the marketing of compound feeding stuffs and the Council adopted a Common Position (EC N$^{\circ}$/2001) published at the Official Journal of the European Communities of 2. 2. 2001. According to the EC (EC N$^{\circ}$ 6/2001) the feeds material contained in compound feeding stufs intended for animals other than pets must be declared according to their percentage by weight, by descending order of weight and within the following brackets (I :< 30%; II :> 15 to 30%; III :> 5 to 15%; IV : 2% to 5%; V: < 2%). For practical reasons, it shall be allowed that the declarations of feed materials included in the compound feeding stuffs are provided on an ad hoc label or accompanying document. However, documents alone will not be sufficient to restore public confidence on the animal feed industry. The objective of the present work is to obtain calibration equations fur the instanteneous and simultaneous prediction of the chemical composition and the percentage of ingredients of unground compound feeding stuffs. A total of 287 samples of unground compound feeds marketed in Spain were scanned in a FOSS-NIR Systems 6500 monochromator using a rectangular cup with a quartz window (16 $\times$ 3.5 cm). Calibration equations were obtained for the prediction of moisture ($R^2$= 0.84, SECV = 0.54), crude protein ($R^2$= 0.96, SECV = 0.75), fat ($R^2$= 0.86, SECV = 0.54), crude fiber ($R^2$= 0.97, SECV = 0.63) and ashes ($R^2$= 0.86, SECV = 0.83). The sane set of spectroscopic data was used to predict the ingredient composition of the compound feeds. The preliminary results show that NIRS has an excellent ability ($r^2$$\geq$ 0, 9; RPD $\geq$ 3) for the prediction of the percentage of inclusion of alfalfa, sunflower meal, gluten meal, sugar beet pulp, palm meal, poultry meal, total meat meal (meat and bone meal and poultry meal) and whey. Other equations with a good predictive performance ($R^2$$\geq$0, 7; 2$\leq$RPD$\leq$3) were the obtained for the prediction of soya bean meal, corn, molasses, animal fat and lupin meal. The equations obtained for the prediction of other constituents (barley, bran, rice, manioc, meat and bone meal, fish meal, calcium carbonate, ammonium clorure and salt have an accuracy enough to fulfill the requirements layed down by the Common Position (EC Nº 6/2001). NIRS technology should be considered as an essential tool in food Safety Programs.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.41 no.2
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• pp.207-214
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• 1996

This study was conducted to obtain fundamental information on forage sorghum breeding in forage crop field of Livestock Experiment Station at Suwon from 1986 to 1991. The charcterization of sorghum germplasm was performed through 1986 to 1987, and after parental lines were selected from diverse sorghum germplasm on the basis of flowering date, plant height and several morphological characters for forage sorghum Fl hybrids. The range of variation of 50% flowering date and plant height were greater in order of forage sorghum sudangrass and male sterile line of grain sorghum. The average flowering date was earlier in sudangrass and male sterile line of grain sorghum than forage sorghum lines from the tested sorghum germplasms. And the average plant height was tall in order of forage sorghum, sudangrass and male sterile lines of grain sorghum. There were remarkable morphological variations between sudangrass lines and male sterile lines of grain sorghum such as plant color, leaf midrib color, glume color, seed coat color, head compactness and shape, awns, grain covering and 100 seed weight.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.spc1
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• pp.29-32
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• 2005

This experiment aimed to estimate the effect of imperfect rice on rice palatability. Rice cultivar, Ilpumbyeo, was cultivated by direct-seeding on flooded paddy surface with 11 kg/10a nitrogen application. Palatability of harvested rice was measured by NIR spectroscope. Brown rice was divided according to their appearance namely, perfect, discolored, green-kerneled, and immature opaque with a composition ratio of $75.7\%,\;11.0\%,\;8.0\%,\;and\;5.3\%$ respectively. When the perfect brown rice was milled, the grain were composed of head, cracked, and white core & belly, at $64.7\%,\;25.3\%\;and\;10.0\%$ respectively. The milled rice of discolored brown rice had similar composition with the perfect rice. The milled green-kerneled vice, on the other hand, had $36\%$ head rice and $64\%$ white core & belly rice. The immature opaque brown rice, when milled, had $25.3\%$ white core & belly and $74.7\%$ damage & opaque rice. With the respect to grain quality, the viscosity of white core at belly rice and damaged & opaque rice was lower than that of head rice. In contrast, their protein content was a little higher than that of head rice. The palatability value of pure imperfect rice was much lower than head rice. The palatability value of damaged & opaque rice was the lowest among the imperfect rices. When mixed with head rice, the damaged & opaque rice impacted on the deterioration of vice palatability. Mixing $1\%$ each of white core at belly rice and damaged h opaque rice decreased the palatability value by $5\%$ as compared with the head rice.