• Journal of The Korean Society of Grassland and Forage Science
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• v.34 no.3
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• pp.209-213
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• 2014

This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical and fermentation parameters of whole crop winter rye silages. A representative population of 216 fresh winter rye silages was used as database for studying the possibilities of NIRS to predict chemical composition and fermentation parameters. Samples of silage were scanned at 1 nm intervals over the wavelength range 680~2,500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in fresh condition. NIRS calibrations were developed by means of partial least-squares (PLS) regression. NIRS analysis of fresh winter rye silages provided accurate predictions of moisture, acid detergent fiber (ADF), neutral detergent fiber (NDF), crude protein (CP) and pH as well as lactic acid content with correlation coefficients of cross-validation ($R^2cv$) of 0.96, 0.86, 0.79, 0.85, 0.82 and 0.78 respectively and standard error of cross-validation (SECV) of 1.89, 2.02, 2.79, 1.14, 1.47 and 0.46 % DM respectively. Results of this experiment showed the possibility of NIRS method to predict the chemical parameters of winter rye silages as routine analysis method in feeding value evaluation and for farmer advice.

• Journal of The Korean Society of Grassland and Forage Science
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• v.33 no.3
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• pp.171-176
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• 2013

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages and feedstuff. This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical parameters of fresh whole crop barley silages. A representative population of 284 fresh whole crop barley silages was used as a database for studying the possibilities of NIRS to predict chemical composition. Samples of silage were scanned at 1 nm intervals over the wavelength range 680~2,500 nm and the optical data were recorded as log 1/Reflectance (log 1/R) and were scanned in fresh condition. NIRS calibrations were developed by means of partial least-squares (PLS) regression. NIRS analysis of fresh whole crop barley silages provided accurate predictions of moisture, acid detergent fiber (ADF), neutral detergent fiber (NDF), crude protein (CP) and pH, as well as lactic acid content with correlation coefficients of cross-validation ($R^2cv$) of 0.96, 0.81, 0.79, 0.84, 0.72 and 0.78, respectively, and standard error of cross-validation (SECV) of 1.26, 2.83, 2.18, 1.19, 0.13 and 0.32% DM, respectively. Results of this experiment showed the possibility of the NIRS method to predict the chemical parameters of fresh whole crop barley silages as a routine analysis method in feeding value evaluation and for farmer advice.

• Korean Journal of Food Science and Technology
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• v.25 no.5
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• pp.475-480
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• 1993

This study was carried out to estimate the bind values of raw meats which are used as an input constaint in the least-cost formulation of an emulsion-type sausage. The least-cost formulation will be useful for Korean meat processore to produce more effectively as meat-grade system is put in force. The analysis results in compositions, functionalities, and pigment contents of raw meats were various according to the difference of species and their parts. The cohesiveness was correlated positively with moisture or protein content and negatively with fat content. Consequently two multiple regression equations for bind value could be derived from the compositions of raw meats. The equations then may be useful for predicting the bind value of a raw meat which presently has not been analysed.

• Applied Biological Chemistry
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• v.35 no.4
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• pp.281-285
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• 1992

Composite seasioning was stored at different temperature with PE/Al/PE/PET packaging. $Q_{10}$ value due to temperature was 2.54 and shelf-lie of composite seasoning was predicted 29, 73 and 185 weeks at $35^{\circ}C$, $25^{\circ}C$ and $15^{\circ}C$, respectively. L value was decreased during storage but a and b value had little change. Correlation coefficient between sensory score and cole. value was low. Browning development and carbonyl value were increased with storage temperature and correlation coefficient between sensory score and those was comparatively high. Moisture content, salinity and total sugar content were within KS-standard during 18 weeks storage.

• Korean Journal of Food Science and Technology
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• v.31 no.6
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• pp.1557-1562
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• 1999

As a series of studies on the preservation methods for boiled-dried anchovies, determination of sorption properties and shelf-life prediction were made for the samples. Dried anchovies, which were gamma-irradiated at pre-established dose (5 kGy) after packaging in both a polyethylene film (PE, 0.1 mm) and a laminated film $(nylon\;15\;{\mu}m/polyethylene\;100\;{\mu}m,\;NY/PE)$, were subjected to a quality evaluation during 4 months at different storage conditions, such as $15^{\circ}C/68%\;RH,\;25{\circ}C/75%\;RH,\;and\;35^{\circ}C/84%$ RH. The sample showed 5.47% of BET monomolecular layer moisture content and the corresponding water activity, 0.15. The velocity constants of browning reaction and organoleptic changes in the sample were in proportion to storage temperature, and $Q_{10}$, values were ranged from 2.17 to 2.40 in a given packaging and irradiation conditions. In the shelf-life prediction of the stored sample at $25^{\circ}C$, non-irradiated groups packaged in PE and NY/PE were 84 days and 125 days. While 5 kGy-irradiated groups in the same packaging were 126 days and 138 days, respectively. This finding proved the efficacy of laminated-film packaging and irradiation treatment in preserving the quality of dried anchovies.

• Journal of Korean Society of Forest Science
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• v.54 no.1
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• pp.36-40
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• 1981

with the chemical equilibrium formula by Hailwood and Horrobin, $$m=a{\cdot}((k_1k_2h)(1+k_1k_2h)^{-1}+(k_2h)_n-k_2h)^{-1})$$, based on absorption theory, monthly equilibrium moisture content(EMC) variations in southern Korea were predicted. The results were as follows: $$k_1=47370272{\cdot}10^{-7}+477345{\cdot}10^{-7}t-502775{\cdot}10^{-8}t^2$$ $$k_2=705940864{\cdot}10^{-9}+16979472{\cdot}10^{-10}t-555336{\cdot}10^{-11}t^2$$ $$w=2233848{\cdot}10^{-4}+694242{\cdot}10^{-6}+185328{\cdot}10^{-7}t^2$$ Here, it is temperature degrees in Celsius, k is the equilibria between hydrate water and dissolved water, k is the equilibria between dissolved water and the water vapour pressure surrounding atmosphere, w is the molecular weight of the polymer unit that forms the hydrate, h is the relative vapour pressure, And the formula was well agreed with the data when the constant values ${\alpha}$ were given to be 2200 in January, February, October, November and December, 1850 in March, April and May, 1920 June, July, August, and September seasonally.

• Journal of The Korean Society of Grassland and Forage Science
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• v.31 no.4
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• pp.409-414
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• 2011

This study was conducted to find out an alternative way of rapid and accurate analysis of forage quality. Near Infrared Reflectance Spectroscopy (NIRS) was used to evaluate the possibility of forage analysis. 175 samples consisted of Italian ryegrass, whole crop barley and pea seeded spring in 2009 were collected. The samples were analyzed for moisture, crude protein (CP), crude ash (CA), acid detergent fiber (ADF), and neutral detergent fiber (NDF), and also scanned using NIRSystem with wavelength from 400~2,500 nm. Multiple linear regression was used with wet analysis data for developing the calibration model and validated unknown samples. The important index in this experiment were SEC, SEP. The r2 value for moisture, CP, CA, ADF, and NDF in calibration set was 0.65, 0.97, 0.93, 0.99, and 0.97 and also was 0.15, 0.94, 0.96, 0.98 and 0.98 in validation set, respectively. The results of this experiment indicates that NIRS was reliable analytical method to assess forage quality for CP, CA ADF and NDF except moisture content in forage when proper samples incorporated into the equation development.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.439-447
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• 2019

In this study, experiments were conducted to establish appropriate measures for slopes with high risk of collapse and to obtain results for minimizing slope collapse damage by detecting the micro-displacement of soil in advance by installing a laser sensor and a vibration sensor in the landslide reduction model experiment. Also, the behavior characteristics of the soil layer due to rainfall and moisture ratio changes such as pore water pressure and moisture were analyzed through a landslide reduction model experiment. The artificial slope was created using granite weathering soil, and the resulting water ratio(water pressure, water) changes were measured at different rainfall conditions of 200mm/hr and 400mm/hr. Laser sensors and vibration sensors were applied to analyze the surface displacement, and the displacement time were compared with each other by video analysis. Experiments have shown that higher rainfall intensity takes shorter time to reach the limit, and increase in the pore water pressure takes shorter time as well. Although the landslide model test does not fully reflect the site conditions, measurements of the time of detection of displacement generation using vibration sensors show that the timing of collapse is faster than the method using laser sensors. If ground displacement measurements using sensors are continuously carried out in preparation for landslides, it is considered highly likely to be utilized as basic data for predicting slope collapse, reducing damage, and activating the measurement industry.

• Korean Journal of Soil Science and Fertilizer
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• v.23 no.3
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• pp.167-172
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• 1990

Some of soil properties already known were selected for the determination of their effect on soil moisture characteristics. Total number of 2,808 representative samples from all over Korea with the exception of Jeju Island were investigated. 1. Available water contents were 4.7 for S, 7.7 for LS, 13.2 for SL, 17.7 for L, 19.2 for SiL, 15.9 for CL, 14.5 for SCL, 18.7 for SiCL, 17.3 for SiC, and 14.9% for C, respectively. 2. Simple regression analysis showed that field capacity and available water content were most strongly associated with sand content in coarse-textured soils, and with organic matter content in fine-textured soils, whereas permanent wilting point was closely associated with clay content. 3. Available water was strongly associated with silt content and also significantly with field capacity, but either not at all or negatively with permanent wilting point. 4. Prediction equations for available water and field capacity were drown out from known soil properties, which can be used for each textural class.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.