• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1622-1622
• /
• 2001

The purpose of this research was to develop a the reflection technique with near infrared (NIR) radiation for estimating physico-chemical properties in compost. The composts (cattle, pig, chicken and waste composts) were air dried and then ground to pass through a 0.5 or 2mm sieve for the physico-chemical properties and spectroscopic determinations. The physico-chemical properties of compost were shown high values ; moisture(30-60%), T-N(0.8-2.9%), organic matter(29-89%), pH(5.89-9.60) K$_2$O(0.27-5.66%), P2O$\sub$5/(0.07-2.62%), CaO(0.03-4.80%), MgO(0.09-1.56%), NaCl(0.01-1.13%), EC(1.41-13.76dS/m). Generally, we should select a simple calibration and prediction method for determining physico-chemical properties in compost under similar accuracy and precision of prediction. It should be remembered that the NIRS approach will never replace the traditional methods. However, NIRS technique may be an effective method for rapid and nondestructive measurements of a large number of compost samples. Near infrared reflectance spectra of composts was obtained by Infra Alyzer 500 scanning spectrophotometer at 2-nm intervals from 1100 to 2500nm. Multiple linear regression(MLR) or partial least square regression (PLSR) was used to evaluate a NIRS method for the rapid and nondestructive determination of physico-chemical properties and humic acid contents in composts. The standard error of prediction(SEP) for finely sized sample(<0.5mm) and coarsely sized sample(<2mm) did not show much difference. The NIR instrument of filter type showed the same accuracy of the monochromator scanning type to estimate the compost properties. The results summarized that NIR spectroscopy can be used as a routine testing method to determine quantitatively the OM, moisture, T-N, color, pH, cation content in the compost samples nondestructively.

• Korean Journal of Food Science and Technology
• /
• v.26 no.5
• /
• pp.579-584
• /
• 1994

Near infrared reflectance(NIR) spectroscopy was used to determine the degree of retrogradation of cooked rice. Cooked rice samples were stored at $4^{\circ}C$ for 120 hours, and the degree of retrogradation was measured at every 6 hour during the storage time. Stored cooked rices were freeze-dried, milled and passed through a 100 mesh sieve. Enzymatic method using glucoamylase was used as reference method for the determination of the degree of retrogradation. Spectral differences due to retrogradation of cooked rice were observed at 1434, 1700, 1928, 2100, 2284 and 2320 nm. 32 samples of which moisture content were below 5% were used for calibration set, and 16 samples were used for validation set. High correlations were achieved between degree of retrogradation determined by conventional enzymatic method and by NIR with multiple correlation coefficient of 0.9753, and a standard error of calibration(SEC) of 3.64%. Comparable results were obtained with 3.91% of standard error of prediction(SEP), when the calibration equation was applied to independent group of samples of which moisture contents were in the range of calibration set. But when the calibration equation was applied to samples of which moisture contents were outer range of calibration set, SEP and bias were increased and correlation coefficient was decreased. The determination of degree of retrogradation was affected by sample moisture content. To determine degree of retrogradation of cooked rice by NIR using this calibration equation, it was suggested that sample moisture content should be controlled to below 5%.

• Korean Journal of Food Science and Technology
• /
• v.26 no.6
• /
• pp.718-725
• /
• 1994

The applicability of near infrared reflectance spectroscopy (NIRS) to determine moisture, protein, fat and amylose content of domestic rice was studied. The standard error of prediction (SEP) of moisture, protein, fat and amylose in polished rice was 0.014, 0.196, 0.098 and 1.427%, and those SEP of brown rice was 0.12, 1.226, 0.153 and 1.923%, respectively. It is concluded that the NIRS method allowed to detect the content of moisture and protein in rice samples with fair precision comparing conventional analysis, but the accuracy for determining amylose and fat was not acceptable.

• Korean Journal of Food Science and Technology
• /
• v.16 no.4
• /
• pp.437-442
• /
• 1984

The color changes and sorption characteristics of dried whole red pepper stored at various relative humidity and temperature were studied. Dried whole red pepper was browned at relative humidity above 67%, and was molded at relative humidity above 84%, and was decolorated at relative humidity below 32%. Therefore, about 50% RH condition was suitable for the preservation of dried whole red pepper and the safe moisture content levels for storge to prevent decolorization were ranging from 15.65% to 19.62% dry basis (DB) with varying temperatures. The moisture contents of monolayer value for the dried whole red pepper were ranging from 7.52% to 9.23% (DB) with varying temperatures. The third order regression equation for the equilibrium moisture content prediction with relative humidity was determined.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1032-1032
• /
• 2001

Artificial Neural Network (ANN) calibration techniques have been used commercially for agricultural applications since the mid-nineties. Global models, based on transmission data from 850 to 1050 nm, are used routinely to measure protein and moisture in wheat and barley and also moisture in triticale, rye, and oats. These models are currently used commercially in approx. 15 countries throughout the world. Results concerning earlier European ANN models are being published elsewhere. Some of the findings from that study will be discussed here. ANN models have also been developed for coarsely ground samples of compound feed and feed ingredients, again measured in transmission mode from 850 to 1050 nm. The performance of models for pig- and poultry feed will be discussed briefly. These models were developed from a very large data set (more than 20,000 records), and cover a very broad range of finished products. The prediction curves are linear over the entire range for protein, fat moisture, fibre, and starch (measured only on poultry feed), and accuracy is in line with the performance of smaller models based on Partial Least Squares (PLS). A simple bias adjustment is sufficient for calibration transfer across instruments. Recently, we have investigated the possible use of ANN for a different type of NIR spectrometer, based on reflectance data from 1100 to 2500 nm. In one study, based on data for protein, fat, and moisture measured on unground compound feed samples, dedicated ANN models for specific product classes (cattle feed, pig feed, broiler feed, and layers feed) gave moderately better Standard Errors of Prediction (SEP) compared to modified PLS (MPLS). However, if the four product classes were combined into one general calibration model, the performance of the ANN model deteriorated only slightly compared to the class-specific models, while the SEP values for the MPLS predictions doubled. Brix value in molasses is a measure of sugar content. Even with a huge dataset, PLS models were not sufficiently accurate for commercial use. In contrast an ANN model based on the same data improved the accuracy considerably and straightened out non-linearity in the prediction plot. The work of Mr. David Funk (GIPSA, U. S. Department of Agriculture) who has studied the influence of various types of spectral distortions on ANN- and PLS models, thereby providing comparative information on the robustness of these models towards instrument differences, will be discussed. This study was based on data from different classes of North American wheat measured in transmission from 850 to 1050 nm. The distortions studied included the effect of absorbance offset pathlength variation, presence of stray light bandwidth, and wavelength stretch and offset (either individually or combined). It was shown that a global ANN model was much less sensitive to most perturbations than class-specific GIPSA PLS calibrations. It is concluded that ANN models based on large data sets offer substantial advantages over PLS models with respect to accuracy, range of materials that can be handled by a single calibration, stability, transferability, and sensitivity to perturbations.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.40 no.2
• /
• pp.139-145
• /
• 2016

In this study, the moisture related hygroscopic characteristics and mechanical properties of epoxy-clay nanocomposites were investigated by experiments as a function of the weight fraction of nanoclay. The hygroscopic and mechanical properties including the moisture saturation amount, moisture diffusivity, adhesive strength, and tensile properties were obtained by moisture absorption test and various tensile tests, respectively. Also, the molecular dynamics (MD) simulation was devised to study of hygroscopic characteristics of nanocomposites and the results were compared to experimental results as a function of the nanoclay content. It was demonstrated that the proposed MD simulation technique can be successfully used for the prediction of the effects of the nanoclay on the moisture diffusion characteristics.

• Asian Journal of Turfgrass Science
• /
• v.23 no.1
• /
• pp.77-90
• /
• 2009

Near Infrared Reflectance Spectroscopy(NIRS) is a quick, accurate, and non-destructive method to measure multiple nutrient components in plant leaves. This study was to acquire a liner regression equation by evaluating the nutrient contents of 'CY2' creeping bentgrass rapidly and accurately using NIRS. In particular, nitrogen fertility is a primary element to keep maintaining good quality of turfgrass. Nitrogen, moisture, carbohydrate, and starch were assessed and analyzed from 'CY2' creeping bentgrass clippings. A linear regression equation was obtained from accessing NIRS values from NIR spectrophotometer(NIR system, Model XDS, XM-1100 series, FOSS, Sweden) programmed with WinISI III project manager v1.50e and ISIscan(R) (Infrasoft International) and calibrated with laboratory values via chemical analysis from an authorized institute. The equation was formulated as MPLS(modified partial least squares) analyzing laboratory values and mathematically pre-treated spectra. The accuracy of the acquired equation was confirmed with SEP(standard error of prediction), which indicated as correlation coefficient($r^2$) and prediction error of sample unacquainted, followed by the verification of model equation of real values and these monitoring results. As results of monitoring, $r^2$ of nitrogen, moisture, and carbohydrate in 'CY2' creeping bentgrass was 0.840, 0.904, and 0.944, respectively. SEP was 0.066, 1.868, and 0.601, respectively. After outlier treatment, $r^2$ was 0.892, 0.925, and 0.971, while SEP was 0.052, 1.577, and 0.394, respectively, which totally showed a high correlation. However, $r^2$ of starch was 0.464, which appeared a low correlation. Thereof, the verified equation appearing higher $r^2$ of nitrogen, moisture, and carbohydrate showed its higher accuracy of prediction model, which finally could be put into practical use for turf management system.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.34 no.4
• /
• pp.277-282
• /
• 2014

Nutritive value analysis of feed is very important for the growth of livestock, and ensures the efficiency of feeds as well as economic status. However, general laboratory analyses require considerable time and high cost. Near-infrared reflectance spectroscopy (NIRS) is a spectroscopic technique used to analyze the nutritive values of seeds. It is very effective and less costly than the conventional method. The sample used in this study was a corn kernel and the partial least square regression method was used for evaluating nutrient composition, digestibility, and energy value based on the calibration equation. The evaluation methods employed were the coefficient of determination ($R^2$) and the root mean squared error of prediction (RMSEP). The results showed the moisture content ($R^2_{val}=0.97$, RMSEP=0.109), crude protein content ($R^2_{val}=0.94$, RMSEP=0.212), neutral detergent fiber content ($R^2_{val}=0.96$, RMSEP=0.763), acid detergent fiber content ($R^2_{val}=0.96$, RMSEP=0.142), gross energy ($R^2_{val}=0.82$, RMSEP=23.249), in vitro dry matter digestibility ($R^2_{val}=0.68$, RMSEP=1.69), and metabolizable energy (approximately $R^2_{val}$ >0.80). This study confirmed that the nutritive components of corn kernels can be predicted using near-infrared reflectance spectroscopy.

• Journal of the Korean Wood Science and Technology
• /
• v.44 no.3
• /
• pp.370-380
• /
• 2016

The black pine logs damaged by pine wilt disease in Jeju-do were heat-treated to extend the utilization of domestic trees damaged by pine wilt disease. The heat-treatment of wood requires wood to be heated to $56^{\circ}C$ for 30 min at the core. The average moisture content and top-diameter of the black pine logs were ranged from 46% to 141% and from 180 mm to 500 mm, respectively. And the basic specific gravity and oven-dry specific gravity of the black pine logs were 0.47 and 0.52, respectively. The time required for heat-treatment at $105^{\circ}C$ temperature was ranged from 7.7 h to 44.2 h, depending on moisture content and top-diameter. The temperature distribution was used to predict the time required for heat-treatment of black pine log with various moisture contents and top-diameters using finite difference method. The thermal properties of wood including the thermal conductivity and specific heat in accordance with moisture content were calculated. Heat transfer coefficient for mixed convection in form of adding natural convection and forced convection was used for heat transfer analysis. The error between the measured and predicted values ranged from 3% to 45%. The predicted times required for heat-treatment of black pine log with 50% moisture content and 200 mm, 300 mm, and 400 mm top-diameter were 10.9 h, 18.3 h, and 27.0 h, respectively. If the initial moisture content of black pine log is 75%, heat treatment times of 13.6 h, 22.5 h, and 32.8 h were predicted in accordance with top-diameter. And if the initial moisture content of black pine log is 100%, heat treatment times of 16.2 h, 26.5 h, and 38.2 h were predicted in accordance with top-diameter. When the physical properties of logs damaged by pine wilt disease are presented, these results can be applicable to the heat-treatment of red pine and Korean pine logs as well.

• Journal of the Korea Concrete Institute
• /
• v.14 no.6
• /
• pp.813-822
• /
• 2002

The purpose of the present study is first to refine the mathematical material models for moisture and temperature distributions in early-age concrete and then to incorporate those models into finite element procedure. The three dimensional finite element program developed in the present study can determine the degree of hydration, temperature and moisture distribution in hardening concrete. It is assumed that temperature and humidity fields are fully uncoupled and only the degree of hydration is coupled with two state variables. Mathematical formulation of degree of hydration Is based on the combination of three rate functions of reaction. The effect of moisture condition as well as temperature on the rate of reaction is considered in the degree of hydration model. In moisture transfer, diffusion coefficient is strongly dependent on the moisture content in pore system. Many existing models describe this phenomenon according to the composition of mixture, especially water to cement ratio, but do not consider the age dependency. Microstructure is changing with the hydration and thus transport coefficients at early ages are significantly higher because the pore structure in the cement matrix is more open. The moisture capacity and sink are derived from age-dependent desorption isotherm. Prediction of a moisture sink due to the hydration process, i.e. self-desiccation, is related to autogenous shrinkage, which may cause early-age cracking in high strength and high performance concrete. The realistic models and finite element program developed in this study provide fairly good results on the temperature and moisture distribution for early-age concrete and correlate very well with actual test data.