• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1613-1613
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• 2001

Fats and molasses are used, at the present time, in a considerable proportion as ingredients for the animal feed industry. They are mainly used as energy sources, but also they provide other characteristics of technological and nutritional interest (dust reduction, increase in palatability, etc). Both semi-liquid ingredients have numerous aspects in common from the point of view of their use in livestock feeds, as well as of their analytical control. Feed manufacturers use several criteria to evaluate the quality of fat and molasses. Furthermore, the traditional methods currently used, for their evaluation (eg. fatty acids, sugars, etc) are expensive and more sophisticated that the traditionally used for solid ingredients. The objective of the present work is to carry out a viability study to evaluate the ability of NIRS technology for the quality control of fat and molasses. Samples of liquid molasses (n = 42) and liquid fat ( n = 61), provided by a feed manufacturer, were scanned in a FOSS-NIR Systems 6500 monochromator equipped with a spinning module. The samples were analysed by folded transmission, using a sample cup of 0.1mm pathlength and gold surface reflector. For molasses, calibration equations were developed for the prediction of moisture (SECV=1.69%; $r^2$=0, 42), gross protein (SECV=0, 14%; $r^2$=0, 99), ashy (SECV=0, 60%; $r^2$=0, 84), NaCl (SECV=0, 05%; $r^2$=0, 99) and sugars (SECV=1, 04%; $r^2$=0, 86). For animal fats calibrations were obtained for the prediction of moisture (SECV=0, 14%, $r^2$=0, 88), acidity index (SECV=0, 83%, $r^2$=0, 82), MIU (SECV=0, 38%, $r^2$=0, 94) and unsaponifiables (SECV=0, 45%, $r^2$=0, 87). High accuracy calibration equations were also obtained for the prediction of the fatty acid profile. The equations have $r^2$values around 0.9 or highest. The results showed that NIRS technology could provide rapid and accurate results and reduce analytical costs associated to the quality control of two Important feed ingredients of a well known chemical variability.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1266-1266
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• 2001

Pasture composition, an important attribute determining sward condition and value, is normally assessed by hand separation, drying and measuring weight contribution of each species in the mixture. This is a tedious, time and labour consuming procedure. NIRS has demonstrated the potential for predicting botanical composition of swards, but most of the work has been carried out on dry samples. The aim of this work was to evaluate the feasibility of developing NIR models for predicting the white clover and ryegrass content in fresh or dry mixtures artificially prepared from pure samples of both species. Mixtures from pure stands of white clover(Trifolium repens) and perennial ryegrass (Lolium perenne) were prepared with different proportions (0 to 100%) of each species (fresh weight). A total of 55 samples were made (11 mixtures,5 cuts). Spectra (400 to 2500 nm) were taken from fresh chopped (rectangular cuvettes, transport sample module) samples, in a NIR Systems 6500 scanning monochromator controlled by the software NIRS 3 (Infrasoft International), which was also utilized for calibration development. Different math treatments (derivative order, subtraction gap and smooth segment) and a scatter correction treatment of the spectra (SNV and Detrend) were tested. Equations were developed by modified partial least squares. Prediction accuracy evaluated by cross-validation, showed that percentage of clover or ryegrass, as contribution in dry weight, can be successfully percentage of clover or ryegrass, as contribution in dry weight, can be successfully predicted either on fresh or dried samples, with equations developed by different math treatments. Best equations for fresh samples were developed including a first, second, or third derivative, whereas for dry samples best equations included a second or third derivative. Standard errors of ross validation were about 6% for fresh and 3.6% for dry samples, Coefficient of determination of cross validation (1-VR) were over 0.95 times the value of SECV for fresh samples and over 8 times the value of SECV for dry samples. Scatter correction (SNV and Detrend) in general improved prediction accuracy. It is concluded more precise on dried and ground samples, it can be used with an acceptable error level and less time and labour, on fresh samples.

• 한국초지조사료학회지
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• 제29권1호
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• pp.31-36
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• 2009

본 연구는 청예용 사초의 질을 신속하고 정확하게 측정할 수 있는 대안을 모색하기 위하여수행하였다. 근적외선분광분석법을 이용한 사초의 분석가능성을 타진하기 위해 2002년부터 2007년에 거쳐 생산된 각종 사초 258점을 시료로 사용하였다. 즉 시료는 조단백질, 조섬유, NDF, ADF 그리고 건물소화율을 분석하였으며, 또한 NIR System으로 $400{\sim}2,400nm$ 사이의 파장을 얻었다. 그리고 파장과 습식분석치를 이용하여 중회귀식을 만들고 이것을 사용하여 미지의 시료를 분석할 수 있는가를 검증하였다. 근적외선 분석법의 중요한 지표인 결정계수 $r^2$와 표준오차이며 본 실험의 결과 검증식의 $r^2$는 CP(crude protein), CF(crude fiber), ADF (acid detergent fiber), NDF (neutral detergent fiber) 그리고 IVTD(in vitro true digestibility)에서 각 각 0.70, 0.86, 0.94, 0.94 그리고 0.89였다. 검량식은 그 값이 0.47, 0.39, 0.89, 0.90 그리고 0.61이었다. 본 실험 결과 근적외선분광법에 의한 청예사료 질평가가 가능하며 특히 조섬유, ADF 그리고 진정인비트로 소화율 (IVTD)에서 유효할 것으로 나타났다. 그러나 보다 정확한 결과를 얻기 위해서는 시료는 한국사초의 대표성을 갖는 것을 수집하여 사용해야 할 것이다. 앞으로 더 많은 시료를 추가하면 모든 사초를 분석할 강고한 검량식이 작성될 수 있을 것으로 기대된다.

• 한국초지조사료학회지
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• 제38권1호
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• pp.53-60
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• 2018

본 연구는 혼파초지 수량예측모형에서 기후특성이 뚜렷한 지역의 자료 제거 및 지역별 구분을 통해 건물수량과 하고일수 간 상관관계를 검토하였다. 데이터세트는 총 582점으로 11개 지역으로 분류되며 혼파조합은 총 41가지였다. 변수에서 반응변수는 건물수량 이었으며 설명변수는 하고일수를 포함한 5가지의 기상변수를 이용하였다. 통계방법은 산점도, 기술통계량 및 상관분석을 거쳐 다중회귀분석을 통해 건물수량과 하고일수 간 상관관계를 확인하였다. 산점도 분석 결과 데이터세트를 지역별로 구분하였을 때 9개 지역 중 7개에서 건물수량과 하고일수 간 부(-)의 상관관계가 나타나 지역을 구분할 필요가 있었으며 대표본 근사이론을 적용할 수 있었던 5개 지역(화성, 수원, 대전, 시흥 및 광주)을 선정하였다. 5개 지역의 상관분석 결과 3개 지역(화성, 수원 및 시흥)에서, 다중회귀분석결과 화성에서 건물수량에 대한 하고일수의 효과가 부(-)로 나타났다. 따라서 혼파초지의 건물수량에 대한 하고일수의 상관관계는 지역별로 구분하였을 때 풀사료 생산이론과 일치하여 수량예측모형의 정밀도를 높일 수 있을 것으로 판단하였다.

• Nutrition Research and Practice
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• 제13권3호
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• pp.256-262
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• 2019

BACKGROUND/OBJECTIVES: The objectives of this study were to evaluate the accuracy of the Dietary Reference Intakes (DRI) for estimating the energy requirements of older adults, and to develop and validate new equations for predicting the energy requirements of this population group. MATERIALS/METHODS: The study subjects were 25 men and 23 women with a mean age of $72.2{\pm}3.9\;years$ and $70.0{\pm}3.3\;years$, and mean BMI of $24.0{\pm}2.1$ and $23.9{\pm}2.7$, respectively. The total energy expenditure (TEE) was measured by using the doubly labeled water (DLW) method, and used to validate the DRI predictive equations for estimated energy requirements (EER) and to develop new EER predictive equations. These developed equations were cross-validated by using the leave-one-out technique. RESULTS: In men, the DRI equation had a -7.2% bias and accurately predicted the EER (meaning EER values within ${\pm}10%$ of the measured TEE) for 64% of the subjects, whereas our developed equation had a bias of -0.1% and an accuracy rate of 84%. In women, the bias was -6.6% for the DRI equation and 0.2% for our developed equation, and the accuracy rate was 74% and 83%, respectively. The predicted EER was strongly correlated with the measured TEE, for both the DRI equations and our developed equations (Pearson's r = 0.915 and 0.908, respectively). CONCLUSIONS: The DRI equations provided an acceptable prediction of EER in older adults and these study results therefore support the use of these equations in this population group. Our developed equations had a better predictive accuracy than the DRI equations, but more studies need to be performed to assess the performance of these new equations when applied to an independent sample of older adults.

• 한국농공학회논문집
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• 제64권3호
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• pp.45-52
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• 2022

Prediction of soil behavior should be interpreted based on the level of axial strain in the actual ground. Recently numerical methods have been carried out focus on the state of soil failure. However considered the deformation of soil the prior to failure, mostly the small strain occurring in the elastic range is considered. As a result of calculating the deformation modulus to 50% of the maximum unconfined compression strength, Deformation modulus (E50) showed a tendency to increase according to the degree of compaction by region. The Poisson's ratio during loading-unloading was 0.63, which was higher than the literature value of 0.5. For the unconfined compression test under cyclic loading for the measurement of permanent strain, the maximum compression strength was divided into four step and the test was performed by load step. Changes in permanent strain and deformation modulus were checked by the loading-unloading test for each stage. At 90% compaction, the permanent deformation of the SM sample was 0.21 mm, 0.37 mm, 0.6 mm, and 1.35 mm. The SC samples were 0.1 mm, 0.17 mm, 0.42 mm, and 1.66 mm, and the ML samples were 0.48 mm, 0.95 mm, 1.30 mm, and 1.68 mm.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1529-1529
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• 2001

The watercore in apple is very important factor in storage and sorting of fruit. Most consumers tend to prefer the apple included watercore in immediately after harvest, however the watercore causes fruit flesh to brown during storage and lose the worth after all. But it is practically impossible to judge to the naked eye whether an apple has watercore or not. Therefore, the rapid, accurate and non-destructive analysis method for discrimination of watercore should be settled without delay. In this study we attempted the discrimination and quantitative analysis of watercore in apple fruit using near-infrared transmittance spectroscopy ‘Fuji’ apple fruits produced in Kyungpook of Korea was used in this experiment. The watercore content in apple was evaluated by graphic treatment of culled slice sections(10 mm). NIR transmittance spectra were collected over the 500 to 1000 nm spectral region with a spectrometer (Sentronic Co., Germany). The calibration models were carried out by partial least squares (PLS) analysis between NIR spectra data of apples and chemical data of watercore content. The spectra were different in absorbance between apple included watercore and not included one. Apple included watercore had higher absorption band than sample not included one at 732 and 820 nm. The calibration model seems to be accurate to predict the watercore content in apple fruit, the correlation coefficient (R) and root mean square error of prediction (RMSEP) were 0.99 and 0.93%, respectively. This result indicates that the PLSR calibration model by using NIR transmittance spectroscopy could be used for discrimination of watercore in apple fruit.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1519-1519
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• 2001

The aim of this work was to test the feasibility of NIRS in analysing textural characteristics of “Pasta Filata” cheese during the shelf-life. For this purpose, 128 samples of “Pasta Filata” cheese, subdivided into two sets on the basis of the wax used to avoid mechanical damages (paraffin, biodegradable wax), were analysed by using an InfraAlyzer 500 (Bran+Luebbe). Analyses were performed at room temperature. Samples were cut into small cylinders (D=3.2 cm, height = 1 cm), in agreement with literature information. Data were processed by using Sesame Software (Bran+Luebbe). Samples were analysed, during the shelf-life, at 90 and 120 days. In parallel, textural characteristics were detected carrying out a compression method by using an Universal Testing Machine Instron model 4301 (Instron Corporation, Canton, Massachusetts). As compression probe was used a cylinder (D = 5.8 cm, height = 3.7 cm) and a speed rate of 20mm/min was applied. The load at 20 mm of compression was recorded on sample cylinders of 1.7 cm (D) by 2 cm (height). Qualitative analysis of full spectra showed the possibility to gather samples on the basis of the days of shelf-life. The textural characteristics of cheese during the shelf-life was evaluated by comparing NIRS data with rheological results. The best correlation was obtained applying MLR to the first derivative of normalized absorbance values at seven wavelengths. Load values were plotted against the NIR prediction values based on first derivatives. NIRS proved to be an useful tool in classifying samples on the basis of the shelf-life period as well as in predicting their textural characteristics ($R^2$= 0.916, SEC = 0.192, SEP = 0.248, SEV = 0.345).

• 한국지능시스템학회논문지
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• 제23권5호
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• pp.412-417
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• 2013

태양광 발전 시스템의 해석적 모델링은 시스템의 동특성을 예측하거나 고장검출 및 진단 등과 같은 고급 공학 기술에 중요하게 적용할 수 있어 최근 많은 각광을 받고 있다. 본 논문은 대용량 학습 데이터를 갖는 태양광 발전 시스템에 대한 확률론적 모델링을 제시한다. 우선 태양광 일사량과 온도 입력 변수에 대한 태양광 시스템의 출력 전력과의 입출력 함수관계를 정의한다. 이 함수관계를 바탕으로 세 확률변수(일사량, 온도, 전력)에 대하여 조건부 확률 식으로 표현한다. 조건부 확률 분포 추정은 대용량 데이터 시스템에 적합한, 전체 표본 데이터 수 대비 관련 변수의 경우의 수에 대한 비율로 나타내었다. 추정한 확률분포를 통해 평균값 이론을 적용하여 시스템의 출력을 추정하게 된다. 본 논문에서 제안한 모델링 기법은 두 태양광 발전 단지의 사례 연구를 통해 성능을 검증하였다.

• Journal of Forest and Environmental Science
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• 제28권4호
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• pp.205-211
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• 2012

Yield tables are a frequently used data base for regional timber resource forecasting. A normal yield table is based on two independent variables, age and site (species constant), and applies to fully stocked (or normal) stands while empirical yield tables are based on average rather than fully stocked stands. Normal and empirical yield tables essentially have many limitations. The limitations of normal and empirical yield tables led to the development of variable density yield tables. Mathematical models for estimating timber yields are usually developed by fitting a suitable equation to observed data. The model is then used to predict yields for conditions resembling those of the original data set. It may be accurate for the specific conditions, but of unproven accuracy or even entirely useless in other circumstances. Thus, these models tend to be specific rather than general and require validation before applying to other areas. Dalbergia sissoo forms a major portion of irrigated plantations in the hot desert of India and is an important timber tree species where stem wood is primarily used as timber. Variable density yield model is not available for this species which is very crucial in long-term planning for managing the plantations on a sustained basis. Thus, the objective of this study was to develop variable density yield model based on the data collected from 30 sample plots of D. sissoo laid out in IGNP area of Rajasthan State (India) and measured annually for 5 years. The best approximating model was selected based on the fit statistics among the models tested in the study. The model develop was evaluated based on quantitative and qualitative statistical criteria which showed that the model is statistically sound in prediction. The model can be safely applied on D. sissooo plantations in the study area or areas having similar conditions.