• International Commerce and Information Review
• /
• v.18 no.4
• /
• pp.285-310
• /
• 2016

The purpose of this study empirically verified impact of strategic and operational integration between first-tier supplier and their supplier and strategic and operational integration between first-tier supplier and their buyer on operation performance. In order to achieve our goal, we tested reliability, validity and path coefficient using structural equation modeling-partial least square (SEM-PLS) over 284 first-tier manufacturing suppliers data that Korea Productivity Center (KPC) surveyed in 2013. This study results indicated that operational integration between first-tier supplier and their supplier or buyer has positive impact on production process flexibility. Meanwhile, strategic integration between first-tier supplier and buyer has positive impact on production flexibility. On the other hand, strategic integration between first-tier supplier and supplier has negative impact on production flexibility. And production process flexibility has positive impact on production flexibility. By empirically testing to departmentalize level and scope of supply chain integration, this study has academic and managerial implications from first-tier perspective on.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2017.04a
• /
• pp.41-41
• /
• 2017

본 연구는 초분광 영상을 이용하여 수박 모종의 수분함량을 비파괴적으로 추정하기 위해 수행되었다. 단계적으로 수분 스트레스를 받은 수박(n=45) 모종을 초분광 영상시스템으로 촬영하여 모종 영역의 반사율을 추출하였고, 매 촬영 후 모종의 생체중과 건물중을 측정하여 수분함량을 계산하였다. 모종의 반사율과 계측된 수분함량을 변수로 하여 Partial Least Square Regression(PLSR) 분석을 이용하여 수분 추정 모델을 구축하였다. 수분 추정모델을 작성한 결과 Calibration(Cal.)의 정확도($R^2$)는 0.66, 정밀도(RMSE 및 RE)는 각각 1.06%, 1.14%로 나타났다. 수박 모종의 수분함량 추정모델의 정밀도는 상당히 높게 나타났으나 정확도는 낮게 나타났다. 정확도를 개선하기 위해 Confidence ellipses의 신뢰구간을 95%로 설정하였을 때 3개의 모종이 타원 밖에 위치하는 것을 발견하였으며 이를 제거 후 재분석을 하였다. 3개의 모종을 제외한 수박 모종의 수분함량 추정모델의 정확도는 0.82, 정밀도는 0.73%, 0.78%로 나타났다. 3개의 모종을 제외함으로서 모델의 정확도 및 정밀도가 상승하여 3개의 모종이 정확도 및 정밀도를 낮추는 원인이라 판단된다. 작물은 가뭄스트레스를 받을수록 반사율이 낮아지지만(Yang et al., 2010) 3개의 모종은 다른 모종의 수분함량에 비해 반사율이 큰 차이를 나타내어 정확도 및 정밀도를 낮춘 것으로 판단된다. 본 연구를 통해 초분광 영상을 이용하여 수박 모종의 수분함량 추정가능성을 시사하였고, 모델의 정확도를 개선하기 위해 샘플 수 및 수분함량의 변이를 증가시키는 것이 필요하다고 판단된다.

• Korean Journal of Food Science and Technology
• /
• v.41 no.6
• /
• pp.609-614
• /
• 2009

Sesame oil was sometimes replaced by mixed oil due to high price in Korean market. To find out authentic sesame oil, electronic nose (E-nose) based on mass spectrometer system was used. Sesame oil was blended with perilla oil at the ratio of 97:3, 94:6, 91:9, 88:12 and 85:15, respectively. Intensities of each fragment from sesame oil by E-nose based on MS were completely different from those of perilla oil. The obtained data was used for discriminant function analysis. For quantitative analysis, the partial least square algorithm was used. The added concentration of perilla oil to sesame oil was correlated with discriminant function first score (DF1) and second score (DF2). From this relationship it could be found out how much perilla oil added. DFA plot indicated a significant separation of pure sesame oil and pure perilla oil. The different geographical origin of sesame oil was used for blending with perilla oil were closed to that of sesame oil. Korean sesame oil mixture and Indian sesame oil one were well separated. And the correlation between mixing ratios and DF1 values was found at the ratio of 97:3, 91:9, and 85:15 (SE vs PE oil), respectively. But the added concentration of perilla oil to sesame oil was correlated with discriminant function first score (DF1). E-nose based on MS system could be used as an efficient method for purity of oil quality.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.35 no.2
• /
• pp.125-130
• /
• 2015

The objective of this study was to investigate the feasibility of using near infrared spectroscopy (NIRS) to discriminate between grass spices. A combination of NIRS and chemometrics was used to discriminate between Italian ryegrass, perennial ryegrass, and tall fescue seeds. A total of 240 samples were used to develop the best discriminant equation, whereby three spectra range (visible, NIR, and full range) were applied within a 680 nm to 2500 nm wavelength. The calibration equation for the discriminant analysis was developed using partial least square (PLS) regression and discrimination equation (DE) analysis. A PLS discriminant analysis model for the three spectra range that was developed with the mathematic pretreatment "1,8,8,1" successfully discriminated between Italian ryegrass, perennial ryegrass, and tall fescue. An external validation indicated that all of the samples were discriminated correctly. The discriminant accuracy was shown as 68%, 78%, and 73% for Italian ryegrass, perennial ryegrass, and tall fescue, respectively, with the NIR full-range spectra. The results demonstrate the usefulness of the NIRS-chemometrics combination as a rapid method for the discrimination of grass species by seed.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1262-1262
• /
• 2001

The rice plant is one of the important staple crops in Korea. The high yield with low cost in rice is required the soil fertility and the development of new precise method of fertilizer application by nutritional diagnosis. Now, in Korea, the nitrogen application system for the rice plant is composed of the basal fertilization, fertilization at tillering stage and fertilization at panicle stage, which the nitrogen fertilization at panicle stage amount to about 30 percent in the total amount. Thus, this experiment carried out to the development of the system that can measure the nitrogen content in the rice plant at panicle stage rapidly with the near infrared spectroscopy, and to predict the appropriate quantity of the nitrogen fertilization at panicle stage based on calibration model for test of nitrogen content in rice plant. The samples were collected from 48 varieties in 4 regions which are mainly cultivated in the southern part of Korea. And then, it collected by classifying into the leaf, the whole plant and the stem since 7 days before the nitrogen fertilization at panicle stage. The ranges of the nitrogen contents were 1.6∼4.0%, 1.7∼3.0% and 1.4∼2.7% in the leaf, the whole plant and the stem, respectively. In the calibration models created by each part of the plant under the Multiple Linear Regression(MLR) method, the calibration model for the leaf recorded the relatively high accuracy. The mutual crossing test on unknown samples were carried out using Partial Least Square(PLS) calibration model. That is, the nitrogen content in the stem was tested by calibration model made by the leaf model and that of stem was tested by calibration model made by whole plant sample. When unknown leaf sample was tested by calibration model made by all sample that collected from each part in rice plant such as leaf, stem and whole plant, it recorded the highest accuracy. As a result, to test the nitrogen content in the rice plant at panicle stage, the nitrogen content in the leaf shall be tested by the calibration model composed of the leaf, the stem and the whole plant. In future, to estimated the amount of nitrogen fertilization at panicle stage for rice plant , it will be calculated based on regression model between rice yield and nitrogen content of leaf measured by calibration model made by mixed sample including leaf, stem and whole plant.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1530-1530
• /
• 2001

The objectives of this study were to develop models to predict quality parameters of Korean bee-honeys by visible and NIR spectroscopic technique. Two kinds of bee-honey fronl acacia and polyflower sources were tested in this study. The honeys were harvested in the spring of 2000 and stored in the storage facility at 20$^{\circ}C$ during experiments. Total of 394 samples of honey were analyzed. Reflectance spectra, moisture contents, ash, invert sugar, sucrose, F/G (fructose/glucose) ratio, HMF (hydroxymethyl furfural), and C12/C13 ratio of honeys were measured. The average values for the tested honeys were 19.9% of moisture contents, 0.12% of ash, 68.4% of invert sugar, 5.7% of sucrose, 1.27 of F/G(fructose/glucose) ratio, 14.4 mg/kg of HMF, and -19.1 of C12/C13 ratio. A spectrophotometer, equipped with a single-beam scanning monochromator (NIR Systems, Model 6500, USA) and a horizontal setup module, was used to collect reflectance data from honey. The reflectance spectra were measured in wavelength ranges of 400∼2,498 nm. with 2 nm of interval. Thirty-two repetitive scans were averaged, transformed to log(1/Reflectance), and then were stored in a microcomputer file, forming one spectrum per measurement. A sample cell and reflectance plate were made to hold honey samples constantly. Spectra of honey samples were divided into a calibration set and a validation set. The calibration set was used during model development, and the validation set was used to predict quality parameters from unknown spectra. The PLS(Partial Least Square) models were developed to predict the quality parameters of honeys. The first and the second derivatives of raw spectra were also used to develop the models with proper smoothing gap. The MSC (multiplicative scatter correction) and the SNV & Dtr.(standard normal variate and detranding) preprocessing were applied to all spectra to minimize sample-to-sample light scatter differences. The PLS models showed good relationships between predicted and measured quality parameters of honeys in the wavelength range of 1100∼2200 nm. However, the PLS analysis was not good enough to predict HMF of honeys.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1281-1281
• /
• 2001

Near-infrared (NIR) spectra have bean measured for high-density (HDPE), linear low-density (LLDPE), and low-density (LDPE) polyethylene in pellet or thin films. The obtained spectra have been analyzed by conventional spectroscopic analysis methods and chemometrics. By using the second derivative, principal component analysis (PCA), and two-dimensional (2D) correlation analysis, we could separate many overlapped bands in the NIR. It was found that the intensities of some bands are sensitive to density and crystallinity of PE. This may be the first time that such bands in the NIR region have ever been discussed. Correlations of such marker bands among the NIR spectra have also been investigated. This sort of investigation is very important not only for further understanding of vibration spectra of various of PE but also for quality control of PE by vibrational spectroscopy. Figure 1 (a) and (b) shows a NIR reflectance spectrum of one of the LLDPE samples and that of PE, respectively. Figure 2 shows a PC weight loadings plot of factor 1 for a score plot of PCA for the 16 kinds of LLDPE and PE based upon their 51 NIR spectra in the 1100-1900 nm region. The PC loadings plot separates the bands due to the $CH_3$ groups and those arising form the $CH_2$ groups, allowing one to make band assignments. The 2D correlation analysis is also powerful in band enhancement, and the band assignments based upon PCA are in good agreement with those by the 2D correlation analysis.(Figure omitted). We have made a calibration model, which predicts the density of LLDPE by use of partial least square (PLS) regression. From the loadings plot of regression coefficients for the model , we suggest that the band at 1542, 1728, and 1764 nm very sensitive to the changes in density and crystalinity.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1522-1522
• /
• 2001

Beverages based on fruit juices are among the most popular commercially available drinks. There is an ever-increasing demand for these juices in the market. Orange juice is one of the most common as well as most favorite flavor. The fruit processing industries have a tremendous responsibility of quality control. For quality evaluation estimation of various components of the juice is necessary. Sucrose, glucose, fructose, citric acid and malic acid are the prime components of orange juice. Little information is available on analysis of orange juice. However, conventional and general wet chemistry procedures are currently being used which are no longer desired by the industry owing to the time involved, labor input and harmful chemicals required for each analysis. Need to replace these techniques with new, highly specific and automated sophisticated techniques viz. HPLC and spectroscopy has been realized since long time. Potential of Near Infrared Spectroscopy in quantitative analysis of different components of food samples has also been well established. A rapid, non-destructive and accurate technique based on Near Infrared Spectroscopy for determination of sugars and organic acids in orange juice will be highly useful. The current study is an investigation into the potential of Near Infrared Diffuse Reflectance Spectroscopy for rapid quantitative analysis of sucrose, glucose, fructose citric acid and malic acid in orange juice. All the Near Infrared measurements were peformed on a dispersive NIR spectrophotometer (ELICO 153) in diffuse reflectance mode. The spectral region from 1100 to 2500nm has been explored. The calibration has been performed on synthetic samples that are mixtures of sucrose, glucose, fructose, citric acid and malic acid in different concentration ranges typically encountered real orange juice. These synthetic samples are therefore considered to be representatives of natural juices. All the Near Infrared spectra of synthetic samples were subjected to mathematical analysis using Partial Least Square (PLS) algorithm. After the validation, calibration was applied to commercially available real samples and freshly squeezed natural juice samples. The actual concentrations were compared with those predicted from calibration curve. A good correlation is obtained between actual and predicted values as indicated by correlation coefficient ($R^2$) value, which is close to unity, showing the feasibility of the technique.

• Journal of Preventive Medicine and Public Health
• /
• v.28 no.3 s.51
• /
• pp.715-725
• /
• 1995

This study was done for identifying the factors which affect psychologic symptoms of low back(LBP) patients. The study subjects were 43 work-related low back pain patients, 28 work-related non-low back pain patients and 47 general low back injury patients. The study materialis SCL 90-R for checking psychologic symptoms and questionnaire for obtaining general information about the subjects. The data were analyzed by model of analysis of covariance adjusted by several variables such as gender, age, education and marital status and then compared the least square means of symptom score between groups. To identify the factors that affect psychologic symptom, duration of suspension, return to work and interaction factor of these two variables were analyzed by multivariate model and we calcuated partial correlation coefficient of these variables. As a result, work-related LBP patients showed higher score of symptoms in somatization, depression and psychosis than work-related non-LBP and non-work-related LBP. Duration of suspension and return to work were significant explanatory variables for psychologic symptom score of work-related LBP. Then, we may conclude that the treatment and rehabilitation programe for work-related LBP should cover the strategy of early return to work.

• Journal of Biosystems Engineering
• /
• v.43 no.2
• /
• pp.138-147
• /
• 2018

Purpose: A narrowband hyperspectral imaging sensor of high-dimensional spectral bands is advantageous for identifying the reflectance by selecting the significant spectral bands for predicting crop yield over the broadband multispectral imaging sensor for each wavelength range of the crop canopy. The images acquired by each imaging sensor were used to develop the models for predicting the Chinese cabbage yield. Methods: The models for predicting the Chinese cabbage (Brassica campestris L.) yield, with multispectral images based on unmanned aerial vehicle (UAV), were developed by simple linear regression (SLR) using vegetation indices, and forward stepwise multiple linear regression (MLR) using four spectral bands. The model with hyperspectral images based on the ground were developed using forward stepwise MLR from the significant spectral bands selected by dimension reduction methods based on a partial least squares regression (PLSR) model of high precision and accuracy. Results: The SLR model by the multispectral image cannot predict the yield well because of its low sensitivity in high fresh weight. Despite improved sensitivity in high fresh weight of the MLR model, its precision and accuracy was unsuitable for predicting the yield as its $R^2$ is 0.697, root-mean-square error (RMSE) is 1170 g/plant, relative error (RE) is 67.1%. When selecting the significant spectral bands for predicting the yield using hyperspectral images, the MLR model using four spectral bands show high precision and accuracy, with 0.891 for $R^2$, 616 g/plant for the RMSE, and 35.3% for the RE. Conclusions: Little difference was observed in the precision and accuracy of the PLSR model of 0.896 for $R^2$, 576.7 g/plant for the RMSE, and 33.1% for the RE, compared with the MLR model. If the multispectral imaging sensor composed of the significant spectral bands is produced, the crop yield of a wide area can be predicted using a UAV.