• Asian-Australasian Journal of Animal Sciences
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• v.27 no.2
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• pp.161-168
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• 2014

This study aimed to investigate dietary concentrate:forage ratios (C:F) and undegraded dietary protein (UDP) on nitrogen balance and urinary excretion of purine derivatives (PD) in lambs. Four Dorper${\times}$thin-tailed Han crossbred castrated lambs with $62.3{\pm}1.9$ kg body weight at 10 months of age were randomly assigned to four dietary treatments in a $2{\times}2$ factorial arrangement of two levels of C:F (40:60 and 60:40) and two levels of UDP (35% and 50% of CP), according to a complete $4{\times}4$ Latin-square design. Each experimental period lasted for 19 d. After a 7-d adaptation period, lambs were moved into individual metabolism crates for 12 d including 7 d of adaption and 5 d of metabolism trial. During the metabolism trial, total urine was collected for 24 h and spot urine samples were also collected at different times. Urinary PD was measured using a colorimetric method and creatinine was measured using an automated analyzer. Intake of dry matter (DM) (p<0.01) and organic matter (OM) (p<0.01) increased as the level of UDP decreased. Fecal N was not affected by dietary treatment (p>0.05) while urinary N increased as the level of UDP decreased (p<0.05), but decreased as dietary C:F increased (p<0.05). Nitrogen retention increased as dietary C:F increased (p<0.05). As dietary C:F increased, urinary excretion of PD increased (p<0.05), but was not affected by dietary UDP (p>0.05) or interaction between dietary treatments (p>0.05). Daily excretion of creatinine was not affected by dietary treatments (p<0.05), with an average value of $0.334{\times}0.005$ mmol/kg $BW^{0.75}$. A linear correlation was found between total PD excretion and PDC index ($R^2$ = 0.93). Concentrations of creatinine and PDC index in spot urine were unaffected by sampling time (p>0.05) and a good correlation was found between the PDC index (average value of three times) of spot urine and daily excretion of PD ($R^2$ = 0.88). These results suggest that for animals fed ad libitum, the PDC index in spot urine is effective to predict daily excretion of PD. In order to improve the accuracy of the spot sampling technique, an appropriate lag phase between the time of feeding and sampling should be determined so that the sampling time can coincide with the peak concentration of PD in the urine.

• The Journal of the Korean life insurance medical association
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• v.4 no.1
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• pp.110-132
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• 1987

A study on hepatomegaly detected by abdominal palpation, and facial telangiectasia in a total of 3,418 insured persons medically examined at the Honam Medical Room of Dong Bang Life Insurance Company Ltd. from February, 1984 to August, 1985 was undertaken. The results were as follows: 1) Hepatomegaly was found in 383 cases(27.5%) among the 1,395 insureds of male and in 163 cases(8.1%) among the 2,023 insureds of female. The difference of incidence of hepatomegaly between all males and females showed statistical significance(p<0.001). In each age group, the incidence of hepatomegaly in :nale was higher than that in female. The incidence of hepatomegaly in each age group in male increased cnosiderably with age; it showed 11.6%,16.2%, 42.6% and 52.9% from second to sixth decade in order, thereafter in seventh decade it decreased to 26.7%, While the incidence of hepatomegaly in female increased slightly in each age group. 2) Facial telangiectasia was found in 318 cases(22.8%) among all males and in 157 cases(7.8%) among all females. The difference of incidence of telangiectasia between all males and females showed statistical significance(p<0.001). In each age group, the incidence of telangiectasia in male was higher than that in female, except of second decade. The incidence of facial telangiectasia in each age group in male increased considerably with age; while it increased slightly in female. 3) Facial telangiectasia accompanied by hepatomegaly was found in 235 cases(61.4%) among 383 cases of hepatomegaly in male and in 69 cases(42.3%) among 163 cases of hepatomegaly in female. The difference of incidence of telangiectasia between males and females show ed statistical significance(p<0.001). 4) Facial telangiectasia without spider angiomata accompanied by hepatomegaly was found in 201 cases(52.5%) among 383 cases of hepatomegaly in all males and in 67 casgs(41.4%) among 163 cases of hepatomegaly in all females; facial spider angiomata accompanied by hepatomegaly was found in 34 cases(8.9%) among 383 cases of hepatomegaly in all males and in 2 cases(1.2%) among 163 cases of hepatomegaly in all females. 5) Abnormal SGOT activity was found in 19 cases(7.9%) among 242 cases of hepatomegaly in all males and in one case(1.5%) among 67 cases of hepatomegaly in all females. The difference of incidence of abnormal SGOT activity showed statistical significance(p<0.001). The incidence of abnormal SGOT activity by the size of hepatomegaly, that is, palpated <1 finger's breadth, <2 fingers' breadth and ${\geqq}2$ fingers' breadth, revealed 2.2%, 6.0% and 60.0% respectively in all males, while abnormal SGOT activity was found only one case in fifth decade among 67 cases of hepatomegaly in all females. 6) In ordinary medical examination(the insured amount is low) abnormal SGOT activity was found in 7 cases(4.8%) among 146 cases of hepatomegaly palpated $1\frac{1}{2}$ fingers' breadth and under, while it was not found in 37 cases of the same sized hepatomegaly in all females. Above mentioned 7 cases are thought to be very significant because 7 cases occupy 35% in 20 cases of abnormal SGOT activity with hepatomegaly. 7) Abnormal SGOT activity was found in 12 cases(4.4%) among 273 cases of hepatomegaly of "not firm" consistency, while it was found in 8 cases(22.2%) among 36 cases of hepatomegaly of "firm" consistency. The difference of incidence of abnormal SGOT activity showed statistical significance(p<0.05). 8) Abnormal SGOT activity was found in 5 cases(17.9%) among 28 cases of spider angiomata with hepatomegaly, while it was found in 10 cases(7.3%) among 166 cases of telangiectasia without spider angiomata with hepatomegaly. Owing to a small number of cases, statistical significance was not recognized, but the incidence of abnormal SGOT activity in spider angiomata cases with hepatomegaly is apt to be higher than that in telangiectasia cases without spider angiomata with hepatomegaly. 9) The incidence of abnormal SGOT activity is apt to be higher with age in male group; abnormal SGOT activity was not found among 4 cases of hepatomegaly in second decade and it was 3.8% in third decade, 4.5% in fourth decade, 9.3% in fifth decade, 17.5% in sixth decade and 33.3% in seventh decade, while the incidence of it was only one case among 67 cases in all females. 10) It is believed that the performance of liver function test to the subjects with hepatomegaly even in ordinary medical examination(the insured amount is low) will give considerable contribution for medical selection of hepatomegaly risk. 11) Age of the insured(young or old), presence of facial telangiectasia or spider angiomata especially and their severity, and consistency of enlarged liver(firm or not) should be considered to increase accuracy in evaluating hepatomegaly risk.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• The Journal of Korean Academy of Prosthodontics
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• v.46 no.2
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• pp.175-184
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• 2008

Purpose: This study was designed to investigate the fitness of adjustable dental impression trays on the Chinese and the Japanese. Material and methods: Initial design of the adjustable dental trays was developed from the results of the dental arch size of Korean adults. This design was applied to the CAD-CAM process in order to create tray model samples. Simple silicon-base molds were then replicated based on these sample models. Polyurethane injection into the silicon- base molds completed the process of creating a large number of test products. 60 Chinese dental students (male:30, female:30) from the Shanghai Second Medical University and 60 Japanese alumni from the Kumamoto high school (male:30, female:30) were selected for taking irreversible hydrocolloid impression with these trays. The width and length of the impression body were measured on several measuring points by Vernier caliper. The results were analyzed statistically to evaluate the fitness of the trays. Results: 1. Uniform impression material thickness was achieved on the Chinese and Japanese by controlling the width of the tray using stops and beveled guides. The material thickness was generally within the range of 3 mm to 6 mm. 2. In the maxillary tray of the Chinese, average thickness of the impression material of the labial vestibule of the incisal teeth was 6.2 mm, the canine was 5.9 mm and the midpalatal part 10.5 mm and the posterior palatal part 9.7 mm. These were relatively large values. 3. In the mandibular tray of the Chinese, average length of the impression material of the lingual vestibule of first, second premolar contact point was 8.9 mm, the incisal teeth was 7.8 mm and thickness of the labial part of canine was 6.8 mm and premolars 7.0 mm. These were relatively large values. 4. In the maxillary tray of the Japanese, average thickness of the impression material of the labial vestibule of the incisal teeth was 7.4 mm, the canine was 7.7 mm and the midpalatal part 9.1 mm. These were relatively large values. 5. In the mandibular tray of the Japanese, average thickness of the impression material of the labial vestibule of first, second premolar contact point was 8.4 mm, and thickness of the labial part of canine was 7.4 mm. These were relatively large values. Conclusion: This adjustable dental tray shows good accuracy to Korean because it was designed by the analysis of the dental arch size of Korean adult model. With this result, it can be applied to Chinese and Japanese, we can take more easy and accurate dental impressions.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• The Journal of Korean Academy of Prosthodontics
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• v.41 no.3
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• pp.369-378
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• 2003

Statement of Problem: Recently, resin cements have become more widely used and have been accepted as prominent luting cements. Current resin cements exhibit less microleakage than conventional luting cements. However, the constant contact with water and exposure to occlusal forces increase microleakage even in resin cements inevitably. Most bonding resins have been modified to contain a hydrophilic resin such as 2-hydroxyethylmethacrylate (HEMA) to overcome some of the problems associated with the hydrophobic nature of bonding resins. By virtue of these modifications, bonding resins absorb a significant amount of water, and there may also be significant stresses at bonding interfaces, which may adversely affect the longevity of restorations. Therefore the reinforcement of water stability of resin cement is indispensable in future study. Purpose: This study was conducted to examine the influence of water retention on microleakage of two resin cements over the period of 6 months. Materials and Methods: 32 extracted human teeth were used to test the microleakage of a single full veneer crown. Two resin cements with different components and adhesive properties - Panavia F (Kuraray Co., Osaka, Japan) and Super-Bond C&B (Sun Medical Co., Kyoto, Japan)- were investigated. The storage medium was the physiological saline solution changed every week for 1 month, 3 months, and 6 months. One group was tested after storage for 1 day. At the end of the each storage period, all specimens were exposed to thermocycling from $5^{\circ}C$ to $55^{\circ}C$ of 500 cycles and chewing simulation of 50,000 cycles, and then stained with 50% silver nitrate solution. The linear penetration of microleakage was measured using a stereoscopic microscope at ${\times}40$ magnification and a digital traveling micrometer with an accuracy of ${\pm}3{\mu}m$. Values were analyzed using two-way ANOVA test, Duncan's multiple range tests (DMRT). Results : Statistically significant difference of microleakage was shown in the 3-month group compared with the1-day or 1-month group in both systems (p<0.05) and there were statistically significant differences in microleakage between the 3-month group and the 6-month group in both systems (p<0.05). The two systems showed different tendency in the course of increased microleakage during 3 months. In Panavia F, microleakage increased slowly throughout the periods. In Super-Bond C&B, there was no significant increase of microleakage for 1 month, but there was statistically significant increase of microleakage for the next 2 months. For the mean microleakage for each period, in the 3-month group, microleakage of Super-Bond C&B was significantly greater than that of Panavia F. On the other hand, in the 6-month group, microleakage of Panavia F was significantly greater than that of Super-Bond C&B (p<0.05). Conclusion: Within the limitation of this study, water retention of two different bonding systems influence microleakage of resin cements. Further studies with the longer observation periods in viro are required in order to investigate water stability and the bonding durability of the resin cement. CLINICAL IMPLICATIONS Microleakage at the Cement-tooth interfaces did not necessarily result in the failure of the crowns. But it is considered to be a major factor influening the longerity of restorations. Further clinical approaches for decreasing the amount of microleakage are required.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• Investigative Magnetic Resonance Imaging
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• v.7 no.2
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• pp.100-107
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• 2003

Purpose : To assess the usefulness of cardiac MR imaging (MRI) in the diagnosis of acute myocardial infarction and in the assessment of myocardial viability in comparision with T1-201 SPECT. Materials and Methods : We retrospectively studied 17 patients who complained of chest pain and dyspnea with cardiac MRI . The patients were evaluated for the presence or absence of high signal intensity on T2-weighted image (T2wI), abnormal wall motion on 2D-FIESTA, perfusion defect on Gd-DTPA enhanced T1WI, and delayed myocardial enhancement on 15-minutes delay Gd-DTPA enhanced T1WI. The results were correlated with the images on T1-201 SPECT, taken at rest and stress, through which reversibility of perfusion defect was assessed. Results : Both cardiac MRI and T1-201 SPECT proved to be useful methods for diagnosing acute myocardial infarction. In order of decreasing correspondence, T2WI, T1-201 SPECT, delayed enhancement study, and wall motion images all showed significant statistical correlation with the clinical diagnosis of myocardial infarction. Perfusion MRI, on the other hand, showed no significant statistical difference was found between T1-201 SPECT and cardiac MRI. The results on T2WI showed high accordance with those on Tl-201 SPECT, while delayed myocardial enhancement and wall motion studies showed no agreement with Tl-201 SPECT. Conclusion : Cardiac MRI is useful method for diagnosis of acute myocardiac infarction. With respect to the assessment of myocardial viability, the results obtained on cardiac MRI showed high agreement with those on Tl-201 SPECT. However, further study is necessary at this point for standardization and establishment of the methods for assessing myocardial viability on cardiac MRI.

• 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.