• Journal of the Korean Society of Food Science and Nutrition
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• v.22 no.4
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• pp.443-447
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• 1993

The physical properties of reconstituted instant rice produced by three different dehydration methods were evaluated. The rehydration ratios of reconstituted instant rice produced by air dehydration at 9$0^{\circ}C$ (process A) had higher values than those produced by freeze dehydration (process C) during entire range of rehydration. After 4min of rehydration time at 95$^{\circ}C$ , the moisture contents of reconstituted instant rice produced by process A and C were higher values than those of control(151.47%, dry basis) which was cooked by electric cooker. Regardless of dehydration method, the sizes of reconstituted instant rice (6min, 95$^{\circ}C$) had larger values than those of control. The values of hardness (H), stickiness (S) and S/H ratio of reconstituted instant rice (6min, 95$^{\circ}C$) produced by high temperature (7min, 15$0^{\circ}C$) air dehydration (process B) as the first stage prior to air dehydration at 9$0^{\circ}C$ were almost the same as those of control. The H of reconstituted instant rice decreased, while S and S/H ratio increased with increasing rehydration time. The higher values of whiteness(L) and lower values of yellowness (b) represented in reconstituted instant rice (6min, 95$^{\circ}C$) than those of control.

• Korean Journal of Food Science and Technology
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• v.42 no.3
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• pp.361-367
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• 2010

Bacterial contamination of cooked rice was analyzed to evaluate the microbial safety. Thirty raw rice samples were collected in Korea and cooked in an electric rice cooker. Mesophilic aerobe, food-poisoning Bacillus cereus group, and their toxin genes were determined on cooked rice. The percentage of total mesophilic aerobe based on 1-3 log CFU/g was 27% among the samples. Bacillus spp. in MYP selective medium was similar to the number of mesophilic aerobe, whileas Bacillus spp. was detected in most samples after enrichment. Thirty-seven isolates from 30 cooked rices were identified as B. thuringiensis, B. cereus, B. valismortis, B. pumilus, B. coagulans, B. licheniformis, Geobacillus stearothermophilus, and Brevibacillus laterosporus. Twenty isolates (54%), more than half of the isolates, were B. thuringiensis while nine (27%) were identified as B. cereus. All B. thuringiensis isolates possessed non-hemolytic toxin genes and interestingly, seven B. cereus among nine isolates possessed emetic toxin genes. More B. thuringiensis was present on the cooked rice than B. cereus and most B. cereus possessed emetic toxin genes rather than diarrheal toxin genes. Therefore, food-borne outbreak due to B.cereus on the cooked rice kept at room temperature might be examples of emetic food-poisoning.

• Korean Journal of Food Science and Technology
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• v.28 no.3
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• pp.490-496
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• 1996

This study was undertaken in order to elucidate the elimination of phenthoate residues by washing and cooking processes of rice which if the most important food crop in Korea. When contaminated rice was washed with distilled water three times, the removal rate of total phenthoate was 51%. The removal rate in the successive washings was 37.3% (wash filtrate 7.8%, wash sediment 29.5%) in the first, 14.3% (wash filtrate 6.2%, wash sediment 8.1%) in the second and 8.9% (wash filtrate 5.8%, wash sediment 3.1%) in the third washings. More than half of the residue was removed by the first washing and most residues were found in the sediment rather than in the filtrate of the rice washings. The residue rate of phenthoate after cooking by an electric rice cooker was 41%, indicating that the removal rate after cooking was 59%, because phenthoate is thermally stable at the cooking temperature. In conclusion, phenthoate residues contaminated in rice grains are grcatly removed in the washing process and it is desirable to wash the grains before cooking in order to decrease the hazards from pesticide residues such as phenthoate. Reduction factor of phenthoate in rice cooking is proposed to be 0.4.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.407-413
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• 2011

This paper presents an electrical power monitoring system for home energy management and an automatic appliance-identification algorithm based on the electricity-usage patterns collected during the monitoring tests. This paper also discusses the results of the field tests of which the proposed system was voluntarily deployed at 13 homes. The proposed monitoring system periodically measures the amount of power consumption of each appliance with a pre-specified time interval and effectively displays the essential information provided by the monitored data which is required users to know in order to save power consumption. Regarding the field tests of the monitoring system, the households responded that the system was useful in saving electricity and especially the electricity-usage patterns per appliances. They also considered that the predicted amount of the monthly power consumption was effective. The proposed appliance-identification algorithm uses 4 patterns: Zero-Crossing Rate(ZC), Variation of On State(VO), Slope of On State(SO) and Duty Cycle(DC), which are applied over the 2 hour interval with 25% of it on state, and it yielded 82.1% of success rate in identifying 5 kinds of appliances: refrigerator, TV, electric rice-cooker, kimchi-refrigerator and washing machine.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.477-484
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• 2016

Small industrial appliances such as fan, audio, electric rice cooker mostly consist of ABS, PP, PS materials. In colored plastics, it is possible to classify by near infrared(NIR) spectroscopy, while in black plastics, it is very difficult to classify black plastic because of the characteristic of black material that absorbs the light. So the RBFNNs pattern classifier is introduced for sorting electrical and electronic waste plastics through LIBS(Laser Induced Breakdown Spectroscopy) spectrometer. At the preprocessing part, PCA(Principle Component Analysis), as a kind of dimension reduction algorithms, is used to improve processing speed as well as to extract the effective data characteristics. In the condition part, FCM(Fuzzy C-Means) clustering is exploited. In the conclusion part, the coefficients of linear function of being polynomial type are used as connection weights. PSO and 5-fold cross validation are used to improve the reliability of performance as well as to enhance classification rate. The performance of the proposed classifier is described based on both optimization and no optimization.