• Preventive Nutrition and Food Science
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• 제1권1호
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• pp.41-45
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• 1996

This study was conducted to develop the fermentation kinetic model for the prediction of acidity and pH changes in Kimchi as a function of fermentation temperatures. The fitness of the model was evaluated using traditional two-step method and an alternative non-linear regression method. The changes in acidity and pH during fermentation followed the pattern of the first order reaction of a two-step method. As the fermentation temperature increased from 4$^{\circ}C$ to 28, the reaction rates of acidity and pH were increased 8.4 and 7.6 times, respectively. The activation energies of acidity and pH were 16.125 and 16.003kcal/mole. The average activation energies of acidity and pH using a non-linear method were 16.006 by the first order and 15.813 kcal/mole by the zero order, respectively. The non-linear procedure had better fitting 개 experimental data of the acidity and pH than two-step method. The shelf-lives based on the time to reach the 1.0% of acidity were 33.1day at 4$^{\circ}C$ and 2.8 day 28$^{\circ}C$.

• The Plant Pathology Journal
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• 제36권1호
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• pp.54-66
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• 2020

This study was conducted to evaluate usefulness of numerical weather prediction data generated by the Unified Model (UM) for plant disease forecast. Using the UM06- and UM18-predicted weather data, which were released at 0600 and 1800 Universal Time Coordinated (UTC), respectively, by the Korea Meteorological Administration (KMA), disease forecast on bacterial grain rot (BGR) of rice was examined as compared with the model output based on the automated weather stations (AWS)-observed weather data. We analyzed performance of BGRcast based on the UM-predicted and the AWS-observed daily minimum temperature and average relative humidity in 2014 and 2015 from 29 locations representing major rice growing areas in Korea using regression analysis and two-way contingency table analysis. Temporal changes in weather conduciveness at two locations in 2014 were also analyzed with regard to daily weather conduciveness (C_i) and the 20-day and 7-day moving averages of C_i for the inoculum build-up phase (C_inc) prior to the panicle emergence of rice plants and the infection phase (C_inf) during the heading stage of rice plants, respectively. Based on C_inc and C_inf, we were able to obtain the same disease warnings at all locations regardless of the sources of weather data. In conclusion, the numerical weather prediction data from KMA could be reliable to apply as input data for plant disease forecast models. Weather prediction data would facilitate applications of weather-driven disease models for better disease management. Crop growers would have better options for disease control including both protective and curative measures when weather prediction data are used for disease warning.

• International journal of advanced smart convergence
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• 제10권4호
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• pp.104-109
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• 2021

In the aftermath of the global pandemic that started in 2019, there have been many changes in the import/export and supply/demand process of agricultural products in each country. Amid these changes, the necessity and importance of each country's food self-sufficiency rate is increasing. There are several conditions that must accompany efficient agricultural activities, but among them, temperature is by far one of the most important conditions. For this reason, the need for high-accuracy climate data for stable agricultural activities is increasing, and various studies on climate prediction are being conducted in Korea, but data that can visually confirm climate prediction data for farmers are insufficient. Therefore, in this paper, we propose an artificial intelligence-based temperature prediction algorithm that can predict future temperature information by collecting and analyzing temperature data of farms in Gyeonggi-do in Korea for the last 10 years. If this algorithm is used, it is expected that it can be used as an auxiliary data for agricultural activities.

• 대한지역사회영양학회지
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• 제19권6호
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• pp.568-580
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• 2014

Objectives: The purpose of this study was to measure the resting metabolic rate (RMR) and to assess the accuracy of RMR predictive equations for Korean farmers. Methods: Subjects were 161 healthy Korean farmers (50 males, 111 females) in Gangwon-area. The RMR was measured by indirect calorimetry for 20 minutes following a 12-hour overnight fasting. Selected predictive equations were Harris-Benedict, Mifflin, Liu, KDRI, Cunningham (1980, 1991), Owen-W, F, FAO/WHO/UNU-W, WH, Schofield-W, WH, Henry-W, WH. The accuracy of the equations was evaluated on the basis of bias, RMSPE, accurate prediction and Bland-Altman plot. Further, new RMR predictive equations for the subjects were developed by multiple regression analysis using the variables highly related to RMR. Results: The mean of the measured RMR was 1703 kcal/day in males and 1343 kcal/day in females. The Cunningham (1980) equation was the closest to measured RMR than others in males and in females (males Bias -0.47%, RMSPE 110 kcal/day, accurate prediction 80%, females Bias 1.4%, RMSPE 63 kcal/day, accurate prediction 81%). Body weight, BMI, circumferences of waist and hip, fat mass and FFM were significantly correlated with measured RMR. Thus, derived prediction equation as follow : males RMR = 447.5 + 17.4 Wt, females RMR = 684.5 - 3.5 Ht + 11.8 Wt + 12.4 FFM. Conclusions: This study showed that Cunningham (1980) equation was the most accurate to predict RMR of the subjects. Thus, Cunningham (1980) equation could be used to predict RMR of Korean farmers studied in this study. Future studies including larger subjects should be carried out to develop RMR predictive equations for Korean farmers.

• 한국식품과학회지
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• 제22권3호
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• pp.325-331
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• 1990

소시지의 세조공정 중의 주 공정인 쿠킹공정에서 가장 영향을 많이 미치는 인자는 쿠킹온도와 상대습도이다. 따라서 쿠킹공정에서 에너지의 효율성을 높이기 위해 상기 인자와 소시지 직경의 변화에 따른 쿠킹시간을 측정하여 쿠킹시간 예측모델식을 수립하였다. 또한 쿠킹 전후의 일반성분 분석과 중량변화 및 각 온도와 상대습도에서의 TPA 분석을 하였다. 쿠킹시간 예측모델식을 SPSS computer program을 이용하여 가장 오차가 적은 범위에서의 예측모델식을 얻었다. 쿠킹시간 예측모델식을 쿠킹온도와 상대습도와 소시지 직경에 대한 각각의 함수관계를 Scattergram을 작성하여 R-square값을 가장 높은 함수를 취하여 각각의 모델식을 수립한 후 독립변수와의 관계를 종합하여 예측값을 구할 수 있는 최종적인 예측모델식을 수립하였다. 또한 소시지 직경 1.5cm에 대한 쿠킹 동안 중량변화는 온도와 상대습도가 적게 소모되어 소시지의 중량변화가 적게 일어남을 알 수 있었다. 물성치를 측정해 본 결과 온도와 상대습도의 변화에 따른 경도와 응집력의 값은 크게 변화가 일어났으나 반면에 탄성과 저작성의 값은 그 변화가 다소 적게 일어남을 알 수 있었다.

• 한국식품영양과학회지
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• 제42권5호
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• pp.832-836
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• 2013

어묵의 유통기한을 예측하기 위해서 어묵을 30, 35, $40^{\circ}C$에서 각각 저장하면서 저장기간 중 총 호기성균 수를 측정하였다. Gompertz model을 이용하여 최대성장속도와 유도기를 구하였고, 각 parameter의 온도 의존성에 대한 식을 통해 유통기한에 관한 예측모델 식을 얻었다. 예측모델 식으로부터 계산된 유통기한은 0, 4, $10^{\circ}C$에서 각각 6.9, 5.5, 3.8일이었다. 이렇게 얻어진 예측모델 식의 적합성 평가를 위해 $A_f$와 $B_f$ 값을 산출한 결과, 각각 1.008, 1.003으로 나타나 예측모델식의 적합성이 뛰어났다. 이러한 결과로부터 본 연구에서 얻어진 유통기한예측 모델 식은 어묵의 유통기한 설정의 기초연구로써 활용될 수 있다고 판단된다.

• Genomics & Informatics
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• 제20권4호
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• pp.45.1-45.7
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• 2022

Food security will be affected by climate change worldwide, particularly in the developing world, where the most important food products originate from plants. Plants are often exposed to environmental stresses that may affect their growth, development, yield, and food quality. Auxin is a hormone that plays a critical role in improving plants' tolerance of environmental conditions. Auxin controls the expression of many stress-responsive genes in plants by interacting with specific cis-regulatory elements called auxin-responsive elements (AuxREs). In this work, we performed an in silico prediction of AuxREs in promoters of five auxin-responsive genes in Zea mays. We applied a data fusion approach based on the combined use of Dempster-Shafer evidence theory and fuzzy sets. Auxin has a direct impact on cell membrane proteins. The short-term auxin response may be represented by the regulation of transmembrane gene expression. The detection of an AuxRE in the promoter of prolyl oligopeptidase (POP) in Z. mays and the 3-fold overexpression of this gene under auxin treatment for 30 min indicated the role of POP in maize auxin response. POP is regulated by auxin to perform stress adaptation. In addition, the detection of two AuxRE TGTCTC motifs in the upstream sequence of the bx1 gene suggests that bx1 can be regulated by auxin. Auxin may also be involved in the regulation of dehydration-responsive element-binding and some members of the protein kinase superfamily.

• Asian-Australasian Journal of Animal Sciences
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• 제19권2호
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• pp.266-272
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• 2006

Numerous reports have evaluated the predictive ability of carcass probes for meat quality using measurements taken early postmortem or near 24 h. The intervening time period, however, has been largely ignored. In this study, the capacity of three probes [pH, electrical conductivity (EC), and grading probe light reflectance (GP)] to predict pork longissimus muscle quality (drip and cooking losses, Warner-Bratzler shear, $L^*$, n = 30) was evaluated at 45 min, 90 min, 3, 6, 12, 24, and 48 h postmortem. The strongest relationships were observed between cooking loss and 6 h EC and GP ($R^2$ = 0.66, 0.72), and $L^*$ and GP ($R^2$ = 0.57-0.66, 12-48 h). pH was most valuable early postmortem ($R^2$ = 0.63, 90 min with cooking loss). GP at 6 h most effectively ($R^2$ = 0.84) predicted a two factor (cooking loss+$L^*$) meat quality index. Results emphasize the predictive value of measures taken between 3 and 12 h postmortem.

• 한국식품과학회지
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• 제21권6호
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• pp.735-741
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• 1989

For the accurate prediction of freezing time, probably the most difficult factor to measure and major error source is the surface heat transfer coefficient. In this work, surface heat transfer coefficient were determined for still air freezing and immersion freezing methods by theory of the transient temperature method and confirmed by using a modification of plank's equation to predict the freezing time of ground lean beef. The results showed the cooling rate of immersion freezing was about 11 times faster than that of still air freezing method. A comparison of surface heat transfer coefficient of copper plate and ground lean beef resulted an difference of 25-30% because the food sample surface is not smooth as copper plate. Also, when h-values measured by ground lean beef were applicated to modified model, the accuracy of its results is very high as difference of about 8%.

• 한국식품과학회지
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• 제37권6호
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• pp.1048-1064
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• 2005

Detection of specific compounds influencing food flavor quality is not easy. Electronic nose, comprised of electronic chemical sensors with partial specificity and appropriate pattern recognition system, is capable of recognizing simple and complex volatiles. It provides fast analysis with simple and straightforward results and is best suited for quality control and process monitoring of flavor in food industry. This review examines application of electronic nose in food analysis with brief explanation of its principle. Characteristics of different sensors and sensor drift. and solutions to related problems are reviewed. Applications of electronic nose in food industry include monitoring of fermentation process and lipid oxidation, prediction of shelf life, identification of irradiated volatile compounds, discrimination of food material origin, and quality control of food and processing by principal component analysis and neural network analysis. Electronic nose could be useful for quality control in food industry when correlating analytical instrumental data with sensory evaluation results.