• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2641-2647
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• 2012

Quality and sensory characteristics such as microbial count, pH, acidity, flavor, taste, color and overall acceptance of bamboo shoot pickle cured with red pepper paste and bamboo shoot pickle cured with soy sauce paste made of Maengjong bamboo shoots were investigated during a long-term storage at different temperature (at $25^{\circ}C$, $35^{\circ}C$ and $45^{\circ}C$). Microbial contamination was not observed, and water content did not showed significant change in all samples of both pickles during the whole storage period of 30 days, regardless of storage temperature. At $25^{\circ}C$, all sensory characteristics of bamboo shoot-red pepper paste pickle did not show a significant change for 30 d. However, at $35^{\circ}C$ and $45^{\circ}C$, the flavor, taste and color of bamboo shoot-red pepper paste pickle did not change remarkably, but the overall acceptance significantly changed from the beginning of storage. Bamboo shoot-soy sauce pickle did not give a significant change in flavor, taste and overall acceptance at $25^{\circ}C$, $35^{\circ}C$ and $45^{\circ}C$. However a remarkable change in color started to be shown at 25 d in case of storage at $45^{\circ}C$. Overall acceptance and color were selected as indicating parameters for the shelf-life estimation of bamboo shoot-red pepper paste pickle and bamboo shoot-soy sauce pickle, respectively. Based on room temperature storage and delivery at $20^{\circ}C$, the shelf-life of bamboo shoot-red pepper paste pickle and bamboo shoot-soy sauce pickle were determined as 308 d (about 10 month) and 447 d (about 14 month), respectively.

• Korean Journal of Food Science and Technology
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• v.37 no.6
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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.

• Food Industry And Nutrition
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• v.10 no.1
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• pp.11-16
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• 2005

식물성 단백질의 주요 공급원이며 우리나라 전통식품 중의 하나인 두부의 유통기한을 정량적으로 예측할 수 있는 수학적 모델을 개발하고자 온도와 초기균수에 따른 두부 부패세균의 성장 실험 결과를 데이터베이스화하여 이를 바탕으로 균의 성장을 정량적으로 평가할 수 있는 수학적 모델을 개발하였다. 근의 증식 지표인 최대증식속도상수(k), 유도기(LT), 세대시간(GT)은 온도에 지배적인 영향을 받았으며, 초기균수에 따른 유의 적 인 차이 는 없었다(p<0.05). 최대증식속도상수와 온도 및 초기균수의 상관관계를 나타내는 수학적 정량평가모델인 square root model을 이 용하여 두부 부패 세균의 성장을 정량적으로 예측할 수 있는 모델$({\surd}{\kappa}=0.016861(T+6.87095))$을 개발하였으며 실험치와 예측치의 상관계수는0.969이었다. 이 예측 정량평가모델로부터 예측한 최대증식속도상수와 두부의 관능적 부패시 점을 반영 한 Gompertz 변형 모델을 이용하여 두부의 유통기한을 예측할 수 있는 모델$(Spoilage-critrion(hr)=\frac{2{\times}Ln2+Ln[(Nmax/No)-1])}{k}$을 개발하였다

• Analytical Science and Technology
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• v.37 no.1
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• pp.12-24
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• 2024

In this study, laser-induced breakdown spectroscopy (LIBS) was used to estimate the degree of rice polishing. As-threshed rice seeds were dehusked and polished for different times, and the resulting grains were analyzed using LIBS. Various atomic, ionic, and molecular emissions were identified in the LIBS spectra. Their correlation with the amount of polished-off matter was investigated. Na I and Rb I emission line intensities showed linear sensitivity in the widest range of polished-off-matter amount. Thus, univariate models based on those lines were developed to predict the weight percent of polished-off matter and showed 3-5 % accuracy performances. Partial least squares-regression (PLS-R) was also applied to develop a multivariate model using Si I, Mg I, Ca I, Na I, K I, and Rb I emission lines. It outperformed the univariate models in prediction accuracy (2 %). Our results suggest that LIBS can be a reliable tool for authenticating the degree of rice polishing, which is closed related to nutrition, shelf life, appearance, and commercial value of rice products.

• Food Science of Animal Resources
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• v.30 no.5
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• pp.851-859
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• 2010

This study was conducted to investigate the application of a model system using an MS-electronic nose based on the discriminative function analysis on volatile flavors, to prediction of the shelf-life of market milk by preservation temperature and differently-loaded heat treatment. On mass spectrum, the ion fragments of volatile flavors of milk obtained from MS-electronic nose could be distinguished at amu 60, 91, 92, and 93. The response levels of volatile flavors at each amu increased in proportion to the heat treatment loaded to the milk, in the order of LTLT, HTST, and UHT. This study indicated that the discriminative function scores of the volatile flavors seemed to correlate with the preservation temperature, storage period, and heat treatment conditions; DF1 (discriminative function first score) showed a strong relationship to storage periods, with $r^2$ of 0.9965, 0.9965, and 0.9911 at temperatures of 4, 7, and $10^{\circ}C$, respectively, while DF2 was influenced by heat treatment conditions with an $r^2$ of 0.9861 at $4^{\circ}C$. It is suggested that the discriminative function analysis given by an MS-electronic nose could be used to construct a new quality control model system for the evaluation of heat treatment loaded during the processing of milk, and for predicting storage periods of market milk.

• Korean Journal of Food Science and Technology
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• v.23 no.5
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• pp.527-531
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• 1991

Frozen mandu, which is one of the main frozen prepared foods, purchased from a local manufacturer, were stored at five constant temperatures ($0,\;-5,\;-10,\;-20\;and\;-30^{\circ}C$) for six months. Effects of the storage temperature and the storage period on the changes in pH, acid value, peroxide value, volatile basic nitrogen, color, sensory score and microbial counts of frozen mandu were studied. The changes in microbiological and physicochemical characteristics were significantly increased in comparison with the initial value after 1 month at $0^{\circ}C$, after 3 months at $-5^{\circ}C$ and after 5 months at $-10^{\circ}C$, but nearly constant in spite of storage periods when the temperature dropped below $-10^{\circ}C$Out of five chemical components, AV and POV were the most reliable components in the quality judgement of frozen mandu and its upper limiting content were 2.56 and 19.35 meq/kg each. Regression equation for shelf life prediction of frozen mandu with sensory scores and POV was determined.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.2
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• pp.56-67
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• 2006

The reliability and failure mode effect analysis are effective means to achieve efficient and cost-reduction design for satellite development. The failure rate of COTS (Commercial-Off-The-Shelf) parts required for reliability analysis is not usually provided from the manufacturer. Space environment factors based on empirical data obtained from MIL-HDBK-217F can be applicable to the reliability calculation. As a radiation environment factor, the occurrence rate of SEL (Single Event Latch-up) is additionally incorporated for the failure rate prediction. In this paper, the statistical reliability analysis method for low-cost small satellite using COTS parts is suggested. This statistical reliability analysis was applied to HAUSAT-2 small satellite whose electronic boxes are consisted of many COTS parts to calculate the system reliability at the end of design mission life.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Korean Journal of Food Science and Technology
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• v.27 no.2
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• pp.193-198
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• 1995

The changes in the rheological properties of soybean curd upon the various storage temperatures ($5{\sim}25^{\circ}C$) were measured by the stress-relaxation test and analysed by time-temperature superposition theory. As the storage temperature was lower, higher initial and equilibrium stress of soybean curd were observed. When the stress-relaxation curves were moved horizontally by using the shift-factor on the basis of reference temperature, the master curve was obtained. By applying master curve and shift-factor to the WLF (Williams-Landel-Ferry) equation, activation energy (30kcal/mol) was calculated and storage time at the specific temperature could be predicted, suggesting the equivalent shelf-life of soybean curd texture.