• Biomedical Science Letters
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• v.23 no.4
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• pp.333-338
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• 2017

This study was performed to assess microbial contamination of Aronia melanocarpa, blueberry, raspberry, and cranberry sold in several markets. We investigated total aerobic bacteria and detected foodborne bacteria by multiplex PCR from Aronia melanocarpa, blueberry, raspberry, and cranberry. Total aerobic bacteria of each sample showed mean 3.54 log CFU/g for Aronia melanocarpa, mean 1.90 log CFU/g for blueberry, and mean 1.40 log CFU/g for raspberry, but not detected in cranberry. Specially, Aronia melanocarpa contained high total aerobic bacteria contamination among various berries and contamination level reached 4.17 log CFU/g in sample 5. To evaluate the effect of distribution conditions, we also investigated total aerobic bacteria of various berries. Total aerobic bacteria showed mean 2.89 log CFU/g for berries in refrigerated distribution and 1.40 log CFU/g in frozen distribution, but not in dry distribution. For assessment of foodborne bacteria contamination, we conducted PCR with multiplex primers of E. coli O157, S. aureus, B. cereus, V. parahaemolyticus, L. monocytogenes, Y. enterocolitica, Salmonella spp., Shigella spp. Among these foodborne bacteria, B. cereus was amplified in Aronia melanocarpa in sample 4 and blueberry in sample 1, 2, 3, and 5. The result of quantitative analysis of B. cereus contamination showed 4.08 log CFU/g of Aronia melanocarpa in sample 4 and higher contamination rate 4.07 log CFU/g of blueberry in sample 3. These results suggest that strict food safety control in harvest and distribution of various berries is necessary to prevent foodborne disease and improve microbiological safety.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.4
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• pp.256-262
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• 2016

The Weibull is widely used in reliability analysis, and several studies have attempted to improve estimation of the distribution's parameters. least squares estimation (LSE) or Maximum likelihood estimation (MLE) are often used to estimate distribution parameters. However, it has been proven that Bayesian methods are more suitable for small sample sizes than LSE and MLE. In this work, the Weibull parameter estimation accuracy of LSE, MLE, and Bayesian method are compared for sample sets with 3 to 30 data points. The Bayesian method was most accurate for sample sizes under 25, and the accuracy of the Bayesian method was similar to LSE and MLE as the sample size increased.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.289-298
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• 1998

We investigate the distribution of likelihood ratio test(LRT) of null hypothesis a sample is from single gamma with unknown shape and scale against the alternative hypothesis a sample is from a mixture of two gammas, each with unknown scale and unknown (but equal) scale. To obtain stable maximum likelihood estimates(MLE) of a mixture of two gamma distributions, the EM(Dempster, Laird, and Robin(1977))and Modified Newton(Jensen and Johansen(1991)) algorithms were implemented. Based on EM, we made a simple structure likelihood equation for each parameter and could obtain stable solution by Modified Newton Algorithms. Simulation study was conducted to investigate the distribution of LRT for sample size n = 25, 50, 75, 100, 50, 200, 300, 400, 500 with 2500 replications. To determine the small sample distribution of LRT, I considered the model of a gamma distribution with shape parameter equal to 1 + f(n) and scale parameter equal to 2. The simulation results indicate that the null distribution is essentially invariant to the value of the shape parameter. Modeling of the null distribution indicates that it is well approximated by a gamma distribution with shape parameter equal to the quantity $0.927+1.18/\sqrt{n}$ and scale parameter equal to 2.16.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.83-90
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• 2000

The chi-squared test statistic is usually employed for testing independence of two categorical variables in a two-way contingency table. It is well known that, under independence, the test statistic has an asymptotic chi-squared distribution under multinomial or product-multinomial models. For the case where both margins fixed, the sampling model of the contingency table is a multiple hypergeometric distribution and the chi-squared test statistic follows the same limiting distribution. In this paper, we study the difference between the small sample and large sample distributions of the chi-squared test statistic for the case with fixed margins. For a few small sample cases, the exact small sample distribution of the test statistic is directly computed. For a few large sample sizes, the small sample distribution of the statistic is generated via a Monte Carlo algorithm, and then is compared with the large sample distribution via chi-squared probability plots and Kolmogorov-Smirnov tests.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.903-914
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• 2014

The inverse Weibull distribution has been proposed as a model in the analysis of life testing data. Also, inverse Weibull distribution has been recently derived as a suitable model to describe degradation phenomena of mechanical components such as the dynamic components (pistons, crankshaft, etc.) of diesel engines. In this paper, we derive the approximate maximum likelihood estimators of the scale parameter and the shape parameter in the inverse Weibull distribution under multiply type-II censoring. We also develop four modified empirical distribution function (EDF) type tests for the inverse Weibull or extreme value distribution based on multiply type-II censored samples. We also propose modified normalized sample Lorenz curve plot and new test statistic.

• IE interfaces
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• v.17 no.1
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• pp.1-12
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• 2004

In a modern semiconductor device manufacturing industry, statistical bin limits on wafer level test bin data are used for minimizing value added to defective product as well as protecting end customers from potential quality and reliability excursion. Most wafer level test bin data show skewed distributions. By Monte Carlo simulation, this paper evaluates methods and sample size effect regarding determination of statistical bin limits. In the simulation, it is assumed that wafer level test bin data follow the Poisson distribution. Hence, typical shapes of the data distribution can be specified in terms of the distribution's parameter. This study examines three different methods; 1) percentile based methodology; 2) data transformation; and 3) Poisson model fitting. The mean square error is adopted as a performance measure for each simulation scenario. Then, a case study is presented. Results show that the percentile and transformation based methods give more stable statistical bin limits associated with the real dataset. However, with highly skewed distributions, the transformation based method should be used with caution in determining statistical bin limits. When the data are well fitted to a certain probability distribution, the model fitting approach can be used in the determination. As for the sample size effect, the mean square error seems to reduce exponentially according to the sample size.

• Journal of the Korean Society of Food Science and Nutrition
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• v.30 no.2
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• pp.204-209
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• 2001

The effects of size distribution of oil particles on the emulsion stability of mayonnaise were studied as follows; The stability of mayonnaise has concerned closely with the viscosity and the size distribution of oil particles. Mostly, if the viscosity was increased, the stability was improved, and the distribution of oil particles was uniform and the less the variation, the more the stability. 75% of oil concentration of sample showed the highest viscosity, also the size of sample was the most uniform, compared to other concentration. Mayonnaise prepared with whole egg was unstable, and the size of oil particles was double larger than the case prepared with only the yolk. Addition of xanthan gum increased, the viscosity and the stability by centrifuge so that the more stable mayonnaise could be prepared. The result of using log-normal density function by Heldmann represented that the normal size of sample adding 0.6%-soluble starch and sample N in non-adding control was increased, while those of sample adding xanthan gum and soluble starch at the same time didn't change.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.57-64
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• 2000

By assuming a Type-I censored sample, we propose the approximate maximum likelihood estimators(AMLE) of the scale and location parameters of the gamma distribution. We compare the proposed estimators with the maximum likelihood estimators(MLE) in the sense of the mean squared errors(MSE) through Monte Carlo method.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1441-1448
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• 2008

We propose the modified quantile-quantile (Q-Q) plot using the approximate maximum likelihood estimators and the modified normalized sample Lorenz curve (NSLC) plot for the extreme value distribution based on multiply Type-II censored samples. Using two example data sets, we picture the modified Q-Q plot and the modified NSLC plot.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1335-1344
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• 2008

The maximum likelihood method does not admit explicit solutions when the sample is multiply censored and progressive censored. So we shall propose some approximate maximum likelihood estimators (AMLEs) of the scale parameter for the power function distribution based on multiply Type-II censored samples and progressive Type-II censored samples when shape parameter is known. We compare the proposed estimators in the sense of the mean squared error (MSE) through Monte Carlo simulation for various censoring schemes.