臺灣博碩士論文加值系統

微生物檢測是食品業的重要任務。致病微生物將食物材料污染成簇或一群個體細胞。細菌在整個食物中遵循零膨脹的負二項分配（ZINB）。為ZINB開發了雙重抽樣計劃（DSP）。我們提供R-code來決定第一階段的計劃參數，例如樣本大小、接受值和拒收值（n1，c1和r1），以及第二階段的樣本數量和接受值（n2和c2）。在給定的允收品質水準(AQL)值，拒絕水準(LTPD)下兩個風險值α和β。使用各種參數（φ，p_1，α，p_2，β）的模擬數據來說明應用。

Microbiological testing is an important task in the food industry. Pathogenic microorganism contaminates the food material as clusters or group of individuals cells. The bacteria follows a zero-inflated Negative Binomial (ZINB) distribution in the entire food material. A double sampling plan (DSP) is developed for an ZINB distribution. We also provide R-code to determine the plan parameters such as the number of sample size, the acceptance number, and the rejection number on the first stage (n1, c1, and r1), and the number of the sample size and the acceptance number on the second stage (n2 and c2). Under given value of (acceptable quality level (AQL), lot tolerance percent defects (LTPD) two risk α and β. Simulated data with various of parameters (φ,p_1,α,p_2,β) are used to illustrate the applications.

Abstract II
Table of contents III
Chapter 1 Introduction 1
1.1 Research background and motivation 1
1.2 Research objectives 3
1.3 Research limitations 4
1.4 Research flow 4
Chapter 2. Literature Review 7
2.1 Single sampling 7
2.2 Double sampling 7
2.3 The zero inflated Negative Binomial 10
Chapter 3. Methodology 13
Chapter. 4 Illustrative examples 15
Chapter 5. Conclusions 21

1. Antoni, C. J., Pere, G. C. (1999). Lot sampling plans in the measure of quality of care indicators. International Journal for Quality in Health Care 11(2):139-145
2. Bohning, D., Dietz, E., schlattman, P. (1999). The zero-inflated Poisson model and the decayed, missing and filled teeth index in dental epidemiology. Journal of Royal Statistical Society, Series A 162:195-209
3. Cameron, J.M (1952). Tables for constructing and computing the Operating Characteristics of Single Sampling Plans. Industrial Quality Control 9(1):37 - 39
4. Deros, B. M., Peng, C. Y., Rahman, M. N., Ismail, A. R., Sulong, A. B. (2008). Assessing acceptance sampling application in manufacturing electrical and electronic products. Journal of Achievements in Materials and Manufacturing Engineering 31(2):622-628.
5. Dhamija, O. P., Hammer, W. C. K. (1990). Manual of food quality control, Vol. 6. Food for Export, FAO Food and Nutrition Paper 146. Rev. 1, Food and Agriculture Organization of the United Nations, Rome.
6. Dodge, H.F., and Romig., 1959, Sampling inspection tables-single, double sampling, 2nd edition, John Wiley and Sons, New York.
7. European Commission. (2005). Commission Regulation No 2073/2005 (OJ L338, p1, 22/12/2005) of 15 November 2005 on microbiological criteria for foodstuffs.
8. Gonzales-Barron, U., Kerr, M., Sheridan, J. J., & Butler, F. (2010). Count data distributions and their zero-modified equivalents as a framework for modelling microbial data with a relatively high occurrence of zero counts. International Journal of Food Microbiology, 136, 268e277.
9. Hasan, M. T., Sneddon, G. (2009). Zero-inflanted Poisson regression for longitudinal data. Communication in Statistic-Simulation and Computation 38:638-653.
10. International Commission on Microbiological Specification of Foods (ICMSF). (1986). Microorganisms in Foods 2. Sampling for Microbiological Analysis: Principles and Specific Applications. 2nd ed. University of Toronto Press.
11. Lambert, D. (1992). Zero-Inflated Poisson regression with an application to defects in manufacturing. Technometrics 34:1-14
12. Johnson, N. L., Kemp, A. W., & Kotz, S. (2004). Univariate Discrete Distributions (3rd ed.). NY: Wiley.
13. Loganathan. A., Shalini, K. (2014). Determination of single sampling plans by attributes under the conditions of zero-Inflated poisson distribution. Communications in Statistics-Simulation and Computation 43(3):538-548.
14. McLachlan, G., Peel, D. (2000). Finite Mixture Models. New York: John Wiley & Sons.
15. Montgomery DC. Introduction to statistical quality control. New York: Wiley, 2013.
16. Mussida, A., Gonzales-Baron, U., and Butler, Francis. (2013). Effectiveness of sampling plans by attributes based on mixture distributions characterizing microbial clustering in food. Food control 34(1):50-60
17. Ponnuraja, C and Santhi S. (2014). Construction of Single Sampling Plan Indexed Through Limiting Quality Level. IOSR Journal of Mathematics (IOSR-JM).
18. R Core Team. (2017). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
19. Ridout, M., Hinde, J., Demtrio, C. G. B. (1998). Models for count data with many zeros. Cape Town: International Biometric Conference.
20. Ridout, M., Hinde, J., Demtrio, C. G. B. (2001). A score test for testing a zero-inflated poisson regressionmodel against zero-inflated negative binomial alternatives. Biometrics 57, 219-223.
21. Schilling, E.G., 1982, Acceptance Sampling in Quality Control, Marcel Dekker, New York.
22. Schilling, E.G., and Johnson, N.C., 1980, Tables for the construction of matched single, double and multiple sampling plan with application to MILSTD-105D under the switching rules,” Journal of Quality Technology., Vol.10, no.3, pp. 104-124.
23. Soundararajan, V., and Arumainayagam, S.D., 1990a, A generalized procedure for selection of attribute double sampling Plan,” Communication in Statistics-Simulation and Computation, Vol. 19, No. 3, pp. 1015-1034.