naa a22 4500 51076469X CHVBK 20180411083152.0 cr unu---uuuuu 180411e20130901xx s 000 0 eng 10.1007/s12393-013-9068-1 doi (NATIONALLICENCE)springer-10.1007/s12393-013-9068-1 On Estimating the Time to the First and Between Successive Accidental Mishaps from Quality Control Records [Elektronische Daten] [Micha Peleg, Mark Normand] In quality control, QC, an accidental mishap can be defined as an entry either falling below a lower or above upper permitted limits without an apparent cause or early indication that this is about to happen. When the randomly fluctuating entries of a QC chart have independent entries with no trend or periodicity, their distribution can be used to estimate the probability of such a mishap. The entries' distribution can also be used to estimate the distribution of the times to the first mishap and that of the times between successive mishaps. This is demonstrated with Monte Carlo simulated QC records whose entries' distribution is normal, representing typical chemical or physical attributes and certain microbial counts, or lognormal, which is frequently encountered in microbial count records. The distribution of the times to cross a lower or upper threshold only is approximately exponential, which has no peak, but whose mean or median can still serve as an intuitive quantitative measure of a process's degree of risk. When the mishap is defined as crossing either the lower or upper threshold, the distribution of the times between successive mishaps is frequently also exponential but not always. It depends on the entries' distribution parameters and the thresholds' magnitudes. In such a case, the most frequent time interval can be identified from the peak of the time intervals' histogram. The procedures to simulate QC charts having normally or lognormally distributed entries, set the thresholds, plot the histogram of the times to the first mishap or time intervals between successive mishaps, and to calculate the corresponding exponential distribution have been automated. They have been made available on the Internet as a series of freely downloadable interactive Wolfram Demonstrations. These demonstrations, where all the parameters can be entered and modified with sliders on the screen, can serve as an auxiliary tool to quantify the risk of accidental mishaps in actual industrial food and nonfood processes. They can also be used to simulate hypothetical line performance scenarios and examine their consequences. Springer Science+Business Media New York, 2013 Quality assurance nationallicence Predictive microbiology nationallicence Risk assessment nationallicence Monte Carlo simulations nationallicence Statistical analysis nationallicence Random time series nationallicence Peleg Micha Department of Food Science, University of Massachusetts, 01035, Amherst, MA, USA aut Normand Mark Department of Food Science, University of Massachusetts, 01035, Amherst, MA, USA aut Food Engineering Reviews Springer US; http://www.springer-ny.com 5/3(2013-09-01), 123-138 1866-7910 5:3<123 2013 5 12393 https://doi.org/10.1007/s12393-013-9068-1 text/html Onlinezugriff via DOI 1 review-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s12393-013-9068-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Peleg Micha Department of Food Science, University of Massachusetts, 01035, Amherst, MA, USA aut NATIONALLICENCE 700 1- Normand Mark Department of Food Science, University of Massachusetts, 01035, Amherst, MA, USA aut NATIONALLICENCE 773 0- Food Engineering Reviews Springer US; http://www.springer-ny.com 5/3(2013-09-01), 123-138 1866-7910 5:3<123 2013 5 12393 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer