Sampling Plan Based on Operating Characteristic Curve
Science Journal of Analytical Chemistry
In the pharmaceutical industry, bulk raw materials are purchased to manufacture the bulk pharmaceutical active ingredients. Some of these bulk raw materials in the packaging is randomly picked up to determine the quality of them. So, the sampling plan is an essential means of testing in quality inspections to make disposition decision. So far, the square root of N plus one rule has been employed to provide a simple mathematical way to calculate the number of items to be inspected for quality of
... cted for quality of raw materials. However, this rule is apparently not devised on the statistical consideration. Now, another sampling plan based on the operating characteristic (OC) curve is established. The OC curve is defined with a sample size and the maximum acceptable number of defective items, describing how well sampling plan discriminates between good and bad lots. This sampling plan is associated with risks such as the producer's risk of incorrected rejection by the consumers and the consumer's risk of incorrect acceptance of the lots with unsatisfied quality. The sampling plan based on the OC curve is exploited to validate the reliability on two levels of quality, such as acceptable quality level (AQL) and lot tolerance percent defective (LTPD). This newly established sampling plan is compared with the principle of the square root of N plus one rule to demonstrate the effectiveness to distinguish the good lots from bad lots for the plants where the individual packaging of raw materials is usually purchased at the level of less than 50. In the case of the number of the individual packaging is less than or equal to16, the capability of the new sampling procedure based on the OC curve for discrimination of the quality of lots inspected is superior or comparable to the principle of square root of N plus one rule. This paper describes the reliability and efficacy of the single-sampling plan under the principles of the OC curve.