Quality Improvement in Routine Inspection and Control of Healthcare Products Using Statistical Intervention of Long-Term Data Trend
Abstract
Commercial products in markets must meet the regulatory and quality control criteria to be acceptable for the intended use. While it is mandatory that each product batch must meet specification limits, the stability and efficiency over the long term are underestimated. The present study was conducted on the chronological trend of a healthcare product using a statistical software package, including correlation matrix and multivariate analysis. The investigated quality characteristics were the average filling weight, relative density, pH and the relative potency of three active components, in addition to a chemical preservative. The database was processed in an Excel sheet and was subjected to descriptive statistical overview, histogram plot, box plot diagram, time series plot, correlation matrix table and Principal Component Analysis. The investigation showed that despite all batches passed quality control tests successfully yet there were signs of instability, fluctuations or oscillations and drifts in all quality metrics, with outlier values that could be observed. Non-parametric correlation demonstrated some level of association between some inspection characteristic indicators. PCA illustrated the major variability influencer and clustering tendency among studied quality markers that guided the grouping of the dataset. The study pinpointed the improvement needed to ensure product stability, efficiency and quality.
Keywords
Box plot, Histogram, Normality test, Matrix plot, Outlier, Principal component analysis
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