Application Of Chemometric Methods In Data Analysis Of Quality Control Of Cosmetics

Data analysis of quality control of cosmetics in Shanghai market from2008to first half of2012is concerned mainly. The related test items are microbial indedes and contents of heavy metals. The microbial indicators include aerobic bacterial count, molds and yeast count, fecal coliforms, pseudomonas aeruginosa and staphylococcus aureus, and the heavy metals include lead, arsenic and mercury. Detection methods of the items mainly refer to national Hygienic Standard for Cosmetics (2007). Chemometric methods of data analysis used are regression analysis and Principal Component Analysis (PCA).Collecting data of quality control from2008to first half of2012, this thesis focuses on statistic analysis of unqualified rate due to different indexes, relationship between index and type of unqualified samples and their reasons. Principal component analysis is used to analyze the data collected for both of microbial indicators and contents of heavy metals for assistant judgement and evaluation of the results of statistic analysis. With score plot of principal component analysis classification and assignment of samples are performed for further analyzing reasons causing the classification and the unqualified samples.The results show that the quality of most samples is good in view of microbial indexes, the unqualified rate being1.66%. Main reason of the unqualified samples is due to indexes of aerobic bacterial count and molds and yeast count. And there is a clear linear relationship between aerobic bacterial count and sample quality with a squared correlation coefficient of R2=0.952. Microbial unqualified products are water, cream and powder samples that contain modified corn starch products. The unqualified rates of hydrosol and powder are47.83%and26.92%respectively. Sample quality is good from index of Heavy metal normally, and the unqualified sample rate is0.259%. The most unqualified samples are mineral and whitening products, and the unqualified indexes are lead and mercury. From score plot of PCA it is found that there exists an obvious classification between qualified and unqualified samples. For the unqualified samples one can distinguish samples due to microbial indexes and those due to heavy metals. It has been also found that aerobic bacterial count, molds and yeast count and arsenic content are the main affecting factors for the unqualified samples.