Abstract:

Some disadvantages such as cumbersome process and difficult microorganism confirmation exist when traditional chemical reagent method is used to study the influence of mask on facial micro-ecology. In order to solve this problem, a method of micro-ecological analysis based on principal component analysis （PCA） was proposed. First, PCA was used to reduce the dimensionality of the facial micro-ecological data which could determine the eigenvector and the dimension k when the cumulative contribution rate was 95%. Then, when the threshold value was also 95%, the mapping relationship between eigenvector and the facial micro-ecological data was used to determine the attributes with large contribution rate in the facial micro-ecological data, namely, the types of microorganisms which could improve the facial skin status. The experimental results showed that the proposed method could effectively overcome the shortcomings of the traditional chemical reagent method, and could be used to quickly and accurately determine the microorganism species which were related to the improvement of the skin status after using the mask. Meanwhile, the differences of the changes of microorganism species caused by different masks were also found, which could provide suggestions for the cosmetics industry to make more beneficial masks for facial skin.

Key words: principal component analysis, dimension reduction, feature selection, mask, micro-ecology