Qualitative and quantitative analysis of human hair in wig products by near-infrared spectroscopy

With the increasing demand for wigs in the fields of beauty, fashion, healthcare, and theater, the requirements for wig quality are also rising. Among them, human hair wigs are the most popular. In pursuit of profit, many merchants counterfeit human hair wigs with substandard products. The methods for human hair identification, such as microscopic observation, DNA analysis, and protein analysis, are almost exclusively targeted at forensic applications. These methods are not suitable for the detection of human hair in wigs, as the hair undergoes certain pre-treatments during the process of making wigs. To address the challenges in human hair identification, we developed a method based on Near-Infrared Spectroscopy (NIR) combined with chemometrics for the qualitative and quantitative analysis of human hair. Statistical methods such as similarity matching, qualification testing, Principal Component-Mahalanobis Distance Discriminant Analysis (PCA-DA), partial least squares-discriminant analysis (PLS-DA), and partial least squares regression (PLS) were employed to analyze the NIR data and establish multiple qualitative and quantitative models. The validation samples were substituted into these models and yielded accurate test results, demonstrating the feasibility of this method. The research results indicate that this method can be applied for the identification and quantitative analysis of human hair in wig products. Our research provides a rapid, convenient, and non-destructive method for the qualitative and quantitative analysis of human hair. This method is effective in combating counterfeit human hair wig products in the market.