Core Viewpoint - The article discusses the advancements in animal hair identification in textiles using the MALDI-TOF mass spectrometry technology developed by Bruker, which addresses the challenges posed by traditional methods and enhances accuracy and reliability in detecting animal hair sources in textile products [1][2][12]. Group 1: Identification Challenges - Traditional methods for identifying animal hair in textiles, such as optical microscopy, are limited, especially when the samples undergo processing like bleaching or dyeing, which can damage DNA [1][2]. - Certain animal fibers, such as cashmere, sheep wool, and yak wool, have overlapping morphological characteristics, making it difficult to establish objective standards for identification [1]. Group 2: MALDI-TOF Technology Implementation - The Shanghai Customs Industrial Products and Raw Materials Testing Technology Center has established a national standard (GB/T 42699.2-2023) for the qualitative and quantitative analysis of animal hair fibers in textiles using MALDI-TOF-MS, which will be implemented in March 2024 [2]. - The MALDI-TOF-MS method allows for both accurate identification of animal hair sources and quantification of adulterated samples, demonstrating good reproducibility [2][11]. Group 3: Methodology and Results - The process involves extracting proteins from animal hair using SDS/DTT/phosphate buffer, purifying them through SDS-PAGE, and analyzing the resulting peptides with MALDI-TOF-MS to identify species based on characteristic peptide segments [3][4]. - Characteristic peptide segments for different animal species are concentrated in the mass-to-charge ratio range of 2450 m/z to 2750 m/z, allowing for precise identification [4][6]. Group 4: Performance and Reliability - The MALDI-TOF-MS method has shown excellent robustness with a relative standard deviation (RSD) of less than 5% over three years of testing different sample concentrations, ensuring data accuracy and reliability [11]. - The technology is not only applicable to textile analysis but can also be utilized in other fields, such as food adulteration detection [12].