Abstract：
The analysis of fingerprint chemical composition is a meaningful way to excavate the multidimensional information of fingerprint, including the donor profiling information and the age of a fingerprint, which broadens the evidential values of fingerprint, especially for the partial and distorted fingerprint. But the research remains still in the pilot phases or is ongoing. Amino acids are the dominant organic substances in latent sweat fingerprint and influenced by many donor factors. Hence, their content reflects personal information of donors. Forensic science will be revolutionized if suspects can be individualized by their amino acid content. The diverse nature, distinct physicochemical properties, and ultra-micro levels of amino acids present in fingerprints make it hard to detect. A high sensitivity method for detecting and quantifying multiple amino acid components is required. UHPLC-QqQ MS/MS offers high sensitivity, high separation, simultaneous multicomponents detection, and no derivatization, making it an ideal method for detecting and analyzing amino acids in fingerprints. Therefore, in this study, we propose and validate an efficient UHPLC-QqQ MS/MS method for the extraction and analysis of 13 amino acids from fingerprint. We compared the results of amino acids of 10 different substrates and found that the inherent amino acids in most porous substrates would have been extracted along with the fingerprint amino acids, making them unsuitable for quantitative amino acid analysis. Instead, plastic sheets are ideal substrates for laboratory studies. Then, extensive experiments were conducted among 30 donors for multidimensional information analysis. The type of samples analyzed were eccrine-rich fingerprints. A Binary Logistic Regression (BLR) model was developed, and the female and male donors were successfully differentiated by amino acids in fingerprints. Two other mathematical models were also developed to verify the accuracy, and all three different mathematical models were able to identify donors of different genders with over 90% accuracy. This demonstrates that amino acids have the potential to provide more information for donors as metabolic markers. In the future, we will conduct a series of experiments to analyze more multidimensional information for individual identification by amino acid content in the fingerprint.
摘要：
指纹化学成分分析是挖掘指纹多维信息的一种有意义的方法,包括捐赠者的特征信息和指纹的年龄，扩大了指纹的证据价值，尤其是部分扭曲的指纹.但这项研究仍处于试点阶段或正在进行中。氨基酸是潜在汗液指纹图谱中的主要有机物质，受许多供体因素的影响。因此，它们的内容反映了捐赠者的个人信息。如果犯罪嫌疑人可以根据其氨基酸含量进行个性化，那么法医学将发生革命性的变化。多样的性质，独特的物理化学性质，指纹中存在的超微水平的氨基酸使其难以检测。需要用于检测和定量多种氨基酸组分的高灵敏度方法。UHPLC-QqQMS/MS提供高灵敏度，高度分离，多成分同时检测，没有衍生化，使其成为检测和分析指纹图谱中氨基酸的理想方法。因此,在这项研究中，我们提出并验证了一种高效的UHPLC-QqQMS/MS方法，用于从指纹图谱中提取和分析13种氨基酸。我们比较了10种不同底物的氨基酸结果，发现大多数多孔底物中的固有氨基酸会与指纹氨基酸一起被提取，使它们不适合氨基酸定量分析。相反，塑料板是实验室研究的理想基材。然后，我们在30名捐献者中进行了广泛的实验,以进行多维信息分析.分析的样品类型是富含内分泌的指纹图谱。建立了二元Logistic回归(BLR)模型，并且通过指纹图谱中的氨基酸成功区分了女性和男性供体。还开发了另外两个数学模型来验证准确性，所有三个不同的数学模型都能够识别不同性别的捐赠者，准确率超过90%。这表明氨基酸具有为供体提供更多信息作为代谢标记的潜力。在未来,我们将进行一系列实验，以分析更多的多维信息，以通过指纹中的氨基酸含量进行个体识别。
