Buchan Emma, Kelleher Liam, Clancy Michael, Stanley Rickard Jonathan James, Oppenheimer Pola Goldberg
School of Chemical Engineering, Advanced Nanomaterials Structures and Applications Laboratories, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
Department of Physics, Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge, CB3 0HE, UK.
Anal Chim Acta. 2021 Nov 15;1185:339074. doi: 10.1016/j.aca.2021.339074. Epub 2021 Sep 18.
Saliva analysis has been gaining interest as a potential non-invasive source of disease indicative biomarkers due to being a complex biofluid correlating with blood-based constituents on a molecular level. For saliva to cement its usage for analytical applications, it is paramount to gain underpinning molecular knowledge and establish a 'baseline' of the salivary composition in healthy individuals as well as characterize how these factors are impacting its performance as potential analytical biofluid. Here, we have systematically studied the molecular spectral fingerprint of saliva, including the changes associated with gender, age, and time. Via hybrid artificial neural network algorithms and Raman spectroscopy, we have developed a non-destructive molecular profiling approach enabling the assessment of salivary spectral changes yielding the determination of gender and age of the biofluid source. Our classification algorithm successfully identified the gender and age from saliva with high classification accuracy. Discernible spectral molecular 'barcodes' were subsequently constructed for each class and found to primarily stem from amino acid, protein, and lipid changes in saliva. This unique combination of Raman spectroscopy and advanced machine learning techniques lays the platform for a variety of applications in forensics and biosensing.
由于唾液是一种复杂的生物流体，在分子水平上与血液成分相关，因此唾液分析作为疾病指示生物标志物的潜在非侵入性来源正受到越来越多的关注。为了使唾液在分析应用中得到广泛应用，至关重要的是要获得基础分子知识，建立健康个体唾液成分的“基线”，并表征这些因素如何影响其作为潜在分析生物流体的性能。在此，我们系统地研究了唾液的分子光谱指纹，包括与性别、年龄和时间相关的变化。通过混合人工神经网络算法和拉曼光谱，我们开发了一种非破坏性分子分析方法，能够评估唾液光谱变化，从而确定生物流体来源的性别和年龄。我们的分类算法成功地从唾液中准确识别出性别和年龄。随后为每个类别构建了可识别的光谱分子“条形码”，发现其主要源于唾液中氨基酸、蛋白质和脂质的变化。拉曼光谱和先进机器学习技术的这种独特结合为法医学和生物传感中的各种应用奠定了平台。
