Comparison and source tracing of multiple stable isotopes in fingernails of residents from three cities in Shaanxi

doi:10.16359/j.1000-3193/AAS.2025.0113

Abstract

Abstract:

Stable isotope ratio information derived from fingernails can be used to infer geographical changes in an individual’s residential area, which holds significant value for personal identification in forensic science. This study utilized Elemental Analyzer-Isotope Ratio Mass Spectrometry (EA-IRMS) to determine the stable isotope ratios of hydrogen (H), oxygen (O), carbon (C), and nitrogen (N) in fingernail samples collected from residents of three cities in Shaanxi Province: Ankang, Xi′an, and Yulin.

These stable isotope ratios act as biological markers that reflect the environmental and geographical conditions experienced by individuals, thereby providing valuable data for tracing an individual’s geographical origin. Three source-tracing analysis models were employed in this research: Discriminant Analysis, Multi-Layer Perceptron (MLP), and Radial Basis Function (RBF). These models were applied to analyze the multivariate stable isotope ratio data obtained from all fingernail samples across the three cities.

Additionally, the study investigated how different combinations of isotopic elements influenced the accuracy of the source-tracing models. The core objective was to evaluate whether the integration of multiple stable isotopes (H, O, C, and N) could enhance the ability to distinguish individuals based on their geographical origins.

The results indicated that all three models effectively differentiated fingernail samples from the three cities, with the MLP model exhibiting the highest accuracy. Compared to single-element or dual-element combinations, the use of multiple isotopes significantly improved classification accuracy. Specifically, the Discriminant Analysis model achieved a cross-validation accuracy of 84.7%, the MLP model reached 88.9%, and the RBF model attained an accuracy of 85.7%. The MLP model performed marginally better, primarily due to its capacity to effectively handle the complex nonlinear relationships between variables in the isotopic data.

These findings underscore the value of integrating multiple isotopic elements for more precise source tracing and highlight the potential of isotope analysis in accurate geographical identification. The research demonstrated that combining different stable isotope elements—particularly H, O, C, and N—provides a more comprehensive and reliable approach to tracing an individual’s origin.

Furthermore, the results emphasize the importance of advanced statistical models in forensic analysis, especially for identifying individuals using biological samples such as fingernails. This study offers valuable insights into the application of stable isotope technology in personal identification and opens new avenues for forensic science applications, particularly in geographical origin tracing. Ultimately, this research contributes to enhancing the accuracy of forensic investigations and personal identification processes in both criminal and civil contexts.

Key words: nails, stable isotopes, traceability, EA-IRMS

CLC Number:

D919/O615.2

CAI Zhuotong, MEI Hongcheng, JIE Zhaowei, ZHU Jun, ZHANG Conghe, XU Aoxiang, LI Yajun, HU Can, WANG Wei, LI Zhi. Comparison and source tracing of multiple stable isotopes in fingernails of residents from three cities in Shaanxi[J]. Acta Anthropologica Sinica, 2026, 45(04): 732-741.

Figures/Tables 7

References 26

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同位素比值Isotope ratio	安康Ankang	西安Xi'an	榆林Yulin
H	-70.18‰±2.06‰^a	-75.07‰±3.63‰^b	-75.18‰±2.94‰^b
O	8.33‰±0.75‰^a	7.03‰±0.69‰^b	8.62‰±0.8‰^a
C	-20.68‰±0.58‰^a	-20.5‰±0.81‰^a	-20.32‰±0.69‰^a
N	7.24‰±0.27‰^b	7.91‰±0.58‰^a	7.03‰±0.63‰^b

同位素比值Isotope ratio	安康Ankang	西安Xi'an	榆林Yulin
H	-70.18‰±2.06‰^a	-75.07‰±3.63‰^b	-75.18‰±2.94‰^b
O	8.33‰±0.75‰^a	7.03‰±0.69‰^b	8.62‰±0.8‰^a
C	-20.68‰±0.58‰^a	-20.5‰±0.81‰^a	-20.32‰±0.69‰^a
N	7.24‰±0.27‰^b	7.91‰±0.58‰^a	7.03‰±0.63‰^b

神经网络模型Neural network model	样本比例 sample ratios		输入层数Number of input layer	输入层协方差变量Input layer covariance	隐藏数/单元数Number of hidden layers/units	隐藏层启动函数Hidden layer startup function	输出层数Number of output layers	输出层变量Output level dependent variable	输出层启动函数Output layer startup function
神经网络模型Neural network model	训练Training	验证validation	输入层数Number of input layer	输入层协方差变量Input layer covariance	隐藏数/单元数Number of hidden layers/units	隐藏层启动函数Hidden layer startup function	输出层数Number of output layers	输出层变量Output level dependent variable	输出层启动函数Output layer startup function
多层感知器MLP	70%	30%	4	C、H、O、N	1/6	双曲正切 Hyperbolic tangent	3	城市 City	归一化指数 Normalized Index
径向基函数RBF	70%	30%	4	C、H、O、N	1/7	归一化指数Normalized Index	3	城市City	恒等式Identity

神经网络模型Neural network model	样本比例 sample ratios		输入层数Number of input layer	输入层协方差变量Input layer covariance	隐藏数/单元数Number of hidden layers/units	隐藏层启动函数Hidden layer startup function	输出层数Number of output layers	输出层变量Output level dependent variable	输出层启动函数Output layer startup function
神经网络模型Neural network model	训练Training	验证validation	输入层数Number of input layer	输入层协方差变量Input layer covariance	隐藏数/单元数Number of hidden layers/units	隐藏层启动函数Hidden layer startup function	输出层数Number of output layers	输出层变量Output level dependent variable	输出层启动函数Output layer startup function
多层感知器MLP	70%	30%	4	C、H、O、N	1/6	双曲正切 Hyperbolic tangent	3	城市 City	归一化指数 Normalized Index
径向基函数RBF	70%	30%	4	C、H、O、N	1/7	归一化指数Normalized Index	3	城市City	恒等式Identity

模型Model	多层感知器MLP			径向基函数RBF
模型Model	安康Ankang	西安Xi'an	榆林Yulin	安康Ankang	西安Xi'an	榆林Yulin
训练集Training set	93.30%	93.80%	100%	85.70%	80%	87.50%
测试集Test set	75%	75%	100%	80%	100%	75%