A stepwise strategy to distinguish menstrual blood from peripheral blood by Fisher’s discriminant function

  • Hongxia He
  • Anquan Ji
  • Yixia Zhao
  • Na Han
  • Sheng Hu
  • Qinglan Kong
  • Li Jiang
  • Jian Ye
  • Yao LiuEmail author
  • Qifan SunEmail author
Original Article


Blood samples are the most common and important biological samples found at crime scenes, and distinguishing peripheral blood and menstrual blood samples is crucial for solving criminal cases. MicroRNAs (miRNAs) are important molecules with strong tissue specificity that can be used in forensic fields to identify the tissue properties of body fluid samples. In this study, the relative expression levels of four different miRNAs (miR-451, miR-205, miR-214 and miR-203) were analysed by real-time PCR, with 200 samples from 5 different body fluids, including two kinds of blood samples (peripheral blood and menstrual blood) and three kinds of non-blood samples (saliva, semen and vaginal secretion). Then, a strategy for identifying menstrual and peripheral blood based on Fisher’s discriminant function and the relative expression of multiple miRNAs was established. Two sets of functions were used: Z1 and Z2 were used to distinguish blood samples from non-blood samples, and Y1 and Y2 were used to distinguish peripheral blood from menstrual blood. A 100% accuracy rate was achieved when 50 test samples were used. Ten samples were used to test the sensitivity of the method, and 10 ng or more of total RNA from peripheral blood samples and 10 pg or more of total RNA from menstrual blood samples were sufficient for this method. The results provide a scientific reference to address the difficult forensic problem of distinguishing menstrual blood from peripheral blood.


Body fluid identification MicroRNA Peripheral blood Menstrual blood Fisher’s discriminant function 



In addition, we want to thank Prof. Guo Xiangqian from Henan University for help with the sample collection, and we would also like to thank all the volunteers who contributed samples to this research.

Authors’ contributions statement

Conceptualization—Anquan Ji, Sun Qifan and Qinglan Kong.

Methodology—Qifan Sun, Hongxia He, Yixia Zhao, and Sheng Hu.

Software—Hongxia He and Li Jiang.

Formal analysis—Hongxia He, Na Han and Qifan Sun.

Statistical modelling—Hongxia He and Qinglan Kong.

Investigation—Qifan Sun, Anquan Ji and Sheng Hu.

Resources—Anquan Ji, Qifan Sun. Jian Ye and Yao Liu.

Data curation—Qifan Sun, Yixia Zhao, and Anquan Ji.

Writing and original draft—Hongxia He, and Qifan Sun.

Writing and review and editing—Hongxia He, Na Han and Qifan Sun.

Project administration—Qifan Sun, Jian Ye and Yao Liu.

Funding acquisition—Qifan Sun and Anquan Ji.

Funding information

This work was supported by grants from the National Key R&D Programme (No. 2017YFC0803503) and the Basic Research Project from the Institute of Forensic Science, Ministry of Public Security, China (No. 2019JB010).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Supplementary material

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ESM 1 (XLSX 105 kb)
414_2019_2196_MOESM2_ESM.xlsx (24 kb)
ESM 2 (XLSX 24 kb)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.MPS’s Key Laboratory of Forensic Genetics, National Engineering Laboratory for Crime Scene Evidence Investigation and ExaminationInstitute of Forensic Science, Ministry of Public Security (MPS)BeijingChina
  2. 2.Faculty of Forensic SciencesShanxi Medical UniversityTaiyuanPeople’s Republic of China
  3. 3.Chinese Center For Disease Control And PreventionState Key Laboratory of Infectious Disease Prevention and ControlBeijingChina
  4. 4.Faculty of Mathematics and StatisticsZaozhuang UniversityZaozhuangPeople’s Republic of China

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