Human Hand Scents: A Novel Tool for Sex Prediction

Summary: A new study reveals that the composition of scent compounds on a person’s hand can accurately determine their sex.

The analysis, using mass spectrometry, successfully predicted an individual’s sex with an impressive accuracy rate of 96.67%. In criminal investigations, this could provide valuable trace evidence where other discriminative evidence like DNA is lacking.

Further validation of these techniques could even reveal other individual characteristics such as age and racial or ethnic group.

Key Facts:

  1. The scent compounds on a person’s hand can be analyzed to predict their sex, with a 96.67% accuracy rate.
  2. The study suggests that scent evidence could be a valuable resource in criminal investigations, particularly when other forms of evidence are lacking.
  3. Further development of these techniques could potentially identify other characteristics of an individual, such as age and racial or ethnic group.

Source: PLOS

The profile of scent compounds from a person’s hand can be used to predict their sex, according to a new study led by Kenneth Furton of Florida International University, publishing July 5 in the open-access journal PLOS ONE.

In criminal investigations, dogs have long been used to reliably identify and track people based on their odor.

But while human scent evidence from the field is well established, researchers have made little progress in analyzing human scent profiles in the lab.

Credit: Neuroscience News

In the new study, researchers used an analysis technique called mass spectrometry to analyze the volatile scent compounds present on the palms of 60 individuals – half male and half female.

After identifying the compounds in each sample, the team performed a statistical analysis to see if they could determine the individual’s sex based on their profile of scents. The analysis successfully predicted a person’s sex with a 96.67% accuracy rate.

Robberies, assaults and rape are all crimes that are often executed with a perpetrator’s hands, and thus have the potential to leave behind valuable trace evidence at a crime scene.

The new study shows that it is possible to predict a person’s sex based on hand scents, and existing human odor research indicates scent compounds can also reveal a person’s age and racial or ethnic group.

With further validation, the chemical and statistical analyses presented in this paper could be used to uncover many details about a potential perpetrator solely through their hand scent profiles.

The authors add: “This approach to analyzing hand odor volatiles can be applied when other discriminatory evidence such as DNA is lacking and allow for differentiation or class characterization such as sex, race and age.”

About this olfaction research news

Author: Hanna Abdallah
Source: PLOS
Contact: Hanna Abdallah – PLOS
Image: The image is credited to Neuroscience News

Original Research: Open access.
Multivariate regression modelling for gender prediction using volatile organic compounds from hand odor profiles via HS-SPME-GC-MS” by Kenneth Furton et al. PLOS ONE


Multivariate regression modelling for gender prediction using volatile organic compounds from hand odor profiles via HS-SPME-GC-MS

The efficacy of using human volatile organic compounds (VOCs) as a form of forensic evidence has been well demonstrated with canines for crime scene response, suspect identification, and location checking.

Although the use of human scent evidence in the field is well established, the laboratory evaluation of human VOC profiles has been limited.

This study used Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) to analyze human hand odor samples collected from 60 individuals (30 Females and 30 Males). The human volatiles collected from the palm surfaces of each subject were interpreted for classification and prediction of gender.

The volatile organic compound (VOC) signatures from subjects’ hand odor profiles were evaluated with supervised dimensional reduction techniques: Partial Least Squares-Discriminant Analysis (PLS-DA), Orthogonal-Projections to Latent Structures Discriminant Analysis (OPLS-DA), and Linear Discriminant Analysis (LDA). The PLS-DA 2D model demonstrated clustering amongst male and female subjects.

The addition of a third component to the PLS-DA model revealed clustering and minimal separation of male and female subjects in the 3D PLS-DA model.

The OPLS-DA model displayed discrimination and clustering amongst gender groups with leave one out cross validation (LOOCV) and 95% confidence regions surrounding clustered groups without overlap. The LDA had a 96.67% accuracy rate for female and male subjects.

The culminating knowledge establishes a working model for the prediction of donor class characteristics using human scent hand odor profiles.

Join our Newsletter
I agree to have my personal information transferred to AWeber for Neuroscience Newsletter ( more information )
Sign up to receive our recent neuroscience headlines and summaries sent to your email once a day, totally free.
We hate spam and only use your email to contact you about newsletters. You can cancel your subscription any time.
  1. Validate it. To predict is not consistent. Of course the analysis is quite sophisticated. I believe using this type of technique will open doors for validation & evaluation. Something to look forward to.

    1. Lol according to “science” gender is just a party game. You can’t identifty anything. Biology doesn’t matter anymore. I identify as a female Kangaroo.

  2. I think I get where you’re coming from, Adam. Still, “prediction” is an appropriate statical term for this study. The model being discussed here is basically a probability equation with multiple variables–given enough known information, a person using this study’s equation is approx 96.67% likely to “solve for” (ie correctly predict, within a particular confidence interval) the unknown outcome variable information (ie sex of hand’s contributor). Because this study relies on probability framework, technically, the multivariate regression models’ independent covariables are considered “predictive” of the target dependent variable. (In regression, an independent variable can even be called a “predictor variable”)


  3. We’ll this thing is going to make some people pretty mad if it can only determine only 2 type of sex, that’s way behind times. There’s has to be about 99 different types by now! I know one thing CA would NOT approve, and man for a tech company we are highly disappointed!

    1. In truth, humanity is not restricted to chromosomal XY (“male”) and XX (“female”)–you might be familiar with the term “intersex,” for example. Perhaps you meant to reference “gender,” which is considered to be socially constructed.

      If you did mean “gender”–although I’m pretty sure your comment was a sarcastic jab at “the Libs”–I think you suggest an interesting point.

      Typically, those who are chromosomally XX/identified as biologically female are assigned “girl” in utero or at birth by a clinical professional. (This assigned gender identity might be invalidated in time by the person living in that body, regardless of chromosomal or identified biological sex.)

      So, your comment could be relevant beyond it’s sarcasm: it’s important that we more consistently consider chromosomal/biological/gender identity diversity in research. The reality of human existence is far too complex to be limited to some convenient binary.

      Now, I’ve only read the article summary so I can’t be sure, but I’d imagine this study defines “sex” as the hand’s donor’s chromosomal or assigned biological sex (e.g., female). I also figure that the study’s researchers would have excluded from their sample as many “complicating” factors as possible, including those who are somehow chromosomally, biologically, and/or gender “discordant.”

      In any case, thank you for your contribution–you really got me thinking.

Comments are closed.