Chemical Fingerprints of Emotional Body Odor

Abstract

Chemical communication is common among animals. In humans, the chemical basis of social communication has remained a black box, despite psychological and neural research showing distinctive physiological, behavioral, and neural consequences of body odors emitted during emotional states like fear and happiness. We used a multidisciplinary approach to examine whether molecular cues could be associated with an emotional state in the emitter. Our research revealed that the volatile molecules transmitting different emotions to perceivers also have objectively different chemical properties. Chemical analysis of underarm sweat collected from the same donors in fearful, happy, and emotionally neutral states was conducted using untargeted two-dimensional (GC×GC) coupled with time of flight (ToF) MS-based profiling. Based on the multivariate statistical analyses, we find that the pattern of chemical volatiles (N = 1655 peaks) associated with fearful state is clearly different from that associated with (pleasant) neutral state. Happy sweat is also significantly different from the other states, chemically, but shows a bipolar pattern of overlap with fearful as well as neutral state. Candidate chemical classes associated with emotional and neutral sweat have been identified, specifically, linear aldehydes, ketones, esters, and cyclic molecules (5 rings). This research constitutes a first step toward identifying the chemical fingerprints of emotion.

Keywords: body odor; chemical fingerprint; chemosignaling; gas chromatography-mass spectrometry; odor perception; pheromones; volatile organic compounds (VOCs); volatilome.

Figure A1

Receiver operating characteristic (ROC) curves of peak 2209 (left panel) and 234 (right panel) for the classifications Happy–Neutral (blue), Fear–Neutral (red), and Fear–Happy (green). For peak 2209 Neutral was the positive level for Happy–Neutral and Fear–Neutral, and Happy was the positive level for Fear–Happy. For 234 fear was the positive level for all three classifications.

Figure A2

Box plot of three condition classes based on the sum of 1655 peak areas per sweat sample.

Figure A3

Example of a typical comprehensive GC×GC TOF MS contour plot of a test person, with x-axis denoting first dimension rt time in min (on non-polar column in Table A1), and y-axis denoting second dimension rt time is seconds (mid polar column in Table A1).

Figure A4

PCA performed on 3796 RT/MS peaks and 169 pads using Pareto scaling. The scores plot (upper panel) displays the variation of the pads classified as clean (from clean pads), reference (from pads deposited in the room), and the condition samples (Neutral, Happy, Fear from the subjects). The loading plot (lower panel) displays the variation of the analytes with tentative assignments.

Figure 1

Effects of emotion induction (fear, neutral, happy) on self-reported emotional state: (A) Spider plot showing participants’ emotion profiles on affective circumplex [46]; (B) experienced core affect (valence, arousal) on affect grid [47]; (C) experienced discrete emotions. Error bars: ± 1 standard error of the mean (SEM).

Figure 2

Partial least squares discriminant analysis (PLS-DA) score plots (left panels) and loading plots (right panels) based on 1655 peak areas and on two condition classes at a time: Neutral versus Fear (upper panels), Neutral versus Happy (middle panels), Fear versus Happy (lower panel). The first two principal components (PCs) t[1] and t[2] are displayed. The loading plots show weighted coefficients of the principal components. The data were not normalized and Pareto-scaled prior to analysis. Q² values > 0.2 were statistically significant.

Figure 3

Principal component analysis (PCA) score plot based on 135 sweat samples and 94 peak areas that were significantly discriminating the condition groups in the PLS-DA models. The principal components t[1], t[2] are displayed, explaining R2X[1]= 0.437 and R2X[2] = 0.141 of the variances, respectively. The data were not normalized and Pareto scaling was applied.

Figure 4

Box plots showing individual peaks per condition class: Panels (A–E) show the boxplots of the peaks 146, 1850, 2578, 2019, and 1842, respectively, per condition for each of the 24 panelists as observed in underarm sweat both left and right (so 48 datapoints for each of the three conditions in each graph).