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.

Encoding fear intensity in human sweat

“…Another necessity [47] is then to characterize molecule quality (actual molecule identities) aside from quantity, to test if the same doses of fear sweat and neutral sweat are qualitatively different, and whether higher doses of fear sweat are represented by 'more of the same' molecules that would also be found in lower doses of fear sweat. If such olfactory signatures of fear (degree) exist [48], how consistent are they across circumstances, and how broadly are these signatures endorsed by the human species (e.g. across gender, cultures, lifespan)?…”

More Data, Please: Machine Learning to Advance the Multidisciplinary Science of Human Sociochemistry

“…tradition. Although several psychological studies have revealed systematic patterns in the behaviors of senders and recipients (in relatively sterile, uniform settings), the chemical message driving this coupling has generally remained enciphered (but see Penn et al, 2007;Smeets et al, 2020). Lessons can be learned from the animal literature, where the combination of rigorous behavioral experiments (bioassay) and chemical analysis (isolating, identifying, and synthesizing the bioactive substance to recreate the bioassay-behavior) forms the golden standard to detect a common chemical "language" for a species: pheromones (Wyatt, 2015(Wyatt, , 2020.…”

“…Other studies found chemical markers suggestive of fear (and happiness). Potential chemical markers for fear were identified by in armpit odor (Smeets et al, 2020), stress levels were also expressed in a person's breath (Preti et al, 2019; acetone, isoprene, dimethyl sulfide), and in a creative field study, (Williams et al, 2016) showed that scary and funny film events reliably changed the emission of molecules from cinema audiences. Taken together, these multidisciplinary studies show the potential for social information to be encoded in a person's smell in predictable ways, thus jumpstarting a sociochemistry approach to identify a common smell language.…”

“…After extracting the molecules using headspace, solvent, or direct extraction techniques, chemical analysis could entail two-dimensional gas chromatography-mass spectrometry (GCxGC-ToF-MS) allowing for comprehensive profiling of the volatile molecules in the sweat samples and their discriminative power between two (or more) states/traits of interest. Because there is little to no background knowledge on chemical classes associated with presumed signals in sweat odor, initial research by Smeets et al (2020) used untargeted screening approaches to distinguish between fear, happiness, and a neutral state, and found a matrix of over a 1,000 chemical volatile peaks. This number could be reduced as a next step to 94 by selecting only those peak intensities that differed significantly with at least one other emotion category.…”

“…Promisingly, Imam and Cleland (2020) placed an array of 72 chemosensors (based on the architecture of the mammalian olfactory bulb) in a wind tunnel, which rapidly learned and identified odor representations, despite various sources of noise. Given that body odor (itself susceptible to noise) contains information about emotions (Smeets et al, 2020) and one's health, ranging from markers for Parkinson's disease (Trivedi et al, 2019), general inflammatory reactions (Olsson et al, 2014), to possibly the presence of COVID-19 in sweat (Grandjean et al, 2020), it would be intriguing to explore whether such algorithms could be used to learn and identify the even more complex language inherent to human odors.…”