New Family of Cannabis Aroma Molecules Identified

Chemists pin the tail on the skunky.

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When Life Hands You (S)-(–)-limonene . . . You Can’t Make Lemonade

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A Skull Full of Mush

 

There’s an interesting paper to be presented later this month at an Association for Computing Machinery virtual conference on the theme of Interactive Media Experiences.

The paper, by three engineers and a psychologist from the University of Liverpool, is titled “Predicting the colour associated with odours using an electronic nose.” The cross-modal associations between scent and color have been of interest for ages; my colleagues and I helped put them on a quantitative basis with a couple of papers in the late 1990s. The Liverpool group’s new twist is getting an e-nose to predict the color associations.

That’s all fine. It’s of a piece with attempts to predict a molecule’s odor character from physical parameters such as molecular weight, shape, charge, etc. The authors of the new study claim to find a 70 to 81% “machine-human similarity rating.” Whatever. I’m not that interested in the details of their e-nose or experimental protocols, but I’m fascinated by their conceptual point of view as expressed in this sentence fragment in the conclusions section:

Thus, highlighting the possibility of using e-noses to predict human olfactory perception and implying that the colour associated with odours are partly written into the molecular properties of the stimulus [5].

Reference 5 is to the color-odor study—using human noses—that I published with Sarah E. Kemp. In it, we discussed the psychological associations that emerged between the test odors and measurable parameters of color. Nowhere in that paper did we attribute those association to the “molecular properties” of the odor stimuli. It is a mistake for the Liverpool researchers to cite that paper in support of their claim.

The bigger problem is with their philosophical view, namely that an odor’s color associations “are partly written into the molecular properties of the stimulus.” This is a dumb but persistent conceptualization that’s popped up before. Here’s what I said about it in 2009 while commenting on a study by Mandairon et al. called “Humans and mice express similar olfactory preferences.”

What I can’t understand is Mandairon’s endorsement of a mathematical model that predicts odor pleasantness. The idea is that odor pleasantness is “partially dependent on the odorants’ physicohemical properties.” Of course this has to be true at some level: different molecules produce different smells because they have different structures. But Mandairon et al. go beyond tautology; the shared response of mice and men suggests

that olfactory preferences are indeed partially engraved in the structure of the odorant molecule

and

there is an initial part of the [odor] percept which is innate and engraved in the odorant structure.

Perceptions engraved on the molecule? This is simply a bizarre way to think. What else is engraved on a molecule of phenylethanol: Visual associations to red roses? The name of my florist? An olfactory memory of my dead grandmother?

Perceptions happen in the central nervous system of an organism. To talk as if odor pleasantness resides in the structural features of a molecule is animistic thinking, pure and simple.

My objection to Mandairon, et al. applies equally to Ward, et al. No matter how cool the math and the engineering, the idea that odor-linked human perceptions are “written” or “engraved” in the structural features of a molecule is rubbish and unworthy of a place in scientific discourse.

The studies discussed here are “Predicting the colour of odours using an electronic nose,” by Ryan J. Ward, Shammi Rahman, Sophie Wuerger and Alan Marshall, published in SensoryX ‘21: Workshop on Multisensory Experiences, together with IMX 2021: ACM International Conference on Interactive Media Experiences, June 21-23, 2021, and “Humans and mice express similarolfactory preferences,” by Nathalie Mandairon, Johan Poncelet, Mousafa Bensafi and Anne Didier, published in PLoS One 4:e4209, 2009.

Things that “Everyone Knows”

 

Among the things that “everyone knows” because it is just so damned obvious is that smoking impairs your sense of smell. Yet when one tabulates the studies looking at tobacco use and odor perception, the conventional wisdom crumbles more easily than the ash on a Macanudo. 

I wrote about this in What the Nose Knows, and studies since then have confirmed my skepticism: some find a link, others don’t.

A newly published “scoping review” collated the results of over 700 studies, seeking statistical associations between “social determinants of health” and olfactory function. They found some significant links (e.g., exposure to environmental and occupational toxins), but “the associations between olfactory dysfunction and education level and lifestyle factors such as smoking and drinking seem to be much more elusive.” (Translation: “we know those factors matter, we just couldn’t find convincing evidence.”)

How much more elusive was smoking as a factor? This elusive:

Of the 28 studies that examined smoking, 12 demonstrated significant positive correlation between smoking status and [olfactory dysfunction].

In other words, the majority of studies (16/28) found no link. The conventional wisdom is still batting less than .500.

The study discussed here is “Association between social determinants of health and olfactory function: a scoping review,” by Joel James, Avraham M. Tsvik, Sei Y. Chung, John Usseglio, David A. Gudis, Jonathan B. Overdevest, published in International Forum of Allergy and Rhinology published online May 28, 2021.

Keeping Up with the News

I’ve tracked new scientific findings about the human sense of smell for a long time. Rather than just file them away for my own interest, I posted one-sentence summaries of clinically relevant findings to the Disorders of Odor Perception website at hyposmia.com. I was doing so on a more or less weekly basis until about a year ago when covid hit the fan and disrupted my routine along with everyone else’s.

Over the course of the year, “regular” publications on smell science were overwhelmed by a flood of papers on covid-related smell loss. I tried to keep up with these for while, but it was simply too much to handle and eventually I stopped updating the site.

Recently, when I decided to resume posting new material to Disorders of Odor Perception, it occurred to me that the web design was hopelessly out of date: the site looked OK on laptops, but was barely legible on phone or tablet. So I redesigned the site to make it “responsive”, i.e., it adapts to the device you view it on and is (hopefully!) much easier to read on mobile devices.

As before, new content appears in three categories: Smell Loss (hyposmia and anosmia); Smell Distortion (parosmia, phantosmia, etc.); and News & Reviews.

I also updated the format of the “new content” emails. They now contain direct links to the original source items—no need to go back to website to find them. This ought to make the emails much more user friendly.

If you are a clinician or patient or just interested in the topic of smell disorders, I encourage you to sign up for the email alerts. It’s a painless way to keep up with new work in the field.

Pardon Me, Do You Have Any Mouse Poupon?

Here’s a tasty new offering from researchers at the Centre des Sciences du Goût et de L’Alimentation in Dijon, France: “Male mice and cows perceive human emotional chemosignals: a preliminary study.”

How do you know if a mouse is perceiving chemosignals of human fear? It poops out more fecal pellets.

How do you know if a cow prefers chemosignals from nonstressed humans? It spends more time smelling the bucket that contains them.

How do you obtain these wondrous human chemosignals? The old cotton pads in the underarms gambit. You get some engineering students to abstain from stinky food and perfume for a few days (major sacrifice—this is France, after all) and wear the pads during a regular class (non-stress) and during an exam (stress).

What can I say? I admire the weirdness of the experimental design. I wonder if members of the ethics panel of the French Ministry of Higher Education and Research managed to keep a straight face during the review meeting.

Sidebar: The title of the paper mentions “male mice” and “cows,” when in fact the animals used were male mice and nulliparous female bovines. Shit-kicking American readers would expect “male mice and heifers,” but evidently the heifer vs cow distinction isn’t recognized in the editorial offices of Animal Cognition.

And finally, with apologies to the man from Nantucket:

There once was a heifer from Dijon,
Whose nostrils scientists seized on,
She found it a balm,
When the students smelled calm,
And the researchers gained a citation.

The study discussed here is “Male mice and cows perceive human emotional chemosignals: a preliminary study,” by Alexandra Destrez, Morgane Costes‑Thiré, Anne‑Sophie Viart, Floriane Prost, Bruno Patris and Benoist Schaal, published online in Animal Cognition, April 11, 2021.

Coming Soon: A Live Virtual Appearance

 

The Cannabis Chemistry Subdivision of the American Chemical Society has been incredibly active in organizing symposia and publications on key topics in the cannabis sector. One of their ongoing events is a monthly “journal club” held as a live webinar.

I’ve been invited to give this month’s presentation on March 25 at 11:00 a.m. Pacific Standard Time. My topic is: Cannabis terpenes: Cultivar markers, aroma sources, or active ingredients?

(Yes, I’ll push all three hot buttons in one talk.)

There’s a Q&A session following my presentation, to be moderated by Nigam Arora, PhD. The webinar is free and open to the public. To register for it go to this link.

Hope to see your there!

Culling the Herd

I have far more books than shelf space. I could buy more bookshelves but then the living room would start to look like the library of an English country house and I’m just not in the mood for Victorian clocks and Wedgewood vases.

Unlike the previous FirstNerve Manor, the current house has no attic where I can stow boxes of books. And when it comes to limited basement storage space, wine takes priority.

So today I hauled out box B219: my collection of books by Stephen Jay Gould. Back in the last century I subscribed to Natural History magazine where Gould had a monthly column. These used to appeal to me as a graduate student and post-doc heavily into natural history and evolutionary theory—and the history of science concerning both. It was easier to buy the book (e.g., The Panda’s Thumb) than keep a stack of old magazines around for reference. With his enormous popularity—every collection of essays received a major review in the NYT—I figured the first editions would hold value.

Gould was something of an academic celebrity back then, despite (or perhaps because of) his thinly veiled Marxism and trendy political views. Even as I kept buying his books, I grew weary of his moralizing and his prose, especially the faux folksiness (his whole “just another nerdy baseball fan” persona really grated). I find it puzzling today that I bought so many of his books, especially as I disliked the leading role he and the odious Richard Lewontin played in the disgraceful attacks on Edward O. Wilson and sociobiology. What was I thinking? [Completionism is a frequent co-morbidity to bibliophilia—Ed.]

So this afternoon I cast a gimlet eye over a stack of 15 volumes by Gould. I’d already determined that there is no market for them (mint condition first edition or not). I decided to keep Ontogeny and Phylogeny (1977) because it is a serious treatment of a major topic in evolutionary theory (no baseball references). Ditto The Structure of Evolutionary Theory (2002), even though I’ve never cracked it open. [Has anyone?—Ed.] Finally, I held on to The Mismeasure of Man (1981) because that book got Gould hoist on his own petard.

All the rest? They’re in the trash can and headed for the Larimer County landfill on Tuesday afternoon.

The SMELL CHANGE STATUS CHECK -- A Quick Way to Assess Recent Smell Loss in Suspected COVID-19

There have been numerous anecdotal reports of smell loss in people with COVID-19. It’s not surprising to find transient or even permanent smell loss associated with upper respiratory tract infections—in fact, it’s one of the leading causes of smell loss. However, in cases of flu, head cold, and sinus infection, the smell loss usually occurs at the same time, or following, the emergence of symptoms. What’s interesting here is the suggestion that the SARS-CoV-2 virus may trigger smell loss before full-blown COVID-19 disease is apparent. Thus, it might be evident in otherwise symptom-free people. If so, smell loss could be a useful marker in deciding whether to test someone for the virus, allow them back to work, etc.

Most people in the medical and chemosensory communities are aware of these possibilities. What has been unaddressed, thus far, is how exactly front-line medical personnel should probe for recent smell changes in potential patients.

Commercially available smell tests, such as the UPSIT and Sniffin’ Stick kits, are designed for full evaluation of smell function (although there are brief versions of each). However, they are relatively time-consuming to administer, at least in the context of evaluating people in the middle of a viral pandemic. They also require close patient contact with the test materials, which raises concerns about virus transfer and hazardous waste disposal.

My old friend and colleague Mark Greenberg, a neuropsychologist with a clinical practice in Boston, were talking about this earlier in the week and decided what was needed was a brief, verbal assessment using standardized questions, to determine if a person has experienced a recent change in smell function. We came up with three questions modified from the NHANES health survey, and added another of our own.

We call the resulting 4-item screener the SMELL CHANGE STATUS CHECK, or SCSC. We’ve made it available via a Creative Commons license. You can download it at this link. We hope healthcare professionals will find it useful. It may help in compiling data regarding how often smell loss is associated with SARS-CoV-2 infection, and when it emerges during the typical clinical course.

Perfume and Big Data: Garbage In, Garbage Out

 A new “big data” study of perfume has been making a splash the past couple of weeks. Titled “Social success of perfumes,” it was published in PLoS ONE on July 4. It’s another in the recent genre of throwing abstruse mathematics at the sense of smell.

The authors, Vaiva Vasiliauskaite and Tim S. Evans, are in the physics department at Imperial College London, more specifically the Theoretical Physics Group and Centre for Complexity Science. [Awesome nameage!—Ed.] Vasiliauskaite is a graduate student and apparently a talented nerd, having graduated from the University of Glasgow with a degree in Theoretical Physics. Evans is a Senior Lecturer.

Here’s the abstract of their paper:

We study data on perfumes and their odour descriptors—notes—to understand how note compositions, called accords, influence successful fragrance formulas. We obtain accords which tend to be present in perfumes that receive significantly more customer ratings. Our findings show that the most popular notes and the most over-represented accords are different to those that have the strongest effect to the perfume ratings. We also used network centrality to understand which notes have the highest potential to enhance note compositions. We find that large degree notes, such as musk and vanilla as well as generically-named notes, e.g. floral notes, are amongst the notes that enhance accords the most. This work presents a framework which would be a timely tool for perfumers to explore a multidimensional space of scent compositions.

Leaving aside the technical terms “network centrality” and “large degree notes,” the claims in the abstract seem clear: the authors have identified odor descriptors that drive the commercial success of specific perfume formulations. Sounds interesting and useful. One dives eagerly into the paper to find the details.

That’s where the problems start.

No actual perfumes were smelled in the making of this study. Nor were any actual perfume formulations examined. Instead, the authors apparently scraped perfume description and ranking data from a website, cleaned it up a bit, and then proceeded to slice, dice, and theorize.

I say “apparently” because nowhere in the paper do they describe where or how they obtained their data. I imagine my amateur readers stammering, “B-b-b-but, don’t scientists have to describe their data?” Yeah, well, uh, no, I guess not.

From clues in the text (e.g., perfumes rated on a five-point scale), Vasiliauskaite and Evans may have tapped Fragrantica.com as their source. In which case, they might have been courteous enough to give the site’s proprietors a shout-out. Or, better yet, have obtained permission to use the site’s data.

Of greater ethical concern is the absence of the source data in a supplemental file or online scientific archive. Making the data available is a requirement of publication in PLoS ONE.

PLOS journals require authors to make all data underlying the findings described in their manuscript fully available without restriction at the time of publication.

I don’t understand how this basic requirement could have been overlooked by the reviewers or the paper’s editor, Yongli Li of the Harbin Institute of Technology in China.

So, let’s look again at the abstract. By “successful fragrance formulas” the authors mean perfume brands with a large number of user reviews and high ratings on an undisclosed fragrance website. By “notes” and “odor descriptors” they mean descriptors provided by anonymous reviewers of undetermined skill level and/or marketing verbiage lifted from advertisements and promotional copy. On Fragrantia.com, gauzy words like “honey,” “amber” and “musk”—which refer to no one specific perfumery material—are given equal weight with clary sage, tonka bean, and oakmoss (see, for example, Paco Rabanne Pour Homme). By “accords” the authors appear to mean co-occurring “notes” on a webpage.

Other passages make one wonder if the authors know what they’re talking about. Take this paragraph, for example:

Information of the precise amounts of each ingredient in the formulation of a perfume is confidential, to prevent duplications of the formula. However, the list of ingredients, the list of notes, is often advertised in order to describe the scent of a perfume. Thus a perfume which smells of rose, vanilla and musk, is described using such notes. In this study we have analysed the notes which make up over ten thousand perfumes without knowing anything about their specific amounts in each perfume. We assume that a note is included in the perfume description as its presence enriches the composition and its smell is detectable.

This paragraph is confused to the point of idiocy. A perfume’s list of ingredients is never revealed, much less advertised. What is publicly promoted by the brand is a short list of note names meant to imply romance, exotic origins, and high quality as much as what the perfume smells like. Even then, it’s unclear whether the “notes” used in this study were provided by the brand or plucked from the website’s crowd-sourced reviews. The authors “have analysed the notes which make up over ten thousand perfumes”? Fairer to say they’ve analyzed notes attributed to perfumes (by persons unknown) that may or may not include all the salient notes in a given perfume. The stated assumption that each note in a description is individually “detectable” is ridiculous. Do the authors believe that the grapefruit and Calabrian bergamot in Coco Noir are individually detectable? One wonders how much smelling they’ve ever done.

At this point it’s clear that no matter how much “network” and “non-network” statistical analysis they apply to this slop bucket of data, the results will lack specificity and insight. For all of its nodes, edges, weighted network representations, permutation tests, d-scores, and one-mode projections, the study doesn’t make much contact with the commercial or sensory realities of perfumery. It could have been interesting as an analysis of brand attributes seen through social media. But even there it fails.

This paper isn’t worth the time it takes to download.

UPDATE September 19, 2019
Called it! The paper has now been retracted by PLoS ONE.

The study discussed here is “Social success of perfumes,” by Vaiva Vasiliauskaite and Tim S. Evans, PLoS ONE 14(7): e0218664.

Did Nicolas Guéguen Really Replicate the Cinnabon Effect?

Some years ago I blogged about a couple of scent-related field studies conducted by French academic researcher Nicolas Guéguen. In 2009, I wrote about a paper of his I had just come across, published in Psychological Reports back in 2001. In “Effect of a perfume on prosocial behavior of pedestrians,” Guéguen found that people were more helpful to a young woman who “accidently” dropped an item when she was wearing perfume, compared to when she was not wearing scent. I thought the paper was worth mentioning because it described a theory-free, empirical approach that could shed light on the natural history of perfume use.

Three years later, in a post called “The Cinnabon Effect Confirmed,” I wrote about another of Guéguen’s studies. This one, published in the Journal of Social Psychology, was titled “The sweet smell of . . . implicit helping: effects of pleasant ambient fragrance on spontaneous help in shopping malls.” The original Cinnabon Effect study was discovered in 1997 by social psychologist Robert Baron who found that shoppers in a mall were more helpful to a stranger when approached next to a pleasant-smelling retail shop (e.g., Cinnabon) rather than an unscented one (e.g., Banana Republic). In his new paper, Guéguen found similar results in a French mall, and extended the paradigm to include male as well as female “strangers.” I wrote approvingly that “Guéguen has confirmed the Cinnabon Effect and extended it to spontaneous acts of kindness.”

I recently became aware that Guéguen’s “sweet smell of” study has been the object of some impressive data sleuthing by Nicholas Brown and James Heathers. A new post on the excellent Retraction Watch website updates the story. In a nutshell, the pair claims that a bunch of papers by Guéguen don’t pass the smell test. The reasons for their skepticism are procedural and statistical. Guéguen is a prolific author yet one with no collaborators, and he may, according to Brown and Heathers, have skirted ethical requirements. Disturbingly, statistical patterns in his underlying data seem consistently fishy. The original B&H posts can be found here and here.

It’s important to note that Brown and Heathers do not claim outright that Guéguen’s studies are fraudulent.

We have not made, and do not make, any specific allegations of fraud, nor are any implied. The initial document that we released is entitled “A commentary on some articles by Dr. Nicolas Guéguen” and details a long series of inconsistencies in research methods, procedures, and data. The words “fraud” and “misconduct” do not appear in this document, nor in any of our communications with the people who helped with the investigation. We restrict ourselves to pointing out that results are “implausible” (p. 2) or that scenarios are “unlikely [to] be enacted practice” (p. 31).

You can download their detailed fifty-two page critique here and judge it for yourself. If you’re less hardcore, this Ars Technica piece covers the story nicely.

I think the points raised by Brown and Heathers are compelling and cast serious doubt on some, if not all, of Guéguen’s work. Accordingly, I’m putting a metaphorical asterisk on the two studies I cited, and flagging this action on my previous blog posts. Data integrity and transparency are core to the scientific endeavor, and when serious doubts are raised they need to be acknowledged.

Hard Copies & Web Metrics

Our cannabis aroma paper in PLoS ONE has now been viewed over 4,000 times and downloaded more than 600 times since February. It’s gratifying to see so many people interested in a new research theme. And yet, because web-based metrics are automated, impersonal counters, I don’t have a clue who these people are. It hasn’t always been this way.

When I was in graduate school in the pre-internet age, the super-efficient way to scan the new scientific literature was to grab a physical copy of Current Contents, the weekly publication of the Institute for Scientific Information (ISI). It was a thick little booklet, printed on thin, bright white paper. At least it was bright white when it arrived in the mail and when your lab head got to leaf through it. By the time it reached the grad students’ offices, it was tattered, marked up, and usually stained with coffee, cheesesteak drippings, and rat urine.



Current Contents listed the contents of the life sciences journals published that week, along with author names and addresses. This enabled you to request a physical reprint of the paper by mailing a letter or postcard. Better funded departments had pre-printed postcards for this purpose. Really well-funded labs signed up for ISI’s pre-printed Request-A-Print® cards—they came with a peel-off return address sticker for the reprint sender’s convenience. But you still had to fill in by hand the citation and the author’s address.



Although it beat going to the library and physically browsing recent, unbound issues of your favorite journals, this process was labor intensive. As a result, it made you think twice about the article in question: was it truly worth the effort of requesting your own personal copy? How many of us today download papers on a whim, only to have them stack up, unread, in our “worth a look” folder?

On the flip side, receiving reprint requests in the mail was a rewarding experience, especially for a graduate student or newly minted faculty member. It meant someone had found your work sufficiently interesting to fill out the postcard. And you knew who they were! A reprint request from a Big Name in the Field was acknowledgment that you had arrived.



Reprint requests also brought a sense of connectedness with scientists around the world. The mail would bring postcards with bright stamps and formal, cursive handwriting. The ones from communist block countries like East Germany, Poland, and Czechoslovakia were sad: printed on pulpy, low quality paper that oxidized in the sunlight and had a bitter smell. I always enjoyed fulfilling these requests—it meant acknowledging these scientists, keeping them connected to the free world, and encouraging freedom of inquiry.

Cannabis in the U.S. Patent Office

Given my current interest in the olfactory properties of cannabis, I monitor the relevant scientific literature and every so often scan the U.S. patent application database. There are a ton of weed-related patents being filed. Some of the inventions are for practical devices, like this one for “Apparatus and related methods for trimming dried Cannabis flowers,” filed by Cullen Raichart of San Diego, California. Mr. Raichart aims to be the Eli Whitney of cannabis. The brief abstract and an illustration are all you need to understand his machine and what it does.

Then there’s “Apparatus and methods for biosynthetic production of cannabinoids,” by Robert Winnicki, et al. This group of inventors is looking beyond the agricultural horizon and proposing to create THC and related cannabinoids in a bioreactor. Neato!

As I browsed along, I found another application that sounded promising: “Method of producing cannabidiol derived products.” When I read the abstract I did a spit-take.

Methods are provided for producing cannabidiol (CBD)-based products in accordance with cycles of the moon and a plurality of biodynamic rituals. In some embodiments, the phase of the moon may be correlated with positions of the moon among the twelve zodiac signs, and the positions used to guide in the cultivation and harvest of cannabis plants. In some embodiments, the phase of the moon and the corresponding astrological phase that the moon is moving through may be used to determine when to plant seeds, when/how to water and fertilize, when to take cuttings, and the right time to harvest mature cannabis plants to produce optimal CBD-derived products. It is contemplated that farming cannabis with consideration of moon cycles may result in healthier plants, increased yields and superior products.

Yes, you read that correctly! Inventor Christine Meeusen of Merced, California, seeks to patent a method of using lunar cycle phase and the moon’s zodiacal position to optimize timing of seed planting, watering, and harvesting of Cannabis.

If the abstract sounds nutty, the body of Ms. Meeusen’s application (which cites no other patents or prior art) is even nuttier. Here’s item 37 from the Detailed Description section:

In one embodiment, the CBD derived products are only manufactured from the new moon to the full moon. On the first day of the cycle, a biodynamic ritual may be held under the stars so as to bless a work environment, such as a table. The ritual may include appreciation with respect to a Creator God and Mother Goddess. Depending on the product, a bundle of sage may be lit, and swept over any plurality of bottles and ingredients that include a high-proof alcohol to dissolve the bud. A plurality of incantations may also be communicated. In one embodiment, the producers may utilize a bundle of sage so as to cleanse one another prior to production. In one embodiment, appropriate dosages of marijuana may be scooped into preparation jars.

Ooooookay, then. Meanwhile, the flowchart in Fig. 1 looks like a game plan drawn up by the Underpants Gnomes:

Fig. 3 achieves peak nuttiness:

So who is Ms. Meeusen? A quick google search reveals that she is also known as Sister Kate the “weed nun.” Like other members of her “order,” she dresses in full habit, coif and wimple while running a sizable marijuana grow in Merced. According to Reuters, she first took up the nun persona in 2011 as part of her involvement in Occupy Wall Street. [This is my shocked face.—Ed.]

Is this a practical joke? Unlikely, given the expense of filing a patent. Is it performance art? Maybe. In any case, pity the poor patent examiner.

The U.S. patent applications discussed here are 20180126578 (Raichart), 20180179564 (Winnicki et al.) and 20180169162 (Meeusen).

Something in the Air

Via Gizmodo

My recent inquiries from online journalists are beginning to form a pattern and it’s not good.

First came an email from Eric Spitznagel at Vice Tonic asking about the science behind the age-old saying “he who smelt it dealt it.” His starting point was the idea that laws of gas diffusion and the concentration gradient of odor dispersion would invariably indict the smeller as the dealer. (From the published piece it appears he got this working hypothesis from an engineering professor at the University of Colorado.) My response was to distinguish between models that describe the behavior of ideal gases, and the more complicated turbulent currents and plumes found in real life. The non-ideal distribution of scented air streams is the basis for the “casting” behavior which many animals species use to localize the source of a smell (they zig-zag back and forth through the odor plume in ever-shorter tacks until they reach it). Given these atmospheric vagaries it is entirely possible that an emission from the guilty party might curl up an innocent person’s nostrils first.

Next heard from was Daniel Kolitz at Gizmodo who was putting together a “GIZ Asks” installment on the legitimate if somewhat feculent question “Why does dog poop smell bad to us but good to dogs?” Kolitz collates answers from a crack team of dog specialists and smell researchers including, beside yours truly, Alexandra Horowitz, Don Wilson, Peter Hepper, Cat Warren, and Charles “I’m publishing as fast as I can” Spence (I kid, I kid). What’s interesting is that several of the experts blithely assume that all human odor responses are cultural, while other take the (correct) view that certain smells or categories of smell are inherently (biologically) offensive. Click over to read the whole thing, but here here’s the pungent part of my answer:

Dogs don’t approach shit as an aesthetic experience—they treat it as a source of social information, like an olfactory Instagram. It answers a lot of questions: Who left it? How recently? Is the pooper healthy? We are able to extract similar information. The lingering cloud in the office restroom tells you who had lunch at P.F. Chang’s. Plumbing and ventilation rob us of the social signals in feces and leave us with mere disgust.

So where is this latest journo-trend heading? What follows farts and dog poop? I could make an educated guess, but I’ll take the lazy way out and just wait for the next email from an inquiring mind.

Cannabis Terpenes: Getting into the Weeds

Fig. 3 from Booth, et al., 2017

The volatile chemical components in cannabis are well known: they consist mainly of terpenes. The aroma profile of cannabis, in contrast, has yet to be nailed down with standard sensory methods. As a result, we have no data-based description of how the various strains smell. Between the chemical cornucopia and the sensory desert lies a largely unexplored zone: the biochemical. It consists of the metabolic means by which the plant produces its characteristic (yet highly variable) smell.

Thanks to three researchers at the University of British Columbia, we finally have some insight into the biochemistry of terpene production in cannabis. Their study, published two months ago in PLoS ONE, is fascinating but it’s not an easy read. In addition to chemical analysis, it involves cDNA cloning, plasmid-transformed E. coli cells, expression vectors, and gene sequence analysis. It’s a dense paper but one that is worth unpacking given the many possibilities it holds for the breeding, cultivation, and commercialization of cannabis.

Let’s start with the basics. The plant, and especially the resin produced in the glandular trichomes of the female flowers, contains a highly variable mix of terpenes. Terpenes are a class of organic molecular structures built from isoprene units (C5H8). Monoterpenes contain two isoprene units (C10H16), sesquiterpenes contain three (C15H24). Among the monoterpenes frequently found in cannabis are α-pinene, β-pinene, limonene, myrcene, β-ocimene, and terpinolene. Cannabis sesquiterpenes include alloaromadendrene, β-caryophyllene, α-humulene, farnesol, and valencene. These are all familiar molecules to fragrance chemists—they show up in all sorts of floral and citrus notes.

This array of cannabis terpenes is produced via two well-known biosynthetic pathways known as MEP and MEV. Each step in these pathways is enabled by one or more enzymes, known as terpene synthases (TPS). These enzymes are coded for by a large gene family found in plants generally; enzymes in the TPS-b gene subfamily produce monoterpenes, while TPS-a enzymes mostly make sesquiterpenes.

What the UBC team has done is identify the enzyme genes active at each step of terpene creation in cannabis. This is a significant achievement. Because terpenes “are responsible for much of the scent of cannabis flowers and contribute characteristically to the unique flavor qualities of cannabis products” we can now begin to understand how and why a particular strain of cannabis smells the way it does.

What the UBC study makes clear is that strain-specific terpene production is complicated. For example, two strains rich in α-pinene may create it using different enzymes. Conversely, the same enzyme may yield different terpenes in different strains. There are probably additional factors in the mix, as “terpene profiles showed considerable variations between individual plants.”

Nevertheless, the pieces are now in place for breeders to select for and genetically manipulate the terpene profiles of a given strain. The authors note:

“Knowledge of the genomics and gene functions of terpene biosynthesis may facilitate genetic improvement of cannabis for desirable terpene profiles.”

Toward the end of the paper the UBC team throws in another fascinating phrase:

The present study highlights the large number of CsTPS genes and the diverse products of the encoded TPS enzyme activities, which contribute to the complex terpene profiles of cannabis. The knowledge of multigene nature of the CsTPS family and the often multiple products of the encoded enzymes will be critical when selecting or breeding, or improving plants by genome editing, for particular terpene profiles for standardized cannabis varieties.

“Standardized cannabis varieties” is an interesting concept. Right now, cannabis cultivation is a farrago of strains and hybrids, raised under an varying greenhouse practices and post-harvest treatments (drying and trimming). Although impressively technological in some respects, the industry presents as a hippie-dippie craft aesthetic with cute varietal names and limited (possibly unrepeatable) production runs. In order for the industry to scale up, it will have to produce enormous volumes of cannabis using strains that respond consistently to uniform growing conditions and handling, a possibility UBC co-author Jonathan Page (who is also CEO of Anandia Labs in Vancouver, B.C.) raises in a new interview.

To me, “standardized cannabis varieties” conjures up more intriguing possibilities, such as branded fragrance and flavor profiles aimed at different segments of the recreational market. Large-scale production will require strains that have consistent aroma, flavor, and potency. Some will no doubt view this as sell-out to corporate agribusiness, a betrayal of traditional cannabis folkways. But it will also bring economies of scale and the high level of safety and quality that consumers expect in branded products. Dedicated hemp-heads will always appreciate the boutique hybrid strain, but a national market will also need to appeal to the casual user who wants a reliable product.

UPDATE June 5, 2017
Beer guru Stan Hieronymus quotes this post, noting that terpenes link hops and cannabis.

The study discussed here is “Terpene synthases from Cannabis sativa,” by Judith K. Booth, Jonathan E. Page, & Jörg Bohlmann, published in PLoS ONE 12(3): e0173911, 2017.

How Nick Zollicker Came to Be

I join Luke Clancy on his Culture File podcast to talk about Nick Zollicker, the protagonist of my new “smell stories.”

Going Fictional: Introducing the Nick Zollicker Stories

I’ve always been fascinated by the portrayal of smell in fiction. I made a brief study of it in What the Nose Knows, and here on FN I’ve taken a close look at various authors and how they weave scent into their novels. Among the writers I’ve discussed or quoted are E.L. James, Tom Robbins, Vladimir Nabokov, Toni Morrison, and P.G. Wodehouse. (You can find these posts via the FN Review, FN Retrospective, and Olfactory Art tags.)

In most of these works scent is mentioned in passing. It functions to characterize a person or place or, especially in the case of Nabokov, to provide a sepia-toned sense of nostalgia. Smell is rarely a central theme in fiction. There are exceptions, like M.J. Rose’s The book of lost fragrances, Süskind’s Perfume: They story of a murderer, and Roald Dahl’s brilliant short story Bitch.

One would think the world of contemporary commercial perfumery is an ideal setting for fiction. The business is an unstable blend of creative fashion and technical chemistry. It straddles the magical and the mundane. It simultaneously touts its innovation and its longstanding traditions. It is filled with characters of Dickensian dimensions.

Another setting ripe for fiction is the world of academic smell research. University labs are stocked with weirdos and drones as well as the rare brilliant scientist. Scientists share a lifestyle—monk-like yet dissipated—that is remote from the experience of most readers. Their scientific obsessions are remarkable if not often bizarre. It’s all fertile ground for fiction.

Well, somebody needed to step up to the plate, and it appears that the someone is me. I have created a contemporary smell expert named Nick Zollicker. He lives in Berkeley, California where he runs a secretive private olfactory research institute. He has wide experience in the hard-edged world of commercial perfumery yet thrills to pushing the boundaries of olfactory science.

My first two Nick Zollicker stores An Imperfect Mimic and Smothering the Savage. They are now available digitally on Amazon. (You can read them on your Kindle or download the Kindle app onto whatever device you prefer.) I hope you enjoy them.

Can We Predict a Molecule’s Smell from Its Physical Characteristics?

An extract of Yuanfang Guan’s winning code for odor prediction

A paper in this week’s edition of Science claims that computer models can predict the smell of a molecule. The paper describes the organization and outcome of an IBM Dream Challenge in which multiple laboratories competed to see whose model best predicts sensory characteristics from chemical parameters.

This crowd-sourced effort began with an olfactory dataset collected and published in 2016 by Andreas Keller and Leslie Vosshall. (Full disclosure: I previously collaborated with Keller and Vosshall on a different smell study.) They had 49 test subjects sniff and rate 476 “structurally and perceptually diverse molecules” using 19 semantic descriptors plus ratings of odor intensity and pleasantness.

In setting up the Dream Challenge, the organizers also “supplied 4884 physicochemical features of each of the molecules smelled by the subjects, including atom types, functional groups, and topological and geometrical properties that were computed using Dragon chemoinformatic software.”

There are several positive aspects to the challenge design. First, instead of recycling the decades-old Dravnieks dataset like so many other attempts at chemometric-based odor prediction, the sponsors supplied a fresh psychophysical dataset. Second, the study included a boatload of odorants, not the handful of smells found in most sensory studies. Third, the odor ratings were gathered from a relatively large number of sensory panelists. Forty-nine is not a super-robust sample size but it’s enough to encompass a lot of the person-to-person variability found in odor perception.

Here’s how the competition worked. Each team was given the molecular and sensory data for 338 molecules. They used these data to build computer models that predicted the sensory ratings from the chemical data. Sixty-nine molecules (absent the sensory data) were used by the organizers to construct a “leaderboard” to rank each team’s performance during the competition. The leaderboard sensory data were revealed to contestants late in the game to let them fine tune their models. Finally, another 69 molecules were reserved by the organizers and used to evaluate performance of the finalized models.

The models were judged on how well their predictions matched the actual sensory data using a bunch of wonky statistical procedures that look reasonable on my cursory inspection. (About the algorithmic structure of the competing models I have nothing useful to say, as “random-forest models” and the like are beyond my ken.) For the sake of argument I will assume that the statistical scorekeeping was appropriate to the task. My concern here is with the sensory methodology, the underlying assumptions, and the claims made for the predictability of odor perception.

Let’s begin with semantic descriptors. The widely used U.C. Davis Wine Aroma Wheel uses 86 terms to describe wine. The World Coffee Research Sensory Lexicon uses 85 terms to describe coffee. The Science paper uses 19 terms to describe a large set of “perceptually diverse” odorants which strikes me as a relatively paltry number. (The descriptors were: garlic, sweet, fruit, spices, bakery, grass, flower, sour, fish, musky, wood, warm, cold, acid, decayed, urinous, sweaty, burnt, and chemical.) Well, you might ask, can’t they just add more descriptors to include qualities like “minty” and “fecal” and “skunky”? It’s not that easy, as I discuss below.

The internal logic of the descriptors presents another issue. Some are quite specific (garlic), other very broad (spices), and still others are ambiguous (chemical). What are we to make of “bakery” as a smell? Is it yeasty like baking bread? Is it the smell of fresh cinnamon buns? (Or would that be “sweet”? Or “spices”?). The problem here is that words that are useful in an olfactory lexicon occur at different levels of cognitive categorization. This is reflected in the wine and coffee examples.

The Wine Aroma Wheel has twelve categories, each with one to six subcategories. For example, the Fruity category includes Citrus which consists of Lemon and Grapefruit. The higher level categories provide overall conceptual structure and are themselves useful as descriptors (e.g. a scent might be citrus-like while not smelling exactly of lemon or grapefruit).

Sensory specialists (including tea tasters, beer brewers, and perfumers) spend a lot of effort setting up lexicons that are concise and hierarchical, and which cover the relevant odor perception space. How were the 19 terms in the Science study arrived at? We do not know. How well do they cover the relevant perception space? We do no know. In fact, the authors state that “the size and dimensionality of olfactory perceptual space is unknown.”

These 19 terms are the basis on which the competing computer models were ranked. Thus a model's success at prediction is locked-in to this specific set of terms (plus intensity and pleasantness). In other words, this is not a general solution to smell prediction: it is specific to these odors and these adjectives. The authors openly admit this:

While the current models can only be used to predict the 21 attributes, the same approach could be applied to a psychophysical dataset that measured any desired sensory attribute (e.g. “rose”, “sandalwood”, or “citrus”).

So if one wants to predict what molecules might smell of sandalwood or citrus, one would have to retest all 476 molecules on another 49 sensory panelists using the new list of descriptors, then re-run the computer models on the new dataset. Easy peasy, right? Alternatively one could assemble a sensory panel and have the members sniff the molecules of interest and rate them on the new attributes of interest. Every fragrance and flavor house has such a panel. That’s how they currently evaluate the aroma of new molecules: they sniff them.

Thus the Dream challenge seems to be tilting at a windmill that the fragrance and flavor industry doesn’t see. The search for new molecules is not done by searching random molecular permutations. It is driven by specific market needs, say for a less expensive sandalwood smell or for a strong-smelling but environmentally safe musk. The parameters are cost, safety, and patentability, along with stability, compatibility in formulations, and (for perfumers) novelty.

Who knows, the smell prediction algorithms of the Dream challenge may turn out to be the first step in automating the exploration of chemosensory space. However I’d be surprised if this approach turns out to be generalizable and amazed if it proves useful in applied settings.

Don’t get me wrong. I like the idea of using Big Data to understand olfaction—have a look at my papers based on the National Geographic Smell Survey. I urged Keller and Vosshall to go big in terms of odorants and the number of sensory panelists for what became our co-authored paper in BMC Neuroscience. At the same time I respect the complexity of odor perception and the effort required to map its natural history. And I think the perceptual side of the equation got short shrift in this study.


The studies discussed here are “Predicting human olfactory perception from chemical features of odor molecules,” by Andreas Keller, et al., published online February 20, 2017 in Science, and “Olfactory perception of chemically diverse molecules,” by Andreas Keller and Leslie B. Vosshall, BMC Neuroscience 17:55, 2016.

Video: AIX Scent Fair Panel at the Hammer Museum

If you couldn’t be there to sniff and see it in person, you still have the chance to watch the May 6, 2016 panel that kicked off the AIX Scent Fair at the Hammer Museum in Los Angeles.

It begins with a welcome by the Hammer’s Darin Klein.

Lizzie “Odette Toilette” Ostrum’s keynote talk begins at 00:07:00.

My presentation begins at 00:29:00

Jacquelyn Ford Morie’s presentation starts at 00:43:00

The presentation by TFFCOTNYT starts at 1:10:00

The panel discussion moderated by the Institute for Art and Olfaction’s Saskia Wilson-Brown begins at 1:25:00.

Enjoy.

A Sensory Analysis of Marijuana Volatiles: Not Such Great S***

The chemistry behind the scent of marijuana is a compelling topic for a number of reasons. First off, it’s a big technical challenge. Pot consists of an extraordinarily complex mixture of volatile compounds and sorting them out is a big job. (Paradoxically, the main psychoactive ingredient tetrahydrocannabinol is odorless.) Secondly, it is of forensic interest: law enforcement seeks to detect the scent while smugglers try to cover it up. Finally, legalization has spurred increased interest in the aromatic qualities of pot as a consumer product. Here in Colorado, it has been fascinating to observe the industry grapple with branding and struggle to formulate edibles that consumers find acceptable.

So I was psyched to find a new paper in PLoS ONE titled “Characterizing the smell of marijuana by odor impact of volatile compounds: an application of simultaneous chemical and sensory analysis.” It is written by a pair of researchers at Iowa State, who use the latest methods of sampling (solid phase micro-extraction) and chemical analysis (multidimensional GC-MS) to characterize the concentration of various volatile molecules in samples of fresh marijuana. They also bring a sensory element to their analysis: they compare chemical concentrations to odor threshold data available in the literature, and have someone sniff and evaluate the various molecules as they emerge from the gas chromatograph. So far, so good.

Based on their results, authors Somchai Rice and Jacek Koziel conclude that when it comes to characterizing the smell of marijuana, “more attention should be focused on highly odorous compounds typically present in low concentrations.” (They mention nonanal, decanol, o-cymene, and benzaldehyde.) This, in principle, is a reasonable and potentially useful conclusion. Unfortunately, it is limited by the paper’s shortcomings in exposition and experimental design.

Rice and Koziel claim to add 200 new molecules to the list of previously known marijuana volatiles. But take a closer look at how they phrase their claim:

Over 200 compounds are being added to the list of what is currently known to be emitted from illicitly packaged marijuana.

They refer to “illicitly packaged marijuana” because they based their analysis on three samples of pot (of unidentified strain), all obtained from the evidence room of the Iowa Division of Criminal Investigation. One of the samples was a gram of pot analyzed along with the plastic baggie it was zipped up in. Another consisted of approximately 50 kilos of pot stuffed into a “US military-style duffel bag.” Thus the chemicals analyzed by Rice and Koziel are not exclusively marijuana-based—they include molecules off-gassing from the packaging.

In this research, the authors are not differentiating between VOC emitted from marijuana samples and VOC emitted from packaging.

Indeed. So Rice and Koziel have now muddied the scientific literature on pot volatiles with who knows how many irrelevant chemicals. It will be up to future researchers to sort through the mess they have created.

What about Rice and Koziel’s conclusion that certain chemicals found in low concentration may have a major impact on the smell of pot? It’s a reasonable idea. Perfume chemists have long known that the most common molecule in a mixture is not necessarily the smelliest. A highly potent odor molecule can impact the overall scent if present even in trace quantities. Despite Rice and Koziel repeatedly patting themselves on the back for being the first to apply the concept to marijuana, one might say that it is the first rule of chemosensory analysis.

The authors use the concept of Odor Activity Value (OAV) to make their point. OAV is an attempt to relate a chemical’s concentration to its sensory impact. One calculates OAV by dividing the chemical’s concentration in the sample by its olfactory threshold concentration (i.e., the lowest limit of detectability to the human nose). If a chemical is present at less than its threshold concentration (OAV < 1.00) it is unlikely to contribute to a mixture’s smell. Rice and Koziel use OAVs to identify potentially important odor components of pot. This is fine as a first pass through the data, but when the authors use OAVs in statistical analyses, they stretch the concept almost beyond its limits.

Why? Because every smell molecule has its own concentration-intensity curve. For every step increase in concentration of molecule A, for example, its odor intensity might increase dramatically. For molecule B, in contrast, it might take many step increases in concentration before its odor intensity is noticeably stronger. Therefore, samples of A and B, set at the same OAV, could have very different odor intensities. This makes OAVs useful as a first pass to identify the smelly ingredients in a mixture, but it is perilous to use OAVs as a measure of comparative odor impact.

Finally, this paper is poorly written and would have benefited from closer editorial attention (yes, I’m talking to you, John Glendinning). The introduction wanders all over the map. It begins by mentioning odor as probable cause for search and seizure, but the ridiculous S1-Table is a random grab-bag of U.S. legal cases which completely omits the fact that at least nineteen states have search and seizure rules for marijuana based on the “in plain smell” doctrine. The intro then discusses analytical techniques, veers into a consideration of drug dogs and scent training, and then into the subject of human olfactory abilities. Like wow, man, everything is connected to everything else, you know?

The chemical and sensory analysis of marijuana scent is an increasingly important topic, and while Rice and Koziel have made a preliminary effort I expect that much better work will be done in the near future.

The study discussed here is “Characterizing the smell of marijuana by odor impact of volatile compounds: an application of simultaneous chemical and sensory analysis,” by Somchai Rice & Jacek A. Koziel, published in PLoS ONE 10(12):e0144160, 2015.