• John Ennis

Dawn Chapman - Re-thinking Sensory


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Dawn is a skilled Sensory and Consumer Scientist with 15+ years of hands-on experience designing, executing, and interpreting multi-faceted research studies to solve business challenges. She is passionate about exploring data sets to make stories come to life.


Dawn studied mathematics and chemistry at DePauw University and received a Ph.D. in food science with a sensory emphasis from UC Davis. Dawn’s doctoral research focused on the relationships between vineyard yield and Cabernet Sauvignon flavor. While at Davis, she helped teach the inaugural year of the UC Davis distance learning Applied Sensory Science and Consumer Testing certificate program.



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Transcript (Semi-automated, forgive typos!)


John: Dawn, welcome to the show.


Dawn: Thank you. It's great to be here.


John: Yeah. It's a pleasure to have you and I just realized, I still get kind of nervous doing these things. It's like for almost our 90 episodes. And I said I'm very happy to have you on the shows that you on the show, so it's happy to have you on. Alright, let's start with your background because I know a lot of people in the field know you either from conference presentations or from UC Davis, from teaching, or from your activity at ASTM. And I know you're involved in another organization. So, can you take us through for people who haven't had a chance to meet you yet? Your background of how you ended up in sensory science and your kind of current responsibilities.


Dawn: Yeah. So I think, like, a lot of people in sensory science. It's kind of this weird convoluted road that a lot of us take to find the field or get into it. And so for me, I went to a small Liberal arts College, and I was really interested in mathematics. And so I did mathematics and chemistry, double major. But during my freshman year, I had a sociology class, and I think sociology was some of my favorite stuff in college. So I have a minor in sociology and what really excited me about that was, I remember my first year we were given, and back in the day, it was like an old three-and-a-half inch disc of census data. And so the task was, here's some data run some cuts, figure out what's the story behind the set of people or tell a story with it.


John: Right.


Dawn: And that just excited me. And so it's so cool that, you know, I was able to create this career that's blending, data science, and mathematics and understanding people and the chemistry of all the flavors as well. So after my first year of school with these interests, I was in an honors program for science research, and I managed to land an internship at International Flavors and Fragrances. That's where I was introduced to sensory. So I got in through the chemistry angle. We were doing gas chromatography and really great research. It was so exciting to be working on brands at the grocery store and have, like the chemistry being used for tangible things in my life that affected my family or that they could see and buy at the store. And the sensory properties, along with the chemistry and the flavors of that. Yeah, so that was my in for sensory. I did two internships at IFF, and then I did also some other tangents during college but ended up choosing UC Davis for grad school. And Davis at the time was just a powerhouse for sensory. They had Ann Noble, and Michael Money, and Jack Seynard and just really great professors in agronomy and psychology that all kind of wove together the discipline. And so I got to work in viticulture and analogy looking at how we treat vines in the vineyard, how we treat great vines lead to differences in flavors. So we went out into the field and we had different irrigation treatments and different Pruning treatments and different Finning treatments. And we're able to show that you can manipulate flavor through all these different ways you treat the vines. And that yield doesn't necessarily isn't a straight line towards quality or flavor. It's how you treat the vines to get the yield that affects the flavor. So pretty fun stuff.


John: Now, that's fascinating. Did you pick Davis specifically for sensory, or did you go there for food science more generally? Like, at what point did you decide this sensory? Did you know before you even went to grad school?


Dawn: Well, when I went to grad school, I was still kind of teetering on the edge. Do I want to go chemistry or do I want to go sensory? And when I chose Davis, because Davis had such great opportunities for sensory, I was strongly leaning that way. But I think regardless of which direction I went sensory would have been a part of my research. But, my research was very multi-faceted, right? Because I had the sensory and then I had the plant biology working the plants of the field and then I also had the chemistry because we were looking at some specific flavor molecules that are very affected by how you treat the vine. So there's a specific molecule called isobutyl methoxy pyrazine that has that bell pepper aroma. So if you have a green bell pepper before it's ripened into a different color, it's a very distinctive smell that many food flavors are there's lots and lots of molecules, like a strawberry has hundreds of molecules that go into the flavor of a strawberry. But for green bell pepper, that one molecule you can recognize as green bell pepper flavor. So you can smell that molecule at low parts per trillion level and so trying to develop methodologies in the lab to quickly assay or make them easier assay for that molecule. So, yeah, I was very involved in chemistry, but the nice thing graduating out of grad school, I work with chemists all the time now, but I'm not wrestling the instrumentation. I can understand what they're doing and I can tie it together with the sensory without having to be hands-on myself anymore.


John: Right. So after your grad school then where was your next stop?


Dawn: Yeah, after grad school, I debated teaching, but sensory professorships, there's usually maybe one when you're graduating if you're lucky, that's open. And so I decided at the time there was one open but I decided I wanted to go into consulting first because I wanted real-world experience and so I landed at the National Food Lab in Northern California and it was fabulous. The hands-on learning was amazing because I think in grad school you're working on one project, and there are lots of things going on with that project but you're focused on one thing. And in consulting, you're exposed to so many different products, so many different companies and so many different methods in developing your own methods based on the business questions, that it was just a great learning environment and really great mentors there in the early years.


John: Okay. So that's nice. You really right away then? It was actually interesting because I'm thinking about how much your career has paralleled mine because I also have a math background. Right? I don't have a chemistry background. I had an undergraduate degree in philosophy, which is a little bit like your sociology. Right? Then I went into psychology so that also is a little bit like physiology. So it's quite interesting and then I've always been a consultant as well. I actually never have worked in the industry. But now you're at Clorox and so you're an internal consultant at Clorox. So can you tell us a little bit about what is similar, what's different when it comes to internal consulting and external consultants?


Dawn: Yeah. I think the great thing about external consulting is that you're exposed to so many different companies and so many different products. And you can really get a sense of kind of the feel of different companies and different kinds of personalities within companies. And so I think as an external consultant, you have your main client, and you're designing research to meet their needs. And it's really important to make sure that you understand not only their needs but the needs of the wider team that the research will impact. Now that I'm internal within a company, it's interesting because I was always thinking about, okay, what does one person need, and then what's the ripple effect? But within a company, it's really interesting that the types of questions we're answering just kind of thinking what's the next step for things and what needs exist that aren't met yet, that we need to build to start being able to answer. So maybe needs that people don't even realize that we could have the tools to answer these questions yet. Yeah, so right now, it's just really exciting building the infrastructure to answer new types of questions within the organization.


John: That's interesting. Now, when you were at the NFL, was it all food? Was it the case there was non-food? Were there non-food projects that would come to?


Dawn: Yeah, there were non-food projects. The vast majority was food, but probably 5-10% of projects would be non-food. And we're using all the same tools for the most part, but we're just using them in different ways. And so now, at Clorox, there are food brands within Clorox and Hidden Valley branches of food brands. But there are lots of non-food brands, obviously, as well, Clorox has lots of brands under its umbrella. Most are non-food. So I think, the methodologies really, it's just understanding how to use and kind of change the methodologies to meet the needs of any given project.


John: Right. Yeah. Well, this case is something I've kind of been thinking about lately myself, which is there is this division that we oftentimes see accomplices food and non-food. In fact, my friend, Christina Rankin, who you might know, she and I were talking recently, and she was I guess this was after the last Pangborn in 2019, where she had a poster on fragrances and methodology for fragrance, trying to raise awareness of the fact that there are things other than food within sensory. I tend to think of it as kind of I mentally classify things according to the senses we talking about chemical senses. We talking about more, like, kind of sight and sound but I'm just kind of wondering, what extent do you think that the sensory tools span all the categories versus there being the need for specific tools within certain categories? How do you mentally kind of classify these different applications?


Dawn: Yeah. That's an interesting question. So thinking out loud here, I think a lot of the methods. Well, if we only have a single method, if you only have a hammer, everything looks like a nail. Right? But there are so many methods available and I think as I said before, it's understanding what tools you have available, and then how can you mix and match those tools for the given situation. So I don't really see it as different methods. I think it's just different applications and understanding what works well and what are the limits for any given thing? And especially a lot of at least the products I'm working on now, non-food products. Most of what I'm working on still has an aroma. Right? Or it has a texture, or, you know, we're using the same methods even though we're not putting them in our mouth. We're still measuring things, measuring human perception of a product to understand the links between objective perception and then consumer preferences or liking or efficacy or perception of efficacy.


John: Right. That's quite interesting. This is a working model for me. Just trying to get myself thinking about this because I do think sometimes it's non-food. You have a situation where the functional benefit, yes, there is an aroma, but there is a kind of physical functional benefits that the products test deliver toothpaste. Right? You know, there's like a feel, maybe there is a flavor component as well, but also is you mentioned texture, but I haven't quite figured this out, so I'm just kind of talking to people about it to try to get my mind around it. But I think you're right that when it comes to sensory measurement, you got to ask yourself, is there a temporal effect? Yeah, that's interesting. Let's talk a little bit about chemistry, too, because this is something to me because I feel like there's still a lot of unsolved problems in the chemical senses, and I'm hoping that the next, I had my father on the show not too long ago, and he has a chemistry background. He was talking about the need for sensory scientists to make more progress in the chemical senses. What do you see as some of the research that might need to happen from a chemical standpoint for us to help understand our products better? Do you think we should be using analytic data more in our analysis? Like, how do you, as a chemist, kind of think about the role of chemistry in our field and how it can help us?


Dawn: That's a great question. Some of the research that I've been doing recently is kind of on the side. I've been partnering with Alison Mitchell at UC Davis, she's an analytical chemist there. And so looking at it, I think one of the big deficiencies that I see right now is for food modeling oxidation. So as you look at products as they oxidize over time as they become ranted. First, if you smell just plain vegetable oil, there really isn't much aroma in it at all. And then it progresses to cardboardy and then painty fishy. So there's this progression of smells, but the main molecules are reactions that are tracked currently, really don't do a good job giving us the predictive ability. So you can't just send a sample out and send it for proxy value or heels or something, because you might miss a peak in those, and it might be really rancid even though your values are low. Or you might have high values with low oxidation like the correlation just isn't there. But I see so many companies relying on these measures. Right?


John: Yeah.


Dawn: And so I think there's a ton of research at Davis, and there's progress, but there's still a lot more work to be done with some of these things where great if we just had an analytical assay that we could send off for quality control purposes, and we're not quite there yet that they don't match the sensory perception.


John: Yes. Well, I definitely agree with that. You know, the Pangborn conference just happened, the one that was going to be in Vancouver, and sad it was just everywhere, I suppose. And the theme there with sustainability. And I think this is one of the areas that we could be more sustainable is through these kinds of models where if we have a better understanding of measurements that can be made at the bench or various chemical analytic measurements to make reasonable predictions about the sensory experience, so we don't have to do as much sensory testing, I think that's definitely. What are some other areas where you think that predictive modeling could be helpful for us as sensory scientist?


Dawn: Oh, my goodness, there are so many ways, you know, bringing in consumers, obviously. But there was a really interesting thing that kind of piqued my interest last week. I was looking at is a new company, and I'm blanking on the name right now, but they have new sensors for haptics. And so modeling basically like fingertip touchpoints, right? So if you're thinking about working with anything that has a texture or skin feel or fabrics, I find that fascinating. And so I haven't veditor, I don't know, you know, how well it works yet, but I think there are lots of opportunities with new technologies too, we can always train a panel to do these things, and that's great. But then with new technologies developing, I don't think we'll ever replace human perception for many things, but some of these if they can provide a shortcut for specific measurements that are helpful and we know are correlated with consumer preferences, it's great.


John: Yeah. I definitely agree with that. We just had Benjamin Cabe from Microsoft on the show, and he was talking about really sensors, the internet of things, the idea of measurements, taking lots of measurements, and figure out what might predict, even if the panel is in some sense the gold standard. As far as the sensory experience, maybe we don't need to do as much sensory testing, or you could have maybe a lot of prototypes, and your screen down the prototype to the ones that you think are most likely to hit the target and then you take those to the panel. I think we can really change the way we work. Now, there's a lot of overlap I find between chemistry and data science and a lot of what you're talking about in your work at Davis, were those designed experiments that you all were doing with the vines, or was it more kind of observational where different vines were treated different ways, and then you're trying to find patterns? Like, how did that research unfold?


Dawn: Yeah, so back in my viticulture analogy days, it was all designed experience experiments. So we were looking at, you know we had multiple levels of irrigation treatments, multiple levels of pruning, and finning. And then, yes, we were looking at those comparing the data across the different treatments to understand how the flavors change over those. Very design but, you know, a real-world application where farmers or vertical girls need to make these decisions every season. Right? And the decisions you're making, if you're just aiming for yield, you could decide to prune a lot or thin a lot or hold back your water. But those lead to different flavor outcomes. And so understanding how the levers you have to manipulate things affect your final output, it's just brilliant.


John: Yeah. Now, that's fascinating. And I do think a design experiment is also the gold standard. I actually think that myself and Thierry Worch and Julien Delarue, you might know, he is a new professor at Davis. We're writing a book on data science for sensory consumer scientists. And in there, I would say one of the biggest differences, it's funny how you don't fully understand something until you like, write something instructional about it. Right? And I hadn't fully appreciated the difference between the classical statistical approach of design experiments and what is very often a more exploratory observational approach to data science. And I definitely have come to appreciate that unless you have a designed experiment, it's very hard to be sure that your results are, especially if you think there's some sort of cause of relationship. Very very difficult to establish that without a designed experiment.


Dawn: Yeah, I agree 100% and you think of even within sensory science, if you have key drivers or drivers of liking study where you're trying to get the landscape of the market. Right? You can get so much good information out of those, but then they always lead to more questions with follow-up. Right? For me, it's never like a one-stop, you know everything. Right? Like you get answers, and then it leads to designed experiments for the next stage. So I find it like multi-step and get a broad understanding and then develop hypotheses to dig in with the design and really test and figure out how things play out in your product type.


John: Yes, that's right. Actually, it's part of writing this book. I got two keys, a classic book on exploratory. Yeah. He talked about his metaphor detectives in judges and jury and that the design experiment is kind of like the judge and jury. But then exploratory analyses are like the detective. Without the Detective, there'd be no case to try. Yeah, it's very good. Alright. Well, actually Dawn, we are going to be out of time soon, so I still have a lot of questions for you. So I'd like to kind of get your thoughts on what are the things you're most interested in, things you're most excited about the topics for the next few years that you think are most important for sensory consumer science? Like what kind of caught your attention right in prioritizing your own research or sort of questions that you're seeing come across your desk?


Dawn: So something that's been interesting for me for a while is really looking at how we can use the sensory properties of products to communicate product benefits. So sometimes if you think about a product that is supposed to be all-natural or healthy or whatever, you could have it from an ingredient standpoint, be all-natural or healthy. But if a consumer has that product and it doesn't resonate with what you're trying to communicate its benefits are, it's not going to be a good thing. So understanding what flavors or what textures really kind of jive with what you're trying to communicate with the product and then, you know, preemptively trying to build those in during product development or creation.


John: I definitely appreciate that. First off, I think sometimes we're too in love with liking as an outcome metric. And this kind of, yeah, that when it came out with efficacy, that oftentimes is a measure. I mean, there's a brand promise. Oftentimes the brand promises that the medicine works or whatever it might be. Right? Or that the cat litter does in fact produce odors, but when you're pouring the cat litter out of the box, there's a question, is it believable? That this is going to do what it's supposed to do?


Dawn: Right.


John: And maybe that should be the outcome measure of the study that we should, I think to stop trying to predict liking so much and start to predict other outcomes that might be more important from in grand harmony perspective.


Dawn: Exactly. Yeah. I think liking is interesting, but all the other things around it is where really I find almost so much of the time. They're really exciting learning or new things are kind of the sidelines things that we're measuring that if you can connect the dots, have a product that's well-liked and it's resonating with what you intended to be or want to communicate to your consumer. I think that's the goal.


John: Yes. I think there has to be a threshold you get ever when it comes to liking, right? But then it's more nuanced than that. Okay and then what about other types of measurements? Are there ways of collecting data right now that you're excited about? Personally, I'm into smart speakers. I think that's an area that opens a lot of doors. But what are some of the other ways that you're thinking about measurements that might be informative to us?


Dawn: So, things I can talk about now. So I think there's a lot of...


John: Nothing confidential here. Just general talking about the field.


Dawn: Yeah. Well, you know, I think smart speakers, which I know you talk about with a lot of your guests. I think that is very exciting to start getting into. Yeah. You know, I think depending on what we're trying to do, I think for so long, we brought panelists and consumers into the lab and during the pandemic, we've had to send a lot of things home now. Right? And so I think there's been a lot of creativity and we've had to rethink things. And I think that is going to continue. Right? Well, as hopefully things return to more normalcy, we will bring some of those things back that we used to do. But I don't think the new methods we've developed with more home evaluations for things that normally we just had CLT or in-person evaluations, it'll be more mixed going forward.


John: Yes, I definitely agree with that. I mean, we haven't really talked at all about focus groups, but I think the back office that we've got much more use of video, you've got people at home on video talking to interviewers. I think that's really giving us literally a window into people's lives that we didn't or maybe some people are doing that kind of research, but now it's pretty mainstream. And then you add to that transcription and text analytics. I think we have a much better, wider, much broader sense of what people are actually doing with our products.


Dawn: Yeah.


John: Alright, Dawn, so the last question I have for you is your advice for young sensory scientists? We always close with this question. So what advice would you have for a young sensory scientist?


Dawn: So my advice would be, take some data science classes, because I feel like, you know, within the sensory consumer, you just can't go wrong if you have at least a core understanding of how they work. And you'll probably have a statistician that you work with. But having an understanding of yourself to help guide that is really critical. And then I think to be curious because the surface level just answering the baseline question. Yeah, that's good. But there's almost always something to dig deeper in to develop more questions and dig in more.


John: Yes, I definitely agree with that. I mean, I talked to our team about that, about what it is to really be a consultant. Right? Think about what is the problem that really needs to solve. You're trying to help somebody what do they really need help with? Because you can kind of be like, basically server and just take orders. Right? So wait and fulfill the order and come back. But honestly, that's not where the value is going to come from like, yes, there is a place for doing something that you're supposed to do. But at the same time, I think that especially as more and more things are automated, the value is going to be in the thinking and the rethinking.


Dawn: Exactly


John: So, Dawn, thank you so much. It's been really nice conversation. How can people will get in touch with you? What's a good way for them to find you?


Dawn: Yeah, so the best ways prior through LinkedIn. So you can just search me, Dawn Chapman on LinkedIn and I'm at Clorox right now.


John: Okay and we'll put your link into the show notes so people can reach out and connect with you. Anything else you want to say to our audience?


Dawn: Yeah, thank you very much for having me.


John: It's been great. Thanks a lot, Dawn.


Dawn: Alright. Thank you, John.


John: Okay, that's it. I hope you enjoyed this conversation. If you did, please help us grow our audience by telling your friend about AigoraCast and leaving us a positive review on iTunes. Thanks.


 

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