Daniel Ennis - The Chemistry of Choice
Welcome to "AigoraCast", conversations with industry experts on how new technologies are transforming sensory and consumer science!
Dr. Daniel Ennis is the President of The Institute for Perception. Danny has more than 40 years of experience working on product testing theory and applications for consumer products. He holds doctorates both in food science and in mathematical & statistical psychology. He is also a Professional Statistician accredited by the American Statistical Association. Among his many accomplishments, Danny has published extensively in the chemical senses and on mathematical models for human decision-making. Danny is a recipient of the Sensory and Consumer Sciences Achievement Award from IFT and also the ASTM David R. Peryam Award in recognition for “outstanding contributions to the field of basic and applied sensory science.” Danny consults globally and has served as an expert witness in a wide variety of advertising cases.
Transcript (Semi-automated, forgive typos!)
John: So, Danny, welcome to the show.
Daniel: Well, thank you, John. And it's very good to be here with you, my son. That you grow up into what he's become. I'm very proud of you, John.
John: Thanks, Dad. Okay, great. So to get started, honestly, it's kind of funny, but going through just even preparing your bio, it is amazing how much you've accomplished. And so I think it would be good for our listeners who may be not as familiar. I mean, I think hard to imagine someone in the field who's never heard of you, but it would be good to kind of do a brief recap of, I know, two of the accomplishments that really stood out to me when we kind of gone through the things you've done in your career. You were the first person to show that humans possess a transducer in the chemical senses. And another thing that, of course, is very important is that you solve the degeneracy problem and multidimensional unfolding. And I think that those two accomplishments are really important to think about because we, of course, on this podcast want to think about the future of sensory. And what I see with those two accomplishments is on the one hand, you've got accomplishment in the chemical senses and on the other hand, you have an accomplishment when it comes to advanced analytics. So I think it would be good to hear you talk briefly about each of these accomplishments and that we can go forward and talk about some of the predictions that you've made regarding the future of sensory and the extent towards those come through. So maybe you could tell people a little bit about your, for starters, your work in the chemical senses.
Daniel: Well, I suppose the best way for me to start is to explain where I came from academically because, you know there's a lot of people think that when they start out in a particular field that they're going to be in that field for the rest of their lives. And young people, I tell them it really doesn't matter where you start, as long as you do a really good job in the area you're in and maybe a twenty five, maybe working in one area. And by the time you're thirty five, you're a completely different area. As long as you keep doing good work, it really doesn't matter. I started out in agriculture. And as I started in agriculture, I was I got an interest to agriculture and in food science. I did my PhD in food science and my PhD advisor was an expert in quality control. And so as a result of his reputation, really, I became a professor at the University of Guelph in Canada where I was teaching statistical quality control. And a part of it was taste testing, which I knew very little, to be honest with you. But I thought it was a developing field. This was around in the 70's and I thought I had an opportunity to do something about it. So that's where I where I began to develop an interest in taste testing. But it wasn't long before I realized that if I was going to have any impact in this area or do anything useful, I was going to have to learn about quantitative psychology. And when I met young Freiders, who was in the bargaining and at the time, he had an enormous mentoring influence on me. He is a psychologist who had worked on the triangle test. And he and I really hooked up well. And I did my second doctorate under his mentorship at bargaining. But I entered really, I really left, I made some initial contributions to the sensory methodology. I sort of left the field and went into cognitive psychology to develop myself and when I did my second doctorate was to be examined on the topic to make sure that I could, for myself, satisfy myself, that I could stand up to scrutiny. So that was that I went into cognitive psychology more likely find me in the meeting or psychometrics meeting than you would in a sensory evaluation meeting. And then I came back again and brought that learning into the field through the social structure. But during that time, I became aware of Freiders in case the graphs work on glucose and fructose mixtures. And I had been interested in pharmacokinetic models and I thought that maybe I could marry them together for the chemical senses, treating odorants as, you know, as if you really treating them as if they were drugs, so I began to develop mathematical models for molecular mixture models and applied them in the chemical senses based on the graph and Freiders work. That was probably one of the most exciting thing I ever did, was developing a model for mixtures based on a receptor transducer system that was common in other areas, like in beta blockers and antihistamines. I got to know James Black's group in the UK. He won the Nobel Prize in, I believe 1989 in medicine. He had applied the same type of models to develop antihistamines and beta blockers, and he is responsible, really, for the two blockbuster drugs of the 20th century, but I extended his work to mixtures and I met his team and we went through that stuff. So that was a very exciting time for me. I published that work at the Chemical Senses in 1991 and then developed a broader model that was published in food chemistry in 1996. That was a very interesting time for me and helped me to understand what was happening at a fundamental level in the chemical senses. You mentioned the other area, the unfolding from the mathematical psychology experiences I had in publications that I had. I was really working on models for the methodologies. You know, sensory evaluation is what we called sensory and consumer science was always concerned with methodology. And this is true from the beginning when Pangborn published her famous book with Ruessier and Amerine. And she organized the methods and linked up with Ruessier really for her statistics. Her connection with Ruessier and the statistics that she did was the closest that she came, in my opinion to developing models. She didn't really go all the way into quantitative psychology or modeling. But she certainly, that was really at what she was really interested in the same thing, but went as far as applying non statistical analytic methods to sensory evaluation. She was also a major player in ACAMPS and the Association Consumer and Perception Sciences and many times at the ACAMPS meetings. So she was also a very big influence for me. But in regard to what you talked about regarding unfolding in my interest in that came from developing models for the triangle test, same difference and so far that based on quantitative psychology principles. And so they're showing you a framework, really, grow at that. I became very aware of the thurstonian structure from Young Freiders. Young done a little bit, well, I mean, contributions large, but he worked on a limited number of methodologies and I expanded that to include many more and got into the multivariate modeling aspects that multivariate thurstonian models. Here I was helped by and mentored along by Norman Johnson, a statistician at North Carolina, and he wrote the encyclopedia statistics. He wrote the series on distributions and statistics as well. He was an important mentor for me and he taught me a lot to publish some papers with him or taught me a lot to appreciate distributions and statistics. But that work helped me to develop the thurstonian framework which turned out to be, I think, useful for something like as you know yourself, you worked on, models yourself and it turned out to be important for the development of the methods of tetrads and popularity is really grasp some thurstonian theory. That's a long answer to your question, John.
John: Yeah. No, that's fascinating. Actually didn't know about everything you actually just said. So, okay, well let's talk a little bit, I there is something I just generally want to talk to you about when it comes to monadic versus sequential-monadic testing. So if we get a chance, I actually would like to hear your thoughts on something, but I would like for the sake of the audience to look at what you predicted about twenty five years ago, you were asked to speak on the future sensory, is that correct?
Daniel: Right. That's true. Yeah.
John: Yeah. And that was at IFP, was it?
Daniel: Yeah. I can tell you what that was. Barbara Klein, professor at the University of Illinois, was a big player in the IFT-Sensory Evaluation Division at the time. She asked me, great respect for her and she's a wonderful person. And she asked me to make a prediction as to what was going to happen over the next twenty five years, in 1998. We're now 22 or 23 years on from there and she asked me to give a talk at the 25th anniversary of the Division on the future of sensory of sensory evaluation. We have coined the term sensory and consumer science for this field now. I think that terminology came later for the IFT people, so I thought I basically organized my thoughts about what the future would bring into three areas. I thought the best way of describing this is what influences are going to occur on the field over the next 25 years in 1998. The first one I thought would be important was the chemical senses itself. I really thought that would have a big influence and in fact, we know that Buck and Axel won the Nobel Prize in 2004 for their work on olfaction, very fundamental of olfaction. And the second area I thought would be important will be mathematical psychology and quantitative psychology in general, and that they would have an influence. In the third area, I thought it was by statistics. If I look at these three areas now, having looked back, what actually happened, I have to say I'm somewhat disappointed that the chemical senses didn't have more of an influence. And then, for instance, the ones I worked on in 91, I was hoping that more people would get interested in developing models like that because they're very fundamental. They connect the periphery to perceptual events and then our reports that we give out when we asked surveys. I was hoping it would have more influence. I don't think it did have as much of an influence as I think it should have. The second area, the mathematical psychology area, certainly did through the thurstonian. And we now have tetrads is largely as a result of that. We now have a well organized way of looking at our methodology. We are interested in, many other people, Michael O'mahony. They've all taken this up and down. Very good things with it. So I think in that area, there's been a lot of progress. In the third area, in terms of biostatistics, I think that played is about in terms of replicated testing. I don't think if I was to think over the next twenty five years or the next period of time, what do I think will occur. I would think that the chemical senses should have an influence. I'm still waiting and still hoping that we would see more and get more people involved. There's a fundamental reason why I think doesn't got to shortly. But I'd like to see that happen. And the second one, I think, is I continue to feel that the quantitative psychology influence will be felt very strongly in our field because it's very fundamental to what we call the science of our field and the third area of biostatistics. I don't really see that continuing on beyond what it did in terms of replication. But instead of that, I do see developments in computational methods, especially combinatorial tools, along with other developments and things like data collection, text analysis, databasing and automated report generation. I think they're all going to be influential. And generally, I see the use of more sophisticated algorithms and faster computer automation. And you can take that all under the umbrella of AI machine learning if you want to. But that's how I would describe what's going to happen over the next number of years. Those three years.
John: Yeah. That's very interesting. Yeah, it matches up. I do think that the chemical senses I agree with you that one of the issues we have, of course in sensory is we don't have nearly as much data as we would really like to have to train some of these more complex models. And I think that one of the ways that we're going to have to start to bring more subject matter expertise into our models will be through models from the chemical senses. So I think that, for example, the work that's coming out of excellent. I think I know a lot of the kind of flavor houses are doing fundamental work. And I think that what's going to have to happen if if people want to truly make predictions for what are the sensory profiles of things based on molecular information or formulation information, they're going to have to get subject matter expertise in there. Yes, to some extent, if you collect a lot of data, you can build machine learning models. But it's a big shortcut if you have some idea what's actually going on when people are reporting certain responses. So I definitely agree with you there. As far as the computational side, of course, I think that's a huge thing. In fact, I would say biostatistics has been one of the big feeder fields for data science. So you might want to say that you count your biostatistics as a success and just call it data science. So that prediction, I think, came true. And then as far as the models, I do think that one of the things that's new is the the number of ways that we have for collecting data has exploded. That we have data coming in from all over the world, right? I mean, one thing, you've got these devices that can collect data. You know, I mean, we, of course, have smart speaker survey platform here. And we've talked about the benefits there. You mentioned text analysis. I mean, I suppose that may be, that second field of the mathematical psychology, the second point you said, might get replaced by technology that bridges the gap between things that used to be separate. Like quantitative and qualitative research or physical and virtual. That's another gap. So I think new technology is giving us new ways for collecting data. So I'd like to hear your thoughts, what do you think has been holding developments in the chemical senses back? And what do you think people need to do in order to make more progress in that area?
Daniel: Well, I think that one thing that I find a little bit surprising is that a lot of the people who work in sensory evaluation or sensory and consumer science, have a very good background in chemistry. If you're trained as a food scientist, I mean, to my food scientist, they have an excellent background in chemistry. It's not a big step for them to go from there to consider the chemical processes that go on that might lead to a perceptual outcome. And I was asked to review a book one time on choice models. In this book on choice models, one of the remarkable things about it is that the brain was never the word brain never appeared in the book at all. And the title of my of my review was "What is the Chemistry of Choice?" And, you know, I had a conversation one time with Bill Estes, who was a big player in math psych, and he asked me what I thought was going to happen in the future. And I said, can we work out the chemistry of choice? He said, well, that's a bit of a stretch. I don't know that's going to happen. That the kind of answer that I got from him. But I'm interested in Francis Crick. He actually entered mathematical psychology himself, Francis Crick. And a lot of parts of his life he was interested in this. And he said he was interested in the bits and pieces that go on in the brain that produce the outcomes that we observe. And I kind of have the same feeling that the chemical senses. So why do I think that we haven't had the influence. A part of it is I don't think people really understand the importance of models. I really think a lot of people in our field are very focused on data. And using class standard statistical approaches to analyzing, processing data, as opposed to thinking about the way in which our data is generated. To ask ourselves how it generated. If you start asking that question, you go back into the science and you realize that there are different things you could do to make those predictions and one of them is to look at what's going on the periphery. When I modeled a graph and Freiders data. I was really developing a chemical basis for the chemical senses, really. It was predicting subjective quality, like what you were collecting was a subjective experience of glucose and fructose mixtures. And so they were getting outcomes from the two AFC. So the question is, can you predict two AFC data from molecular models out of periphery? So connected to each other? And yes, you can if you make certain monotonic assumptions, you can take what's happening at the periphery, unpredictable occur in terms of points of subjective quality. And so that puts chemosensory psychophysics on a molecular foundation. So that's why I'm saying I would like to see chemosensory psychophysics on a molecular foundation where we consider models from fields like pharmacokinetics and apply them in the area to make that prediction. There are a lot of people who work on the periphery, but not enough people working on the part between the periphery and the perceptual outcomes that come from that. But I think it's partly a lack of I think it's a lack of understanding of what science is which for me was a revelation itself. I didn't understand what science when I was doing my PhD. It was not large, about 35 that actually starts we take an interest in the philosophy of science and understand what it is. And if more people did that, they might have more appreciation for the role of models in any area.
John:Yeah, well, I think that's one of the ironies about the term data science, is that a lot of times what you see from data science is not science at all. Right? There is not any interest in theory or models or the processes that are leading to data being generated. You know, there's, I think, a very shallow engagement with the data. And so it definitely resonates with me what you're saying, that yes, you should be aware of all these kind of fancy computational tools, but there needs to be more than that. There has to be some. I think it should be some place for subject matter expertise, and there has to be a way to, you know, to instantiate the subject matter expertise in your analysis somehow. Yeah. Okay, well, that's yeah, I think that is definitely good. What else do you see for the future? What do you think, if you're going to make your predictions now for the next 25 years. Chemical senses again, computational tools and what would be your third?
Daniel: Well, I was separating computational tools like we talked about more clever algorithms for what we were discussing. I'm separating them from, some of which are really engineering. But I'm separating them from the quantitative psychology of mathematical psychology area as having an influence in the future. I hope that over the next twenty five years we get to have a comprehensive framework that we all can understand, at least if challenged, that we can use to make comparisons among methodologies. I mean, one of the weaknesses of the field is a lack of general understanding of how all of these different methodologies relate to each other and under a common framework. I'd like to see that. Now, your various ways of approaching that, I mean, that takes us into what is science about is not a fixed thing. There are various ways of approaching. You can approach it from a lecture standpoint. You can approach it from a conceptual the search on your framework point of view which should involve any kind of peripheral events. So you can do it from either, but you'd have to have some kind of framework. Otherwise, our field ends up just chasing after the latest methodological breakthrough, as people might call it, a new way of collecting data or a new way of getting data. And it doesn't address what, you know what's fundamental. So I hope, too, that people have a better understanding or a better appreciation for what science is. And if I could just say a little bit about that without because that is one of the things I hope that develops and I see a weakness in society about this. So regarding science and including the science in our field, I hope people think more about what it means. So with issues like global climate change and the current pandemic. I hear a lot about the science as if there's only one version and all of the thinking is heretical. That's the kind of a very destructive view and it's an opposition to a good and useful perspective on what science can provide to society.
John:Yeah, science is skeptical. Science is skeptical by its nature, it’s not dogmatic and that's where the capitalist science is very dangerous, I think. That people should have room to disagree and should be encouraged, not discouraged.
Daniel: Well, even more than skeptical scientific models are all wrong anyway. I mean, it's very hard not to believe in our models. I think we can use them, we can make policy and it's the best game in town. But in the long run, all this will just above the representation for what we can what we see. And they can also limit young students who might otherwise consider a career in science where they can enter onto their own voyage of discovery. That's what happened to me and free from the restrictions of their thinking by understanding the philosophy science. It has helped me a great deal to understand what I am doing and what gives me so much joy in being a scientist. I mean, if you're a young scientist and you look at a field like chemistry and you think, oh my God, I'll never get to the end of this. There are so many people that have done so much stuff. And if I'm going to do anything in this field, I'm going to have to go to the edge. And that's going to take you to 20 years. That's very discouraging. Children can think in a scientific way. They can have ideas. You know your son can work with them all the time. He has his own ideas about how the world works. He asked some very, you know, good questions. And my other grandchildren ask great questions and they can develop their own view. That's what they're trying to do.
John: Yeah. Children are great scientists. They're natural scientists. You can see even our daughter Artemis, you know her well, one year old. I see her formulating hypotheses and testing them. Trying this toy against the wall makes this sound. The toy against the great, makes a different sound. You know, trying to explain to me that they're different. I mean, you see her running little experiments. So, yes, you're totally right. You don't have to learn every bit of established knowledge before you can start thinking about forming your own ideas about how the world works. So let's keep going on this. Let's talk then about advice for young scientists. So someone who's getting into the world, what would be your advice? What would be a good way for them to move forward with their careers?
Daniel: When someone finishes a degree or gets a qualification, whether it's a bachelor’s degree, masters or PhD. I would strongly recommend that they find and think about a nurturing environment. And, Betina, you know well. Betina, I had a chat with her after she won the award from her company. And she asked me that kind of question. She ended up Wageningen. And I was delighted to hear that. I heard the Wageningen is ranked number one in the world in agriculture last year. I didn't know that. But that's where she is. And she made a very very good choice in doing that. But I think that if you join a company, you have to remember the companies are in the business of making products and selling them to people. That's what they want to do. That's their ultimate objective. And their interests and nurturing their people as long as that helps with their main objectives. There are companies vary in the level of interest they have in basic science, and they vary in the level of interest they have in developing their scientists. The more long term ones have a much healthier view than the shorter term ones. So I think that would be very careful where you go because you only have so much time, and especially on the first five years, you need a place where you can grow and develop and whatever you end up, you can be stifled. I've seen this happen to people. Some brilliant people go into an area. It can happen to university. You could be loaded down with courses and you might not have enough time to think about things. So I think find a nurturing environment. That's my strongest advice. Develop an interest in the philosophy of science. Understand what science is and what your role is. Don't be afraid to come up with new ideas and persist with them and expect challenges and also try to work in an excellent way where you focus on quality rather than quantity of output.
John: Okay. And what are the things you're most excited about in the field now? I mean, it's hard to believe, I think you're 70 now, but you're just getting started? It is just warming up. So what do you think when you look at the field now, the things that you see are the most exciting things for people? Like what are the opportunities in the field that you think have the most potential for the next five years?
Daniel: Well, I mean, someone very heavily influenced by what I think what I'm doing myself, of course, because I'm looking on inside my head and not somebody else's head. John, you know you and I worked on some things that I thought were really fantastic and involving combinatorial tools and you taught me a lot. Actually, you mentored me as well. So you taught me a lot of mathematics. But the use of mathematics, at least in our field, I think that the work you did on coming to terms with your use of graph theory, your use of linear programming, your advances in turf analysis has opened up in the last five years an enormous potential to sensory with marketing science in a way that couldn't have been done before. I think marketing science had been a bit stuck in may be critical of this, but I think that they've been a bit stuck with hierarchical Bayesian models and maybe max stiff and there's a lot more to be done and a lot more that can be done. There are things that we can do now that we couldn't do, you know, 5 year or 6 years ago that we ought to be pursuing. I'm excited about the use of advanced algorithms and computers and helping us to get out to some of the drudgery that we waste time on that we can automatically generate reports. Like you said, one of the things that you mentioned and I think it's true, is that there could be a marriage of quantitative and qualitative research. I think the qualitative research can be put on a more quantitative foundation and a better marriage can be achieved with them. I'm hoping that the chemical senses will have an influence. That we will be able to answer the question, what is the chemistry of choice?
John: Fascinating. Alright, Dad, well, amazingly we are actually out of time here, so if people want to get in touch with you, what are the best ways for them to reach out? I mean, we'll put links in the show notes. Should they just reach out on LinkedIn and connect with you that way?
Daniel: Well, the way in which I really get to know people is through my short courses and also by interacting with own projects. People read our papers. When they read our books, they learn. But if you want to interact with us, a good way of doing it is email me. You can call me or if we can meet up at conferences, but also attending our courses. Attending our short courses is a great way for us to explain our perspective and for them to tell us really what they want. I would love to hear from people. So if they were designing a curriculum, what would they want in it themselves? Because a lot of things that we can do, but we don't always know the right things to do. So that would be very helpful if people communicate to us in that way.
John: Okay, we'll put the link to the IFP website also in the show notes so people can find those. Okay, well, this has been great, Dad. Any last comments that you want to make to the audience here?
Daniel: No, that's fine. That's all I have to say, John. I don't know anything else.
John: I don't believe that. But okay, thanks a lot for being on the show.
Daniel: Okay, thanks, John.
John: Okay, that's it. 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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