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Anne Goldman is an IFT Fellow, a Certified Food Scientist, and has been honored with IFT’s 2016 Sensory and Consumer Sciences Achievement Award. Among other honors, Anne is a recipient of the Honorary David R. Peryam Award for applied sensory science and co-chair of the 14th Pangborn Sensory Science Symposium in Vancouver, Canada, in August of this year.
A food science graduate of the Universities of London and Leeds Anne began her professional career in the UK food industry, followed by research positions with DSIR, Fonterra, and Massey University in New Zealand and an Assistant Professorship at the University of Guelph, Canada.
Anne is most proud of the fact that she has been a principal of ACCE International since it was founded in 1986.
Anne on LinkedIn
Transcript (Semi-automated, forgive typos!)
John: Anne, thanks a lot for being on the show.
Anne: Thank you, John. It's a pleasure. And thank you for inviting me. It's always a pleasure to get to talk to a fellow sensory scientist and to discuss words of wisdom that might help others in the field, particularly from somebody who's been around so long.
John: Well, you certainly have a lot of wisdom, and I've always enjoyed talking with you at ASTM from the previous conferences where our paths have crossed. So it's a pleasure to have you on the show, Anne as someone who has, as you said been in the field for some time. So are you now 50 years in the field? Is that correct?
Anne: Yes, actually, 50 plus years. I started my career in 1968 after I graduated with my Master's in food science from the University of Leeds, where I must say, we didn't learn very much about sensory evaluation.
John: So I think that's great, because you have now obviously become a true expert in the field. So it would be nice to hear your kind of take on how you've seen the field evolve. You know, what resources you've seen kind of come online and you know how you have, I guess what I'm most interested to know is how you've seen the field accelerate. But if you could just kind of share some of your thoughts on where the field was when you started and then how you've seen it changed to the current day. I think that'll be really interesting.
Anne: Well, certainly when I started in the field, there was nothing called online research. So you couldn't just tap into the Internet and find out a whole lot of information. The resources that were available back in those dim and distant days were the very esteemed text by Amarone Roessler in Pangborn, which I think, you know, people could possibly still dip into this day and learn some interesting pieces. But there were also some quite good publications from people like Elizabeth Lamonte at the Agriculture Canada. Back in those days, there was a very strong sensory group starting in Canada. And then they had a handbook which was okay, fairly basic, but a useful tool. It even never even showed you how to do ANOVA by hand, which, you know, that's the kind of thing we did back in those days. And then, of course, there was ASTM. They had a manual that had been published in the 60's. So there were publications, but they were fairly basic, but they still were very useful. And as far as my beginnings, I learned quite a bit of my sensory practices when I worked in New Zealand. What was the origins of Fonterra, was the Dairy Research Institute and they had already been guided by Arthur D. Little in their flavor profile. And they had started to adopt them. So I was very fortunate to work with a group that had already embraced sensory evaluation in my early career, which was very fortunate.
John: Here's something I would like to get your take on when it comes to that kind of early sensory and the ideas that were around the day. Do you think that there were ideas that didn't really it didn't have the technology yet to support them that we should still be thinking about? Do you think that there are lessons from the early days of sensory where, I mean, I can give you one example of this from talking with Howard Moscowitz, but I'd like to kind of get your take first on either ideas that you from the early days that are still relevant or ideas that are maybe even more relevant now with all the new technology we have for data collection and data analysis. What are your what's your take on the kind of wisdom of the early sensory?
Anne: Well, I think we were still very much engaged in the descriptive part of our sensory. And I think that's something that potentially can get overlooked these days, the descriptive sensory testing using panels. It is something that tends to be, oh, it's too expensive. It takes too much time. Obviously, we can speed it up much more these days with some data collection and we can get the answers more quickly. However, as I said, you know, I think we've all been saying on these charts, you still need to take time to evaluate that data. You still have to really dig in and take a step back to think through the data and not be swept up in this. The speed that we have, you know, we do go at breakneck speed these days. It's not to say that we didn't we didn't go at breakneck speed and our way back then, because we still have to get the answers. We still have to get to the root of the whatever the problem was. But I think it's that connection between not just objective measurements done with instrumentals, but also with the descriptive and then relating that to the consumer. And that's obviously been an evolution relating the descriptive into the consumer dimensions. And that continues.
John: Yeah, definitely. And it is exciting to see the developments when it comes to, I think, more informative consumer measurements that you see. I mean, there's every move to to go past liking to try to get some of the more, you know, maybe you call them predictive or more system one type measurements. Measurements that might actually tell you more about consumer behavior. Before we go into that, I would like to kind of go back to what you're saying, but descriptive analysis, because it's, the more I've learned about machine learning, the more I think that having good quality data that you understand is essential. There is this drive among I don't know if it's computer scientists or if it's just people who have, I don't know, desire to scale everything because they want to have the next unicorn or whatever that they maybe it's the venture capital influence. I'm not sure. But there's this this desire to just take whatever numbers you can get and throw them into a model and get some predictions. And it's funny how the more I learn about data science, the machine learning, the more I believe. No, no. We really need good quality measure. I'd rather have a few good quality measurements than, like, a lot of junk. I guess I'd be interested in your thoughts on this as far as, you know, the importance of science in our field. You know, whether you feel like is there more of a need now to focus on science than there was historically?
Anne: I think, I mean, certainly as scientists, we've always focused on getting decent data. I mean, we've always been as good scientists. We've always been very rigorous about saying whatever data we are analyzing. Is it relevant and is it good, robust data? And can we can you know, because anybody who's ever had to defend a thesis or defend their research has always had to show that the data that they gathered was sound and relevant. So I agree. We are kind of rushing into, yes, let's just pile all this data into an algorithm and come out with that algorithm. Well, what data is it you're putting in? Where did it go come from? Does it actually relate to each other or is it relevant? So, yes, I think we need to abide by our scientific principles in terms of how we address this. And it's never been actually more important. I think it always was. And it's always been something that's been very much part of the scientific rigor. And it should continue to be so. And I think that's something that you obviously learn, you learn, you know, when you've been trained as a scientist, but you absorb it more as you go through life, too. And, you know, come across instances where maybe that wasn't followed. And then you see the results of that where, you know, where it was something was uncovered that hadn't been done correctly. So, you know, you really don't want that to happen.
John: I totally agree, and I actually think nowadays where you have all the fancy analytics. It's kind of more dangerous because in the sense that if you've got poor quality data, it can be hiding behind fancy analytics. And, you know, you're getting numbers and you have a nice maybe a lot of money was spent on the design of the software. And so the software looks beautiful. But at the end of the day, you've got to build things on good science. And yeah, I am concerned a little bit about that.
Anne: Yeah, just on that point, you know, I'm certainly not a statistical expert, but I've always felt that, you know, you need to understand the methodologies behind whatever these analytics are. What are they actually doing? And anybody who is blindly using some tool that they don't really know what it's doing. That's very dangerous.
John: Yeah, I completely agree with that, and I actually think this is a big debate, obviously, in the machine learning community about black boxes. And I think they have their place. I mean, you're talking about, you know, computer vision and you're trying to recognize. One example, you know, from my client work is interest in influencer surveillance. I don't know if you've, I mean crazy these words that wouldn't made any sense five years ago. In this case you have a company who's thinking about which influencers to sponsor and what they want to do. Their idea is, well, if we know which influencers already use our products or are already appearing with our products in their hands, then it'll be more congruent if we pay those influencers, because then when they start showing our products more often, it won't seem out of place. Whereas if you have an influence or you suddenly changes, you know, they're always, I don't know, wearing Ray-Ban's and suddenly they're wearing Maui Jim's or something like, look, it might seem strange. So the idea is okay, let's build a neural network so that we can identify whether or not our product appeared in a video. And then you set up bots to go through and watch all the videos of all these influencers. And I mean this is totally crazy, like it's a very strange world we live in. But this is the sort of thing that's happening. So in that situation, I don't think it's important to understand why the bot would say this video has Maui Jim sunglasses in it and this one doesn't. It's always about getting the right answer. I think that's probably fine. But I think if you're going to start...
Anne: That whole area also brings up this issue of privacy and using data of people when they're not aware of it, right?
John: Well, you know, the argument would be that this day they put this video out there, it's in the public sphere.
Anne: I know that's right yeah.
John: They put it out there so that people could watch it. And so now it's being watched it's scale. Yes, that's right. That is an interesting question, too. But then I think if you want to have confidence in your prediction, suppose you're trying to predict whether or not people are going to be interested in the product. I do think that from a scientific standpoint that it is important to have some idea why your model is making the prediction that it's making. Otherwise, it's a very empty kind of knowledge. It's the sort of knowledge you know the Babylonians would predict where things were going to be in the sky and they would have pretty good predictions, but they didn't have any kind of model that was accurate about the universe. They just don't make good predictions. You know, whereas, you know, modern astronomical models are very complex and, you know, they make a predictions, but they also have the virtue that there's some sort of deeper understanding of what's going on. So I didn't think about that. But let me ask you, too. So as you think, seeing things kind of evolve for this kind of thing I been wondering about. Do you feel like the last five years have been unusual in terms of the pace of progress? I mean, have you seen a kind of acceleration or have you seen sort of steady rate? Like, how is the rate of change looked to you as you've been making your career as a sensory scientist?
Anne: Certainly there's been tremendous changes in the consumer behavior area in the last few years with the like we've just been talking about this reliance on Internet and fast, you know, access to data and in terms of the consumer being able to change their behavior even through things like ordering the food or, you know, the car, you know. But basically what I call the uberization of behavior. I mean, from, you know, ordering a car ride to ordering a dinner to ordering a snack or whatever, you know, or just easy access for everything. So this is something that certainly when you're doing consumer work, you need to be aware of and somehow figure out what are the implications for that when we're doing some consumer projects that have required that input to know how that input might affect the way the product is going to be consumed or evaluated. These changes in the way consumers are getting some of their information and behaving certainly has implications in terms of the industry, the actual, you know, the client industry that we deal with. The changes really have been, yes, we want information much more quickly because we're doing much more fast adapt product development. We needing the results much more quickly, you know, we've gone are the days of a two year cycle for, you know, a product development cycle. It's much faster. And it also needs to be much more cost effective. The budgets are not what they used to be. So this is or shall we say the money is being spooled into other other areas, there have been changes, that's for sure. And it's all to do, I think, with this need to be a lot faster and damaging. Totally. So we just need to be more efficient and more cognizant of how to deal with that.
John: Right. Yeah, I think I saw a statistic recently that since 2013, large CPG companies have really not even been profitable. That they only barely been profitable compared to rather large profits prior to 2013. So what do you think that changed in society that's been driving that difference, that the sudden challenges that CPG is facing?
Anne: Consumers are really, I think looking at a much broader range of choices now, they're demanding more. Well, certainly in the food side, that demanding a lot more fresh, healthy items that deal with different lifestyles on the go. You know, much more eating on the go. Much more cross generationally thing. You know, people don't eat together the way they do. So there's some need for just so much more choice. And so the standard CPG products are certainly being threatened. They are definitely, you know, as so many are doing, are looking for more entrepreneurial startup companies. New food ideas, and they're starting to try to embrace that, which, of course, comes into this fast adapt. We can't we can't spend two years doing this development like we used to. So, you know, I think that's been a struggle to try and create these new products and situations that consumers appear to be demanding. So it's tough. It's a very tough work.
John: Yeah, it's very interesting how there's so many factors. but something I had really thought about the demand for kind of healthier eating options is, you know, those are gonna be lower profit items, right? That it's you know, at some point, if you only have an item that's got, you know, three or four ingredients in it, then there's not really much you can do other than have high quality ingredients with those items. So that's an interesting challenge. At the same time, of course, social media has been made it a lot easier for people to take any product to market. That once upon a time you're gonna need television dollars, right? Now you can have some viral campaign and launch a new product. So, yeah, it's really challenging. Do you think in today's environment that it's more important for researchers to have a kind of holistic view of the business? Do you think it's important to seek input from all sides of the business before kind of taking on your research? Or what's your take on how sensory researchers should be conducting the research these days?
Anne: Certainly, sensory researcher is more than ever have to be showing their value within companies. You know, we obviously know of instances where they have been disbanded because maybe they haven't been able to show the value to the bean counters in the business. And it's always been important to be able to engage with all the business. Parts of the business to show what it is you bring, how useful your research can be, what it is you can actually bring to the whole business. It's no use being sort of just in your little home, buried somewhere in an R&D function and never seeing the light of day. It's always been important to show this is what this piece of the puzzle can help and how it can you know, how it can help to drive your business. You know, it's so important to know all about your product. And this doesn't isn't just sensory, but any anybody who is in a business who is making product products for consumers needs to know what it is about the product that drives it. What are sensory dimensions that are important. And you don't just have to be the sensory scientist. The role of the business needs to understand what is it about this dishwashing liquid or this toothpaste or this bread? What is it that makes it a superior product or a better product the way people will like it. So. You know, I've always found it's interesting how parts of the business just don't seem to engage with the product sometimes. They don't even ever seem to use it sometimes.
John: That's right. Well, definitely. Yeah. I have some stories from interacting with certain marketing groups that they were definitely back up of what you're saying, you know. Yeah. Basically, in the beer industry believing well, it doesn't matter what's in the bottle. Like we can sell it, but it does matter what's in the box.
Anne: Yes, it sure does.
John: So, Anne we actually I mean, it's amazing how time flies in these conversations. But I would like to get your take on what you see as the important issues, because you're in a very, what the best word to say this, I mean, I guess to say powerful is probably the best word. As one of the co-chairs of Pangborn, you're going to have a big influence on the field. So what are the things you're thinking about as far as the topics that you are going to be encouraging sensory scientists to think about at Pangborn next year?
Anne: Well, the theme of, and thank you for giving us customary that plug. We're both very young. We're thrilled to be the co-chairs for 2021 Pangborn. The theme of this conference is going to be sustainability, and that's to embrace sustainability as we know it, environmental sustainability and its relevance. But it also means the sustainability of our field, too. And now you know how our field can sustain itself. So it's quite a broad topic. And I think it's very relevant to what the world we're facing at the moment, too. So we do hope that we will have we already know we've got some engaging keynotes that we've managed to agree to come and speak and that will be announced very shortly. And we'll also be having workshops and other streams, just as we did in Edinburgh. So there'll be lots of opportunity to engage with all the different facets of our field, which we will look forward to welcoming everybody in 2021 and to Vancouver will be wonderful.
John: I'm sure it'll be great. I know you both do a great job. And how do you see new technologies factoring into sustainability? I mean, I already think a bunch of things myself I'd be curious about, like your your take, you know, given the theme of the show, how our new technologies are helping to support sustainability efforts within sensory?
Anne: Well, we certainly hope that we can have some presentations that, again, back to our earlier discussion about good sound data that we'll be able to show some movements on the AI front that are helping to further our research and give us answers to certain areas that we feel we still need answers to. We still need to somehow explore. I think we'll have lots of opportunity for some really good discussion on these different aspects. And I really encourage people to put forward their ideas to to make this a good sounding board.
John: Yeah, it's interesting and I have exciting thought about this, but the fact is, for example, databasing is a topic that I'm extremely passionate about. And then graph database, it's getting raising awareness of graph databases and their like, usefulness within sensory consumer sciences, like my mission for 2020. If it's the case that someone has their data well organized and they're able to to begin their research with a good awareness, what they already know, then that means that research will be much more efficient, right? And that means that you won't have to do as many studies or you'll get more mileage out. You have a better designed study. So that I mean, when you think about a central location test, I never actually really thought about the carbon footprint of like a category appraisal. But it's probably non-trivial, like all these people driving to a central location test or whatever. If it was the case that you could do much more targeted research because you know what you already know and you realize you don't need to do these large scale tests that involve, you know, a lot of logistics or you maybe can do more online testing or you can do testing in virtual environments, people's homes or, whatever is coming technologically. It's very interesting thing about that. Reducing the carbon footprint.
Anne: Yes, that's true.
John: That's really interesting. I never thought about that. Okay. Alright. Well, we are actually pretty much out of time. So with the time we have left, Anne I think it'd be great if you had some short advice, you know, for young members of the field. I mean, you're someone who's really I think, as what I said, you're kind of viewing the field from a great height in terms of your amazing experience, the fact you've been in so many walks of life. What would you recommend for the next two years? What should sensory other than coming to Pangborn, what should a young sensory researcher be doing over the next couple of years?
Anne: Well, a young sensory researcher, if they're not already engaged in one of the professional associations that we all support. I would encourage them to do that if they're wherever they are in the globe. There are opportunities to join things like ASTM or their local sensory group within maybe the Food Science Society, whatever. I encourage them to get an engaged with that because it's often very difficult for sensory scientists who aren't attached to academia to speel they're staying up to date. And this is definitely a way of staying engaged and also being able to interact with some other sensory scientists and be able to gain some exposure and experience from other sensory scientists. So I'd always recommend try and be engaged with whatever you can, even if it means funding yourself. It's definitely worth it because it's how you can stay engaged and get perspective on what it is, you know, with the new techniques or new ideas that are coming through. I also would give some advice on trying to think of ways in which you can be sure you're being as relevant as possible to the business that your working in or wherever it is you're working, and to be able to help them to see what your true value is. Just keep that in mind, because it's so important to sustain, you know, the relevance of who we are. You know, if everybody could just be their own little marketer in their field. I think it would be very helpful.
John: I agree to that for sure. Okay. Well, Anne, thank you so much for being on the show. It's really a pleasure and an honor to have you as a guest here. How can people get in touch with you? How can they find you? Are you active on LinkedIn or how should they reach out?
Anne: Yes, I'm on LinkedIn. My email is on LinkedIn as well. And you can also find me through our website, acceintl.com. And I think probably if you find Goldman on LinkedIn, you'll definitely find me there.
John: Okay, great. And we'll put the links to your LinkedIn page and the ACCE in the show notes as well so we can find you that way. Thank you, Anne.
Anne: It was a pleasure. John.
John: Okay. That's it. Hope you enjoyed this conversation. If you did, please help us grow our audience by telling a friend about AigoraCast and leaving us a positive review on iTunes. Thanks.
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