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Food choice: What people say and what people do

Applegg recently participated in an exciting publication. The paper is co-authored by researchers at Valio (FI), SOK media (FI), Radboud University (NL) and NIZO (NL). It appears in the December 2019 issue of "Food Quality and Preference".

We all have been there: Craving for a meal or snack after a tough day at work: "Shall I prepare a nutritious balanced meal, full of vegetables and environmentally-friendly meat-replacing pea-protein?" or "Will I surrender to the temptation of that juicy double-cheese hamburger with extra french fries?" In spite of good intentions and resolutions, much of what we eat we choose by impulse. Even when ingredients and nutrional values are the same, the package familiarity, the portion size or even the package color drives the impulse choice.

Given that consumers often choose on impulse, they may find it difficult to predict their own choices when asked. How to predict consumer behavior then?

Consumers do not always choose what they say

In traditional consumer research, people express preferences by checking boxes or rating scales. In the case of comparing products that are all socially desirable, all responses would appear acceptable. Ratings would then reflect true product preferences. However, when adding highly endulging yet calorie-rich foods to the comparison, consumers may tend towards choosing the socially acceptable healthy alternative. Clearly, such outcome does not predict product success: Product success depends on choice impulses in the store, not on socially desirability.

In recent years, alternative consumer measures have been developed that reveal hidden choice impulses towards products. Applegg applies these measures, named Applegg Attract, to complement regular consumer measures in regular consumer studies. When people feel compelled to give a socially desirable answer, Applegg Attract measures will still signal the products that they really feel attracted to.

The Question

Imagine comparing very similar, healthy products of competing brands. Would product brand and package still influence purchase behavior? And, would Applegg Attract measures still predict purchase behavior? Which consumer measure would predict that behavior best: the classical consumer test, Applegg Attract or a combination of both?

A team of scientists including Applegg 's Harold Bult studied these questions with a large population in Helsinki using three different brands of fruit-quarks.

The Experiment

Consumer test: Fruity quark lovers (n=134) participated in this study. Besides a variety of classical consumer measures, we collected brain measures and involuntary hand movements. On day 1, participants evaluated three quarks using classical consumer test measures. On day 2, a random selection of these consumers (n=52) watched images of the three quarks, while producing EEG brain measures and joystick responses.

Purchase behavior: During the month following the consumer tests, participants kept a diary of supermarket purchases.

Results: Brain-measures predicted purchase behavior very well (see figure below). This is remarkable, considering that the compared products differed only in brand and graphic design. In fact, portion sizes, package shape and ingredients (high-protein) were all the same. Explicit consumer measures like brand-loyalty and willingness to eat also predicted purchase behavior very well. Yet, the best predictions resulted from classical and Applegg Attract measures combined. This is truly remarkable considering the lack of reasons to produce socially desirable answers.

This image has an empty alt attribute; its file name is wanting-vs-purchase.png
Averaged over 52 regular quark consumers, purchased amounts of 3 fruity quarks were well-predicted by immediate brain responses following presentation of the quark images.

Conclusion

Food purchases are best predicted by a combination of (1) unconscious brain responses and joystick responses to food images and (2) a set of classical consumer measures.

This technology allows for more reliable testing of healthy and sustainable foods, utilities and services that usually receive positive socially desirable evaluations.

Applegg publishes its first scientific paper

paper, co-authored with researchers at NIZO, published in the recent proceedings of the Weurman Symposium

The Question

This paper is the culmination of decades of research into the phenomenon of aroma-induced sweetness enhancement (AISE). Indeed, food aromas (as in’strawberry smell’) can produce vivid taste alterations (as in ‘sweet’). Imagine the benefits: Reducing caloric sweeteners while maintaining sweetness.

The main concern with AISE has always been that the effect may wear off after repeated consumption. The lack of sucrose may get noticed over time.

We put this concern to the test: “Is the perceived AISE robust for repeated exposure?” Or even stronger put: “is AISE robust for the explicit message that there is not as much sugar in there as one may think?”

The Experiment

Ethyl hexanoate (HEX) is an odorant produced by apples at advanced ripening stages, when sugars are also synthesised. After adding HEX to apple juice, 45 naive subjects indeed judged the juices sweeter, in line with the amounts of HEX added (left column in the figure). This effect was most pronounced for low-sugar apple juices. However, AISE collapsed after informing panellists repeatedly on the actual sugar contents of juices (columns 2 and 3). Did subjects acquire the ability to distinguish sugar-induced sweetness from AISE? The answer is no: Over time, during which subjects were not reminded of  sucrose contents, AISE did recover (column 4). Apparently, subjects forgot to apply the rule to trade aroma for sweetness.

Conclusion

Humans do not acquire the ability to distinguish between aroma-induced sweetness and sucrose-induced sweetness, even after intensive training. Hence, long-term sugar-reduction by AISE is possible in food!

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Measuring Emotions while Eating Cookies at Home

Smart cookies

The Virtual Emotion Reader project is a collaboration between the companies VicarVision, NIZO and Noldus, co-funded by the Dutch government. The project's main objective?  To optimize and validate improved software by FaceReader.

The improvement allows more robust  facial expression analysis when faces are partially obscured by objects. This happens when hands, cups or cutlery block the camera view of the face. As a result, the FaceReader can be applied in food and cosmetics R&D.

Applegg contributed with the acquisition and analysis of facial expression results in relation to preference ratings in an online cookie tasting experiment. This marked the final validation of the new Facereader technology AND the successful integration of expression analysis with online consumer testing (see also Applegg Online).

The final report of the validation study was submitted earlier this month, which concludes the three-year collaboration. Our partner VicarVision, producer of the FaceReader software, shares a sneak preview of the results here.

We will keep you updated on new exciting developments in this field of consumer research. An example of this is the use of elementary muscle actions (action unit responses) specific for true hedonic impressions of the evaluated product.

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