Packaging Research in Food Product Design and Development

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This is to ensure that we give you the best experience possible. Continuing to use www. If you would like to, you can learn more about the cookies we use. Author s : Moskowitz, H. Author Affiliation : Moskowitz Jacobs Inc. Author Email : mjihrm sprynet. Editors : Moskowitz, H. Book : Packaging research in food product design and development pp.

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ISBN : Record Number : Publisher : Wiley-Blackwell. Location of publication : Ames. Country of publication : USA. Language of text : English. Language of summary : English. Identifier s : drinks, food distribution and marketing, labeling labeling Subject Category: Techniques, Methodologies and Equipment see more details , labels. In line with our Privacy Policy, we want to make you aware about what we do with the information you provide when you create your My CABI account. We collect your name, email address, institutional affiliation and login credentials.

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Packaging Research in Food Product Design and Development

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Visit our updated privacy and cookie policy to learn more. This Website Uses Cookies By closing this message or continuing to use our site, you agree to our cookie policy. Learn More This website requires certain cookies to work and uses other cookies to help you have the best experience. Food Engineering logo. January 16, Consumer researchers are accustomed to looking at subgroups of respondents in addition to the data from the total panel. These subgroups are typically defined either by geo-demographics i. In our candy study, all of the respondents were recruited to be category purchasers, so there is no question that we are dealing with the correct target sample.

The exceptionally high performance of three products and the poor performance of two other products cannot, therefore, be traced to an unusual group of respondents. All respondents were appropriate for the study, and furthermore, all respondents evaluated every single one of the candies. By having each respondent evaluate all eight samples, we eliminate any bias that may be caused by the respondent.

Each respondent serves as his own control. It is a simple precaution that, at once, increases the strength of the data by reducing a host of biases.

We will look at our statistic, the percent top-3 box. Keep in mind that this statistic tells us the proportion of individuals who we classify as acceptors for each candy, based on how they rate the picture of the product and the schematic of the package. Table 3.

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The numbers in the body of the table are percent top-3 box ratings of 7, 8, 9 for purchase intent, rated on a 9-point scale. Each respondent rated all eight pictures.

How To Design: a Packaging in 3 Steps!

Or course we see the data in a neat tabular form! But, again, what do we really see? Certainly we see some differences. Yet, being realistic, can we legitimately say that we see differences among groups, or are the differences that we see merely the result of random variability that always occurs in scientific studies, no matter how well controlled the studies may be? One way to convince ourselves that there are no differences by groups is by plotting the data in a scattergram. From the deviations we see that the complementary subgroups really respond quite similarly.

Differences in the top-3 box are probably due to random error; there are no major discrepancies.


Plotting the data is better than a lot of the inferential statistics, when we look for similarities and differences in populations. Finding Individual Differences Through Segmentation 80 80 Females Females Throughout this book we will be talking about the value of segmentation as a way to understand designs, packages, and of course the people behind them, the consumers Ennis et al. The notion of percent top-3 box brings that idea to life.

Since we focus on the percent of the respondents who rated a candy 7—9 on a 9-point scale, we instantly realize there are the others who did not rate the candy 7—9. We saw no differences in general patterns when we looked at the summary statistics, by candy, across gender and across age look at Table 3. One way is to plot the distribution of the ratings for each of our eight candies, to see how these distributions appear.

We see our eight candy bars, with a so-called density distribution. The trick here is to see whether the people distribute across the scale, or whether they clump at one location. Distribution means that people differ; clumping means that the people are identical. A good example is Mounds. Mounds scored at the bottom of the group, at least based on the picture of the product, the structure of 60 40 20 0 60 40 20 0 20 40 60 Males 80 0 0 20 40 60 Teens 80 Figure 3.

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Each circle corresponds to one of the candies. The respondents were divided into complementary subgroups and plotted against each other. Yet, look at Figure 3. We see the ratings for Mounds distributed in a way to suggest that there are some people who truly love the product. We might as well start here with the simplest data—liking ratings for a set of different products.

We begin with the recognition that all we have are the ratings of these eight products.


Certainly we know about these products from other sources of information, but the only criteria that we will use for dividing the population will come from the eight ratings themselves. Lay out the products in an easy to use matrix.

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This first step is really a bookkeeping step, necessary for statistical analysis. The typical layout comprises rows as people, and columns as products, or columns as other measures on which we are going to divide the people. This layout is done to follow the requirements of statistical programs. Reduce some of the redundancy in the variables on which you are going to segment. We have eight products. We would really like to make sure that we segment or divide people on variables that are truly different from each other.

A good example of the logic behind this comes from dividing people on the pattern of their responses to questions, not products. Suppose we asked respondents to answer eight questions, but six of the eight questions had to do with taste, one had to do with appearance, and one had to do with texture. And, furthermore, most of the six questions on taste dealt with different aspects of the chocolate flavor. Dividing people on the pattern of their answers to these eight questions, six of which are flavor, and five of which deal with chocolate flavor, would give us a distorted division of people.

We just start again, reducing the redundancy of our eight questions or, in this case, of our eight products. We use the method of principal components factor analysis to reduce the redundancy of our eight products. We divide the product set into three logical or pseudoproducts that differ from each other. The number three is not fixed. The number of such logical or pseudoproducts comes from statistical considerations in the analysis. It could be two, three, four, five, or even more pseudo-products.