Generalized Pairwise Correlation and method comparison: Impact assessment for JAR attributes on overall liking

Consumers with high frequency of 'just about right' in JAR scales may use lower cognitive effort: Evidence from the concurrent 9-point hedonic scale and CATA question

JAR scales are widely used to evaluate the suitability of attributes and guide product optimization. However, the reliability and validity of the results from JAR scales had been widely doubted. In the current research, it was hypothesized that respondents with high just about right (jar) frequency may have more satisficers according to Krosnick's survey satisficing theory, herein they were more likely to employ low cognitive effort in the tests. To search relevant evidence to prove this, a strategy of indirect method was employed that consumer with different jar frequencies may also perform differently in other concurrent tests such as hedonic scaling and CATA questions. A total of 716 consumers were recruited in four studies with four different sets of products involving coffee and chrysanthemum tea. These consumers were then divided into two groups in each study according to their jar frequency to examine the above hypothesis and their performance on the concurrent tests were compared between the two groups. It was found that consumers with high jar frequency tended to use a narrower range of scales on 9-point hedonic scale, and use terms more narrowly and repeatedly in CATA questions. This confirmed the above hypothesis. Meanwhile, the low cognitive effort could noticeably influence responses in other questions. For instance, it led to lower product discrimination based on hedonic scores when samples were similar and altered results of related to terms discrimination and term configurations in CATA questions. Thereby, survey satisficing problem should be taken seriously in both experimental design and statistical analysis in consumer testings.

Effect of Serving Plate Types and Color Cues on Liking and Purchase Intent of Cheese-Flavored Tortilla Chips

Foods' overall liking (OL) and purchase intent (PI) are influenced by visual inputs, such as color cues and serving plate types. Cheese-flavored tortilla chips (CFTC) from two formulations (A and B) with a noticeable color difference (∆E = 4.81) were placed on different serving plates (plastic, foam, and paper) and presented monadically to N = 83 consumers using a randomized/balanced block design in two sessions. Consumers evaluated likings of overall visual quality, color, crunchiness, saltiness, overall flavor (OF), and OL using a 9-point-hedonic scale, attribute appropriateness on a 3-point-just-about-right (JAR) scale, and PI using a binomial (Yes/No) scale. Color differences between A and B influenced crunchiness and saltiness liking and perception, which together with OF liking and formulation, mainly determined OL of CFTC. Although having similar fracturability (N) and sodium content, formulation A had higher crunchiness and saltiness likings. PI was influenced by crunchiness, saltiness, and OF liking with 37, 49, and 60% increases in PI odds per liking-unit increase, respectively. Plate type had minimal effect on the sensory liking of CFTC. The brighter and less-yellow color of CFTC could positively influence liking of crunchiness and saltiness, which significantly contributed to OL and PI. These findings are useful to understand consumers' acceptability and perception of foods when varying visual inputs.

Multicriteria decision making for evergreen problems in food science by sum of ranking differences

Finding optimal solutions usually requires multicriteria optimization. The sum of ranking differences (SRD) algorithm can efficiently solve such problems. Its principles and earlier applications will be discussed here, along with meta-analyses of papers published in various subfields of food science, such as analytics in food chemistry, food engineering, food technology, food microbiology, quality control, and sensory analysis. Carefully selected real case studies give an overview of the wide range of applications for multicriteria optimizations, using a free, easy-to-use and validated method. Results are presented and discussed in a way that helps scientists and practitioners, who are less familiar with multicriteria optimization, to integrate the method into their research projects. The utility of SRD, optionally coupled with other statistical methods such as ANOVA, is demonstrated on altogether twelve case studies, covering diverse method comparison and data evaluation scenarios from various subfields of food science.

Liking Product Landscape: Going Deeper into Understanding Consumers' Hedonic Evaluations

The use of graphical mapping for understanding the comparison of products based on consumers’ perceptions is beneficial and easy to interpret. Internal preference mapping (IPM) and landscape segmentation analysis (LSA) have successfully been used for this propose. However, including all the consumers’ evaluations in one map, with products’ overall liking and attributes’ perceptions, is complicated; because data is in a high dimensional space some information can be lost. To provide as much information as possible, we propose the liking product landscape (LPL) methodology where several maps are used for representing the consumers’ distribution and evaluations. LPL shows the consumers’ distribution, like LSA, and also it superimposes the consumers’ evaluations. However, instead of superimposing the average overall liking in one map, this methodology uses different maps for each consumer’s evaluation. Two experiments were performed where LPL was used for understanding the consumers’ perceptions and compared with classic methodologies, IPM and cluster analysis, in order to validate the results. LPL can be successfully used for identifying consumers’ segments, consumers’ preferences, recognizing perception of product attributes by consumers’ segments and identifying the attributes that need to be optimized.

Sensory characterization of cucumbers pickled with verjuice as novel acidifying agent

Verjuice is an acidic unfermented unripe grape juice that has recently been proposed as an alternative to vinegar in different food preparations. In this study, pickled cucumber preserves were industrially prepared using two dilutions of verjuice as acidifying agent and compared with the traditional vinegar-pickled samples. Samples were chemically characterized and subjected to sensory analyses using a trained panel and consumers. The different acidifying agents resulted in pickled cucumbers giving different olfactory and gustatory evaluations. No differences in appearance and texture were found. Despite showing chemical and sensory differences, pickles preserved with verjuice and vinegar had similar overall liking scores for visual, olfactory and gustatory aspects. A key feature of pickles prepared with verjuice is their lack of acetic acid potentially conferring them an additional value, suggesting that verjuice represents a viable alternative to vinegar for the production of pickles with distinctive sensorial properties.

Use of JAR-Based Analysis for Improvement of Product Acceptance: A Case Study on Flavored Kefirs

A common question of dairy product developments is the possible success of the new product. Several publications reported successful results using just-about-right (JAR) scales; although there is some debate about their advantages/disadvantages. This study highlights the limitations and opportunities of JAR scales and penalty analysis of fruit flavored kefirs. The first question is whether penalty analysis results help to improve the product and thus its overall liking (OAL)? The second question is what happens to those who rated the products "ideal" (JAR) before product development when evaluating the new products? Fruit flavored live-flora stirred-type kefir samples were formulated and evaluated by 92 consumers before and after the JAR-based product development. The OAL of two products significantly increased after product development. A new visualization tool is introduced, which shows what happens to those who rated the attribute as JAR but the attribute has been modified. A general product development scheme is also introduced for JAR-based kefir product development.

Improving the sensory quality of flavored liquid milk by engaging sensory analysis and consumer preference

Developing innovative products that satisfy various groups of consumers helps a company maintain a leading market share. The hedonic scale and just-about-right (JAR) scale are 2 popular methods for hedonic assessment and product diagnostics. In this paper, we chose to study flavored liquid milk because it is one of the most necessary nutrient sources in China. The hedonic scale and JAR scale methods were combined to provide directional information for flavored liquid milk optimization. Two methods of analysis (penalty analysis and partial least squares regression on dummy variables) were used and the results were compared. This paper had 2 aims: (1) to investigate consumer preferences of basic flavor attributes of milk from various cities in China; and (2) to determine the improvement direction for specific products and the ideal overall liking for consumers in various cities. The results showed that consumers in China have local-specific requirements for characteristics of flavored liquid milk. Furthermore, we provide a consumer-oriented product design method to improve sensory quality according to the preference of particular consumers.