A case study of the use of experimental design and multivariate analysis in product improvement

Ellekjær, Marit Risberg; Ilseng, M.A.; Næs, Tormod

doi:10.1016/0950-3293(95)00018-6

Cited by 45 publications

(26 citation statements)

References 2 publications

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“…Treatment A is preferable to maintain or improve the sensory qualities of the product. The data also show that experimental design and sensory evaluation work well in combination as has been shown in various previous studies (10,15,16). However, in experiments involving evaluation by a sensory panel, it is important to ensure that the differences generated by the experimental variations are sufficiently large to be detected by the sensory panelists.…”

Section: Resultsmentioning

confidence: 54%

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Commercial spread optimization using experimental design and sensory data

Wikström

Sjöstróm

2004

J Americ Oil Chem Soc

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The cost and quality of food products are issues that concern both the consumer and producer. In this research, the process used for the production of a commercial spread was subjected to a statistical experimental design for the purpose of reducing the cost of production while maintaining or improving the sensory quality. Three factors-the amount of oil added (x 1 ), the speed of puddling (x 2 ), and the temperature treatment (A or B; x 3 )-were varied according to a full-factorial design at two levels. The experiments were performed over 2 d, and the factorial design was complemented with three replicates for temperature treatments A and B, which were performed on different days. The products were evaluated with both sensory and physicochemical measurements. Special attention was paid to the hardness of the product since it was permissible to reduce it slightly. In contrast, sensory quality aspects of the product including butter-aroma and off-flavor, as well as other quality properties such as spreadability, shine, and meltability, had to be maintained at the present level or improved. Statistical evaluation of the data showed that it was possible to add high amounts of oil (x 1 ) without impairing the sensory quality of the product and, hence, reduce the cost of production. The hardness of the product was also slightly reduced when using the high level of oil. In maintaining other sensory qualities such as shine and spreadability at the present levels, the choice of temperature treatment (x 3 ) was important.

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Section: Resultsmentioning

confidence: 54%

“…The relationship between the investigated variables and the sensory quality of the product determined by sensory evaluation is then modeled by multivariate methods such as PLS or MLR. Another common way is to use ANOVA in combination with principal component analysis (10).…”

mentioning

confidence: 99%

Commercial spread optimization using experimental design and sensory data

Wikström

Sjöstróm

2004

J Americ Oil Chem Soc

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“…In a fractional factorial design of seven factors and 12 response variables (sensory attributes), Ellekjaer et al 8 performed separate ANOVA on all the single sensory responses, using a cross-classification model for samples, assessors and their two-factor interactions. Responses that were found to separate between samples, as identified by the ANOVA, were subject to a principal component analysis (PCA), see e.g.…”

Section: The Real World Is Multivariatementioning

confidence: 99%

Restricted randomization and multiple responses in industrial experiments

Bjerke¹,

Langsrud²,

Aastveit

2007

Quality & Reliability Eng

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Two issues regarding designed experiments are discussed; restrictions on randomization and multiple responses. The former is typically related to hard-to-vary factors and factors appearing in separate stages of a process experiment. Randomization restrictions should be taken into account in the construction of the design as well as in the statistical modelling. In the paper, a case study of sausage production is discussed, having a split-plot model with correlated multiple responses. Multiple responses are handled in two ways, by principal component analysis (PCA) followed by ANOVA of the principal components, and by a newly developed alternative, the '50-50 MANOVA'. Multiple tests of correlated response variables are also described. Practical aspects of the planning, performing, response measurements, and statistical analysis are emphasized throughout. Hence, the paper aims to extend the utility of statistical methods in industry by linking design of experiments to multivariate analysis of the responses.

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“…On the other hand, the so called internal preference mapping only uses consumers' ratings, and it is applied to discover market segments formed by consumers with similar tastes that can be differentiated from other segments (Van Kleef, Van Trijp, and Luning, 2006). The available methods for tackling these problems employ a combination of analysis of variance (ANOVA, see for instance (Lea, Naes, Rodbotten, 1997)), principal component analysis (PCA, see (Ellekjaer, Ilseng, and Naes, 1996)), and regression (McEwan, 1996;Tenenhaus, Pagès, Ambroisine, and Guinot, 2005).…”

Section: Introductionmentioning

confidence: 99%