Designs for Mixture Experiments Involving Process Variables

Hare, Lynne B.

doi:10.1080/00401706.1979.10489746

Cited by 16 publications

(3 citation statements)

References 19 publications

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“…Conditions are given which can be used to determine when the use of the transformation is preferable to that of other transformations. Hare (1979) considers the development of designs for the situation where some of the design variables, called process variables, are not subject to the simplex constraint, E-x, 1. Of concern here is the choice of settings for the mixture variables as well as the process variables.…”

Section: Design Of Mixture Experimentsmentioning

confidence: 99%

Response Surface Methodology

Myers¹,

Montgomery²

1996

IIE Transactions

1,108

525

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Approved for Public Release: Distribution Unlimited 17. OiSTAiSuTiON STATEMENT (of the ob.trocl -ntoted IA Block 20, It different 1-m. Roporl) If. SUPPLEMENTARY NOTES 19. KEY *ORDS (Comntin~ue onrvrse 00.... It necateottv aid Identity by block number) Response Surface, Review, Design, Analysis. 20 ABSTRACT (Contln..e an roers*9 old* It flec*esorYan id hentity by block number)This paper provides a thorough review of response surface methodology since 1966. The review covers topics pertaining to the design and analysis of response surfaces. Multirespone techniques associated with optimization and lack of fit testing are also covered.

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Section: Design Of Mixture Experimentsmentioning

confidence: 99%

Response Surface Methodology

Myers¹,

Montgomery²

1996

IIE Transactions

1,108

525

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“…Experimental designs for these problems must specify settings for both the mixture variables and the process variables. Hare (1979) generated designs by restricting the mixture variables to a cuboidal subset contained within the simplex defined by (7.1) and then crossing that subset with a cuboidal region for the process variables. A different approach to designing experiments with both mixture and process variables was taken by Vuchkov, Damgaliev, and Yontchev (1981).…”

Section: Mixture Designsmentioning

confidence: 99%

Experimental Design: Review and Comment

Steinberg¹,

Hunter²

1984

Technometrics

240

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We review major developments in the design of experiments, offer our thoughts on important directions for the future, and make specific recommendations for experimenters and statisticians who are students and teachers of experimental design, practitioners of experimental design, and researchers jointly exploring new frontiers. Specific topics covered are optimal design, computer-aided design, robust design, response surface design, mixture design, factorial design, block design, and designs for nonlinear models.

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“…The major ingredients in a chocolate peanut spread – peanut (P), chocolate (C) and sugar (S) – were combined to obtain a spreadable product using proportions of ingredients in a mixture design. In addition to the three mixture components, a processing variable (Cornell 1971; Hare 1979; Cornell 1990, 2000), roast (R), was included to study the effects of different roasting levels. It has been shown that roasting time has a significant influence on the odor and flavor of roasted peanuts (Buckholtz et al.…”

Section: Introductionmentioning

confidence: 99%

Sensory Profiling and Characterization of Chocolate Peanut Spread Using Response Surface Methodology

Chu

Resurreccion

2005

Journal of Sensory Studies

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Response surface methodology was used to profile and characterize formulations of chocolate peanut spread. A constrained mixture design for 36 different formulations with varying peanut (P), chocolate (C) and sugar (S) was used. A processing variable, roast (R), was included where peanuts were roasted to light, medium and dark levels. A descriptive panel (n = 10) identified and rated 24 attributes, using 150‐mm unstructured line scales. Regression analysis was performed and models were reduced. Models having R2 > 0.70 were selected for prediction. Contour maps were constructed to: (1) visualize the effects of mixture components and roasting level and (2) characterize optimum formulations at light, medium and dark, which were determined as (30–49% P, 23–40% C and 21–31% S); (29–65% P, 0.9–41% C and 17–36% S) and (27–56% P, 19–45% C and 18–35% S), respectively, adding up to 100% of the mixture. Analyses of optimum and nonoptimum formulations and significant differences were not found between predicted and observed values for most attributes.

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Designs for Mixture Experiments Involving Process Variables

Cited by 16 publications

References 19 publications

Response Surface Methodology

Response Surface Methodology

Experimental Design: Review and Comment

Sensory Profiling and Characterization of Chocolate Peanut Spread Using Response Surface Methodology

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