Mixture designs and models for the simultaneous selection of ingredients and their ratios

Muteki, Koji; MacGregor, John F.; Ueda, Toshihiro

doi:10.1016/j.chemolab.2006.08.003

Cited by 65 publications

(47 citation statements)

References 20 publications

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“…The first is that the mixing rules are approximately valid (see discussions in [2,3]). The second is that the available raw material properties correlate well with the final product properties (Y).…”

Section: Mixture-property Pls Modelmentioning

confidence: 99%

“…The mixture PLS model of Equation (2) represents the relationship between the raw material properties of the mixture (X mix ), based on the use of ideal mixing rules [2,3], and the final product properties (Y). Alternatively, any other known mixing rules could be used.…”

Section: Mixture-property Pls Modelmentioning

confidence: 99%

“…Muteki et al [1][2][3] presented a combined mixture-property modeling approach based on ideal mixing rules that allows for the incorporation of information on the raw material properties (X) as well. The model is expressed as…”

Section: Mixture-property Pls Modelmentioning

confidence: 99%

“…In order to obtain successful results with that general approach [1,2], it is required that the data on the blends are sufficiently rich. Muteki et al [3] present a mixture-property DOE approach that effectively combines statistical molecular design (SMD) and D-optimal mixture design methods to ensure a sufficiently rich database for this product development purpose. It is useful when the objective is to obtain a mixture-property model that is valid over a wide range of the latent variable space of the mixture-property data.…”

Section: Introductionmentioning

confidence: 99%

“…In Section 2, the data structure generally available in mixture design is first shown, and the mixture-property partial least square (PLS) model [1][2][3] is briefly described. In Section 3, the new sequential DOE approach with the objective of achieving a set of specified final product properties is presented.…”

Section: Introductionmentioning

confidence: 99%

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Sequential design of mixture experiments for the development of new products

Muteki

MacGregor

2007

Journal of Chemometrics

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This paper presents a new sequential approach to mixture DOE that simultaneously selects new materials and ratios in which to combine them with the objective of achieving a set of specified final product properties. In each sequentially designed experiment the approach selects new materials and formulation ratios through an optimization based on mixture-property partial least square (PLS) models that account for similarities among the properties of different materials (Muteki et al. [1,2]). The approach is useful for the case where one has existing experiments some of which may not be too far from those required for the desired new product. The sequential DOE methodology is demonstrated on an industrial polymer blending problem.

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Section: Mixture-property Pls Modelmentioning

confidence: 99%

Section: Mixture-property Pls Modelmentioning

confidence: 99%

Section: Mixture-property Pls Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sequential design of mixture experiments for the development of new products

Muteki

MacGregor

2007

Journal of Chemometrics

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Optimal purchasing of raw materials: A data‐driven approach

Muteki

MacGregor

2008

AIChE Journal

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in Wiley InterScience (www.interscience.wiley.com).An approach to the optimal purchasing of raw materials that will achieve a desired product quality at a minimum cost is presented. A PLS (Partial Least Squares) approach to formulation modeling is used to combine databases on raw material properties and on past process operations and to relate these to final product quality. These PLS latent variable models are then used in a sequential quadratic programming (SQP) or mixed integer nonlinear programming (MINLP) optimization to select those raw materials, among all those available on the market, the ratios in which to combine them and the process conditions under which they should be processed. The approach is illustrated for the optimal purchasing of metallurgical coals for coke making in the steel industry. However, it is well suited to many similar problems such as the purchasing of crude oils for refining, of ingredients for processed foods and of polymeric materials to blend into functional polymers.

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Nutritional and sensory quality of composite extruded complementary food

Forsido

Duguma

Lema

et al. 2019

Food Science & Nutrition

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Complementary foods in Ethiopia have nutritional and sensory limitations which can be attributed to cereal‐dominated ingredients and lack of appropriate processing techniques. This study aimed to optimize the nutritional and sensory quality of complementary food product through compositing and extrusion of various local ingredients. A constrained D‐optimal mixture experiment with 13 runs was designed. Accordingly, 55–65 g/100 g oats, 11–23 g/100 g soybean and 6–11 g/100 g linseed, and a premix of 9.9 g/100 g sugar, 0.6 g/100 g table salt, three g/100 g moringa and 1.5 g/100 g fenugreek were blended and extruded using a co‐rotating twin screw extruder with set parameters. Statistical model evaluation and optimization were done using Minitab version 16 software package. There is a statistically significant (p < 0.05) association between the blend of oats and soybean, oats and linseed, soybean and linseed, and the protein, fat, carbohydrate, fiber, ash, β‐carotene content as well as aroma, taste, and consistency. On the contrary, there is a no statistically significant (p < 0.05) association between the blends and moisture, energy, and zinc content together with appearance and overall acceptability. The optimal blending ratio was 55.0 g/100 g oats, 21.0 g/100 g soybean, and 9.0 g/100 g linseed plus 15.0 g/100 g premix. Evidence‐based selection of locally grown plant‐based ingredients, an optimal mixture of these ingredients and optimal processing, can result in a complementary food product with an improved dietary quality for children in low‐income settings.

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Mixture designs and models for the simultaneous selection of ingredients and their ratios

Cited by 65 publications

References 20 publications

Sequential design of mixture experiments for the development of new products

Sequential design of mixture experiments for the development of new products

Optimal purchasing of raw materials: A data‐driven approach

Nutritional and sensory quality of composite extruded complementary food

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