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Date of Award

Fall 2011

Degree Type

Thesis

Degree Name

Master of Applied Science (MASc)

Department

Chemical Engineering

Supervisor

John F. MacGregor

Language

English

Abstract

Accommodating changing consumer tastes, nutritional targets, competitive pressures and government regulations is an ongoing task in the food industry. Product development projects tend to have competing goals and more potential solutions than can be examined efficiently. However, existing databases or spreadsheets containing formulas, ingredient properties, and product characteristics can be exploited using latent variable methods to confront difficult formulation issues. Using these methods, a product developer can target specific final product properties and systematically determine new recipes that will best meet the development objectives.

Latent variable methods in reformulation are demonstrated for a product line of frozen muffin batters used in the food service industry. A particular attribute is to be minimized while maintaining the taste, texture, and appearance of the original products, but the minimization is difficult because the attribute in question is not well understood. Initially, existing data is used to develop a partial least squares (PLS) model, which identifies areas for further testing. Design of experiments (DOE) in the latent variable space generates new data that is used to augment the model. An optimization algorithm makes use of the updated model to produce recipes for four different products, and a significant reduction of the target attribute is achieved in all cases.

Latent variable methods are also applied to a difficult classification problem in oat milling. Process monitoring involves manually classifying and counting the oats and hulls in the product streams of groats; a task that is time-consuming and therefore infrequent. A solution based on near infrared (NIR) imaging and PLS-discriminant analysis (PLS-DA) is investigated and found to be feasible. The PLS-DA model, built using mixed-cultivar samples, effectively separates the oats and groats into two classes. The model is validated using samples of three pure cultivars with varying moistures and growing conditions.

McMaster University Library

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