Design of experiments and regression modelling in food flavour and sensory analysis: A review

TLDR: A critical review of computer-based approaches to flavour and sensory analysis, including optimal design approaches to sensory experimental designs, and incorporation of nonlinear modelling methods such as artificial neural network into the analysis of results are provided.

Abstract: Background Food sensory science and flavour analysis are key processes in new product development, and is essential in understanding consumers by bridging the gap between product characteristics and consumer perception and acceptance. Scope and approach This article provides a critical review of computer-based approaches to flavour and sensory analysis, including optimal design approaches to sensory experimental designs, and incorporation of nonlinear modelling methods such as artificial neural network into the analysis of results. The advantages and disadvantages of these methods, as well as their statistical background will be discussed. The incorporation of these statistical and mathematical methods into existing analytical processes is briefly covered, along with an overview of available computer software packages. Key findings and conclusions Food flavour and sensory analysis is an information gathering process, and can be divided into two main stages: (1) the design of the experiment; (2) analyses and interpretation of results. The choice of an analytical procedure in sensory and flavour science is crucial in obtaining information correlating food products and consumers. Traditionally, sensory analysis is based on classical experimental designs and linear multivariate analysis techniques. Computer algorithm-based methods such as optimal designs in the design of experiments, and artificial neural network as a non-linear regression method may be used in conjunction with current methods, or adopted to overcome potential shortfalls of existing methods.