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.

中文翻译：

---

食品风味和感官分析的实验设计和回归建模：综述

背景

食品感官科学和风味分析是新产品开发中的关键过程，并且通过弥合产品特性与消费者感知和接受之间的差距，对于理解消费者至关重要。

范围和方法

本文对基于计算机的风味和感官分析方法进行了严格的评述，包括用于感官实验设计的最佳设计方法，以及将非线性建模方法（例如人工神经网络）纳入结果分析。将讨论这些方法的优缺点以及其统计背景。简要介绍了将这些统计和数学方法合并到现有的分析过程中，并概述了可用的计算机软件包。

主要发现和结论

食品风味和感官分析是一个信息收集过程，可以分为两个主要阶段：（1）实验设计；（2）分析和解释结果。感官和风味科学中分析程序的选择对于获得与食品和消费者相关的信息至关重要。传统上，感官分析基于经典的实验设计和线性多元分析技术。基于计算机算法的方法（例如实验设计中的最佳设计）和作为非线性回归方法的人工神经网络可以与当前方法结合使用，或用于克服现有方法的潜在不足。