Sensory-informed experimental design and modelling time-intensity

感官知情的实验设计和时间强度建模

基本信息

  • 批准号:
    434864-2012
  • 负责人:
  • 金额:
    $ 7.29万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Collaborative Research and Development Grants
  • 财政年份:
    2013
  • 资助国家:
    加拿大
  • 起止时间:
    2013-01-01 至 2014-12-31
  • 项目状态:
    已结题

项目摘要

The design of new products for consumer markets has undergone a major transformation over the last 50 years. Traditionally, inventors would create a new product that they thought might address a perceived need of consumers. Such products tended to be developed to meet the inventors own perception and not necessarily that of consumers. The social consequence of a top-down approach to product development has been a large failure rate in new product introduction. By surveying potential customers, a refined target is created that guides developers and reduces the failure rate. Today, however, the proliferation of products and the emergence of consumer choice has resulted in the identification of segments within the market. Consumers were originally segmented on demographics but the proportion of disposable income has increased so much for people in the developed world that choice is now based upon liking rather than financial means. Therefore, understanding your target market is essential for building successful products. Understanding your target market typically involves conducting a product category assessment, where 12 to 30 commercial products are tested with consumers to create a preference map. Every consumer gets to test every product in a complete-block design; however, several classes of products do not lend themselves to such approaches. These tend to be "high-fatigue" products, such as beverage alcohol products, intensely flavoured products, such as coffee or chocolate, and highly seasoned products, such as salsa or curry. The work outlined herein will see the development of methodologies that will allow incomplete-block designs to be tailored for high-fatigue data such that 'similar' products are not tasted consecutively. Work will also be carried out on the development of statistical methods for modelling consumer reaction to a product over the time course of a tasting; e.g., as one tastes strawberry jam, how does the sweetness of the flavour sensation change over time. These novel statistical approaches will allow the extraction of otherwise hidden information from time-intensity data.
在过去的50年里,面向消费市场的新产品设计经历了重大变革。传统上,发明者会创造一种他们认为可能满足消费者感知需求的新产品。这类产品的开发往往是为了满足发明者自己的看法,而不一定是消费者的看法。自上而下的产品开发方法的社会后果是新产品推出的失败率很高。通过调查潜在客户,创建了一个精细化的目标,以指导开发人员并降低失败率。然而,今天,产品的激增和消费者选择的出现导致了市场细分的识别。消费者最初是根据人口结构进行细分的,但对于发达国家的人们来说,可支配收入的比例增加了很多,以至于现在的选择是基于喜好,而不是经济能力。因此,了解您的目标市场对于打造成功的产品至关重要。了解你的目标市场通常包括进行产品类别评估,其中12到30种商业产品会与消费者一起测试,以创建偏好图。每个消费者都有机会以完整的模块设计测试每一种产品;然而,有几类产品不适合这种方法。这些往往是“高度疲劳”的产品,如饮料酒精产品,强烈风味的产品,如咖啡或巧克力,以及高度调味的产品,如萨尔萨酱或咖喱。本文概述的工作将看到方法学的发展,这些方法学将允许针对高疲劳数据量身定做不完整区块设计,以便不会连续品尝“类似”的产品。还将开展开发统计方法,以模拟消费者在品尝过程中对产品的反应;例如,当人们品尝草莓果酱时,味道的甜味如何随着时间的推移而变化。这些新的统计方法将允许从时间强度数据中提取原本隐藏的信息。

项目成果

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科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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McNicholas, Paul其他文献

Characterization of Emergent HIV Resistance in Treatment-Naive Subjects Enrolled in a Vicriviroc Phase 2 Trial
  • DOI:
    10.1086/652189
  • 发表时间:
    2010-05-15
  • 期刊:
  • 影响因子:
    6.4
  • 作者:
    McNicholas, Paul;Wei, Yi;Strizki, Julie M.
  • 通讯作者:
    Strizki, Julie M.

McNicholas, Paul的其他文献

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{{ truncateString('McNicholas, Paul', 18)}}的其他基金

Computational Statistics
计算统计
  • 批准号:
    CRC-2021-00494
  • 财政年份:
    2022
  • 资助金额:
    $ 7.29万
  • 项目类别:
    Canada Research Chairs
Computational Statistics
计算统计
  • 批准号:
    CRC-2015-00200
  • 财政年份:
    2022
  • 资助金额:
    $ 7.29万
  • 项目类别:
    Canada Research Chairs
Clustering and Classification Using Mixture Models: Towards Big Data
使用混合模型进行聚类和分类:迈向大数据
  • 批准号:
    RGPIN-2017-05255
  • 财政年份:
    2021
  • 资助金额:
    $ 7.29万
  • 项目类别:
    Discovery Grants Program - Individual
Computational Statistics
计算统计
  • 批准号:
    CRC-2015-00200
  • 财政年份:
    2021
  • 资助金额:
    $ 7.29万
  • 项目类别:
    Canada Research Chairs
Clustering and Classification Using Mixture Models: Towards Big Data
使用混合模型进行聚类和分类:迈向大数据
  • 批准号:
    RGPIN-2017-05255
  • 财政年份:
    2020
  • 资助金额:
    $ 7.29万
  • 项目类别:
    Discovery Grants Program - Individual
Nomination for NSERC Steacie Memorial Fellowship
NSERC Steacie 纪念奖学金提名
  • 批准号:
    537596-2019
  • 财政年份:
    2020
  • 资助金额:
    $ 7.29万
  • 项目类别:
    EWR Steacie Fellowships - Supplement
Modelling Consumer Preferences for Roses
模拟消费者对玫瑰的偏好
  • 批准号:
    532177-2018
  • 财政年份:
    2018
  • 资助金额:
    $ 7.29万
  • 项目类别:
    Engage Grants Program
Clustering and Classification Using Mixture Models: Towards Big Data
使用混合模型进行聚类和分类:迈向大数据
  • 批准号:
    RGPIN-2017-05255
  • 财政年份:
    2018
  • 资助金额:
    $ 7.29万
  • 项目类别:
    Discovery Grants Program - Individual
Computational Statistics
计算统计
  • 批准号:
    CRC-2015-00200
  • 财政年份:
    2018
  • 资助金额:
    $ 7.29万
  • 项目类别:
    Canada Research Chairs
Computational Statistics
计算统计
  • 批准号:
    CRC-2015-00200
  • 财政年份:
    2017
  • 资助金额:
    $ 7.29万
  • 项目类别:
    Canada Research Chairs

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