Feasibility of the expectation-maximization algorithm for assessing individuals with different sensory perceptions in discrimination of specialty coffees
Larissa Karolina de Oliveira, M. Resende, F. M. Borém, M. A. Cirillo
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引用次数: 0
Abstract
The results of sensory evaluations of coffees are associated with latent factors, such as the particular subjectivity of each individual. Based on the foregoing, assessing the quality of a sensory panel for product discrimination basically depends on the statistical methodology to be used in data analysis. Following this argument, this study aimed to evaluate the feasibility of the EM - Expectation Maximization algorithm in discriminating groups of individuals, characterized by the degree of experience and knowledge in sensory analysis of coffees of different varieties, produced in the Serra da Mantiqueira micro-region, with different processing and altitudes. The main advantage of this algorithm is the fast convergence, when the current solution approaches the optimal solution with high precision. The disadvantage is because it is a deterministic optimization technique, which can only achieve a local optimization depending on the initialization, i.e., initial values input in the iterative procedure. It can be concluded that estimates of the correlation matrices obtained by the EM algorithm showed that the final grade has a greater influence of sweetness, in addition to discriminating groups of consumers with different sensory perceptions and in situations where the number of individuals in each group is unknown, the EM algorithm was accurate in estimating the proportion of individuals belonging to each group, assuming that the correlations of sensory responses follow a bivariate normal distribution.
对咖啡的感官评价结果与潜在因素有关,比如每个人的特定主观性。基于上述,评估产品歧视感官面板的质量基本上取决于数据分析中使用的统计方法。根据这一论点,本研究旨在评估EM -期望最大化算法在区分个体群体中的可行性,该群体以不同加工和海拔的Serra da Mantiqueira微地区生产的不同品种咖啡的感官分析经验和知识程度为特征。该算法的主要优点是收敛速度快,当当前解以高精度逼近最优解时。缺点是它是一种确定性优化技术,只能依靠初始化,即迭代过程中输入的初始值来实现局部优化。可以得出结论,EM算法得到的相关矩阵的估计值表明,最终的等级对甜度的影响更大,除了区分不同感官知觉的消费者群体之外,在每一群体中个体数量未知的情况下,EM算法在估计属于每一群体的个体比例方面是准确的。假设感觉反应的相关性遵循二元正态分布。
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