基于秩相关与聚类分析的评价员排序能力评估

将最常用的甜、酸基本味分别设置成6个浓度体系作为评价员排序能力筛选的参比样,考虑到感官疲劳的客观现象,12位评价员对每个味道系列浓度进行3次重复排序,在分别采用Spearman检验和Kendall检验评估评价员排序正确性与重复性的基础上,采用聚类分析融合正确性与重复性数据特征,综合评估分析每位评价员的排序能力。发现5号评价员表现最差需要剔除;3号评价员需要进一步训练;6位评价员(编号为2、4、9、10、11、12号)排序能力表现优异,可直接开展检测试验;4位评价员(编号为1、6、7、8号)排序能力良好。同时发现Kendall检验系数W值与Spearman检验系数rs平均值具有整体正相关趋势,而W值能更精确区分评价员之间的能力差异。该研究可为感官分析实验室中评价员的筛选与维护提供理论依据和实践经验。     

豆腐干质构感官分析及评价小组能力评估

经过培训的感官评价小组对市售豆腐干质构进行蛛网描述性感官分析,得到区分不同品质豆腐干的主要质构属性。通过计算信噪比变化趋势,研究了培训过程中评价小组的表现;采用Panel Check软件,评估了培训后的评价小组一致性、评价员区分能力和重复性;进一步通过主成分分析法,研究了豆腐干之间的质构差异及豆腐干与各属性相关程度。结果表明,经过培训的豆腐干感官评价小组给出的数据较为准确、可靠;区分不同品质豆腐干的主要质构属性为:表面油感、凝聚性、硬度、成团性、咀嚼次数。     

感官分析评价小组及成员表现评估技术动态分析

作为感官评价仪器的“评价员/评价小组”是获得可靠感官分析数据的关键,其性能表现评估技术是有效 管理该仪器的重要手段。本文将信度与效度作为该“仪器”的评估指标,发现21世纪以后该类研究进入高峰期,其 超过85%的研究成果发表于Food Quality and Preference与Journal of Sensory Studies这两个感官研究类权威杂志中, 其中欧美国家在此技术中占主导地位。透过技术内容发现,基于定量描述能力的评价小组及成员性能表现评估技术 研究频繁并趋于成熟,技术手段重点采用单参数或多参数方差分析、多元统计方法（主成分分析、广义普罗克分析 等）,涌现了以PanelCheck、Compusense Five等为代表的评估软件,同时国际标准化组织和美国材料与试验协会机 构也进入相关标准研制阶段。而有关差别与排序的评估技术研究相对缺乏。本文提出性能评估所用样品性质及数 量、性能评估所用数据表现形式、性能评估感官实验设计要求、性能评估期望值及置信区间、性能评估所用感官分 析方法选择、性能评估的数学统计方法选择等关键要素将成为该领域未来努力的方向,并有助于形成统一的、系统 的感官分析评价小组及成员表现评估技术规范。     

建立食品质地参照物体系中量值估计法的改进

当前,在已经建立食品质地参照物体系的国家中,其建立方法的不统一性,以及数据统计分析的模糊性,使将要建立食品参照物体系的国家面临许多困难。在研究现行食品质地参照物体系的基础上,结合中国国情,选用国内具有消费规模的相关知名品牌食品为样品,采用感官排序法和改进的量值估计法对10人评价小组进行培训及评分,建立硬度、黏附性、碎裂性、咀嚼性4个主要参照物体系。研究结果表明:改进的量值估计法用于评价员定量评估参照物体系其量值结果与顺序标度值呈高度相关性(R20.98);通过培训建立起的感官评价小组稳定可靠,可对同类型多种食品进行评价。     

酱香型白酒勾调酒样感官排序结果的统计学最优估计

以5批次酱香型白酒勾调酒样感官质量排序结果为实例，综合采用Friedman和Kendall统计学检验方法，开展感官排序结果一致性和重复性评价，剔除感官品评人员在整体排序结果中一致性和重复性差的结果，然后开展Friedman检验及多重比较数据分析。统计分析结果表明，在显著性水平(α=0.05)下，该方法很好的实现了5批次酱香型白酒勾调酒样感官排序结果的统计学最优估计的科学表达。基于研究成果，研发感官数据在线分析平台，该平台可以广泛推广实际应用于白酒企业不同批次勾调酒样感官质量科学评价及优质品酒员的筛选。     

基于品评小组可靠性筛选葡萄酒的香气描述符

利用定量描述性分析（0～5标度法）评价9 款国产‘赤霞珠’干红葡萄酒的香气特性,结合方差分析和多元统计分析方法,评估品评小组可靠性和规范葡萄酒香气特性感官描述符的筛选方法。结果表明,利用平均重现性指数Ri、检验统计量F值、均方误差（mean-square error,MSE）值和Manhattan图等能够有效评估品评员的重现性、辨别能力、可重复性以及品评小组的一致性。以可靠品评小组的感官数据为原始变量,通过几何平均值（M值）计算和排序,结合主成分分析（principal component analysis,PCA）和聚类分析（cluster analysis,CA）,规范了葡萄酒特征香气描述符的科学筛选方法,明确了实验‘赤霞珠’干红葡萄酒的香气特征：以黑醋栗、红醋栗等果香为主,有突出的焦糖、烟熏等焙烤类香气,兼有胡椒等佐料香气。     

基于主成分分析法的描述性面包感官参数评估

针对标准GB/T 14611-2008《粮油检验小麦粉面包烘焙品质试验直接发酵法》中4个面包定量感官参数,依据感官评价小组表现,分析验证各参数稳定性、难易程度和特性,为参数和标度设置提供依据。选择人员和样品不同的四组感官评价小组,按该标准对面包外观、面包芯色泽、质地和纹理结构评价打分,通过方差和主成分分析四组评价人员的各参数感官评价表现,分析4个参数特性。面包外观样品间差异显著、易区分判别、稳定性好、小组一致性好;面包芯色泽样品间差异较显著,但混杂了光泽和颜色两个属性,不易理解区分,小组一致性低于其他参数,稳定性差;面包质地差异较显著、较易区分,但稳定性较差,可通过培训改善人员的区分力和小组的一致性;面包芯纹理结构差异不明显、难于区分、稳定性差,良好培训可改善区分能力和一致性;标准化和统一标度主成分分析显示四个参数都是面包主要参数。建议将面包芯的颜色和光泽分开评分,或给出更详细的描述,提高面包外观和面包芯色泽的评分比例。     

PanelCheck检测评价小组对乳酸菌饮料的感官评估能力

经过培训的感官评价小组对市售乳酸菌饮料进行定量描述性感官分析,得到区分不同品质乳酸菌饮料的主要感官属性,分别为:花香、果香、酸奶味、牛奶味、塑料味、甜味、酸味和涩味。采用Panel Check软件,通过Line plot、Correlation Plot、Manhattan plot、F plot和MSE plot等多种可视化图型,可以探讨评价小组个体的评估能力和检测个体差异。结果表明,不同评价员对乳酸菌饮料的特定方向属性表现和重复性都有不同。从不同图表中获得的信息能够作为后续培训评价小组以及提高每个评价员感官属性评估能力的基础。     

Flash Profile法在豆豉感官评价中的应用

Flash Profile（FP）法是一种快速描述食品感官性质的方法,具有操作简便,周期短的特点。该实验对10种市售豆豉的感官特征采用FP法进行分析,由9名未经培训的感官评价员从10种样品中产生了颜色、黏附性、光泽、饱满度等19个能描述豆豉感官差异的描述词。对每个评价员对自己选择的感官描述词的强度排序结果进行广义普鲁克分析（GPA）及层次聚类分析（AHC）。结果表明,未经培训的感官评价员能够区分窖藏一年的豆豉与其他豆豉之间的感官差异,也能区分不同产地豆豉之间的感官差异。FP法能快速有效区分豆豉样品之间的感官差异。     

模糊综合评价法在米酒感官评价中的应用

用三种甜酒曲酿制米酒,应用模糊数学综合评价法对这三种米酒的感官质量进行评定。结果表明三种米酒感官评定级别由高到低的顺序为酒曲C酒曲A酒曲B。该方法可科学、客观地鉴别出多种米酒样品的感官质量,并为之排序,为米酒及其他食品的感官评价提供了一个行之有效的参考方法。     

Questionnaire Design: Carry‐Over Effects of Overall Acceptance Question Placement and Pre‐evaluation Instructions on Overall Acceptance Scores in Central Location Tests

Question placement and usage of pre‐evaluation instructions (PEI) in questionnaires for food sensory analysis may bias consumers’ scores via carry‐over effects. Data from consumer sensory panels previously conducted at a central location, spanning 11 years and covering a broad range of food product categories, were compiled. Overall acceptance (OA) question placement was studied with categories designated as first (the first evaluation question following demographic questions), after nongustation questions (immediately following questions that do not require panelists to taste the product), and later (following all other hedonic and just‐about‐right [JAR] questions, but occasionally before ranking, open‐ended comments, and/or intent to purchase questions). Each panel was categorized as having or not having PEI in the questionnaire; PEI are instructions that appear immediately before the first evaluation question and show panelists all attributes they will evaluate before receiving test samples. Postpanel surveys were administered regarding the self‐reported effect of PEI on panelists’ evaluation experience. OA scores were analyzed and compared (1) between OA question placement categories and (2) between panels with and without PEI. For most product categories, OA scores tended to be lower when asked later in the questionnaire, suggesting evidence of a carry‐over effect. Usage of PEI increased OA scores by 0.10 of a 9‐point hedonic scale point, which is not practically significant. Postpanel survey data showed that presence of PEI typically improved the panelists’ experience. Using PEI does not appear to introduce a meaningful carry‐over effect.     

Quantifying Nonhomogeneous Colors in Agricultural Materials. Part II: Comparison of Machine Vision and Sensory Panel Evaluations

ABSTRACT: The average colors of mangos and apples were measured using machine vision. A method to quantify the perception of nonhomogeneous colors by sensory panelists was developed. Three colors out of several reference colors and their perceived percentage of the total sample area were selected by untrained panelists. Differences between the average colors perceived by panelists and those from the machine vision were reported as ΔE values (color difference error). Effects of nonhomogeneity of color, and using real samples or their images in the sensory panels on ΔE were evaluated. In general, samples with more nonuniform colors had higher ΔE values, suggesting that panelists had more difficulty in evaluating more nonhomogeneous colors. There was no significant difference in ΔE values between the real fruits and their screen image, therefore images can be used to evaluate color instead of the real samples.     

基于心理物理学的食品硬度参比样建立研究

硬度是食品重要的质地特性,参比样是统一对感官特性及其强度认识和评价的基础。目前国际以及我国国家标准中质地特性参比样的建立主要基于西方饮食习惯,不少产品在国内难以获得且不为人们所熟悉,因此建立适合我国国情的食品质地特性参比样十分必要。本研究以构建食品硬度参比样为例,建立统计分析与质地剖面分析（texture profile analysis,TPA）相结合的参比样快速筛选方法,通过对样品硬度仪器测量值的聚类分析、主成分分析和方差分析,对符合参比样原则要求（如典型性、代表性、稳定性等）的样品进行筛选,同时选用10 人定量描述分析小组,采用15 cm线性标度对筛选出的样品进行感官评价。最终从16 类100多种硬度备选参比样中确定出9 个硬度参比样,分别是白玉北豆腐、蜡笔小新果味果冻、金锣鱼肉火腿肠、恒慧鸡蛋干、北京一鸣马蹄罐头、惠宜香脆花生、中粮大杏仁、瑞士糖和北京二商单晶冰糖,其硬度的感官标度值分别为：1.0、2.4、4.3、5.6、7.4、9.8、10.8、12.2、14.0。硬度的感官标度值的自然对数与仪器测量值的自然对数之间存在线性关系：y＝0.46x－2.424（R2＝0.977）,符合心理物理学定律。食品硬度参比样的建立不仅为我国食品硬度感官评价提供了一把统一的“尺子”,也为其他感官特性参比样的建立提供了研究思路和技术方法。     

How reliable are the consumers? Comparison of sensory profiles from consumers and experts

This study compares expert and consumer sensory profiles for the same 12 perfumes in different ways: the discriminatory ability and reproducibility are analyzed through ANOVA and the panelists’ consensus through the correlation coefficients. Next, the two product spaces are first analyzed separately for each panel, and then compared through multiple factor analysis. Finally, the two panels are compared using the confidence ellipses methodology. These analyses show that the two panels give similar results with respect to the important criteria for panels (discrimination, consensus, reproducibility). The comparison of the two products spaces shows high similarity. From the confidence ellipses, it can be concluded that no significant differences exist for a given product between the two panels. Hence, in this particular case, the use of consumers appears to be a good alternative to the classical sensory profile provided by a trained panel.     

Proficiency testing for sensory ranking panels: measuring panel performance

Proficiency testing in sensory analysis is an important step towards demonstrating that results from one sensory panel are consistent with the results of other sensory panels. The uniqueness of sensory analysis poses some specific problems for measuring the proficiency of the human instrument (panel). As part of an EU supported project, ProfiSens, 14 panels undertook ranking of sweetness on five samples of apple juice. Four panels were designated ‘validation’ panels, whose data were used firstly to establish the expected ranking results, and secondly to set the performance criteria that a trained sensory panel would be expected to achieve. Four key measures of a panel’s performance were investigated: the ability to rank the samples in the correct order; the significance level associated with differences between samples; the number of pairs of samples that a panel found to be different at a specified level of significance; and the degree of agreement between assessors within a panel. For each of these criteria an ‘expected result’ was considered, as well as an overall measure of performance. The data from the remaining panels were analysed, and the level of performance was recorded for each of the stated criteria. Results indicated different levels of performance, and the research also revealed the importance of choice of validation panels and the screening of samples prior to testing. A simpler performance scheme was proposed to address issues relating to attaching arbitrary weightings to each of the performance criteria, and to address potential problems associated with combining different measurement criteria into a single performance score. While there is still potential for further development and refinement, ProfiSens has made a significant contribution to proficiency testing for sensory analysis.