定量描述法在食醋感官评定中的应用

采用定量描述法(QDA)对三种食醋进行了感官评定,并建立了三种食醋的QDA数据的蜘蛛网图,结果表明,此方法能区别三种食醋的感官特性,适用于食醋感官品质的评价。     

定量描述分析(QDA)在葡萄酒感官评定中的应用研究

采用定量描述分析（QDA）方法对以普通葡萄和冰冻葡萄为原料生产的葡萄酒进行感官上的定性和定量评定,探讨了QDA的原理及应用。通过QDA方法对冰冻葡萄酒和普通葡萄酒进行感官分析表明,以冰冻葡萄为原料生产的葡萄酒在色泽、留香、芳香度、透明度和香气等感官特性都明显强于普通葡萄酒。     

QDA在火腿肠感官评定中的应用

采用定量描述法（QDA）对三种火腿肠进行了感官评定,并建立了三种火腿肠的QDA数据的蜘蛛网图,结果表明,此方法能区别三种火腿肠的感官特性,适用于火腿肠感官品质的评价。     

定量描述分析在茶汤滋味评定中的应用

目的研究用量化数据来判断茶叶的滋味品质,并确定茶叶感官品质特征与化学成分间的相关性。方法采用定量描述分析方法,对同一鲜叶原料加工的炒青、云尖和龙井的8种感官滋味特征分别进行定量描述,并绘制出反映滋味特征的雷达图,应用Excel和SPSS18.0软件进行相关的数据分析。结果云尖在苦度、浓度、强度和涩度这4个感官特性上要弱于炒青和龙井,在鲜度这一感官特性上要强于炒青和龙井;8种茶汤滋味特征中的浓度、鲜度、苦度、涩度与茶叶化学成分具有相关性。结论定量描述分析方法可以有效地判别茶叶滋味特征,更直观地表现茶叶的滋味差异,阐明了浓度、鲜度、苦度、涩度与不同茶叶化学物质间的相关性。     

辣条感官描述词的建立及感官特征比较

目的 建立辣条感官描述词。方法 以5种辣条为研究对象, 建立由13人组成的辣条专用感官评价小组。首先利用M值法对感官描述词进行初步筛选, 再采用聚类分析、主成分分析、相关性分析对辣条感官描述词进行二次筛选。结果 首先得到了筋道、甜味、辣味等15个描述词, 选取了4个主成分PC1 (24.218%)、PC2 (21.694%)、PC3 (13.346%)、PC4 (9.12%), 代表辣条在口味和口感方面的感官特征, 前4个主成分的累计贡献率为68.378%, 可以解释辣条感官品质的绝大部分信息。结论 最终得到咸味、麻味、辣味、甜味、鲜味、香精香料味、筋道及油滑感8个辣条关键感官属性描述词, 并能体现不同产品间的差异。通过感官特征载荷图说明样品LPP与SZS可归为一类, 样品WLQ、MLW与FWS差异明显。     

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.     

描述性分析在食品感官评定中应用进展

该文简介食品科学领域所应用几种描述性分析方法,如定量描述性分析、蛛网描述性分析、属性引用次数法、时间―强度曲线描述性分析法、自由选择描述法等,举例其应用,并分析描述性分析发展趋势。     

Friedman检验和Kramer检验在感官排序测试中的比较

排序法是按指定特性强度或喜好程度对数个样品排列出顺序的一种感官分析方法。介绍了排序法中分析数据使用的Friedman检验和Kramer检验，这两种检验都可判定排序样品间是否存在显著性差异。通过大量实例的计算，对Friedman检验和Kramer检验的判定结果进行了比较，并归纳出两种检验结果相同的一种情况。最后，在感官排序测试中如何应用Friedman检验和Kramer检验提出了建议。     

Monitoring calibration of descriptive sensory panels using distance from target measurements

Training targets can be established from product profiles that provide an objective representation of the underlying sensory characteristics of a group of products. If available to a panel leader, these training targets can be used to calibrate a panel and measure the accuracy of their responses. Response accuracy can be determined either by frequency counts—how often the target was hit versus how many opportunities there were to hit it—or by distance from target measurements—which attempt to further quantify the degree to which the target was hit or missed. In addition to the frequency counts, four distance-from-target methods are presented and discussed—Distance from Target, Distance from Range, Adjusted Distance from Target, and Adjusted Distance from Range—each of which provides insights into the degree to which the panel is calibrated.