A COMPARISON OF TWO MULTIVARIATE METHODS FOR THE ANALYSIS OF SENSORY PROFILE DATA

Tables of means, over assessors, are often used to summarize the results of sensory profile experiments. These tables are sometimes further summarized by Principal Components Analysis (PCA) to give plots of the samples in the principal sensory dimensions. An alternative procedure is to use Generalized Procrutes Analysis (GPA) on the assessor data to allow for differences in usage of the vocabulary and in the proportion of the scale used. It is shown that these methods give different configurations in the principal sensory dimensions when applied to the data from a study of cheeses (Muir et al. 1995). Using a Jackknife method to calculate the variability of the samples in the principal sensory dimensions, the results from the GPA method are shown to have a higher dimensionality than from the PCA method. Jackknife estimates of variability are used to calculate confidence ellipses to attach to the sensory space maps.     

A SIMPLE ALTERNATIVE TO GENERALIZED PROCRUSTES ANALYSIS: APPLICATION TO SENSORY PROFILING DATA

A statistical method for analyzing sensory profiling data obtained by means of fixed vocabulary or free choice profiling is discussed. The most interesting feature of this method is that it involves only simple statistical treatment and can therefore be performed using standard software packages. The outcomes of this method are compared to those of Generalized Procrustes Analysis on the basis of two data sets obtained, respectively, by means of fixed vocabulary and free choice profiling. A significance test is also discussed in order to assess whether the overall configuration of the products is meaningful. This significance test is based upon a simulation study involving the permutation procedure.     

APPLICATION OF FUZZY SETS AND NEURAL NETWORKS IN SENSORY ANALYSIS

This paper describes an initial but fundamental attempt to lay some groundwork for a fuzzy-set-based paradigm for sensory analysis and to demonstrate how fuzzy set and neural network techniques may lead to a natural way for sensory data interpretation. Sensory scales are described as fuzzy sets, sensory attributes as fuzzy variables, and sensory responses as sample membership grades. Multi-judge responses are formulated as a fuzzy membership vector or fuzzy histogram of response, which gives an overall panel response free of the unverifiable assumptions implied in conventional approaches. Neural networks are used to provide an effective tool for modeling and analysis of sensory responses in their naturally fuzzy and complex forms. A maximum method of defuzzification is proposed to give a crisp grade of the majority opinion. Two applications in meat quality evaluation are used to demonstrate the use of the paradigm and procedure. It is hoped that this work will bring up some new ideas and generate interest in research on application of fuzzy sets and neural networks in sensory analysis.     

CROSS VALIDATION OF A SENSORY LANGUAGE FOR CHEDDAR CHEESE

Communication and replication of sensory data from different sites are important to track progress on fundamental research issues and to ensure that research efforts are not duplicated. A uniform anchored Cheddar cheese sensory language has previously been identified and refined. The objective of this study was to demonstrate application of the defined sensory language for Cheddar cheese for communication between sensory panels at three different sites. The defined and referenced sensory language for Cheddar cheese was disseminated to panel leaders at the three sites and sensory panels (n ≤ 8) were trained for 40 to 80 h at each site. Ten forty-pound blocks of Cheddar cheese representing different ages were collected and evaluated by the panels. Cheeses were differentiated by the three panels by univariate and multivariate analysis (P<0.05). Cheeses were differentiated by the three panels in a similar manner. Results indicate that it is possible to calibrate panels using a standardized defined sensory language.     

DISSIMILARITIES IN COGNITION OF FLAVOUR TERMS RELATED TO VARIOUS SENSORY LABORATORIES IN A MULTIVARIATE STUDY

A study of dissimilarities in cognitive perception of 20 common flavor terms was carried out by the Sensory Group of Norway. An average cognitive pattern of the flavor terms was revealed from multidimensional scaling (MDS) and cluster analysis (CLU). In general, small but interesting deviations between different sensory laboratories working with various food products were found by a multivariate pattern recognition technique based on principal component analysis (PCA). Suggestions for finding general reference standards for flavor terms are discussed.     

THE USE OF FOURIER ANALYSIS TO COMPARE THE SENSORY PROFILES OF PRODUCTS

A consumer panel comprising 60 females evaluated three coffee whiteners and fresh milk for a number of sensory attributes including liking of flavor and appearance, overall liking, natural flavor and odor, creaminess and watery flavor. Fourier analysis was used to produce a plot which readily identified the whiteners with sensory profiles most similar to fresh milk. More importantly, the whitener whose sensory profile was significantly different from that of fresh milk was identified from the Fourier plot, something which could not be done by traditional univariate methods or even by an overall discriminant analysis.     

USE OF COMPUTER-GENERATED IMAGES AND CONJOINT ANALYSIS TO INVESTIGATE SENSORY EXPECTATIONS

Consumers unfamiliar with passion-fruit juice were presented with 24 computer generated package images on which six packaging factors (background color, picture, information, brand, language and shape) had been manipulated and asked to give scores for six expected sensory attributes (sweetness, pureness, sharpness, refreshing, freshness, naturalness) and liking. Significant effects were obtained for each attribute, with background color and information being the most important. Consumers varying in Need For Cognition (NFC) were affected differently by the investigated features of the package. High NFC individuals used less of the packaging factors than low NFC subjects did.     

新城疫病毒单克隆抗体的特性及应用

建立了8个分泌抗新城疫病毒(NDV)特异性单克隆抗体(McAb)的杂交瘤细胞株,根据它们的免疫生物学特性可以分为三种类型:(1)具有FA和ELISA特性(FN1、FN4、FN29、FN30、FN35、FNl22);(2)具有FA、ELISA和HI特性(FN7);(3)具有ELISA、HI特性和中和能力(FN106),根据FN30和FN106的ELISA试验,可将11个NDV毒株分为二种不同的抗原群,应用FN4-FITC,FN7-FITC和FN29-HRP试剂,对人工感染NDV和野外送检病例检测结果表明,单抗试剂的DFA阳性率(92.3％)高于病毒分离阳性率(87.2％),两种方法的符合率89.7％,这些单抗试剂用于临床诊断敏感性和特异性高,且方法快速、简便。     

RQA在肌电分析中的应用

介绍了递归图的生成方法和定量递归分析（RQA）中递归点的百发数,确定性线段的百分数,线段分布香农熵等分析量的意义。应用RQA分析肱二头肌及肱桡肌在不同负重下的肌电信号,发现肽二头肌肌电信号的递归点百分数的比肱肌高,有较强的周期性嵌入。与同一信号所作的FFT谱分析相比较,RQA法有较同的区分灵敏性,是肌电分析的一种新方法,它在其他复杂的生理信号处理中也有十分广阔的应用前景。     

THE APPLICATION OF TWO FREE-CHOICE PROFILE METHODS TO INVESTIGATE THE SENSORY CHARACTERISTICS OF CHOCOLATE

The concept of free-choice profiling allows untrained assessors to participate in sensory profiling. However, it is sometimes the case that such individuals are unable to describe what they perceive when the samples are presented in the isolation of the sensory testing booths. This paper reports an experiment to compare the usual free-choice profile procedure, with a more structured approach based on Kelly's repertory grid method. Generalized Procrustes analysis was used to analyse the data. The sample configurations obtained from both approaches were very similar, as were the interpretations of the main perceptual dimensions.