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.     

SENSORY DIRECTIONALS FOR PIZZA: A DEEPER ANALYSIS

This paper presents a set of analyses on sensory directional attributes used to rate experimentally designed pizza products. Consumers may or may not know the 'optimal' sensory level of attributes for pizza, so that the usefulness of the sensory directional varies by attribute. Furthermore, the sensory magnitude of each sensory directional attribute varies, as shown by the slope (B) relating the two attributes (Sensory Magnitude = A + B (Directional Rating)). The study incorporated sensory directionals into evaluation of products varied according to an experimental design. The optimal product emerging from the design does not necessarily exhibit a sensory directional profile where all attributes are 'on target', nor does a product whose sensory attributes are all on 'target' exhibit the highest level of liking.     

FUZZY MULTIATTRIBUTE DECISION MAKING APPROACH FOR DEVELOPMENT AND COMPARISON OF SOY FORTIFIED PANEER

Soyfortified paneer (SFP) samples prepared from blends containing different proportions of buffalo milk of varying fat content and soy milk (7.5 °B) were evaluated organoleptically for assessing the quality attributes like body and texture, flavor and taste, color and appearance and the overall acceptability. Sensory data were analyzed using fuzzy logic approach, which addresses the problem of data classification in a unified qualitative and quantitative manner. Results of the study indicated that the fuzzy multiattribute decision making approach provide an adequate and reliable system for product formulation and comparison, based on sensory data. The developed fuzzy mathematical model performed remarkably well in the evaluation and ranking of various SFP samples. The SFP sample made from blend of buffalo milk (4.5% fat) and soy milk (7.5 °B) in the proportion of 90:10 was found to be the most acceptable one for different classes of consumers irrespective of their preferences for a particular sensory quality attribute.     

Separability indexes and accuracy of neuro-fuzzy classification in Geographic Information Systems for assessment of coastal environmental vulnerability

The aim of this study was the development, evaluation and analysis of a neuro-fuzzy classifier for a supervised and hard classification of coastal environmental vulnerability due to marine aquaculture using minimal training sets within a Geographic Information System (GIS). The neuro-fuzzy classification model NEFCLASS‐J, was used to develop learning algorithms to create the structure (rule base) and the parameters (fuzzy sets) of a fuzzy classifier from a set of labeled data. The training sites were manually classified based on four categories of coastal environmental vulnerability through meetings and interviews with experts having field experience and specific knowledge of the environmental problems investigated. The inter-class separability estimations were performed on the training data set to assess the difficulty of the class separation problem under investigation. The two training data sets did not follow the assumptions of multivariate normality. For this reason Bhattacharyy and Jeffries–Matusita distances were used to estimate the probability of correct classification. Further evaluation and analysis of the quality of the classification achieved low values of quantity and allocation disagreement and a good overall accuracy. For each of the four classes the user and producer values for accuracy were between 77% and 100%.In conclusion, the use of a neuro-fuzzy classifier for a supervised and hard classification of coastal environmental vulnerability demonstrated an ability to derive an accurate and reliable classification using a minimal number of training sets.     

Semiparametric estimation of treatment effect in a pretest-posttest study

Inference on treatment effects in a pretest-posttest study is a routine objective in medicine, public health, and other fields. A number of approaches have been advocated. We take a semiparametric perspective, making no assumptions about the distributions of baseline and posttest responses. By representing the situation in terms of counterfactual random variables, we exploit recent developments in the literature on missing data and causal inference, to derive the class of all consistent treatment effect estimators, identify the most efficient such estimator, and outline strategies for implementation of estimators that may improve on popular methods. We demonstrate the methods and their properties via simulation and by application to a data set from an HIV clinical trial.     

VISUAL CHARACTERIZATION OF THE ESTHETIC QUALITY OF THE ROSEBUSH

Since its introduction into the analysis of foodstuffs, sensory analysis has been applied in several contexts. This work seeks to widen the field of sensory analysis to include ornamental plants and to characterize their esthetic quality. Using the rosebush as a plant model, an attribute generation protocol is proposed in order to develop a conventional profile of such products. Further to statistical treatments aiming to verify the unambiguity, discrimination and independence of these attributes, a reduced list of 18 attributes has been set up. These attributes make up the very core of the conventional profiling studies currently undertaken .

PRACTICAL APPLICATIONS

The generation of a list of attributes that is not too long, in order to describe plants as exhaustively as possible, is one of the first steps of extending sensory analysis methods to ornamental horticulture. This list will be used to train a panel of assessors to characterize the rosebush.
Two applications are in progress. The first application consists of evaluating the impact of nitrogen nutrition on the visual quality of the rosebush. The second has the objective of determining which characteristics influence consumer preferences.     

Using supervised fuzzy clustering to predict protein structural classes

Prediction of protein classification is both an important and a tempting topic in protein science. This is because of not only that the knowledge thus obtained can provide useful information about the overall structure of a query protein, but also that the practice itself can technically stimulate the development of novel predictors that may be straightforwardly applied to many other relevant areas. In this paper, a novel approach, the so-called "supervised fuzzy clustering approach" is introduced that is featured by utilizing the class label information during the training process. Based on such an approach, a set of "if-then" fuzzy rules for predicting the protein structural classes are extracted from a training dataset. It has been demonstrated through two different working datasets that the overall success prediction rates obtained by the supervised fuzzy clustering approach are all higher than those by the unsupervised fuzzy c-means introduced by the previous investigators [C.T. Zhang, K.C. Chou, G.M. Maggiora. Protein Eng. (1995) 8, 425-435]. It is anticipated that the current predictor may play an important complementary role to other existing predictors in this area to further strengthen the power in predicting the structural classes of proteins and their other characteristic attributes.     

Constructing a fuzzy rule-based system using the ILFN network and Genetic Algorithm.

In this paper, a method for automatic construction of a fuzzy rule-based system from numerical data using the Incremental Learning Fuzzy Neural (ILFN) network and the Genetic Algorithm is presented. The ILFN network was developed for pattern classification applications. The ILFN network, which employed fuzzy sets and neural network theory, equips with a fast, one-pass, on-line, and incremental learning algorithm. After trained, the ILFN network stored numerical knowledge in hidden units, which can then be directly interpreted into if then rule bases. However, the rules extracted from the ILFN network are not in an optimized fuzzy linguistic form. In this paper, a knowledge base for fuzzy expert system is extracted from the hidden units of the ILFN classifier. A genetic algorithm is then invoked, in an iterative manner, to reduce number of rules and select only discriminate features from input patterns needed to provide a fuzzy rule-based system. Three computer simulations using a simulated 2-D 3-class data, the well-known Fisher's Iris data set, and the Wisconsin breast cancer data set were performed. The fuzzy rule-based system derived from the proposed method achieved 100% and 97.33% correct classification on the 75 patterns for training set and 75 patterns for test set, respectively. For the Wisconsin breast cancer data set, using 400 patterns for training and 299 patterns for testing, the derived fuzzy rule-based system achieved 99.5% and 98.33% correct classification on the training set and the test set, respectively.     

CATEGORY APPRAISAL WITHIN A LIMITED PRODUCT RANGE: A METHODS-ORIENTED CASE HISTORY

This paper presents the author's approach to synthesizing useful direction from product testing when the stimuli are not systematically varied. The approach presented here comprises a research design and data analysis strategy, rather than a conventional product optimization with subsequent validation. The design steps comprise stimulus selection, attribute selection, and product evaluation. The data analysis comprises univariate modeling to show how sensory attributes drive overall liking, reduction of the matrix to factor scores for multivariate modeling, and then the creation of an integrated product model. The outcome is a set of factor scores that can be translated to sensory attributes and in turn to target products.     

Ozone prediction on the basis of neural networks,support vector regression and methods with uncertainty

The article presents modeling of daily average ozone level prediction by means of neural networks, support vector regression and methods based on uncertainty. Based on data measured by a monitoring station of the Pardubice micro-region, the Czech Republic, and optimization of the number of parameters by a defined objective function and genetic algorithm a model of daily average ozone level prediction in a certain time has been designed. The designed model has been optimized in light of its input parameters. The goal of prediction by various methods was to compare the results of prediction with the aim of various recommendations to micro-regional public administration management. It is modeling by means of feed-forward perceptron type neural networks, time delay neural networks, radial basis function neural networks, ε-support vector regression, fuzzy inference systems and Takagi–Sugeno intuitionistic fuzzy inference systems. Special attention is paid to the adaptation of the Takagi–Sugeno intuitionistic fuzzy inference system and adaptation of fuzzy logic-based systems using evolutionary algorithms. Based on data obtained, the daily average ozone level prediction in a certain time is characterized by a root mean squared error. The best possible results were obtained by means of an ε-support vector regression with polynomial kernel functions and Takagi–Sugeno intuitionistic fuzzy inference systems with adaptation by means of a Kalman filter.     

Object-oriented fuzzy expert system for on-line diagnosing and control of bioprocesses

Summary An object-oriented fuzzy expert system to support on-line control of an automated fermentation plant is described. The major elements of the system consist of a fuzzy inference engine, a database, a knowledge base, and an expression evaluater. The expression evaluater calculates specific rates for growth, and substrate and product formation at different physiological states during the cultivation from the measured data. The specific rates are then compared with the standard target rates stored in the database. If differences outside the set tolerances were observed, the inference engine analyses the reasons for the faults on the basis of the knowledge represented in the form of a knowledge network and fuzzy membership functions of the process variables. The fuzzy expert system was developed on the basis of a shell constructed by using the object oriented Smalltalk/V Mac programming environment, with Lactobacillus casei lactic acid fermentation as the example of process application.Visiting scientist from Helsinki University of Technology at RIKEN Correspondence to: P. Linko or I. Endo     

Effect of Encapsulation on Viability of <Emphasis Type="Italic">Bifidobacterium longum</Emphasis> CFR815j and Physiochemical Properties of Ice Cream

The health beneficial attributes of bifidobacteria and its safe association with the host gut has increased its significance as a probiotic. However delivering probiotic bifidobacteria with Minimum Biological Value (MBV) through product has always been a challenge. In the present study, an attempt was made to maintain the viability of native isolate of Bifidobacterium longum CFR 815j and deliver through ice-cream. B. longum CFR815j was microencapsulated in alginate starch capsules by emulsification followed by evaluation of bead stability in simulated gastrointestinal conditions. After incorporation in ice-cream, the effect on chemical properties, sensory parameters and meltdown characteristics of the product were also evaluated. Survival studies of B. longum revealed higher counts than 10⁷ in the product which is essential for probiotic bacteria to exhibit beneficial effect. Further, all the properties of this ice-cream were comparable to the regular ice-cream. Our studies conclude that encapsulation was able to maintain the requisite MBV of bifidobacteria in ice-cream without affecting the sensory characteristics.     

PRODUCT OPTIMIZATION AND THE ACCEPTOR SET SIZE

The acceptability of a product, measured by the acceptor set size (percentage of consumers rating the product acceptable), is a function of the perception of its attributes. The attributes are themselves a function of the inputs to the product (such as ingredients, processing or storage variables). These assumptions lead to the following model:
Acceptor set size = F (attribute₁, attribute₂, …, attribute_n)
Attribute j = f (input₁, input₂, …, input_m)
If we assume that these functions are differentiable, we can estimate the partial derivatives of the acceptor set size, with respect to the input variables. The gradient vector obtained indicates the fastest way to maximize the acceptor set size. The gradient search method, using the acceptor set size as an objective measure, can be applied in a variety of situations: to improve existing products, to maximize the acceptability of new products, and to study the relationship between shelf-life and acceptability.     

Sensitive pattern discovery with 'fuzzy' alignments of distantly related proteins

MOTIVATION: Evolutionary comparison leads to efficient functional characterisation of hypothetical proteins. Here, our goal is to map specific sequence patterns to putative functional classes. The evolutionary signal stands out most clearly in a maximally diverse set of homologues. This diversity, however, leads to a number of technical difficulties. The targeted patterns-as gleaned from structure comparisons-are too sparse for statistically significant signals of sequence similarity and accurate multiple sequence alignment. RESULTS: We address this problem by a fuzzy alignment model, which probabilistically assigns residues to structurally equivalent positions (attributes) of the proteins. We then apply multivariate analysis to the 'attributes x proteins' matrix. The dimensionality of the space is reduced using non-negative matrix factorization. The method is general, fully automatic and works without assumptions about pattern density, minimum support, explicit multiple alignments, phylogenetic trees, etc. We demonstrate the discovery of biologically meaningful patterns in an extremely diverse superfamily related to urease.     

Learning cluster-based classification systems with ant colony optimization algorithms

Classification is a data mining task the goal of which is to learn a model, from a training dataset, that can predict the class of a new data instance, while clustering aims to discover natural instance-groupings within a given dataset. Learning cluster-based classification systems involves partitioning a training set into data subsets (clusters) and building a local classification model for each data cluster. The class of a new instance is predicted by first assigning the instance to its nearest cluster and then using that cluster’s local classification model to predict the instance’s class. In this paper, we present an ant colony optimization (ACO) approach to building cluster-based classification systems. Our ACO approach optimizes the number of clusters, the positioning of the clusters, and the choice of classification algorithm to use as the local classifier for each cluster. We also present an ensemble approach that allows the system to decide on the class of a given instance by considering the predictions of all local classifiers, employing a weighted voting mechanism based on the fuzzy degree of membership in each cluster. Our experimental evaluation employs five widely used classification algorithms: naïve Bayes, nearest neighbour, Ripper, C4.5, and support vector machines, and results are reported on a suite of 54 popular UCI benchmark datasets.     

Multisensory Oddity Detection as Bayesian Inference

A key goal for the perceptual system is to optimally combine information from all the senses that may be available in order to develop the most accurate and unified picture possible of the outside world. The contemporary theoretical framework of ideal observer maximum likelihood integration (MLI) has been highly successful in modelling how the human brain combines information from a variety of different sensory modalities. However, in various recent experiments involving multisensory stimuli of uncertain correspondence, MLI breaks down as a successful model of sensory combination. Within the paradigm of direct stimulus estimation, perceptual models which use Bayesian inference to resolve correspondence have recently been shown to generalize successfully to these cases where MLI fails. This approach has been known variously as model inference, causal inference or structure inference. In this paper, we examine causal uncertainty in another important class of multi-sensory perception paradigm – that of oddity detection and demonstrate how a Bayesian ideal observer also treats oddity detection as a structure inference problem. We validate this approach by showing that it provides an intuitive and quantitative explanation of an important pair of multi-sensory oddity detection experiments – involving cues across and within modalities – for which MLI previously failed dramatically, allowing a novel unifying treatment of within and cross modal multisensory perception. Our successful application of structure inference models to the new ‘oddity detection’ paradigm, and the resultant unified explanation of across and within modality cases provide further evidence to suggest that structure inference may be a commonly evolved principle for combining perceptual information in the brain.     

Fuzzy cluster analysis of simple physicochemical properties of amino acids for recognizing secondary structure in proteins.

Fuzzy cluster analysis has been applied to the 20 amino acids by using 65 physicochemical properties as a basis for classification. The clustering products, the fuzzy sets (i.e., classical sets with associated membership functions), have provided a new measure of amino acid similarities for use in protein folding studies. This work demonstrates that fuzzy sets of simple molecular attributes, when assigned to amino acid residues in a protein''s sequence, can predict the secondary structure of the sequence with reasonable accuracy. An approach is presented for discriminating standard folding states, using near-optimum information splitting in half-overlapping segments of the sequence of assigned membership functions. The method is applied to a nonredundant set of 252 proteins and yields approximately 73% matching for correctly predicted and correctly rejected residues with approximately 60% overall success rate for the correctly recognized ones in three folding states: alpha-helix, beta-strand, and coil. The most useful attributes for discriminating these states appear to be related to size, polarity, and thermodynamic factors. Van der Waals volume, apparent average thickness of surrounding molecular free volume, and a measure of dimensionless surface electron density can explain approximately 95% of prediction results. hydrogen bonding and hydrophobicity induces do not yet enable clear clustering and prediction.     

Transitive or Not: A Critical Appraisal of Transitive Inference in Animals

Transitive inference has been historically touted as a hallmark of human cognition. However, the ability of non‐human animals to perform this type of inference is being increasingly investigated. Experimentally, three main methods are commonly used to evaluate transitivity in animals: those that investigate social dominance relationships, the n‐term task series and the less well known associative transitivity task. Here, we revisit the question of what exactly constitutes transitive inference based upon a formal and habitual definition and propose two essential criteria for experimentally testing it in animals. We then apply these criteria to evaluate the existing body of work on this fundamental aspect of cognition using exemplars. Our evaluation reveals that some methods rely heavily on salient assumptions that are both questionable and almost impossible to verify in order to make a claim of transitive inference in animals. For example, we found shortcomings with most n‐term task designs in that they often do not provide an explicit transitive relationship and/or and ordered set on which transitive inference can be performed. Consequently, they rely on supplementary assumptions to make a claim of transitive inference. However, as these assumptions are either impossible or are extremely difficult to validate in non‐human animals, the results obtained using these specific n‐term tasks cannot be taken as unambiguous demonstrations (or the lack thereof) of transitive inference. This realisation is one that is generally overlooked in the literature. In contrast, the associative transitivity task and the dominance relationship test both meet the criteria for transitive inference. However, although the dominance relationship test can disambiguate between transitive inference accounts and associative ones, the associative transitivity test cannot. Our evaluation also highlights the limitations and future challenges of current associative models of transitive inference. We propose three new experimental methods that can be applied within any theoretical framework to ensure that the experimental behaviour observed is indeed the result of transitive inference whilst removing the need for supplementary assumptions: the test for the opposite transitive relation, the discrimination test between two separate and previously non‐reinforced items, and the control for absolute knowledge.