Az érzékszervi minőség fogyasztói megítélésének mérése standard mutatószámmal = Evaluation of sensory quality through a standard consumer preference index

A kedveltségi mutatószám segíti a vizsgált minták fogyasztói megítélésének standard megjelenítését az alábbi esetekben is: • azonos időszakban, azonos fajtaszámmal, de különböző bírálati számmal végzett preferencia vizsgálatok (eltérő arányok a fogyasztói preferencia csoportok között), • különböző időszakokban (tárolási kísérletek) eltérő számú bírálóval végzett kísérletek, • eltérő időpontokban (fajta-specifikus érési idők) különböző fajtaszámmal és eltérő számú bírálóval végzett bírálatok. A kidolgozott kedveltségi mutatószám a magasabb preferenciát magasabb értékkel fejezi ki, így az eredmény szemléletes és közérthető módon ábrázolható az ilyen jellegű kísérleteknél megszokott diagramokon is. Consumer preference tests usually apply simple ranking. In this case, representing the test results in a graph might be misleading, because of the ordinal nature of the rank numbers. The rank sum of a more preferred product will be low, while the rank sum of the rejected products will be high. Even if attention is called to this with a legend (e.g. the lowest rank sum indicates the highest preference), one can misread the graph, since the usual way of interpretation of data is: high value – preference, low value – rejection. The new index, I have developed, provides an objective value, which is independent of the number of assessments and samples, thus facilitating the comparison of ranking test results. The name of the index is ‘preference index’, which refers to its application in my current research (the abbreviation is Rmax%conv, which reads: converted rank sum in the percentage of the maximum). The problem of evaluating high number of samples causes physiological and psychological exhaustion in th panel, which biases the result. This problem can be solved by the application of Balanced, Incomplete Block Design (BIB). In BIB designs each assessor analyzes only a subset of the samples, but the data analysis provides identical results, like in the case when each assessor evaluates every sample. The statistical procedure (Friedman-analysis) has to be optimized for the special data structure. I also defined the calculation method of the preference index (Rmax%conv) for BIB design tests

Mesterséges ideghálózatok (ANN) alkalmazása az érzékszervi minősítés gyakorlatában = Application of artificial neural network (ANN) in praxis of the sensory evaluation

A mesterséges ideghálózatok (ANN) alkalmazásának terjedése a módszernek köszönhetően dinamikus és széleskörű, mivel ezek képesek az adatokban rejlő komplex, nem lineáris mintázatok, kiugró értékek, korrelációk azonosítására, valamint a nem lineáris változók közötti regresszió alkalmazására, értékek és kategóriák előrejelzésére. A különböző neurális hálók (MLF-NNs, RBF-NN, Kohonen hálók) élelmiszertudományi alkalmazása megvalósult, köszönhetően a nagy szoftverfejlesztő cégeknek, amelyek ezeket termékeikbe/programjaikba beépítették. Ezek többek között a következők: MatLab (Neural Network Toolbox), Statistica (Neural Networks), Palisade (NeuralTools), SPSS (Modeler), Alyuda (Neurointelligence), NeuroDimension (Neurosolution). Ennek ellenére a nemzetközi szakirodalomban csak néhány humán érzékszervi vizsgálatokkal kapcsolatos kutatási eredményt publikáltak. A kutatási célunk az ANN módszerének alkalmazása a panelteljesítmény általános jellemzésére, és a panel értékelésétől eltérő tagok azonosítására. Kutatásunkban 5 kereskedelmi forgalomban kapható csemegekukorica mintát elemeztünk, melyeket egy képzetlen és egy képzett 10 fős panel értékelt, 2 ismétlésben. A neurális háló felépítését a 'Best Net Search segítségével végeztük, a képzetlen bírálók esetében a 4 nóduszos MLFN, míg a képzett bírálók esetében az 5 Nóduszos MLFN adta a legjobb predikciót a tréning 80%, teszt 20% beállításokkal, véletlen mintavétellel (Palisade, Neural Tools 5.5). A modellek validációját elvégeztük az érzékszervi panelek teljesítményértékelésére kifejlesztett célszoftver a PanelCheck szoftver Tucker-1 és tojáshéj diagram (eggshell) módszereinek alkalmazásával. Összefoglalásként megállapítottuk, hogy mind a képzetlen (3,6,7,8,9), mind a képzett bírálók (8,9,10) esetében ugyanazokat a bírálókat azonosítottuk eltérő paneltagként a két szoftverrel. The application of the Artificial Neural Networks is more and more widespread in several fields of scientific research due to the flexibility of this method. With the use of an ANN model it can be easily identified outliers, correlation and the complex nonlinear patterns in the data set. Consequently ANN can be applied to space reduction, numerical and/or categorical prediction and of course regression between nonlinear variables. The different types of neural networks (MLF-NNs, RBF-NN, Cohonen networks) have already been transferred to the food sector thanks to those software developing companies who had integrated this method in their products/software, some of them are MatLab (Neural Network Toolbox), Statistica (Neural Networks), Palisade (NeuralTools), SPSS (Modeler), Alyuda (Neurointelligence) and NeuroDimension (Neurosolution). In spite of these there is only a limited number of publications dealing with ANN and human sensory evaluation. The aim was to apply the ANN method to evaluate the performance of a human sensory panel and to identify panel members who perform differently compared to the rest of the panel. In this research five commercially available sweet corn samples were evaluated by a trained and an untrained sensory panel using two replicates. Both of the panels consisted of 10 panelists. The creation of the neural net was performed using ‘Best Net Search’ algorithm, which resulted in case of the untrained panel a 4 nodes MLFN net and in case of the trained panel a 5 nodes MLFN net. The training and testing conditions were set to 80% and 20% with random sampling (Palisade, Neural Tools 5.5). The models were validated by PanelCheck software which is designed to evaluate the performance of sensory panels by the Norwegian Nofima Research Institute. The applied mathematical methods were the Tucker1 and eggshell plots. As a conclusion we have defined that in case of the untrained and trained panel the same assessors were marked as different with the application of the two softwares

Revision of the performance evaluation methods of sensory panellists performing descriptive analysis

Sensory tests form the basis for sensory science. Sensory science uses human senses as measurement tools. During sensory tests, the properties of a product are evaluated by sensory panelists and by a sensory panel consisting of them. Decisions made after sensory tests are fundamentally determined by the quality of the data experienced, therefore, the quality of sensory data is determined by the trained and expert sensory sensory panel and its members. In our work, revision of the correlation and regression methods recommended by the standard titled „MSZ ISO 11132:2013 Sensory analysis. Methodology. Guidelines for monitoring the performance of a quantitative sensory panel” are described, and corrections are suggested

Ásványvizek érzékszervi minőségének vizsgálata ProfiSens szoftver alkalmazásával = Computer supported sensory evaluation of mineral waters by the application of the ProfiSens software

A csendes ásványvizek érzékszervi vizsgálatnak kísérleti eredménye szerint a csapvíz uszoda illata és klóros íze miatt elkülönült a Mohai Ágnes, a Veritas, az Óbudai Gyémánt, a Balfi és a Fonyódi palackozott vizektől. Fontos kiemelni ugyanakkor, hogy a csapvíz érzékszervi minősége akár Budapest, akár Magyarország viszonylatában nagy eltéréseket mutathat. A savanykás ízben a magas HCO3 tartalmú Mohai és Balfi elkülönült a többi víztől, míg az alacsonyabb tartalommal rendelkező vizek között nem volt érzékszervi különbség. A legtermészetesebb jelleggel a Fonyódi rendelkezett. Szájérzetben, fémességben nem adódott matematikailag igazolható szignifikáns különbség a vizsgált termékek között. Korábbi kutatásaink eredményeit alátámasztva arra a következtetésre jutottunk, hogy amennyiben az ásványvizek több tulajdonságban is érzékelhetően eltérőek, úgy a profilanalítikus eljárás, az ásványvizek érzékszervi leírásának megfelelő módszere. A ProfiSens segítségével korrekt módon és megbízhatóan automatizálható a bírálat megtervezése, kivitelezése és értékelése. A szoftveres támogatásnak köszönhetően a módszer időigénye lecsökken, segítségével a bírálók és a szakemberek azonnal megismerhetik az eredményeket, amelyeket visszacsatolhatnak, integrálhatnak az előállítási, kutatási folyamatokba. Sensory quality involves all attributes perceived through the human senses. The subjective character of the assessors taking part in the evaluation might influence sensory testing data. However, several techniques are known which reduce bias to an acceptable level. Information Technology (IT) is a great help in designing and performing sensory tests in accordance to the relevant ISO standards. Our laboratory has developed a VBA software supporting sensory profile analysis of food products. Mainly the dissolved materials determine the sensory properties of mineral waters. Our study was carried out to determine whether non-expert assessors (consumers) could differentiate between different types of mineral water. In the group there were still (non-carbonated) bottled water samples are: Mohai Ágnes, Veritas, Óbudai Gyémánt, Balfi, Fonyódi and tap water

Correlations between value-based segmentation and eye movements during a food choice task: case study with breads

Connections between eye-movements and self-reported values were analyzed in this study. Cluster analysis of self-reported values of 140 participants was performed using internal cluster validity measures and the obtained optimal number of clusters was six. Differences between clusters were found in decision times where eco-ra-tionalists, who rated true friendship, comfortable life, environment and energy conscious life and economical living as highly important, needed more time to choose one from the three bread alternatives. These results were strengthened by the analysis of the eye-tracking variables which showed that members of the eco-rationalist and family-oriented clusters spent more time to gaze at the stimuli. Our results confirmed that there is a significant link between the self-reported values and eye-movements; hence it is advisable to split the participants into groups according to their self-reported values prior to eye-tracking in order to avoid false conclusions

Application of eye-tracking methodology in food researches

Eye-tracking is a widely applied tool to follow the human gaze direction. Due to the excessive technical development of eye-trackers, nowadays several fields of applications are available. A new field is food research where numerous questions can be answered by the analysis of gazing behaviour. The methodology might be applied not only in food marketing, but also in sensory analysis and consumer studies. In our article, we aim to introduce the principles of eye-tracking along with the major eye-movements and describe the meanings of the measured variables. Furthermore, several applications are introduced from food sciences

Eye-tracking tests in consumer perception of food

Summary Eye-tracking analyses provide an opportunity to record the eye movements of the participants, and then to evaluate the data obtained. The application of eye-tracking cameras is not yet typical in the food industry in Hungary, as opposed to the practice in Western Europe, where this technology is an important and commonly used tool of product development and marketing support. To the best of our knowledge, no eyetracking analyses related to beets have been published so far in the domestic and international literature. During the research, eye-tracking analyses were carried out in the Sensory Analysis Laboratory of the Faculty of Food Science of Szent István University, using a Tobii X2- 60 eye-tracker and the Tobii Studio (version 3.0.5, Tobii Technology AB, Sweden) data processing software. The results draw attention to the fact that the decisions of the consumers interviewed were only slightly influenced by their knowledge of the treatment of the beets analyzed. On the other hand, extra information regarding the antioxidant content changed their decision regarding the selection. Eye-tracking analysis results showed that consumer decision can be monitored much more accurately than using traditional market research methods. The reason for this is that eye movement is very hard to control consciously, and so objective information can be obtained about consumer decision mechanisms which is practically impossible to get using subjective questionnaire methods based on self-declaration or focus group testing

Prediction of sensory preference by artificial neural networks, using sweet corn varieties as an example

According to our knowledge, there are only a few publications in available literature sources on the sensory characteristics and consumer preferences of sweet corn varieties. In our research, practical application of artificial neural networks (ANNs) is presented. In our study, 41 frozen sweet corn varieties were evaluated by a panel of expert sensory panelists (14 persons), by the method of profile analysis (MSZ ISO 11035:2001; ISO 13299:2003), on an unstructured scale of 0 to 100, then, in large-scale tests, 6 of the 41 varieties were evaluated by consumers (167 people) according to preference, on a structured scale of 1 to 9. Artificial neural networks require large amounts of data, therefore, on the expert and consumer data for the 6 varieties, 1,000 Monte Carlo simulations were run. 80% of the resulting dataset was used to train the created neural networks, and 20% was utilized to test them. The best prediction was given by the 4-node multi-layer feedforward neural network (MLFN), the smallest residues were obtained in this case during the training and the test, which were also validated by predictions on random numbers and cross-checking. Preference values of the other 35 corn varieties were predicted by this model. The most preferred variety was ‘Shinerock’ (8.46), while the least preferred ones, according to the predictions, were ‘Madonna’ and ‘Rustler’, with and average preference value of 2.7 (on a scale of 1 to 9). During the establishment of the artificial neural network model, product characteristics that are the main drivers of consumer acceptance were successfully identified: sweet taste, global taste intensity and juiciness. In general, it can be stated that prediction of the preference of different varieties is made possible by the validated product-specific artificial neural network presented

QUALIFICATION OF ASSESSORS IN FOOD PROFILE ANALYSIS AND OTHER NEW DEVELOPMENTS OF PROFISENS

Sensory analysis is a fundamental tool in food quality assurance. Beside consumer tests (that focus on the acceptance of products), trained panels provide much detailed sensory data on the intensity of the most relevant attributes. The analysis of descriptive sensory data is a very complex task of this science. One of the key issues is the reliability of the panel members in making decisions. The research group of BME and BCU has created a specialized software - ProfiSens - for food profile analysis. ProfiSens was applied in research and education, in designing and carrying out profile analyses by different panelists in hundreds of cases. Several times not only the food samples, but also the group of panelists were to be qualified. In our paper we discuss a new method based on geometrical properties of the profile polygon, which offers a fast way for the qualification of the assessors. We also often met the problem of willing to use earlier defined profile analysis scoresheets or even only some of their attributes. The solution for these problems is to create a DataBase, containing all the data of designed scoresheets, and to make possible searching and picking up any wanted attributes from the DB. We discuss our results in this field as well