Effects of different kind of sesame materials on the physical properties of gomatofu (sesame tofu)

Gomatofu (sesame tofu) is one of the traditional Japanese healthy foods and is representative of all shojin (vegetarian) dishes. Gomatofu, one of the mixed gels consisting of kudzu (arrowroot) starch and sesame, possesses an extremely unusual textural characteristic which is soft, smooth, and springy. The textural properties are greatly influenced by preparation, ingredients and roasting conditions of sesame seeds. Hence there are many kinds of sesame materials which can be used to prepare gomatofu such as white, black, huskless or not and roasted or unroasted sesame materials, in this study, the effects of these materials on the physical properties of gomatofu were studied. The sample of Ra-W prepared with unroasted huskless sesame seed had the least hardness, but mouthfeel of this sample were the highest. The samples of Ro-B and Ro-W prepared with roasted husk (black and white) sesame seed were evaluated to have the best palatability because of their superior springiness. It was clarified that different kinds of sesame materials affected the forming of structure and physical properties of gomatofu, because the chemical components were different from varieties of sesame materials.     

The influence of additives on the rheological and sensory properties of nonfat yogurt

In this work, response surface methodology was applied to a study of the effects of the addition of skim milk powder and stabilizers on the apparent viscosity and sensory attributes of nonfat yogurt. The addition of stabilizers has been shown to produce a significant effect on sensory attributes, while high concentrations of xanthan and guar gum are seen to decrease the overall acceptability of the yogurt, with gelatin producing the higher apparent viscosity and consistency. The correlation among apparent viscosity, which is an instrumental measurement, and consistency, which is a sensory evaluation, was 0.72.     

Frying Performance of No-<Emphasis Type="Italic">trans</Emphasis>, Low-Linolenic Acid Soybean Oils

Two extruded-expelled physically refined soybean oils with reduced contents of linolenic acid, ultra-low- linolenic acid (ULL, 1.5%) and low-linolenic acid (LL, 2.6%), and a extruded-expelled physically refined control oil (control, 5.3% linolenic acid) were evaluated by frying French fries in a commercial-like setting for 6 h day⁻¹ during 23 days. The oils became darker, increased in yellow color at the beginning, and became redder and less green throughout the process. Free fatty acids levels were not different among the oils until day 14, after which, ULL was different from the control for the remainder of frying. The conjugated dienoic acid values were greatest in the control. Generally, ULL and LL oils had lower percentages of polar compounds than did the control, providing a frying life 2 days longer than the control and ~30% increase in frying time. A trained sensory panel evaluated the French fries on days 2, 5, and 6. Buttery and potato flavors decreased, and rancid and painty flavors increased over frying time for all products. Rancid flavor was highest in the fries from the control oil. Overall, the ULL and LL oils performed better than did the control oil and ULL tended to perform better than the LL.     

基于证据理论的陶瓷智能感观评估

陶瓷感官评估结果的表达形式包括语言词汇、象征性符号和数值描述等，它们具有不确定性和不精确性。本文首先运用模糊逻辑技术对这些评估结果定量化，并基于证据理论提出了一种陶瓷智能感观数据融化方法，得到了基于感观数据的质量分级，并取得了较好的实验结果。     

Shifts in sensory dominance between various stages of user-product interactions

In the area of product design, sensory dominance can be defined as the relative importance of different sensory modalities for product experience. It is often assumed that vision dominates the other senses. In the present study, we asked 243 participants to describe their experiences with consumer products in various situations: while buying a product, after the first week, the first month, and the first year of usage. The data suggest that the dominant sensory modality depends on the period of product usage. At the moment of buying, vision is the most important modality, but during the usage the other sensory modalities gain importance. The roles of the different modalities during usage are product-dependent. Averaged over 93 products analyzed in this study, after one month of usage touch becomes more important than vision, and after one year vision, touch and audition appear to be equally important. We conclude that to create a long-lasting positive product experience, designers need to consider user-product interaction at different stages of product usage and to determine which sensory modality dominates product experience at each stage.     

Modeling wine preferences by data mining from physicochemical properties

We propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. A large dataset (when compared to other studies in this domain) is considered, with white and red vinho verde samples (from Portugal). Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful to support the oenologist wine tasting evaluations and improve wine production. Furthermore, similar techniques can help in target marketing by modeling consumer tastes from niche markets.     

Optimization of media processing workflows with adaptive operator behaviors

In this paper, we present the ARIA media processing workflow architecture that processes, filters, and fuses sensory inputs and actuates responses in real-time. The components of the architecture are programmable and adaptable; i.e. the delay, size, and quality/precision characteristics of the individual operators can be controlled via a number of parameters. Each data object processed by qStream components is subject to transformations based on the parameter values. For instance, the quality of an output data object and the corresponding processing delay and resource usage depend on the values assigned to parameters of the operators in the object flow path. In Candan, Peng, Ryu, Chatha, Mayer (Efficient stream routing in quality- and resource-adaptive flow architectures. In: Workshop on multimedia information systems, 2004), we introduced a class of flow optimization problems that promote creation and delivery of small delay or small resource-usage objects to the actuators in single-sensor, single-actuator workflows. In this paper, we extend our attention to multi-sensor media processing workflow scenarios. The algorithms we present take into account the implicit dependencies between various system parameters, such as resource consumption and object sizes. We experimentally show the effectiveness and efficiency of the algorithms.

Classification of non-alcoholic beer based on aftertaste sensory evaluation by chemometric tools

Sensory evaluation is the application of knowledge and skills derived from several different scientific and technical disciplines, physiology, chemistry, mathematics and statistics, human behavior, and knowledge about product preparation practices. This research was aimed to evaluate aftertaste sensory attributes of commercial non-alcoholic beer brands (P1, P2, P3, P4, P5, P6, P7) by several chemometric tools. These attributes were bitter, sour, sweet, fruity, liquorice, artificial, body, intensity and duration. The results showed that the data are in a good consistency. Therefore, the brands were statistically classified in several categories. Linear techniques as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were performed over the data that revealed all types of beer are well separated except a partial overlapping between zones corresponding to P4, P6 and P7. In this research, for the confirmation of the groups observed in PCA and in order to calculate the errors in calibration and in validation, PLS-DA technique was used. Based on the quantitative data of PLS-DA, the classification accuracy values were ranked within 49-86%. Moreover, it was found that the classification accuracy of LDA was much better than PCA. It shows that this trained sensory panel can discriminate among the samples except an overlapping between two types of beer. Also, two types of artificial networks were used: Probabilistic Neural Networks (PNN) with Radial Basis Functions (RBF) and FeedForward Networks with Back Propagation (BP) learning method. The highest classification success rate (correct predicted number over total number of measurements) of about 97% was obtained for RBF followed by 94% for BP. The results obtained in this study could be used as a reference for electronic nose and electronic tongue in beer quality control.