Agricultural product intelligence is a new way for biotechnology that can be made multiple food products with a variety of characteristics, enhanced flavor and nutritional quality of foods. To evaluate food products of bio-food products for improvement of the food quality in global bio-food markets, cross-cultural customer behaviors are mostly influenced with healthy food in global markets. In this study, we propose a new approach using Kansei Evaluation integrated with fuzzy rules and Self-Organizing Map (SOM) model, together with customers and expert sensibilities and preferences for selection of the appropriate alternatives (bio-food and food technology), matched with mass customer and expert group behaviors. To confirm the model's performance, the proposed approach has been tested in experiments for the validated model and applied in several domains in Asian countries.
The potential food biotechnology is a key biotechnology engineering to
produce good bio-food, attractive marketing, or healthy nutrition in daily life. Food
biotechnology has been widely produced healthy foods in daily life [
2.1
In the preliminary experiment, the surveys were done by 30 customers including local, international students and experts from Hanoi Fresh Vegetable and Fruit Institute with export companies for the markets. There were 5 experts and 25 customers who participated in the surveys for selection of 14 adjectives as pairs of Kansei words in total of 16 Kansei words influenced to these criteria. In the final data collection, we have selected 14 Kansei words of the most relevant words with redundance of 02 Kansei words using Factor Analysis that are influenced to these criteria for evaluation of biology-food products, as shown in Table 1.
A Kansei matrix is constructed using Kansei words for the steps as follows: 1. Collect customer surveys in the preliminary experiment using Semantic Differential (SD) method with a five-point scale definition (0: oppose, 0.25: almost oppose, 0.5: have no preference, 0.75: almost agree, 1: agree). 2. Consider the most appropriate Kansei words in the bio-food market belonging to Kansei criteria evaluation. 3. Structure Kansei words using Factor Analysis to identify conditional factor loadings and commonalities in the model. 4. Select the most appropriate Kansei words and construct a Kansei matrix from the average of Kansei weights. 5. Update Kansei weights to the Kansei matrix in a Database. (1) 2.2
Let XS = {X1S , X2S , ..., XmS} be a set of Kansei words that use to evaluate biofood products, where m is the number of Kansei words. In order to quantify customer’s sensibilities in evaluating bio-food products, we have refined for the most important Kansei words in XS to evaluate a bio-food product with respect to criteria in stock market S.
Let WmS = {Wm−, Wm+} be opposite pairs of Kansei words with respect to XmS. Let P customers collect their preferences by surveys. To evaluate bio-food product CjS , its Kansei weight witj represents by the i-th Kansei word of the j-th bio-food product evaluated by the t-th expert. Hence, the average weight of i-th Kansei word is evaluated by P customers, as given by Eq.(1). kiSj =
P 1 X wij
t P t=1
KnS×m = (kiSj )(n×m) is a Kansei matrix construction, where n and m is the number of bio-food products and Kansei words respectively. Table 2 illustrates a sample of the Kansei matrix consisting of alternatives in Kansei evaluation.
3.1
Human emotions in decision making can be presented as an integration of logical rules, quantitative knowledge and reasoning evidence. Figure 1 shows the quantification of expert sensibilities and emotions.
In common sense human reasoning, linguistic expressions represent rules for expert decision situations. To quantify emotions and sensibilities in reasoning of expert in dynamic market environments, we use fuzzy rules as illustrations of an example as follows: Rule 1: IF Japanese people prefer an organic apple juice with its red color AND satisfaction with its fresh fruit THEN The system marks positive emotion status liked++ Rule 2: IF Chinese people do not like an organic apple juice AND Chinese markets have many fresh apples with these low prices THEN The system marks negative emotion status disliked- Note that emotion expert status represents in the subset disliked- -, disliked-, neutral, satisfied+, satisfied++, as described human reasoning emotion decisions.
Common Sense Human Reasoning can be presented as an integration of fuzzy rules, quantitative knowledge and reasoning evidence. Linguistic expressions can be used to represent rules for expert decision situations. To quantify the Common Sense Human Reasoning of expert ei in dynamic market environments, we use the following logical rules as Rule i can be expressed by Eq.(1): IF Condition 1 AND...AND Condition m
THEN Actions
Note that expert decision status is represented in a five step scale {invest++,
invest+, neutral, risk-, risk- -}
The proposed model aims to explain how we evaluate bio-food products in
terms of expert preferences and customer behaviors. This uses uncertainty of
quantitative and qualitative factor weights, together with the quantification of
customer sensibilities in bio-food products environments. These weights can be
transformed through the framework, representing in interval values [
Step 1. Kansei Evaluation to quantify expert sensibilities and
preferences. Pairs of adjectives called Kansei words are collected from
bio-food markets. Based on the experts’ experiences, Semantic Differentials
(SD) method is applied to refine pairwised Kansei words. All refined-adjective
words use to evaluate alternatives based on expert preferences. In Kansei
evaluation, expert sensibilities and emotions are quantified in weights, representing
in an internal value [
erences in Bio-food Evaluation. Experts surveys are collected that divided into several groups. Each group of experts makes surveys and provides its preferences using collaborative decision making.
together with expert sensibilities and emotions. Kansei bio-food evaluation matrix, as discussed in Section 2.2 is divided into two sub matrices. Kansei Bio-food matrix and Kansei decision matrix are the same as bio-food alternatives, indicators and Kansei words. The Expert decision matrix using collaborative decision making of expert groups by calculating weights. These matrices are visualized by SOM to aggregate customer/expert preferences.
preferences. The outcomes of the systems are shown in the best bio-food products, matched with customer/expert preferences using collaborative decision making. To select the quantity of bio-foods, we apply logical fuzzy rules to consider the bio-food results by dealing with group customer/expert decisions as follows:
updating Kansei bio-food matrix. In order to aggregate all decision maker preferences, the decision maker preference distance dS ei→ej between two vectors DeSi and DeSj represents by decision maker preferences, in food market S as defined by Euclidean distance given by Eq.(2).
deSi→ej =k DeSi − DeSj k
tances. To select bio-food products ptS |(t = 1, ..., c) in food market S, the decision maker preference distance dS
ei→ej is represented by mitj calculated from the Kansei product attribute distance vitj evaluated by decision maker eiS of his/her group at iteration t and the Kansei bio-food weight witj of the decision maker group. The weight of mitj is expressed by Eq.(3).
k mitj =k P1 X witj − vitj k j=1 (2) (3) (4) where (i = 1,...,q, j = 1,...,k ) and p is the number of decision makers in each group.
MnS×p.
Step 4.3. Updating Kansei bio-food weights. A Decision matrix AqS×k is updated by its weights given by Eq.(4). After that, the Decision matrix AqS×k is joined with Kansei bio-food matrix MnS×p and its weights are updated to m mitj+1 = mitj + βjS (k P1 X witj − vitj k) j=1 where βjS is a set of decision maker preferences as defined in a five-point scale (0: oppose, 0.25: almost oppose, 0.5: have no preference, 0.75: almost agree, 1: agree)
To select c bio-food product (p1S , p2S , ..., pcS ), the similar steps are repeated between Step 4.1 and Step 4.3 until c decision maker groups with updated weights to the Decision matrix completely. 5
The proposed model has been employed several domains of Asian markets for demonstration of the proposed approach. In data collection, web-based application is designed to allow any authorized user via the Intranet / Internet connection. Figure 2 shows an example of results for the illustration of collaborative decision making of expert and customer groups for evaluation of bio-food products on the Asian markets. The proposed model is used to evaluate dynamic solutions of bio-food technology, based on collaborative expert preferences and customer behaviors. The simulation results showed that customer behaviors, together with expert preferences matched with bio-food products. Decision makers can dynamically evaluate bio-food properties on map results as well as optimal decisions. Compared to conventional methods in evaluating bio-food products, most approaches have been used statistic methods in evaluation, the proposed model has been figured out dynamic evaluation in quantification of customer sensibility and behaviour. The new approach using Kansei evaluation is to quantify human sensibilities and emotions about bio-food quality in market and bio-food research environments.
This approach is also illustrated with a case study of experimental results to validate the model. In future works, the proposed approach is extended to evaluate multiple food products in daily life. 6
The proposed approach has been applied in evaluation of bio-food products, based on collaborative expert preferences and customer behaviors. The simulation results showed that customer behaviors, together with expert preferences matched with bio-food products. Decision makers can dynamically evaluate biofood properties on map results as well as optimal decisions. The new approach using Kansei evaluation is to quantify expert sensibilities and emotions about bio-food quality in market and bio-food research environments. This approach is also illustrated with a case study of experimental results to validate the model. In future works, the proposed approach is extended to evaluate multiple food products in daily life.