Fuzzy Logic Model for Sensory Evaluation of Commercially ...

1 Journal of Ready to Eat Food | April-June, 2014 | Vol 1 | Issue 2 | Pages 78-84 2014 Jakraya Publications (P) Ltd JOURNAL OF READY TO EAT FOOD Journal homepage: ORIGINAL ARTICLE Fuzzy Logic Model for Sensory Evaluation of Commercially Available Jam Samples Shinde1* and Pardeshi2 1 Assistant Professor of Mathematics, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola ( )- 444 104. 2 Associate Professor, Department of Agricultural Process Engineering, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola ( )-444 104. *Corresponding Author: Shinde Email: Received: 31/05/2014 Revised: 29/06/2014 Accepted: 29/06/2014 Abstract The four different jam samples available in market were evaluated for their liking by the consumers using Sensory Evaluation . A Sensory study was conducted for analysis of acceptability of these samples. The analysis of this Sensory study was conducted using Fuzzy Logic . The preference ranking of the different samples was obtained using Fuzzy Logic .

2 This methodology takes an account of the judge s behavior to articulate the preference ranking. The Evaluation of the sample depends on the color, flavor, texture and overall appearance. These quality attributes are considered as mathematical variable and based on these variables the Fuzzy Logic mathematical Model was developed. The concept of triplets and value membership function of standard Fuzzy scale is used to determine the overall Sensory scores of jam samples. This procedure leads to obtain the preference ranking of the jam sample as per the choice of judges. The output given by Fuzzy Logic Model , the samples are ranked very good, good, satisfactory and unsatisfactory. It was observed that the Fuzzy Logic technique could be satisfactorily utilized for Evaluation of the samples. The results of Sensory analysis showed that the sample X4 was ranked highest followed by X1, X3 and X2. Keywords: Fuzzy Logic , Sensory Evaluation , jam, MATLAB.

3 Introduction Fuzzy sets theory introduced by Zadeh (1965), which allows uncertain phenomena to be treated mathematically. Chen et al. (1988) developed a Model for the analysis of Sensory data. Zhang and Litchfield (1991) developed a Fuzzy comprehensive Model for ranking of foods and developing new food products. Ranking of food samples and their quality attributes are based on triplets associated with Sensory scales, triplets for Sensory score, triplets for Sensory score of quality attributes, triplets for relative weightage of quality attributes, triplets for overall Sensory score, values of membership function of standard Fuzzy scale, values of overall membership function of Sensory scores on standard Fuzzy scale, similarity values, quality attributes ranking in general. Multiple experts are involved in subjective Evaluation . In most cases the expert s opinion rather comes in linguistic form, which contains a lot of subjectivity, vagueness and ambiguity.

4 Fuzzy Logic is an important tool by which vague and imprecise data can be analyzed and important conclusions regarding acceptance, rejection, ranking, strong and weak attributes of food can be drawn. In Fuzzy modeling, linguistic variables ( , not satisfactory, good, excellent, etc.) are used for developing relationship between independent ( colour, flavour, texture, overall appearance etc.) and dependent ( acceptance, rejection, ranking, strong and weak attributes of food) variables (Das, 2005; Routray and Mishra, 2011). Fuzzy sets can be used for analysis of Sensory data instead of average scores to compare the samples attributes (Lincklaen et al., 1989; Kavdir and Gayer, 2003), since Fuzzy sets are not confined to deterministic value and have a merit in Sensory Evaluation because human expressions on filling for foods are Fuzzy rather than deterministic.

5 The developed Fuzzy mathematical models perform remarkably well in the Evaluation and ranking of food products (Uprit and Mishra, 2002). In Fuzzy theory, a subject can be represented by Fuzzy sets with a series of elements and their membership degrees compared to crisp sets without membership (Zimmermann, 1991). Shinde and Logic Model for Sensory Evaluation of Commercially Available Jam Samples Journal of Ready to Eat Food | April-June, 2014 | Vol 1 | Issue 2 | Pages 78-84 2014 Jakraya Publications (P) Ltd 79 Such Fuzzy sets provide the mathematical methods that can represent the uncertainty of human s expressions (Lazim and Suriami, 2009). Sensory Evaluation is the ultimate criterion for acceptance or rejection of food (Falade and Omojola, 2008) especially Commercially available foods like jam. Sensory quality of food can be evaluated from an estimation of total impression the food creates in the mind of person who consumes it (Reinoso et al.)

6 , 2008; Giusti et al., 2008). Attributes of jam that are evaluated by human senses are its colour, texture, flavour and appearance. The objective of this study was to conduct a Sensory analysis using Fuzzy Logic in order to analyze the acceptability of these jams. Materials and Methods The data available from subjective Evaluation of four Commercially available jam samples were analyzed by using Fuzzy Logic . The four market samples were coded as X1, X2, X3 and X4. A panel of five judges (as against 11 judges mentioned by Das, 2005) was selected for sample study purpose based on good health, interests in Sensory Evaluation , ability to concentrate and learn and familiarity with jam. The researchers have reported the involvement of more than 100 judges (Singh et al., 2012) for better utility of the Fuzzy Logic technique for Sensory Evaluation . Quality attributes selected for Sensory Evaluation were: color, flavor, texture and overall appearance.

7 Judges were familiarized with quality attributes of jam before the actual Sensory Evaluation . They were advised to take two short sniffs of samples before tasting them and give the score for flavor first in scorecard (Ranganna, 1987). They were also advised to rinse their mouth with water between testing the consecutive samples (Jaya and Das, 2003). Judges were instructed to give tick ( ) mark to appropriate respective Fuzzy scale factor for each of the quality attributes of the sample after evaluating the samples. The samples were rated as Not satisfactory , Fair , Medium , Good and Excellent . Judges were also instructed to give rank to quality attributes of jams in general, by giving tick ( ) mark to the respective scale factors, viz. Not at all important , Somewhat important , Important , Highly important and Extremely important . The set of observations were analyzed using Fuzzy analysis of Sensory scores.

8 This method has been successfully applied for mango drinks (Jaya and Das, 2003), dahi powder (Routray and Mishra, 2011), instant green tea powder (Sinija and Mishra, 2011) and bread prepared from millet-based composite flours (Singh et al., 2012). This method utilizes linguistic data obtained by Sensory Evaluation . Ranking of the jam samples was done by using triangular Fuzzy membership distribution function, which has been explained in detail by Das (2005). Sensory scores of the jam samples were obtained by using Fuzzy scores given by the judges, which were converted to triplets and used for estimation of similarity values used for ranking of samples. The major steps involved in the Fuzzy modeling of Sensory Evaluation were (1) calculation of overall Sensory scores of jam samples in the form of triplets; (2) estimation of membership function on standard Fuzzy scale; (3) computation of overall membership function on standard Fuzzy scale; (4) estimation of similarity values and ranking of the jam samples; and (5) quality attribute ranking of jam samples in general.

9 A program in Matlab (The MathWorksTM, McGarrity, 2008) was developed for the calculation of all the above mentioned steps (Das, 2005). Set of three numbers known as triplet is used to represent triangular membership function distribution pattern of Sensory scales and the distribution pattern of 5-point Sensory scales consists of Not satisfactory/Not at all important (0,0,25) , Fair/Somewhat important (25,25,25) , Medium/Important (50,25,25) , Good/Highly important (75,25,25) and Excellent/Extremely important (100,25,0) (Fig 1). The three numbers shown in the brackets with the 5-point Sensory scales are the triplets, where the first number of the triplet denotes the coordinate of the abscissa where the value of the membership function is 1 (Fig 1), and the second and third numbers of the triplet designate the distance to the left and right, respectively, of the first number, where the membership function is zero (Routray and Mishra, 2011).

10 The MATLAB program of Sensory Evaluation is given by Das (2005). Results and Discussion The Sensory scores as given by the panel have been shown in Table 1 and Table 2. The analysis of Sensory attributes of jam samples was conducted using Fuzzy Logic as adopted by Das (2005), Triplets associated with Sensory scale Triangular membership function distributions of Sensory scales were given by triplets , which were sets of three numbers. Fig 1 shows distribution pattern of five point Sensory scales, viz. Not satisfactory/Not at all important (0,0,25), Fair/Somewhat important(25,25,25), Medium/Important(50,25,25), Good/Highly important (75,25,25), and Excellent/Extremely important (100,25,0). Table 3 shows 'triplets' associated with five point Sensory scales. First number of the triplet denotes the value of abscissa at which the value of membership function is one. Second and third number of the triplet indicate the distance to left and right respectively of the first number where the membership function is zero.