Fuzzy logic (similarity analysis) approach for sensory evaluation of chhana podo

Abstract Chhana podo is a baked traditional dairy product of India. The present study was undertaken with the following objectives: (1) to find acceptable levels of ingredients on a dry mass basis to constitute the chhana podo feed-mix and (2) to conduct a sensory evaluation study of chhana podo samples. In addition to chhana and sugar, different additional ingredients were tried in various proportions, namely, cornflour, refined wheat flour, raw semolina and roasted semolina. Acceptable levels of roasted semolina and sugar in the feed-mix was found to be 0.1 kg (db) and 0.5 kg (db) respectively per kg of chhana (db). Five samples (two market samples, two samples produced at other conditions and one produced at optimum conditions, wherein constrained optimization was done using genetic algorithm) were evaluated and results were analyzed using fuzzy logic (similarity analysis). Analysis of samples using fuzzy logic showed that product produced at optimum conditions as obtained from constrained optimization using genetic algorithm was indeed better than other samples. Importance of quality attributes for chhana podo in general was (in decreasing order): taste, color, aroma and mouthfeel. For the optimized product, the most important quality attribute was taste, followed by mouthfeel, color and aroma.

[1]  S. Jaya,et al.  SENSORY EVALUATION OF MANGO DRINKS USING FUZZY LOGIC , 2003 .

[2]  P. Molnár A model for overall description of food quality , 1995 .

[3]  S. De,et al.  Outlines of Dairy Technology , 2001 .

[4]  Luis Martínez,et al.  Sensory evaluation based on linguistic decision analysis , 2007 .

[5]  Shrilekha Das,et al.  Aggregation of sensory data using fuzzy logic for sensory quality evaluation of food , 2013, Journal of Food Science and Technology.

[6]  H. Burton Ultra-High-Temperature Processing of Milk and Milk Products , 1989 .

[7]  Hari Niwas Mishra,et al.  Fuzzy analysis of sensory attributes of bread prepared from millet-based composite flours , 2012 .

[8]  M. A. Lazim,et al.  Sensory Evaluation of the Selected Coffee Products Using Fuzzy Approach , 2009 .

[9]  Sarah E. Kemp,et al.  Sensory evaluation: a practical handbook. , 2009 .

[10]  Herbert Stone,et al.  Sensory Evaluation Practices , 1985 .

[11]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[12]  Shyi-Mig Chen,et al.  A new approach to handling fuzzy decision-making problems , 1988, [1988] Proceedings. The Eighteenth International Symposium on Multiple-Valued Logic.

[13]  Hari Niwas Mishra,et al.  SENSORY EVALUATION OF DIFFERENT DRINKS FORMULATED FROM DAHI (INDIAN YOGURT) POWDER USING FUZZY LOGIC , 2012 .

[14]  H. Mishra,et al.  Fuzzy Analysis of Sensory Data for Quality Evaluation and Ranking of Instant Green Tea Powder and Granules , 2011 .

[15]  K. Parameswara Rao,et al.  Fuzzy logic based optimization of ingredients for production of mango bar and its properties , 2003 .

[16]  Ji Hye Lee,et al.  Sensory quality index (SQI) for commercial food products , 2011 .

[17]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[18]  Qin Zhang,et al.  Applying fuzzy mathematics to products development and comparison , 1991 .

[19]  Hari Niwas Mishra,et al.  FUZZY MULTIATTRIBUTE DECISION MAKING APPROACH FOR DEVELOPMENT AND COMPARISON OF SOY FORTIFIED PANEER , 2002 .

[20]  F. M. E. Emerald,et al.  Moisture sorption characteristics of chhana podo at 5 °C and 35 °C , 2006 .

[21]  Debjani Chakraborty,et al.  Structural quantization of vagueness in linguistic expert opinions in an evaluation programme , 2001, Fuzzy Sets Syst..

[22]  Robert P. W. Duin,et al.  FUZZY SET THEORY APPLIED TO PRODUCT CLASSIFICATION BY A SENSORY PANEL , 1989 .

[23]  Xianyi Zeng,et al.  Intelligent sensory evaluation: Concepts, implementations, and applications , 2008, Math. Comput. Simul..