Application of Fuzzy Logic in Sensory Evaluation of Food Products: a Comprehensive Study

Sensory evaluation plays a vital role in the assessment of acceptance of novel food products and preferences for different cuisines. This process provides significant and valuable information to the food-processing industries and food scientists regarding the sensory quality of food products. Traditional techniques generally employed for the sensory evaluation assess only in a qualitative sense and cannot perform a precise quantitative assessment. However, recently, novel techniques such as fuzzy set theory have been effectively used in assessing the sensory characteristics of various traditional as well as novel food products developed through fortification and modified processing techniques. The aim of the fuzzy set theory is to treat ambiguous phenomena mathematically and express the degree of incomprehensibility in human thinking along with connecting it to a real number. Furthermore, fuzzy logic mimics human behavior for reasoning and decision-making. In fuzzy modeling, linguistic entities such as “not satisfactory, fair, medium, good and excellent” are employed for describing the sensory attributes of food products (including color, aroma, taste, texture, and mouthfeel) obtained through subjective evaluation, which are combined with the accurate and precise data attained through objective evaluation to draw conclusions regarding acceptance, rejection, and ranking, along with strong and weak characteristics of the food under study. This analysis also assists in finding the preference of quality attributes and sets criteria for acceptance or rejection of the newly developed foods. This review provides an overview of the application of fuzzy concepts to the sensory evaluation of traditional and novel food products (often enriched with nutraceuticals) in the food industry, along with the corresponding advantages.

[1]  Flores Mónica Sensory Descriptors for Dry-Cured Meat Products , 2008 .

[2]  Nitin Kumar,et al.  Fuzzy Analysis of Sensory Attributes of Gluten Free Pasta Prepared From Brown Rice, Amaranth, Flaxseed Flours and Whey Protein Concentrates , 2019, Journal of Food Science and Nutrition Research.

[3]  I. Burhan Turksen,et al.  Consumer preference models: fuzzy theory approach , 1993, Other Conferences.

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

[5]  R. Pradhan,et al.  Physico-chemical and sensory analysis of Kendu (Diospyros melaxoxylon Roxb.) jam using fuzzy logic , 2017, Journal of Food Measurement and Characterization.

[6]  M. Tripathi,et al.  Sensory Quality Evaluation of MA Packaged Fruits Applying Fuzzy Logic , 2013 .

[7]  Holger Voos,et al.  Fuzzy control of a drying process in sugar industry , 1998 .

[9]  Conor M. Delahunty,et al.  Descriptive sensory analysis: past, present and future , 2001 .

[10]  Keith C. C. Chan,et al.  A new fuzzy approach to improve fashion product development , 2006, Comput. Ind..

[11]  Marco Pintore,et al.  SENSORY ANALYSIS OF RED WINES: DISCRIMINATION BY ADAPTIVE FUZZY PARTITION , 2008 .

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

[13]  Ioannis Paraskevopoulos,et al.  Fuzzy logic tool for wine quality classification , 2017 .

[14]  C. K. Sahu,et al.  Sensory evaluation of kokum drinks by fuzzy logic and a simple method , 2017 .

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

[16]  Herbert L. Meiselman,et al.  Critical evaluation of sensory techniques , 1993 .

[17]  I. L. Pardeshi,et al.  Fuzzy Logic Model for Sensory Evaluation of Commercially Available Jam Samples , 2014 .

[18]  Thomas Becker,et al.  Fuzzy logic control and soft sensing applications in food and beverage processes , 2013 .

[19]  V. Davidson,et al.  A LINGUISTIC METHOD FOR SENSORY ASSESSMENT , 1998 .

[20]  Constantina Tzia,et al.  Extraction optimization in food engineering , 2003 .

[21]  R. Jager,et al.  Fuzzy Logic in Control , 1995 .

[22]  Gilles Trystram,et al.  Back-propagation of imprecision in a cheese ripening fuzzy model based on human sensory evaluations , 2006, Fuzzy Sets Syst..

[23]  Zahra Sadat Zolfaghari,et al.  Application of fuzzy linear regression method for sensory evaluation of fried donut , 2014, Appl. Soft Comput..

[24]  Ritu Tiwari,et al.  Fuzzy Inference Systems , 2010 .

[25]  Sensory Profiling of Dulce de Leche, a Dairy Based Confectionary Product , 1992 .

[26]  A. Rai,et al.  Sensory analysis of bar samples prepared from mahua ( Madhuca longifolia ) flower syrup using fuzzy logic , 2019 .

[27]  R. Farahmandfar,et al.  Comparison of adaptive neuro-fuzzy inference system and artificial neural networks (MLP and RBF) for estimation of oxidation parameters of soybean oil added with curcumin , 2015, Journal of Food Measurement and Characterization.

[28]  Evaluation of Food Quality , 2008 .

[29]  Ali Vahidian Kamyad,et al.  Application of fuzzy logic to classify raw milk based on qualitative properties , 2012 .

[30]  Zou Xiaobo,et al.  Discrimination of honeys using colorimetric sensor arrays, sensory analysis and gas chromatography techniques. , 2016, Food chemistry.

[31]  Gilles Trystram,et al.  Dry sausage ripening control integration of sensory-related properties , 2002 .

[32]  B. Rolls Sensory evaluation practices, Herbert Stone, Joel L. Sidel. Academic Press, Orlando, FL (1985), 311, $39·50 , 1986 .

[33]  H. Mishra,et al.  Response Surface Optimization of Process Parameters and Fuzzy Analysis of Sensory Data of High Pressure-Temperature Treated Pineapple Puree. , 2015, Journal of food science.

[34]  S. Hussain,et al.  Soluble fiber-fortified sponge cakes: formulation, quality and sensory evaluation , 2017, Journal of Food Measurement and Characterization.

[35]  H. Bolini,et al.  Passion fruit juice with different sweeteners: sensory profile by descriptive analysis and acceptance , 2015, Food science & nutrition.

[36]  Rachna Rana,et al.  Application of fuzzy logic technique for sensory evaluation of high pressure processed mango pulp and litchi juice and its comparison to thermal treatment , 2015 .

[37]  Rania Hodhod,et al.  AI Cupper: A Fuzzy Expert System for Sensorial Evaluation of Coffee Bean Attributes to Derive Quality Scoring , 2018, IEEE Transactions on Fuzzy Systems.

[38]  Suradeep Basak The use of fuzzy logic to determine the concentration of betel leaf essential oil and its potency as a juice preservative. , 2018, Food chemistry.

[39]  Brigitte Charnomordic,et al.  Fuzzy Inference Systems to Model Sensory Evaluation , 2004 .

[40]  X. Gao,et al.  APPLICATION OF FUZZY SETS AND NEURAL NETWORKS IN SENSORY ANALYSIS , 1999 .

[41]  S. Alavi,et al.  Novel Fortified Blended Foods: Preference Testing with Infants and Young Children in Tanzania and Descriptive Sensory Analysis. , 2018, Journal of food science.

[42]  Paula Varela,et al.  Exploring consumer product profiling techniques and their linkage to a quantitative descriptive analysis. , 2010 .

[43]  S. Hutchings,et al.  Novel techniques to understand consumer responses towards food products: A review with a focus on meat. , 2018, Meat science.

[44]  D. Hout,et al.  Comparative categorization method: Using 2-AFC strategy in constant-reference duo-trio for discrimination of multiple stimuli from a reference , 2017 .

[45]  Quality assessment of fried potato wedges by fuzzy logic and texture analyses , 2015 .

[46]  Gilles Trystram,et al.  Fuzzy concepts applied to food product quality control: A review , 2006, Fuzzy Sets Syst..

[47]  C. R. Stampanoni,et al.  The Use of Standardized Flavor Languages and Quantitative Flavor Profiling Technique for Flavored Dairy Products , 1994 .

[48]  Sigfredo Fuentes,et al.  Integration of non-invasive biometrics with sensory analysis techniques to assess acceptability of beer by consumers , 2019, Physiology & Behavior.

[49]  U. Enneking,et al.  How important intrinsic and extrinsic product attributes affect purchase decision , 2007 .

[50]  Hui Liu,et al.  Sensory and physical quality characteristics of bread fortified with apple pomace using fuzzy mathematical model , 2017 .

[51]  Luis Martínez-López,et al.  Sensory evaluation based on linguistic decision analysis , 2007, Int. J. Approx. Reason..

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

[53]  Snehasis Chakraborty,et al.  Bread from wheat flour partially replaced by fermented chickpea flour: Optimizing the formulation and fuzzy analysis of sensory data , 2018 .

[54]  Harry T. Lawless,et al.  Sensory Evaluation of Food: Principles and Practices , 1998 .

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

[56]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[57]  M. J. Beriain,et al.  Relationship between biochemical and sensory quality characteristics of different commercial brands of salchichon , 2000 .

[58]  H M A Bolini,et al.  Survival analysis: A consumer-friendly method to estimate the optimum sucrose level in probiotic petit suisse. , 2015, Journal of dairy science.

[59]  Jérôme Pagès,et al.  Comparison of three sensory methods for use with the Napping® procedure: Case of ten wines from Loire valley , 2008 .

[60]  Seung Ju Lee,et al.  Investigation of sensory attributes contributing to beer preference among Koreans by using fuzzy reasoning , 2017 .

[61]  Macarena Espinilla,et al.  Fuzzy Linguistic Olive Oil Sensory Evaluation Model based on Unbalanced Linguistic Scales , 2014, J. Multiple Valued Log. Soft Comput..

[62]  Da Ruan Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms , 1997 .

[63]  N. Alavi,et al.  Date grading using rule-based fuzzy inference system , 2012 .

[64]  Patareeya Lasunon,et al.  Fuzzy analytical modeling for sensory evaluation of water meal (Wolffia arrhiza (L.) Wimm.) - Rice cracker , 2016 .

[65]  Susana Fiszman Quality of Battered or Breaded Fried Products , 2008 .

[66]  Fakhreddin Salehi,et al.  Sensory Acceptability Modeling of Pistachio Green Hull’s Marmalade using Fuzzy Approach , 2011 .

[67]  Rosires Deliza,et al.  THE CONSUMER SENSORY PERCEPTION OF PASSION-FRUIT JUICE USING FREE-CHOICE PROFILING , 2005 .

[68]  Brigitte Charnomordic,et al.  Knowledge discovery for control purposes in food industry databases , 2001, Fuzzy Sets Syst..

[69]  Hari Niwas Mishra,et al.  Fuzzy logic (similarity analysis) approach for sensory evaluation of chhana podo , 2013 .

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

[71]  Gilles Trystram,et al.  Development of a control system using the fuzzy set theory applied to a browning process––a fuzzy symbolic approach for the measurement of product browning: development of a diagnosis model––part I , 2004 .

[72]  Mohammad Saber Iraji,et al.  Comparison between soft computing methods for tomato quality grading using machine vision , 2018, Journal of Food Measurement and Characterization.

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

[74]  Satyawati Sharma,et al.  Technological, nutritional, functional and sensorial attributes of the cookies fortified with Calocybe indica mushroom , 2019, Journal of Food Measurement and Characterization.

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

[76]  G. Pastore,et al.  Effect of galactooligosaccharide addition on the physical, optical, and sensory acceptance of vanilla ice cream. , 2015, Journal of dairy science.

[77]  Frank Hsu,et al.  Knowledge Discovery , 2014, Encyclopedia of Social Network Analysis and Mining.

[78]  Valerie J. Davidson,et al.  Fuzzy models to predict consumer ratings for biscuits based on digital image features , 2001, IEEE Trans. Fuzzy Syst..

[79]  Min-A Kim,et al.  Affective discrimination methodology: Determination and use of a consumer-relevant sensory difference for food quality maintenance , 2015 .

[80]  Morten Meilgaard,et al.  Sensory Evaluation Techniques , 2020 .

[81]  J. Brimblecombe,et al.  Consumer acceptance of reformulated food products: A systematic review and meta-analysis of salt-reduced foods , 2017, Critical reviews in food science and nutrition.

[82]  S. Kupongsak,et al.  SET POINT DETERMINATION FROM SENSORY EVALUATIONS FOR FOOD PROCESS CONTROL , 2004 .

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

[84]  Xianyi Zeng,et al.  Intelligent Sensory Evaluation: Methodologies and Applications , 2004 .

[85]  M. Hautus,et al.  Sensory discrimination by consumers of multiple stimuli from a reference: Stimulus configuration in A-Not AR and constant-ref. duo-trio superior to triangle and unspecified tetrad? , 2016 .

[86]  Didier Dubois,et al.  Readings in Fuzzy Sets for Intelligent Systems , 1993 .

[87]  John R. Piggott,et al.  SENSORY ASPECTS OF MATURATION OF CHEDDAR CHEESE BY DESCRIPTIVE ANALYSIS , 1991 .

[88]  Physico-chemical characterization and fuzzy logic modeling of sensory evaluation for market Ghewar , 2017 .

[89]  Gilles Trystram,et al.  The Fuzzy Symbolic Approach for the Control of Sensory Properties in Food Processes , 2004 .

[90]  Seung Ju Lee,et al.  Study on fuzzy reasoning application for sensory evaluation of sausages , 2007 .

[91]  A. Jha,et al.  Fuzzy Analysis of Sensory Data for Ranking of Beetroot Candy , 2016 .

[92]  M. Niakousari,et al.  Sensory evaluation of selected formulated milk barberry drinks using the fuzzy approach , 2017, Food science & nutrition.

[93]  Rebecca Ford,et al.  Deciding Which Test to Use in Discrimination Testing , 2017 .

[94]  H. Zimmermann,et al.  Latent connectives in human decision making , 1980 .

[95]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[96]  Vivek Kumar,et al.  Optimization of process parameters for the production of taro chips using RSM with fuzzy modeling , 2015, Journal of Food Measurement and Characterization.

[97]  R. Banerjee,et al.  Sensory Preference Modeling of Probiotic Shrikhand Employing Soft Computing , 2016, Agricultural Research.

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

[99]  E. Hunter,et al.  SENSORY PROPERTIES OF FERMENTED MILKS: OBJECTIVE REDUCTION OF AN EXTENSIVE SENSORY VOCABULARY , 1993 .

[100]  Georges Corrieu,et al.  Decision support system design using the operator skill to control cheese ripening––application of the fuzzy symbolic approach , 2004 .