AGRICULAGRICULAGRICULAGRICULAGRICULTURAL RESEARCH COMMUNICATURAL RESEARCH COMMUNICATURAL RESEARCH COMMUNICATURAL RESEARCH COMMUNICATURAL RESEARCH COMMUNICATION CENTRETION CENTRETION CENTRETION CENTRETION CENTRE

wwwwwwwwwwwwwww.ar.ar.ar.ar.arccjourccjourccjourccjourccjournals.com / indianjournals.com / indianjournals.com / indianjournals.com / indianjournals.com / indianjournals.comnals.comnals.comnals.comnals.comJ. of Dairying & Food Sci., 3030303030 (1) : 25 - 31, 2011

PSYPSYPSYPSYPSYCHORHEOLCHORHEOLCHORHEOLCHORHEOLCHORHEOLOGICAL STUDOGICAL STUDOGICAL STUDOGICAL STUDOGICAL STUDY ON VISCOSITY OF MILKY ON VISCOSITY OF MILKY ON VISCOSITY OF MILKY ON VISCOSITY OF MILKY ON VISCOSITY OF MILK

Amol S. VAmol S. VAmol S. VAmol S. VAmol S. Vyawaharyawaharyawaharyawaharyawahare, Pe, Pe, Pe, Pe, Pratik Nawale, Sushim Kumar*, Krushna Pratik Nawale, Sushim Kumar*, Krushna Pratik Nawale, Sushim Kumar*, Krushna Pratik Nawale, Sushim Kumar*, Krushna Pratik Nawale, Sushim Kumar*, Krushna Papinwarapinwarapinwarapinwarapinwar,,,,,PPPPPatel Dhinalkumar H. and K. Jayaraj Raoatel Dhinalkumar H. and K. Jayaraj Raoatel Dhinalkumar H. and K. Jayaraj Raoatel Dhinalkumar H. and K. Jayaraj Raoatel Dhinalkumar H. and K. Jayaraj Rao

Dairy Technology Section, National Dairy Research Institute (Southern Campus),Adugodi, Bangalore – 560 030, India.

Received : 24-08-2009 Accepted : 31-03-2010

ABSTRACTABSTRACTABSTRACTABSTRACTABSTRACTVVVVViscosity of miscosity of miscosity of miscosity of miscosity of milk with TS levels ranging from 4 – 40% was deterilk with TS levels ranging from 4 – 40% was deterilk with TS levels ranging from 4 – 40% was deterilk with TS levels ranging from 4 – 40% was deterilk with TS levels ranging from 4 – 40% was determined at 40mined at 40mined at 40mined at 40mined at 40oooooC by objectiveC by objectiveC by objectiveC by objectiveC by objective

method using a capillary viscometer and also evaluated by subjective method. Five equationsmethod using a capillary viscometer and also evaluated by subjective method. Five equationsmethod using a capillary viscometer and also evaluated by subjective method. Five equationsmethod using a capillary viscometer and also evaluated by subjective method. Five equationsmethod using a capillary viscometer and also evaluated by subjective method. Five equationsviz. linearviz. linearviz. linearviz. linearviz. linear-linear-linear-linear-linear-linear, linear, linear, linear, linear, linear-log, polynomial second order-log, polynomial second order-log, polynomial second order-log, polynomial second order-log, polynomial second order, polynomial third order and linear –, polynomial third order and linear –, polynomial third order and linear –, polynomial third order and linear –, polynomial third order and linear –exponential were fitted to the instrumental and subjective viscosities and the goodness of fit wasexponential were fitted to the instrumental and subjective viscosities and the goodness of fit wasexponential were fitted to the instrumental and subjective viscosities and the goodness of fit wasexponential were fitted to the instrumental and subjective viscosities and the goodness of fit wasexponential were fitted to the instrumental and subjective viscosities and the goodness of fit wasevaluated. The results indicated that instrumental viscosity and sensory viscosities betweenevaluated. The results indicated that instrumental viscosity and sensory viscosities betweenevaluated. The results indicated that instrumental viscosity and sensory viscosities betweenevaluated. The results indicated that instrumental viscosity and sensory viscosities betweenevaluated. The results indicated that instrumental viscosity and sensory viscosities betweenthemselves and with TS content of milk were related by polynomial second and third orders withthemselves and with TS content of milk were related by polynomial second and third orders withthemselves and with TS content of milk were related by polynomial second and third orders withthemselves and with TS content of milk were related by polynomial second and third orders withthemselves and with TS content of milk were related by polynomial second and third orders withcorrelation coefficient values ranging from 0.73 – 0.99. The study indicated that subjective testscorrelation coefficient values ranging from 0.73 – 0.99. The study indicated that subjective testscorrelation coefficient values ranging from 0.73 – 0.99. The study indicated that subjective testscorrelation coefficient values ranging from 0.73 – 0.99. The study indicated that subjective testscorrelation coefficient values ranging from 0.73 – 0.99. The study indicated that subjective testsalso have an important role to plaalso have an important role to plaalso have an important role to plaalso have an important role to plaalso have an important role to play in determining the quality of milk.y in determining the quality of milk.y in determining the quality of milk.y in determining the quality of milk.y in determining the quality of milk.

Key words :Key words :Key words :Key words :Key words : Psychorheology, Viscosity, Milk, Correlation, Subjective evaluation

INTRODUCTIONINTRODUCTIONINTRODUCTIONINTRODUCTIONINTRODUCTION Psychorheology is a branch of psychophysicsdealing with the sensory perception of rheologicalproperties of foods. It is also defined as therelationship between the consumer preferences andrheological properties of foods (Bourne, 2002).Psychorheology is known as a special area devotedto response of human sensory organs to variousrheological properties and also their acceptance. Itanalyses mathematical relationship betweensubjective and objective rheological or texturalcharacteristics of food products. In case of subjectiveanalysis, the signals generated at the nerve endingsof the senses are transmitted via the central nervoussystem to the brain where they are integrated withpast experience, expectations and other conceptualfactors before the opinion of the response issummarised (Amerine et al., 1965). Hence,psychorheological aspects play a vital role inconsumer acceptance and product development andmarketing studies. Several methods are in vogue forobjective measurement of viscosity of milk –

fundamental and empirical. Of these, the formermethods are followed by research and academicorganisations, and the latter methods are followedby the industry. Fundamental methods need to bedone under controlled conditions, whereas empiricalones are carried out under convenient conditions.Ultimately, the objective of all types of analysis is toevaluate the sensory acceptance of products. In thisregard, subjective methods determine the finalacceptance of products and objective methods onlyplay a supportive role. However, if one wants to studythe influence of treatments on the rheologicalparameters of a product irrespective of sensoryacceptance, then fundamental methods come inhandy. Subjective evaluations also help inovercoming certain disadvantages of objectivemethods – the main one being wide deviationscaused by the inherent variations in food products. Viscosity of milk-an important rheologicalcharacteristic- can be determined by variousobjective methods and is generally expressed ascentipoise or milli Pascal second (mPa.s). It can also

26 J. DAIRYING, FOODS & H.S.

be expressed as low, thin, thick, flowable etc. insensory evaluation procedures and quantified interms of scores. The mathematical correlations wouldhelp in predicting the objective values with the helpof subjective analysis results. Such correlation studieswere carried out in products like chocolate milk(Hough and Sánchez, 1998), cheese (Everard et al.,2006), stirred yoghurt (Thomas et al., 2006) andtartar sauce (2006), but few were reported in otherdairy products. It is a common practice for consumersto pronounce consistency of milk as thin (alludingto adulteration with water) or thick (referring to fatcontent). Many experienced consumers may alsotell the extent of adulteration of milk with water basedon the latter’s thinness or consistency. This is theresult of the sensory perception recorded in the mindabout the normal milk and any variation in theconsistency will immediately be discerned by mind.Determining mathematical correlations between theobjective and subjective data will help in predictingone value from the other.

Milk has a viscosity of 1.24 cP at 40oC(Kessler, 1981), and is highly dependent ontemperature of measurement and components(Webb et al., 1974). Fat is the major contributor forviscosity of milk, hence consumers often correlatethe ‘consistency’ of milk with the fat content. Quiteoften persons with intuitive and analytical mind willbe able to say the consistency of products in termsof either acceptance or actual rheological unitswithout the aid of any instrument. In the presentstudy, milk viscosity was determined objectively bya bulb pipette, which is a simple capillary type tubeviscometer, and the data were analysed visavis thesensory acceptance of milk’s consistency. Attemptswere made to see whether viscosity of milk can bequantified by human mind with a fair degree ofaccuracy.

MAMAMAMAMATERIALS AND METHODSTERIALS AND METHODSTERIALS AND METHODSTERIALS AND METHODSTERIALS AND METHODSPPPPPrrrrreparat ion of mi lk wi th di f fereparat ion of mi lk wi th di f fereparat ion of mi lk wi th di f fereparat ion of mi lk wi th di f fereparat ion of mi lk wi th di f ferent Tent Tent Tent Tent Tota lo ta lo ta lo ta lo ta lSolids Levels: Solids Levels: Solids Levels: Solids Levels: Solids Levels: Skim milk powder (380 g) (SagarBrand) procured from local market was thoroughlymixed with 500 ml warm distilled water and the

mixture filtered through muslin cloth. This milkcontained 40% TS and was termed as High TotalSolids Milk (HTSM). The HTSM was taken inbeakers in different aliquots and diluted with distilledwater progressively as shown in Table -1 to obtainmilk samples of increasing TS levels. The beakerswith different TS milks were kept in water bath at40oC for tempering and then used for determinationof viscosity by objective and subjective methods.

Determinat ion of v iscos i ty of mi lk byDeterminat ion of v iscos i ty of mi lk byDeterminat ion of v iscos i ty of mi lk byDeterminat ion of v iscos i ty of mi lk byDeterminat ion of v iscos i ty of mi lk byobjective method: objective method: objective method: objective method: objective method: The viscosity of milk wasdetermined using a 50 ml-bulb pipette adapted as acapillary viscometer, which worked based oncapillary principle (Bourne, 2002). The bottom andtop marks on the bulb of the pipette were marked,and distilled water was pipetted in up to the top markand the level held with a finger. The pipette was thenclamped to a stand at a fixed height. A beaker wasplaced beneath the pipette and the finger wasremoved allowing the water to flow, simultaneouslystop watch (Racer Brand, 0.1 sec accuracy) wasswitched on. The time (sec) taken for distilled waterto cross the bottom mark was recorded to the nearest0.1 sec. Like wise, the procedure was repeated 3-4times for all the milk samples. Average time takenwas recorded for each milk sample and convertedto viscosity by the following formula:

d2.t2

n2 = n1. ——— d1.t1

Where,d1: density of water (1 g / cc); d2: density of milksample; t1: time taken by water in sec; t2: time takenby milk sample in sec; n1: Relative viscosity of water(1); n2: Relative viscosity of milk sample (instrumentalviscosity).

Determinat ion of v iscos i ty and sensoryDeterminat ion of v iscos i ty and sensoryDeterminat ion of v iscos i ty and sensoryDeterminat ion of v iscos i ty and sensoryDeterminat ion of v iscos i ty and sensoryacceptance of milk by subjective method:acceptance of milk by subjective method:acceptance of milk by subjective method:acceptance of milk by subjective method:acceptance of milk by subjective method:The milk samples were evaluated by a panel of

judges. Ten judges were selected from the Institute

faculty and students based on their willingness,

ability, experience and scientific knowledge etc (ISI,

27Vol. 30, No. 1, 2011

1971). They were acquainted with milk of varyingconsistency levels by serving milk with differentdilutions and viscosities. The milk samples withdifferent TS levels (4 - 40%) were tempered to 40oCand served to judges for evaluation of viscosity aswell as acceptance. The sensory evaluation wascarried out in Laboratory under fluorescent lamps.The judges were then asked to examine the samplescarefully and record the following with reference towater: 1) viscosity of the milk samples in terms ofcentipoise (sensory viscosity) and 2) body andtexture acceptance score on a 9-point Hedonic scale(Amerine et al., 1965). For sensory viscosity, thejudges were asked to pour the milk sample from onebeaker to another and critically examine the flowbehaviour and consistency for estimating theviscosity. They were asked to quantify the viscosityin terms of centipoises (cP) with reference to that ofwater (one cP) as per perception of their mind.

DeteDeteDeteDeteDeterminat ion of corre lat ion amongrminat ion of corre lat ion amongrminat ion of corre lat ion amongrminat ion of corre lat ion amongrminat ion of corre lat ion amonginst rumental v iscos i tyins t rumental v iscos i tyins t rumental v iscos i tyins t rumental v iscos i tyins t rumental v iscos i ty, sensor, sensor, sensor, sensor, sensory v iscos i tyy v iscos i tyy v iscos i tyy v iscos i tyy v iscos i ty,,,,,sensory acceptance and TS%: sensory acceptance and TS%: sensory acceptance and TS%: sensory acceptance and TS%: sensory acceptance and TS%: The objectivedata i.e. viscosity values determined by the capillaryviscometer (termed as instrumental viscosity), andthe subjective data (termed as sensory viscosity) andsensory acceptance were tabulated and correlationanalysis was done between the following:Instrumental viscosity Vs Sensory viscosity,Instrumental viscosity Vs Sensory acceptance,Instrumental viscosity Vs Per cent TS and Sensoryviscosity Vs Per cent TS. The paired data were fittedto the following regression equations using MS Excelprogramme:

i) y = a + bx (linear – linear)ii) y = a + b. ln x (linear-log)iii) y = b0 + b1x + b2x

2

(polynomial 2nd order)iv) y = b0 + b1x + b2x

2 + b3x3

(polynomial 3rd order)v) y = a. ebx (linear – exponential)

Where,y = Instrumental / Sensory viscosityx = Sensory / Instrumental viscosity/ % TSa, b0 , b1, b2, b3 = Regression coefficients

The degree of relationship was measured bycoefficient of determination (R2) values, andgoodness of fit was evaluated by root mean square(RMS) values. The RMS was determined as follows(Sawhney et al., 1994):

Where,Wi = experimental observationsWi*= calculated parametersN= number of observations.

The lower the RMS value, the better was thegoodness of fit. All the mathematical calculationswere carried out by computing in MS-Excelprogramme of MS-Office 2007 version. An RMSvalue of up to eight was considered as a fairly goodfit (Sawhney et al., 1994).

RESULRESULRESULRESULRESULTS AND DISCUSSIONTS AND DISCUSSIONTS AND DISCUSSIONTS AND DISCUSSIONTS AND DISCUSSIONRelationship between instrumental viscosityRelationship between instrumental viscosityRelationship between instrumental viscosityRelationship between instrumental viscosityRelationship between instrumental viscosityand sensory viscosity: and sensory viscosity: and sensory viscosity: and sensory viscosity: and sensory viscosity: It is common practice inday to day life to judge the viscosity of liquids asthin, thick, flowable etc. and often relate the same totheir acceptance. It suggests that there is a scope forour sensory perception to be quantified as outputsequal to those of any instruments that measureviscosity. How accurately we can predict theviscosity sensorily can be gauged by correlating theinstrumental viscosity with sensory viscosity values.In Table-2, various relationships along withregression coefficients are listed. Coefficient ofdetermination values (R2) ranged from high (0.98)to low (0.76) and the RMS values ranged from 6.25to 23.65. Since RMS values up to eight areconsidered as good fit, polynomial second order, thirdorder and exponential relationships were found tobe good for describing the relationship betweeninstrumental and sensory viscosities. The best fitrelationship is illustrated in Fig.1 along withtrendline. This suggests that age-old practice of

28 J. DAIRYING, FOODS & H.S.

determining the quality of milk based on ‘thickness’ofmilk is not without basis. It means, to a certain extent,we can rely on our senses to gauge the viscosity ofmilk.

Relationship between instrumental viscosityRelationship between instrumental viscosityRelationship between instrumental viscosityRelationship between instrumental viscosityRelationship between instrumental viscosityand sensory acceptance: and sensory acceptance: and sensory acceptance: and sensory acceptance: and sensory acceptance: Correlation study wasdone to see whether we can judge acceptance ofmilk by viscosity determined by instrument. All therelationships studied showed medium or lowcorrelations, indicating that it is a complexphenomenon. As the viscosity increased, theacceptance increased as indicated by increasingscores, but after a certain viscosity, the scoresdecreased, forming a skewed bell shaped curve(Fig.2). Bourne (2002) stated that the correlationsbetween acceptance and objective parametersnormally follow Wundt Curve, while that betweenintensity scale and objective analysis may followlinear or any of the four patterns reported. The curveindicated that viscosities between 1.11 and 1.24 cPare acceptable to judges as normal milk. Above theseviscosities, milk was perceived as thick and wasconsidered as not normal milk, so scored low inacceptability. Kessler (1981) also reported a viscosityvalue of 1.24 cP for milk at 40oC. Among the fiverelationships studied, the RMS values ranged from13.51 – 18.98, the least being in the case ofpolynomial third order relationship (Table-2).Possibly, other models with multiple regression wouldhave fitted better, but those were not tried in thisstudy, because the objective of the study was toevaluate simple regression procedure rather thancomplex multiple regression analyses.

Viscosity is a measure of the extent to whicha fluid resists flow (Walstra, 2003) and the inherentviscosity is the cumulative and combinedcontribution of various ingredients. Of these, fat andproteins play a major role in determining the viscosity.In case of milk, fat content controls the viscous natureand thereby profoundly influences consumerpreference. At low viscosity levels, judges felt thatthe milk was watery, but acceptance was maximumonly when the viscosity reached normal values.

TTTTTable 1:able 1:able 1:able 1:able 1: Milk samples used in the study

Beaker No. TS% High Total Distilled water, g

Solids Milk (HTSM), g

1 4 10 90

2 8 20 80

3 12 30 70

4 16 40 60

5 20 50 50

6 24 60 40

7 28 70 30

8 32 80 20

9 36 90 10

10 40 100 0

y = ‐0.0134x3 + 0.2038x2 ‐ 0.571x + 1.4911

R2 = 0.9782

0

0.5

1

1.5

2

2.5

3

3.5

0 1 2 3 4 5 6 7 8

Sensory   viscosity, cP

Instrumental viscosity, cP

FFFFFig. 1 : ig. 1 : ig. 1 : ig. 1 : ig. 1 : Instrumental Viscoisty Vs Sensory viscoistywith trendline.

y = 1.2465x3 ‐ 8.7376x2 + 17.015x ‐ 3.2296

R2 = 0.7319

2

3

4

5

6

7

8

9

0.50 1.00 1.50 2.00 2.50 3.00

Instrumental viscosity, cP

Sensory acceptability score

Fig. 2 :Fig. 2 :Fig. 2 :Fig. 2 :Fig. 2 : Instrumental viscoisty Vs Sensory acceptabilityscore with trendline.

Sensory methods may appear to lack the precisionthat is desirable in scientific research because of thevariability from person to person, variability fromhour to hour and day to day in likes and dislikes ofeach person. In spite of these obstacles, sensory

29Vol. 30, No. 1, 2011

measurement of texture is very importantaspect of food quality that cannot be ignored(Bourne, 2002).

R e l a t i o n s h i p b e t w e e n i n s t r u m e n t a lR e l a t i o n s h i p b e t w e e n i n s t r u m e n t a lR e l a t i o n s h i p b e t w e e n i n s t r u m e n t a lR e l a t i o n s h i p b e t w e e n i n s t r u m e n t a lR e l a t i o n s h i p b e t w e e n i n s t r u m e n t a lviscosity and TS content: viscosity and TS content: viscosity and TS content: viscosity and TS content: viscosity and TS content: Viscosity of milkis highly dependent on its fat and proteincontents (Webb et al., 1974). The viscosity canalso be correlated with total s l id content,because fa t and pro te in a re the majoringredients of total solids. Relationship betweenfat content and viscosity of milk was reportedby Van Vliet and Walstra (1979). In the presentstudy, polynomial second order showed a fairre lat ionship between the TS content andins t rumenta l v i scos i ty. The var iousrelationships along with equation constants areshown in Table-3. The best fit relationshipamong those studied is shown in Fig.3 with anR2 of 0.96 and RMS value of 11.12. It wasobserved that the viscosity of milk increasedas TS increased, but not linearly. This couldpossibly be attributed to interaction effectsamong milk constituents.

Re la t i onsh ip be tween senso ry v i s cos i t yRe la t i onsh ip be tween senso ry v i s cos i t yRe la t i onsh ip be tween senso ry v i s cos i t yRe la t i onsh ip be tween senso ry v i s cos i t yRe la t i onsh ip be tween senso ry v i s cos i t yand TS content : and TS content : and TS content : and TS content : and TS content : The relationships studiedare shown in Table-3 along with R2 and RMSvalues. It can be discerned that polynomialth i rd order equat ion bes t desc r ibed therelationship between sensory viscosity and TS

content (R2: 0.99, RMS: 3.88), followed bysecond order relationship (R2: 0.99, RMS:7.96). The almost coinciding trendline in Fig.4demonstrates that the TS content can bepredicted with a good accuracy by sensoryviscosi ty. In other words, by exper ience,

TTTTTable 2able 2able 2able 2able 2: Mathematical relationships bewteen instrumental viscoisty and sensory viscosity / sensory acceptability score of

milk, their regression coefficients, coefficients of determination (R2) and root mean square (RMS) values

Type of relationship Instrumental viscosity (y) Vs sensory viscosity (x) Sensory acceptability score (y) Vs Instrumental

viscosity (x)

Equation R2 RMS Equation R2 RMS

linear-linear y= 0.3232x + 0.4735 0.92 13.84 Y = -1.930x + 8.979 0.67 16.76

linear-log y = 0.9533ln(x) + 0.5826 0.76 23.65 Y = -3.3398ln(x) + 7.2132 0.62 18.98

polynomial 2nd order y = 0.0398x2 – 0.0006x 0.97 6.57 Y = -1.322x2 + 3.180x 0.72 14.22

+ 0.9623 + 4.760

polynomial 3rd order y = -0.0134x3 + 0.2038x2 – 0.98 6.25 Y = 1.246x3 – 8.737x2 0.73 13.51

0.571x + 1.4911 + 17.01x - 3.229

linear- exponential y=0.7881e0.182x 0.95 8.22 Y = 10.55e-0.39x 0.73 17.76

y = 4E‐05x3 + 0.0008x2 + 0.0769x + 0.7167

R2 = 0.9972

0

1

2

3

4

5

6

7

8

0 10 20 30 40

% TS

Sensory  Viscosity,cP

Fig.4 :Fig.4 :Fig.4 :Fig.4 :Fig.4 : Sensory viscosity Vs per cent TS with trendline.

y = 0.0022x2 ‐ 0.046x + 1.2584

R2 = 0.9643

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

0 10 20 30 40

% TS

Instrumental Viscosity,cP

Fig. 3: Fig. 3: Fig. 3: Fig. 3: Fig. 3: Instrumental viscosity Vs per cent TS withtrendline.

30 J. DAIRYING, FOODS & H.S.

persons would be able to tell the TS content ofmilk based on perception of mind. This furthercorroborates the ability of certain consumersto pass judgement on the quality of milk basedon its consistency. The viscosity of a liquid orsemisolid food is normally evaluated sensorilyby watching the rate of flow as it pours from acontainer or flows across the food or the plate(Shama and Sherman, 1973). Detai ls onstudies on sensory testing of textural parameterswere recorded (Szczesniak et al., 1962) andreviewed by Szczesniak (1986).

In spite of development of sophisticatedinstruments for measuring viscosity, sensorymethods are the ultimate methods used forcalibrating instruments of texture measurement,because sensory eva lua t ion i s the mos timportant criterion for product development

TTTTTable 3able 3able 3able 3able 3: Mathematical relationships bewteen instrumental /sensory viscosity and per cent TS of milk, their regression

coefficients, coefficients of determination (R2) and root mean square (RMS) values

Type of relationship Instrumental viscosity (y) Vs per cent TS (x) Per cent TS (y) Vs Sensory viscosity (x)

Equation R2 RMS Equation R2 RMS

linear-linear y = 0.053x + 0.466 0.81 21.0 Y = 5.6142x + 1.873 0.96 31.1

linear-log y = 0.732ln(x) - 0.488 0.57 29.72 Y = 18.535ln(x) + 1.5981 0.99 13.24

polynomial 2nd order y = 0.002x2 - 0.046x + 1.258 0.96 11.12 Y = - 0.5868x2 + 10.39x – 5.3384 0.995 7.96

polynomial 3rd order y = 1E-05x3 + 0.001x2 - 0.029x 0.96 17.0 Y = 0.0939x3 - 1.7344x2 + 14.382x 0.997 3.88

+ 1.182 - 9.0385

linear- exponential y = 0.775e0.030x 0.87 13.75 Y= 6.0412e0.3062x 0.78 40.54

(Bourne, 2002) . S imple ins t ruments a reindustry friendly and very useful because oftheir empir ical nature. The present studyreports good correlation between the viscosityvalues measured by simple instrument such asbulb pipette and sensory evaluation.

C O N C LC O N C LC O N C LC O N C LC O N C LU S I O NU S I O NU S I O NU S I O NU S I O NThe relationship between instrumental

viscosity and sensory viscosity as well as betweenthese parameters and TS content of milk could bewell described by second and third order polynomialequations (R2= 0.99) as compared to otherequations (R2 = 0.78-0.96). Also accuracy in termsof lower RMS value was found to be higher in secondand third order polynomial equations. Hence, it wasconcluded that like objective tests, subjective testsalso have an important role to play in determiningthe quality of milk.

REFERENCESREFERENCESREFERENCESREFERENCESREFERENCESAmerine, M.A. et al. (1965). Principles of Sensory Evaluation of Foods. Academic Press, New York, USA.Bourne, M.C. (2002). Food texture and Viscosity : Concept and Measurement. 2nd Edn., Academic Press, New York,

USA, p. 23, 238-296.Everard, C.D. et al..... (2006). J. Text. Stud., 3737373737: 361-382.Hough, G. and Sánchez, R. (1998). Food Quality and Preferences, 99999:197-204.ISI (1971). Guide for Sensory Evaluation of Foods . Part I: Optimum Requirements . IS : 6273, Bureau of Indian

Standards, Manak Bhavan, Bahadur Shah Jafar Marg, New Delhi – 1, pp. 5 – 6.Kessler, H. (1981). Food Engineering and Dairy Technology. Verlag A, Friesing, Germany.Sawhney, I.K. et al. (1994). J. Food Sci. Technol., 3131313131: 252-254.Shama, F. and Sherman, P. (1973). J. Text. Stud., 44444: 111 – 118.Szczesniak, A.S. (1986). J. Text. Stud., 1818181818: 1-15.Szczesniak, A.S. et al. (1962). J. Food Sci., 2828282828: 397-403.

31Vol. 30, No. 1, 2011

Tern, P. et al. (2006). J. Text. Stud., 37 37 37 37 37: 580-596.Thomas, J. et al. (2006). J. Text. Stud., 37 37 37 37 37: 276-299.Van Vliet, T. and Walstra, P. (1979). J. Text. Stud.,,,,, 11 11 11 11 11:65-68.Walstra, P. (2003). Physical Chemistry of Foods. Marcel Dekker Inc., NewYork, USA, p. 91.Webb, B.H. et al. (1974). Fundamentals of Dairy Chemistry. The AVI Publishing Co., Westport, Connecticut,

USA, p. 417.

Copyright of Journal of Dairying Foods & Home Sciences is the property of Agricultural Research

Communication Centre and its content may not be copied or emailed to multiple sites or posted to a listserv

without the copyright holder's express written permission. However, users may print, download, or email

articles for individual use.