influence of fat on the perceived texture of set acid milk gels: a sensory perspective

Influence of fat on the perceived texture of set acid milk gels:

a sensory perspective

Rogerio Pereiraa,1, Lara Matia-Merinoa, Veronika Jonesb, Harjinder Singha,*

aRiddet Centre, Massey University, Private Bag 11 222, Palmerston North, New ZealandbFonterra Research Centre, Private Bag 11 029, Dairy Farm Rd, Palmerston North, New Zealand

Abstract

Correlation of sensory perception of texture with instrumental parameters is key to understanding how specific structure formation can

influence the consumer acceptability of a food product. Experimental acid milk gels with different solids-non-fat content (10–20%, w/v),

with and without added fat (0–4% fat, w/v), were manufactured and characterised using quantitative descriptive analysis, confocal

microscopy and small/large deformation rheology. Confocal micrographs of the gels showed that the gel structures were in agreement with

the perceived sensory textural differences, in that the addition of fat, up to 4% (w/v), caused major changes in the microstructure of the

network and in the overall perception of texture. This was observed mainly at low total solids levels (10–14%); no significant changes in

microstructure or sensory perception of texture were detected at high total solids levels (above 18%), regardless of fat addition. The main

effects of increasing fat content in the gels were a decrease in the mean pore size and an increase in the average cluster size. Added fat also

caused the gels to become firmer, more resistant to penetration, more cohesive and sticky, creamier and less compressible before fracture

(less ‘give’). Both instrumental and quantitative microstructural image analysis results correlated with perceived texture, and, when used in

combination, these data sets generated an estimated model with satisfactory predictive ability for textural parameters as assessed by a trained

panel (pred r2Z96.3%).

q 2005 Elsevier Ltd. All rights reserved.

Keywords: Acid milk gels; Sensory; Texture; Correlation

1. Introduction

Understanding the relationship between the microstruc-

ture of food gels and their sensory quality is important for

producing foods with the properties that consumers look for

in a food choice situation. For milk-based gels, textural

attributes can be as important as, or sometimes even more

important than, flavour in determining a consumer’s

acceptability of the product (Bourne, 2002). Therefore,

characterising the texture is a crucial first step to defining

how uniform in quality, and thus how successful, a specific

product will be in comparison with similar products from

which consumers can choose.

0268-005X/$ - see front matter q 2005 Elsevier Ltd. All rights reserved.

doi:10.1016/j.foodhyd.2005.01.009

* Corresponding author. Tel.: C64 6 350 4401; fax: C64 6 350 5655.

E-mail address: [email protected] (H. Singh).1 Present Address: HortResearch, 120 Mt Albert Rd, Private Bag 92 169,

Mt Albert, Auckland, New Zealand.

Fundamentally, texture is a sensory attribute that we

try to simulate and understand in the food laboratory

using mostly physical methods. The texture of an acid

milk gel (such as yoghurt), in particular, is created from

the manner in which the constituent particles interact to

form a continuous colloidal network or microstructure,

with variations in dimensions and shapes of droplets,

protein strands and pores (Langton, Astrom, & Hermans-

son, 1997). The way in which this continuous network is

perceived by the human senses will, ultimately, define the

texture.

Most of the published work regarding correlations

between sensory perception and instrumental parameters

has been based on rheological measurements, many of

which vary considerably with the instrument used and/or the

test principles and conditions (Bourne, 2002; Irigoyen,

Castiella, Ordonez, Torre, & Ibanez, 2002; Meullenet,

Lyon, Carpenter, & Lyon, 1998; Pereira, Singh, Munro, &

Luckman, 2003; Ronnegard & Dejmek, 1993). Instrumental

evaluation of texture has been reported to have advantages

over sensory assessment, such as better reproducibility of

Food Hydrocolloids 20 (2006) 305–313

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http://www.elsevier.com/locate/foodhyd

R. Pereira et al. / Food Hydrocolloids 20 (2006) 305–313306

results, speed of result generation and lower running costs,

and has been recommended as a routine procedure when

good correlation with sensory perception of texture can be

demonstrated (Bourne, 2002; Skriver, Holstborg, & Qvist,

1999).

The quality of the correlation between the sensory

perception of texture and rheological measurements has

been investigated (Bourne, 1983; Drake, Gerard, Truong, &

Daubert, 1999; Hough et al., 1996; Pereira, Bennett, Hemar,

& Campanella, 2001), but very few studies have attempted

to establish correlations between the sensory perception of

texture and structural data from image analysis of

micrographs (Hullberg & Ballerini, 2003; Langton et al.,

1997; Thybo, Szczypinski, Karlsson, Dønstrup, Stødkilde-

Jørgensen, & Andersen, 2004).

Any physical or rheological response of a gel will be

derived directly from its microstructure; therefore, it would

be of interest to correlate the microstructure directly with

sensory assessments. Texture characterisation at a micro-

scopic level using image analysis has been reported, but

only to a limited extent (de Bont, van Kempen, & Vreeker,

2002; Hullberg & Ballerini, 2003; Langton et al., 1997;

Thybo, Bechmann, Martens, & Engelsen, 2000; Thybo,

Szczypinski, Karlsson, Dønstrup, Stødkilde-Jørgensen and

Andersen, 2004). Current advances in microscopy technol-

ogy and image analysis software may prove to be valuable

in allowing accurate modelling of perceived texture as a

function of microstructure and, in turn, in engineering

specific microstructures to deliver the desired perception of

texture that consumers seek.

This study used a controlled experimental design and a

range of texture characterisation techniques to illustrate the

effect of fat addition, at different levels of total solids, on the

formation of structure in acid milk gels made from

reconstituted skim milk. In addition, attempts were made

to model correlations between sensory data and instru-

mental data to describe how structural changes influence

sensory perception. Predictive models that used instru-

mental parameters for correlation with perceived texture

were also developed.

2. Materials and methods

2.1. Preparation of acid milk gels

Low heat skim milk powder (Fonterra Co-operative

Group Ltd., New Zealand) was reconstituted in deminer-

alised water to protein concentrations ranging from 3.3 to 6.

6% (w/v). Frozen fat for milk recombination (FFMR)

(Fonterra Co-operative Group Ltd., New Zealand) was

added to some of the milks at concentrations of 2 and 4%

(w/v). The reconstituted milks were heated to 90 8C in a

pilot-scale indirect ultra high temperature (UHT) plant (Alfa

Laval, Australia), and were then transferred at that

temperature to stainless steel beakers placed in a

thermostatically controlled water bath and held at 90 8C

for 15 min under agitation. Rapid cooling of the milks to

20–23 8C was achieved using ice water. The mean fat

droplet size distribution (d32 or volume–surface average

diameter) was measured using a Mastersizer MSE static

laser light-scattering analyser (Malvern, Worcestershire,

UK). The parameters used to analyze the particle size were

defined by the presentation code 2NAD.

The milks were subsequently acidified at 35 8C over a

period of 15 h, using a mother culture prepared with a

freeze-dried mixture of Lactococcus delbrueckii subsp.

bulgaricus and Streptococcus salivarius subsp. thermo-

philus (Joghurt 709, VISBYVAC Series 1000, Danisco,

Niebull, Germany). The milks were inoculated with 2%

(w/w) mother culture prior to incubation at 35 8C. The

experimental design used allowed for the manufacture of 18

different gel samples. The final pH of the gels was found to

range between 3.9 and 4.2.

2.2. Sensory evaluation

Sensory evaluation was carried out with a trained panel

of 10 panelists, using both oral and non-oral attributes.

Attributes evaluated orally were: firmness, thickness,

adhesiveness, creaminess, coarseness and dissolvability.

Non-oral attributes were: firmness, give, resistance to

penetration, cohesiveness of mass, adhesiveness to spoon,

moisture release in-hand, adhesiveness and breakdown

consistency. Definitions for each of these attributes are

given in Table 1. Non-oral and oral evaluations were carried

out using separate containers, independently coded and

randomised, to avoid bias. Samples were presented to the

panelists in individual, three-digit-coded plastic containers

(50 ml) placed in cooled metal blocks to keep the gel

temperatures low and uniform during testing. The gel

temperature during testing was 7G0.8 8C and evaluation

was done on 3- to 5-day-old gels that were kept under

refrigeration after the incubation period. Testing was

conducted under white light conditions. Each panelist

evaluated each of the 18 different gels six times (three

times orally, three times non-orally) over nine sessions.

They evaluated 12 gels in each session. On each test day, the

presentation order was randomly assigned by computer (C5,

Compusense Inc., Guelph, Ontario, Canada). Evaluations

were made using a 150-mm line scale anchored with

references for each of the tested attributes. The references

used were the softest (10% total solids, no added fat) and the

firmest (24% total solids, 4% added fat) gels in the range

studied.

2.3. Instrumental measurements

2.3.1. Compression

The mechanical properties of 3-day-old gels were

evaluated at 6–7 8C using a pseudo-compression (‘back

extrusion’) test on a TA-XT2 Texture Analyser (Stable

Table 1

Definitions of the sensory textural attributes (non-oral and oral) of acid milk gels

Sensory attribute Definition

Firmness Force required to compress the gel, before fracture and permanent structural damage, using the middle or index

finger

Give Extent to which the gel can be compressed before fracture and permanent structural damage

Resistance Extent of resistance offered by the gel to penetration with a plastic spoon, using a constant speed, down to the

bottom of the container

Cohesiveness of mass Degree to which the gel holds together when scooped up with a plastic spoon

Adhesiveness to spoon Ease of removal/slipping off of the gel from a plastic spoon when the spoon is slowly tipped at an angle of 458

Moisture release Amount of liquid observed around the gel edges when a spoonful of the gel sample is placed in the hand (palm)

Breakdown consistency Lumpy (0) to mushy (150) consistency of the gel after being squashed three times, in orthogonal directions,

between the thumb, and index and middle fingers

Adhesiveness in hand Amount of residual gel stuck on to the fingers after squishing it for evaluation of breakdown consistency

Firmness (mouth) Force required to compress a sample of the gel between the tongue and the palate

Thickness (mouth) Density or consistency of the gel after initial compression between the tongue and the palate

Adhesiveness (mouth) Amount of gel stuck to the tongue or palate after compression of the sample

Coarseness (mouth) Presence of coarse particles in the mouth (felt with tongue) after initial compression

Creaminess (mouth) Combined sensation of smoothness and thickness of the gel, associated with a fatty mouthcoating experience

Dissolvability (mouth) Ease with which a sample of the gel dissolves in the mouth

R. Pereira et al. / Food Hydrocolloids 20 (2006) 305–313 307

Micro Systems, Surrey, England). Gel samples, 40 mm

deep, were compressed using a flat aluminium upper plate,

15 mm in diameter, at a crosshead speed of 10 mm/s and to

a depth of 5 mm. Curves of force versus time were analysed

using Microcal Origin version 5 (Microcal Software Inc.,

Northampton, USA) to generate rheological parameters for

subsequent correlation analysis. Five rheological par-

ameters were derived: maximum force in compression

(Fmax), deformation to peak force (TFmax), work in

compression (Area C), work in decompression (Area K)

and slope of the force versus time curve (Slope). All

measurements were made in quadruplicate.

2.3.2. Frequency sweep

Gels were prepared using 10 ml of cultured milk in 60-

mm diameter Petri dishes (Biolab Scientific, Auckland,

New Zealand) and the evaluation was performed on 3-day-

old gels at 20 8C. The equipment used for the measurements

was a Rheometrics SR-5000 (Rheometric Scientific, New

Jersey, USA) and a special plate/dish holder was built for

fixing the Petri dishes during the test. The parallel plate

geometry was used (upper plate Z40 mm diameter, strain

Z1%, frequency range Z0.01–4 Hz). Any visible whey on

the surface of the gels was carefully removed with a

micropipette prior to running the test. G 0, G 00, G* and tan d

were the measured parameters.

2.3.3. Syneresis

Syneresis was evaluated using 100 ml grade A glass

volumetric flasks (Fortunaw NS 12.5/21, Germany) accord-

ing to the procedure described by Lucey, Teo, Munro, and

Singh, (1998c). A volume of 85 ml was added to each flask

so that they were filled to just below the base of the neck.

The gels were kept sealed at 4–6 8C for up to 3 days after

gelation and measurements were made 0, 6, 12, 24 and 48 h

after the end of the incubation period.

2.4. Microstructure

Confocal laser scanning microscopy has been described

as a useful technique for studying the formation of

microstructure in yoghurt (Hassan, Frank, Farmer, Schmidt,

& Shalabi, 1995) and was used in this study for

microstructural analysis of the experimental acid milk

gels. The sample preparation method using the fluorescent

dye Fast Green FCF (Merck, Darmstadt, Germany) for

proteins has been described previously (Lucey et al.,

1998c). Nile Blue (BDH, Poole, England) was used for

staining the fat droplets. The gels were examined on a Leica

DM RBE confocal microscope LS 510 (Leica Lasertechnik

GmbH, Heidelberg, Germany) with a 100x oil immersion

objective (numerical aperture 1.4) and an Ar/Kr mixed gas

laser source using excitation wavelengths of 568 and

488 nm for Fast Green and Nile Blue respectively.

Experiments were done in duplicate, with 12 images

captured per experiment. Image analysis was carried out

using Image-Pro Plus, version 4.5 for Windows (Media

Cybernetics, Silver Spring, USA), and five parameters were

quantified: mean cluster size, mean cluster numbers, mean

end point number (number of unconnected protein branches

of a protein cluster), mean pore size and mean pore

numbers.

2.5. Statistical analysis

Statistical analysis was performed using MINITABw

release 14 (Minitab Inc., State College, USA). The results

were analysed using analysis of variance (ANOVA) and

Tukey’s HSD significance test, principal component

analysis (PCA) and principal component regression

(PCR), as well as partial least squares (PLS) regression.

Canonical correlation was performed using SAS version 8

(SAS Institute Inc., Cary, North Carolina, USA), using

PROC GLM and PROC CANCORR.

Table 2

Analysis of variance for individual sensory textural attributes of acid milk gels

Attribute Model Mean squares F-value p-value

Firmness

(hand)

Judge (J) Solids-Non-Fat

(SNF) Fat (F) SNF*F

1434.33 170253.52 17426.10 2512.61 2.75 326.03 33.37 4.81 !0.01!0.001!0.001!0.001

Give Judge (J) Solids-Non-Fat

(SNF) Fat (F) SNF*F

6181.84 34241.32 60584.19 9787.14 7.41 41.07 72.66 11.74 !0.001!0.001!0.001!0.001

Resistance Judge (J) Solids-Non-Fat

(SNF) Fat (F) SNF*F

1383.56 161235.07 24146.14 1504.74 4.24 494.38 74.04 4.61 !0.001!0.001!0.001!0.001

Cohesiveness

of mass


(SNF) Fat (F) SNF*F

906.63 158525.30 18617.16 3811.30 2.41 421.84 49.54 10.14 !0.05!0.001!0.001!0.001

Adhesiveness

to spoon


(SNF) Fat (F) SNF*F

3329.87 172867.80 33561.80 2179.46 8.54 443.27 86.06 5.59 !0.001!0.001!0.001!0.001

Moisture

release


(SNF) Fat (F) SNF*F

5753.42 158027.15 24088.83 2660.57 12.85 352.82 53.78 5.94 !0.001!0.001!0.001!0.001

Breakdown

consistency


(SNF) Fat (F) SNF*F

2389.44 164294.75 30997.64 2266.13 8.00 549.89 103.75 7.58 !0.001!0.001!0.001!0.001

Adhesiveness

in hand


(SNF) Fat (F) SNF*F

2940.90 114892.70 29196.79 1641.04 10.08 393.94 100.11 5.63 !0.001!0.001!0.001!0.001

Firmness

(mouth)


(SNF) Fat (F) SNF*F

4114.68 165950.61 20049.50 599.22 12.77 514.87 62.20 1.86 !0.001!0.001!0.001!0.05

Thickness

(mouth)


(SNF) Fat (F) SNF*F

3197.19 159308.57 22840.88 876.09 10.57 526.58 75.50 2.90 !0.001!0.001!0.001!0.01

Adhesiveness

(mouth)


(SNF) Fat (F) SNF*F

5397.39 146902.78 21962.17 875.50 16.70 454.43 67.94 2.71 !0.001!0.001!0.001!0.01

Coarseness

(mouth)


(SNF) Fat (F) SNF*F

21294.60 86886.95 2359.63 1304.72 30.77 125.53 3.41 1.89 !0.001!0.001!0.05!0.05

Creaminess

(mouth)


(SNF) Fat (F) SNF*F

19183.24 107939.91 45967.85 1866.85 32.57 183.24 78.04 3.17 !0.001!0.001!0.001!0.001

Dissolvability

(mouth)


(SNF) Fat (F) SNF*F

4160.99 162767.73 22320.77 1197.43 7.67 299.89 41.12 2.21 !0.001!0.001!0.001!0.05


3. Results and discussion

10H0

10H2

10H4

12H0

12H2

12H4

14H0

14H214H4

16H0

16H216H4

18H018H2 18H4

20H020H2

20H4

50–5

1

0

–1

sensory PC1 (93.8%)

sens

ory

PC

2 (3

.2%

)

give

moisturebreakdown

dissolvability

coarse mouth

firm mouththick mouth

adhesive mouth

adhesive handcream mouth

cohesive hand

resistanceadhesive spoonfirm hand

Fig. 1. Principal component plot for the Sensory textural attributes (for gel

samples: initial numberZ% SNF, HZHeat-treated milk, final numberZ% Fat).

3.1. Sensory evaluation

Previous studies investigating the influence of protein

content and fat content on the structural characteristics of

stirred acidified milk gels (Schkoda, Hechler, & Hinrichs,

2001a,b) have shown that, whereas the concentration of

casein governs the structural properties of the final product,

fat addition prior to acidification seems to improve the

serum-holding capacity of these products, when assessed

instrumentally. Little is known about how these effects on the

structural properties of the acidified gels are perceived using

the human senses, and how closely sensory perception can be

correlated with microstructural information about the gels.

The sensory evaluation scores from the panel were

analysed using ANOVA. The model used included effects of

session, panelists, solids-non-fat (SNF) content, fat content

and SNF-fat interaction. Mean squares and probability

values for each textural attribute are shown in Table 2.

For all textural attributes, both SNF content and fat

content played a significant role in producing perceivable

differences between the gels. Overall, however, the effect of

SNF in producing sensory differences between the gels in

this study was predominant, as shown by the mean square

values in Table 2. ‘Give’ was the only textural attribute that

was primarily influenced by the fat content of the gel

samples.

The significant differences between the panelists for all

descriptors were not entirely unexpected, especially because

of the narrow textural range of the gels explored in the

study. Although the training of panelists seeks to reduce this

inherent variability, significance of the effect of panelists in

sensory data analysis has been reported previously (Pereira

et al., 2003). All interactions of the panelists with sessions,

samples and replicates were included in the error term of the

ANOVA, maximising the term against which all other

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

8 10 12 14 16 18 20 22

Total solids (%)

Fm

ax (

N)

(a)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

8 10 12 14 16 18 20

SNF (%)

Fm

ax (

N)

(b)

Fig. 2. Maximum force in compression (Fmax) of acid milk gels plotted as a

function of (a) total solids level (TS) or (b) solid-non-fat level (SNF) at

different fat contents 0%(B), 2%(-) or 4%(6).


effects were tested. Table 2 shows that, over and above the

‘noise’ or variability inherent to the panelists over sessions,

samples and replicates, significant differences could still be

detected between the many gels evaluated.

PCA of the sensory results (Fig. 1) shows that, as the SNF

content increased from 10 to 20% (w/v), the gels became

firmer, more resistant to cutting, more adhesive or sticky on

the hands and palate, more cohesive, coarse and thick, less

prone to whey separation, less dissolvable in the mouth and

more brittle under small applied stresses, regardless of the

amount of fat added to the samples. Within each level of fat

addition (0, 2 and 4%, w/v), differences between the

experimental gels were more easily detectable at lower SNF

levels, the gels being increasingly perceived as similar in

texture as the total solids content was increased above 18%.

Another observation from Fig. 1 is that ‘give’ was the

major sensory attribute that differentiated gels with and

without added fat. Samples without added fat were

perceived as being more compressible than those to which

fat was added, which was probably the result of the stronger

gel network formed in the gels to which fat had been added.

Similarly, differences between the gels were more

pronounced when the total solids content was below 18%.

Both oral and non-oral sensory attributes were effective

at discriminating between the textures of the gels, with a

high overall interrelationship of the attributes (Pearson

correlation coefficient, rO0.9). ‘Give’ was, as expected, the

one non-oral attribute that could be only marginally

correlated with the oral subset. The possibility of using

either oral or non-oral assessments in characterising the

texture of acid gels has already been shown for skim milk

gels (Pereira, Singh, Jones, & Munro, 2004). Confirmation

of the correlation between the two data subsets for gels to

which fat was added supports the use of non-oral attributes

as a routine method for characterising gel texture, with the

advantage of minimising oral and mental fatigue of

panelists.

3.2. Instrumental characterisation and image analysis

In this study, the experimental gels were subjected to a

range of different instrumental methods of texture charac-

terisation, all of which were analysed using PCA. Amongst

other factors, the textural properties of protein-based gels

are influenced by the droplet size of the lipid filler (Mor,

Shoemaker, & Rosenberg, 1999). Analysis of the fat droplet

size distributions of the recombined milks, prior to gelation,

revealed that all samples exhibited similar average volume-

to-surface droplet diameters (d32), ranging from 0.46 to

0.52 mm, at all levels of skim milk powder and fat used. As

all samples were heat treated at 90 8C, it was expected that

denatured whey proteins would associate with casein

micelles via intermolecular disulphide bonds, participating

in the gel matrix and improving the rheological properties of

the gels, as has been reported previously (Lucey, Tamehana,

Singh, & Munro, 1998b). As all the fat-containing systems

were homogenised prior to acidification, the homogenised

fat globules with adsorbed caseins would be expected to

contribute actively to the formation of the gel network and

to the gel properties (Schkoda et al., 2001b).

All the instrumental measurements in this study were

made after storage for 3 days, to match with the sensory

assessments. As one example, the effects of fat content and

total solids content on compression strength are shown in

Fig. 2. As expected, an increasing level of total solids

increased the compression strength (Fmax) of the gels. For

the same level of SNF, only the high level of fat addition

(4%) increased Fmax significantly (Fig. 2(b)), whereas

Fmax of the gels decreased with increasing fat content at a

given total solids content (Fig. 2(a)). Previous studies on

dairy gels have shown that addition of emulsified fat to skim

milk powder networks improves the mechanical properties

of milk gels (Aguilera & Kessler, 1989; Aguilera, Kinsella,

Liboff, Dickinson, Morr, & Xiong, 1993), as also found in

this study, especially at added fat levels S4%. However,

partial replacement of SNF by fat seems to change the

compression properties of the gels by reducing the force

needed to fracture, with fat possible acting as a lubricant.

The instrumental principal component plot (Fig. 3)

shows that, in this study, instrumental results (from

10H0

10H210H4

12H012H2

12H4

14H0

14H2

14H4

16H0

16H2

16H4

18H0

18H218H4

20H0

20H2

20H4

–4 –3 –2 –1 0 1 4 6

–3

–2

–1

0

1

2

instrumental PC 1 (53.9%)

inst

rum

enta

l PC

2(

16.5

%) Area -

tan delta

TFmax

syn 0syn 6

syn 12syn 24

syn 48

Area +FmaxSlope

G'G"

532

Fig. 3. Principal component plot for the Instrumental textural attributes (for

gel samples, initial numberZ% SNF, HZHeat-treated milk, final

numberZ% Fat).


syneresis measurements, small deformation rheology and

compression tests) were generally not as effective as the

sensory panel in discriminating between the textures of the

gels. Samples with higher total solids content, above 18%,

were discriminated from those with lower total solids in

terms of their more elastic behaviour under compression and

in a frequency sweep test. In general, experimental gels

without added fat and the group in which fat was added at

Fig. 4. Confocal micrographs of acid milk gels at different levels of SNF (1

the 4% level could be well discriminated, with the latter

group showing very little whey separation and higher tan d.

In contrast, gels with the intermediate level of fat addition

showed considerable variability when tested instrumentally.

Confocal micrographs were used both qualitatively and

quantitatively for correlation with the sensory results

obtained from the trained panel. The correlation between

the first principal components of the instrumental and

microstructural data was found to be significant (p!0.001),

but without a relatively high correlation coefficient (rZ0.69). This could have been caused by the variability in the

microstructural results, which is highly dependent on the

number of replicates and the magnification used to obtain

the micrographs.

During acidification of milk, casein particles and

denatured whey proteins associated with the surfaces of

micelles aggregate into chains and clusters that are linked

together to form a three-dimensional network (Bremer,

Bijsterbosch, Schrijvers, van Vliet, & Walstra, 1990; Lucey

et al., 1998c). Previously, scanning electron microscopy has

revealed similar microstructure (porosity of recombined

yoghurt protein matrices) with or without the inclusion of

1.5% fat (Barrantes, Tamime, Sword, Muir, & Kalab, 1996).

It was suggested that the fat globules could become hidden

within the protein clusters and chains of the matrix.

However, transmission electron microscopy made it

0–20%) where protein is replaced by fat (0–4%). Scale barZ10 mm.


possible to visualise the fat globules, showing their

interaction with casein micelles and their participation in

the formation of the gel matrices (Barrantes et al., 1996).

Fig. 4 shows representative confocal micrographs of the

microstructures of acid gels with 14 and 20% total solids

and containing different proportions of SNF and fat. In

agreement with previous observations (Aguilera & Kinsella,

1991; Lucey, Munro, & Singh, 1998a), in gels containing

fat, the recombined fat globules were embedded and fully

connected into the matrix. The presence of fat caused major

microstructural changes at a relatively low level of total

solids (14%), whereas no significant changes were detected

at a high total solids level (20%). These major changes

resulted in a denser and less open microstructure, with a

smaller pore size and increased ‘interconnectivity’ of the

network. The same effect was detected when the SNF level

was fixed and fat was incorporated into the network (Fig. 5).

The average mean cluster size and the average mean pore

size measurements at 10% SNF illustrated the microstruc-

tural changes that occurred with increasing fat (or increasing

number of interacting particles) (Fig. 5(a)), as opposed to

the relatively constant values obtained at 20% SNF

(Fig. 5(b)). This correlates well with the findings from the

sensory panel, who perceived the samples at high total

solids levels (O18%) as having similar textural properties,

regardless of the fat content (Fig. 1).

(a)

(b)

1

10

100

1000

0 1 2 3 4 5% fat

Mea

n cl

uste

r si

ze (

µm2 )

0.1

1

10

100

Mea

n po

re s

ize

(µm

2 )

10

100

1000

0 1 2 3 4 5% fat

Mea

n cl

uste

r si

ze (

µm2 )

0.1

1

10

Mea

n po

re s

ize

(µm

2 )

Fig. 5. Mean cluster size (open symbols) and mean pore size (filled

symbols) of acid gel microstructures, containing varying amounts of fat and

(a) 10% SNF, (C,B) or (b) 20% SNF (:, 6).

3.3. Statistical correlation

Canonical correlation has been used to investigate the

relationship between the sensory and instrumental and/or

microstructural sets of data, in a procedure that maximises

the correlation coefficients without assumptions about

dependences of one set on another (MacFie & Hedderley,

1993) or necessarily a cause-effect relation between the data

sets (Dijksterhuis, 1995). For predictive purposes, however,

PLS regression (Martens & Martens, 1986) has been widely

recommended because of the inherent testing for predictive

performance by calculation of a cross-validated r2, using

leave-one-out procedures (MacFie & Hedderley, 1993).

Although both techniques were used for analysis of the data

in this study, the outputs of the canonical correlation

analysis are not presented; instead, models derived from

PLS regression are presented.

Because of the strong interrelationship between the

variables within each data set in this study, as identified

through principal component analysis, PLS regression was

performed using the principal components themselves as the

independent variables or predictors, and the sensory

principal component as the response to be predicted. This

procedure was a modified form of PCR, in which the leave-

one-out cross-validation commonly used in PLS was

applied.

The analysis showed that when regressing the first

sensory principal component (SPC1) against the principal

components of the combined instrumental and microstruc-

tural data (XPC1, XPC2, XPC3), an estimated model with

high predictive ability (pred r2Z96.3%) was generated.

This means that it is possible to predict, with good accuracy,

new observations for perceived sensory results from data

gathered from confocal micrographs and instrumental

texture analysis. Fig. 6 shows the model, based on

Actual Response

Cal

cula

ted

Res

pons

e

5.02.50.0-2.5-5.0–7.5

5.0

2.5

0.0

–2.5

–5.0

–7.5

VariableFittedCrossval

PLS Response Plot for SPC1SPC1= 0.9832 XPC1+ 0.0761 XPC2 + 0.0428 XPC3

Fig. 6. PLS response plot for the first sensory principal component (SPC1),

modelled as a function of instrumentalCmicrostructural data (XPC1,

XPC2, XPC3). Pred r2Z96.3%.

Actual Response

Cal

cula

ted

Res

pons

e

120100806040200

120

100

80

60

40

20

0VariableFittedCrossval

PLS Response Plot for GIVEGIVE = –07732 XPC1 - 0.1756 XPC2 - 0.3935 XPC3

Fig. 7. PLS response plot for the sensory attribute ‘give’, modelled as a

function of instrumentalCmicrostructural data (XPC1, XPC2, XPC3). Pred

r2Z68.9%.


standardised coefficients and including fitted and cross-

validated fitted values. ‘Give’ is the one sensory attribute

that could not be as well modelled as the other textural

descriptors. Regression analysis showed that a model could

be produced, with pred r2Z68.9%, which was still a

reasonable predictive ability (Fig. 7).

Canonical correlation analysis confirmed the good

correlation found between the sensory data set and the

group of instrumental and microstructural parameters (data

not shown). However, no information on the predictive

power of the model derived from this statistical procedure

was available.

PLS regression for SPC1 using only instrumental

analysis resulted in a predictive model with pred r2Z86.9%, with data generated from compression tests and gel

permeability being those that more closely correlated with

and predicted sensory response. Use of microstructural

image analysis data only produced, in turn, a model with

pred r2Z47.2%, which was quite a poor model for

estimating perceived texture. The lack of published results

showing good correlation between microstructure and

perceived texture was identified previously by Langton

et al. (1997), and was attributed to improperly defined

experimental design.

It is important to note that any minor lack of structural

homogeneity of the samples tends to be highly magnified

during image analysis, because of the small section of the

sample that is being analysed and the high magnifications

required for proper visualisation of structural information.

To generate a good image of the texture to be quantified in

image analysis, two assumptions need to hold: that

significant variation in intensity levels between nearby

pixels exists, and that homogeneity at some spatial scale

larger than the resolution of the image occurs (Thybo et al.,

2004). Large variability in image analysis results is

therefore not uncommon, unless a large number of

replicated measurements are produced, in order to generate

a more accurate estimate of each parameter being

quantified. This inherent variability could have contributed

to the lack of good correlation results reported to date.

4. Conclusions

The present study showed how differences in the

microstructure of acid milk gels, caused by differences in

the SNF and fat contents, influenced the perception of the

textural attributes of these products, and how image analysis

could be used to model these changes in perception. Fat

addition resulted in a decrease in the mean pore size and an

increase in the mean cluster size. But these observations

alone were not sufficient to predict, with a satisfactory

degree of accuracy, the perceived texture of the gels.

However, a combination of image analysis measurements

with whey separation and rheological properties derived

from a compression test enabled models to be estimated that

had excellent predictive power for sensory textural

attributes evaluated non-orally (firmness, moisture release,

cohesiveness, adhesiveness, resistance to penetration) and

orally (firmness, thickness, adhesiveness and

dissolvability).

Although this study was of a preliminary nature,

advances in computer technology and software for image

analysis may prove to be useful in generating more accurate

and reproducible quantitative information from the gel

microstructural fingerprints that correlate with the percep-

tion of texture and that can be used as a quality control tool

for delivery of the sensory experience that consumers expect

from milk gels.

Acknowledgements

The authors acknowledge the assistance of the sensory

panel at Fonterra Research Centre (Palmerston North). We

would also like to thank Warwick Johnson, Liz Nickless,

Thomas Huber, Stephen Hubbes, Raul Jacobsen Neto and

Stephan Wullschleger for their valuable help in the

manufacture and instrumental testing of the experimental

gels. The financial support provided by the Fonterra Co-

operative Group Ltd., New Zealand, is gratefully

acknowledged.

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