The perpetual preoccupation of the companies is the satisfaction of their consumers
who aim at their comfort. In order to improve the comfort of the products use,
a new approach has been set tries to integrate the human perceptions into the
design process. It is the analysis of the sensory characteristics of the products.
The textile industrials are interested in the characterization of the touch
quality or the fabric hand, thus to the clothing comfort. The term fabric hand
or handle has been defined as the quality of a fabric or yarn assessed
by the reaction obtained from the sense of touch or the total sum of the sensations
expressed when a textile fabric is handled by touching and flexing with the
fingers (Bishop, 1996). It implies the ability of the
fingers to make a sensitive and discriminating assessment and the ability of
the mind to assimilate and express the results in a single judgment (Ellis
and Garnsworthy, 1980). This term is treated in several manners in the literature.
The investigators like Binns (1926), Pierce
(1930), Houghton and Yaglou (1923), Winslow
et al. (1937a, b) and others began systematic
analyses of subjective responses to textile and clothing. From these early efforts
evolved the conceptual bases for the study of fabric handle and
overall clothing comfort.
Howorth and Oliver (1958) and Howorth
(1964) studied the subjective assessment of fabric hand. They used a panel
of 25 people with no special experience in handling fabrics to rank 27 samples
of suit fabrics which were ranked according to hand by the method of comparison
Brand (1994) is one of several researchers who made
differences between experts and untrained judges of textile hand. Hui
et al. (2004) have trained the panelists to understand the definitions
of the fourteen significant bipolar pairs of sensory attributes of fabric hand.
In order to assess the reliability of these panelists, they conducted a test-retest
Yick et al. (1995) studied the influence of
the judges experiences on the results of subjective handle assessment.
They used a panel of 199 judges with different academic and industrial experiences
in the textile and clothing industries. They concluded that the more experienced
judges exhibited a higher percentage of significance and gave a higher level
of overall agreement.
In the work of Cardello et al. (2003) a standardized
hand evaluation methodology (HSDA; Handfeel Spectrum Descriptive Analysis method)
was checked for its sensitivity and reliability and was used to characterize
military fabrics. They concluded that in conjunction with the panel training
program, result in a sensory hand evaluation method is highly sensitive and
reliable over extended period of time.
In this study, we have developed a sensory assessment method which involves
experts as a measuring instrument. These experts have taken part in a training
program. During this program, we have controlled their performance on the level
of repeatability to diminish the variableness of the provided measurements and
a list of handle attributes is drawn up thanks to the judges
contest. This panel is validated following the sensitivity and reliability test
carried out after a test-retest reliability study at the completion of training.
MATERIALS AND METHODS
Condition of the sensory evaluation: The judges of the panel were selected
from volunteer employees at SITEX Company (weaving and finishing factory), chosen
on the basis of interest, availability and tactile acuity/sensitivity. It is
composed of administrators, laboratory technicians and engineers.
According to the norm NF-ISO-11035 (AFNOR, 1995), the
panel must be composed, at least, of 6 judges and it is advised to recruit and
train 1.5 to 3 times more people wanted ultimately. In our case, fifteen judges
(8 men and 7 women) are recruited at the beginning to end up with ten after
the selection. They have experience on the procedures of fabric manufacture,
but do not have any idea about the sensory analysis methods. Their age varies
between 25 and 45 years. Panelists participated in a one year training program
that consisted of training in the basic methodology and operational evaluation
techniques employed in the handfeel.
The tests were carried out in a room where the temperature and hygrometry are
constant and are felt comfortable by the subjects. The hands of the subjects
are washed and dried before each evaluation sitting, in order to avoid the skews
gotten by the cosmetic products and the contact with other products. The time
of each evaluation session was limited to 30 min., because hands become less
sensitive if the test is too long. The number of samples to be evaluated by
sitting was fixed to six.
|| List of samples used in training
Characteristics of the evaluated fabric: These panelists were trained
with textile fabrics (Table 1) of different aspects and structures;
furnishing fabrics, knitting, clothing fabrics. The fabrics were cut into 30x25
cm swatches with the longest edge are in parallel to the warp direction. Later
samples were given for the panelists one after the other in a mixed order
and they were asked to rank the fabrics. Before testing, specimens were kept
in standard atmosphere conditions (temperature 20±2°C, humidity 65±2%)
not less than 24 h (AFNOR, 1992).
RESULTS AND DISCUSSION
Establishment of the attributes list: With the aim of obtaining the
maximum number of descriptive terms and to avoid skews due to the influence
of an individual on the group, a certain number of people of various ages, sexes,
regions and experiences were queried using a form to fill,. Each person of this
questioned population was invited to argue his/her decision of acceptance or
rejection of a textile fabric in general by descriptive terms, thus to explain
the significance of each attribute. The questionnaire was made in absence of
the samples so that the terms given by the questioned persons are not limited
by the characteristics of the provided samples. The result of this questionnaire
was a preliminary list of 57 diversified terms. They are classified alphabetically
in the Table 2.
We noted that there are other original terms suitable for the Tunisian Arab
language which signify well
We also obtained several terms which describe the material handfeel: cotton,
wool, silk, linen, cashmere, polyester. In the list also appear the terms which
describe the fabric structure: fabric, knitting, satin, Jean, Denim.
A preliminary qualitative sorting (AFNOR, 1994, 1993)
is carried out to eliminate from this generated list the hedonic terms, terms
characterizing a material, the irrelevant terms and the attributes referring
to the visual feel. We considered as irrelevant, the terms which do not describe
the textile hand, such as; hard, absorbent, full, porous etc.
|| Preliminary list of the 57 attributes
We also removed the terms which are rarely employed by the consumers and the
terms which do not have the same meaning, for example; unified, it can relate
to the color or to the fabric structure, fits the shape of body, it can be elastic
or flexible, etc. But for the terms which represent the same meaning, we kept
the more significant ones, for example; cool is eliminated and cold is kept,
marrowy is eliminated and hairy is kept, extensible is eliminated and elastic
is preserved and soft, tender are gathered.
Table 3 presents the list of 25 attributes preserved after
the preliminary sorting. In this list appear some antagonistic terms (bipolar),
such as; thin-thick, light-heavy, supple-stiff, cold-heat, moist-dry and smooth-grooved.
The terms preserved in the preliminary list are classified by 200 Tunisians
of various regions and various experiences using a second questionnaire. Where,
we took account of the antagonistic terms, in order to decrease the dispersion
of information. The results of this last questionnaire are studied using SPSS
software which yielded the following histogram (Fig. 1).
|| List of the descriptors preserved
This histogram shows that there are 11 attributes (soft/tender, supple-stiff,
sleek, falling, elastic, light-heavy, thin-thick, smooth-grooved, cold-heat,
silky, flexible) which are classified by more than 50% of the questioned peoples,
in the first 10 ranks.
The attributes grainy, dry, loose, hairy, wrinkly and sticking, are classified
in rows superior to 10. Among these last terms, we find the terms which were
not understood by some questioned people, for example grainy.
A first analysis of the results obtained of this evaluation consists in classifying
the attributes according to the importance of the geometric mean M of the sum
of the attributes quotation frequencies and the sum of the cumulated intensities
allotted to the attribute. It is calculated by the following equation (AFNOR,
||The frequency of the quotation. It is the ratio of the number
of the attribute quotations to the total number of possible quotations for
the attribute. In our case, the total number of quotations is 117 (13 subjects
and 9 fabrics)
||The relative intensity which is the ratio of the sum of the intensities
given by all the members of the jury for an attribute to the sum of the
possible maximum intensities for this attribute. In our case, the sum of
the maximum possible intensities is 585 (maximum intensity on the scale
is 5 per 9 fabrics and 13 subjects)
|| Diagram of attribute classification at first sitting
In the Table 4, the attributes are classified in the decreasing
order according to the importance of their geometric mean M. thirteen attributes
have a geometric mean more than 50%. The attributes smooth, flexible, elastic
and tender have the most significant averages. The sticking attribute has the
weakest average (25.135%). This last attribute will be eliminated from the list
of attributes with grainy classified in the last rows according to the second
questionnaire. The grainy term is classified according to the geometric mean
among the last before the terms moist and grooved. We did not eliminate these
last two terms because we considered them as the antagonistic terms respectively
of dry and smooth. There are certain panelists who preferred to use moist instead
of dry and grooved instead of smooth.
A second analysis of the results, by the method of Principal Components Analysis
(PCA), allowed us a second reduction. This reduction consists of a regrouping
of the synonymous attributes (correlated positively) or antonyms (correlated
negatively) and an elimination of the attributes which contribute very little
to highlight differences between the tested fabrics in a sensory profile.
|| Classification of attributes
To determine these correlations, the circles of correlations were presented
in the Fig. 2a and b. On the first circle,
42.34% of information is gathered on the first two axes. To visualize the maximum
of information (63.162%), it was necessary to present the circles of correlations
on the first five axes. These circles of correlation have allowed to highlight
the antagonistic terms. Indeed, they show a significant negative correlation
between; thin-thick, heavy-light and cold-heat which is obvious especially on
the circle of the axes F4 and F5 (Fig. 2b). Nevertheless,
there is no significant correlation between; moist-dry, supple-stiff and smooth-grooved.
These attributes are antagonistic by definition. This shows that these latters
are badly interpreted by the panelists.
These correlation circles also show a significant correlation between; attributes
tender, soft and silky and attributes slipping and sleek.
|| (a) Circle of correlation on axis F1/F2 and (b) Circle of
correlation on axis F4/F5
|| Final list of attributes
In this step, we didnt take into account this positive correlation because
the definitions of these attributes are different, we only took into account
the antagonistic attributes and the attributes reduced by the first (grainy)
and the second classification (sticking), thus the reduced list of 17 attributes
are represented in Table 5.
These 17 attributes are classified in three categories (Table
||Surface handle: Group of terms which characterize the
properties of the textile fabric surface
||Physical handle: Group of terms which characterize the physical
properties of the textile fabric
||Dynamic handle: Group of terms which characterize the dynamic properties
of the textile fabric and which require the application of a light force
on the fabric to determine them
During these first sittings a precise definition and an evaluation method gave
for each attribute of the established list. To each one of these attributes
was also associated a stable and adapted reference fabric which will be preserved
like memory assistance for the training of panel to define the perception intensity
associated to each attribute.
Training to the use of scale and the identification of tactile perceptions:
The general goal of the training program is to provide to the panelists
knowledge techniques of sensory analysis and to develop their ability to detect
and describe the sensory stimuli. To train the subjects to evaluate and note
the intensity of perceptions corresponding to each attribute according to a
structured scale, tests of comparison were carried out in the presence of negative
reference (corresponding to the lowest intensity) and positive reference (corresponding
to the strongest intensity). The test of comparison consists to represent these
two references to the subject and when this last acquire certain knowledge the
references will be replaced by the sample to evaluate.
Before the beginning of the training program, the subjects were informed about
the concept covered by the scale of quotation. The scale used for notation is
graduated from 0 to 10. This type of scale is preferred to make it easier on
the one hand to the subject the notation of the detected perception intensity
and on the other hand to make easier the later recording of the results.
During the first sitting, the subjects are asked to evaluate each attribute
for only two samples, with the assessment method indicated in the provided form.
They are started with two samples so that the subjects focus all their attention
on the evaluation methods. In each following sitting, the number of samples
was increased until six. On the total, six different samples are evaluated during
these first sittings by thirteen subjects, because two of them were already
eliminated from the group considering their unavailability.
To study the way of notation of the attributes by the subjects, we represented
the diagram (rank according to panelists) of the results obtained for the sample
1341 during the first sitting.
|| Cold-heat attribute
It was logical to obtain, for the first sitting, dispersed notes and to have
dissensions between the subjects, since they do not have experience in tactile
The study of the diagrams (Fig. 3) showed that the notes
are very dispersed for the terms; cold-heat, moist-dry, soft, tender, silky,
flexible, hairy, supple-stiff and wrinkly. These attributes present a standard
deviation which varies between 2.29 (silky) and 3.17 (tender). This result was
discussed again and the evaluation procedure was rectified for these attributes
with the group in the presence of the references.
For the remainder of the attributes, the notes are not very dispersed, only
there were some panelists, who did not agree with the rest of the group. These
panelists are re-examined the samples and readjusted the notes for the attributes
which there is a dissension. For example for the thin-thick term, most of the
subjects noted between 6 and 8 except two (lc noted 4 and ea noted 0), for the
elastic term the majority of the subjects noted between 0 and 1 except three
(ts noted 5 and rr&mk noted 8).
This study was re-carried out during the second and third training session,
in order to decrease the dissension between the panelists.
Such training allowed to familiarize the subjects with the notation method
of the perceptions intensity and also allowed a certain selection of the attributes.
Indeed, some attributes were eliminated following a statistical PCA study from
the results obtained at the last training sessions.
The circle in Fig. 4 shows a positive correlation between
soft and tender with a value of 0.913 and between sleek and slippery 0.867 on
a level of significance alpha equal to 0.05. Indeed, soft is eliminated and
tender is kept which is the closest in Arab terminology, because they are also
defined with the same manner by all the subjects. But, it did not hold in account
the correlation between sleek and slippery, because they are different by definition.
So that, the subjects identify the difference between these two terms, their
assessment methods and the definition were rectified for each two terms.
|| Circle of correlation after training
The term loose also changed with the term compact this is considered by the
panelists more significant.
Following these analyses, the form used for the evaluation was rectified. Indeed,
on this questionnaire is indicated the selected references as well as the new
evaluation method for each attribute. The order of evaluation also was changed
so that the attributes qualifying the surface and physical handle will be evaluated
the first according to the assessment method and that the attributes qualifying
the dynamic handle will be evaluated the last.
After establishing the assessment methodology judges were trained individually
to use the prescribed techniques. They were also provided with explanatory and
visual information how to assess these features. Descriptions how to assess
each fabric attribute are given in Table 5.
Training and control of the performance of the panel: After several
training sessions, the judges have received a certain competence in standardized
tactile sensory evaluation. They, then, became qualified subjects. In order
to assess the reliability and sensitivity of these judges, test-retest reliability
study was conducted at the completion of training program. During the test-retest
sessions, the panelists have examined six different samples. The same sample
was evaluated several times, in two evaluations not successively in each time.
The results obtained were treated by the variance analysis, in order to study
the repetitivity and the reproducibility of each judge. This analysis consists
in studying the variation of the residual standard deviation for each attribute,
for all the panelists during the sittings of assessments. The standard deviation
calculated during the sitting N°0, after the 6th sitting and the 12th for
and represented for each judge.
The Fig. 5 shows that the judge hm represents a significant
standard deviation which exceeds 2.5 in the beginning, but after six sittings
his standard deviation decreased enormously and approximately become stable
for the most of the attributes (lower than 1).
According to the curve of repeatability of the subject mk (Fig.
6), we noted that this subject, since the beginning, has a weak standard
deviation except for some attributes, but after six sittings his standard deviation
is stabilized for all the attributes.
The repeatability of the subject ss (Fig. 7), after six sittings,
was improved for most of the attributes, except for some of them (sleek, slippery
and smooth-grooved) the standard deviation increased. But after 12 training
sessions his standard deviation was decreased for all the attributes.
|| Repetitivity diagram of panelist hm
|| Repetitivity diagram of panelist mk
|| Repetitivity diagram of panelist ss
|| Repetitivity diagram of panelist lc
|| Long-term repeatability of the group
On the other hand, the repeatability of the subject lc (Fig.
8) did not represent a significant improvement, his standard deviation did
not change too much after 6 sittings, but it was improved after 12 training
This case was noted with other panelists during training sessions, but it was
accidental and was recovered in the following sittings. For this reason, the
training program did not stopped after the sixth sitting and it is extended
until most of subjects give the same evaluations for the same stimuli from one
sitting to another taking into account a dispersion around an average of 1.
To assess the long-term repeatability of the group the total hand obtained
during three distant dates was represented in Fig. 9. This
diagram shows that there is not a great difference between the notes given by
the group especially during the sixth sitting and afterwards during the 12th
sitting for all the descriptors. This implies that the repeatability of the
group is improved according to the number of training sessions and that the
group acquired certain reliability. This also shows that there is an improvement
of agreement degree between the panelists.
In order to check the individual repeatability of the subject during the same
training session and to visualize his manner of noting on the intensity scale,
the standard deviation of two notes is represented for each panelist according
to the average of these two notes for each couple attribute/sample. The graph
is thus a cloud of dots the distribution of which reflects the behavior of each
subject. The value 1is considered like reference of standard deviation to better
visualize the evolution of repeatability of the subjects at the beginning and
the end of the training program.
Figure 10 and 11 show that the two panelists
rr and hn have a homogeneous and continuous dispersion of points along the notation
scale. This shows that these two subjects are able to use quantitatively all
the scale from the beginning of the training program. The repeatability of hn
is significant from the first sitting and improved towards the last sitting
to reach a maximum standard deviation of 1.4 from 2.3 at the beginning with
3 points only located at the top of reference 1. The repeatability of rr is
also improved at the last training session, its maximum standard deviation was
decreased from 6 to 1.4 with two points which values of standard deviation are
higher than 1.
However, the repeatability of the panelist lc (Fig. 12)
is not significantly improved, because several points are at the top of reference
1 after 12 training sessions. Thus, the distribution of the points on the graph
is not uniform which shows that the panelist lc assimilates the scale of notation
to categories and that he does not respect the quantitative aspect.
|| Individual repeatability of panelist rr during
the same training session (a) rr s0 and rr s12
|| Individual repeatability of panelist hn during
the same training session (a) hn s0 and (b) hn s12
|| Individual repeatability of panelist lc during
the same training session (a) lc s0 and (b) lc s12
For this reason, this panelist is eliminated from the group with the panelist
gr who represents the same problem, thus the panelist bs who represents an unstable
standard deviation during the same sitting and a poor repeatability.
After this study, only ten subjects have been selected to form our panel of
experts among 15 recruited in the beginning. The remainder of panelists represented,
along the training program an improvement of repeatability and of notation scale
We applied the PCA analysis also to verify the degree of agreement between
the panelists. Indeed the circle of correlation is represented at the beginning
and at the end of the training program with repetition.
The Fig. 13a shows that the panelists are dispersed on three
independent groups. This implies that the intensity of correlation between the
panelists was poor at the beginning of the training program. On the contrary,
at the end of the program, the degree of agreement between the panelists was
improved, that is legible on the second circle of correlation (Fig.
|| Degree of agreement during (a) S0 and (b) S12
On the second circle all the panelists are gathered on the positive edge (1)
of first axis F1 excepts the panelist bs who does not agree with the others.
For this reason also this panelist is eliminated from the group.
In this study, we tried to establish a practical organization adapted for the
tactile sensory evaluation of the textile fabrics. To do that, 56 tactile attributes
were generated following a preliminary questionnaire, after a qualitative and
quantitative reductions, 17 attributes are revealed relevant to describe the
textile fabrics. During the first training sessions, an analysis of correlation
enabled us to eliminate another one to have a final list of 16 attributes. During
the training of the group to use the notation scale (0 to 10), discussions are
established with the group to define the evaluation method and the negative
and positive references of each attribute.
The PCA and ANOVA analyses carried out in this study, permitted to set up a
group of 10 experts of textile handle, they are selected among 15 trained judges
according to their repeatability and their discriminative capacity. These selected
experts represent a very weak standard deviation, lower than 0.8 and agreement
with the remainder of the group. This group is ready to carry out a sensory
profile of 16 attributes to describe the tactile quality of the textile fabrics.
We would like to express our sincere thanks to the director and to all the
group of development service of the SITEX Ksar Hellal company for their practical
assistance. We are also grateful to the group of panelists for their collaboration
at the sensory assessment.