=Paper=
{{Paper
|id=Vol-2416/paper43
|storemode=property
|title=Expert system of food sensory evaluation for mobile and tablet
|pdfUrl=https://ceur-ws.org/Vol-2416/paper43.pdf
|volume=Vol-2416
|authors=Marina Nikitina,Yuri Ivashkin
}}
==Expert system of food sensory evaluation for mobile and tablet ==
https://ceur-ws.org/Vol-2416/paper43.pdf
Expert system of food sensory evaluation for mobile
and tablet
M A Nikitina1, Y A Ivashkin2
1V.M. Gorbatov Federal Research Center for Food Systems of Russian Academy of
Sciences, Talalikhina str., 26, Moscow, Russia, 109316
2Moscow Technical University Communication and Informatics, Aviamotornaya str.,
8a, Moscow, Russia, 111024
e-mail: nikitinama@yandex.ru
Abstract. One of the main directions of statistics in sensory evaluation is an
assessment of the dependence between experimental variables and measured
characteristics. Statistical criteria are used to assess a degree of interaction between
variables, a level of experimental effects, and allow accepting or rejecting hypothesis
proposed. In sensory evaluation, people act as measurement instruments, and a
variation associated with the human factor arises. This proves that the use of statistical
methods is necessary. This article represents a network computer system for collection
and evaluation of food sensory indicators based on the methods of rank correlation and
multifactorial analysis of variance in real time. The article describes information
technology of expert sensory evaluation of food quality by individual panelists and
sensory panels regarding the indicators that are not measured by technical means of
control, based on client-server network architecture. The software implementation of
system for collecting and statistical processing of sensory data based on the principles of
multifactorial analysis of variance in real-time mode makes it possible to evaluate the
influence of the human factor on objectiveness and reliability of sensory evaluation
results, as well as to visualize the data of expert scores by various expert panels.
1. Introduction
Sensory analysis and evaluation of food quality is the basis for commodity examination of
food products and prediction of consumer demand. The usage of modern methods of sensory
analysis requires from panelists not only specialized knowledge of methodology and application
of, procedures for generating lexical dictionaries or scaling, but also the provision of consolidated,
consistent scores that confirm the objectiveness of the results [1].
Regardless of the experience and degree of training of panelists, individual differences always
arise in their scores of food quality, associated with sensory sensitivity, knowledge, and the
subjectivity of the scales for sensory indicator measurement [2, 3]. Therefore, the adequate
interpretation and objectification of individual panelist scores requires the development of
IT technologies for factor analysis of food sensory evaluation results based on mathematical
statistics methods [4-6] and computer software for processing and visualizing data from sensory
evaluation of a product [7-8].
An objective assessment of food quality, taking into account a variety of parameters,
alternatives and criteria, may be implement by using intelligent computer technologies for
processing and formalizing knowledge with the adoption of optimal decisions based on statistical
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M A Nikitina, Y A Ivashkin
methods of multivariate data analysis with an objective assessment of adequacy and confirmation
of hypotheses.
The probabilistic spread of characteristics and properties of initial biological raw material,
as well as subjectivity of individual panelist opinions determine the complexity of this problem.
The samples for testing cannot be identical in nutritional and biological value (protein, fat,
connective tissue, etc.). If the expert does not have enough knowledge and competencies, then
even the most sophisticated software will not conduct a qualitative analysis of the data [9].
Therefore, the adequacy of the scores in each case essentially depends on the influence of
the human factor with the individual psycho-physiological capabilities of sensory perception
and needs correction and checking the objectivity of expert scores, considering the coherence of
certain panel opinions and experience [10-13].
Information technology in a client-server network architecture is proposed based on the
methods of ranking correlation and multivariate analysis of variance, for sensory evaluation of
food quality by individual panelists and sensory panels regarding the indicators that are not
measured by any instruments [14].
2. Rank correlation in sensory evaluation of food quality
Formal characterization of final product sensory evaluation obtained as a result of expert survey is
achieved by rank correlation [8,9,11,15,16], according to which a group of quantitatively non-
measurable factors is ranked by each expert independently of each other in order of decreasing or
increasing their influence on the assessment of product quality. The ranking results are recorded in
a rank matrix xij , i = 1, m, j = 1, n, specifying the place of j-th parameter among n other
parameters by i-th expert.
Since opinions of panelists not always match each other, total ranks are determined to obtain
an objective assessment.
m
X
Rj = xi,j ; j = 1, n, (1)
i=1
and the coefficient of concordance W is calculated, which characterizes the objective relationship
between the scores of m independent panelists using the equation:
12 · S(d2 )
W = , (2)
m2 · (n3 − n)
n
" m #2
X X 1
S(d2 ) = xi,j − m(n + 1) , (3)
j=1 i=1
2
ranging from 0 in the absence of relationship between rankings by experts, to 1 with their full
consent in ranking the impact on the product quality Q.
After assessing the significance showing that the indicator of consistency W of panelist
opinions is not accidental, weighting factor gj is assigned to each sensory indicator:
M
gj = Pm ; j = 1, n, (4)
i=1 xij
where M – scale factor.
Weighting factors gj reflect the practical experience of qualified experts and characterize
comparative impact of j-th factor on total quality assessment in regression:
n
X
Q = Q0 + gj xj , (5)
j=1
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where Q0 – score of reference product, and allows to objectively identify the most significant
factors of deviations from the specified quality.
The vector of weight coefficients is presents as a bar chart in accordance with the index
number of the indicator or in descending order of the absolute values of the coefficients. If the
distribution of weights is uniform or close to it, then the level of a priori knowledge is low and
further accumulation and processing of statistical data is necessary. In turn, uneven distribution
with an exponential decrease in the weights corresponds to a high degree of a priori knowledge
about the product quality.
3. Multifactorial analysis of variance for sensory evaluation
The data with a multi-level structure is analyzed by multifactorial statistical procedures [17],
which allow determining differences between two or more data sets for all dependent variables
simultaneously. This helps to reduce the level of overall mistake of the first kind and to assess the
degree of relationship between dependent variables, and makes it possible to establish
combinations of sensory variables, which allow distinguishing samples in the case of non-
manifestation of differences in each variable separately[18-28].
The general algorithm for processing the results of sensory evaluation includes the
determination of the sample size from the general population and formulation of a null (H0) and
alternative (H1) hypotheses; choice of significance level (α = 0.01; or α = 0.05; or α = 0.1) and
conducting an assessment; data collection and calculation of total statistical criteria; acceptance
or rejection of the null (H0) hypothesis and result interpretation.
In the case of two-factor analysis of variance, pooling by two factors is used, and in addition to
the experimental error, the variance of scores due to individual differences between panelists in
the panel is taken into account [29]. In this case, the order of samples A, B and C should be
individual for each panelist, and their combinations ABC, ACB, BAC, BCA, CAB, CBA are
distributed in equal proportions to ensure complete randomization. The total sample variance for
all experiments is equal to sum of the intergroup and intragroup variances. The value of Fisher
test is calculated not only as the ratio of the intergroup and intragroup variances of panelist
scores, but also as the ratio of the variance of scores between individual experts to the intragroup
variance.
Analysis of variance [17, 18] is the basis for software development with a client-server
architecture for collection and statistical processing of sensory data. Fisher’s exact test goes
through all possible options of contingency table with the same total frequencies in rows and
columns, i.e. carries out all kinds of construction of null-models, which built on the assumption of
no influence on the factor under study [30-32].
The general algorithm for processing the results of sensory evaluation (Figure 1) includes
determining the sample size from the general population and formulating null (H0 ) and
alternative (H1 ) hypotheses; selecting the level of significance (α = 0.01; or α = 0.05; or
α = 0.1) and conducting an assessment; data collection and calculation of total statistical
criteria; accepting or rejecting the null (H0 ) hypothesis and interpreting the results.
The influence of the factor is estimated by the Fisher-Snedecor test at the chosen level of
significance, not only as the ratio of the intergroup variance of scores from the relationship
M S2
of factors to the intragroup variance from the experimental error F = M Sx1x2 2 , but also as
E
2
M Sx1 2
M Sx2
a relationships F = 2
M SE
and F = 2
M SE
of factor variances of expert scores for intragroup
variance.
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Figure 1. The integrated block
diagram of two-factor analysis of
variance.
4. Software implementation of expert system for food sensory evaluation with a
client-server network architecture
The client-server expert software consists of two subsystems: the server software and the client
software (Figure 2). The client software is installed on the user’s computer and transmits
requests to the server subsystem to process data and requests from clients and to return them
back to user’s computer.
The functional structure of the system includes six modules that enter parameters and
evaluate product descriptors; creating a data set for analysis; sensory profile; comparison with
reference; help (for user and administrator).
The list of parameters (Figure 3) determined by the purpose of the sensory evaluation,
includes:
• the number of samples and evaluated descriptors;
• type of scale (structured or unstructured); structured five- and nine-point scales are used,
according to which each indicator has 5 or 9 degrees of quality, respectively. According to a five-
point scale: 5 – excellent quality; 4 – good; 3 – satisfactory; 2 – unsatisfactory, but acceptable;
1 – unsatisfactory.
Nine-point scale recommended in the V.M. Gorbatov Federal Research Center for Food
Systems expands the range of sensory scores with the introduction of quantitative characteristics:
9 – optimum quality; 8 – very good; 7 – good; 6 – above average; 5 – medium; 4 or 3 – acceptable,
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Figure 2. General scheme of the Figure 3. Start menu of the
information system. computer system.
but undesirable; 2 or 1 – unacceptable.
• name of the evaluated descriptors;
• folder for saving files with sensory evaluation results (text format *.txt);
• instruction for the sensory panel.
After setting the evaluation parameters, the panelist connects to the server software and
enters his identification data (for example, full name) and further, using the intensity scale of the
descriptors in the product samples, individually evaluates the intensity of the product descriptors
recording the results from the beginning of the scale. After evaluating all the descriptors in the
first product, the panelist proceeds with the next product or finishes the sensory evaluation.
Figure 4 represents a table with results of two-factor analysis of variance by the descriptor
of “smoke odor”.
As a null hypothesis (H0 ), the system proposes: – tthe products do not affect the “smoke
odor” tdescriptor, and the alternative hypothesis (H1 ) – the products affect the “smoke odor”
descriptor. To verify them, Fisher’s exact test was used at a significance level of α = 0.05.
From the data in Figure 5, the calculated value of F-test for x1 factor (products) as F ≈ 19.85,
and the critical region is formed by the right-hand interval (4.46; +∞). Since F falls into the
critical region, the null hypothesis (H0 ) is rejected and the alternative (H1 ) hhypothesis is
accepted, i.e. x1 factor (products) affects the “smoke odor”.
Figure 4. Result of the panelist’s
consistency check by the “Smoking
odor”: SS – variance; df – degree
of freedom; MS – unbiased scores;
F – calculated Fisher test; P-value
– function of F distribution; F
critical value – table value of Fisher
test.
Similarly, assessment of the second factor takes place, i.e. “panelists”. With a null (H0 )
hypothesis, panelists do not affect the “smoke odor” descriptor and with alternative (H1 )
hypothesis, panelists affect the “smoke odor” descriptor.
The values in Figure 5 show, that calculated F -test for x2 factor (panelists) is F = 1, and
the critical region is formed by the right-hand interval (3.84; +∞). Since F does not fall in the
critical region, the null (H0 ) hypothesis is accepted, i.e. influence of x2 factor (panelists) on the
“smoke odor” was not confirmed.
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Figure 5. Sensory profile for three cooked Figure 6. Sensory profile for the reference
sausage samples. product and three cooked sausage samples.
Sample determination coefficient:
SSx1 17.2
ρx 2 = = ≈ 0.77, (6)
1 SSx1 + SSx2 + SSx 17.2 + 17.3 + 3.47
shows that 77% of the total sample variation in the descriptor (smoke odor) is related to the
influence of the product type on it.
In the P-value column P-value is determined, which corresponds to the calculated value of
F-test.
In our example, P-value for x1 factor (products) depends on the values of F ; df and MS of
this factor in the first row of the table, and has a value of 0.00079.
P-value for the x2 factor (panelists) depends on the values of F ; df and MS of this factor in
the second row of the table, and is equal to 0.46.
According to the Fisher-Snedecor test, when P-value is less than 0.05 (P < 0.05), the data are
not consistent. Based on the calculation, analysis and comparison, the system makes conclusion
“Products differ in this descriptor; the scores of the panelists are consistent”.
In the case of a consistent and reliable evaluation, the software allows to build a sensory
profile (profilogram) of the product characteristic being evaluated (Figure 6) with a number of
intensity score axes equal to the number of specific descriptors.
Figure 5 shows an example of the sensory profile for three samples of cooked sausage in the
form of a polygon with vertices combining the obtained product characteristics.
Using similar procedures, the software allows to determine the position of a product among
competitors based on a comparison of its profile with competitors’ product profiles.
For comparison of the product profile with reference, reference product is preliminarily
produced, which is the basis for comparing all the products involved in the evaluation. The
characteristics of the reference sample determine the reference sensory profile, which is compared
with the profile of a similar sample from another batch (Figure 6).
The computer software also allows to identify changes in the sensory characteristics of the
product when replacing food ingredients, additives or spices in the formulation or using new
types of packaging, etc.
5. Experimental testing of the software
The given example of the network expert system and its dialog interface along with the individual
numerical scores and statistical evaluation by panelists provides the objectivized conclusion and
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recommendations concerning the product quality based on processing of the subjective data from
expert panels of up to 20 panelists by 15 descriptors and 6 product types with the construction
of profilograms with up to 15 descriptors and a possibility of data export to MS Excel. Thus,
the accuracy and reliability of the objectivized scores presented in Figure 4 is determined by the
criteria values for the specific case, as well as by the degree of agreement and competence of the
opinions from the qualified panelists evaluating technically uncontrollable sensory properties of
food products and their influence on evaluation.
6. Conclusion
Therefore, the computer software with the client-server architecture based on the multivariate
analysis of variance realizes the information technology for support of decision making in sensory
food evaluation contrary to the traditional expert systems and software packages. It performs
real-time collection, accumulation and statistical processing of sensory data from individual
panelists and geographically distributed panels and visual presentation of the objectified results in
different graphic forms.
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