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 e ects, 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 in uence 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.
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 con rm the objectiveness of the results [
Regardless of the experience and degree of training of panelists, individual di erences always
arise in their scores of food quality, associated with sensory sensitivity, knowledge, and the
subjectivity of the scales for sensory indicator measurement [
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 methods of multivariate data analysis with an objective assessment of adequacy and con rmation 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 [
Therefore, the adequacy of the scores in each case essentially depends on the in uence 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 [
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 [
Formal characterization of nal product sensory evaluation obtained as a result of expert survey is
achieved by rank correlation [
Since opinions of panelists not always match each other, total ranks are determined to obtain an objective assessment. and the coe cient of concordance W is calculated, which characterizes the objective relationship between the scores of m independent panelists using the equation: (1) (2) (3) (4) (5) W =
12 S(d2) m2 (n3 n)
; n " m S(d2) = X X xi;j j=1 i=1 gj =
M Pim=1 xij ; j = 1; n; n Q = Q0 + X gj xj ; j=1 where Q0 { score of reference product, and allows to objectively identify the most signi cant factors of deviations from the speci ed quality.
The vector of weight coe cients 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 coe cients. 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.
The data with a multi-level structure is analyzed by multifactorial statistical procedures [
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 signi cance level ( = 0:01; or = 0:05; or = 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 di erences between panelists in
the panel is taken into account [
Analysis of variance [
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 signi cance ( = 0:01; or = 0:05; or = 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 in uence of the factor is estimated by the Fisher-Snedecor test at the chosen level of signi cance, not only as the ratio of the intergroup variance of scores from the relationship of factors to the intragroup variance from the experimental error F = MMSSx212x2 , but also as
E
MMSSx2E22 of factor variances of expert scores for intragroup
MMSSx2E21 and F = a relationships F = variance.
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 pro le; 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 ve- and nine-point scales are used, according to which each indicator has 5 or 9 degrees of quality, respectively. According to a vepoint 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, but undesirable; 2 or 1 { unacceptable.
name of the evaluated descriptors; folder for saving les 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 identi cation 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 rst product, the panelist proceeds with the next product or nishes 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 a ect the \smoke odor" tdescriptor, and the alternative hypothesis (H1) { the products a ect the \smoke odor" descriptor. To verify them, Fisher's exact test was used at a signi cance level of = 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; +1). 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) a ects the \smoke odor".
Similarly, assessment of the second factor takes place, i.e. \panelists". With a null (H0) hypothesis, panelists do not a ect the \smoke odor" descriptor and with alternative (H1) hypothesis, panelists a ect 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; +1). Since F does not fall in the critical region, the null (H0) hypothesis is accepted, i.e. in uence of x2 factor (panelists) on the \smoke odor" was not con rmed. shows that 77% of the total sample variation in the descriptor (smoke odor) is related to the in uence 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 rst 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 di er 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 pro le (pro logram) of the product characteristic being evaluated (Figure 6) with a number of intensity score axes equal to the number of speci c descriptors.
Figure 5 shows an example of the sensory pro le 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 pro le with competitors' product pro les.
For comparison of the product pro le 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 pro le, which is compared with the pro le 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.
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 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 pro lograms 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 speci c case, as well as by the degree of agreement and competence of the opinions from the quali ed panelists evaluating technically uncontrollable sensory properties of food products and their in uence on evaluation.
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 objecti ed results in di erent graphic forms.