CC BY
15UDC 004.94:664 DOI: 10.21323/2414-438X-2020-5-1-4-8
Review paper
ABOUT A «DIGITAL TWIN» OF A FOOD PRODUCT
Marina A. Nikitina*, Irina M. Chernukha, Andrey B. Lisitsyn
V. M. Gorbatov Federal Research Center for Food Systems of Russian Academy of Sciences, Moscow, Russia
Key words: digital twin, simulation model, chemical composition, functional-technological properties, food product Abstract
The paper presents definitions of digital twins. The authors examine a hypothesis that a digital twin of a food product is a mathematical (simulation) model that includes the whole variety of factors influencing quality and safety. An approach to the mathematical setting of the structural optimization task at different stages of description of the technology for a food product digital twin is analyzed. The first stage, which has several levels, is connected with correspondence of the nutritional and biological values to the medico-biological requirements. The second stage is linked with predetermination of structural forms, the third with perception of sensory characteristics (color, odor and so on). The universal method for assessment of quality and efficiency of a food product digital twin using the generalized function (integral index) is described. Different individual responses can be components of the additive integral index: physico-chemical, functional-technological and organoleptic.
Introduction
The term digital twin appeared in 2003 in the framework of the Course on Product Lifecycle Management (PLM) in Florida Institute of Technology (https://www.fit.edu/) [1].
Over the last decade, many definitions of a digital twin have appeared. The most widespread definitions are given in [2]:
1) A digital twin is an integrated multiphysics, multiscale, probabilistic simulation of an as-built vehicle or system, which uses the best available physical models, sensor updates, history and so on [3];
2) A joint model of a real machine, which works on the cloud platform and simulate the health condition with integrated knowledge both from manageable data of analytical algorithms and from other available physical knowledge [4];
3) A digital twin is a digital replication of a living or nonliving physical entity. Combining physical and virtual worlds, data are transformed unnoticeably allowing a virtual entity to exist simultaneously with a physical entity [5];
4) The use of digital replication of a physical system for real-time optimization [6];
5) A dynamic virtual representation of a physical object or system throughout the life cycle using real-time data for understanding, learning and reasoning [7];
6) A digital twin is a real mapping of all components in the product life cycle using physical data, virtual data and data of their interaction [8].
A digital twin has been introduced in the KAMAZ sites. KAMAZ has already created the 3D models of 28 units of machine tools with CNC and 20 universal machine tools as well as more than 50 units of different technological equipment (robots, manipulators, turn-over devices, roller tables). The 3D models are used in simulation of mechanical processing and assembly as well as for arrangement of equipment in the 3D plant design.
Digital copies came into use for effective operation of trains Sapsan and Lastochka. Virtual models are used for optimization of rail transportation. Thereby, costs of repair work are reduced and operations that duplicate each other are eliminated. In 2018, introduction of a production digital twin was also announced by «Transmashholding». The system calculates the results of production plan fulfillment with given parameters in a matter of minutes and quickly reacts on the customer requests [9].
Definition No.4 is the most suitable for the term digital twin of a food product.
The present paper examines a possibility of using the theory of a digital twin in description of food products. The authors of the paper propose a hypothesis that a food product digital twin is a virtual model of a product, namely its mathematical model (simulation model*) that combines the whole variety of factors from the chemical composition and functional-technological properties to organoleptic indices. Using a digital twin of a food product before its launch into production, engineers-technologists can analyze the nutritional, biological and energy values as well as other product characteristics.
The dispersion of parameters and properties of biological raw materials can be compensated in the real-time operational conditions by selection of optimal strategies of component redistribution and alteration of technological schemes depending on the actual resource and component composition of biological raw materials. Therefore, each possible condition of the input flow of biological raw materials will be contrasted with a certain structural and regime alternative that ensure maximum product processing from a raw material unit at maximum approximation to the normative indices.
* A simulation model is a logical mathematical description of an object that can be used for computer experiments for design, analysis and assessment of object function [10].
FOR CITATION: Nikitina M. A., Chernukha I. M., Lisitsyn A. B. About a digital twin of a food product. Theory and practice of meat processing.
2020;5(1): 4-8. DOI 10.21323/2414-438X-2020-5-1-4-8
Main part
By a mathematical (simulation) model is meant an equation that links an optimization parameter with factors
y = P(xi, x2-xk )i
where cp{xx, x2...xk ) is a response functi.n.
To design an experiment, a factor should have a certain number of discrete levels. A fixed set of factor levels determines one of possible states of the object under investigation. At the same time, this is a condition for conducting one of possible experiments. If we try all possiMe sets oft ntates, we will have many different states of the object under investigation. The number of possible experiments is determined by the equation
N = pk,
where, N — number of experiments; p — number of levels; k —
number of factors.
The real objects usually have huge complexity.For example, a system with 5 factors at 5 levels that mighn tppeor an first glance to be simple has 3125 condinions 31pn),
and for 10 factors at 4 levels their number will tee above f million (N = 410 = 1048576). In these cases, performance of all experiments is practically impossible. Therefore, advantages of using the digital twin technology become immediately evident.
Setting up the task of structural optimization at different levels of description of the digital twin technology for a food product is reduced to minimizaticso on deviatione of the actual parameters from the given normative (rtferenie, desired) values with finding a balance regarding; the chosen indices between the input and output material flows.
At the first stage of the balance analysii bp raw materials for manufactured products, the syttem structural optimization is reduced to redistribution ol raw materialt and the supply stream that ensure minimum deviation Sioto the given (reference, desired) product ttructure undsr the given conditions and restrictions. The first stagt consists f f several levels.
The first level represents the description of the product chemical composition, and a criterion of minimum devialion P(z) from the set structure of nutritional faluu ingredients across the whole variety of product componands ii introduced for product quality assessment:
N X
f
i=1 k=1
;=i
with A,a — the coefficients of significance of deviations of the dth index; of the chemicaf composition in fhe gh product determined depending on the biological value, deficiency, cost and other component characteristics.
The seconf level is linked with quantitative assessment of mono-structutes — fngredientc of tine product biological value (essential amino acids, polyunsaturated fatty acids and others); that is, components of the chemical composition
elements. In this ctse, a criterion is ex]tccssed as a sum of squated deviafions of the confeat of month-structure elements from their falues in a certain reference balanced puoduct (for example egg protein or broast milcf
C mi ~\2
j=1
/ \ N X T
P(s)=IH fit
i=1 k=1 i=1
ikt
*jktbjkXij
S --
Zb
j=1
jkXj
with the coefficients /a/p odsignificanf e oO Ceviation od the ith ingredient of fhe fC ele ment of1 the fh product.
According to the concett of the minimum tegoee of assimilability by the body ot the elements of the product chemical compasition (minimal acore),
GT = mi-
X>,kAkXi,
j=1
Skt!b,kx j
j=1
>; k = 1, x, i = 1, N
which shows thee minimum content relative to the reference oy the iilt microtlnment oi che kth ggnttiap» m tine fh product.
The total losses of the biological valnm of product comi )con ento can ltn^ use d at a criterion tf the system efficien cy fuf nptimization:
i(if) = fh ( (i - ^ g a].lttbf xi}.
¿=1 fi y=i
Th^e seconpstaig iit linliiei1 with designing structural formn of a food prodyct.
The optimal tecipe ol a food product at the fst sttge does not; guarantee transformation into a ctabfe system with re-guired structural-mechanical and functional-lechnological indiccs during technological processing.
A^cttv:il_s;ii^iLon of certain structural forms (consistency appearfncc, cohesiveness, iexiure and so on)1 by a food com-poaition is conthtioned by peculiarities of collmid-ichenfical precises of the «protein-protein», «protem-wster», «protain-fat» amd «water-protein-fnt» ^yfpxes. It is impocsible to be suta ehyt recipe ingredienfs wsiif^l Heey tsansformo d into a scable tiisperse ^.y^tem with required properties as -s reeult of t^ch^nologgi^ccil ^roci^siting [11].
To realize; ^hie second stiige, it it necessary co have infitt-rnatiion about actyal valucs of the eunceionalrtechnologiosi properties (FTP) of main raw materials, auxiliary ingredients, kinetics of biochemical and coltoid-chemicalprocesses (first of all, stcucturization) in multi-compgnent fotd syttems, nnalytical and empirical dependences tfae dtaracterize the main regulasities oi behavior ofheterogeneous disperse systems upon variation of the physico-chemical factors.
With eppearance of more and more convenient tools for processing and storage of large data, an opportunity for increasing the number of variants of using and alternatives for the development of a food product digital twin arises, which in turn, increases adequacy and validity in decision-making.
Nowadays, food product databases should contain not only information about the main indices (moisture, protein,
Table 1 . The main functional-technological properties of certain types of meat raw materials [12]
Before thermal processing
Raw material type Water-binding capacity to total moisture, % Plasticity, x10-1 m2/kg Water absorption, % of total mass Fat absorption, % of total mass
Beef, 2nd grade Cutlet meat 3 mm 78. 0 ± 2 .8 cutter 87 . 0 ± 5 . 0 3 mm 9 . 74 ± 0 . 4 cutter 11. 76 ± 0 . 8 3 mm 56 .2 ± 4. 4 cutter 55 . 3 ± 3 .8 3 mm 28. 4 ± 3 . 0 cutter 30 . 9 ± 1. 6
85 . 9 ± 3 . 4 IX Q -L A 88. 4 ± 4 . 6 HA ÎL-X-X Q 10 . 6 ± 0 . 8 O Ad-X-A 1 12.13 ± 1.0 11 C J.A C 52 .3 ± 3 . 7 XA Û4-H 54. 8 ± 6. 2 £LA 1 -l- A O 26. 3 ± 3 . 0 I'") C 4. 1 ^ 31.4 ± 1.8 OQ a -i- o i
Semifat pork Fat pork 73 . 8 ± 6. 0 7A /ill') /4. 6 ± 3 . 8 H Q Q _L 1 C. 9 . 46 ± 0 . 3 10 . 52 ± 0 . 2 H Q _L ft 4 11. 5 ± 0 .5 12 . 31 ± 0 . 3 O OxA 34 .9 ± 2 . 3 29 . 2 ± 2 . 4 1 J.2 7 64 2 ± 4 2 32 . 2 ± 2 . 6 jll A A 22 . 5 ± 1. 2 19.8 ± 2 .0 Ij) n i 1 c. 28 6 ± 2 1 23 .4 ± 1. 7 ir Q i i -2
Mutton, single grade Meat from beef heads 70 4 ± 3 2 64 . 4 ± 1. 8 /8. 8 ± 2 .6 68 . 6 ± 2 . 0 7.8 ± 0 .4 6. 2 ± 0 . 2 C Q -I- A is. 8 . 9 ± 0 . 4 6 .6 ± 0 . 2 is. Q J- A C 40 . 2 ± 3 J 36. 9 ± 1. 3 43 4 ± 3 4 37 .3 ± 1. 2 24. 9 ± 1.6 12 . 9 ± 0 .9 1 Q Q _i_ A 7 35 8 ± 1 3 17 . 5 ± 0 . 5 Ol O -L 1 "X
Meat from pork heads Beef rumen 49 . 8 ± 3 . 7 O/i I±l c 65 . 4 ± 4 . 2 OC Ail'» 5 . 8 ± 0 . 6 5 . 8 ± 0 . 6 11 1 j. n n 6 . 8 ± 0 . 5 7 . 7 ± 0 . 4 11 cj.no 26 . 6 ± 2 . 4 53 . 2 ± 4 . 4 in /T I 1 Q 28 7 ± 2 1 58 .3 ± 3 . 8 ir ft i ft 18. 8 ± 0 .7 26 .1 ± 2 . 2 21 9 ± 1 3 37 . 8 ± 2 . 5 Q i 1
Beef lungs Beef spleen 94 3 ± 1 6 66. 6 ± 3 . 4 7Q •■> _i_ -X X 96 . 0 ± 1. 2 64 . 2 ± 4. 0 on 10.10 11. 3 ± 0 . 9 18.1 ± 0 .4 Q 1 J- ft £ 11.5 ± 0 . 8 18 .8 ± 0 . 3 Q H J- ft A 30 . 6 ± 2 . 8 26. 0 ± 2 . 0 Kill 1 35 0 ± 4 0 29 . 3 ± 2 . 7 u C 1 I A 25 . 2 ± 2 . 2 16.6 ± 1.1 oou.no 32 8 ± 3 1 18. 2 ± 0 .8 11 1 -I- 1 "X
Esophagus meat Beef lips 78 2 ± 3 3 100±0 . 0 80 . 2 ± 2 . 8 100±0 . 0 8 .1 ± 0 . 6 4 . 2 ± 0 . 5 8.7 ± 0 . 4 4. 8 ± 0 .5 15 .1 ± 1.1 5 .1 ± 0 . 6 16. 5 ± 1. 4 6.1 ± 0 . 6 9 . 8 ± 0 . 9 9 . 34 ± 0 . 8 12 2 ± 1 3 9 . 8 ± 0 . 4
fat, energy value, amino acid, fatty acid, vitamin and mineral compositions) but also information about functional-technological properties of raw materials of animal and plant origin.
The data presented in Table 1 [12], which contain the main characteristics of functional-technological properties of certain types of protein-containing raw materials, can be used to determine conditions of component compatibility in a recipe, optimize a choice of an ingredient ratio with consideration for a probability of inter-regulation of properties of individual constituents and the resulting system in general.
N
1) a model of the water-binding capacity — WBC = X wixi,
i=i
where w; — where is the water-binding capacity of the ith recipe component; N
2) a model of the fat-holding capacity — FBC = X lixi,
i=1
where l. — is the water-binding capacity of the ith recipe component;
3) a model of the water-holding capacity — WHC = X vtxt,
i=1
where vi — is the water-holding capacity of the ith reci-pecomp onent; N
4) a model of the ultimate shear stress — USS = X qixi,
i=1
where qi — is an index of the ultimate shear stress of the ith recipe component;
N Vx
5) a model of the dynamic viscosity (n) — n = X
i=i n
where n — is the dynamic viscosity of the ith recipe component; V is the volume fractions of recipe components; ^
N x 1
6) a model of the density (p) — p= X~ , where
X—
r-1 p;)
pi — is the density of the ith recipe component;
7) a model of the active acidity index (pH) — pH =
f N
= - lgl P c,10"
V i=1
- pH
, where — is the active acidity
index of the i'th recipe component; x. is the mass fraction of the ith recipe component in the given recipes from (1) to (7).
The third stage is the determination of the organoleptic properties of a product under design by the methods of expert assessment with the control of agreement.
Therefore, analysis and control of optimality of different structural variants are carried out based on the complex simulation model of a food product, that is, on a digital twin.
Assessment of the efficiency of the developed food product is possible only upon analysis of many different indices.
It is convenient to generalize (convolve) a set of indices into the united quantitative non-dimensional index. To this end, it is necessary to introduce for each of them a non-dimensional scale, which should be of the same type for all unified indices. This approach makes them comparable.
After building a non-dimensional scale for each index, the next difficulty appears — a choice of a rule for combining initial individual indices into an overall index. There is no unified rule.
One of the most common overall indices is the Harrington generalized function. The basis for building this function is an idea of transformation of natural values of individual indices into the non-dimensional scale of desirability or preference. The desirability scale belongs to psychophysical scales.
The authors use a functional [13], an integral index in a form of an additive convolution, to assess quality and adequacy of a food product.
A functional, first of all, determines a correspondence to specified requirements by the chemical composition, functional-technological properties and organoleptic indices. A functional reflects an average weighted total deviation of actual values of condition parameters from the normative values.
With regard to weight coefficients and separation of certain groups of factors, the equation has the following form:
0(x) = 1 -
- Ë a Ë
j=1
¿=1
i - 0 ^ Xj_Xj
M
^ max
n
where, n — is the number of the combined indices; x.., x0 — are
j j
the actual and desirable values; Ax4 — is the maximum de-
j
viation from a desirable value for the kth quality level; b. — is
the weighting coefficient of the j1 parameter in the ith group;
a. — is the coefficient of group significance.
A value of the quality coefficient changes from 1 upon complete agreement of the obtained values with recommended (the best quality) to 0 upon reaching the limit of the quality level (the limiting value), so that at negative values of a functional, there is no correspondence to the specified quality level.
A value of the quality coefficient changes from 1 upon complete agreement of the obtained values with recommended (the best quality) to 0 upon reaching the limit of the quality level (the limiting value), so that at negative values of a functional, there is no correspondence to the specified quality level.
To determine weighting coefficients, the method of a full factorial experiment can be used, when the following values are put into the columns of the response function
yir of the rth repetition of the kth experiment: 1-0.7 — when a product has a very good quality level; 0.7-0.3 — good; 0.3-0 — satisfactory; 0-(-0.2) — bad; less than (-0.2) — a very bad quality level.
Conclusion
A «digital twin» of a food product is its simulation model associated with processing of a large number of information about the chemical composition, functional-technological properties and organoleptic indices. «New» simulation technologies allow engineers-technologists to use digital twins to carry out tests in the virtual world saving time, money and resources for physical scientific experiments on primary trial of recipes for new food products with complex composition and characteristics.
Acknowledgment
The article is published in the framework of execution of scientific-research work theme No. 0585-2019-0008 of the State task of the V. M. Gorbatov Federal Research Center for Food Systems of RAS.
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AUTOOR INFORMATION
Marina A. Nikitina — candidate of technical sciences, docent, leading scientific worker, the Head of the Direction of Information Technologies
of the Center of Economic and Analytical Research and Information Technologies, V. M. Gorbatov Federal Research Center for Food Systems of
Russian Academy of Sciences. 109316, Moscow, Talalikhina str., 26 Tel: +7-495-676-92-14
E-mail: [email protected]
ORCID: https://orcid.org/0000-0002-8313-4105
^corresponding author
Irina M. Chernukha — doctor of technical sciences, professor, Academician of the Russian Academy of Sciences, leading research scientist of
Experimental clinic — laboratory «Biologically active substances of an animal origin, V. M. Gorbatov Federal Research Center for Food Systems
of Russian Academy of Sciences. 109316, Moscow, Talalikhina str., 26. Tel: +7-495-676-97-18
E-mail: [email protected]
ORCID: https://orcid.org/0000-0003-4298-0927
Andrey B. Lisitsyn — doctor of technical sciences, professor, Academician of the Russian Academy of Sciences, Scientific supervisor, V. M. Gor-batov Federal Research Center for Food Systems of Russian Academy of Sciences, 109316, Moscow, Talalikhina str., 26. Tel: +7(495)-676-95-11. E-mail: [email protected]
ORCID: https://orcid.org/0000-0002-4079-6950
All authors bear responsibility for the work and presented data.
All authors made an equal contribution to the work.
The authors were equally involved in writing the manuscript and bear the equal responsibility for plagiarism. The authors declare no conflict of interest.
Received 23.01.2020 Accepted in revised 25.02.2020 Accepted for publication 05.03.2020
