Food science applications and international trends of artificial neural networks

Recently, research has been focusing increasingly on the system of artificial neural networks, and its results are used in many places by industrial practices. The success of these networks lies in their ability to recognize the complex relationships and patterns in data, as well as to predict unknown samples, thus enabling value and category predictions with high certainty. Artificial neural networks are very efficient tools for modeling non-linear trends within data. In many cases, they perform well where traditional statistical tools provide unsatisfactory results or unable to solve a given research problem. In our work, the operation principle and structure (topol-ogy) of artificial neural networks are summarized, as well as the classification and application possibilities of the networks. The latest food science applications are presented separately, based on the usage type (prediction, classification, optimiza-tion). Results show that artificial neural networks possess many beneficial properties, making them especially suitable for solving food science tasks

Nemzetközi büntetőjog és nemzetközi büntető igazságszolgáltatás = International criminal law and international criminal justice

A projekt megvalósítása során a magyar szakirodalomban elsőként végeztünk kutatásokat az un. nemzetközi jogi bűncselekmények (háborús- és emberiesség elleni bűncselekmények, a kínzás, illetve a népirtás) jogtörténeti és dogmatikája körében, illetve a nemzetközi büntetőbíróságok vonatkozó judikatúrájának bemutatására. A kutatás során a hangsúlyt a nemzetközi büntetőjogi felelősség speciális formáinak - az elöljáró (parancsnok) speciális felelőssége, joint criminal enterprise, az állam büntetőjogi felelőssége - feldolgozására helyeztük, illetve elemzésre kerültek a nemzetközi büntetőjogban és a nemzetközi jogban szabályozott büntethetőségi akadályok. Végül vizsgálat tárgyát képezte a nemzetközi büntetőbíráskodás kérdésköre, figyelemmel elsősorban a jogtörténeti előzményekre, illetve az ad hoc nemzetközi törvényszékek, illetve a Nemzetközi Büntetőbíróság működésére, az ezek előtt folyó bizonyításra. Ezen túlmenően kutatások kiterjedtek az EU büntetőjogi együttműködési rendszerére a jövőbeni fejlődési irányaira. Ezen a területen belül a magyar kutatások között elsőként foglalkoztunk a terrorizmus és a titkos információszerzés európai dimenzióinak jogi aspektusaival. | In this OTKA project we made first in Hungary research work on both historical and doctrinal aspects as well as court practice analysis of international crimes (war crimes, crimes against humanity, torture and genocide). In our research work we put the emphasis on the special forms of international criminal responsility (criminal responsibility of commanding officer, of joint criminal enterprises and of state). We analysed the problems of justification and excuse in international law and international criminal law as well. We also examined the historical aspects of the main issues of international criminal jurusdiction, the role of, and the rules of evidence before the ad hoc international criminal tribunals and International Criminal Court. Beyond these issues the project focused first in Hungary on the evolution and the future perspectivesof the criminal law cooperation system of the EU with special referece to the legal problems of terrorism and covert operations

Possibilities for the analysis of fruit and vegetable consumption based on a transtheoretical dynamic COM-B model

The objective of the transtheoretical dynamic COM-B (Capability, Opportunity, Motivation-Behavior) model is to understand why people take risks when it comes to their health and why they do not follow the instructions to protect their health. The model has been developed as the central part of a larger behavioral system called the Behavior Change Wheel (BCW), the goal of which is to assist the designers of a given intervention with factual data during the process leading from the behavioral analysis of the problem to the planning of the intervention. The COM-B model has been successfully applied in many cases. When increasing the consumption of fruits and vegetables, an essential condition for behavior change is that people have the ability, opportunity and motivation to change. The behavior was measured by the annual per capita spending on vegetables, potatoes and fruits, based on HKF (Household Budget Surveys), the latter being published in the STADAT issued by the Hungarian Central Statistical Office. It was assumed that the capability can be approximated by the expenditure on “Higher education“, the opportunity by the expenditure on “Gardens, plants and flowers, and motivation by the expenditure on “Sport, camping goods “, “Indoor sports equipment” and “Sports equipment, camping equipment “. A correlation was demonstrated between the expenditure on fruits, vegetables and potatoes and the expenditure on flowers, gardening and sports, however, there was no correlation in the case of money spent on higher education

Prediction of sensory preference by artificial neural networks, using sweet corn varieties as an example

According to our knowledge, there are only a few publications in available literature sources on the sensory characteristics and consumer preferences of sweet corn varieties. In our research, practical application of artificial neural networks (ANNs) is presented. In our study, 41 frozen sweet corn varieties were evaluated by a panel of expert sensory panelists (14 persons), by the method of profile analysis (MSZ ISO 11035:2001; ISO 13299:2003), on an unstructured scale of 0 to 100, then, in large-scale tests, 6 of the 41 varieties were evaluated by consumers (167 people) according to preference, on a structured scale of 1 to 9. Artificial neural networks require large amounts of data, therefore, on the expert and consumer data for the 6 varieties, 1,000 Monte Carlo simulations were run. 80% of the resulting dataset was used to train the created neural networks, and 20% was utilized to test them. The best prediction was given by the 4-node multi-layer feedforward neural network (MLFN), the smallest residues were obtained in this case during the training and the test, which were also validated by predictions on random numbers and cross-checking. Preference values of the other 35 corn varieties were predicted by this model. The most preferred variety was ‘Shinerock’ (8.46), while the least preferred ones, according to the predictions, were ‘Madonna’ and ‘Rustler’, with and average preference value of 2.7 (on a scale of 1 to 9). During the establishment of the artificial neural network model, product characteristics that are the main drivers of consumer acceptance were successfully identified: sweet taste, global taste intensity and juiciness. In general, it can be stated that prediction of the preference of different varieties is made possible by the validated product-specific artificial neural network presented

Mesterséges neurális hálózatok élelmiszertudományi alkalmazásai és nemzetközi trendjei Food science applications and international trends of artificial neural networks

A mesterséges neurális hálózatok rendszere napjainkra egyre inkább a kutatások fókuszába került, melynek eredményeit az ipari gyakorlatok számos helyen alkalmazzák. Sikerességük abban rejlik, hogy képesek az adatokban rejlő komplex kapcsolatok, és az adatokban rejlő mintázatok felismerésére, valamint az ismeretlen minták előrejelzésére is, így segítségükkel érték és kategória előrejelzések tehetők meg nagy biztonsággal. A mesterséges neurális hálózatok nagyon hatékony eszközök a nem lineáris trendek adatokon belüli modellezéséhez. Sok esetben ott is jól teljesítenek, ahol a hagyományos statisztikai eszközök nem kielégítő eredményeket mutatnak, vagy nem képesek adott kutatási probléma megoldására. Munkánkban összefoglaljuk a neurális hálózatok működési elvét, felépítését (topológiáját), a hálózatok csoportosítását és alkalmazási lehetőségeit. Külön részben mutatjuk be a felhasználási típusok alapján − predikció, osztályozás, optimalizálás − a legújabb élelmiszertudományi alkalmazásokat. Az eredmények azt mutatják, hogy a mesterséges neurális hálózatok számos előnyös tulajdonsággal rendelkeznek, melyek kifejezetten alkalmassá teszik őket az élelmiszertudományi feladatok megoldására. Recently, research has been focusing increasingly on the system of artificial neural networks, and its results are used in many places by industrial practices. The success of these networks lies in their ability to recognize the complex relationships and patterns in data, as well as to predict unknown samples, thus enabling value and category predictions with high certainty. Artificial neural networks are very efficient tools for modeling non-linear trends within data. In many cases, they perform well where traditional statistical tools provide unsatisfactory results or unable to solve a given research problem. In our work, the operation principle and structure (topology) of artificial neural networks are summarized, as well as the classification and application possibilities of the networks. The latest food science applications are presented separately, based on the usage type (prediction, classification, optimization). Results show that artificial neural networks possess many beneficial properties, making them especially suitable for solving food science tasks

Mesterséges neurális hálózatok élelmiszertudományi alkalmazásai és nemzetközi trendjei

A mesterséges neurális hálózatok rendszere napjainkra egyre inkább a kutatások fókuszába került, melynek eredményeit az ipari gyakorlatok számos helyen alkalmazzák. Sikerességük abban rejlik, hogy képesek az adatokban rejlő komplex kapcsolatok, és az adatokban rejlő mintázatok felismerésére, valamint az ismeretlen minták előrejelzésére is, így segítségükkel érték- és kategória-előrejelzések tehetők meg nagy biztonsággal. A mesterséges neurális hálózatok nagyon hatékony eszközök a nem lineáris trendek adatokon belüli modellezéséhez. Sok esetben ott is jól teljesítenek, ahol a hagyományos statisztikai eszközök nem kielégítő eredményeket mutatnak, vagy nem képesek adott kutatási probléma megoldására. Munkánkban összefoglaljuk a neurális hálózatok működési elvét, felépítését (topológiáját), a hálózatok csoportosítását és alkalmazási lehetőségeit. Külön részben mutatjuk be a felhasználási típusok alapján - predikció, osztályozás, optimalizálás - a legújabb élelmiszertudományi alkalmazásokat. Az eredmények azt mutatják, hogy a mesterséges neurális hálózatok számos előnyös tulajdonsággal rendelkeznek, amelyek kifejezetten alkalmassá teszik őket az élelmiszertudományi feladatok megoldására

Vision tests of sensory judges - review

In sensory examinations, judges must be in good general health. They should not have any deficiencies that could affect their perception or adversely affect their sensory performance, and thus can affect the reliability of their judgments. The vision of a judge is basically determined by three factors: visual acuity, contrast sensitivity and color vision. In the international practice of sensory analyses, color vision is generally examined. Color blindness is typically tested using the Ishihara pseudo-isochromatic color test, while color discrimination ability is examined using the Farnsworth-Munsell 100 hue test [1]. The most accurate tool to detect color blind people is the anomaloscope. Screening for color blind people is important because they have both poorer color discrimination abilities and poorer color identification abilities. The results of online color vision tests are significantly affected by the display device and its settings (monitor resolution, color-correct calibration), as well as test conditions: test geometry (relative position of the light source, the test book and the eye), photometric and spectral nature of the light source and the monitor, and the adaptation state of the eye. Unfortunately, the specifications for standard sensory tests do not require the visual acuity and contrast sensitivity testing of sensory judges, however, these properties obviously affect visual perception, so testing them is necessary

Organoleptic validation of a color masking system specified for green and black tea (Camellia sinensis L.) brews

Numerous studies focusing on organoleptic tests have shown that the visual characteristics of the product under study cause a preconception (expectation error) in the judge, which distorts the perception of the other sensory characteristics to varying degrees. In cases where the rating is not based on visual judgment, it is advisable to ensure test conditions where it can be ruled out that the color stimulus of the product does not influence the decision of the judges [1]. Consequently, it is necessary to reduce the intensity of color sensation or the quality of the sensation, but most of all to mask them. The methods widely used in practice (blindfolding, colored vessels, colored lenses, etc.) are subject to distortions, therefore, a spectrally adjustable lighting system specified for the types of the given product can provide a solution to eliminate these by optimizing the parameters of the observation and by the sensory validation of them. The said spectrally adjustable LED measuring system with a homogeneous light distribution is controlled by arduino (an open-source electronic prototyping platform enabling users to create interactive electronic objects - ed.). In our study, the organoleptic validation of a color masking system specified for green and black tea (Camellia sinensis L.) brews is presented. Participants of the experiment were tested according to international standards [2, 3]; based on our test results, they had normal vision in all respects. The results showed that, by color masking the smallest detectable threshold value and by determining the spectral characteristics, differences in visual perception between sample pairs with a certain difference in color stimulus can be partially or completely masked. As a result, under perfectly masking illumination, expectation errors due to perception do not distort the judgment of the other organoleptic characteristics (such as smell, taste, texture and mouth coating) of tea brews. Partial masking eliminates color differences in many cases, increases judgment time 4 to 8-fold, however, differences due to brightness remain observable

How to objectively determine the color of beer?

Beer color is an important sensory attribute, the first one that the consumer observes. There are two standard methods accepted for determining the color of these products, one related to the European Brewery Convention (EBC) and the other is the Standard Reference Method (SRM). Both methods are based on absorbance, but in case of the more and more popular fruit beers these methods give false result since these products appear in varied colors and have different spectra than regular beers. In this study 39 different types of beers were investigated, including fruit beers and beer based mixed drinks to compare their color in CIE 1976 L*a*b* color space, absorption-based colors and transmission spectra. DE*ab values of products with less than 5% EBC difference ranged from 4.5 to 17.4. There were magnitude differences in the transmission spectra of these products, fruit beers showed different tendencies due to the added fruit or fruit juice. The highest DE*ab value belonged to two traditional Weissbiers. Absorption-based methods are not able in many cases to differentiate between products which have nearly the same EBC or SRM color but visually are different. A multi-wavelength method would be reasonable to be developed for more objective and accurate beer color determination

A zöldség- és gyümölcsfogyasztás vizsgálatának lehetősége az elméleteken átívelő dinamikus COM-B modell alapján = Possibilities for the analysis of fruit and vegetable consumption based on a transtheoretical dynamic COM-B model

Az elméleteken átívelő és dinamikus COM-B (Capability, Opportunity, Motivation – Behaviour - Képességek, Lehetőségek, Motiváció – Viselkedés) modell célja, hogy megérthessük, miért vállalnak az emberek kockázatot, amikor az egészségükről van szó, illetve miért nem követik az előírásokat az egészségük védelme érdekében. A modellt egy viselkedésváltoztatási keréknek (BCW – Behaviour Change Wheel) nevezett nagyobb viselkedési rendszer középpontjaként fejlesztették ki, amelynek célja, hogy tényadatokkal segítse egy adott beavatkozás tervezőit a probléma viselkedési elemzésétől a beavatkozás megtervezése felé vezető folyamat során. Számos esetben alkalmazták sikeresen a COM-B modellt. A gyümölcs- és zöldségfogyasztás növelése során a viselkedés megváltoztatásának elengedetlen feltétele az, hogy az embereknek meglegyenek a képességeik, a lehetőségeik és a motivációjuk a változtatáshoz. A viselkedést zöldségre, burgonyára és gyümölcsre fordított éves egy főre jutó kiadással mértük a HKF (Háztartási Költségvetési Felvételek) alapján, ez utóbbi a Központi Statisztikai Hivatal által közzétett STADAT-ban jelent meg. Feltételeztük, hogy ezt a képességet a „Felsőfokú oktatás”-ra, a lehetőségeket a „Kertek, növények és virágok”- ra, és a motivációt a „Sport, kempingcélú javak”-ra, a „Szobai sporteszközök”-re és a „Sportszerek, kempingcikkek”-re költött kiadással szimbolizálhatjuk. Korrelációt mutattunk ki a zöldségekre, gyümölcsökre és burgonyára, illetve a virágokra, kertészkedésre és a sportra költött kiadások között, viszont nem adódott korreláció a felsőfokú oktatás esetében. The objective of the transtheoretical dynamic COM-B (Capability, Opportunity, Motivation - Behavior) model is to understand why people take risks when it comes to their health and why they do not follow the instructions to protect their health. The model has been developed as the central part of a larger behavioral system called the Behavior Change Wheel (BCW), the goal of which is to assist the designers of a given intervention with factual data during the process leading from the behavioral analysis of the problem to the planning of the intervention. The COM-B model has been successfully applied in many cases. When increasing the consumption of fruits and vegetables, an essential condition for behavior change is that people have the ability, opportunity and motivation to change. The behavior was measured by the annual per capita spending on vegetables, potatoes and fruits, based on HKF (Household Budget Surveys), the latter being published in the STADAT issued by the Hungarian Central Statistical Office. It was assumed that the ability can be approximated by the expenditure on “Higher education“, the opportunity by the expenditure on “Gardens, plants and flowers, and motivation by the expenditure on “Sport, camping goods “, “Indoor sports equipment” and “Sports equipment, camping equipment “. A correlation was demonstrated between the expenditure on fruits, vegetables and potatoes and the expenditure on flowers, gardening and sports, however, there was no correlation in the case of money spent on higher education