Metrika

  • citati u SCIndeksu: 0
  • citati u CrossRef-u:0
  • citati u Google Scholaru:[]
  • posete u poslednjih 30 dana:15
  • preuzimanja u poslednjih 30 dana:8

Sadržaj

članak: 2 od 6  
Back povratak na rezultate
Food and Feed Research
2021, vol. 48, br. 1, str. 9-21
Veštačke neuronske mreže za predviđanje kvaliteta različitih genotipova paradajza
Pestorić Mladenka V.a ORCID, Mastilović Jasna S.a ORCID, Kevrešan Žarko S.a, Pezo Lato L.b ORCID, Belović Miona M.a ORCID, Glogovac Svetlana K.c, Škrobot Dubravka J.a, Ilić Nebojša M.a, Takač Adam J.c
aInstitut za prehrambene tehnologije, Novi Sad, Srbija
bInstitut za opštu i fizičku hemiju, Beograd, Srbija
cNaučni institut za ratarstvo i povrtarstvo, Novi Sad, Srbija

e-adresa: miona.belovic@fins.uns.ac.rs
Projekat:
Razvoj i primena novih i tradicionalnih tehnologija u proizvodnji konkurentnih prehrambenih proizvoda sa dodatom vrednošću za evropsko i svetsko tržište - Stvorimo bogatstvo iz bogatstva Srbije (MPNTR - 46001)
Stvaranje sorata i hibrida povrća za gajenje na otvorenom polju i u zaštićenom prostoru (MPNTR - 31030)
Ministarstvo prosvete, nauke i tehnološkog razvoja Republike Srbije (institucija: Institut za prehrambene tehnologije, Novi Sad) (MPNTR - 451-03-68/2020-14/200222)
Ministarstvo prosvete, nauke i tehnološkog razvoja Republike Srbije (institucija: Naučni institut za ratarstvo i povrtarstvo, Novi Sad) (MPNTR - 451-03-68/2020-14/200032)

Ključne reči: kvalitet svežeg paradajza; senzorska ocena; fizičko-hemijska svojstva; model veštačkih neuronskih mreža
Sažetak
Senzorska analiza predstavlja najbolje sredstvo za precizno opisivanje kvaliteta svežih namirnica. Međutim, to je skupa i dugotrajna metoda koja se ne može koristiti za merenje pokazatelja kvaliteta u realnom vremenu. Cilj ovog rada bio je da doprinese proučavanju odnosa između podataka dobijenih primenom senzorske analize i instrumentalnih metoda i da definiše odgovarajući model za predviđanje senzorskih svojstava svežeg paradajza pomoću određivanja fizičko-hemijskih svojstava. Analiza glavnih komponenti (RSA) primenjena je na eksperimentalne podatke da bi se okarakterisali i diferencirali posmatrani genotipovi, objašnjavajući 73,52% od ukupne varijanse, koristeći prve tri glavne komponente. Model veštačke neuronske mreže (ANN) korišćen je za predviđanje senzorskih svojstava na osnovu rezultata dobijenih osnovnim hemijskim i instrumentalnim određivanjima. Razvijeni ANN model predviđa senzorska svojstva sa visokom adekvatnošću, sa ukupnim koeficijentom determinacije od 0,859.
Reference

*** (2012) Sensory analysis: General guidelines for the selection, training and monitoring of selected assessors and expert sensory assessors: ISO 8586. Geneva: International Organization for Standardization

*** (2014) Methodology: Guidelines for monitoring the performance of a quantitative sensory panel: ISO 11132. Geneva: International Organization for Standardization

*** (2007) Sensory analysis: General guidance for the design of test rooms: Amendment 1: ISO 8589. Geneva: International Organization for Standardization

*** (1991) United States Standards for grades of fresh tomatoes. United States Department of Agriculture-Agricultural Marketing Service

2

AOAC International (2000) Official methods of analysis. Arlington: Association of Official Analytical Chemists, 17th ed

Arias, R., Lee, T.C., Logendra, L., Janes, H. (2000) Correlation of lycopene measured by HPLC with the L*, a*, b* color readings of a hydroponic tomato and the relationship of maturity with color and lycopene content. Journal of Agricultural and Food Chemistry, 48: 1697-1702

Bahramparvar, M., Salehi, F., Razavi, S.M. (2014) Predicting total acceptance of ice cream using artificial neural network. Journal of Food Processing and Preservation, 38(3): 1080-1088

1

Baldwin, E.A., Scott, J.W., Shewmaker, C.K., Schuch, W. (2000) Flavour trivia and tomato aroma: Biochemistry and possible mechanisms for control of important aroma components. HortScience, 35: 1013-1021

4

Basheer, I.A., Hajmeer, M. (2000) Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1): 3-31

1

Batu, A. (2004) Determination of acceptable firmness and colour values of tomatoes. Journal of Food Engineering, 61(3): 471-475

Cancilla, J.C., Wang, S.C., Díaz-Rodríguez, P., Matute, G., Cancilla, J.D., Flynn, D., Torrecilla, J.S. (2014) Linking chemical parameters to sensory panel results through neural networks to distinguish olive oil quality. Journal of Agricultural and Food Chemistry, 62(44): 10661-10665

1

Canene-Adams, K., Campbell, J.K., Zaripheh, S., Jeffery, E.H., Erdman, J.W. (2005) The Tomato As a Functional Food. Journal of Nutrition, 135(5): 1226-1230

Carbonell, L., Izquierdo, L., Carbonell, I., Costell, E. (2008) Segmentation of food consumers according to their correlations with sensory attributes projected on preference spaces. Food Quality and Preference, 19: 71-78

Corollaro, M.L., Endrizzi, I., Bertolini, A., Aprea, E., Demattè, M.L., Costa, F., Biasioli, F., Gasperi, F. (2013) Sensory profiling of apple: Methodological aspects. Postharvest Biology and Technology, 77: 111-120

Geeson, J.D., Browne, K.M., Maddison, K., Shepherd, J., Guarald, F. (1985) Modified atmosphere packaging to extend the shelf life of tomatoes. Journal of Food Technology, 20: 339-349

3

Hu, X., Weng, Q. (2009) Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment, 113(10): 2089-2102

1

Kaiser, H.F., Rice, J. (1974) Little Jiffy, Mark IV. Educational and Psychological Measurement, 34(1): 111-117

Kyriacou, M.C., Rouphael, Y. (2018) Towards a new definition of quality for fresh fruits and vegetables. Scientia Horticulturae, 234: 463-469

1

Lenucci, M.S., Cadinu, D., Taurino, M., Piro, G., Dalessandro, G. (2006) Antioxidant Composition in Cherry and High-Pigment Tomato Cultivars. Journal of Agricultural and Food Chemistry, 54(7): 2606-2613

2

Maul, F., Sargent, S.A., Sims, C.A., Baldwin, E.A., Balaban, M.O., Huber, D.J. (2000) Tomato Flavor and Aroma Quality as Affected by Storage Temperature. Journal of Food Science, 65(7): 1228-1237

Oraguzie, N., Alspach, P., Volz, R., Whitworth, C., Ranatunga, C., Weskett, R., Harker, R. (2009) Postharvest assessment of fruit quality parameters in apple using both instruments and an expert panel. Postharvest Biology and Technology, 52: 279-287

6

Otto, M. (1999) Chemometrics statistics and computer application in analytical chemistry. Weinheim: Wiley-VCH

Pezo, L.L., Ćurčić, B.L., Filipović, V.S., Nićetin, M.R., Koprivica, G.B., Mišljenović, N.M., Lević, L.B. (2013) Artificial neural network model of pork meat cubes osmotic dehydration. Hemijska industrija, vol. 67, br. 3, str. 465-475

Pinela, J., Barros, L., Carvalho, A.M., Ferreira, I.C. (2012) Nutritional composition and antioxidant activity of four tomato (Lycopersicon esculentum L.) farmer' varieties in North Eastern Portugal homegardens. Food and Chemical Toxicology, 50: 829-834

Pinheiro, J., Alegria, C., Abreu, M., Gonçalves, E.M., Silva, C.L.M. (2013) Kinetics of changes in the physical quality parameters of fresh tomato fruits (Solanum lycopersicum, cv. 'Zinac') during storage. Journal of Food Engineering, 114(3): 338-345

Rouphael, Y., Kyriacou, M.C., Petropoulos, S.A., de Pascale, S., Colla, G. (2018) Improving vegetable quality in controlled environments. Scientia Horticulturae, 234: 275-289

Ruiz, J.J., Alonso, A., García-Martínez, S., Valero, M., Blasco, P., Ruiz-Bevia, F. (2005) Quantitative analysis of flavour volatiles detects differences among closely related traditional cultivars of tomato. Journal of the Science of Food and Agriculture, 85: 54-60

Sabio, E., Lozano, M., Montero, D.E.V., Mendes, R.L., Pereira, A.P., Palavra, A.F., Coelho, J.A. (2003) Lycopene and b-carotene extraction from tomato processing waste using supercritical CO 2. Industrial & Engineering Chemistry Research, 42: 6641-6646

Singh, R.R.B., Ruhil, A.P., Jain, D.K., Patel, A.A., Patil, G.R. (2009) Prediction of sensory quality of UHT milk: A comparison of kinetic and neural network approaches. Journal of Food Engineering, 92(2): 146-151

Slimestad, R., Verheul, M. (2009) Review of flavonoids and other phenolics from fruits of different tomato (Lycopersicon esculentum Mill.) cultivars. Journal of the Science of Food and Agriculture, 89: 1255-1270

Turan, D., Capanoglu, E., Altay, F. (2015) Investigating the effect of roasting on functional properties of defatted hazelnut flour by response surface methodology: RSM. LWT - Food Science and Technology, 63: 758-765

Zaborowicz, M., Boniecki, P., Koszela, K., Przybył, J., Mazur, R., Kujawa, S., Pilarski, K. (2013) Use of artificial neural networks in the identification and classification of tomatoes. u: Proceedings of the Fifth International Conference on Digital Image Processing (ICDIP 2013), Beijing, 8878: 88782-88782
   

O članku

jezik rada: engleski
vrsta rada: izvorni naučni članak
DOI: 10.5937/ffr48-29661
primljen: 02.12.2020.
revidiran: 02.02.2021.
prihvaćen: 05.02.2021.
objavljen onlajn: 15.02.2021.
objavljen u SCIndeksu: 10.07.2021.
metod recenzije: jednostruko anoniman
Creative Commons License 4.0

Povezani članci

Nema povezanih članaka