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An attempt to predict conformation and fatness in bulls by means of artificial neural networks using weight, age and breed composition information
Marjeta Čandek-Potokar, Maja Prevolnik Povše, Martin Škrlep, Maria Font-I-Furnols, Marjana Novič, 2015, original scientific article

Abstract: The present study aimed to predict conformation and fatness grades in bulls based on data available at slaughter (carcass weight, age and breed proportions) by means of counter-propagation artificial neural networks (ANN). For chemometric analysis, 5893 bull carcasses (n=2948 and n=2945 for calibration and testing of models, respectively) were randomly selected from the initial data set (n≈27000; one abattoir, one classifier, three years period). Different ANN models were developed for conformation and fatness by varying the net size and the number of epochs. Tested net parameters did not have a notable effect on models’ quality. Respecting the tolerance of ±1 subclass between the actual and predicted value (as allowed by European Union legislation for on-spot checks), the matching between the classifier and ANN grading was 73.6 and 64.9% for conformation and fatness, respectively. Success rate of prediction was positively related to the frequency of carcasses in the class.
Keywords: govedo, goveje meso, mastnost mesa, struktura mesa, klavna teža, ANN modeli, modeli za napovedovanje
Published in DKUM: 24.07.2017; Views: 1258; Downloads: 361
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Chemometric characterisation of the quality of ground waters from different wells in Slovenia
Ernest Vončina, Darinka Brodnjak-Vončina, Nataša Sovič, Marjana Novič, 2007, original scientific article

Abstract: The quality of ground water as a source of drinking water in Slovenia is regularly monitored. One of the monitoring programmes is performed on 5 wells for drinking water supply, 3 industrial wells and 2 ground water monitoring wells. Two hundred and fourteen samples of ground waters were analysed in the time 2003-2004. Samples were gathered from ten different sampling sites and physical chemical measurements were performed. The following 13 physical chemical parameters were regularly controlled: temperature, pH, conductivity, nitrate, AOX (adsorbable organic halogens), metals such as chromium, pesticides (desethyl atrazine, atrazine and 2,6-dichlorobenzamide), highly-volatile halogenated hydrocarbons (trichlorometane, 1,1,2,2-tetrachloroethene and 1,1,2-trichloroethene). For handling the results different chemometrics methods were employed, such as basic statistical methods for the determination of mean and median values, standard deviations, minimal and maximal values of measured parameters and their mutual correlation coefficients, cluster analysis (CA), the principal component analysis (PCA), the clustering method based on Kohonen neural network, and linear discriminant analysis (LDA). The study gives the opportunity to follow the quality of ground waters at different sampling sites within the defined time period. Monitoring of general pollution of ground waters and following measuring can be used to search the pollution source, to plan prevention measures and to protect from pollution, as well.
Keywords: ground waters, water quality, principal component analysis, classification, Kohonen neural networks
Published in DKUM: 21.12.2015; Views: 1665; Downloads: 115
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Multivariate data analysis of erythrocyte membrane phospholipid fatty acid profiles in the discrimination between normal blood tissue and various disease states
Zdenka Cencič-Kodba, Darinka Brodnjak-Vončina, Marjana Novič, Uroš Potočnik, 2010, original scientific article

Abstract: The investigation presented here aims to compare the fatty acid composition of red blood cells of patients with different disease states and to test the hypothesis that the changes in fatty acid profiles derived from erythrocyte phospholipids might be relevant to various diseases. The study sample consisted of 342 blood donors, among them 135 with inflammatory bowel disease, 53 with uterine leiomyoma, 14 with verified absence of uterine leiomyoma, 52 with asthma, 18 with colon adenomas, and 70 blood samples without any of mentioned diseases that was used as a control group. After the isolation of erythrocytes from blood samples, total extracted lipids were separated by solid-phase extraction (SPE) into non polar lipids and polar phospholipids. After the saponification of phospholipid fraction, the esterification process followed with boron trifluoride-methanol reagent. The fatty acid methyl ester (FAME) composition of the total red blood cell phospholipid fraction was analyzed by gas chromatography (GC) with flame ionization detector (FID). Additionally two fatty aldehyde dimethyl acetals (hexadecanal and octadecanal dimethyl acetals; 16:0 DMA and 18:0 DMA) derived from erythrocyte membrane plasmalogen phospholipids were also determined. The resulting fatty acid and plasmalogen linked fatty acid composition was evaluated by the principal component analysis (PCA). We demonstrated decreased levels of omega-3 polyunsaturated fatty acids (n-3 PUFAs) in red blood cell membrane of patients with colon adenomas. Also, a large negative correlation was observed among all samples between the quantity of saturated acids and arachidonic (20:4n6) acid as well as saturated acids and adrenic (22:4n6) acid. In PCA score plot a group of female donors is distinguished mainly by the content of linoleic (18:2n6) acid; a small subgroup shows its concentration highly above the average value. At the same time, the same subgroup has both dimethyl acetals below the average concentrations. The study demonstrates feasibility of multivariate data analysis in discrimination of patients with different diseases according to fatty acid profile and suggests considerable differences in membrane fatty acid profiles in patients with various disease states.
Keywords: erythrocyte phospholipids, cell membrane, fatty acid profiles, differentiation, disease state, gas chromatography, principal component analysis
Published in DKUM: 31.05.2012; Views: 2479; Downloads: 72
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Uporabnost NIR spektroskopije za določanje lastnosti mesa
Maja Prevolnik, 2011, dissertation

Abstract: V predstavljenih raziskavah smo preucevali uporabnost bližnje infrardece (NIR) spektroskopije za napovedovanje kemijske sestave in kakovosti mesa. Spektralne analize smo izvedli s spektrofotometrom NIR Systems model 6500 (Silver Spring, MD, USA), dobljene podatke pa obdelali s statisticnim programom WinISI II. Kar zadeva kemijsko sestavo, smo napovedovali vsebnost intramuskularne mašcobe, beljakovin, vode, soli, neproteinskega dušika in prostih aminokislin v razlicnih vrstah mesa in mesnih izdelkov, pri cemer smo zaradi velike prakticne vrednosti namenili posebno pozornost dolocanju intramuskularne mašcobe v mesu prašicev in goveda ter napovedovanju kemijske sestave kraškega pršuta. NIR spektroskopija se je za dolocanje kemijske sestave izkazala kot izjemno zanesljiva metoda, ki bi lahko nadomestila konvencionalne kemijske analize. V zvezi s kakovostjo pa smo v mesu prašicev napovedovali tehnološke parametre, kakršni so pH, barva in sposobnost za vezavo vode. Tocnost napovedovanja lastnosti kakovosti mesa je bila v primerjavi s kemijsko sestavo manjša, toda napake napovedovanja so bile primerljive s ponovljivostjo referencnih metod, ki so v tem primeru hitre, enostavne, odvisne od okolja in zato manj natancne. Prednosti NIR spektroskopije (hitrost, nedestruktivnost, hkratnost dolocanja razlicnih lastnosti) skupaj z visoko zanesljivostjo napovedovanja dajejo velike možnosti za dolocanje kakovosti mesa v industriji. Z ustrezno izbiro nekaterih dejavnikov tocnosti (priprava vzorca, spektralno obmocje, vrsta vzorca, kemometricna metoda) lahko uspešnost napovedovanja z NIR spektroskopijo še izboljšamo.
Keywords: NIR spektroskopija, meso, mesni izdelki, kemijska sestava, kakovost mesa
Published in DKUM: 28.03.2011; Views: 5432; Downloads: 468
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