Marinkovski, Mirko

Preferred name
Marinkovski, Mirko
Official Name
Marinkovski, Mirko
Main Affiliation
Email
mirko@tmf.ukim.edu.mk

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    ANN modeling and RSM optimization of ultrasound-assisted extraction of protodioscin-rich extracts from Tribulus terrestris L
    (Elsevier BV, 2024-12)
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    Dimitrievska, Isidora
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    Stojchevski, Martin
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    Tribulus terrestris L. is a herb renowned for its abundance of saponins, flavonoids, and alkaloids, which are utilized in treating various health conditions. Protodioscin functions by enhancing the conversion of testosterone into potent dihydrotestosterone, stimulating an increase in libido, red blood cell production from the bone marrow, and muscle development. Contemporary ultrasound-assisted-extraction (UAE) process employing green extraction solvents was selected to design the required separation system. The experimental plan was developed based on the independent operating variables - extraction time, operating temperature and solvent system composition in order to determine the influence of defined parameters and their interactions on the extraction yield and the presence of protodioscin. The 3D-RSM approach was introduced to determine the optimal values of studied independent variables in the area of maximal extraction yield. UAE process performed at optimal operating conditions generated maximal extraction yield (31 %, w/w) and protodioscin content of 5.9 mg/g dry plant matrix. Experimental data was used to develop an ANN for the defined extraction system using the operating variables values as the input matrix and observed yield as target matrix. Successfully designed and trained ANN generated high correlation (r = 0.9992) between observed data and predictive model' outputs, and MSE value of 0.29107.
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    Valorization of tomato processing by-products: Predictive modeling and optimization for ultrasound-assisted lycopene extraction
    (Elsevier BV, 2024-11)
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    Stojchevski, Martin
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    Lycopene is a carotenoid highly valuable to the food, pharmaceutical, dye, and cosmetic industries, present in ripe tomatoes and other fruits with a distinctive red color. The main source of lycopene is tomato crops. This bioactive component can be successfully isolated from tomato processing waste, commonly called tomato pomace, mostly made from tomato skins, seeds, and some residual tomato tissue. The main investigative focus in this work was the application of green engineering principles in each stage of the optimized ultrasound-assisted extraction (UAE) of enzymatically treated tomato skins to obtain functional extracts rich in lycopene. The experimental plan was designed to determine the influence of studied operating parameters: enzymatic reaction time (60, 120, and 180 min), extraction time (0, 5, 10, 15, 30, 60, and 120 min), and temperature (25, 35 and 45 ℃) on lycopene yield. Process optimization was performed based on the yield of lycopene [1018, 1067, and 1120 mg/kg] achieved at optimal operating conditions. An artificial neural network (ANN) model was developed and trained for predictive modeling of the closed extraction system, with operating parameters used as input neurons and experimentally obtained values for lycopene content defined as the output neural layer. Applied ANN architecture provided a high correlation of experimental output with ANN-generated data (R=0.99914) with a model deviation error for the entire data set of RMSE=5.3 mg/kg. The k-Nearest Neighbor algorithm was introduced to predict lycopene yield using experimental key features: operating temperature, extraction time, and time of enzymatic treatment, split into training and testing sets with an 85/15 ratio. The model interpretation was conducted through the SHAP (SHapley Additive exPlanations) methodology.
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    Optimization of Oil Extraction Process from White Mustard Seeds Using Response Surface Methodology
    (National and University Library of the Republic of Srpska, 2024-02-21)
    Stojchevski, Martin
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    Slaveski, Angelina
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    In this study, response surface methodology (RSM) was used for the determination of the effect of two process parameters (extraction temperature and extraction time) and their interaction on the yield of the total extract from white mustard seeds (Sinapis alba l.), as well as the creation of a mathematical model, 3D response surface, and the establishment of an optimal extraction region. Ethanol 96% as a green solvent was employed to separate biological compounds from white mustard (Sinapis alba l.) seeds by applying ultrasound-assisted extraction (UAE). UAE was performed at the temperature of 25, 30, and 35℃ for a duration of 30, 60, and 90 min. UAE as a modern extraction technique showed great performance, while the ethanol solvent provided a high yield of compounds. The utilized RSM design adequately fit the experimental data with a high coefficient of determination (R2= 0.9365) and low mean absolute error (MAE= 1.12242). Estimated effects for the yield of the total extract showed that the extraction time, the temperature, and the interaction between time and temperature influenced positively, while the time-squared and the temperature-squared influenced negativity on the yield of the total extract. Analysis of variance showed that 2 effects have P-values less than 0.05. The optimal yield of the total extracted oil was 15.19% at the temperature of 35℃ for a duration of 70.3 min.