Umjetna inteligencija i matematičko modeliranje kinetike sušenja prethodno obrađenih cjelovitih plodova marelice

This study involved monitoring and modelling the drying kinetics of whole apricots pre-treated with solutions of sucrose, NaCl, and sodium bisulphite. The drying was performed in a microwave oven at different power levels (200, 400, and 800 W). Two artificial intelligence models were used for the prediction of drying time (DT) and moisture ratio (MR): artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). On the other hand, the MR prediction was also done with 21 semi-empirical models, one of which we created. The results showed that the drying time decreased with the increase in microwave oven power for the three treatments. The treatment with NaCl was the most suitable for our work. The correlation coefficients of drying time (0.9992) and moisture ratio (0.9997) of ANN were high compared to the ANFIS model, which were 0.9941 and 0.9995, respectively. Among twenty semi-empirical models that were simulated, three models were fitted to our study (Henderson & Papis modified, Henderson & Pabis, and Two Terms). By comparing the three models adapted to our work and the model that we proposed, as well as ANN for MR prediction, it was observed that the model that we created was the most appropriate for describing the drying kinetics of NaCl-treated apricot. This solution opens the prospect of using this potential model to simulate fruit and vegetable drying kinetics in the future.Ovim istraživanjem obuhvaćeno je praćenje i modeliranje kinetike sušenja cjelovitih plodova marelice prethodno obrađenih otopinama saharoze, natrijeva klorida i natrijeva bisulfita. Sušenje je provedeno u mikrovalnoj pećnici pri različitim snagama (200, 400 i 800 W). Za predviđanje vremena sušenja (DT) i omjera vlage (MR) primijenjena su dva modela umjetne inteligencije: umjetna neuronska mreža (ANN) i prilagodljivi sustav neizrazitog zaključivanja zasnovanog na neuronskoj mreži (ANFIS). S druge strane, za predviđanje MR-a upotrijebljeno je 20 postojećih poluempirijskih modela te jedan koji su autori izradili sami. Rezultati su, kod sve tri primijenjene obrade, pokazali redukciju vremena sušenja s povećanjem snage mikrovalne pećnice. Tretman otopinom natrijeva klorida pokazao se najpogodnijim. Koeficijenti korelacije ANN modela za vrijeme sušenja (0,9992) i omjer vlage (0,9997) bili su viši nego kod ANFIS modela (0,9941 i 0,9995). Za dvadeset primijenjenih polu-empirijskih modela, tri modela pokazala su se podudarnim s rezultatima ovog istraživanja (modificirani model Hendersona i Pabisa, model Hendersona i Pabisa te model dvaju pojmova). Uspoređujući tri spomenuta modela i model predložen u ovom radu, kao i predviđanje MR-a ANN-om, uočeno je da je model predložen u radu najprikladniji za opisivanje kinetike sušenja marelice tretirane otopinom natrijeva klorida. Takvi rezultati ukazuju da bi se predloženi model potencijalno mogao ubuduće primjenjivati za simulaciji kinetike sušenja voća i povrća

Predviđanje količine bikarbonata u pitkoj vodi regije Médéa modeliranjem umjetnom neuronskom mrežom

The region of Médéa (Algeria) located in an agricultural site requires a large amount of drinking water. For this purpose, the water analyses in question are imperative. To examine the evolution of the drinking water quality in this region, firstly, an experimental protocol was done in order to obtain a dataset by taking into account several physicochemical parameters. Secondly, the obtained data set was divided into two parts to form the artificial neural network, where 70 % of the data set was used for training, and the remaining 30 % was also divided into two equal parts: one for testing and the other for validation of the model. The intelligent model obtained was evaluated as a function of the correlation coefficient nearest to 1 and lowest mean square error (RMSE). A set of 84 data points were used in this study. Eighteen parameters in the input layer, five neurons in the hidden layer, and one parameter in the output layer were used for the ANN modelling. Levenberg Marquardt learning (LM) algorithm, logarithmic sigmoid, and linear transfer function were used, respectively, for the hidden and the output layers. The results obtained during the present study showed a correlation coefficient of R = 0.99276 with root mean square error RMSE = 11.52613 mg dm–3. These results show that obtained ANN model gave far better and more significant results. It is obviously more accurate since its relative error is small with a correlation coefficient close to unity. Finally, it can be concluded that obtained model can effectively predict the rate of soluble bicarbonate in drinking water in the Médéa region. This work is licensed under a Creative Commons Attribution 4.0 International License.Regija Médéa (Alžir) smještena na poljoprivrednom zemljištu zahtijeva veliku količinu pitke vode te je stoga analiza vode od iznimne važnosti. Da bi se ispitao razvoj kvalitete pitke vode u toj regiji, najprije je napravljen eksperimentalni protokol za dobivanje skupa podataka uzimajući u obzir nekoliko fizikalno-kemijskih parametara. Zatim je dobiveni skup podataka podijeljen na dva dijela za stvaranje umjetne neuronske mreže, gdje je 70 % skupova podataka upotrijebljeno za trening, a preostalih 30 % dodatno je podijeljeno na dva jednaka dijela: jedan za testiranje, a drugi za validaciju modela. Dobiveni inteligentni model procijenjen je kao funkcija koeficijenta korelacije najbližeg 1 i najnižeg korijena srednje kvadratne pogreške (RMSE). U ovom istraživanju upotrijebljen je skup od 84 podatkovnih točaka. Za modeliranje ANN-a upotrijebljeno je osamnaest parametara u ulaznom sloju, pet neurona u skrivenom sloju i jedan parametar u izlaznom sloju. Za skriveni i izlazni sloj upotrijebljeni su algoritam učenja Levenberg Marquardt (LM), logaritamski sigmoid i funkcija linearnog prijenosa. Rezultati dobiveni tijekom ovog istraživanja pokazali su koeficijent korelacije R = 0,99276 s korijenom srednje kvadratne pogreške RMSE = 11,52613 mg dm–3. Ti rezultati pokazuju da je dobiveni model neuronske mreže dao daleko bolje rezultate, jer je točniji a njegova relativna pogreška je mala s koeficijentom korelacije blizu 1. Konačno, zaključeno je da taj model može učinkovito predvidjeti brzinu topljivosti bikarbonata u vodi za piće u regiji Médéa. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Predicting the concentration of sulfate using machine learning methods

International audienceContinuous water monitoring is expensive and time consuming. Because it requires sampling information throughout 12 months and restricts the conduct of water aid management studies as well as the calibration and validation of excellent water models. To overcome this obstacle to better water quality management, improving water quality models is a necessary step. Various modelling strategies have been developed in recent years to improve the accuracy of predictions of major water parameters. In this work, for the prediction of raw water sulfate, we used five machine learning models were considered in this work: artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR), and decision tree (DT) and ensemble tree (ET). Moreover, the DT model was used to know the influence of the other physicochemical parameters (inputs) on the, and the ET model to improve the DT result and ensure the influence of the other physicochemical parameters on the sulfate. The experimental results indicate that all models were found to be effective in predicting sulfate levels, due to their very high correlation coefficients (close to 1) and very low statistical errors (close to 0); however, the most suitable water quality models were GPR and ANN, as their coefficients and statistical indicators do not show much difference between them. Indeed, the coefficients and the statistical indicators of the GPR model were R = 0.9991, R-2 = 0.9982, R-2 adj = 0.9978, RMSE = 0.0182, MSE = 0, 0003. MAE = 0.0073 and EPM = 1.5386; while those of the ANN model were: R = 0.9989, R-2 = 0.9978, R-a(dj)2 = 0.9972, RMSE = 0.0124, MSE = 0.0001, MAE = 0.0083 and EPM = 2.0639. The only difference that favored the GPR model if compared to the ANN was the number of parameters, namely 70 parameters and a very weak loss, 3.3404e-04. In contrast, the ANN model was run with 190 parameters. The model tests (interpolation) confirmed this result, owing to the values of the the correlation coefficient (R = 0.99834) and the coefficient of determination (R-2 = 0.9966), as well as that of statistical indicators (RMSE = 0.0309, MSE = 9.5219e-04, EPM = 3.0267 and MAE = 0.0122). In light of these results it can be concluded that the GPR model is the more efficient to predict sulfate in raw water. Additionally, its ability to deal with missing values, outliers, and the updating ability shows its relevance, which should be kept in the future. This efficiency seems to be due to the fact that the sulfate concentration in the raw water is linked to the physico-chemical characteristics of the environment by non-linear relationships. It is confirmed by a tree and ensemble model decision which provided information on how sulfate reacts with other physicochemical characteristics

Polyphenols and Flavonoids Contents of Fresh and Dried Apricots Extracted by Cold Soaking and Ultrasound-assisted Extraction

This study was carried out to verify the influence of drying parameters on phenolic and flavonoid compounds of apricots (Prunus armeniaca L.) treated with sucrose, NaCl, and sodium bisulphite solutions dried by microwave at different powers (200, 400, and 800 W). We used two extraction methods, namely, cold soaking and ultrasound-assisted extraction (UAE). Total phenolics and flavonoids in fresh and dried apricots and apricot dough were estimated using the Folin-Ciocalteu reagent and the aluminium trichloride method, respectively. Fresh apricot contained considerable amounts of polyphenols and flavonoids by the cold soaking and UAE (285.43 and 165.49 mg GAE/100 g DM and 48.57 and 12.11 mg QE/100 g DM, respectively). Analysis of the data showed that the decrease in polyphenol and flavonoid contents of the dried treated apricots compared to the fresh material was significant. The greatest losses of these nutrients were recorded when applying the ultrasonic extraction method

Sadržaj polifenola i flavonoida u svježim i sušenim marelicama ekstrahiranim hladnim namakanjem i ekstrakcijom potpomognutom ultrazvukom

This study was carried out to verify the influence of drying parameters on phenolic and flavonoid compounds of apricots (Prunus armeniaca L.) treated with sucrose, NaCl, and sodium bisulphite solutions dried by microwave at different powers (200, 400, and 800 W). We used two extraction methods, namely, cold soaking and ultrasound-assisted extraction (UAE). Total phenolics and flavonoids in fresh and dried apricots and apricot dough were estimated using the Folin-Ciocalteu reagent and the aluminium trichloride method, respectively. Fresh apricot contained considerable amounts of polyphenols and flavonoids by the cold soaking and UAE (285.43 and 165.49 mg GAE/100 g DM and 48.57 and 12.11 mg QE/100 g DM, respectively). Analysis of the data showed that the decrease in polyphenol and flavonoid contents of the dried treated apricots compared to the fresh material was significant. The greatest losses of these nutrients were recorded when applying the ultrasonic extraction method.U ovom radu ispitan je utjecaj parametara sušenja na fenolne i flavonoidne spojeve marelica (Prunus armeniaca L.) tretiranih otopinama saharoze, natrijeva klorida i natrijeva bisulfita te sušene u mikrovalovoj pećnici pri različitim snagama (200, 400 i 800 W). Primijenjene su dvije metode ekstrakcije, hladno namakanje i ekstrakcija potpomognuta ultrazvukom (UAE). Ukupni fenoli u svježim i suhim marelicama te tijestu marelica određeni su primjenom Folin-Ciocalteu reagensa, dok su flavanoidi određeni metodom s aluminijevim(III) kloridom. Obje metode ekstrakcije pokazale su da je svježa marelica sadržavala znatne količine polifenola i flavonoida. U suženim marelicama zabilježene su znatno manje količine polifenola i flavanoida. Najveći gubici tih nutrijenata zabilježeni su prilikom primjene ekstrakcije potpomognute ultrazvukom

Optimisation and Prediction of the Coagulant Dose for the Elimination of Organic Micropollutants Based on Turbidity

In this study, four different mathematical models were considered to predict the coagulant dose in view of turbidity removal: response surface methodology (RSM), artificial neural networks (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). The results showed that all models accurately fitted the experimental data, even if the ANN model was slightly above the other models. The SVM model led to almost similar results as the ANN model; the only difference was in the validation phase, since the correlation coefficient was very high and the statistical indicators were very low for the ANN model compared to the SVM model. However, from an economic point of view, the SVM model was more appropriate than the ANN model, since its number of parameters was 22, i.e. almost half the number of parameters of the ANN model (43 parameters), while the results were almost similar in all the data phase. To further reduce the economic costs, the RSM model can also be used which remained very useful due to its high coefficients related to the number of parameters – only 13. In addition, the statistical indicators of the RSM model remained acceptable

Assessment of Surface Water Quality Using Water Quality Index and Discriminant Analysis Method

Given the complexity of water quality data sets, water resources pose a significant problem for global public order in terms of water quality protection and management. In this study, surface water quality for drinking and irrigation purposes was evaluated by calculating the Water Quality Index (WQI) and Irrigation Water Quality Index (IWQI) based on nine hydrochemical parameters. The discriminant analysis (DA) method was used to identify the variables that are most responsible for spatial differentiation. The results indicate that the surface water quality for drinking is of poor and very poor quality according to the WQI values, however, the IWQI values indicate that the water is acceptable for irrigation with restrictions for salinity sensitive plants. The discriminate analysis method identified pH, potassium, chloride, sulfate, and bicarbonate as the significant parameters that discriminate between the different stations and contribute to spatial variation of the surface water quality. The findings of this study provide valuable information for decision-makers to address the important problem of water quality management and protection

Assessment of Surface Water Quality Using Water Quality Index and Discriminant Analysis Method

Given the complexity of water quality data sets, water resources pose a significant problem for global public order in terms of water quality protection and management. In this study, surface water quality for drinking and irrigation purposes was evaluated by calculating the Water Quality Index (WQI) and Irrigation Water Quality Index (IWQI) based on nine hydrochemical parameters. The discriminant analysis (DA) method was used to identify the variables that are most responsible for spatial differentiation. The results indicate that the surface water quality for drinking is of poor and very poor quality according to the WQI values, however, the IWQI values indicate that the water is acceptable for irrigation with restrictions for salinity sensitive plants. The discriminate analysis method identified pH, potassium, chloride, sulfate, and bicarbonate as the significant parameters that discriminate between the different stations and contribute to spatial variation of the surface water quality. The findings of this study provide valuable information for decision-makers to address the important problem of water quality management and protection

Aleppo pine seeds (Pinus halepensis Mill.) as a promising novel green coagulant for the removal of Congo red dye: Optimization via machine learning algorithm

International audienceConsideration is now being given to the use of metal coagulants to remove turbidity from drinking water and wastewater. Concerns about the long-term impact of non-biodegradable sludge on human health and the potential contamination of aquatic systems are gaining popularity. Recently, alternative biocoagulants have been suggested to address these concerns. In this study, using a 1 M sodium chloride (NaCl) solution, the active coagulating agent was extracted from Pinus halepensis Mill. Seed, and used for the first time to remove Congo red dye, the influence of numerous factors on dye removal was evaluated in order to make comparisons with conventional coagulants. The application of biocoagulant was shown to be very successful, with coagulant dosages ranging from 3 to 12 mL L(-1) achieving up to 80% dye removal and yielding 28 mL L(-1) of sludge. It was also found that biocoagulant is extremely pH sensitive with an optimum operating pH of 3. Ferric chloride, on the other hand, achieved similar removal rate with higher sludge production (46 mL L(-1)) under the same conditions. A Fourier Transform Infrared Spectroscopy and proximate composition analysis were undertaken to determine qualitatively the potential active coagulant ingredient in the seeds and suggested the involvement of proteins in the coagulation-flocculation mechanism. The evaluation criteria of the Support vector machine_Gray wolf optimizer model in terms of statistical coefficients and errors reveals quite interesting results and demonstrates the performance of the model, with statistical coefficients close to 1 (R = 0.9998, R(2) = 0.9995 and R(2) adj = 0.9995) and minimal statistical errors (RMSE = 0.5813, MSE = 0.3379, EPM = 0 0.9808, ESP = 0.9677 and MAE = 0.2382). The study findings demonstrate that Pinus halepensis Mill. Seed extract might be a novel, environmentally friendly, and easily available coagulant for water and wastewater treatment

Antioxidant and Biological Activities of Fresh and Dried Apricot Extracts Obtained by Cold Soaking and Ultrasonic Extraction

The objective of this study was to evaluate the antioxidant (AA), antibacterial, and antifungal activity of fresh and pre-treated apricot extracts, dried by microwave at different powers (200, 400, and 800 W), extracted by the cold soaking method, and ultrasound-assisted extraction (UAE). Biological activity (bacterial and fungal) was estimated by agar disk diffusion test against four bacterial strains (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Streptococcus sp.), and two fungal strains (Candida spp. and Geotrichum capitatum). Methanolic extracts of apricot fruits: fresh, dried processed, and apricot dough extracted by cold soaking showed a higher AA ranging from 34.22 to 96 % than the other extracts extracted by UAE with values ranging from 14.37 to 66.88 %. The results of tested extracts from fruits (Prunus armeniaca L.) extracted by both extraction methods showed the highest inhibitory activity against most of the tested bacterial and fungal strains with inhibition zones ranging from 4 to 45 mm. The biological activity (antibacterial and antifungal activity) has been improved using different treatments and microwave powers for drying apricots. In addition, the results of the biological activity of the extracts obtained by UAE are the best compared to cold soaking. However, it was determined that the UAE extraction method of cold soaking and drying of apricot fruits was more appropriate for the food industry due to the obtaining of extracts with good antibacterial and antioxidant potential, and their incorporation in foods would increase their production and uses, thus leading to the development of superior biofunctional foods, and the use of bio-solvent, such as methanol, which is easily available in high purity, highly safe, and completely biodegradable