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Monitoring stations have been established to combat water pollution, improve the ecosystem, promote human health, and facilitate drinking water production. However, continuous and extensive monitoring of water is costly and time-consuming, resulting in limited datasets and hindering water management research. This study focuses on developing an optimized K-nearest neighbor (KNN) model using the improved grey wolf optimization (I-GWO) algorithm to predict dry residue quantities. The model incorporates 20 physical and chemical parameters derived from a dataset of 400 samples. Cross-validation is employed to assess model performance, optimize parameters, and mitigate the risk of overfitting. Four folds are created, and each fold is optimized using 11 distance metrics and their corresponding weighting functions to determine the best model configuration. Among the evaluated models, the Jaccard distance metric with inverse squared weighting function consistently demonstrates the best performance in terms of statistical errors and coefficients for each fold. By averaging predictions from the models in the four folds, an estimation of the overall model performance is obtained. The resulting model exhibits high efficiency, with remarkably low errors reflected in the values of R, R2, R2ADJ, RMSE, and EPM, which are reported as 0.9979, 0.9958, 0.9956, 41.2639, and 3.1061, respectively. This study reveals a compelling non-linear correlation between physico-chemical water attributes and the content of dry tailings, indicating the ability to accurately predict dry tailing quantities. By employing the proposed methodology to enhance water quality models, it becomes possible to overcome limitations in water quality management and significantly improve the precision of predictions regarding critical water parameters.

Apricot is the fruit of the common apricot tree, Prunus armeniaca L., of the Rosaceae family (subfamily Pomoides). In 2019, Algerian apricot production amounted to 256.890 t. 1 The fruit of the apricot is distinguished by its high dietary value, and is a valuable raw material for processing. 2 It is highly appreciated by consumers due to its delicious taste, appealing smell, vivid colours, and numerous nutritional properties which are a result of its rich content of minerals, fibres, sugars and bioactive phytochemicals, including vitamin C, β-carotene, thiamine, ri-boflavin, niacin, and pantothenic acid, as well as phenols, carotenoids, and tocopherols. 3 Recently, research trends are focused on studying healthy foods and natural products due to their potential use as biologically active compounds and their consecutive connection in treating and mitigating diseases. 4 Natural antioxidants such as phenolic, vitamins, minerals, and carotenoids are contributing substantially to the prevention of oxidation and quenching of free radicals species. These antioxidants are highly effective against oxidative damage related to health disorders, including cardiovascular diseases, neurological syndromes, aging process, cancer, cataract development, weakness in the immune system, and inflammation. 5,6 The use of antioxidant-rich fruits has been

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.