2018
DOI: 10.1016/j.fct.2018.04.036
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Quality assessment and artificial neural networks modeling for characterization of chemical and physical parameters of potable water

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Cited by 47 publications
(21 citation statements)
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“…The solubility of metals is high at low pH [45]. A pH level above 8.5 enhances the conversion of nontoxic ammonium to the toxic form of unionized ammonia [53]. The pH also determines biological constituents of water.…”
Section: Water Quality Status Of Rungiri Quarry Reservoirmentioning
confidence: 99%
“…The solubility of metals is high at low pH [45]. A pH level above 8.5 enhances the conversion of nontoxic ammonium to the toxic form of unionized ammonia [53]. The pH also determines biological constituents of water.…”
Section: Water Quality Status Of Rungiri Quarry Reservoirmentioning
confidence: 99%
“…Na Tabela 1 estão dispostos os resultados encontrados para os parâmetros de pH das amostras de água, coletados de residências localizadas na mesorregião de Para Salari et al (2018), os padrões físico-químicos da água remetem-se ao termo qualidade, tornando-a adequada para o consumo humano, doméstico, agrícola e industrial, além de proporcionar uma maior estabilidade do cloro na água de abastecimento, reduzindo, em paralelo a possibilidade de proliferação de microrganismos patogênicos. E Telles e Costa (2010), associa qualidade da água com problemas de saúde, alegando que está relação, por conta de inúmeros casos, tornase complexa e grave, onde envolve efeitos negativos e positivos.…”
Section: Resultados E Discussõesunclassified
“…2019) and lack of a proper statistical approach to handle a large pool of data (Salari et al . 2018). Analysing metabolites in a large volume of milk and milk products for quality and safety evaluation, adulterant detection and milk processing control requires enhancement of sensitivity, data processing quality and precision of instruments (Begou et al .…”
Section: Discussionmentioning
confidence: 99%
“…Aim of study et al 2020), which are of great potential in maintaining quality attributes during milk and milk product processing. However, metabolomic analysis has not achieved its full potential due to limitations in compound identification, sensitivity analysis (Pinu et al 2019) and lack of a proper statistical approach to handle a large pool of data (Salari et al 2018). Analysing metabolites in a large volume of milk and milk products for quality and safety evaluation, adulterant detection and milk processing control requires enhancement of sensitivity, data processing quality and precision of instruments (Begou et al 2017).…”
Section: )mentioning
confidence: 99%