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dc.creatorBenedet, Lucas-
dc.creatorSilva, Sérgio Henrique Godinho-
dc.creatorMancini, Marcelo-
dc.creatorAndrade, Renata-
dc.creatorAmaral, Francisco Hélcio Canuto-
dc.creatorLima, Geraldo Jânio-
dc.creatorCarneiro, Marco Aurélio Carbone-
dc.creatorCuri, Nilton-
dc.date.accessioned2023-06-27T15:29:27Z-
dc.date.available2023-06-27T15:29:27Z-
dc.date.issued2023-03-
dc.identifier.citationBENEDET, L. et al. Clean quality control of agricultural and non-agricultural lime by rapid and accurate assessment of calcium and magnesium contents via proximal sensors. Environmental Research, [S.l.], v. 221, Mar. 2023.pt_BR
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0013935123000920pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/57078-
dc.description.abstractCa and Mg are the most important chemical elements in lime. Properly measuring Ca and Mg contents is essential to assess the quality of lime products. Quality control guarantees the adequate use of lime in industrial processes, in soils, and helps avoiding adulteration. Proximal sensors can aid in this process by determining Ca and Mg contents easily, rapidly and without producing chemical waste. The objective of this study was to evaluate the use an environmentally-friendly method of analyzing the quality of lime. We studied 1) the use of portable X-ray fluorescence (pXRF) to predict concentrations of Ca and Mg in lime, 2) tested if NixPro™ sensor can improve prediction accuracy and 3) tested if sample preparation methods (grinding) affect analyses. 74 samples of lime were analyzed by two different laboratories (lab. 1 = 38, lab. 2 = 36). All samples submitted to pXRF and NixPro™ analyses. Sensor analyses were done in whole (CP) and ground (AQ) samples to test the effect of sample preparation in prediction performance. High correlation was found between Ca and Mg contents measured via pXRF and laboratory analyses. Mg-CP presented the highest correlation coefficient (r = 0.81); Mg-AQ, the lowest (0.57). Predictions presented good performance (R2 > 0.68); Mg had the best results (0.86). Separating models per laboratory showed that some datasets are harder to model, probably due to variability in the source material (limestone). The addition of NixPro™ data contributed to improve prediction accuracy, although slightly. Predictions using CP samples presented the best results, especially for Mg, indicating that grinding is not necessary. This pioneer study demonstrated that fused proximal sensors can be used to rapidly and easily determine contents of Ca and Mg in soil amendments without producing chemical waste.pt_BR
dc.languageen_USpt_BR
dc.publisherElsevierpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceEnvironmental Researchpt_BR
dc.subjectPortable X-ray fluorescence spectrometry (pXRF)pt_BR
dc.subjectNix propt_BR
dc.subjectWaste reductionpt_BR
dc.subjectLimestonept_BR
dc.subjectMachine learningpt_BR
dc.subjectRandom forestpt_BR
dc.titleClean quality control of agricultural and non-agricultural lime by rapid and accurate assessment of calcium and magnesium contents via proximal sensorspt_BR
dc.typeArtigopt_BR
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