Accuracy analysis of iron content of galvanized coating using an X-ray fluorescence spectrometer with an L-spectrum

The accuracy(repeatability and reproducibility) of the iron content analysis of galvanized coating using an X-ray fluorescence spectrometer with an L-spectrum is not better than that of flame atomic absorption spectrometry, sometimes it exceeds the quality control limit.Influences, such as current, voltage, equipment(internal circulating water, 10%CH4+90%Ar, and vacuum) checking, instrument monitoring, sample cleaning, and oper-ators, were investigated by means of 6-sigma and lean operations to improve accuracy.

Application of a Portable XRF Spectrometer for <i>In-Situ</i>and Nondestructive Investigation of Pigments in Two 15th Century Icons

A simple portable X-Ray Fluorescence (XRF) spectrometer was successfully used for in-situ and nondestructive identification of the painting materials in two 15th century icons from the Onufri Museum in Beart, Albania. The spectrometer is based on a low power X-ray tube, a thermoelectrically cooled Si PIN detector and the spectrum acquisition system. It was assembled and adjusted at our laboratory for the investigation of the icons. A small number of pigments were clearly identified by X-Ray Fluorescence (XRF) measurements in both icons. This includes Lead white for the white color, gold and yellow ochre for the yellow color, red lead, cinnabar and red ochre for the red color, as well as cooper based pigments for the green color. At the same time, the investigation raised some new questions that need further investigations by the use of additional analytical techniques. The results show that in both icons are used similar pigments, which are in accordance with the Byzantine icon painting tradition.

Novel Method for Indirect Determination of Iodine in Marine Products by Atomic Fluorescence Spectrometry

A method for the determination of iodine based upon compound H2HgI4, formed between I- and Hg1＋ in nitric acid and extracted in methyl isobutyl ketone（MIBK）, was developed via atomic fluorescence spectrometry（AFS）. After the compound is reduced with potassium borohydrid（KBH4）, the resultant mercury vapor was injected into the instrument and iodine was, therefore, indirectly determined. Experimental parameters such as the conditions of extraction reagents, aqueous phase acidity, elemental mercury diffusion temperature in a vial and other factors were investigated and optimized. Under the optimum experimental conditions, this method shows a detection limit of 0.038μg/L iodine and a linear relationship between 0.04-20 p.g/L. The method was applied to determining the iodine content in marine duck eggs, kelps, laver and Ganoderma lucidum spirulina, showing a relative standard deviation（RSD） of 2.15% and the recoveries in the range of 98.1%-102.5%.

Proximal sensor-enhanced soil mapping in complex soil-landscape areas of Brazil

Portable X-ray fluorescence(pXRF) spectrometry and magnetic susceptibility(MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging in many countries with tropical soils. The objective of this study was to use proximal soil sensor data associated with terrain variables at varying spatial resolutions to predict soil classes using the Random Forest(RF) algorithm. The study was conducted on a 316-ha area featuring highly variable soil classes and complex soil-landscape relationships in Minas Gerais State, Brazil. The overall accuracy and Kappa index were evaluated using soils that were classified at 118 sites, with 90 being used for modeling and 28 for validation. Digital elevation models(DEMs) were created at 5-, 10-, 20-, and 30-m resolutions using contour lines from two sources. The resulting DEMs were processed to generate 12 terrain variables. Total Fe, Ti, and SiO_(2) contents were obtained using pXRF, with MS determined via a magnetometer. Soil class prediction was performed using the RF algorithm. The quality of the soil maps improved when using only the five most important covariates and combining proximal sensor data with terrain variables at different spatial resolutions. The finest spatial resolution did not always provide the most accurate maps. The high soil complexity in the area prevented highly accurate predictions. The most important variables influencing the soil mapping were MS, Fe, and Ti. Proximal sensor data associated with terrain information were successfully used to map Brazilian soils at variable spatial resolutions.