Regression Analysis of Orthogonal, Cylindrical and Multivariable Color Parameters for Colorimetric Surface pH Measurement of Materials

The surface pH is a critical factor in the quality and longevity of materials and products. Traditional fast colorimetric pH detection-based tests such as water quality control or pregnancy tests, when results are determined by the naked eye, cannot provide quantitative values. Using standard pH pap...

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Bibliographic Details
Main Authors: Katarína Vizárová, Izabela Vajová, Naďa Krivoňáková, Radko Tiňo, Zdenko Takáč, Štefan Vodný, Svetozár Katuščák
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Molecules
Subjects:
Online Access:https://www.mdpi.com/1420-3049/26/12/3682
Description
Summary:The surface pH is a critical factor in the quality and longevity of materials and products. Traditional fast colorimetric pH detection-based tests such as water quality control or pregnancy tests, when results are determined by the naked eye, cannot provide quantitative values. Using standard pH papers, paper-printed comparison charts, or colorimetric microfluidic paper-based analytical devices is not suitable for such technological applications and quality management systems (QMSs) where the particular tested material should contain a suitable indicator in situ, in its structure, either before or after the process, the technology or the apparatus that are being tested. This paper describes a method based on the combination of impregnation of a tested material with a pH indicator in situ, its exposure to a process of technology whose impact on pH value is to be tested, colorimetric pH measurement, and approximation of pH value using derived pH characteristic parameters (pH-CPs) based on CIE orthogonal and cylindrical color variables. The hypotheses were experimentally verified using the methyl red pH indicator, impregnating the acid lignin-containing paper, and preparing a calibration sample set with pH in the range 4 to 12 using controlled alkalization. Based on the performed measurements and statistical evaluation, it can be concluded that the best pH-CPs with the highest regression parameters for pH are <i>√</i>∆<i>E, ln (a),</i><i>√</i>∆<i>H (ab), a/L, h/b</i> and <i>ln (b/a)</i>. The experimental results show that the presented method allows a good estimation of pH detection of the material surfaces.
ISSN:1420-3049