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Abstract
This paper reports the chemometric predictive models developed for near infrared spectroscopy (NIRS) for the quantitative determination of the kinematic viscosity (37.1–93.1 cSt) of lubricant oils for gear motors. The gear motor is a complete motive force system that consists of an electric motor and a reduction gear train integrated into one easy-to-mount and configure package. The method used for measuring the viscosity of the lubricating oil was ASTM D445, the Standard Test Method for Kinematic Viscosity of Transparent and Opaque Liquids. A comparison was made among several multivariate calibration techniques and algorithms for pre-processing and variable selection of data, including partial least squares, interval partial least squares (iPLS), a genetic algorithm (GA), and a successive projections algorithm. Finally, the results obtained for the root mean square errors of prediction in cSt and relative average error were, respectively, 1.86 and 2.97% (GA) and 2.36 and 2.97% (iPLS). The method proposed in this study is a useful alternative for the determination of the kinematic viscosity in oils for gear motors.
Acknowledgments
The authors would like to thank the Propesq-UFRN and PG Lubrificantes Ltda for providing samples and the reference method.
Notes
The number of PLS factors or in PLS, iPLS, PLS-SPA, and PLS-GA models are represented in parentheses.
The number of PLS factors or in PLS, iPLS, PLS-SPA, and PLS-GA models are represented in parentheses.