PARTIAL LEAST SQUARES REGRESSION CALIBRATION OF AN ULTRAVIOLET-VISIBLE SPECTROPHOTOMETER FOR MEASUREMENTS OF CHEMICAL OXYGEN DEMAND IN DYE WASTEWATER

Wastewater from the dye industry is typically analyzed using a standard method for measurement of chemical oxygen demand (COD) or by a single-wavelength spectroscopic method. To overcome the disadvantages of these methods, ultraviolet-visible (UV-Vis) spectroscopy was combined with principal component regression (PCR) and partial least squares regression (PLSR) in this study. Unlike the standard method, this method does not require digestion of the samples for preparation. Experiments showed that the PLSR model offered high prediction performance for COD, with a mean relative error of about 5% for two dyes. This error is similar to that obtained with the standard method. In this study, the precision of the PLSR model decreased with the number of dye compounds present. It is likely that multiple models will be required in reality, and the complexity of a COD monitoring system would be greatly reduced if the PLSR model is used because it can include several dyes. UV-Vis spectroscopy with PLSR successfully enhanced the performance of COD prediction for dye wastewater and showed good potential for application in on-line water quality monitoring.

сточные воды красильной промышленности, спектрофотометр для ультрафиолетовой и видимой областей спектра, химическая потребность в кислороде, частичная регрессия на основе метода наименьших квадратов, dye wastewater, UV-Vis spectrophotometer, chemical oxygen demand, partial least squares regression

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