Extraction of indirectly captured information for use in a comparison of offline pH measurement technologies

Understanding the causes of discrepancies in pH readings of a sample can allow more robust pH control strategies to be implemented. It was found that 59.4% of differences between two offline pH measurement technologies for an historical dataset lay outside an expected instrument error range of ±0.02pH. A new variable, Osmo_Res, was created using multiple linear regression (MLR) to extract information indirectly captured in the recorded measurements for osmolality. Principal component analysis and time series analysis were used to validate the expansion of the historical dataset with the new variable Osmo_Res. MLR was used to identify variables strongly correlated (p<0.05) with differences in pH readings by the two offline pH measurement technologies. These included concentrations of specific chemicals (e.g. glucose) and Osmo_Res, indicating culture medium and bolus feed additions as possible causes of discrepancies between the offline pH measurement technologies. Temperature was also identified as statistically significant. It is suggested that this was a result of differences in pH-temperature compensations employed by the pH measurement technologies. In summary, a method for extracting indirectly captured information has been demonstrated, and it has been shown that competing pH measurement technologies were not necessarily interchangeable at the desired level of control (±0.02pH).