Representative subset selection and standardization techniques. A comparative study using NIR and a simulated fermentative process UV data

Several algorithms related to the calibration transfer process were tested with the objective of comparing them in terms of its performance. Firstly, three algorithms for representative subset selection (Kennard–Stone, Leverage and OptiSim) were used and compared to select appropriate samples to make the calibration transfer. Secondly, three algorithms of spectra standardization were tested using different values for input parameters. The three methods tested were direct standardization (DS), piecewise direct standardization (PDS) and wavelet hybrid direct standardization (WHDS), the latter was implemented testing a large number of available filters.Two data sets were used: a) NIR data acquired from different NIR instruments, previously employed in other publications and available from the PLS Toolbox, and b) UV data obtained in our laboratory from samples of penicillin dissolved in a culture medium representative of an industrial process of penicillin production. The spectra were recorded at different rates and from samples undergone to drastic modification on the pH, originating diverse data sets.Results show that OptiSim can be recommended on Kennard–Stone and Leverage for representative subset selection for data with similar structure that the analyzed herein. On the other hand, the performance of WHDS was remarkably better than the other tested methods when NIR data were analyzed. Finally, a strategy combining PDS optimization with WHDS results a good choice for more complex data. In addition, when the data become more complex, using wider windows improve the PDS performance.