Systematic prediction error correction: a novel strategy for maintaining the predictive abilities of multivariate calibration models.

The development of reliable multivariate calibration models for spectroscopic instruments in on-line/in-line monitoring of chemical and bio-chemical processes is generally difficult, time-consuming and costly. Therefore, it is preferable if calibration models can be used for an extended period, without the need to replace them. However, in many process applications, changes in the instrumental response (e.g. owing to a change of spectrometer) or variations in the measurement conditions (e.g. a change in temperature) can cause a multivariate calibration model to become invalid. In this contribution, a new method, systematic prediction error correction (SPEC), has been developed to maintain the predictive abilities of multivariate calibration models when e.g. the spectrometer or measurement conditions are altered. The performance of the method has been tested on two NIR data sets (one with changes in instrumental responses, the other with variations in experimental conditions) and the outcomes compared with those of some popular methods, i.e. global PLS, univariate slope and bias correction (SBC) and piecewise direct standardization (PDS). The results show that SPEC achieves satisfactory analyte predictions with significantly lower RMSEP values than global PLS and SBC for both data sets, even when only a few standardization samples are used. Furthermore, SPEC is simple to implement and requires less information than PDS, which offers advantages for applications with limited data.

In situ monitoring of the seed stage of a fermentation process using non-invasive NIR spectrometry.

Non-invasive NIR spectrometry has been used to monitor in situ the seed stage of a streptomyces fermentation process. The main spectral change occurred at 7263 cm(-1) in the 1st derivative spectrum, and from comparison with off-line NIR spectra acquired of components present in the fermentation broth, can be attributed to biomass. The biomass signal was constant for the first 20 h of the seed stage, after which it decreased before increasing again. The time at which the minimum occurred in the NIR profile was either the same or slightly earlier than that at which a maximum in the carbon dioxide evolution rate (CER) occurred. The changes observed for the biomass signal in the NIR spectra can be attributed to growth and then fragmentation of mycelia, which indicates a change in metabolic activity. Hence, it may be possible to use NIR spectrometry in situ to determine the optimum transfer time for the seed stage of a fermentation process. Spectra were also acquired of the final stage of the same fermentation process. The variation in the biomass signal in the NIR spectra was more complicated in the final stage owing to changes in stir rate, and biomass concentration and morphology.