Application of FT-MIR spectrometry in wine analysis
Introduction
Recent developments in design and performance of FT-MIR spectrometers combined with advances in chemometrics (multivariate data analysis) software has provided an interesting analytical tool that is suitable for rapid product screening and process control. The first application in wine has already been reported by Schindler et al. [1] and the first results comparing the FT-MIR prediction with reference analysis was published by Patz et.al (1999) [2].
From the start of grape ripening to harvest, the maturity, the grape reception, and fermentation control up to the ready made wine there is a need for fast and reliable analytical techniques for monitoring and screening purposes [3]. The primary requirements for these approaches are speed, high degree of automation, a good reproducibility, precision and accuracy, comparability to the reference methods, as well as cost effective. The information collected by the rapid method may determine that additional reference analysis is required. Reference analysis may be more accurate but it is also more time consuming and expensive.
There are two main problems associated with wine characterisation by FT-MIR spectroscopy and the associated multivariate data analysis. First of all, the components under investigation are chemically very similar and therefore display similar IR absorption characteristics. The second problem is that the dominating absorption of ethanol, water and in some cases, sugars strongly influences the determination of other components. Chemometric techniques are normally used for solving problems in which several groups are to be determined and are particularly suited for working with large data sets. In the case of FT-MIR wine calibration it is necessary to select the relevant spectral wavelengths first and then to use PLS regression mathematics to optimise the calibration equations for each of the different parameters. A completely independent set of samples is required to validate the quality and the robustness of the finished calibration model.
Section snippets
Samples for wine calibration and validation
Because FT-MIR is a secondary analytical method, it was first necessary to calibrate the instrument against the chemical reference methods for the different components. A total of 327 typical German wines (all qualities, from vintage 1989 to 2001) were representatively selected from the different winegrowing regions. The samples were analysed with reference methods and also tested by FT-MIR to obtain the infrared spectra. In Table 1, the German wine quality designations are listed in order of
German wine validation
As previously described under Section 2, the samples were split into two unique sample sets, identified as “A” and “B”. These two sets were then used independantly for both calibration and validation, i.e. “B” was used to validate “A” and vice versa. This technique was adopted to display the stability of the calibration.
The results obtained in terms of the range of concentration, the median of the data set, the correlation factor (R2), and standard error of prediction (RMSEP) are listed in
Conclusion
The method presented here is rapid (more than 12 parameters can be determined in less than 90 s) and has a high degree of automation (data processing, an autosampler is optional). The FT-MIR is well proven in daily routine analyses—has low maintenance costs, and is environment friendly.
The validation procedure performed with German wines of all available qualities demonstrated that every calibration model is a compromise between the accuracy of the predicted results and the sample range which is
Acknowledgements
Financial support of this work has been provided by the Federal Agency for Agriculture and Food (BLE) in Germany under project no. 99HS003, the “Federal Institute of Risk Assessment” for the accomplishment of the Ring test and the attended laboratories for the analyses.
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