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  • 1 Faculty of Food Science, Szent István University H-1118 Budapest, Ménesi út 45. Hungary
  • | 2 Department of Refrigeration and Livestock Products Technology, Faculty of Food Sciences, Szent István University H-1118 Budapest, Ménesi út 45. Hungary
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The near infrared spectra are useful information sources relating to quality (e.g. composition) of a material examined. To obtain and interpret useful information requires in most cases the application of sophisticated methods of mathematical statistics. A method different from those mentioned above, implementing a large scale data reduction based on geometrical consideration is the PQS. According to this method, the quality of a material can be characterised by the centre of its spectrum represented in a polar co-ordinate system. In many cases it is enough to know whether the investigated product deviates in a certain degree from a given “standard product” or not. This can be decided by determining special “distances” between the two (investigated and standard) products using their near infrared spectra. Besides the successfully used Euclidean and Mahalanobis distances a new one, the “polar distance” was introduced giving the distance between the two centres (quality points) of the spectra of the two products examined. A method was elaborated to select the optimal wavelength range giving the maximum normalised distance between the two quality points of the investigated products. The so called “wavelength range optimisation” can not be used to work with non spectral data sets. While in case of NIR spectra the sequence of the data are determined by nature, in several cases the order of the data can be freely varied and the goal is the determination of the optimal data sequence. By introducing the “sequence optimisation” PQS could be generalised and used from the field of near infrared spectroscopy to solve any kind of multivariate tasks. The advantage of the PQS optimisation method is its simplicity. Since PQS was developed to extract the needed information from NIR spectra, the basic principles of the technique are introduced with the help of near infrared spectra of some milk powder samples of different fat content. The sequence optimisation is demonstrated with the sensor signal responses of an electronic nose (chemosensor array) instrument measuring different steam distilled volatile oil samples.

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