Radiant energy – Ionic separation or analysis – Methods
Reexamination Certificate
1999-09-27
2002-12-10
Anderson, Bruce (Department: 2881)
Radiant energy
Ionic separation or analysis
Methods
C250S281000
Reexamination Certificate
active
06492639
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to a method for recognizing properties of a material that are of interest, based on the volatile and/or vaporizable portions of this material. The invention is used in particular to be able to make comparisons of materials/substances basically considered identical/similar, in order to recognize whether there are actually differences between these compared substances with respect to the concerned properties. A specific field of application is the food sector, for example, in order to be able to make quality categorizations of batches thereof.
BACKGROUND ART
It is known that in organic chemical analysis, mass spectrometry is the most important detection method for identifying unknown individual substances.
In particular the combination of mass spectrometric detection with static headspace gas chromatography is a well-established routine analysis method for qualitative and quantitative determining of the volatile components of an unknown sample. This method of analysis is based on the fact that in a closed container, the very volatile and moderately volatile components of a sample spread out between the sample matrix and the gaseous phase over this matrix. In this connection, the sample matrix itself may be liquid or solid. In the state of thermodynamic equilibrium, the gaseous phase contains a qualitatively and quantitatively representative equivalent of all volatile components. For this reason, for identifying the volatile components of an unknown sample, a defined gas volume, what is referred to as the aliquot, is removed from the gaseous phase and is fed to the gas chromatographic separation with subsequent mass spectrometric detection.
It is known to use static headspace gas chromatography in combination with mass spectrometry in different areas of organic chemical analysis.
It is known from Ragunathan, N. et al. “Multispectral detection for gas chromatography” JOURNAL OF CHROMATOGRAPHY A, vol. 703, no. 1, 26th May 1995, pp. 335-382 XPOO4023371, to timewise separate an unknown mixture of substances into its single components, which can be sequentially analyzed by a mass spectrometer. For each single component an individual mass spectrum can be recorded within a respective time window. In the following spectral analysis the individual mass spectrum of each single component is compared to a given catalogue (library) containing the mass spectra of various reference substances. To reduce library search time the system selects only a few ionized fragments from the individual mass spectrum to be compared with the reference spectra by introducing a threshold for the minimum peak height. The intensity of the spectral line determines which spectral lines are used for the spectral comparison. The system selects only a reduced number of spectral lines for evaluation.
From Belitz and Grosch, “Lehrbuch der Lebensmittelchemie” [Textbook of Food Chemistry] (1992), p. 312, it is known that this method of analysis, known under the acronym HSGC/MS, is well-established in particular in the food industry sector, because the type as well as the number of volatile components contained in a food sample are of decisive significance for the quality of a food. In particular the presence of certain odor-active volatile components that mark the aroma of a food sample, and their concentration in the food sample, are used in this connection as the basis for quality assessment.
Likewise, in numerous sectors of environmental analysis, in particular in utilization of waste products, in environmental monitoring, in emission studies of packaging material and in emission analysis, the routine decoding of the volatile components of a sample to be examined takes place by means of the HSGC/MS method. In this case as well, the odor-active volatile components are particularly significant because they are used to assess the annoyance of emissions. HSGC/MS analysis has similar importance for determining maximum workplace concentration values.
From Newman, “Electronic Noses”, Analytical Chemistry 63 (10), pp. 585A-588A, it is known that in recent times, in particular in the field of food quality control, instruments that are referred to as “electronic noses” are used for the objective, rapid measuring/characterization of odors. These are sensor arrays of several unselective individual sensors that are housed together in a measuring chamber. Just as in HSGC/MS analysis, the “sample” arriving for measuring is an aliquot of the gaseous phase that is situated above the actual liquid or solid sample matrix. The individual signals delivered by the individual sensor elements when volatile substances are present in the gaseous phase are evaluated by methods of pattern recognition, known from Gardner and Bartlett, Sensors and Sensory Systems for an Electronic Nose (1992), p. 161. In this connection, cluster analysis in the multidimensional space or neural networks are preferably used.
In particular when studying odorous samples with the help of sensor arrays, there is no identification of the volatile components contained in the sample. For this reason, neither the components decisive for the odor of the sample nor the odorless components become known thereby. If two odorous samples differ only in that non-odor-active components are present in different concentrations, both samples are classified as different by the “electronic nose”, although they are assessed identically in human sensory perception. Such an erroneous classification limits the ability of “electronic noses” to be used universally.
In practical experience, in particular in the food sector, the technical problem that is often presented is to recognize materials, e.g. from batches, goods deliveries and the like in order, for example, to be able to carry out a quality classification thereof, or to allow evaluation comparisons between substances considered identical/similar, where this supposed parity actually does not hold true. A simple example is provided not for limitation, but rather only as an illustration. Different batches, e.g., of parsley delivered should actually be of uniform quality. For example, these batches cannot be differentiated optically from each other, yet they display odor differences. The recognition to be executed with the invention is intended to allow objective differentiation of the individual batches, for example for the purpose of classification into merchandise categories.
A technical problem addressed by of the present invention is to indicate a method that makes such recognition or differentiation executable at the least possible expense and in particular in a brief time.
This technical problem is solved with the teachings of patent claim
1
, and in further developments according to the subclaims.
The invention is based on basically known recording and evaluation of mass spectra of complex substance mixtures, but including, according to the invention, measures that allow mass spectrometry, familiar in principle, to be applied in a simplified manner in that a given (random) reference sample is taken as a basic pattern, and the actual series of tests are conducted only as comparisons with simplified expenditure. In particular, when examining and evaluating the individual (series) samples of the testing series, the method according to the invention does so without conducting gas chromatographic separation of the components of these samples in each case, namely without loss of quality or general validity of the (quality) assessment achieved.
The method according to the invention has the advantage that in (merchandise) classification not every individual sample is to be examined in detail in a time-consuming manner. Rather, as is also shown more thoroughly in the following detailed description, only a one-time calibration procedure is carried out with a (random) reference sample for this merchandise, namely preferably with an arrangement also to be used for conducting the subsequent series tests, as is described in
FIG. 2
, for example.
With respect to a pattern analysis/e
Dittman Brigitte
Horner Gerhard
Nitz Siegfried
Parlar Harun
Aker David
HKR Sensorsysteme GmbH
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