Method for classifying wine and coffee

• US 7,244,902 B2
• Filed: 05/06/2002
• Issued: 07/17/2007
• Est. Priority Date: 05/28/2001
• Status: Expired due to Fees

• Current Assignee: Bionorica AG
• Original Assignee: Bionorica AG
• Inventors: Guggenbichler, Wolfgang, Bonn, Gu, Huck, Christian, Popp, Michael A.
• Primary Examiner(s): Mackey; Patrick
• Assistant Examiner(s): Hageman; Mark
• Application Number:
  US10/479,064
• Publication Number:
  US 20040222136A1
• Time in Patent Office: 1,898 Days
• Field of Search: 250/910, 209/552, 209/559, 209/576, 209/577, 209/587, 209/588
• US Class Current: 209/577
• CPC Class Codes: G01N 33/14 Beverages
• CPC Class Codes: G01N 33/146 containing alcohol
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: Y10S 250/91 Food sample analysis using invisible radiant energy source

Abstract

A method for classifying beverages of natural origin, such as wine or coffee. Classification is carried out by means of NIR spectroscopy and corresponding numerical-mathematical conditioning of the spectral data of the individual beverage samples, wherein respective obtained spectra are then correlated with a predetermined beverage, class. By means of the method of the invention it is possible to classify wines by sort of wine, growing regions, grape, vine, vintage, kind of material or wood of the wine cask used, and varying degree of maturity of the wine, and by other chemical parameters. Coffee, for example, may be classified by coffee sort, country of origin, coffee growing region, roasting method and defined chemical parameters, e.g., caffeine content, or chlorogenic acid content.

Claims

1. A method for classifying beverages of natural origin, comprising the following steps:

a) providing a plurality of beverage classes, with a plurality of known beverage samples per class, each beverage class having a plurality of known class properties;

b) irradiating the known beverage samples with irradiated light from a predetermined wavelength range

c) measuring detected light over a range of wavelengths, the detected light being of at least one of the following types;

light passed through the known beverage samples, light reflected from the known beverage samples, light re-emitted from the known beverage samples, or light dispersed by the known beverage samples;

d) determining a ratio of the irradiated light to the detected light at one or more wavelengths for each known beverage samples of each class, to obtain spectral data;

e) performing numerical-mathematical conditioning of the spectral data of the individual known beverage samples, to obtain conditioned spectral data;

f) correlating the conditioned spectral data of a plurality of known beverage samples of each beverage class to one another, to determine a class correlation;

g) compiling a database from the conditioned spectral data with different beverage classes based on the measured known beverage samples of the individual classes for calibration of a class correlation;

h) providing at least one unknown beverage sample, said unknown beverage sample having at least partially unknown properties;

i) irradiating the unknown beverage sample with irradited light from a predetermined wavelength range;

j) measuring detected light over a range of wavelengths, the detected light being of at least one of the following types;

light passed through the unknown beverage samples, light reflected from the unknown beverage samples, light re-emitted from the unknown beverage samples, or light dispersed by the unknown beverage samples;

k) determining a ratio of the irradiated light to the detected light at one or more wavelengths for each unknown beverage sample of each class, to obtain spectral data;

l) performing numerical-mathematical conditioning of the spectral data of the individual unknown beverage samples, to obtain conditioned spectral data;

m) determining the beverage classes to which the unknown beverage sample is to be associated, with the aid of a class correlation of the measured spectra, by using the compiled calibration database of step g), to arrive at a classification result;

n) at least one of representing the classification result to a user, and recording the classification result;

o) repeating steps h-n as necessary to classify additional unknown beverage samples;

wherein correlation of the numerically-mathematically conditioned spectral data is performed by cluster formation.

2. The method according to claim 1, wherein said beverages of natural origin are wines, and wherein the class properties of the individual wine classes comprise properties selected from the group consisting of:

type of wine;

growing region;

grape;

vine;

vintage;

kind of material, including species of wood of the wine cask used for storage/maturing, including American oak, French oak, Hungarian oak, and mixed forms of woods;

degree of maturity of storage in cask;

chemical parameters, including ethanol content, sugar content, acidity, and 802 content;

tannin content;

pH value;

water content;

dry residue;

polyphenol content; and

toxicological parameters, including glycol content and methanol content.

3. The method according to claim 1, wherein said beverages of natural origin are coffees, and wherein the class properties of the individual beverage classes comprise properties selected from the group consisting of:

coffee sort;

country of origin;

coffee growing region;

roasting method; and

chemical parameters, including caffeine content, bittering content, acidity, and chlorogenic acids content.

4. The method according to claim 1, wherein the wavelength range of detected light is in the range of approximately 700 to 2,200 nm.

5. The method according to claim 1, wherein a light waveguide is employed for at least one of the following purposes;

introduction of the irradiated light to the known beverage samples through the waveguide, measurement of detected light from the known samples through the waveguide, introduction of the irradiated light to the unknown beverage samples through the waveguide, or measurement of detected light from the unknown samples through the waveguide.

6. The method according to claim 1, wherein the thickness of the sample is between about 0.2 and 5 mm.

7. The method according to claim 1, wherein the known beverage samples and the unknown beverage samples are thermostated for measurement.

8. The method according to claim 1, wherein said numerical-mathematical conditioning comprises at least one data reduction selected from the group consisting of normalization, smoothing, 1st derivation, 2nd derivation, multiplicative scatter correction, reciprocal value, square, mean centering, Kubelka Munc transformation, absorption, baseline correction, addition of a constant, and shift negative to zero.

9. The method according to claim 1, wherein said correlation of numerically-mathematically conditioned spectral data comprises at least one multivariate method.

10. The method according to claim 1, wherein the tolerance circles of the individual clusters are adjustable in calibration.

11. The method according to claim 1, wherein approximately 3 to 20 spectral scans per known beverage sample are recorded and wherein approximately 3 to 20 spectral scans per unknown beverage sample are recorded.

12. The method according to claim 1, wherein at least approximately 10 beverage samples per class property are used for calibration.

13. The method according to claim 1, wherein about 70% of all beverage samples measured per class are used for calibration, and about 30% for validation of the method.

14. The method according to claim 1, wherein the classification result is represented on a screen as a 3-D plot.

15. The method according to claim 4, wherein the wavelength range of detected light is in the range of approximately 1,000 to 2,200 nm.

16. The method according to claim 6, wherein the thickness of the sample is between about 0.5 mm and about 3 mm.

17. The method according to claim 7, wherein the known beverage samples and the unknown beverage samples are thermostated for measurement at about 23°

C.

18. The method according to claim 9, said at least one multivariate method is selected from the group consisting of a principal components analysis (PCA), a smoothing, a series development, a Taylor series development, an artificial neuronal network algorithm, a backpropagation network, a dynamic learning vector quantization (DLVQ algorithm), a radial basis function (RBF networks), and a RBF networks (RBF-DDA network) trained with a dynamic decay adjustment algorithm (DDA algorithm).

19. The method according to claim 2, wherein the class properties of the individual wine classes comprise properties selected from the group consisting of type of wine;

growing region;

grape;

vine;

vintage;

kind of material, including species of wood of the wine cask used for storage/maturing, including American oak, French oak, Hungarian oak, and mixed forms of woods; and

degree of maturity of storage in cask.

20. The method according to claim 3, wherein the class properties of the individual beverage classes comprise properties selected from the group consisting of coffee sort;

country of origin;

coffee growing region; and

roasting method.