DE102007045449B4 - Method and device for calibrating a sensor by means of a drying balance - Google Patents

Abstract

Method for calibrating a spectrophotometric sensor by means of a drying balance (1), comprising a heating source (5) for heating and drying a sample (4), a weighing system (2) for measuring the moisture-dependent mass of the sample (4) and a sensor (11) for measuring the moisture-dependent transmission and / or reflection spectrum of the sample (4) in at least one predetermined spectral range, wherein the assignment of the output signals of the sensor (11) to the measured moisture contents of the sample (4) and storage of these measured value pairs in the electronics ( 7) the drying balance (1) a calibration of the sensor (11) is performed
characterized,
- That the measured value pairs of several samples that belong to a common substance class, the moisture-dependent transmission and / or reflection spectra, however, vary due to other physical and / or chemical parameters are evaluated together and using statistical calculation methods, a substance class calibration in the form of a Computational model is determined and
- That this substance class calibration is used as a calibration for the sensor (11).

Description

The The invention relates to a method for calibrating a spectrophotometric Sensors by means of a drying balance, which is a heating source for heating and Drying a sample, a weighing system for measuring the moisture-dependent Mass of the sample and a sensor for measuring the moisture-dependent transmission and / or reflection spectrum of the sample in at least one predetermined Having spectral range, wherein the assignment of the output signals the sensor to the measured moisture content of the sample a calibration performed by the sensor becomes. The invention further relates to a drying balance to carry out this procedure.

A method of this type and the necessary for carrying out the drying balance are from the DE 10 2004 053 734 B4 known.

at This known method is provided that the calibration the sensor for measuring the moisture-dependent transmission and / or Reflection spectrum of the sample in at least one predetermined spectral range by a single run of a single sample. However, since the optical properties of a sample are not limited to depend on the material moisture, but a whole series of other variables such. B. composition, Temperature, color, grain size, bedding and for arable crops also growing area, season and weathering is using a single one Sample of a substance or substance class for a calibration is not always representative. Such a created calibration for the moisture content is at a significant change in optical properties due to changes in the other variables no longer applicable. Therefore, the calibration is strict always taken only for exactly the substance used and after each change in the production process a new calibration is necessary. For coffee, for example, must in the worst case Case for every variety, every vintage, every roasting process etc. (just a few To call variables) a new calibration. As a result, a large number of calibrations must be created at great expense and the assignment of new samples to the individual calibrations is very difficult.

Besides, they are all calibrations are subject to certain measuring errors; this applies as well as the output signal of the sensor for measuring the moisture-dependent transmission and / or reflection spectrum, as well as for the determination of moisture by means of of the weighing system. Since each calibration is based on only one measurement, they all affect each other Error in the calibration fully on the final result. Besides, it is not Ensured that all substance-specific variables are correct Dimensions the calibration is taken into account are.

task The invention is therefore, the calibration by means of o. a. drying balance to optimize, in particular a higher reliability of the calibration and improved robustness over substance-specific others To reach variables.

• – dass die gemessenen Transmissions- und/oder Reflexionsspektren zusammen mit den dazugehörigen Feuchtegehalten der Probe in der Elektronik der Trocknungswaage als Messwertpaare gespeichert werden,
• – dass die Messwertpaare von mehreren Proben, die einer gemeinsamen Substanzklasse angehören, deren feuchteabhängige Transmissions- und/oder Reflexionsspektren jedoch aufgrund von anderen physikalischen und/oder chemischen Parametern variieren, gemeinsam ausgewertet werden und unter Benutzung von statistischen Rechenmethoden eine Substanzklassen-Kalibration in Form eines Rechenmodells bestimmt wird und
• – dass diese Substanzklassen-Kalibration als Kalibration für den Sensor benutzt wird.

According to the invention, this is achieved by

• That the measured transmission and / or reflection spectra are stored together with the associated moisture contents of the sample in the electronics of the drying balance as measured value pairs,
• - That the measured value pairs of several samples that belong to a common substance class, the moisture-dependent transmission and / or reflection spectra, however, vary due to other physical and / or chemical parameters are evaluated together and using statistical calculation methods, a substance class calibration in the form of a Computational model is determined and
• - That this substance class calibration is used as a calibration for the sensor.

It So those from the transmission and / or reflection spectrum the sample and its associated Moisture content formed measured value pairs in the electronics of the drying balance saved. Furthermore Different samples are grouped into substance classes - for example "roasted coffee". The measurement results for this Samples are analyzed together and from this a substance class calibration derived. Through the joint mathematical analysis of the measured values of different samples and at different wavelengths may have characteristic features This class of substances are much better recognized. And through the Using statistical calculation methods can error the individual measurements be largely compensated. This is the quality in the Substance class calibration better than a single calibration. Also the influences of Substance-specific variables are much easier to analyze and eliminate: By multivariate computing models can be from the pairs of measured values of the different samples even then a safe Statement about Derive the moisture content, if at individual wavelengths the Variables have a bigger impact on the transmission and / or Reflectance spectrum of the sample have as the moisture content to be measured.

The calibration of the sensor by a mathematical model is thus generally much more accurate and compared to the influence of others ren variables significantly less sensitive. The greater the number of measured value pairs and the more completely the bandwidth of the other variables was recorded, the better the robustness and quality of the calibration.

All in all is the measurement accuracy of such a calibrated sensor during the measurement new samples of unknown moisture content significantly better than Use of a single calibration.

A Substance class is characterized by a similar composition of individual samples defined. The already mentioned substance class "roasted coffee" is one Example; other examples of substance classes are tea, flour, milk powder etc. In the context of a production can also different production batches to a substance class summarized be and with the associated chemical or organizational product designation.

advantageously, is the calculation model not only determining the moisture content, but in addition at least one reliability characteristic value. This Reliability characteristic or these reliability characteristics should / should be a measure of the quality of the calibration be. For example, the measurement uncertainty can be such a reliability characteristic value be. This can be checked when measuring new samples with the sensor, whether the calculation model is a reliable determination of the moisture content for the new sample allowed. Only if the reliability characteristic within given limits, becomes the value determined by the calculation model for the Moisture content output, however, the reliability value is outside the predetermined limits, an error signal is output. Of the Humidity meter operator can then check if there is an error happened in the assignment of the sample to the substance class, that is, whether the correct calibration for selected the present substance has been. If the assignment is correct, the operator must do this Measure the sample once in the drying scale. The electronics of the drying scale can then the calculation model of the substance class calibration due adjust or recalculate the new measured values.

The Determination of the transmission and / or reflection spectrum of the sample done by measuring at several predetermined wavelengths; for higher accuracy requirements the transmission and / or reflection spectrum advantageously recorded quasi-continuously. The distance of each used wavelength is smaller than the resolution of the spectrometer used or less than the width of the transmission and / or reflection bands of the sample.

The Drying scale for carrying the described method has a heating source for heating and Drying a sample, a weighing system for measuring the moisture-dependent Mass of the sample and a sensor for measuring the moisture-dependent transmission and / or reflection spectrum of the sample in at least one predetermined Spectral range on, further features the electronics of the drying scale over storage means for the formed the output signals of the sensor and the associated moisture contents Measured value pairs and over Calculation means for the determination of a substance class calibration in form a calculation model from the stored measured value pairs of several Samples belonging to a common substance class whose moisture-dependent However, transmission and / or reflection spectra due to others physical and / or chemical parameters vary, and over output means for the calculated Classes of substances calibration.

In an advantageous embodiment of the sensor from the drying balance removable.

The The invention will be described below with reference to the schematic figures. Showing:

1 a block diagram of the drying balance and

2 a flowchart for explaining the method of calibration.

In 1 is the drying balance 1 , as used for calibration, is shown as a block diagram. The drying balance consists of a weighing system 2 with a balance bowl 3 on which a sample 4 for which a calibration is to be determined. As a heat source 5 for heating and drying of the sample, an annular radiant heater is indicated, but it is also the use of any other known heating sources possible. The power of the heating source 5 is controlled by an electronic control system 6 controlled, wherein the target size for the heating power and its time dependence by a central electronics 7 is given. The output signal of the weighing system 2 - So the momentary mass of the sample 4 - is through an analog / digital converter 8th digitized and fed to the central electronics. During the drying process, for example, the instantaneous mass of the sample becomes in relation to the initial mass of the sample in a display 9 displayed; After completion of the drying process, the determined moisture content is displayed (as in 1 shown). The drying scale is operated via a keyboard 10 ,

The drying balance 1 also has a sensor 11 for measuring the transmission and / or Reflection spectrum of the sample 4 on. The measurement of the transmission and / or reflection spectrum is carried out, for example, in the near infrared. The dependence of the reflection coefficient on the moisture content of the sample is particularly great, for example in the wavelength range around 1.4 μm and in the wavelength range around 1.9 μm, so that these spectral ranges are preferably used. The sensor 11 consists in the example shown from a radiation source 12 in the form of an infrared LED, one or more fiber optic cables 13 that the radiation of the radiation source 12 transferred to the sample. The sample end of the fiber optic cable 13 is in a tubular end piece 14 fixed. The sample 4 So is by the infrared light of the radiation source 12 illuminated. The light reflected from the sample is transmitted through one or more optical fibers 13 ' whose ends are also in the tail 14 are fixed, collected and become a radiation detector 15 , z. B. in the form of a grating spectrometer transferred. From this radiation detector are only the grid 15 ' and two photo elements 15 '' and 15 ''' indicated that symbolize the used photodiode array. The photodiodes can z. B. InGaAs diodes. Through the photodiode array, the intensity of the reflected light at a plurality of predetermined wavelengths can be measured and thus with knowledge of the incident light intensity and the geometric conditions, the reflection coefficient at these wavelengths and thus the reflection spectrum. Instead of the infrared LED, it is also possible to use a halogen light source, an IR emitter or another infrared radiation source. Instead of the optical waveguide, a free-beam optics can also be used. The spectrometer can also be constructed with a scanning optical system (eg scanning mirror).

In the foregoing, it is assumed that this is at the surface of the sample 4 diffuse reflected infrared light is measured and thus therefore the reflection coefficient or the reflection spectrum of the sample material. This is true for highly absorbent sample materials where the penetration depth of the radiation is low. However, there are also materials that absorb only a small amount of radiation in the given spectral range. For these materials, the penetration depth of the radiation is greater than the layer thickness of the sample 4 , Then you give the balance 3 a highly reflective surface, so also the non-absorbed part of the radiation, after he has penetrated the sample a second time, gets back into the optical waveguide 13 ' and is measured. In this case you can do so in the geometry according to 1 For a given layer thickness also measure the superposition of the reflection spectrum and the transmission spectrum. This measuring method, also referred to as transflection, is described in detail in the US 4,278,887 A. shown. Of course, for the measurement of the pure transmission spectrum, a geometry is necessary in which the radiation detector (or the end of the associated optical waveguide) is arranged below the (radiation-permeable) weighing pan with the sample (not shown).

In 1 is further by an arrow 16 hinted that the tail 14 the optical waveguide is arranged vertically displaceable. This may cause the tail 14 during the measurement of the transmission and / or reflection spectrum just above the surface of the sample 4 to achieve a good radiation output for the radiation detector; between the individual measurements may be the tail 14 higher so that there is no shadowing of the sample for the radiation of the heating source 5 produced and thus the uniformity of the heating of the sample does not deteriorate. In 1 is the tail 14 drawn in a middle position. - When measuring the transmission spectrum, in which it depends on a given layer thickness of the sample., It is also conceivable, the lower end position of the tail 14 to use for setting the predetermined layer thickness.

Is the heat source 5 a radiant heater that emits (heating) radiation in the same spectral range, in which also the (measuring) radiation source 12 (Measurement) emits radiation, so in a known manner by a clocked operation of the (measurement) radiation source 12 a trouble-free measurement can be achieved (chopper operation).

If the measured value for the transmission and / or reflection spectrum over a larger surface area of the sample 4 Measured averaged, this is easily done by a rotatable weighing pan with rotary drive and a slightly eccentric arrangement of the tail 14 possible.

For the creation of a record for calibration z. B. a sample 4 used with maximum moisture content. During the drying process, the signal of the sensor becomes at short intervals 11 for each wavelength used - ie the spectrum - recorded and in the central electronics 7 saved; on the other hand, the weighing system is practically the same time 2 recorded instantaneous sample weight and recorded in the central electronics 7 saved. After completion of the drying process and the dry weight known therewith, the respective intermediate sample weights can then be converted to moisture contents. The resulting value pairs for the spectrum and the associated moisture content are also calculated in the central electronics 7 and represent the calibration data set for this sample. It is expressly pointed out that the first value of each value pair, viz the spectrum is composed of a number of subvalues, namely the transmission and / or reflection coefficients at the different wavelengths used.

Of course it is it also possible the pairs of values just stated do not occur in a single drying cycle but from different samples with different Moisture content and with each sample only one value pair, namely the value pair for the initial moisture content, to measure. Through the different samples is thereby simultaneously the influence of other variables, such as z. B. the various beds the sample, recorded.

The summary and joint evaluation of several measurements provided according to the invention will be described below with reference to the flowchart in FIG 2 explained. First, the substance class is determined for which the sensor 11 to be calibrated (box 21 ). In the example of coffee already mentioned, this would be the substance class "roasted coffee". From this class of substance then different samples in the drying balance 1 measured as described in the previous paragraphs (box 22 ). It is advantageous if the different samples differ significantly in the other physical and / or chemical parameters, so that the occurring variables are recorded as far as possible. In the example "roasted coffee" z. B. different coffees, different bed, different degrees of browning by different degrees of roasting or possibly by different roasting processes, etc.

From the calibration data records of various samples of a substance class measured and stored in this way, a mathematical model representing the substance class calibration is then produced according to known statistical calculation methods, also referred to as chemometric calculation methods (Box 23 ). The calculation model becomes the later measurement of a new sample of unknown humidity with the sensor 11 from the signals of the sensor 11 at the different wavelengths the associated moisture value of the sample is calculated. In addition, the computational model can determine at least one reliability characteristic value, which gives a measure of how consistently the measurement results at the individual wavelengths lead to a specific moisture value. - With the determination of this calculation model is the calibration of the sensor 11 completed for substances of this class.

Will be the calibrated sensor 11 If now used outside the drying weigher for the measurement of an unknown moisture content of a new sample from this substance class, not only the moisture content can be determined with the aid of the calculation model (Box 24 ), but by the monitoring of the co-calculated reliability characteristic advantageously also a statement about the reliability of the result are made (box 25 ). The output of the humidity measurement result (box 26 ) can z. B. be made dependent on the reliability characteristic value is within predetermined limits.

If the reliability characteristic value is outside the specified limits, an error signal is expediently output (box 27 ). The user of the humidity measuring system can then check whether z. B. a completely different substance was incorrectly measured (box 28 ). If several substance class calibrations have already been stored, the transmitter of the sensor can check whether the measured value pair with another substance class calibration may yield a value for a moisture content with a reliability characteristic value within the specified limits. The transmitter may then ask the user if the sample may belong to that class of substance and thus aid troubleshooting. If the limit value overrun of the reliability characteristic value is caused by the fact that the measured sample has other, previously unrecognized variables or the degree of deviations of certain variables is greater than in the underlying calibration data set, the user can repeat one or more additional measurements in the drying balance Create calibration records. Thus, the electronics of the drying scale can adapt the calculation model for substance class calibration or recalculate it (Box 29 ) that it also gives a reliable determination of the moisture content for the new sample of the same substance class. (Box 30 ).

When measuring further samples of the same substance class, the measurement cycle then begins in the process step 24 ,

Instead of the surveillance the reliability characteristic value on compliance with the given limits, it is also possible to get out the reliability characteristic value to calculate a (percentage or absolute) measurement uncertainty and this measurement uncertainty together with the measurement result for the moisture content issue. The operator of the humidity measuring system receives thereby not just a good / bad statement, but a continuously variable one Accuracy, from which he z. B. gradually inaccurate results already detects before the preset limit is exceeded.

The storage and computation means mentioned in the above description are stock part of the microprocessor or microcomputer electronics 7 without these ingredients in 1 are shown separately.

1

drying balance

2

weighing system

3

pan

4

sample

5

heating source

6

control electronics

7

electronics

8th

Analog / digital converter

9

display

10

keyboard

11

sensor

12

radiation source

13 13 '

optical fiber

14

tail

15

radiation detector

15 '

grid

15 '', 15 '' '

Photo elements (Photodiode array)

16

arrow for displacement

21 ... 30

steps

Claims (8)

Method for calibrating a spectrophotometric sensor by means of a drying balance ( 1 ), which is a heat source ( 5 ) for heating and drying a sample ( 4 ), a weighing system ( 2 ) for measuring the moisture-dependent mass of the sample ( 4 ) and a sensor ( 11 ) for measuring the moisture-dependent transmission and / or reflection spectrum of the sample ( 4 ) in at least one predetermined spectral range, wherein the assignment of the output signals of the sensor ( 11 ) to the measured moisture contents of the sample ( 4 ) and storing these measured value pairs in the electronics ( 7 ) of the drying balance ( 1 ) a calibration of the sensor ( 11 ), characterized in that the measured value pairs of a plurality of samples belonging to a common substance class whose moisture-dependent transmission and / or reflection spectra vary on the basis of other physical and / or chemical parameters are evaluated jointly and using statistical calculation methods a substance class calibration is determined in the form of a computer model and that this substance class calibration is used as calibration for the sensor ( 11 ) is used. Method according to claim 1, characterized in that that the calculation model both a value for the moisture content, as well at least one reliability characteristic value calculated. A method according to claim 2, characterized in that in the measurement of further samples with the sensor ( 11 ) is tested, if the at least one reliability characteristic value is within predetermined limits, and that an error signal is output, if the reliability characteristic value is outside the predetermined limits. A method according to claim 3, characterized in that, if in the measurement of another sample, the reliability characteristic value is outside the predetermined limits, this further sample in the drying balance ( 1 ) and the substance class calibration is adjusted or recalculated on the basis of the new measured value pairs and together with the stored measured value pairs. A method according to claim 1, characterized in that the moisture-dependent transmission and / or reflection spectrum of the sample ( 4 ) is determined quasi-continuously within the at least one predetermined spectral range. Method according to claim 1, characterized in that the calibrated sensor ( 11 ) outside the drying scale ( 1 ) is used for the measurement of new samples of unknown moisture content. Drying scale ( 1 ), which is a heat source ( 5 ) for heating and drying a sample ( 4 ), a weighing system ( 2 ) for measuring the moisture-dependent mass of the sample ( 4 ), a sensor ( 11 ) for measuring the moisture-dependent transmission and / or reflection spectrum of the sample ( 4 ) in at least one predetermined spectral range and storage means in the electronics ( 7 ) for storing the output signals from the sensor ( 11 ) and the associated moisture contents measured pairs, characterized in that the electronics ( 7 ) of the drying balance ( 1 ) additionally by means of calculating means for determining a substance class calibration in the form of a computer model from the stored measured value pairs of several samples belonging to a common substance class whose moisture-dependent transmission and / or reflection spectra however vary on the basis of other physical and / or chemical parameters; that the drying balance ( 1 ) has output means for the calculated substance class calibration. Drying balance according to claim 7, characterized in that the sensor ( 11 ) from the drying balance ( 1 ) is removable.