WO2000017611A1 - Technique et dispositif de commande de procede - Google Patents

Technique et dispositif de commande de procede Download PDF

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Publication number
WO2000017611A1
WO2000017611A1 PCT/DK1999/000494 DK9900494W WO0017611A1 WO 2000017611 A1 WO2000017611 A1 WO 2000017611A1 DK 9900494 W DK9900494 W DK 9900494W WO 0017611 A1 WO0017611 A1 WO 0017611A1
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Prior art keywords
als
alternating
dairy
samples
monitoring
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PCT/DK1999/000494
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English (en)
Inventor
Per Waaben Hansen
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Foss Electric A/S
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Application filed by Foss Electric A/S filed Critical Foss Electric A/S
Priority to AU57269/99A priority Critical patent/AU5726999A/en
Publication of WO2000017611A1 publication Critical patent/WO2000017611A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28—Investigating the spectrum
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/85—Investigating moving fluids or granular solids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/02—Food

Definitions

  • the present invention relates to a method and apparatus for monitoring and/or controlling a process involving food and/or beverages.
  • the invention also relates to evaluation of spectroscopic data, and more specifically the use of multivariate curve resolution, such as alternating least-squares ALS.
  • NIR/NIT Near infrared reflection/transmission
  • MIR mid-infrared
  • Milk analysis using MIR equipment is generally more accurate than the corresponding NIR/NIT method. This is because MIR contains more specific information (fundamental absorptions) and stronger signals than NIR/NIT, which detects derived information (overtones and combination bands).
  • full spectrum instruments based on Fourier transform infrared (FT-IR) spectroscopy for dairy product analysis in the laboratory are showing promising results with regard to the number of components (e.g. specific sugars (13), casein (7, 9), urea (6)) that can be measured.
  • FT-IR Fourier transform infrared
  • PARAFAC requires the data to be of an order higher than two, which is the case when e.g. a spectral landscape is obtained for each sample and the whole data set can be arranged in a cube.
  • the landscape could be obtained by measuring a reacting sample with fixed time intervals during the process.
  • the individual spectra constituting the landscape will be related and these relationships implicitly contain information on the concentrations of all infrared absorbing compounds in the sample.
  • PARAFAC is able to resolve these variations and to produce concentration profiles and pure spectra corresponding to the absorbing species present in the sample.
  • the concentrations will be arbitrary, but proportional to the true concentrations. If correlating species are present, the concentration profiles will be the sums of such correlating compounds.
  • PARAFAC is normally the most useful method for multivariate curve resolution, as it can handle more than one sample at a time, the solutions to the mathematical problem are unique (there is only one solution to each problem), and they might resemble real spectra and concentrations. Good results have been obtained on resolving absorption and emission profiles from fluorescence spectra of sugar samples using PARAFAC (1).
  • Alternating Least Squares (ALS), sometimes referred to as Alternating Regression (8), is a two-way method which handles one landscape or unfolded data set at a time.
  • Ref. (15) shows how such curve resolution methods are working in practice.
  • ALS produces pure spectra and concentration profiles in a way similar to PARAFAC, but ALS is performed on the unfolded data set. The method has been used for resolution of infrared process data with excellent results (3, 14).
  • ALS Alternating Least Squares
  • AR Alternating Regression
  • CVS Cross Validation Segments
  • FT-IR Fourier Transform Infrared
  • MIR Mid-Infrared
  • NIR Near Infrared Reflection
  • NIT Near Infrared Transmission
  • PLS Partial Least Squares
  • RMSEP Root Mean Square Error of Prediction
  • SEC Standard Error of Calibration
  • SEP Standard Error of Prediction
  • SSE Sum of Squared Errors
  • R 2 Correlation
  • s r Repeatability.
  • the aim of the present invention is to provide a spectroscopic method and apparatus for monitoring processes in the food industry, and specifically dairy processes, e.g. the lactose hydrolysis process. Further it is the aim to provide a method for generating calibrations without using reference analysed samples. Specifically on-line monitoring is contemplated. Summary of the invention
  • the present invention relates to a method and apparatus for monitoring and/or controlling a process involving food and/or beverages, e.g. proceeding in a fluid or medium in a process line or a reactor.
  • infrared spectroscopic analysis equipment provides spectroscopic data of the fluid or medium, the spectroscopic data covering a period of time and being evaluated by use of curve resolution, such as multivariate curve resolution, such as Alternating Least Squares (ALS), and/or Alternating Regression, (AR).
  • curve resolution such as multivariate curve resolution, such as Alternating Least Squares (ALS), and/or Alternating Regression, (AR).
  • said method and apparatus is used for determining the properties or constituents in food products, such as dairy products and/or monitoring a food process, e.g. a dairy process. Further the method may be used for generating a calibration to be used for future determining the properties of or constituents in food products, such as dairy products and/or monitoring a dairy process.
  • the method and apparatus used according to the invention is specifically advantageous in that no reference samples has to be measured by a reference method; the method can be used for monitoring processes, such as chemical reaction processes. Further the method can be used to recognize (or identify) unknown constituents in the sample and predict their relative concentrations.
  • the method can be used to provide calibrations for a spectroscopic instrument, such as an FTIR instrument arranged to monitor a reaction process such as the lactose hydrolysis process in milk.
  • the calibrations are derived on basis of the multivariate curve resolution and the calibrations can then be stored in a data memory in the instrument and be applied for future monitoring of the process.
  • Figure 1 shows an infrared landscape from a chemical process.
  • Figure 2 is a reference versus predicted plot for lactose showing 44 calibration samples.
  • Figure 3 shows a reference versus predicted plot for lactose showing 23 test samples predicted using a PLS model with 5 factors.
  • Figure 4 shows the best 3 component ALS solution with lowest SSE (out of 100 runs) for set 1.
  • Figure 5 shows the best 3 component ALS solution with lowest SSE (out of 100 runs) for set 2
  • Upper part shows the concentration profiles, lower part the pure spectra.
  • Figure 6 shows the four component ALS solution with lowest SSE for set 1. Upper part shows the concentration profiles, lower part the pure spectra.
  • Figure 7 shows the four component ALS solution with lowest SSE for set 2. Upper part shows the concentration profiles, lower part the pure spectra.
  • Figure 8 shows a lactose profile and reference lactose results plotted against each other. The profile relates to calibration set 2 with 3 components
  • Figure 9 shows a lactose profile and reference lactose results plotted against each other.
  • the profile relates to calibration set 2 with 4 components.
  • Figures 10 A and B show concentration profiles for 2 test runs (or batches).
  • Figure 11 shows a schematic diagram of an apparatus for carrying out the method according to the invention.
  • Figure 12 shows a schematic diagram of the optical system and the control system.
  • An embodiment of an on-line apparatus or a system for carrying out the method according to the invention may comprise the following major components: A flow system 100, an optical MID-IR spectrometry system 200 and a control system 300 as shown schematically in figures 11 and 12.
  • the flow system 100 which is the object of a separate patent application, now published as WO 98/20338, may comprise the following components as shown in the example in Fig. 11 :
  • Sample intake means comprising a tube 20 and a pump and valve means, e.g. a piston pump 40 having at least one one-way valve 21 , 22 at the pump inlet and at least one one-way valve 23, 45 at the pump outlet.
  • the sample intake means 20 is connected to or introduced into a process line or reactor 10 from which the samples are taken through a filter 15 and through a detachable connection e.g. mini clamps 13 comprising two flange parts and a gasket. Preferably all such connections are made according to the hygienic standards for food processing plants.
  • the process line or reactor 10 is part of a food processing plant such as a dairy, which is not shown.
  • Temperature controlling means 30 preferably comprising: a preheater or cooler, e.g. a coiled steel tube embedded in or wound around an electrically heated copper cylinder, providing e.g. from 1 to 5 ml, preferably 1.5 ml of heated milk or a heated copper cylinder having an inner volume of about 1.5 ml and assigned temperature sensoring means (not shown) connected to control means 300 for controlling the preheater or cooler.
  • the heating means 30 is designed to heat e.g. 1.5 ml milk from 1°C to a temperature about 40°C - 50°C in about 25 seconds.
  • a high pressure pump 40 (e.g. a MSC50-h-pump as used in a FOSS ELECTRIC
  • MILKOSCAN 50 or a single stroke pump providing a whole sample volume - e.g. 1.5 ml in one single stroke) provides the high pressure (e.g. about 400-500 bar)for homogenization of the sample. Typically at least a pressure of 200 bars is needed for homogenizing.
  • the pump yield 40 will ensure a high flow rate through the IR-cuvette during a flushing period, so that the cuvette is cleaned by means of the flow rate of the milk, making further cleaning unnecessary for a number of hours.
  • the pressure across the cuvette may reach 100 - 200 bars.
  • the pressure of the measuring branch is maintained at at least the same pressure as the pressure at the location on which the sample is extracted from the process plant. Preferably the pressure in the measuring branch exceeds the pressure in the process plant.
  • the pressure is maintained at a substantially constant level by the use of a back pressure valve 88 as explained later.
  • an in-line filter 35 provides a filtered milk passing through the measurement branch comprising the cuvette 70.
  • a valve 45 allows the milk to bypass the filter, the milk running directly towards waste 90.
  • the high flow rate of milk along the inside of the filter 35 will provide a cleaning of the filter 35 when the valve 45 is open.
  • the valve 45 can be controlled by the control means 300.
  • the valve 45 also act as a safety valve which is set to open if the pressure exceeds e.g. 400 bars.
  • a homogenizer 50 e.g a S4000 homogenizer as used in FOSS ELECTRIC MILKOSCAN
  • a thorough homogenization of the liquid food product is preferred in order to obtain a representative sample (a sample containing all components in the liquid food product) inside the very thin cuvette (typically having a width of 37-50 ⁇ m).
  • a further reason for including homogenization is that the scattering of the infrared light passing through the cuvette depends on the particle size of the liquid sample. Accordingly a uniform homogenization is essential in order to have reproducible measurement conditions.
  • the pressure drop across the homogenizer is about 200 bars.
  • a further preheater or cooler 60 e.g. a coiled tube preferably wound on the periphery of a temperature stabilised IR cuvette, having an electrical resistor soldered to a copper body adjusting the temparature of the milk sample to a predetermined temperature, e.g. to about 40 °C and preferably to 50 °C before entering the cuvette, and preferably comprising assigned temperature sensonng means connected to the control means 300 for controlling the temperature of the preheater or cooler.
  • a predetermined temperature e.g. to about 40 °C and preferably to 50 °C before entering the cuvette
  • preferably comprising assigned temperature sensonng means connected to the control means 300 for controlling the temperature of the preheater or cooler.
  • the sample may be returned to the milk processing plant.
  • the optical system 200 for measuring the IR absorption can be chosen between several known IR spectrometric systems, and realised in several ways.
  • a scanning interferometer i.e a FT-IR instrument is used, e.g. an MID-IR-unit as used in FOSS ELECTRIC MILKOSCAN 120 and ProcesScan FT.
  • the optical system may instead include a filter wheel, comprising a plurality of IR filters appropriate for the desired measurements, e.g.
  • FIG. 11 A simplified diagram of a suitable optical arrangement appears from Fig. 11 and Fig. 12.
  • the Box 120 is an IR-source and scanning interferometer, Scanning interferometers are well known.
  • 140 is a detector, and 160 is a computer.
  • 305 is communication paths between the control means and the optical means, including the path for transferring spectroscopic signals to the control system.
  • an IR probe may be submerged directly into the process container. It is however the inventors experience that the use of the flow system 100 provides more accurate measurements results.
  • the control system comprises means for controlling the flow system 100 and the optical system 200 to regularly and/or repetitively perform the measurements and means for collecting the signals from the detector 140 as well as means for converting the spectrometric signal into spectrometric data which are stored in known manner.
  • the data are processed according to the present invention by use of software means comprising an ALS procedure stored in the processing means in the computer 160.
  • the calculations for the determination of the quantities of the components in the milk or food product are performed in the computer 160, and they are performed by methods according to the present invention as explained in further details below.
  • the computer 160 may be an integral part of the control system 300 or an ordinary PC (preferably containing a Pentium) communicating with the control system 300.
  • Curve Resolution such as Multivariate Curve Resolution, and more specifically e.g. Alternating Regression, AR. Simplisma
  • GRAM Generalised rank annihilation method
  • Evolving factor analysis Evolving factor analysis
  • RAFA Rank annihilation factor analysis
  • TLD Trilinear decomposition
  • X CA [2] where X is a landscape containing the spectra in its rows and C is a matrix containing the concentrations corresponding to each spectrum. In this context one sample is named X, i.e. a collection of spectra from one process run.
  • a + is the pseudo-inverse of A.
  • Non-negativity can be applied in various ways.
  • the most straightforward approach is to force negative values to zero (e.g. in C) after each iteration. This is very simple and does not necessarily lead to the optimal description of X (i.e. the least squares solution).
  • the approach employed here (adopted from The N-Way Toolbox by C. A. Andersson, Internet site: http://www.models.kvl.dk/source/nwaytoolbox/index.htm) forces only one concentration profile (or spectrum) at a time to zero, followed by a correction of the pure spectrum matrix, A (or concentration matrix, C). This modification leads to the optimal result.
  • the data analysis and calibration is performed on the computer 160, e.g. a PC, preferably using Matlab 5.2.1 software (The MathWorks Inc., MA, USA).
  • the pseudo-inverse in equations [3] and [4] can be calculated using the built-in functions of Matlab.
  • the calibration routines can be taken from the PLS_Toolbox Version 1.5 (Eigenvector Technologies, WA, USA).
  • q is the number of samples
  • n is the number of replicates
  • x is the result of the i'th replicate of the j'th sample
  • x j is the average result of the j'th sample.
  • RMSEP Root Mean Square Error of Prediction
  • N is the number of determinations (number of samples (q) times number of replicates (n) from above) and x, reference and x liP redicted are the reference and predicted values corresponding to the i'th determination, respectively.
  • SEP Standard Error of Prediction
  • N preference and X llP red ⁇ cted are defined above and X reference, Sreference, reference and S pre d ⁇ cted are the mean and standard deviations of the reference and predicted results, respectively.
  • x,, is an element in X
  • c is as row vector containing the concentrations of the i'th sample and a, is a column vector containing the absorbencies of the j'th wavelength.
  • M is the number of wavelengths in the spectra. Note that the reference results have not been used in the calculation of the SSE.
  • Calibration samples This set contains 124 samples. They were collected from nine process runs (five based on skim milk, four based on whole milk) carried out in May 1997 using an experimental set-up in the laboratory. Lactozym 3000 (Novo-Nordisk, Bagsvaerd, Denmark) was the enzyme used. Samples were taken from the reaction mixture at various time points over a three hour period, and they were immediately heated to 80 °C in a 750 W microwave oven in order to deactivate the enzyme. Duplicate samples were taken, and the following reference analyses and spectral measurements were carried out independently. Thus, the set of 124 samples comprises two very similar sets of 62 samples.
  • Test samples This set contains 23 samples obtained from two process runs carried out in the laboratory in November and December 1997 using the same experimental set-up. Samples were taken at various intervals, and this time only the sub-sample used for reference analysis had the enzyme deactivated, (the reference analysis described here is carried out only to check the applicability of the new method, it is not included in the method according to the invention) The spectral measurement was carried out on the non-deactivated sample immediately (i.e. less than one minute) after sampling in order to make the FT-IR measurements as close to an on-line application as possible. The sub-samples for reference analysis were still subjected to a heat treatment.
  • MilkoScan FT 120 (Foss Electric A/S, Hiller ⁇ d, Denmark, the presently preferred instrument for carrying out these measurements is however a ProcesScan FT from the same company).
  • the infrared spectrum from 925 to 5000 cm "1 was recorded.
  • the calibration samples were measured in duplicate and the test samples were measured in triplicate. In the data analysis only the ranges 964-1542, 1724-1847 and 2699-2965 cm '1 were used, as these are the areas containing useful chemical information.
  • Figure 3 shows the reference versus predicted plot for lactose for the 23 test samples (three replicates for each sample) predicted using a PLS model with 5 factors.
  • R 2 0.987
  • RMSEP 2.49
  • SEP 1.55
  • bias 1.96 bias 1.96
  • s r 0.23.
  • the reference results range from 0 to 40% dry base lactose.
  • the reference results and lactose predictions correlate well which is the main issue in this context.
  • the skim and whole milk samples had a start guess for fat of 0 and 1 , respectively
  • lactose is known to be the only sugar present in the beginning of the process, the lactose concentration of the first sample of each run was set to 1 , while all others were set to 0
  • Table 2 ALS results of the two calibration sets (part A and B) and the independent test set (part C and D) using 3, 4 and 5 components.
  • the reference results range from 0 to 45 % dry base lactose.
  • the component decreasing rapidly through each batch is lactose.
  • the SSE and/or the R 2 may be used as the selection criterion. When three components are used, both criteria give almost the same result, while the SSE criterion gives a somewhat higher prediction error in the case of four and five components. In both cases there is a significant improvement over the three-component result. As use of SSE and R 2 give similar results SSE is preferred as it does not involve reference data.
  • the remaining problem allowing implementation of ALS for practical use in dairy process monitoring is how to select the optimal number of components in the ALS model. This corresponds to the problem of selecting factors in PLS, but in the ALS case there is no prediction error (e.g. RMSEP) to minimise. In the present case the obvious choice would have been three components, as this gave the most stable result. Only the comparison of the profiles to actual lactose results indicated that four components were optimal. Methods for determining the number of independently varying species present in the samples are therefore required.
  • Scores and/or loadings obtained through Principal Component Analysis (PCA) could solve the problem.
  • the scores (not shown) contain structure (originating from the batch structure of the data) revealing up to four or five components.
  • four or five components would be expected to be optimal in ALS, which supports the actual findings shown above.
  • ALS is a promising method for use in dairy process optimisation. Without the need for reference analyses it is possible to extract e.g. four components from lactose hydrolysis process data (fat, lactose and two other sugar components) and to obtain a lactose prediction error similar to the one obtained from an ordinary PLS regression.
  • Such use of ALS for reference-independent prediction of process parameters is not limited to dairy products only, but is likely to be useful for process monitoring and identification of intermediates in all branches of the food and beverage industry.
  • the monitoring may be performed by collecting data at regular time intervals or at intervals defined in other ways, eg. by certain recognisable changes in concentrations, the process could be e.g. a batch reaction, a dairy process, a distillation or fractioning process, a fermentation process or other reaction process involving food feed or beverages and it is, therefore, contemplated that the appended claims shall cover any such modifications as fall within the true spirit and scope of the invention.

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  • General Physics & Mathematics (AREA)
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Abstract

L"invention concerne une technique et un dispositif qui permettent de surveiller et/ou de commander un procédé portant sur des produits alimentaires et/ou des boissons. Un équipement d"analyse spectroscopique par infrarouges, de préférence un équipement à infrarouges moyens, fournit des données spectroscopiques pendant une certaine durée (figure 1). La technique peut s"appliquer par exemple à un fluide ou à un milieu, dans un réacteur, les données spectroscopiques concernant ledit fluide ou milieu contenu dans le réacteur. Le procédé peut être un procédé discontinu, tel qu"un procédé de transformation de produits laitiers. Les données sont évaluées au moyen d"une résolution de courbe, telle qu"une résolution de courbe à plusieurs variables, moindres carrés alternés (ALS) ou régression alternée (AR) par exemple. Cette technique, qui peut être utilisée pour déterminer les propriétés d"un procédé et/ou les composantes employées durant ledit procédé et/ou pour la surveillance, notamment pour la surveillance en ligne du procédé, consiste à stocker et à traiter les données spectroscopiques collectées pendant une pluralité de tranches de temps discrètes sur une certaine durée. L"avantage du procédé réside en ce que des analyses de référence ne sont pas nécessaires pour traiter les données.
PCT/DK1999/000494 1998-09-18 1999-09-16 Technique et dispositif de commande de procede WO2000017611A1 (fr)

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Cited By (7)

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WO2002097431A2 (fr) * 2001-05-28 2002-12-05 Vinorica S.L. Procede pour classer du vin et du cafe
DE102007027914A1 (de) 2007-06-18 2008-12-24 Siemens Ag Sterilisierbarer Sensor zur Überwachung von biochemischen Prozessen in Fermentern
DE102007029215A1 (de) * 2007-06-24 2009-01-08 Global Sience Gmbh Eine Methodik zur objektiven Herstellung von Weinen mit vordefiniertem Alkoholgehalt inklusive der Definition von spezifischen Charakteristika im Bezug auf das Aroma und den Geschmack eines spezifischen Weines
WO2011018440A1 (fr) * 2009-08-12 2011-02-17 Siemens Aktiengesellschaft Procédé et dispositif pour déterminer des propriétés chimiques et/ou des propriétés physiques de produits consommables dans une installation de machine
WO2020130778A1 (fr) 2018-12-19 2020-06-25 Sigma Alimentos, S.A. De C.V. Procédé et système pour formuler une composition requise à partir d'au moins un ingrédient de composition variable
CN114184573A (zh) * 2021-11-01 2022-03-15 华中农业大学 牛奶中κ-酪蛋白的中红外快速批量检测方法
CN116964437A (zh) * 2020-12-23 2023-10-27 红牛有限公司 用于生产含水食品的设施及其用途和用于生产含水食品的方法

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WO1995016201A1 (fr) * 1993-12-10 1995-06-15 Foss Electric A/S Detection d'eau etrangere ou de l'abaissement du point de congelation concernant des echantillons de lait
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