DETERMINATION OF A CONSTITUENT RELATED PROPERTY OF A MULTI-CONSTITUENT SAMPLE

Abstract

A method of determining a constituent related sample property of a multi-constituent sample comprising: subjecting the sample to a perturbation selected to induce a time dependent change in measurement data associated with a constituent related to the sample property to be determined; recording a time-series of measurement data following subjecting the sample to the perturbation; and determining the sample property from the application to the recorded time-series of measurement data of a calibration correlating the sample property with time-series of measurement data, said calibration being empirically derived from chemometric time-series modelling of time-series measurement data recorded for each of a plurality of reference samples following subjecting each reference sample to the perturbation, each reference sample having a different known values of the sample property.

Description

DESCRIPTION

The present invention relates to a method of and a monitor for the determination of a constituent related property of a multi-constituent sample and in particular to such a determination using chemometric analysis of electromagnetic radiation after it's interaction with the sample.

In the food industry for example, there is an increasing interest both from the consumer and the producer in obtaining more detailed information regarding one or more constituent related properties of a product, for example, going from total fat content to fatty acids profiling in meat and dairy products; going from total protein content to protein fractions (such as from milk protein to casein and whey fractions); going from total carbohydrate to sugar profiling in milk, wine and juice products; or identifying low concentration components, such as malic acid in wine and juice products or acetone in milk; or determining physical or functional properties of a food.

In the present context the phrase ‘constituent related property’ means a compositional, physical or functional property of a multi-constituent sample which is affected by one or more of the constituents of the sample. Similar phrases will have similar meanings.

Thus it is expected that information regarding a constituent related property may be obtained from measurements made on the constituent itself.

It is well known that different constituents of a multi-constituent product interact differently with optical radiation, particularly within the infra-red region of the electromagnetic spectrum, to produce more or less distinctive ‘spectral fingerprints’. Stretching and bending vibrations of chemical bonds of sample constituents, for example, can provide characteristic absorption bands within the electromagnetic spectrum, particularly the near and mid infra red portions of the spectrum. Different particle size, which for ground cereal grains is related to hardness, may cause a sample to exhibit differential light scattering and/or absorption characteristics which again can be monitored through the interaction of optical radiation with the sample to provide information on a property of interest within the sample. Chemometric analysis of optical spectra which derive from the detection of electromagnetic radiation in one or more wavelength regions from within ultra violet to infra red portions of the electromagnetic spectra (referred to herein as ‘optical radiation’) after it has interacted with a sample is now commonly employed as a means to derive quantitative or qualitative information about a property of the sample. Chemometric analysis is a so-called ‘indirect’ technique, meaning that the constituent related information is not directly available from the recorded spectral data. Rather a calibration must be established by linking spectral features of reference samples with information regarding a property of interest of those samples, which information is obtained for each reference sample using other, typically direct, analysis techniques. However, advantageously, chemometric analysis offers the ability to mathematically extract the relevant information about the property of interest of the sample through the development of a model that subsequently can be used for quantitative property prediction of new samples as well as for detection of deviating samples not taken into account in the calibration samples.

Unfortunately, constituents with similar chemical bonds often produce similar or strongly overlapping spectral fingerprints. This will make it difficult to employ chemometric analysis of conventional optical spectra in order to determine properties of the sample which are related to these constituents.

According to a first aspect of the present invention there is provided a method of determining a sample property of a multi-constituent sample comprising: subjecting the sample to a perturbation selected to induce a time dependent change in measurement data associated with a sample property to be determined; recording a time-series of the measurement data following subjecting the sample to the perturbation; and determining the sample property from the application to the recorded time-series of the measurement data of a calibration correlating the sample property with time-series of measurement data, said calibration being empirically derived from multivariate chemometric modelling of time-series measurement data recorded for each of a plurality of reference samples following subjecting each reference sample to the perturbation, each reference sample having a different known values of the sample property.

By changing the known, static measurement situation into a dynamic measurement situation through inducing sample perturbations affecting only a constituent related to the sample property to be determined, either hardware or in-sample perturbations, then temporal evolution data may be employed to provide a characteristic temporal development profile and changes in its content associated only with the constituent of interest. Since it is a specific change in the measurement data which is now being employed even constituents which are present in low (even previously undetectable) concentrations may generate relatively large changes which allow their detection and a property determination, such as constituent concentration or constituent dependent physical or functional properties.

Moreover, by employing the dynamic time-resolved measurement data the method of the present invention can be made much more robust towards interferences and the amount of required calibration reference samples may be reduced by using advanced chemometric dynamic time-series modelling.

Physical or chemical perturbations are induced directly in the sample during measurement by means of, for example, temperature change, addition of a chemical, addition of one or more enzymes, salt or pH changes. In every event the in-sample perturbation employed will be selected to induce a physical or chemical change in the sample which manifests as a change in measurement data associated with the constituent related information of interest. A hardware perturbation is one which is applied external of the sample to cause perturbations in the sample, for example, to induce a movement of dispersed particles, such as molecules, in a fluid sample held in a sample presentation unit, comprising for example a cuvette, as the result of applying a current or magnetic field to the fluid sample.

According to a second aspect of the present invention there is provided A sample property monitor (2) comprising an output unit (4) for outputting an electromagnetic wave for interaction with a multi-constituent sample; a detection unit (6) for detecting a property of the electromagnetic wave after its interaction with the multi-constituent sample and outputting the detected property of the electromagnetic wave as measurement data; a perturbations unit (14) adapted to generate a perturbation in the multi-constituent sample selected to induce a change associated with a sample property to be determined, said change manifesting as a change in the property of the electromagnetic wave detected by the detection unit (6); and a determinations unit (8) for determining the sample property from the output measurement data; wherein the detection unit (6) is adapted to operate in a timed relationship with the operation of the perturbations unit (14) to detect the property of the electromagnetic radiation a plurality of times following generation of the perturbation and to output a time-series of measurement data; and wherein the determinations unit (8) is adapted to process the time-series of measurement data to determine the constituent related sample property by the application of a calibration linking the sample property to the time-series of measurement data, which calibration is empirically derived from multivariate chemometric modelling of time-series measurement data recorded for each of a plurality of reference samples following the generation of the perturbation in each sample, each reference sample having a different known value of the sample property.

In the following the invention is further explained and exemplified with reference to the figures, of which:

FIG. 1 Illustrates a typical mid-IR spectrum of milk;

FIG. 2 Illustrates schematically a representative sample property monitor according to the present invention;

FIG. 3 Show exemplary temporal evolution profiles obtained using the analyser of FIG. 2;

FIG. 4 Show a PARAFAC kinetic mode retrieved from profiles as illustrated in FIG. 3;

FIG. 5 Shows a calibration for K-casein in milk obtained by PARAFAC modelling of temporal evolution profiles exemplified in FIG. 3; and

FIG. 6 Shows schematically a sampling unit employed in the monitor of FIG. 1 for use in electrophoretic based determinations according to the present invention.

An embodiment of the present method will now be described by way of example only. According to this exemplary embodiment a determination of the amount of Kappa-casein (‘K-casein’) in milk is made as the constituent related sample property. This determination is made by analysing perturbation induced time dependent changes in a property of an electromagnetic wave, here intensities of wavelength components of the electromagnetic wave, after the wave has interacted with the milk.

Considering now FIG. 1, a representative mid-infrared absorbance spectrum of milk in the wavenumber region 1000 cm⁻¹ to 1600 cm⁻¹ (corresponding to the wavelength region 10,000 nm to 6250 nm) is illustrated. The protein (P) related feature of the milk sample spectral fingerprint is identified. Casein is known to represent around 80% of the total protein content. Since, according to Beer's law, absorption of the electromagnetic energy (here mid-infrared energy) is proportional to the amount of absorbing component then detection of casein using chemometric analysis of conventional, static, optical spectra should be possible. However, as can be seen from FIG. 1, the protein spectral feature (P) shows no clearly distinguishable structure. Spectral features related to K-casein are essentially indistinguishable from those of the other proteins in milk. As will be appreciated by those skilled in the art a conventional chemometric model developed to predict K-casein content from such measurement data would, in these circumstances, be inaccurate.

It is well known that gelation of casein micelles with addition of the enzyme chymosin is the first step in the manufacture of cheese. The enzyme specifically removes the C-terminus of the K-casein causing destabilization and subsequent agglomeration of the casein micelles which agglomeration causes changes in the optical spectral fingerprint of the milk sample. By way of example only, using this enzyme as a chemical perturbation on a milk sample a time-series of spectral data can be recorded for each sample as a monitor of the temporal evolution of K-casein agglomeration. Two enzymatic preparations (chymosin preparations) were used in the present exemplary embodiment, namely CHYMAXPLUS™ and CHYMAXM1000™, both obtained from Chr. Hansen, Bøge Allé 10-12, DK-2970 Hørsholm, Denmark. CHYMAXM1000 shows highest specificity for K-casein and was therefore used to establish an empirical calibration as described below which links the sample property (here K-casein concentration) with time-series spectral data (here representing temporal evolution of spectral absorption). Both enzymatic preparations show very comparable spectral evolutions and act in a similar manner concerning the agglomeration of K-casein in milk.

According to the present embodiment, and by way of example only, all electromagnetic spectral measurement data is recorded using a sample property monitor 2 as illustrated schematically in FIG. 2 and operating to record spectral data in the wavenumber region between 1000 cm⁻¹ to 1600 cm⁻¹. According to FIG. 2 the exemplary sample property monitor 2 comprises an output unit 4; a detection unit 6; a determinations unit 8; a control unit 10; and a sampling unit 12, which sampling unit 12 comprises a perturbations unit 14 and a sample presentation unit 16.

The output unit 4 is adapted to output an electromagnetic wave towards a multi-constituent liquid sample (here milk) in the sample presentation unit 16. In the present embodiment the electromagnetic wave is a mid-infrared electromagnetic wave having wavelength components at least extending in the region between 6250 nm (1600 cm⁻¹) and 10000 nm (1000 cm⁻¹). It will be appreciated that generally the wavelengths of electromagnetic energy emitted by the output unit 4 are selected dependent on the constituent(s) within the sample which gives rise to the constituent related sample property being monitored.

The detection unit 6 detects a property of the electromagnetic wave output from the output unit 4 after the wave interacts with the liquid sample in the sample presentation unit 16. In the present embodiment the detection unit 6 is show configured to monitor an energy (wavelength or wavenumber) dependent intensity variation of the output electromagnetic wave that is induced by its interaction with the liquid sample and to provide this electromagnetic absorption spectral data as output measurement data. Here, and by way of example only, the detection unit 6 is configured to detect the electromagnetic energy transmitted through the sample and may, as in the present example, comprise a Fourier Transform infrared (FTIR) spectrometer although other known spectrophotometric devices, such as a monochromator or detector diode array (DDA), which are adapted to provide an output of intensity indexed against energy (wavelength or wavenumber) may be substituted.

The determinations unit 8 comprises a data processor configured to determine a concentration of a constituent (sample property) within the sample based on the measurement data provided to it by the detection unit 6, as will be described in more detail below, and to output the same Conc., for example in a human discernible format on a video display or in digital format for transmission, storage in a memory device or for use in other electronic systems.

The control unit 10 is operably connected to the perturbations unit 14 to trigger the perturbations unit 14 to induce a perturbation in the liquid sample which causes time dependent variations in the degree of interaction between the output electromagnetic energy and the liquid sample which are associated with a component of the liquid sample which is related to the property of the sample being monitored. In the present example the control unit 10 is also operably connected to the detection unit 6 to trigger the detection unit 6 to make detections for a plurality of times after the triggering of the perturbation by the perturbations unit 14. The thus generated time-series electromagnetic spectral data is the measurement data employed by the determinations unit 8 to determine a concentration of a component of interest within the liquid sample as the component related property of the sample.

The sampling unit 12 includes a perturbations unit 14 which, in the present embodiment, may be operated to automatically induce a suitable perturbation in the liquid sample, for example directly in the sample held in the sample presentation unit 16. Here the perturbations unit 14 operates to introduce a chemical, such as an enzyme for example, into the liquid sample to cause a reaction which manifests itself as a detectable change in the interaction of the liquid sample with the electromagnetic waves output from the output unit 4. The chemical/enzyme is selected to induce a change that is specific to the constituent related to the property of the sample to be determined, in the present example the constituent concentration. Other perturbations may be substituted for a chemical one, dependent on the constituent. Such other perturbations may be of a thermal, electric or magnetic nature. In an alternative embodiment the perturbations unit 14 may be manually operable and may for example be a pipette or syringe containing the appropriate chemical and may operate to introduce this chemical into the sample before it enters the sample presentation unit 16.

The sample presentation unit 16 of the sampling unit 12 is configured according to the property of the electromagnetic wave being detected by the detecting unit 16. In the present embodiment then, the sample presentation unit 16 comprises opposing wall sections which are transparent to the electromagnetic wave output by the output unit 16 and may be formed as a removable cuvette into which a sample (with or without a perturbing chemical added) may be introduced manually. Alternatively, the sample presentation unit 16 may be formed as a flow-through cuvette having an inlet connected to a liquid flow system by which an external sample may be flowed into the cuvette for analysis, and an outlet through which analysed sample may be removed from the cuvette. In this embodiment an end of an externally accessible pipette of the liquid flow system may be immersed in a liquid to be analysed, for example as may be held in a beaker or a test tube, and the flow system operated to automatically introduce a liquid sample into the sample presentation unit 16. The perturbations unit 14 may be fluidly connected to the flow system to introduce a perturbing chemical/enzyme into liquid flowing towards the sample presentation unit 16.

According to the present embodiment, in order to establish the empirical calibration for K-casein concentration reference samples were prepared using different dilutions of milk which is sourced from a from a consumer milk carton. According to the dilution the K-casein concentration was calculated simply by multiplying the total protein content (here obtained from milk package nutrition information but which may be obtained using standard analysis techniques such as a one employing Kjeldahl chemistry) with a factor of 0.8. It is generally accepted in the art, for example as evidenced in the text book: Mejeri-lre, authors E. Waagner Nielsen and Jens A. Ullum, ISDN 87-7510-536-5, pg. 62, that K-casein represents around 80% of total protein in milk.

The diluted milk reference samples employed in establishing the calibration are listed in Table 1 and are selected so that the K-casein concentration extend to cover the range of concentrations expected in samples of unknown K-casein concentrations to which the calibration will eventually be applied.

TABLE 1
Sample				K-casein
Number	Milk (ml)	Water (ml)	Milk ratio	(g/100 ml)
1	25	5	0.83	2.33
2	24	6	0.80	2.24
3	23	7	0.77	2.15
4	22	8	0.73	2.05
5	21	9	0.70	1.96
6	20	10	0.67	1.87
7	19	11	0.63	1.77
8	18	12	0.60	1.68
9	17	13	0.57	1.59
10	16	14	0.53	1.49
11	15	15	0.50	1.40

Temporal evolution profiles of the energy dependent absorption of the output electromagnetic wave which were recorded for reference samples identified by sample numbers 5 and 11 are presented in FIGS. 3 (a) and (b) respectively. As the acquisition time for each spectrum is known then the scan number illustrated in FIG. 3 represents a time scale for the collected spectral data. It can be clearly observed that the intensity of the spectral evolution for electromagnetic energy in the wavelength region between 6250 nm (1600 cm⁻¹) and 10000 nm (1000 cm⁻¹) increases with the K-casein concentration (i.e. increase from FIG. 3(b) to FIG. 3(a)) and increases with time after introduction of the perturbation/spectrum number (see FIG. 3 (a)). All perturbations have been induced by an introduction of 20 μl of CHYMAXM1000 enzyme solution.

To establish a K-casein calibration multivariate chemometric, preferably multi-way, analysis methodology, such as for example PARAFAC, TUCKER3 or NPLS, are employed. Alternative multivariate chemometric methodologies are unfolded PLS, unfolded PCR, unfolded Ridge regression or similar, as well as variable selection techniques in connection with these methods (including Multivariate Linear Regression ‘ MLR’). For the true multi-way methodologies in principle only one calibration sample is sufficient to calibrate the system as opposed to the two way methods where several samples are needed to span the variations expected. In this present embodiment PARAFAC was applied to the temporal evolution profiles in order to extract the underlying spectral patterns without reference data (which is unsupervised and therefore unbiased). PARAFAC is employed to decompose the tensor into sample mode, kinetic mode and spectral fingerprint mode. The retrieved PARAFAC kinetic mode is presented in FIG. 4 which shows a plot of Loading on PARAFAC Component 1 against scan number (equivalent to time after perturbation) and illustrates that the loadings on Component 1 increase with time (increase in scan number).

The final calibration was thereafter established by correlating the PARAFAC retrieved score for Component 1 with the K-casein concentration from Table 1. Calibration accuracy and precision were calculated for different models using different amounts of spectra (=different observation times). As shown in FIG. 5, which is a plot of PARAFAC Component 1 scores against K-casein concentration in milk (g/100 ml) for reference samples listed in Table 1, the K-casein concentration correlates well with the Component 1 scores retrieved by PARAFAC. Precision was determined by measuring nine replicates of one K-casein concentration.

For the present example the optimal observation time for good calibrations in terms of accuracy and precision are rather short. The PARAFAC model for an observation time of 1.9 min (7 spectra; each spectra having 16.6 seconds acquisition time) showed best performance.

To determine a K-casein concentration in a test sample of unknown concentration a time-series of electromagnetic spectra in the wavelength region between 6250 nm (1600 cm⁻¹) and 10000 nm (1000 cm⁻¹) are recorded for this test sample essentially in the same manner as the time-series were obtained for the reference samples. Generally then, the enzyme is added to the test sample and electromagnetic spectra including the wavelength region between 6250 nm (1600 cm⁻¹) and 10000 nm (1000 cm⁻¹) are recorded by a sample property monitor equivalent to that monitor 2 used to generate the model and illustrated in FIG. 2. By the term ‘equivalent’ it is to be understood to mean a sample property monitor which would generate spectral data from a sample that would be detectably indistinguishable from spectral data generated using the monitor employed in calibration generation. These spectra are obtained over the same observation time and acquisition time as employed in the derivation of the calibration model stored in the system. In the present exemplary embodiment the recorded time-series data is subjected to PARAFAC decomposition in order to obtain a score for the Component 1, the stored calibration model is then applied to the obtained score in the concentration determining unit 8 and a concentration (amount) of K-casein in the test sample is obtained.

In other embodiments more than one component is required and then a multivariate chemometric regression method such as MLR is employed to correlate these components with the sample property of interest.

In a further embodiment of the method according to the present example proteins are detected in a in a multi-component food sample, such as milk or an emulsified solid food product. Here, the technique of protein denaturation is employed as the perturbation. Protein denaturation is any modification in conformation not accompanied by the rupture of peptide bonds involved in primary structure and according to the present invention it is the temporal evolution in conformation modifications that is monitored. Heat, acids, alkalis, concentrated saline solutions, solvents and electromagnetic radiations are all perturbations which are known to cause denaturation. It is known to monitor protein denaturation by measuring sedimentation; viscosity; protein migration through electrophoresis; optical rotary dispersion, dichroism, wavelength dependent intensity variations or other changes in a property of an electromagnetic wave. Thus according to the present invention a suitable perturbation is induced in a test sample and a time-series of measurement data is recorded. A suitably derived empirical calibration correlating the constituent (protein) related sample property with changes in time-series of measurement data is applied in a data processor to the recorded time-series of measurement data in order to determine the constituent related property of the sample, such as concentration, for the test sample. Such an empirical calibration may be generated in a manner analogous to that described above in respect of the K-casein calibration. Thus, time-series measurement data is obtained for a plurality of reference samples having known amounts of the protein of interest using the same methodology as employed in obtaining the time-series measurement data of the test sample. Multivariate chemometric modelling, such as multi-way modelling described above, is applied to the time-series measurement data for each reference sample in order to generate the calibration.

It will be appreciated that calibrations linking temporal evolution measurement data with other sample properties of interest can be generated by using reference samples having known values of that sample property and subjecting temporal evolution profiles of these reference samples to a multivariate chemometric modelling.

According to a second embodiment of a sample property monitor according to the present invention a sampling unit 812 is illustrated in FIG. 8 which substitutes for the sampling unit 12 of FIG. 2 in order to adapt the component analyser 2 of FIG. 2 to operate to determine a concentration (Conc.) of a constituent of interest in a multi-constituent liquid sample using an electrophoretic perturbation. The sampling unit 812 comprises a perturbations unit 814 and a sample presentation unit 816.

The perturbations unit 814 is a direct current (D.C.) generator operably connected to the sample presentation unit to generate an electric field within a multi-constituent liquid sample held in the sample presentation unit 816.

The sample presentation unit 816 is, in the present embodiment and by way of example only, configured as a flow-through cuvette provided with liquid inlet 818 and liquid outlet 820 in fluid connection with a liquid flow system (not shown) of the sample property monitor. The flow-through cuvette 816 has opposing window portions 822 and 824 (dashed) which are formed of a material transparent to electromagnetic energy output from the output unit 4 and dimensioned to provide an observation region (shaded region) within the cuvette 816 which is less than the liquid holding region of the cuvette.

In use the perturbations unit 814 generates a D.C. electric field within liquid sample in the liquid holding region (which liquid holding region is the entire liquid holding volume of the sample presentation unit 816) which causes charged constituents of the liquid sample to move through the observation region 826 towards either the negative (−) or positive (+) poles of the cuvette 816, in a direction dependent on their charge. A plurality of electromagnetic spectra are recorded at different times after application of the electric filed by the perturbations unit 814 and this time-series of electromagnetic spectral data is employed in the determinations unit 8 to determine a concentration (amount) or the presence/absence of a constituent of the multi-constituent liquid sample and to output the same (Conc.). A calibration is obtained empirically from the application of multivariate chemometric modelling to time-series reference sample data. This calibration, which thus correlates a sample property to be determined with features in the time-series of electromagnetic spectral data, is made available for use, for example stored within an accessible electronic memory or other storage device, within the determinations unit 8 for this purpose. This calibration is derived in basically the same manner as that for K-casein described above. In the present embodiment however, and as will be appreciated by the skilled artisan, temporal evolution profiles are recorded for reference samples of known concentration of the constituent not consequent on the addition of an enzyme but, rather, consequent on the application of the D.C. electric field. Moreover the output unit 4 of the present embodiment will be adapted to output the electromagnetic wave in an appropriate region of the electromagnetic spectrum, which is sensitive to the electrical perturbation induced changes to the constituent. As mentioned above this embodiment of a sample property monitor may, without limitation as to its other uses, be applied to the determination of protein related information as the sample property of interest through monitoring time related changes which are manifestations of the denaturation of the protein of interest.

Claims

1. A method of determining a concentration of a particular constituent in a multi-constituent sample, the method comprising: inducing a particular constituent-related perturbation of the multi-constituent sample, the particular constituent-related perturbation associated with the particular constituent of the multi-constituent sample, such that the particular constituent-related perturbation causes a change related to the particular constituent in the multi-constituent sample, the change manifesting as a change in a property of electromagnetic waves interacting with the multi-constituent sample;directing an electromagnetic wave through the multi-constituent sample, separately from inducing the particular constituent-related perturbation of the multi-constituent sample, such that the electromagnetic wave interacts with the multi-constituent sample to establish a post-interaction electromagnetic wave;implementing a plurality of detections of a property of the post-interaction electromagnetic wave over a period of time following the inducing of the particular constituent-related perturbation to generate a time-series of perturbation-dependent measurement data associated with the concentration of the particular constituent in the multi-constituent sample, such that the perturbation-dependent measurement data is time-varying based on the particular constituent-related perturbation of the multi-constituent sample over the period of time; anddetermining a quantitative value of the concentration of the particular constituent in the multi-constituent sample based on applying the time-series of perturbation-dependent measurement data to a calibration association of values of the concentration of the particular constituent with corresponding time-series of perturbation-dependent measurement data.

2. The method of claim 1, wherein the calibration association is empirically derived from multivariate chemometric modelling of one or more time-series of perturbation-dependent measurement data recorded for each reference sample of a plurality of reference samples following subjecting each reference sample to the particular constituent-related perturbation, each reference sample having a different known quantitative value of the concentration of the particular constituent in the multi-constituent sample.

3. The method as claimed in claim 1, wherein the property of the post-interaction electromagnetic wave is a wavelength dependent intensity of the post-interaction electromagnetic wave having wavelength components, intensities of the wavelength components changing dependent on perturbation induced changes associated with the particular constituent in the multi-constituent sample.

4. The method as claimed in claim 2, wherein the multivariate chemometric modelling includes application of a multi-way modelling methodology to the time-series of perturbation-dependent measurement data.

5. The method as claimed in claim 4, wherein the multi-way modelling methodology is a methodology selected from PARAFAC, TUCKER3 and NPLS.

6. The method as claimed in claim 1 wherein inducing the particular constituent-related perturbation of the multi-constituent sample includes subjecting the multi-constituent sample to a chemical perturbation.

7. The method as claimed in claim 6, wherein the chemical perturbation includes introducing an enzyme to the multi-constituent sample.

8. The method as claimed in claim 7, wherein the multi-constituent sample is a food sample and the particular constituent is a protein in the food sample.

9. The method as claimed in claim 8, wherein the food sample is milk,the enzyme is an enzyme selected to facilitate K-casein agglomeration, andthe determining the quantitative value of the concentration of the particular constituent in the multi-constituent sample includes determining a concentration of K-casein in the food sample.

10. The method as claimed in claim 9, wherein the enzyme is a chymosin preparation.

11. The method as claimed in claim 1, wherein inducing the particular constituent-related perturbation of the multi-constituent sample includes subjecting the multi-constituent sample to a physical perturbation.

12. The method as claimed in claim 11, wherein the physical perturbation is one or more of an electric perturbation and a magnetic perturbation configured to create a movement of dispersed particles within the multi-constituent sample.