Patent ID: 12216035

DETAILED DESCRIPTION

ConsideringFIG.1, a flow diagram exemplifying a method100of preparing an organic material sample for laser induced breakdown spectroscopy (LIBS) according to the present invention is illustrated as comprising: a step102of obtaining a sample pellet of compressed granular organic material having an organic matrix in which is disposed elements of interest for LIBS investigation; and a step104of searing the sample pellet at an exposed surface to produce a searing event, which comprises a number of individual searing instances, during each of which searing instance the organic matrix at the exposed surface undergoes a thermochemical decomposition.

Searing the sample pellet at step104comprises a step106of controlling a heater unit to supply heat to the exposed surface for one or more known exposure times to produce a same number of searing instances; a step108of measuring reflectance values of the exposed surface before and after one or more of the one or more searing instances using a reflectance unit; a step110of determining by a data processor a searing parameter as an indicator of a susceptibility of the organic matrix to searing determined based on the measured reflectance values; a step112of calculating by the data processor a time value indicative of a time to achieve an optimum exposure time from the application by the data processor of a predictive model to the determined searing parameter, the predictive model linking the searing parameter to time value; step114of generating a new exposure time using the calculated time value; and a step116of heating the exposed surface for the new exposure time TN+1to complete the searing event. This may be performed in a single new searing instance or may be performed in a plurality of searing instances, where all of the plurality of searing instances together extend for the new exposure time TN+1.

By way of example only, according to a first embodiment the step106of supplying heat and the step108of measuring the reflectance values is repeated once. With each repetition the exposure time, TN, where N is the number of searing instances, is here held constant but in other embodiments the exposure time TNmay differ between the repetitions.

In this embodiment the searing parameter, X, is determined at the step110according to the equation:

X=BN⁢e-2⁢AN-1(1)
where ANis relative difference between the reflectance values RN measured at the step108and BNis its cumulative sum. These are determined according to the equation:

AN=RN-RN-1RN-1,BN=Σn=1N⁢An(2)
R0is the value of reflectance of the exposed surface measured before any searing instance, Sn. The searing parameter, X, uses the evolution of the reflectance level to estimate how susceptible a sample is to the searing process. The reflectance level evolution is captured by the cumulative sum parameter, BN. The higher the value of searing parameter X, for a given sample, the more difficult it is to sear that sample.

In this embodiment the predictive model which is employed at the step112to calculate the time value, Tcorrindicative of a time necessary to achieve an optimum exposure time is an exponential model of the form:

Tcorr=a⁢eb⁢X+c(3)
and is determined empirically, as is described in greater detail below.

The new exposure time, TN+1, that is generated at step114is then expressed as:

TN+1=TN+Tc⁢o⁢r⁢r(4)

In this embodiment the time value Tcorris a correction to be added to the previous exposure time TN. In other embodiments, the time value Tcorrmay be a time value representing completely a new exposure time needed to achieve an optimum searing (that is, TN+1=Tcorr).

According to a second exemplary embodiment, the step106of supplying heat and the step108of measuring the reflectance values is repeated once (that is, two searing instances, S1and S2). With each repetition the exposure time, TN, where N is the number of searing instances, is again held constant but in other embodiments the exposure time TNmay differ between the repetitions.

In this embodiment the searing parameter, X, is determined at the step110according to the equation:

X=RNR0(5)
where R0is again the value of reflectance of the exposed surface measured before any searing instance and here RN is the value of reflectance (that is, R2) of the exposed surface measured after the second searing instance, S2.

In this embodiment the predictive model which is employed at the step112to calculate the time value, Tcorrindicative of a time necessary to achieve an optimum exposure time is a second order polynomial model of the form:

Tcorr=a⁢X2+b⁢X+c(6)
and is determined empirically, as described in greater detail below

The predictive models used at step112depend on the form of searing parameter, X, chosen. The predictive model is determined empirically from observations on a reference sample set of pellets of compressed granular organic material having different organic plant matrices. The sample set which is employed to illustrate how the prediction model may be determined is set out in Table 1 below:

TABLE 1TCorr▪Optimum SearingMatrix(ms)Time (ms)Hay4001300Soy9001800Plant10001900Grass and Whole crop silage6001500Barley and Peas silage2001100Grass and Clover1001000Grass Silage1001000Grass Bushel1501050Grass + Clover Silage3001200Alfalfa silage7501650Barley (whole crop silage)6001500Hay2501150Legume Hay9001800Small Grain Hay6001500Straw Hay3001200

The reference time value Tcorri.e. the time required for each sample pellet of the reference sample set to achieve an optimum searing, is estimated based on the following protocol:Each sample is exposed to 3 searing instances, S1, S2and S3The 1st and 2nd searing instances, S1and S2have a duration, T1and T2respectively, of 300 msThe 3rd searing instance S3duration, T3, is adjusted as: T3=300 ms+Tcorr, that is, according to equation (4).Each reference sample has at least 3 replicas which are exposed to different searing times by modifying the Tcorrvalue.The optimum searing is determined by visually inspecting the 3 or more replicas of a reference sample and the reference time value Tcorris determined as the time required to achieve optimum searing. The optimum searing is that searing for which the complete sample surface just turns black leaving no un-seared spots. The reference time value Tcorrthus obtained for each reference sample is stored in a memory for subsequent access by a data processor. In other embodiments the optimum searing may be determined by monitoring the effects of searing on a LIBS spectral signature of a characteristic constituent element which is present in the organic matrix of test samples in a known manner, as described for example in US2019/0170617A1.For each pellet that is seared, the reflectance value (RN) is measured before any searing instance, R0, after the first searing instance, R1and after the second searing instance, R2. The measured values R0, R1, and R2are stored in the memory for subsequent access by the data processor.

FIG.2illustrates a flow diagram exemplifying a method200for determining the prediction model for use in step112to calculate a time value Tcorr.

At a step202reference time values Tcorrare determined and reflectance values (RN) before (R0) during (R1) and after (R2) one or more (for example two) searing events are measured for a set of sample pellets of a reference sample set (such as set out in Table 1) and stored in a memory for access by a data processor. This may be achieved according to the protocol described above.

At a step204searing parameters (X) are calculated in the data processor for each sample of the reference sample set using the reflectance values stored in memory at step202.

At a step206regression analysis is performed in the data processor to model the relationship between the dependent variable of reference time values (Tcorr) determined at step202and the corresponding independent variable (X) calculated at step204.

At a step208a prediction model is constructed from the analysis performed at step206for use in the method according to the first aspect of the present invention.

In embodiments using the searing factor X of the form set out in equation (1) a model of the form set out in equation (3) may be established with the variables a=1076; b=0.2677 and c=0, established using a least squares fit. This relationship is illustrated inFIG.3where the variation of Tcorrwith X is mapped graphically together with the ‘best-fit’ line calculated using equation (3) and the values of a, b and c stated above to minimize the sum of the squared residuals.

In embodiments using the searing factor X of the form set out in equation (5) a model of the form set out in equation (6) may be established with the variables a=−1238; b=2494 and c=0, established using a least-squares fit. This relationship is illustrated inFIG.4where the variation of Tcorrwith X is mapped graphically together with the ‘best-fit’ line calculated using equation (6) and the values of a, b and c stated above to minimize the sum of the squared residuals.

However the searing parameter X and the time value Tcorrare calculated, it will be appreciated that the same method of calculation must be applied at step110and112(in this example, three searing instances, the first two of which are for the same known duration of 300 ms and the third having a duration which is adjusted dependent on the value Tcorr) and the new exposure time must be generated at step114accordingly (that is, for example according to equation (4) or for example using the calculated value of Tcorrdirectly as the new exposure time generated at step114).

An example of a searing device500is illustrated schematically inFIG.5. The searing device500comprises a housing502which houses some or all of the remaining components of the searing device500. The searing device500further comprises a heating unit504; a holder506for holding a sample pellet508, for example retained in an open ended die in which the sample pellet508was pressed, with an exposed surface510accessible to heat from the heating unit504; a controller512adapted to control the operation of the heating unit504to supply heat to the exposed surface510for an exposure time to generate a searing instance Sn; a reflectance unit514adapted to supply optical radiation to and detect the supplied optical radiation reflected from the exposed surface510for use in measuring reflectance values of the exposed surface510; and a data processor516adapted to provide control signals to the controller512, the data processor516being configured to implement program coding dedicated to processing the measured reflectance values to determine a searing parameter therefrom as an indicator of a susceptibility of the organic matrix to searing; to calculate a time value indicative of an optimum exposure time by applying the determined searing parameter to a predictive model which links searing parameter to time value; and to generate a control signal representing new exposure time obtained using the calculated time value for use by the controller512in controlling the operation of the heating unit504to supply heat to the exposed surface510for a period equal to the new exposure time.

In some embodiments, the heating unit504comprises a gas burner and a gas supply, for example bottled propane gas, which may be sited internal or external of the housing502. In other embodiments different sources of heat, such as infra-red or resistive heating, may substitute for the gas burner of the present heating unit504.

In some embodiments and as illustrated inFIG.5, the holder506comprises a rotatable disc adapted to receive and hold one or more sample pellets (in the present embodiment a single sample pellet508) and rotatable by means of a connected motor unit518to move the received sample pellet508selectively between a first position (solid line construction) in which the exposed surface510of the sample pellet508is accessible to heat from the heating unit504and a second position (broken line construction) in which reflectance measurements can be performed by the reflectance unit514.

The reflectance unit514is of a known construction and may comprise a light source520which is adapted to emit light along a light path522towards the exposed surface510of a sample pellet508when in the second position (broken line construction) and a complementary detector524configured to detect light emitted by the light source520after being reflected, for example diffusely reflected, from the exposed surface510of a sample pellet508when in the second position (broken line construction). In some embodiments one or more reflectance standards (illustrated two reflectance standards526,528) are provided, either internal or external of the reflectance unit514, and are each moveable into and out of the light path522to intercept light emitted by the light source and to reflect a known percentage of the intercepted light for detection by the detector524for use in calibrating the reflectance unit514.

The data processor516generally includes a memory module530, a programmable computing module532and an input/output (I/O) module534. The memory module530holds program code which when implemented by the computing module532causes the data processor516to, amongst other things, process reflectance values measured by the reflectance unit514to determine a searing parameter therefrom as an indicator of a susceptibility of the organic matrix to searing; to calculate a time value indicative of an optimum exposure time by applying the determined searing parameter to a predictive model which links searing parameter to time value; and to generate a control signal representing new exposure time obtained using the calculated time value and transmitting it via the I/O module534to the controller512for use in controlling the operation of the heating unit504. In the present embodiment the memory module530also holds accessible suitable program code which when implemented by the computing module532causes the data processor516to control the operation of one or more of the other elements of the searing device500, such as the reflectance unit514, the motor unit518and the movement of the reflectance standard(s)526,528, in order to implement the method according to the first aspect of the present invention.

Examples of the data processor516include but are not limited to one or more of a laptop computer; a desktop computer, a remotely connected server. The memory module530may include but is not limited to one or more of a disk drive, an EPROM, a CD-ROM.

Upon completing the preparation of the organic material sample according to the method100of the first aspect of the present invention, as exemplified with reference to the description ofFIG.1above, preferably utilizing the searing device500according to the second aspect of the present invention, as exemplified with reference to the description ofFIG.5above, then laser induced breakdown spectroscopy (LIBS) may be performed on the so prepared sample pellet by subjecting the seared granular organic material sample, here the exposed surface510of the prepared sample pellet508, to a laser beam pulse so as to produce a plasma ablation event and then performing a spectrometric analysis of light generated in the plasma ablation event in a manner well known in the art of LIBS.