A method for analysing data from 1- and 2-dimensional NMR experiments by determination of their expectation values and standard deviations

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It combines a repeated use of the discrete Anahess approach for analysing the dynamic data where a regrouping of the noise in the data is performed for each repetition.. These resulting expectation values are used as initial and restricting values to produce a distribution using the Inverse Laplace Transform, where the position and volume of the distribution can then be reported with expectation value and standard deviation. The method is verified on synthetic data and tested on real data in both one and two dimensions. Anahess Inverse Laplace Transform T1 and/or T2 distribution expectation value standard deviation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1 Introduction When analysing relaxation and/or diffusion data acquired with various Nuclear Magnetic Resonance (NMR) techniques followed by the use of the Inverse Laplace Transform (ILT) [ 1 – 9 ], the smoothing of the data is essential to produce distributions of relaxation times and/or diffusion coefficients. Even though there has been some work in non-uniform smoothing the most common way to smooth the data is to apply a uniform smoothing on the data, i.e. a constant smoothing throughout the processing irrespective of the content of the data. Consequently, there will be a different broadening of the peaks depending on the peak position or fraction of the data in which the peak contributes to an attenuating signal [ 10 ]. Alternatively, discrete methods have been developed that fits the data to a limited number of components [ 11 – 13 ]. However, these methods suffer from the lack of information on the distributivity of the dataset. Recently a method was proposed that combines a discrete processing of the dataset together with the ILT, namely the Anahess distribution [ 10 ]. Here the number of components in the solution is limited to a minimum, which makes it possible to divide the solution into sub-groups that can be transformed and processed as superimposed datasets using the ILT with conditions set by the discrete solution. This approach has shown to reproduce synthetic distributions better than the ILT only. An extension of this method has now been developed and will be presented here. It aims at finding the expectation values for the fitted discrete components and the corresponding distribution. This should provide a measure allowing to evaluate the quality of the fit. In short, the procedure is as follows: After fitting the exponentially decaying data to a limited number of components according to the Bayesian Information stop Criterion (BIC) [ 14 ], a set of residual data or noise from the fit is produced. The residuals are then rearranged with respect to position. That is, the residuals or a group of such are interchanged in a random way so that new noise data is produced but with same expectation value and standard deviation as the original set of residuals. A new exponentially decaying data set can then be produced from the regrouped noise and the fitted components are determined from the discrete fitting procedure. The new raw data set is then analysed using the discrete Anahess, resulting in a new set of fitted components. If there is an impact of noise on the fitted components, the values will vary due to the different noise present. This procedure is repeated until enough data with rearranged residuals are produced to find an expectation value and a standard deviation for the fitted components. In the following we will recapture the combination of the discrete Anahess approach with ILT [ 10 ] and provide the method for determining the expectation values of the fitted components and distributions. 1.1 The Anahess approach In experimental data, the noise, ε, is superimposed on the exponentially decaying signal. Data arising from relaxation and/or diffusion NMR experiments can be described by a multi-exponential decaying signal S(t) $$\:\varvec{S}\left({\varvec{t}}_{\varvec{i}}\right)={\sum\:}_{\varvec{k}}A\left({\varvec{T}}_{\varvec{k}}\right)\varvec{exp}\left(-\frac{{\varvec{t}}_{\varvec{i}}}{{\varvec{T}}_{\varvec{k}}}\right)+{\varvec{\epsilon\:}}_{\varvec{i}}$$ 1 Here A(T k ) is the distribution of intensities to be fitted to the corresponding T k , which could be the corresponding relaxation time or the diffusion coefficient. The most common way to fit the equation above to experimental data is to use the Inverse Laplace Transform initially developed by Provencher [ 1 – 3 ]. Then, a predefined grid of a fixed number of points are defined, on which the solution ( A(T k ) ) have to be found. Another approach is to use a discrete method where the number of components is limited [ 11 ]. In this work we apply the discrete Anahess approach, where the number of points (components) in the solution is minimized according to a Bayesian Information criterion and the points are allowed to move anywhere in the space of solutions. As in the ILT routine, a function involving the sum squared relationship is to be minimized [ 11 , 15 ] $$\:S{S}_{res}\hspace{0.33em}={\sum\:}_{j}^{NX}\{{a}_{0}+{\sum\:}_{p}^{NCO}{a}_{p}{e}^{(-(1/{T}_{p}\left){t}_{j}\right)}-{R}_{j}{\}}^{2}$$ 2 Where $$\:{R}_{j}={a}_{0}+{\sum\:}_{p}^{NCO}{a}_{p}{e}^{(-(1/{T}_{p}\left){t}_{j}\right)}+{\epsilon\:}_{j}$$ 3 a 0 is a baseline offset which may be positive or negative. The data matrix R have the corresponding number of data points NX . The parameters a p , T p are thus characteristic properties of the component with index p out of all the NCO components. As SS re s will decrease with increasing number of fitted components, a stop criterion is needed. The Bayesian information criterion ( BIC ) [ 14 ] is such a criterion. Let n be the number of observed data points, let p be the number of free model parameters, and SS res be the sum of squared residuals. Then if the residuals are normally distributed, the BIC has the form: $$\:BIC\hspace{0.33em}\hspace{0.33em}=\hspace{0.33em}n{ln}(\frac{s{s}_{res}}{n})+p{ln}(n)$$ 4 In this equation, a good model fit gives a low first term while few model parameters give a low second term. When comparing a set of models, the model with the minimal BIC value is selected. The discrete solution fits to a relatively small number of components that provides a satisfactorily fit of the raw data. This is due to the discreteness of the fitting routine when using the BIC as a stop criterion [ 14 ]. As most of data reflects continuous distributions of components, a method for probing the distributivity using the discrete Anahess results has been developed. This is done by applying the ILT where the results from the discrete Anahess fit is fed into the ILT as initial and restricting conditions [ 8 ]. Because of the limited number of components in the Anahess, one may group various regions in the solution and provide a superimposed fit of each group. Consequently, prior to the use of the ILT the data is grouped and then transformed in t according to the following equation [ 10 ] $$\:{t}^{k}\to\:\:\:t\bullet\:\left(\frac{5{T}_{k}}{{t}_{max}}\right)\:\:$$ 5 where t is the original observation time, T k is the result from the Anahess fit for component k , and t max is the longest observation time in the data set. With this approach one may process data with the discrete Anahess method and probe the distributivity. 1.2 The method for determining the expectation value and its standard deviation When the exponentially decaying data are fitted using the Discrete Anahess [ 11 , 15 ], a resulting set of residuals is produced. These residuals are the difference between the fitted and the original data. As the noise is the crucial part that affects the result of the fitting procedure, both using discrete and continuous fittings, we here propose to produce new raw data to be subjected for processing through redistributing the noise as follows; divide the residual noise into several compartments for both 1- and 2-dimensional noise, as shown in Fig. 1 . The compartments should be set so small that when moving them around in a random way such that the end product is a different noise dataset but with the same expectation value and standard deviation. $$\:{\varvec{n}\varvec{o}\varvec{i}\varvec{s}\varvec{e}}^{\varvec{N}\varvec{E}\varvec{W}}\left({\varvec{c}\varvec{o}\varvec{m}\varvec{p}\varvec{a}\varvec{r}\varvec{t}\varvec{m}\varvec{e}\varvec{n}\varvec{t}}_{\varvec{i}}\right)={\varvec{n}\varvec{o}\varvec{i}\varvec{s}\varvec{e}}^{\varvec{O}\varvec{R}\varvec{I}\varvec{G}\varvec{I}\varvec{N}\varvec{A}\varvec{L}}\left({\varvec{c}\varvec{o}\varvec{m}\varvec{p}\varvec{a}\varvec{r}\varvec{t}\varvec{m}\varvec{e}\varvec{n}\varvec{t}}_{\varvec{j}}\right)$$ 6 where j is a random number within the number of compartments. Using the fitted components and intensities from the Discrete Anahess method one may then perform a Laplace Transform and produce new data sets where the noise is different due to the random interchanging of the compartments in the original noise data. $$\:{\varvec{S}}^{\varvec{N}\varvec{E}\varvec{W}}\left({\varvec{t}}_{\varvec{i}}\right)={\sum\:}_{\varvec{p}}{a}_{p}\varvec{exp}\left(-\frac{{\varvec{t}}_{\varvec{i}}}{{\varvec{T}}_{\varvec{p}}}\right)+{\varvec{n}\varvec{o}\varvec{i}\varvec{s}\varvec{e}}^{\varvec{N}\varvec{E}\varvec{W}}$$ 7 These datasets can then be analysed using the discrete Anahess approach and a variation in the fitted intensities and new relaxation times and/or diffusion coefficients will be found. When this procedure has been repeated until the effect of the noise has been probed properly, it will result in an expectation value for the various components, and these values can then be used as initial and restricting values to probe the distributivity of the original dataset. This approach assumes that there is no dependency of the noise as a function of observation time and/or applied gradient strength. That is, the discrete Anahess fit returns a Gaussian distribution of residuals that has an expectation value of 0 and the same insignificant skew [ 16 ]. Also, this method provides a way to check if the fit to the original raw data is a stable one. That is, the minimum BIC number is achieved at the same number of components and the variation in the expectation values is acceptable. 2 Experimental A set of synthetic data sets in 1- and 2 dimensions were used to verify the proposed method for determining the expectation values [ 10 ]. The one-dimensional synthetic data set was produced from a distribution located on a grid of 200 T 2 -values (0.0001-10 seconds) having three identical but separated peaks. After imposing a Laplace Transform to produce a decaying signal synthetic Gaussian noise was added to mimic the experimental noise from an NMR experiment. The inter-echo spacing (2τ) was set to 0.4 ms and the data set contained 8000 echo points to mimic a Carr-Purcel-Meiboom-Gill (CPMG) decay. The two-dimensional synthetic data set was produced from distributions located on a 64x32 grid of T 1 ’s (0.001-10 seconds) and T 2 ’s (0.0001-10 seconds). As for the one-dimensional case, synthetic Gaussian noise was added, and the attenuation mimicked a combined Stimulated Echo - CPMG experiment [ 10 ]. In addition to the synthetic data sets real NMR data sets were acquired from a sample of oat flakes. The NMR instrument applied was a 0.5 Tesla permanent magnet supplied by Advance Magnetic Resonance Ltd. [ 17 ] with the possibility of measuring samples of 18 mm in diameter. The one-dimensional experiment was a CPMG with inter-echo spacing of 0.2 ms acquiring 4000 echoes, and this was enough to secure the attenuating signal to reach the noise level. The Inversion Recovery (IR)-CPMG experiment was applied to produce two-dimensional data. After measuring on the oat flakes, the sample was dried at 105 C for 12 hours and remeasured with the IR-CPMG experiment. The aim of this procedure is to identify the location of the moisture in the processed data prior to drying, and the oat flakes are chosen as sample because the T 2 signal from moisture and fat is found to partially overlap. The discrete Anahess is the processing method that provides the fitted components used to find the expectation values and their standard deviation [ 18 ]. The application starts with fitting one component to a data set and calculate its BIC number, then proceeds to two components and finds a new BIC number. If the current BIC number is lower than the previous one, the application increases the number of components to be fitted by one, and a new BIC number is found. This procedure continues until the current BIC number is larger than the previous one. The best fit for the data set is then the NCO that produces the lowest BIC number. This is shown in Fig. 2 for NCO ∈ [ 3 , 7 ] using the data from the two-dimensional data set produced from oat flakes (Fig. 9 ), where the best fit is found at 6 components. 3 Results and discussion In the following the results from processing of synthetic and real data in one and two dimensions are presented together with the expectation values and standard deviations achieved from the method proposed in section 2.2 3.1 The expectation value for the one-dimensional synthetic data set In Fig. 3 the attenuation of the synthetic data set is shown together with the residuals from the discrete Anahess fit, and it resulted in a three-component fit for the lowest BIC number. In the upper right corner of the figure the noise is plotted and fitted to a Gaussian distribution. The skew of the noise is found to be 0.003, which shows that the distribution is symmetrical around the expectation value 0. In other words, the discrete Anahess fit returns Gaussian noise as residuals, and on may produce new data sets by doing a random permutation of the noise compartments. For this experiment, the 8000 points of noise were divided into 80 compartments of 100 points each, and they were randomly permuted 10 times to provide 10 new raw data sets. As the 10 datasets were processed, it turned out that the minimum BIC number was achieved at NCO = 3 for all data sets, and one could easily identify the components’ position throughout the series of fitted data. The results for the three components are found in Table 1 , while the synthetic and Anahess distribution are shown in Fig. 4 . The Anahess distribution is based on the average values found in Table 1 , and one may notice that the Anahess distribution is broadened due to the Gaussian noise that was added to the synthetic distribution when producing the original raw data. The areas of the synthetic peaks were 200 each, and the average T 2 values were 2.33, 35.80 and 568.57 ms All average values in Table 1 were within the standard deviation except for the average intensity of the peak at longest T 2 , so the there is a good agreement between the fitted and key average values . What is evident from the data in Table 1 is an increasing relative standard variation as T 2 is reduced. For the peak at shortest T 2 , the relative standard deviation is 4.2% for T 2 and 7.5% for the intensity, for the peak in the middle the standard deviation is 1.3% for T 2 and 2.2% for the intensity while for the peak at longest T 2 the standard deviation is 0.4% for T2 and 0.6% for the intensity. The reason for the decreasing uncertainty of the fitted values as T 2 increases is because the number of significant datapoints where the components contributes in the attenuating signal is increased as T 2 increases. For the component fitted to 2.39 ms its signal has reduced to a fraction less than 0.01 at 24 ms. With a τ-value of 0.2 ms this corresponds to 60 data points. Consequently, in the data that contains 8000 data points the components with shortest T 2 contribute only in 0.75% of the data. The component with T 2 of 35.38 contributes in 5.6% of the data while the component with T2 of 568.57 ms contributes in 88.9% of the data. Table 1 Anahess results Component # Average T 2 / ms Average intensity / arbitrary units 1 2.39 ± 0.18 200.0 ± 8.5 2 35.38 ± 0.78 198.1 ± 2.6 3 568.57 ± 3.58 198.4 ± 0.8 3.2 The expectation value for the one-dimensional real data set In Fig. 5 the attenuation of the data set arising from oat flakes is shown together with the residuals from the discrete Anahess fit resulting in three-components with the lowest BIC number. The three components are one isolated component with T 2 ~ 0.6 ms and two components rather close to each other with T 2 ~ 50 and 180 ms respectively. In the upper right corner of the figure the noise is plotted and fitted to a Gaussian distribution. The skew of the noise is found to be 0.03, which is the same skew as found from the synthetic Gaussian noise distribution, i.e. an insignificant skew. Thus, one may conclude that the distribution is symmetrical around the expectation value 0.001, and again the discrete Anahess fit returns Gaussian noise as residuals, and on may produce new data sets by doing a random permutation of the noise compartments. In this experiment, the 4000 points of noise were divided into 40 compartments of 100 points each, and they were randomly permuted 20 times to provide 20 new raw data sets. Table 2 Average results from 20 processed datasets Component # Average T 2 / ms Average intensity / arbitrary units 1 0.59 ± 0.08 3892.3 ± 355.5 2 47.2 ± 10.1 829.0 ± 117,9 3 178.9 ± 12.5 1313.0 ± 152.0 2 + 3 127.6 ± 4.1 2142.0 ± 54.5 As the 20 datasets were processed, it turned out that the minimum BIC number was achieved at NCO = 3 for all data sets, and one could easily identify the components’ position throughout the series of fitted data. The results for the three components are shown in Table 2 . The isolated component at shortest T 2 is reported with a standard deviation of 9.1% in intensity and 13.3% in T 2 , while the component with intermediate T 2 (47.1 ms) is reported with higher standard deviations (14.2% in intensity and 21.4% in T 2 ). This does not fit the picture of an improved accuracy for the intermediate T 2 component as more datapoints are available. The reason for this is that the two components at longer T 2 do not correspond to two unique components as the varying noise significantly interfere with the fitted components. The solution is to group the two components together, as shown in Table 2 , where the standard deviation for the group is then down to 2.5% for the intensity and 3.2% for the T 2 . This knowledge can be applied when probing the distributivity of the data, and components 2 and 3 must be processed as a group. In Fig. 6 , the Anahess T 2 -distribution is shown for the oat flakes, and it is based on the fit of the 20 data sets with different noise produced from the residuals of the fitting of the original data. Consequently, the intensity of the peaks and their average T 2 values (i.e. peak position) can be reported with a standard deviation given in Table 2 for component 1 (left peak) and component 2 + 3 (right peak). Alternatively, one may produce 20 data sets by running the experiment 20 times. 3.3 The expectation value for the two-dimensional synthetic data set Figure 7 shows the results from the discrete Anahess fit of the two-dimensional synthetic data set, with a six-component fit for the lowest BIC number. In the right part of the figure the noise is shown plotted and fitted to a Gaussian distribution. The skew of the noise was found to be -0.003, confirming that the distribution is symmetrical around the expectation value 0 and thus the discrete Anahess fit returns Gaussian noise as residuals. New data sets can then be produced by doing a random permutation of the noise compartments. For this example, the 3000 points of noise was divided into 30 compartments of 100 points each, and they were randomly permuted 10 times to provide 10 new raw data sets. Table 3 Average results from 10 processed datasets Component # Intensity T 1 /ms T 2 /ms 4 27.0 ± 0.6 28.6 ± 6.1 10.6 ± 1.2 3 138.6 ± 17.3 183.4 ± 8.5 109.4 ± 5.4 5 126.1 ± 28.9 405.2 ± 36.6 132.5 ± 8.7 6 160.0 ± 26.3 377.1 ± 22.3 63.7 ± 4.3 1 55.9 ± 23.2 703.2 ± 106.0 837.6 ± 87.1 2 72.2 ± 23.2 1147.5 ± 81.1 761.6 ± 64.8 3 + 5 + 6 424.7 ± 1.5 321.0 ± 2.1 98.8 ± 1.1 1 + 2 128.1 ± 1.7 951.7 ± 12.7 786.7 ± 4.4 In Table 3 the average results based on the 10 fitted data sets with varying noise are shown. As for the one-dimensional case on oat flakes, the components can be regrouped into component 4, components 1 + 2 and components 3 + 5 + 6. Without this grouping the standard deviations for individual components at higher T 2 ’s are higher than the standard deviation for the component with shortest T 2 , while a lower standard deviation is to be expected due to more data points available for fitting the components of longer T 2 ’s. When grouping the components as shown in Table 3 , the standard deviation decreases as T 2 of the group increases. The Anahess distribution based on the average values of the components is shown in Fig. 8, and it fits well to the synthetic distribution provided in [ 10 ]. 3.4 The expectation value for the two-dimensional real data set Figure 9 shows the results from the discrete Anahess fit for a dataset recorded from a combined IR-CPMG experiment, and the lowest BIC number is found with a six-component fit. In the right part of the figure, the noise is shown plotted and fitted to a Gaussian distribution. The skew of the noise is found to be -0.002 confirming that the distribution is symmetrical around the expectation value 0 and thus the discrete Anahess fit returns Gaussian noise as residuals. New data sets can then be produced by doing a random permutation of the noise compartments. For this example, the 4000 points of noise was divided into 40 When the 10 new datasets were processed using the discrete Anahess method, it was found that the lowest BIC number is 6 for 9 of the 10 datasets while for one data set the lowest BIC number is found at NCO = 5. For this dataset, component 5 in Fig. 9 disappears and the neighbouring components (6 and 3) are shifted slightly in position. This is a consequence of the Gaussian noise in combination with the fact that component 5 in Fig. 9 has an intensity of ~ 80 while the noise varies between ± 100. In short, in 10% of the processed data we will fit to 5 components using Anahess while in 90% of the incidents it will be reported 6 components. So, based on the processed data we can report the 6-component fit shown in Table 4 with a 90% probability. As for the synthetic two-dimensional dataset we find that the standard deviation reduces as T 2 and T 1 increases if one regroups the components as component 4, components 1 + 2 + 3 and components 5 + 6. However, when producing the Anahess distribution as shown in Fig. 10 , it turns out that components 5 + 6 does not produce one peak as components 1 + 2 + 3 does. Thus, the probing of the distributivity indicates that component 5 should be separated from component 6, and the increased standard deviations of these components are due to the low signal intensity. The finding above provides a new tool using the discrete Anahess processing; for datasets where the noise is significant, as in NMR logging data, the discrete Anahess in combination with a random permutation of the fitted noise (assumed Gaussian) can be used to estimate the likelihood of finding a given number of components, and whether a component should be grouped with other components or not. Table 4 Average results from 10 processed datasets. Component # Intensity T 1 /ms T 2 /ms 4 147.1 ± 12.9 2.74 ± 0.32 0.77 ± 0.06 6 932.6 ± 21.8 73.9 ± 1.9 0.48 ± 0.06 5 78.5 ± 6.4 117.3 ± 12.6 2.86 ± 0.34 3 88.8 ± 14.6 72.7 ± 5.4 41.0 ± 0.5 2 293.5 ± 9.7 153.5 ± 6.6 105.8 ± 4.6 1 171.6 ± 8.5 369.3 ± 4.9 284.8 ± 5.8 1 + 2 + 3 553.8 ± 2.4 207.4 ± 0.6 150.9 ± 0.8 5 + 6 1011.6 ± 19.8 951.7 ± 12.7 0.66 ± 0.02 In order to establish the location of the moisture and fat signal in the T 1 T 2 -distribution, the sample was dried at 105 °C for 12 hours and remeasured using the IR-CPMG sequence. The processing resulted in 5 components as shown in Fig. 11 , and the residuals from the discrete Anahess method were used to produce 10 new datasets that were processed. Then, it turned out that five of the processed datasets resulted in 4 components while the other five resulted in 5 components. The T 1 T 2 -distributions are shown in Fig. 13 for the two equally probable results, and the largest variations in the results for NCO = 4 and NCO = 5 are found at the shortest T 1 ‘s and T 2 ’s. In particular the component with shortest T 2 appears at 1 ms for NCO = 4 while it appears at 0.35 ms for NCO = 5. The intensity is approximately the same, around 160. Thus, it is evident that the variation of the noise affects the part with the smallest number of attenuating data points significantly, and it is not possible to establish what the best solution is based on the 10 data sets. A new experiment with improved signal-to-noise ratio was therefore conducted, where the number of scans was increased from 64 to 128. Then all processed data were reported with 5 components as the lowest BIC number (Fig. 11 ). Also, the average values and standard deviations are shown in Table 5 . A shift in T 1 ‘s andT 2 ’s towards shorter values is observed, the large component at the shortest T 2 and highest T 1 /T 2 has been reduced to a small component and the component at the shortest T 2 and lowest T 1 /T 2 , component 6 in Fig. 9 , has vanished. Component 4 in Fig. 9 can then most likely be assigned to the moisture, component 6 is believed to be the tail of protein signal which becomes undetectable at the time of the first echo at 0.2 ms because T 2 is reduced due to the drying. What remains then in Fig. 12 is the fat signal that can be divided into more (long T 2 ) and less (short T 2 ) mobile fat [ 15 , 19 , 20 ]. When correcting for the different number of scans the total signal from Fig. 12 fits to the signal from components 1,2,3 and 5 in Fig. 9 . This indicates that it could be possible to determine the fat content without drying using this method. Table 5 Average results from 10 processed datasets. Component # Intensity T 1 /ms T 2 /ms 1 101.2 ± 3.6 354.8 ± 5.1 236.2 ± 4.8 2 275.3 ± 6.1 124.7 ± 2.5 70.2 ± 1.6 3 338.5 ± 10.8 76.5 ± 1.7 17. 1 ± 0.6 4 282.5 ± 7.7 50.6 ± 2.1 3.4 ± 0.3 5 333.9 ± 15.3 72.2 ± 6.8 0.34 ± 0.04 1 + 2 + 3 + 4 996.0 ± 12.5 110.8 ± 0.6 50.1 ± 0.6 4 Conclusion Provided that a dataset reflects a multiexponential decay or recovery in one or two dimensions, the discrete Anahess processing tool returns a set of residual data or noise from the fit than can be regarded as symmetric and Gaussian. New raw datasets can then be produced from random permutations of the residuals, and they can be reprocessed using Anahess to find expectation values and standard deviations for T 1 , T 2 and the intensity. Also, the results from the fitted datasets can be used to find the likelihood of fitting to a certain number of components with the lowest BIC number. Declarations Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Geir Humborstad Sørland, Henrik Walbye Anthonsen, and Sebastien Simon. The first draft of the manuscript was written by Geir Humborstad Sørland and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Provencher, S.W., CONTIN: A general purpose constrained regularization program for inverting noisy linear algebraic and integral equations. Computer Physics Communications 27(3), 229–242 (1982) Provencher, S.W., A constrained regularization method for inverting data represented by linear algebraic or integral equations. 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Biometrika. 63 (1): 201–203 (1976) www.admagres.com www.antek.no Sørland, G.H., Larsen P.M, Lundby, F., Anthonsen, H.W., Foss, B.j., On the Use of Low-Field NMR Methods for the Determination Of Total Lipid Content in Marine Products , in Magnetic Resonance in Food Science: The Multivariate Challenge . 2005, The Royal Society of Chemistry. p. 20–27. Nordic-Baltic Committee on Food Analysis, "Fat determination in fish, fish feed and fish meal by low field nuclear magnetic resonance (LF-NMR)", NMKL 199, 2014 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4887848","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":349595964,"identity":"3206c406-cd13-4851-b66d-a000c318a62f","order_by":0,"name":"Geir Humborstad Sørland","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYFACxgZmBgYbEIuZJC1pDAxsxGsBm36YBC3y7s1tnwv+nJc3l28+bMC4w4awFsMzB5tnz2y7bbizjS05gfFMGhFaZiQ2M/M23GbccIzH+ABj22EitMx/2MzM8+ec/YZj/J+J0yIvwQjUwnYgEWgLcwJRWgx4QA5rS07ecCzN2CCxjQi/yLcffwx0mJ3thsOHH0t8bCMixAwOIPMSCGsA2tJAjKpRMApGwSgY2QAAcW03vAotmHMAAAAASUVORK5CYII=","orcid":"","institution":"Norwegian university of science and technology (NTNU)","correspondingAuthor":true,"prefix":"","firstName":"Geir","middleName":"Humborstad","lastName":"Sørland","suffix":""},{"id":349595966,"identity":"d9fd46e0-1e98-472d-a62b-f896840a73e4","order_by":1,"name":"Henrik Walbye Anthonsen","email":"","orcid":"","institution":"Anvendt Teknologi AS","correspondingAuthor":false,"prefix":"","firstName":"Henrik","middleName":"Walbye","lastName":"Anthonsen","suffix":""},{"id":349595967,"identity":"ce246cc6-1a5e-40f4-86b6-37a9b1433217","order_by":2,"name":"Sebastien Simon","email":"","orcid":"","institution":"Norwegian university of science and technology (NTNU)","correspondingAuthor":false,"prefix":"","firstName":"Sebastien","middleName":"","lastName":"Simon","suffix":""}],"badges":[],"createdAt":"2024-08-09 14:42:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4887848/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4887848/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00723-024-01718-z","type":"published","date":"2024-09-20T15:56:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64776174,"identity":"016aeee3-d1fe-463a-9d9b-54be89caa0b9","added_by":"auto","created_at":"2024-09-18 16:32:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":133021,"visible":true,"origin":"","legend":"\u003cp\u003eThe synthetic gaussian noise in one (left) and two (right) dimensions. The black dashed lines indicate the separation of the noise into different compartments\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4887848/v1/1d3795eb0e633883c4157552.jpg"},{"id":64164419,"identity":"ec040252-98c2-43f8-a8e7-a1dffc7f108b","added_by":"auto","created_at":"2024-09-09 08:50:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38397,"visible":true,"origin":"","legend":"\u003cp\u003eThe fitted BIC number as a function of number of components\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4887848/v1/b298192542c0d346541763ef.png"},{"id":64164849,"identity":"71a5f26a-91c9-4d25-9d10-207f14ee4748","added_by":"auto","created_at":"2024-09-09 08:58:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":124496,"visible":true,"origin":"","legend":"\u003cp\u003eThe attenuation of the multiexponential decaying signal (red curve) arising from a synthetic data distribution. The blue curve corresponds to the fitted residuals. In the upper right corner, the distribution of residuals is shown together with the fitted Gaussian curve\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4887848/v1/7eefddbc8418442416a8e66d.png"},{"id":64165618,"identity":"c100567a-2926-4f49-83ba-257c5a8dd3bf","added_by":"auto","created_at":"2024-09-09 09:14:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":153943,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between the synthetic and the Anahess distributions produced from the average values for T\u003csub\u003e2\u003c/sub\u003e and initial intensities\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4887848/v1/a99aa8da53df268c5a886d31.png"},{"id":64165205,"identity":"0fa8e913-6be7-4bfc-8888-c4c19c87f173","added_by":"auto","created_at":"2024-09-09 09:06:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":171017,"visible":true,"origin":"","legend":"\u003cp\u003eThe attenuation of a decaying signal (red curve) arising from a CPMG experiment performed on oat flake. The blue curve corresponds to the fitted residuals. In the upper right corner, the distribution of residuals is shown together with the fitted Gaussian curve\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4887848/v1/3a960cb1394b299f22afbed6.png"},{"id":64163801,"identity":"8348afd2-9bdf-444c-9333-8d82ed834d0b","added_by":"auto","created_at":"2024-09-09 08:42:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":11644,"visible":true,"origin":"","legend":"\u003cp\u003eThe Anahess T\u003csub\u003e2\u003c/sub\u003e distribution from oat flakes based on the average values for T\u003csub\u003e2\u003c/sub\u003e and initial intensities of the oat flakes components\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4887848/v1/e4c4685caaf710ffa0ee440a.png"},{"id":64164424,"identity":"79eb679e-fa11-46e0-84a9-6406c3ec0ed6","added_by":"auto","created_at":"2024-09-09 08:50:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":399338,"visible":true,"origin":"","legend":"\u003cp\u003eThe discrete components from the Anahess fit to the left, the resulting residuals to the lower right and the distributions of residuals to the upper right\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4887848/v1/b48e34001cff5a0ab467b144.png"},{"id":64163802,"identity":"4c73131d-8037-4aa2-8931-15df232df0a9","added_by":"auto","created_at":"2024-09-09 08:42:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":28740,"visible":true,"origin":"","legend":"\u003cp\u003eThe Anahess T\u003csub\u003e1\u003c/sub\u003e-T\u003csub\u003e2\u003c/sub\u003e synthetic distribution based on the average values for T\u003csub\u003e1\u003c/sub\u003e, T\u003csub\u003e2\u003c/sub\u003e and initial intensities\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4887848/v1/fa60c670d7a4b89ce8193d8d.png"},{"id":64164853,"identity":"0b3e9c3f-b4c2-4315-959e-8676b5f323b3","added_by":"auto","created_at":"2024-09-09 08:58:46","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":409725,"visible":true,"origin":"","legend":"\u003cp\u003eThe discrete components from the Anahess fit to the left, the resulting residuals to the lower right and the distributions of residuals to the upper right\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4887848/v1/7d8845027d71ac91592e8e0e.png"},{"id":64163810,"identity":"90ef6d32-2354-4f15-970b-107e833641fd","added_by":"auto","created_at":"2024-09-09 08:42:46","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":35385,"visible":true,"origin":"","legend":"\u003cp\u003eThe Anahess T\u003csub\u003e1\u003c/sub\u003e-T\u003csub\u003e2\u003c/sub\u003e distribution from oat flakes based on the average values for T\u003csub\u003e1\u003c/sub\u003e, T\u003csub\u003e2\u003c/sub\u003e and initial intensities\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-4887848/v1/dbdac8899c74cd007b97c5d1.png"},{"id":64163812,"identity":"bf0cd879-62f9-456c-8552-95ed27397c47","added_by":"auto","created_at":"2024-09-09 08:42:46","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":318104,"visible":true,"origin":"","legend":"\u003cp\u003eThe discrete components from the Anahess fit to the left, the resulting residuals to the lower right and the distributions of residuals to the upper right\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-4887848/v1/31a6bcbd6194692bc4a01fb9.png"},{"id":64164425,"identity":"0f62e53f-e7f3-4fdd-97ea-5890ef7139fb","added_by":"auto","created_at":"2024-09-09 08:50:46","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":160594,"visible":true,"origin":"","legend":"\u003cp\u003eThe Anahess T\u003csub\u003e1\u003c/sub\u003e-T\u003csub\u003e2\u003c/sub\u003e distribution from dried oat flakes either with lowest BIC at 5 components (left) or at 4 components (right)\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4887848/v1/35e01e6728a7f74128fc581d.jpg"},{"id":65103999,"identity":"bddec115-0a25-4b7b-b077-bdb85d63e42f","added_by":"auto","created_at":"2024-09-23 16:10:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2408396,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4887848/v1/b92c39de-9239-48b1-b467-bb6ac7d25929.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A method for analysing data from 1- and 2-dimensional NMR experiments by determination of their expectation values and standard deviations","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWhen analysing relaxation and/or diffusion data acquired with various Nuclear Magnetic Resonance (NMR) techniques followed by the use of the Inverse Laplace Transform (ILT) [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the smoothing of the data is essential to produce distributions of relaxation times and/or diffusion coefficients. Even though there has been some work in non-uniform smoothing the most common way to smooth the data is to apply a uniform smoothing on the data, i.e. a constant smoothing throughout the processing irrespective of the content of the data. Consequently, there will be a different broadening of the peaks depending on the peak position or fraction of the data in which the peak contributes to an attenuating signal [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Alternatively, discrete methods have been developed that fits the data to a limited number of components [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, these methods suffer from the lack of information on the distributivity of the dataset.\u003c/p\u003e \u003cp\u003eRecently a method was proposed that combines a discrete processing of the dataset together with the ILT, namely the Anahess distribution [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Here the number of components in the solution is limited to a minimum, which makes it possible to divide the solution into sub-groups that can be transformed and processed as superimposed datasets using the ILT with conditions set by the discrete solution. This approach has shown to reproduce synthetic distributions better than the ILT only. An extension of this method has now been developed and will be presented here. It aims at finding the expectation values for the fitted discrete components and the corresponding distribution. This should provide a measure allowing to evaluate the quality of the fit. In short, the procedure is as follows: After fitting the exponentially decaying data to a limited number of components according to the Bayesian Information stop Criterion (BIC) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], a set of residual data or noise from the fit is produced. The residuals are then rearranged with respect to position. That is, the residuals or a group of such are interchanged in a random way so that new noise data is produced but with same expectation value and standard deviation as the original set of residuals. A new exponentially decaying data set can then be produced from the regrouped noise and the fitted components are determined from the discrete fitting procedure. The new raw data set is then analysed using the discrete Anahess, resulting in a new set of fitted components. If there is an impact of noise on the fitted components, the values will vary due to the different noise present. This procedure is repeated until enough data with rearranged residuals are produced to find an expectation value and a standard deviation for the fitted components.\u003c/p\u003e \u003cp\u003eIn the following we will recapture the combination of the discrete Anahess approach with ILT [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and provide the method for determining the expectation values of the fitted components and distributions.\u003c/p\u003e \u003cp\u003e1.1 The Anahess approach\u003c/p\u003e \u003cp\u003eIn experimental data, the noise, ε, is superimposed on the exponentially decaying signal. Data arising from relaxation and/or diffusion NMR experiments can be described by a multi-exponential decaying signal S(t)\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{S}\\left({\\varvec{t}}_{\\varvec{i}}\\right)={\\sum\\:}_{\\varvec{k}}A\\left({\\varvec{T}}_{\\varvec{k}}\\right)\\varvec{exp}\\left(-\\frac{{\\varvec{t}}_{\\varvec{i}}}{{\\varvec{T}}_{\\varvec{k}}}\\right)+{\\varvec{\\epsilon\\:}}_{\\varvec{i}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere \u003cem\u003eA(T\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e is the distribution of intensities to be fitted to the corresponding \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e, which could be the corresponding relaxation time or the diffusion coefficient. The most common way to fit the equation above to experimental data is to use the Inverse Laplace Transform initially developed by Provencher [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Then, a predefined grid of a fixed number of points are defined, on which the solution (\u003cem\u003eA(T\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e) have to be found. Another approach is to use a discrete method where the number of components is limited [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In this work we apply the discrete Anahess approach, where the number of points (components) in the solution is minimized according to a Bayesian Information criterion and the points are allowed to move anywhere in the space of solutions. As in the ILT routine, a function involving the sum squared relationship is to be minimized [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:S{S}_{res}\\hspace{0.33em}={\\sum\\:}_{j}^{NX}\\{{a}_{0}+{\\sum\\:}_{p}^{NCO}{a}_{p}{e}^{(-(1/{T}_{p}\\left){t}_{j}\\right)}-{R}_{j}{\\}}^{2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{R}_{j}={a}_{0}+{\\sum\\:}_{p}^{NCO}{a}_{p}{e}^{(-(1/{T}_{p}\\left){t}_{j}\\right)}+{\\epsilon\\:}_{j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003ea\u003c/em\u003e \u003csub\u003e0\u003c/sub\u003e is a baseline offset which may be positive or negative. The data matrix \u003cem\u003eR\u003c/em\u003e have the corresponding number of data points \u003cem\u003eNX\u003c/em\u003e. The parameters \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e are thus characteristic properties of the component with index \u003cem\u003ep\u003c/em\u003e out of all the \u003cem\u003eNCO\u003c/em\u003e components. As \u003cem\u003eSS\u003c/em\u003e\u003csub\u003e\u003cem\u003ere\u003c/em\u003es\u003c/sub\u003e will decrease with increasing number of fitted components, a stop criterion is needed. The Bayesian information criterion (\u003cem\u003eBIC\u003c/em\u003e) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] is such a criterion. Let \u003cem\u003en\u003c/em\u003e be the number of observed data points, let \u003cem\u003ep\u003c/em\u003e be the number of free model parameters, and \u003cem\u003eSS\u003c/em\u003e\u003csub\u003e\u003cem\u003eres\u003c/em\u003e\u003c/sub\u003e be the sum of squared residuals. Then if the residuals are normally distributed, the \u003cem\u003eBIC\u003c/em\u003e has the form:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:BIC\\hspace{0.33em}\\hspace{0.33em}=\\hspace{0.33em}n{ln}(\\frac{s{s}_{res}}{n})+p{ln}(n)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this equation, a good model fit gives a low first term while few model parameters give a low second term. When comparing a set of models, the model with the minimal \u003cem\u003eBIC\u003c/em\u003e value is selected.\u003c/p\u003e \u003cp\u003eThe discrete solution fits to a relatively small number of components that provides a satisfactorily fit of the raw data. This is due to the discreteness of the fitting routine when using the BIC as a stop criterion [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. As most of data reflects continuous distributions of components, a method for probing the distributivity using the discrete Anahess results has been developed. This is done by applying the ILT where the results from the discrete Anahess fit is fed into the ILT as initial and restricting conditions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Because of the limited number of components in the Anahess, one may group various regions in the solution and provide a superimposed fit of each group. Consequently, prior to the use of the ILT the data is grouped and then transformed in \u003cem\u003et\u003c/em\u003e according to the following equation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:{t}^{k}\\to\\:\\:\\:t\\bullet\\:\\left(\\frac{5{T}_{k}}{{t}_{max}}\\right)\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003et\u003c/em\u003e is the original observation time, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e is the result from the Anahess fit for component \u003cem\u003ek\u003c/em\u003e, and \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e is the longest observation time in the data set. With this approach one may process data with the discrete Anahess method and probe the distributivity.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.2 The method for determining the expectation value and its standard deviation\u003c/h2\u003e \u003cp\u003eWhen the exponentially decaying data are fitted using the Discrete Anahess [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], a resulting set of residuals is produced. These residuals are the difference between the fitted and the original data. As the noise is the crucial part that affects the result of the fitting procedure, both using discrete and continuous fittings, we here propose to produce new raw data to be subjected for processing through redistributing the noise as follows; divide the residual noise into several compartments for both 1- and 2-dimensional noise, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The compartments should be set so small that when moving them around in a random way such that the end product is a different noise dataset but with the same expectation value and standard deviation.\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{n}\\varvec{o}\\varvec{i}\\varvec{s}\\varvec{e}}^{\\varvec{N}\\varvec{E}\\varvec{W}}\\left({\\varvec{c}\\varvec{o}\\varvec{m}\\varvec{p}\\varvec{a}\\varvec{r}\\varvec{t}\\varvec{m}\\varvec{e}\\varvec{n}\\varvec{t}}_{\\varvec{i}}\\right)={\\varvec{n}\\varvec{o}\\varvec{i}\\varvec{s}\\varvec{e}}^{\\varvec{O}\\varvec{R}\\varvec{I}\\varvec{G}\\varvec{I}\\varvec{N}\\varvec{A}\\varvec{L}}\\left({\\varvec{c}\\varvec{o}\\varvec{m}\\varvec{p}\\varvec{a}\\varvec{r}\\varvec{t}\\varvec{m}\\varvec{e}\\varvec{n}\\varvec{t}}_{\\varvec{j}}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ej\u003c/em\u003e is a random number within the number of compartments. Using the fitted components and intensities from the Discrete Anahess method one may then perform a Laplace Transform and produce new data sets where the noise is different due to the random interchanging of the compartments in the original noise data.\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{S}}^{\\varvec{N}\\varvec{E}\\varvec{W}}\\left({\\varvec{t}}_{\\varvec{i}}\\right)={\\sum\\:}_{\\varvec{p}}{a}_{p}\\varvec{exp}\\left(-\\frac{{\\varvec{t}}_{\\varvec{i}}}{{\\varvec{T}}_{\\varvec{p}}}\\right)+{\\varvec{n}\\varvec{o}\\varvec{i}\\varvec{s}\\varvec{e}}^{\\varvec{N}\\varvec{E}\\varvec{W}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThese datasets can then be analysed using the discrete Anahess approach and a variation in the fitted intensities and new relaxation times and/or diffusion coefficients will be found. When this procedure has been repeated until the effect of the noise has been probed properly, it will result in an expectation value for the various components, and these values can then be used as initial and restricting values to probe the distributivity of the original dataset.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis approach assumes that there is no dependency of the noise as a function of observation time and/or applied gradient strength. That is, the discrete Anahess fit returns a Gaussian distribution of residuals that has an expectation value of 0 and the same insignificant skew [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Also, this method provides a way to check if the fit to the original raw data is a stable one. That is, the minimum BIC number is achieved at the same number of components and the variation in the expectation values is acceptable.\u003c/p\u003e \u003c/div\u003e"},{"header":"2 Experimental","content":"\u003cp\u003eA set of synthetic data sets in 1- and 2 dimensions were used to verify the proposed method for determining the expectation values [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The one-dimensional synthetic data set was produced from a distribution located on a grid of 200 T\u003csub\u003e2\u003c/sub\u003e-values (0.0001-10 seconds) having three identical but separated peaks. After imposing a Laplace Transform to produce a decaying signal synthetic Gaussian noise was added to mimic the experimental noise from an NMR experiment. The inter-echo spacing (2τ) was set to 0.4 ms and the data set contained 8000 echo points to mimic a Carr-Purcel-Meiboom-Gill (CPMG) decay. The two-dimensional synthetic data set was produced from distributions located on a 64x32 grid of T\u003csub\u003e1\u003c/sub\u003e\u0026rsquo;s (0.001-10 seconds) and T\u003csub\u003e2\u003c/sub\u003e\u0026rsquo;s (0.0001-10 seconds). As for the one-dimensional case, synthetic Gaussian noise was added, and the attenuation mimicked a combined Stimulated Echo - CPMG experiment [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to the synthetic data sets real NMR data sets were acquired from a sample of oat flakes. The NMR instrument applied was a 0.5 Tesla permanent magnet supplied by Advance Magnetic Resonance Ltd. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] with the possibility of measuring samples of 18 mm in diameter. The one-dimensional experiment was a CPMG with inter-echo spacing of 0.2 ms acquiring 4000 echoes, and this was enough to secure the attenuating signal to reach the noise level. The Inversion Recovery (IR)-CPMG experiment was applied to produce two-dimensional data. After measuring on the oat flakes, the sample was dried at 105 C for 12 hours and remeasured with the IR-CPMG experiment. The aim of this procedure is to identify the location of the moisture in the processed data prior to drying, and the oat flakes are chosen as sample because the T\u003csub\u003e2\u003c/sub\u003e signal from moisture and fat is found to partially overlap.\u003c/p\u003e \u003cp\u003eThe discrete Anahess is the processing method that provides the fitted components used to find the expectation values and their standard deviation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The application starts with fitting one component to a data set and calculate its BIC number, then proceeds to two components and finds a new BIC number. If the current BIC number is lower than the previous one, the application increases the number of components to be fitted by one, and a new BIC number is found. This procedure continues until the current BIC number is larger than the previous one. The best fit for the data set is then the NCO that produces the lowest BIC number. This is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for NCO \u0026isin; [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] using the data from the two-dimensional data set produced from oat flakes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e), where the best fit is found at 6 components.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3 Results and discussion","content":"\u003cp\u003eIn the following the results from processing of synthetic and real data in one and two dimensions are presented together with the expectation values and standard deviations achieved from the method proposed in section 2.2\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The expectation value for the one-dimensional synthetic data set\u003c/h2\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e the attenuation of the synthetic data set is shown together with the residuals from the discrete Anahess fit, and it resulted in a three-component fit for the lowest BIC number. In the upper right corner of the figure the noise is plotted and fitted to a Gaussian distribution. The skew of the noise is found to be 0.003, which shows that the distribution is symmetrical around the expectation value 0. In other words, the discrete Anahess fit returns Gaussian noise as residuals, and on may produce new data sets by doing a random permutation of the noise compartments. For this experiment, the 8000 points of noise were divided into 80 compartments of 100 points each, and they were randomly permuted 10 times to provide 10 new raw data sets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs the 10 datasets were processed, it turned out that the minimum BIC number was achieved at NCO\u0026thinsp;=\u0026thinsp;3 for all data sets, and one could easily identify the components\u0026rsquo; position throughout the series of fitted data. The results for the three components are found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, while the synthetic and Anahess distribution are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The Anahess distribution is based on the average values found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and one may notice that the Anahess distribution is broadened due to the Gaussian noise that was added to the synthetic distribution when producing the original raw data. The areas of the synthetic peaks were 200 each, and the average T\u003csub\u003e2\u003c/sub\u003e values were 2.33, 35.80 and 568.57 ms All average values in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were within the standard deviation except for the average intensity of the peak at longest T\u003csub\u003e2\u003c/sub\u003e, so the there is a good agreement between the fitted and key average values .\u003c/p\u003e \u003cp\u003eWhat is evident from the data in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is an increasing relative standard variation as T\u003csub\u003e2\u003c/sub\u003e is reduced. For the peak at shortest T\u003csub\u003e2\u003c/sub\u003e, the relative standard deviation is 4.2% for T\u003csub\u003e2\u003c/sub\u003e and 7.5% for the intensity, for the peak in the middle the standard deviation is 1.3% for T\u003csub\u003e2\u003c/sub\u003e and 2.2% for the intensity while for the peak at longest T\u003csub\u003e2\u003c/sub\u003e the standard deviation is 0.4% for T2 and 0.6% for the intensity. The reason for the decreasing uncertainty of the fitted values as T\u003csub\u003e2\u003c/sub\u003e increases is because the number of significant datapoints where the components contributes in the attenuating signal is increased as T\u003csub\u003e2\u003c/sub\u003e increases. For the component fitted to 2.39 ms its signal has reduced to a fraction less than 0.01 at 24 ms. With a τ-value of 0.2 ms this corresponds to 60 data points. Consequently, in the data that contains 8000 data points the components with shortest T\u003csub\u003e2\u003c/sub\u003e contribute only in 0.75% of the data. The component with T\u003csub\u003e2\u003c/sub\u003e of 35.38 contributes in 5.6% of the data while the component with T2 of 568.57 ms contributes in 88.9% of the data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnahess results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent #\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage T\u003csub\u003e2\u003c/sub\u003e / ms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage intensity / arbitrary units\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e200.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e35.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e198.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e568.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e198.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The expectation value for the one-dimensional real data set\u003c/h2\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e the attenuation of the data set arising from oat flakes is shown together with the residuals from the discrete Anahess fit resulting in three-components with the lowest BIC number. The three components are one isolated component with T\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;~\u0026thinsp;0.6 ms and two components rather close to each other with T\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;~\u0026thinsp;50 and 180 ms respectively. In the upper right corner of the figure the noise is plotted and fitted to a Gaussian distribution. The skew of the noise is found to be 0.03, which is the same skew as found from the synthetic Gaussian noise distribution, i.e. an insignificant skew. Thus, one may conclude that the distribution is symmetrical around the expectation value 0.001, and again the discrete Anahess fit returns Gaussian noise as residuals, and on may produce new data sets by doing a random permutation of the noise compartments. In this experiment, the 4000 points of noise were divided into 40 compartments of 100 points each, and they were randomly permuted 20 times to provide 20 new raw data sets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage results from 20 processed datasets\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent #\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage T\u003csub\u003e2\u003c/sub\u003e / ms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage intensity / arbitrary units\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3892.3\u0026thinsp;\u0026plusmn;\u0026thinsp;355.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e47.2 \u0026plusmn; 10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e829.0\u0026thinsp;\u0026plusmn;\u0026thinsp;117,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e178.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1313.0\u0026thinsp;\u0026plusmn;\u0026thinsp;152.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026thinsp;+\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e127.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2142.0\u0026thinsp;\u0026plusmn;\u0026thinsp;54.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs the 20 datasets were processed, it turned out that the minimum BIC number was achieved at NCO\u0026thinsp;=\u0026thinsp;3 for all data sets, and one could easily identify the components\u0026rsquo; position throughout the series of fitted data. The results for the three components are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The isolated component at shortest T\u003csub\u003e2\u003c/sub\u003e is reported with a standard deviation of 9.1% in intensity and 13.3% in T\u003csub\u003e2\u003c/sub\u003e, while the component with intermediate T\u003csub\u003e2\u003c/sub\u003e (47.1 ms) is reported with higher standard deviations (14.2% in intensity and 21.4% in T\u003csub\u003e2\u003c/sub\u003e). This does not fit the picture of an improved accuracy for the intermediate T\u003csub\u003e2\u003c/sub\u003e component as more datapoints are available. The reason for this is that the two components at longer T\u003csub\u003e2\u003c/sub\u003e do not correspond to two unique components as the varying noise significantly interfere with the fitted components. The solution is to group the two components together, as shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, where the standard deviation for the group is then down to 2.5% for the intensity and 3.2% for the T\u003csub\u003e2\u003c/sub\u003e. This knowledge can be applied when probing the distributivity of the data, and components 2 and 3 must be processed as a group. In Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the Anahess T\u003csub\u003e2\u003c/sub\u003e-distribution is shown for the oat flakes, and it is based on the fit of the 20 data sets with different noise produced from the residuals of the fitting of the original data. Consequently, the intensity of the peaks and their average T\u003csub\u003e2\u003c/sub\u003e values (i.e. peak position) can be reported with a standard deviation given in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for component 1 (left peak) and component 2\u0026thinsp;+\u0026thinsp;3 (right peak). Alternatively, one may produce 20 data sets by running the experiment 20 times.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The expectation value for the two-dimensional synthetic data set\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the results from the discrete Anahess fit of the two-dimensional synthetic data set, with a six-component fit for the lowest BIC number. In the right part of the figure the noise is shown plotted and fitted to a Gaussian distribution. The skew of the noise was found to be -0.003, confirming that the distribution is symmetrical around the expectation value 0 and thus the discrete Anahess fit returns Gaussian noise as residuals. New data sets can then be produced by doing a random permutation of the noise compartments. For this example, the 3000 points of noise was divided into 30 compartments of 100 points each, and they were randomly permuted 10 times to provide 10 new raw data sets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage results from 10 processed datasets\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent #\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e /ms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e /ms\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e138.6\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e183.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e109.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e126.1\u0026thinsp;\u0026plusmn;\u0026thinsp;28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e405.2\u0026thinsp;\u0026plusmn;\u0026thinsp;36.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e132.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e160.0\u0026thinsp;\u0026plusmn;\u0026thinsp;26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e377.1\u0026thinsp;\u0026plusmn;\u0026thinsp;22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e63.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e55.9\u0026thinsp;\u0026plusmn;\u0026thinsp;23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e703.2\u0026thinsp;\u0026plusmn;\u0026thinsp;106.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e837.6\u0026thinsp;\u0026plusmn;\u0026thinsp;87.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e72.2\u0026thinsp;\u0026plusmn;\u0026thinsp;23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1147.5\u0026thinsp;\u0026plusmn;\u0026thinsp;81.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e761.6\u0026thinsp;\u0026plusmn;\u0026thinsp;64.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u0026thinsp;+\u0026thinsp;5\u0026thinsp;+\u0026thinsp;6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e424.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e321.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e98.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u0026thinsp;+\u0026thinsp;2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e128.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e951.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e786.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e the average results based on the 10 fitted data sets with varying noise are shown. As for the one-dimensional case on oat flakes, the components can be regrouped into component 4, components 1\u0026thinsp;+\u0026thinsp;2 and components 3\u0026thinsp;+\u0026thinsp;5\u0026thinsp;+\u0026thinsp;6. Without this grouping the standard deviations for individual components at higher T\u003csub\u003e2\u003c/sub\u003e\u0026rsquo;s are higher than the standard deviation for the component with shortest T\u003csub\u003e2\u003c/sub\u003e, while a lower standard deviation is to be expected due to more data points available for fitting the components of longer T\u003csub\u003e2\u003c/sub\u003e\u0026rsquo;s. When grouping the components as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the standard deviation decreases as T\u003csub\u003e2\u003c/sub\u003e of the group increases. The Anahess distribution based on the average values of the components is shown in Fig.\u0026nbsp;8, and it fits well to the synthetic distribution provided in [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.4 The expectation value for the two-dimensional real data set\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows the results from the discrete Anahess fit for a dataset recorded from a combined IR-CPMG experiment, and the lowest BIC number is found with a six-component fit. In the right part of the figure, the noise is shown plotted and fitted to a Gaussian distribution. The skew of the noise is found to be -0.002 confirming that the distribution is symmetrical around the expectation value 0 and thus the discrete Anahess fit returns Gaussian noise as residuals. New data sets can then be produced by doing a random permutation of the noise compartments. For this example, the 4000 points of noise was divided into 40\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen the 10 new datasets were processed using the discrete Anahess method, it was found that the lowest BIC number is 6 for 9 of the 10 datasets while for one data set the lowest BIC number is found at NCO\u0026thinsp;=\u0026thinsp;5. For this dataset, component 5 in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e disappears and the neighbouring components (6 and 3) are shifted slightly in position. This is a consequence of the Gaussian noise in combination with the fact that component 5 in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e has an intensity of ~\u0026thinsp;80 while the noise varies between \u0026plusmn;\u0026thinsp;100. In short, in 10% of the processed data we will fit to 5 components using Anahess while in 90% of the incidents it will be reported 6 components. So, based on the processed data we can report the 6-component fit shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e with a 90% probability. As for the synthetic two-dimensional dataset we find that the standard deviation reduces as T\u003csub\u003e2\u003c/sub\u003e and T\u003csub\u003e1\u003c/sub\u003e increases if one regroups the components as component 4, components 1\u0026thinsp;+\u0026thinsp;2\u0026thinsp;+\u0026thinsp;3 and components 5\u0026thinsp;+\u0026thinsp;6. However, when producing the Anahess distribution as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e, it turns out that components 5\u0026thinsp;+\u0026thinsp;6 does not produce one peak as components 1\u0026thinsp;+\u0026thinsp;2\u0026thinsp;+\u0026thinsp;3 does. Thus, the probing of the distributivity indicates that component 5 should be separated from component 6, and the increased standard deviations of these components are due to the low signal intensity.\u003c/p\u003e \u003cp\u003eThe finding above provides a new tool using the discrete Anahess processing; for datasets where the noise is significant, as in NMR logging data, the discrete Anahess in combination with a random permutation of the fitted noise (assumed Gaussian) can be used to estimate the likelihood of finding a given number of components, and whether a component should be grouped with other components or not.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage results from 10 processed datasets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent #\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e /ms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e /ms\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e147.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e932.6\u0026thinsp;\u0026plusmn;\u0026thinsp;21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e73.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e78.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e117.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e88.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e72.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e41.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e293.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e153.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e105.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e171.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e369.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e284.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u0026thinsp;+\u0026thinsp;2\u0026thinsp;+\u0026thinsp;3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e553.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e207.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e150.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u0026thinsp;+\u0026thinsp;6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1011.6\u0026thinsp;\u0026plusmn;\u0026thinsp;19.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e951.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn order to establish the location of the moisture and fat signal in the T\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e2\u003c/sub\u003e -distribution, the sample was dried at 105 \u0026deg;C for 12 hours and remeasured using the IR-CPMG sequence. The processing resulted in 5 components as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e, and the residuals from the discrete Anahess method were used to produce 10 new datasets that were processed. Then, it turned out that five of the processed datasets resulted in 4 components while the other five resulted in 5 components. The T\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e2\u003c/sub\u003e -distributions are shown in Fig.\u0026nbsp;13 for the two equally probable results, and the largest variations in the results for NCO\u0026thinsp;=\u0026thinsp;4 and NCO\u0026thinsp;=\u0026thinsp;5 are found at the shortest T\u003csub\u003e1\u003c/sub\u003e\u0026lsquo;s and T\u003csub\u003e2\u003c/sub\u003e\u0026rsquo;s. In particular the component with shortest T\u003csub\u003e2\u003c/sub\u003e appears at 1 ms for NCO\u0026thinsp;=\u0026thinsp;4 while it appears at 0.35 ms for NCO\u0026thinsp;=\u0026thinsp;5. The intensity is approximately the same, around 160. Thus, it is evident that the variation of the noise affects the part with the smallest number of attenuating data points significantly, and it is not possible to establish what the best solution is based on the 10 data sets. A new experiment with improved signal-to-noise ratio was therefore conducted, where the number of scans was increased from 64 to 128. Then all processed data were reported with 5 components as the lowest BIC number (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Also, the average values and standard deviations are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. A shift in T\u003csub\u003e1\u003c/sub\u003e\u0026lsquo;s andT\u003csub\u003e2\u003c/sub\u003e\u0026rsquo;s towards shorter values is observed, the large component at the shortest T\u003csub\u003e2\u003c/sub\u003e and highest T\u003csub\u003e1\u003c/sub\u003e/T\u003csub\u003e2\u003c/sub\u003e has been reduced to a small component and the component at the shortest T\u003csub\u003e2\u003c/sub\u003e and lowest T\u003csub\u003e1\u003c/sub\u003e/T\u003csub\u003e2\u003c/sub\u003e, component 6 in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e, has vanished. Component 4 in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e can then most likely be assigned to the moisture, component 6 is believed to be the tail of protein signal which becomes undetectable at the time of the first echo at 0.2 ms because T\u003csub\u003e2\u003c/sub\u003e is reduced due to the drying. What remains then in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e is the fat signal that can be divided into more (long T\u003csub\u003e2\u003c/sub\u003e) and less (short T\u003csub\u003e2\u003c/sub\u003e) mobile fat [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. When correcting for the different number of scans the total signal from Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e fits to the signal from components 1,2,3 and 5 in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e. This indicates that it could be possible to determine the fat content without drying using this method.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage results from 10 processed datasets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent #\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e /ms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e /ms\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e101.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e354.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e236.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e275.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e124.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e70.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e338.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e76.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e17. 1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e282.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e50.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e333.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e72.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u0026thinsp;+\u0026thinsp;2\u0026thinsp;+\u0026thinsp;3\u0026thinsp;+\u0026thinsp;4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e996.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e110.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e50.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eProvided that a dataset reflects a multiexponential decay or recovery in one or two dimensions, the discrete Anahess processing tool returns a set of residual data or noise from the fit than can be regarded as symmetric and Gaussian. New raw datasets can then be produced from random permutations of the residuals, and they can be reprocessed using Anahess to find expectation values and standard deviations for T\u003csub\u003e1\u003c/sub\u003e, T\u003csub\u003e2\u003c/sub\u003e and the intensity. Also, the results from the fitted datasets can be used to find the likelihood of fitting to a certain number of components with the lowest BIC number.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Geir Humborstad S\u0026oslash;rland, Henrik Walbye Anthonsen, and Sebastien Simon. The first draft of the manuscript was written by Geir Humborstad S\u0026oslash;rland and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eProvencher, S.W., CONTIN: A general purpose constrained regularization program for inverting noisy linear algebraic and integral equations. Computer Physics Communications 27(3), 229\u0026ndash;242 (1982)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProvencher, S.W., A constrained regularization method for inverting data represented by linear algebraic or integral equations. Computer Physics Communications 27(3), 213\u0026ndash;227 (1982)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProvencher, S.W., Inverse problems in polymer characterization: Direct analysis of polydispersity with photon correlation spectroscopy. Die Makromolekulare Chemie, 180(1), 201\u0026ndash;209 1979\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkataramanan L., Song Y., H\u0026uuml;rlimann M.D., Solving Fredholm integrals of the first kind with tensor product structure in 2 and 2.5 dimensions, IEEE Transactions on Signal Processing 50,1017\u0026ndash;1026 (2002)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong, Y.Q., L Venkataramanan, M D H\u0026uuml;rlimann, M Flaum, P Frulla, C Straley., T1\u0026ndash;T2 Correlation Spectra Obtained Using a Fast TwoDimensional Laplace Inversion, Journal of Magnetic Resonance 154(2), 261\u0026ndash;268 (2002)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorgia G.C., Brown R.J.S and Fantazzini P., Uniform-Penalty Inversion of Multiexponential Decay Data, Journal of Magnetic Resonance 132, 65\u0026ndash;77 (1998)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorgia G.C., Brown R.J.S and Fantazzini P., Uniform-Penalty Inversion of Multiexponential Decay Data II, Journal of Magnetic Resonance 147, 273\u0026ndash;285 (2000)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeal P.D and Eccles C. Adaptive truncation of matrix decompositions and efficient estimation of NMR relaxation distributions, Inverse Problems 31(4), (2015)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVilliam Bortolotti V., Brizi L., Nagmutdinova A., Zama F., and Landi G.: MUPen2DTool: A new Matlab Tool for 2D Nuclear Magnetic Resonance relaxation data inversion, SoftwareX, 20 (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026oslash;rland Geir H., Anthonsen Henrik W., Ukkelberg, \u0026Aring;smund \u0026amp; Zick Klaus: A robust method for Analysing One and Two-Dimensional Dynamic NMR Data. Applied Magnetic Resonance 53, 1345\u0026ndash;1359 (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUkkelberg \u0026Aring;. S\u0026oslash;rland G.H, Hansen E.W: Wider\u0026oslash;e H.C., Anahess, a new second order sum of exponential fits, compared to the Tikhonov regularization approach, with NMR applications International Journal of Recent Research and Applied Studies, IJRRAS 2(3), 2010\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBabak P., Kryuchkov S., and Kantzas A.: Parsimony and goodness-of-fit in multi-dimensional NMR inversion, Journal of Magnetic Resonance 274, 46\u0026ndash;56 (2017)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYarman C.E, Monz\u0026oacute;n L., Reynolds M., Heaton N.: A new inversion method for NMR signal processing. 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 260\u0026ndash;263 (2013)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGideon, S., Estimating the Dimension of a Model, The Annals of Statistics 6(2), 461\u0026ndash;464 (1978)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026oslash;rland G.H. \u003cem\u003eDynamic Pulsed-Field_Gradient NMR\u003c/em\u003e, Springer Verlag (2014)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Hagan, A., Leonard, T., \u003cem\u003eBayes estimation subject to uncertainty about parameter constraints\u003c/em\u003e\". Biometrika. 63 (1): 201\u0026ndash;203 (1976)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ewww.admagres.com\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ewww.antek.no\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026oslash;rland, G.H., Larsen P.M, Lundby, F., Anthonsen, H.W., Foss, B.j., \u003cem\u003eOn the Use of Low-Field NMR Methods for the Determination Of Total Lipid Content in Marine Products\u003c/em\u003e, in \u003cem\u003eMagnetic Resonance in Food Science: The Multivariate Challenge\u003c/em\u003e. 2005, The Royal Society of Chemistry. p. 20\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNordic-Baltic Committee on Food Analysis, \"Fat determination in fish, fish feed and fish meal by low field nuclear magnetic resonance (LF-NMR)\", NMKL 199, 2014\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"applied-magnetic-resonance","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apmr","sideBox":"Learn more about [Applied Magnetic Resonance](http://link.springer.com/journal/723)","snPcode":"723","submissionUrl":"https://submission.nature.com/new-submission/723/3","title":"Applied Magnetic Resonance","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Anahess, Inverse Laplace Transform, T1 and/or T2 distribution, expectation value, standard deviation","lastPublishedDoi":"10.21203/rs.3.rs-4887848/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4887848/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA method for analysing ill posed multi exponentially decaying data from 1- and 2-dimensional experiments by determination of the expectation values and their standard deviation has been developed. It combines a repeated use of the discrete Anahess approach for analysing the dynamic data where a regrouping of the noise in the data is performed for each repetition.. These resulting expectation values are used as initial and restricting values to produce a distribution using the Inverse Laplace Transform, where the position and volume of the distribution can then be reported with expectation value and standard deviation. 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