1H

PLS Regression Model Comparison of 60 and 300 MHz qNMR of EPA and DHA
Omega-3 Fatty Acids Obtained at Different Points...
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SMASH - NMR of Fish Oil Poster - 9-24-13

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SMASH - NMR of Fish Oil Poster - 9-24-13

  1. 1. 1H PLS Regression Model Comparison of 60 and 300 MHz qNMR of EPA and DHA Omega-3 Fatty Acids Obtained at Different Points in a Fish Oil Nutritional Supplement Manufacturing Process John C. Edwards and Paul J. Giammatteo Process NMR Associates, LLC, 87A Sand Pit Rd, Danbury, CT 06810 USA Abstract 1H NMR of a series of samples taken from different points in the manufacturing process of a fish oil nutritional supplement were obtained on a 300 MHz superconducting NMR system and a cryogen-free bench-top 60 MHz NMR system. The resulting spectra were utilized to develop single wide-range partial least-squares regression models of the EPA and DHA omega-3 fatty acid content of the fish oils at the various sampling points on the process. The process involves initial concentration of the fatty acids by solvent precipitation, molecular distillation, ethyl ester esterification, clathration, and centrifugation. For each of the fatty acids a single full range PLS model was obtained utilizing the entire integral binned/normalized spectrum or utilizing a series of normalized peak integrations rather than the entire spectrum. It was demonstrated that 60 MHz NMR spectra yielded identical model performance to the higher resolution 300 MHz spectra. The 60 MHz system is compact enough that it can be placed in the manufacturing plant environment for at-line utilization. Alternatively it can be packaged to provide on-line/in-line process control. NMR – 60 MHz Experimental In chain Aspect AI – 60 MHz Cryogen-Free NMR Spectrometer CH2 24 pulse on pure sample in 5 mm tube Locked on 1H NMR signal In MNova 8.1.2 SPC Files Imported, Stacked, Binned at 3 Hz interval, Area Normalized to 100 Saved as Transposed Ascii Matrix For Peak Integrals Used Advanced Feature – Create Integral Graph from Stacked Plot PLS Regression Performed Thermo Grams IQ and Eigenvector Solo Eicosaoentaenoic Acid (EPA) 20:5(n-3) Docosahexaenoic Acid (DHA) 22:6(n-3) Ethyl CH3 EtOOC-CH2-R Olefins =C-CH2-C= R-CH2-C= CH3-CH2-O-OC-CH2-R DHA 300 MHz 1H CH3 EPA NMR ID FO3h001 FO3h002 FO3h003 FO3h004 FO3h005 FO3h006 FO3h007 FO3h008 FO3h009 FO3h010 FO3h011 FO3h012 FO3h013 FO3h014 FO3h015 FO3h016 FO3h017 FO3h018 FO3h019 FO3h020 FO3h021 FO3h022 FO3h023 FO3h024 FO3h025 EPA (Area %) 0.64 21.55 62.97 29.43 14.21 52.74 15.21 7.18 16.95 36.35 61.09 13.32 71.78 41.40 1.19 11.73 43.38 6.07 9.77 58.93 10.62 43.91 54.05 0.00 26.97 Sample Description First Esterification First Esterification Clathration Mol Dist Pollock Oil Separator PolyUnsat Ester First Esterification First Esterification Clathration Mol Dist MSC Pollock Oil Separator PolyUnsat Ester First Esterification First Esterification Clathration Clath Raffinate First Esterification Mol Dist MSC Pollock Oil Separator PolyUnsat Ester First Esterification First Esterification 50 sample initial data set from All points in manufacturing process. First round of PLS regression analysis revealed concentration outliers that were linked to limitations in the GC method. This study was performed on improved GC data values. Final 80 Sample models were utilized to validate the calibrations on a 24 sample validation set (below). 600 NMR ID FO3h026 FO3h027 FO3h028 FO3h029 FO3h030 FO3h031 FO3h032 FO3h033 FO3h034 FO3h035 FO3h036 FO3h037 FO3h038 FO3h039 FO3h040 FO3h041 FO3h042 FO3h043 FO3h044 FO3h045 FO3h046 FO3h047 FO3h048 FO3h049 FO3h050 Expansion of Correlated Region NMR Experimental Varian Mercury - 300 MHz NMR Supercon Spectrometer 4 pulse on pure sample in 5 mm tube, Run Unlocked In MNova 8.1.2 SPC Files Imported, Stacked, Binned at 3 Hz interval, Area Normalized to 100, Saved as Transposed Ascii Matrix For Peak Integrals Used Advanced Feature – Create Integral Graph from Stacked Plot PLS Regression Performed Thermo Grams IQ and Eigenvector Solo NMR Processing for Multivariate Analysis DHA (Area %) 0.01 13.34 15.66 18.16 9.54 28.90 10.51 0.23 10.04 16.47 21.26 5.95 7.43 25.91 0.06 12.23 19.30 2.78 0.72 23.41 5.18 21.52 28.18 0.00 12.82 EPA EPA (Area %) DHA (Area %) 44.55 19.30 16.69 9.89 6.87 4.53 62.79 19.43 37.29 22.03 9.71 0.38 32.00 15.10 38.79 26.75 41.87 23.25 35.49 43.99 0.30 0.00 34.09 8.09 15.44 9.82 60.08 24.52 8.77 5.75 12.41 57.59 36.36 21.49 3.79 0.15 29.23 13.78 45.99 33.51 12.10 5.69 24.57 14.32 45.86 33.61 6.39 3.68 58.13 24.66 Sample Description Clathration First Esterification Crude Pollock Oil Separator PolyUnsat Ester First Esterification First Esterification Clathration Mol Dist Separator First Esterification Clathration MonoUnsat Ester PolyUnsat Ester Crude Salmon Oil Separator PolyUnsat Ester First Esterification Clath Raffinate PolyUnsat Ester MSC Pollock Oil Separator PolyUnsat Ester First Esterification Clathration 400 300 200 100 0 -100 4 Partial Least Squares calculated with the SIMPLS algorithm X-block: 1H NMR - 50 Fish Oil Samples - Transposed.xlsx 50 by 135 Preprocessing: Mean Center Y-block: EPA GC Content.xlsx 50 by 1 Preprocessing: Autoscale Num. LVs: 8 Cross validation: venetian blinds w/ 7 splits RMSEC: 1.20344 RMSECV: 1.67825 Bias: -3.55271e-015 CV Bias: -0.0822205 R^2 Cal: 0.996441 R^2 CV: 0.993139 1 0 -1 -2 0 5 10 15 20 Hotelling T^2 (99.89%) 25 30 0 0.2 0.4 Leverage 0.6 100 50 2 1 0 -1 -2 0.8 0 4 5 6 Variables 7 8 GC DHA 20.64 20.93 23.94 4.5 33.56 12.63 23.09 23.79 12.53 0.27 0.15 8.57 5.6 4.92 23.36 0.67 5.87 14.59 20.8 26.39 20.59 36.98 0.09 13.59 9 NMR EPA 45.53 32.74 36.01 4.22 44.56 26.48 42.18 35.23 20.33 7.79 3.62 17.25 15.95 8.88 57.17 1.85 13.23 7.09 41.61 46.93 61.34 44.45 -0.52 26.77 SEV = 10 11 Difference -2.24 -0.48 0.52 -2.53 -1.83 -2.37 -1.37 -2.36 1.21 -0.43 -0.56 -1.87 4.66 -1.88 0.27 0.03 -0.11 -23.54 -3.52 -6.79 -0.93 1.76 -0.82 1.49 1.64 ** ** # ** Partial Least Squares calculated with the SIMPLS algorithm X-block: 1H NMR - 55 Fish Oil Samples - Transposed.xlsx 50 by 135 Preprocessing: Mean Center Y-block: DHA GC Content.xlsx 50 by 1 Preprocessing: Mean Center Num. LVs: 8 Cross validation: venetian blinds w/ 7 splits RMSEC: 0.837073 RMSECV: 1.08906 Bias: 0 CV Bias: 0.0445129 R^2 Cal: 0.994978 R^2 CV: 0.991523 3 -3 0 3 4 Y Stdnt Residual 1 50 2 Q Residuals (0.15%) Y Stdnt Residual 1 Q Residuals (0.11%) 100 150 2 NMR GC DHA Difference EPA 20.71 0.07 47.77 19.84 -1.09 33.22 25.7 1.76 35.49 3.81 -0.69 6.75 33.21 -0.35 46.39 12.11 -0.52 28.85 22.76 -0.33 43.55 23.9 0.11 37.59 12.66 0.13 19.12 0.38 0.11 8.22 0.67 0.52 4.18 8.55 -0.02 19.12 10.23 4.63 11.29 6.34 1.42 10.76 24.27 0.91 56.9 1.74 1.07 1.82 4.99 -0.88 13.34 15.15 0.56 30.63 20.71 -0.09 45.13 27.61 1.22 53.72 20.86 0.27 62.27 35.92 -1.06 42.69 5.27 5.18 0.3 12.83 -0.76 25.28 SEV= 0.81 *** - Viscosity Related Spectrum Issue and/or M-Distance Outlier # - Possible GC Error 3 M-Distance Spectral Outliers And Possible GC Error Not included in SEV Calculation 3 1 24 Sample Validation Set Sample Sample ID 1 Finished Product 2 Finished Product 3 Mol Dist 4 First Esterification 5 Mol Dist 6 Mol Dist 7 First Esterification 8 Clathration 9 Clathration 10 First Esterification 11 First Esterification 12 First Esterification 13 Crude Fish Oil 14 Crude Fish Oil 15 Finished Product 16 Clathration 17 Crude Fish Oil 18 Clathration 19 Clathration 20 Clathration 21 Separator 22 Separator 23 First Esterification 24 Separator DHA 150 Integration of Peaks to Produce Multivariate Spectra 500 Data 1H -3 0 10 20 30 Hotelling T^2 (99.85%) Variables/Loadings Plot for 1H NMR - 55 Fish Oil Samples - Processed - 8-9-13 - Transposed.xlsx 0.04 40 0 0.2 0.4 Leverage 0.6 0.8 Variables/Loadings Plot for 1H NMR - 55 Fish Oil Samples - Processed - 8-9-13 - Transposed.xlsx 0.6 20 0.02 0 -20 0.01 0 40 30 20 10 50 0.2 Reg Vector for Y 1 20 0.4 Scores on LV 2 (1.01%) 40 100 50 Y CV Predicted 1 60 60 0.03 Reg Vector for Y 1 Scores on LV 2 (0.45%) 40 0 -0.01 0 0 20 40 Y Measured 1 60 -40 -400 80 0 -200 0 200 Scores on LV 1 (95.40%) 400 0 10 20 30 40 Y Measured 1 -0.02 -0.03 20 40 60 80 100 50 -50 -400 60 -200 0 200 Scores on LV 1 (94.90%) 120 30 15 10 1 0 -1 -2 5 -3 0 5 10 15 Hotelling T^2 (99.91%) 20 0 0.2 0.4 Leverage 0.6 20 15 10 0.8 0 40 60 80 100 120 Variable Partial Least Squares calculated with the SIMPLS algorithm X-block: SPC Files in MNova.xlsx 49 by 220 Preprocessing: Mean Center Y-block: DHA GC Content.xlsx 49 by 1 Preprocessing: Mean Center Num. LVs: 8 Cross validation: venetian blinds w/ 7 splits RMSEC: 0.613656 RMSECV: 1.13119 Bias: 0 CV Bias: -0.118407 R^2 Cal: 0.99739 R^2 CV: 0.99137 2 0 -2 5 0 5 10 15 20 Hotelling T^2 (99.91%) Variables/Loadings Plot for SPC Files in MNova.xlsx 1 80 20 4 Y Stdnt Residual 1 20 -0.8 25 Q Residuals (0.09%) 2 Y Stdnt Residual 1 Q Residuals (0.09%) 25 0 DHA PLS Regression Calibration – 300 MHz Partial Least Squares calculated with the SIMPLS algorithm X-block: SPC Files in MNova.xlsx 50 by 220 Preprocessing: Mean Center Y-block: EPA GC Content.xlsx 50 by 1 Preprocessing: Mean Center Num. LVs: 7 Cross validation: venetian blinds w/ 7 splits RMSEC: 1.54867 RMSECV: 2.1229 Bias: 0 CV Bias: -0.112162 R^2 Cal: 0.993826 R^2 CV: 0.988456 3 -0.2 -0.6 EPA PLS Regression Calibration – 300 MHz 30 0 -0.4 Variable 25 -4 30 0 0.2 0.4 Leverage 0.6 0.8 20 Variables/Loadings Plot for SPC Files in MNova.xlsx 0 0 20 40 Y Measured 1 60 0 -10 -20 80 -200 -100 0 100 Scores on LV 1 (98.04%) 200 0 -0.5 -1 1 40 30 20 10 10 0.5 0 Reg Vector for Y 1 20 50 Scores on LV 2 (0.36%) 40 20 10 Y CV Predicted 1 Y CV Predicted 1 60 60 0.5 Reg Vector for Y 1 Scores on LV 2 (0.64%) 1.5 -10 0 -0.5 0 -1.5 20 40 60 80 100 120 Variable 140 160 180 200 220 EPA PLS Regression Calibration – 60 MHz 0 10 20 30 40 Y Measured 1 50 -20 60 -200 -100 0 100 Scores on LV 1 (97.83%) 200 -1 DHA PLS Regression Calibration – 60 MHz -1.5 20 40 60 80 100 120 Variable 20 10 0 0 5 10 15 Hotelling T^2 (99.98%) 0 -2 -4 20 0 0.1 0.2 Leverage 0.3 0.2 0 3 40 30 20 2 0 -1 2.13 1.13 1.68 1.09 2.71 2.24 Fused - NMR-FTIR 1.62 1.08 -0.2 -0.4 0.4 0 -0.2 220 Peak Integral Data 0 -2 10 200 300 MHz NMR 0.2 1 180 60 MHz NMR PLS Regression Data 0.4 Reg Vector for Y 1 30 0.4 Reg Vector for Y 1 EPA 40 50 0.6 2 4 Y Stdnt Residual 1 Y Stdnt Residual 1 EPA Q Residuals (0.02%) 50 60 160 DHA - SECV (Wt%) 0.8 4 Q Residuals (0.03%) 60 140 EPA - SECV (Wt%) Variables/Loadings Plot for aaa-1H-300MHz_IntelligentIntegrals.xlsx 0.6 Variables/Loadings Plot for aaa-1H-300MHz_IntelligentIntegrals.xlsx -3 0 5 10 15 20 Hotelling T^2 (99.97%) 25 30 0 0.2 0.4 Leverage 0.6 0.8 -0.6 -0.4 -0.8 40 20 -0.6 0 -20 -40 0 0 20 40 60 Y Measured 1 EPA 1 2 3 4 20 80 -500 0 Scores on LV 1 (98.20%) 500 EPA PLS Regression Calibration – Peak Integrals 5 6 Variable 7 8 9 10 11 50 Partial Least Squares calculated with the SIMPLS algorithm X-block: aaa-1H-300MHz_IntelligentIntegrals.xlsx 49 by 11 Preprocessing: Mean Center Y-block: EPA GC Content.xlsx 49 by 1 Preprocessing: Mean Center Num. LVs: 5 Cross validation: venetian blinds w/ 7 splits RMSEC: 2.20056 RMSECV: 2.70883 Bias: 1.77636e-014 CV Bias: -0.116845 R^2 Cal: 0.987561 R^2 CV: 0.98129 40 30 20 10 2 3 4 5 0 10 20 30 40 Y Measured 1 50 60 6 Variable 7 8 9 10 11 140 Partial Least Squares calculated with the SIMPLS algorithm X-block: aaa-1H-300MHz_IntelligentIntegrals.xlsx 49 by 11 Preprocessing: Mean Center Y-block: DHA GC Content.xlsx 49 by 1 Preprocessing: Mean Center Num. LVs: 5 Cross validation: venetian blinds w/ 7 splits RMSEC: 1.8631 RMSECV: 2.24092 Bias: -1.24345e-014 CV Bias: 0.00464222 R^2 Cal: 0.974603 R^2 CV: 0.963534 50 0 -50 0 1 100 Scores on LV 2 (1.20%) 60 60 Y CV Predicted 1 Scores on LV 2 (0.66%) Y CV Predicted 1 EPA 40 -500 0 Scores on LV 1 (98.13%) 500 120 Fused 1H NMR and FTIR-ATR 100 80 Data 80 60 40 DHA PLS Regression Calibration – Peak Integrals 20 -2 250 200 150 100 50 Variables/Loadings Plot for NMR_FTIR Scaled - Sample 24 Removed.xlsx 0 5 10 15 Hotelling T^2 (99.85%) -4 20 0.2 0 0.1 0.2 Leverage 0.3 20 0 20 40 60 Y Measured 1 EPA 80 10 20 30 Hotelling T^2 (99.87%) -4 40 0 0.2 0.4 Leverage 0.6 0.8 Variables/Loadings Plot for NMR_FTIR Scaled - Sample 24 Removed.xlsx 100 0 100 0.05 0 -0.05 -0.1 -100 0.25 60 -0.15 50 40 30 20 10 0.2 0.15 50 0 -50 0.1 0.05 0 -0.05 -0.1 -100 0 -0.2 0 -2 0.3 Y CV Predicted 1 DHA Reg Vector for Y 1 EPA Scores on LV 2 (8.12%) 40 0 0.15 200 60 0 0.4 0.1 80 2 0.25 0 0 4 Reg Vector for Y 1 DHA 50 0 300 Scores on LV 2 (4.42%) 100 2 Partial Least Squares calculated with the SIMPLS algorithm X-block: NMR_FTIR Scaled - Sample 24 Removed.xlsx 49 by 998 Preprocessing: Mean Center Y-block: DHA GC Content - Sample 24 Removed.xlsx 49 by 1 Preprocessing: Mean Center Num. LVs: 7 Cross validation: venetian blinds w/ 7 splits RMSEC: 0.677763 RMSECV: 1.08301 Bias: -5.32907e-015 CV Bias: 0.010442 R^2 Cal: 0.996822 R^2 CV: 0.992286 DHA Correlation with Combined and Scaled 1H NMR and FTIR-ATR Y Stdnt Residual 1 DHA 4 Y Stdnt Residual 1 EPA Q Residuals (0.15%) 150 Partial Least Squares calculated with the SIMPLS algorithm X-block: NMR_FTIR Scaled - Sample 24 Removed.xlsx 49 by 977 Preprocessing: Mean Center Y-block: EPA GC Content - Sample 24 Removed.xlsx 49 by 1 Preprocessing: Mean Center Num. LVs: 6 Cross validation: venetian blinds w/ 7 splits RMSEC: 1.16039 RMSECV: 1.62525 Bias: 3.55271e-015 CV Bias: -0.0432842 R^2 Cal: 0.996587 R^2 CV: 0.993315 Q Residuals (0.13%) EPA Correlation with Combined and Scaled 1H NMR and FTIR-ATR Y CV Predicted 1 EPA Y CV Predicted 1 80 -200 -500 0 Scores on LV 1 (87.67%) 500 -0.25 200 400 600 800 1000 Variable 1200 1400 1600 -0.15 0 10 20 30 40 Y Measured 1 DHA 50 60 -500 0 Scores on LV 1 (85.17%) 500 -0.2 200 400 600 800 1000 Variable 1200 1400 1600 0 -20 200 400 600 800 1000 Variables 1200 1400 1600 Conclusion Wide range PLS correlation models can be readily built based on 60 MHz NMR data. Various spectral ‘de-resolution’ techniques may make these models transferable between NMR data sets obtained at varying magnetic field strengths. At-line and in-line permanent magnet NMR systems can yield the same high quality correlations as data obtained on much higher field superconducting NMR systems.

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