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»,.»,.. .. » ,., A., ,,. To.»,.»,.,,,,.,,,,,..,,. , ( ), (10 ml, 15 ml )., (120 L, 300 L ),..,,,,.,. : ) 3 )..,,. : :,,,,. ) ( 4 ) : ,,,,. ) ( 5 ) : ( Accuracy ), (Repeatability), Reproducibility,

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»,.»,.. .. » ...,., A., ,,. To.»,.»,.,,,,.,,,,,..,,. , ( ), (10 ml, 15 ml )., (120 L, 300 L ),..,,,,.,. : ) 3 )..,,. : :,,,,. ) ( 4 ) : ,,,,. ) ( 5 ) : ( Accuracy ), (Repeatability), Reproducibility, Detectability. :, ( ) ( matrix effect ). (2002/657/ ).,. ( 16 ) 7 L,., 9 L 0,6% 0,2% CV.,,,,,,. ABSTRACT The cultivation of olive trees existed since ancient times, especially in areas around the Mediterranean basin, and has offered one of the main foods of man and connected with all the cultures that developed them. To oil used in a variety of uses beyond the diet being the major fuel-lighting material was one of the few known pharmaceutical agents for many centuries. Liquid gold by Homer. 'Fix', mentions Asclepius. With the passage of time in the presence of factors such as humidity, temperature, enzymes, microorganisms, metals released fatty acids from the glycerides resulting hydrolysis of olive oil. Which leads initially acidification, then the increase of the peroxide, followed by increased ketones, aldehydes and ultimately change the taste, aroma and ultimately degrade the beneficial value of, the human body. Obviously the above changes have negative impact in the commercial value. Which makes it necessary to control the oxidation state of the oil during the production, storage, and handling of standardization.the formal method used for the determination of the peroxide is time consuming with low productivity few analyzes per shift, are manually (subjective), large consumption of organic solvents (10 ml chloroform, 15 ml acetic acid) by determining peroxide value.in order to improve the above features of the official method creates the need for automation of analytical methods to parse large number of tests in little time, with friendly manner (acetic acid 120 L, 300 L propanol per measurement) to the environment, and maximum reliability. This thesis describes the development and application of measuring the peroxide value of the oil and potentially in other fat using advanced medical technology analysts. In these analyzers all stages of analysis, sampling, metering, data analysis, and finally calculates the results are fully automated through command computer, giving excellent accuracy and repeatability.biochemical analyzers have the advantages of ease of use, high speed analysis.this research paper includes the following sections: (A) Design Protocol measuring peroxide value in biochemistry analyzer (Chapter 3). Preliminary checks resistance equipment in contact with the reagents required with this method. Were checked, the cell strength was tested, pipework and samplers where the results were positive. Optimizing Protocol: Optimization: sample volume, measurement wavelength, concentration sodium iodide, stirring, time of incubation. (B) Validation Protocol (Chapter 4): Sample preparation standards receipt samples analyzed by the official method, calibration using olive oil samples, analysis of samples by the automated method, Correlation results of the two methods. (C) Validation method (Chapter 5): Determination of Precision (Accuracy), Repeatability,Reproducibility, Detectability of the method. Conclusion: The results are fully compatible with the official method, since the proposed method was standardized (calibrated) with real samples analyzed by the official method eliminated the effect matrix. Also verification-validation of the method was in accordance with the standards of Greek texts EU Directive (2002/657/EC) had excellent results.finally, the characteristics of the method can be improved by increasing the sample volume. This is evident from the study repeatability done to measure the volume (Table 16) showing that increased volume of 7 L, used in this work determining A.Y., 9 L repeatability is improved CV from 0.6% to 0,2%. SCIENTIFIC AREA / KEYWORDS Biochemical, Peroxide, Olive, Automation, numbers, p-anisidine, free radicals, autoxidation. :,...Keep improving,.,. ,,.... , ,, ( ) UV F test t test Accuracy Repeatability Reproducibility..85 Detectability COMMISSION REGULATION (EEC) No 2568/913.2 Panayotis G. Nouros, Constantinos A. Georgiou x, Moschos G. Polissiou. (1998).,,.,,, ,.,,. 50 L.,, 48 L.,,,. 1 1.1.,,.. 16,,,,. Jules Duboscq ,.,, , o Örjan Ouchterlony Norman Anderson,. 1974,,.,. 1 .,, [1] ). 2 1.2.., :,,. : max:,. 3 2. max: 3..,. 4 1.2.3., , : ,,.,.. 5 K 6 ,,,,,..,. ( )..,.,.. 6. K [30] 7 , 400, 900, 900,. 8 1.3.5,, 7. [34],.. 8. ( integrated ) 9 ,, , 10 nm 1000 m. ( nm).. [2]. I 0, s I 0 ( ) = s / I 0... ( ) R I ( 100% ).,,,. ( ) : 11 I A log I s R logt 10. Beer. Beer. Beer : A = bc A = bc : A = = (molar absorptivity), c mol/l =, c gr/l. b = c =. 12 A = bc.,, PH. 11. Beer A dc A1 b c 1 1 A2 b c 2 2 A c A c A c 1 2 c u c =, u =. 1 2 c A c u Ac c c A c u u c u Au A c xc c c u cc Au x A c A xk u Ac c c ( ),. Zn. ( ) ( ) : : ( ) / ( ) ( ). [32]. = C = x C, = ,, Ca, Li, Mg.,,,,. [33]. :,,,.. ( ), 15 S 2 S 1 T 1 A S r r 3 4 :.,,. 16 .. Beer-Lambert : F I bc : F = = ( ). I 0 =. =. b =. c = mol/l. 4 2% (inner filter effect)[2]. 17 13..,,.. :, (sinlget state) .,,,,,, (, ), ( Cu 2+,Fe 3+ )... [2]. [2]. 19 1.4.5.,.,,,,,. ( 15) 16. cyclodextrin-2,7-dihydroxy-naphthalene 4.6 H2O. 21 17. (a) ( b ) (c) 18 ( ).. 18 ( ) Rayleigh ( 1) I s I o. /10.,. 22 [2]. I s I sin 4 2 r : 1 I s = I 0 =. a =. =. =. r =. I I s ( dn / dc) Mcsin 4 2 N r a 2.. I s =. I 0 =. = (g/mol) C = (g/ml). =. dn / dc = N a = Avogadro =. r =. 23 ,, 180,.., : I bt I 0 e 1 I ln b I t 0 : T =. b =. I =. I 0 = ,,.,,.,.. =,,,. -,.,, (+ ) -,.., apob, C3, C4,,, , 2. :, : F, Cl, I (, ) , C , ), ( ), ( ). LED Laser (ultra violet visible UV-Vis), ( photomultiplier tube ).. (solid state). 28 ,,,.,,. [34]. 1.7,,,, ( ), [34]. 29 2 2.1,,. To [2]. ,.»,.», ( ),. 30 2.2. [3,4,5] % 3,5-21 % 0-1,5 % 1. 7,5-20 % 0,5-5 % 2.. [10] 3. 31 3....,. [7] [7] 32 . 0-0,0 1 2,9 0,6 0,5 0,3 0,2 0,2 2 1,2 18,4 2,3 0,1 0,7 5,1 2,2 3 0,2 5,9 0,9 2,4 0,7 1,3 43,5 4 0,4 0,2 6,8 3,5 0,2 0,1 =, =, =, =, = ( ) [8] 2.2.3., (, ) 30,... (mg/100g ) 30-50 % 15 % - 10 % - -, % [8]. T,,,.[9] (C 40 H 56 O 2 ). C 40 H 5. 35 23., n- (C 27,C 32 ) ( -, ).,. 0,1 %.. 36 ( ). ( ) [9]. O ( Gas Liquid Chromatography ) [7] [10] 88,5 %, 1,6 % ppm [11].....H [7] ,, (Cinquanta et al,1977).. 25.,. 38 / (Tsimidou et al,1992). (3,4 ) (3,4 - ) [9]., ppm ,.,. Mg,., [13]. 39 ,,, 40 2.5 [14,15] (Olea Europa Sativa) ,,.. : ) (extra virgin olive oil), 0.8%. meq0 2 / kg µ 20, 270 µ µ 0,22 µ µ 0,01. 41 ) (virgin olive oil),, 2%., 270 0,25. ) (virgin olive oil lampante) µ, 2% , µ,, 0,3g 100g,. 20 meq 2 /kg µ 5, 270 µ 1,1 µ 0, µ ( µ ),, 1% µ. 42 2.5.5., µ. µ ,, 0,3%. meq02 /kg µ µ 10, 270 µ µ 2 µ 0, [14,17] ,,,,..,,.,.. 43 1. Karl Fischer ,, ,,.,. 44 ..,,,, [18]., 2.7.2.,, ,. [19] K 1 1. ( ) (R, ROO ), Fe, Cu) 2. k 2 R + O2 ROO K 3 ROO + RH ROOH + R K 4 3. R + R R-R K 5 ROO + R ROOH K 6 ROO + ROO ROOR + O 2 46 2.8...,, [20]. (ROOH I 2 ). meq O 2 /kg,.. [26] [21].,. 350 nm. 47 , ( TBARS). ( ) 2-.,,, [22]. 28. (Malondialdehyde) 48 2-. : 2.., UV [7]. 232 nm ( ) 270nm (, ),. 270 nm ). 232, 270,,. 49 2.9, & (,, )..,.,. ( ). lipace [23,24,25,26]. ( ) LDL (Low density lipoprotein), [27]. LDL. H HDL, ( ) LDL ( ),. 50 .,. [29]. ( -6 ) -3 ).. lzheimer. ( )... 51 L 1,0% (w / v) NaI 7 L ( ) 310 L 44% ( / ) ( ) 340 nm x 700 nm nm x 700 nm,. 52 3.1.2 Fresf _ solution 2 I I 2 2 e 29. NaI 1% (w/v) in n-propanol., [28] ( ) - (K NaI ) 2 : 2 I I 2 2 e 2, nm,, I 3. ( ) I nm 3,52 x 10 4, 2,32 x10 4 mol -1 cm -1, [45]. 53 30. : I nm. ' ' ROOR 3 I H 2 O I 3 ROH R OH 2 OH 2 (Na 2 S 2 O 3 ) : 2 I 2 S O I S O ,, -,,,., 99,9%. 0,25-3,0% (w / ) 54 % ( / ). 2, ,35 PV 100 PV, , : : ( ),..,,.. : 0,2-2 ml.. : 0,3 0,9 L C [17].. 56 1,0% (w / v) NaI, 43.2% ( / ) -, 360 nm. [20] [17] (matrix effect). 293 nm [29]. n-propanol 1 7 n-propanol ( 8) 1 ) R = 0, n-propanol R = 0, mau ,2 0,4 0,6 0,8 1 1,2 L mau 1 649,63 0,5 326,24 0,25 139,88 0,125 65,79 7., n = 10,.. 21,6±0,8 20,9±0,8 19,5±0,8 19,9±0,8 19,3±0,8 19,1±0,8 18,8±0,8 18,7±0,8 19,8±0,8 mean 19,6±0,8 sd 0,8 cv 4,5 8. n=10. 59 , [30], 293 nm 700 nm. nm Microsoft Office Excel % ( ). 3.4, ( univariate procedure),,,,. 60 ( 9) (. NaI I I 2 2 e 5 L, nm A (mau) ± SD, (n=5) A(mAu)±SD, (n=5) ,8±1,3 43,4±2, ±2,4 27,5±3, ,47±3,4 23,7±3, ,96±4,2 8,2±3, 3.4.1., CV., NaI,. 7 L ( 10 ). A ± SD, A ± SD, Sample volume (n=5) 383 nm CV A ± SD, (n=5) 405 nm CV (n=5) 452 nm CV 2 L 4,01±0,14x10 2 3,6 1,56±0,06x10 2 3,5 0,35±0,43x ,09 3 L 5,72±0,13x10 2 2,6 2,60±0,06x10 2 2,8 0,62±0,09x10 2 2,5 4 L 6,70±0,21x10 2 3,1 3,06±0,09x10 2 2,9 0,70±0,02x10 2 3,7 5 L 9,28±0,21x10 2 2,4 4,24±0,01x10 2 2,4 0,96±0,03x10 2 3,3 6 L 10,3±0,27x10 2 2,6 4,74±0,13x10 2 2,8 1,09±0,04x10 2 9,3 7 L 11,8±0,08x10 2 0,7 5,42±0,03x10 2 0,6 1,26±0,01x10 2 0,5 8 L 12,9±0,04x10 2 0,3 5,92±0,03x10 2 0,5 1,35±0,01x10 2 0,7 9 L 14,5±0,05x10 2 0,3 6,66±0,01x10 2 0,2 1,54±0,01x10 2 0,5 10.., 62 , ( 10 ) nm Sample in L R² = Absorbance in mau 63 nm 383 nm Sample volume in L y = 0,0066x - 0,7084 R² = 0, Absorbance in mau nm sample optimization Absorbance in mau R = 0,998 R = 0,997 R= 0,994 2ul 3ul 4ul 5ul 6ul 7ul 8ul 9ul 383nm 405nm 452 nm Linear (383nm) Linear (405nm ) Linear (452 nm) Sample in ul nm 64 ) 293 nm,340 nm,383nm,405nm., 510 nm 700 nm. 3 L, nm A ± SD, (n=5) RSD % ,1±0,3 x ,0±0,1x ,7±0,1x10 2 2, ,6±0,06x10 2 2, ,6±0,02x10 2 4, ,09±0,02x ,01±0,8x10 2 1, NaI, NaI 65 ,. NaI, ( 13) 0,5-1 NaI (v/v) in n-propanol. NaI (w/v) in-propanol RSD % 293 nm 340 nm 383 nm 405 nm 12,5x10-2 3,8 17,2 * 7,5x10-2 3,2 13,3 107,60 165,0 0,5x10-1 2,4 3,1 2,8 5,0 0,5 2,3 2,6 2,2 2,3 1,0 3,0 3,0 2,9 2,0 2,2 2,2 2,2 2,5 8,9 8,9 * 13. RSD % NaI (v/v) in n-propanol 66 RSD % 18,00 16,00 14,00 12,00 10,00 8,00 6,00 4,00 2,00 0,00 0,0125 0,0750 0,0500 0,5000 1,0000 2,0000 2,5000 Concentration NaI (w/v)% RSD % NaI (v/v) in n-propanol 1600, , ,00 Absorbance in mau 1000,00 800,00 600, , , ,00 0,0125 0,0750 0,0500 0,5000 1,0000 2,0000 2,5000 Concentration NaI (w/v)% 9. NaI (v/v) in n-propanol 67 ( ), ), ( ) 14. (n = 5) 340nm 383 nm 405 nm A ± SD CV A ± SD CV A ± SD CV 11,9±0,17x ,5 11,2±0,16x10-2 1,46 5,14±0,07x10-2 1,4 12,4±0,86x ,7 11,8±0,11x10-2 0,94 5,40±0,06x10-2 1,2 12,7±0,16x ,3 12,1±0,15x10-2 1,31 5,56±0,07x10-2 1,3 452 nm 510 nm A ± SD CV A ± SD CV 1,17±0,01x10-2 1,0 0,14±0,01x10-2 4,4 1,24±0,04x10-2 3,2 0,14±0,03x ,29±0,02x10-2 1,9 0,16±0,02x (n = 5) RSD % t,s 293 nm 340 nm 383 nm 120 2,4 2,4 2, ,1 1,3 1, ,6 2,6 2, ,1 1,2 1, ,7 0,6 0, ,5 1, ,5 2, ,5 1,3 15. RSD % 69 3,00 2,50 2,00 CV % 1,50 1, ,50 0, Incubation Time 10. RSD %. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,. 70 293nm 340nm 383nm 405nm 452 nm O1 3,1x10-2 1,3x10-2 1,2x10-2 5,8x10-2 0,1x10-2 O2 3,0x10-2 1,3x10-2 1,2x10-2 5,6x10-2 0,1x10-2 O3 1,8x10-2 7,8x10-2 7,3x10-2 3,4x10-2 0,9x10-2 O4 3,5x10-2 1,5x10-2 1,5x10-2 6,7x10-2 0,1x10-2 O5 1,8x10-2 7,9x10-2 7,4x10-2 3,4x10-2 0,8x10-2 O6 8,2x10-2 3,5x10-2 3,3x10-2 1,5x10-2 0,4x10-2 O7 1,7x x10-2 7,4x10-2 1,7x10-2 O8 4,0x10-2 1,7x10-2 1,6x10-2 7,7x10-2 0,2x10-2 O9 3,3x10-2 1,4x10-2 1,4x10-2 6,5x10-2 0,2x10-2 O10 4,9x10-2 2,1x10-2 2,0x10-2 9,4x10-2 0,2x nm Absorbance mau nm 383nm 405nm 452nm nm 0 O1 O2 O3 O4 O5 O6 O7 O8 O9 O10 600nm Sample random ) ( 32 ) 32. ( ) 32. ( KI ) 72 [31] 18., 0,001 g, ( ),, : ml.. 15 ml 1 ml.,, 22º C. 75 ml. 0,002 Mol / L 12, 0,01 Mol / L 12), (6.5).. 0,05 ml 0,01 mol / L. 73 [17]..,,.,,.., ( ) x. (calibration function): y = g(x). ( Lambert Beer): ( ) = b c : F ( ) = 2,3 P 0 b c = k c (fitting) ( y C). (linear), (origin).. : Beer (,, )., : y = a + b x y =, x =, a = y b = [28]. (calibration curve). ; ( ); ; 75 ; (univariate regression), ( ),. (Least Squares Method) (estimates) a b. a b ( e i ) : e 2 0 e i : e i = y i -, e i = (residual),y i =, = = a + bx i : e i = y i - a - bx i a b, R: R = e 2 i = (y i - a - bx i ) 2 (normal equations). : ( xi i xi n xi ( xi ) x)( yi y) n yi yi i i i b ( x x) x i i i i xi i i ( xi ) 2 yi xi xi y i y bx 2 2 n x i i i i i i 76 4.3. OLITECN (IOOC).. n=2,. OLITECN 04 2,30 2, ,53 6, ,67 9, [28] t F test o Microsoft Office Excel % , 77 R=0, ,22 1, ,45 2, ,89 4, ,89 6, ,94 6, ,94 6, ,06 6, ,22 7, ,07 8, ,14 9, ,49 9, ,88 9, ,59 10, ,66 11, ,23 12, ,66 12, ,03 12, ,74 13, ,07 15, ,4 16, ,35 20, ,3 24,23 78 19., ( ), ( ) y = (1,002±0,04)x - 0,04±0,003 R= 0, F test F test. F,. s F s 2 2 1 s2 3. s F ( 3) 21,. 20. F = (s1) 2 / (s2) 2 95% 80 F-Test Two-Sample for Variances Mean.. 10,01 9,95 Variance 33,01 32,82 Observations df F. 1,01 P(F =f) one-tail 0,49 F Critical one-tail.. 2,04 P(F =f) one-tail F Critical one-tail t test Student t,,.,. t ( 21). t t,. t,.. t. t.,., t, 81 , D D, sd. t ( 4). D t sd 2 N ( Di D) Sd N t D, D. t-test: Paired Two Sample for Means Mean. 10,01 9,95 Variance. 33,01 32,82 Observations Pearson Correlation. 0,99 Hypothesized Mean Difference 0 df 22 t Stat.. 3,67 P(T =t) one-tail.. 6,62x10-4 t Critical one-tail 1, P(T =t) two-tail. 1,3x10-3 t Critical two-tail 2,07 P(T =t) one-tail t Critical one-tail & P(T =t) two-tail t Critical two-tail 82 21. t 83 [31-32] Accuracy (, error bias) mean,, Repeatability ( inter-day),,,,,., 1, 1,5 2 0,5, 1 1, , (%) CV. 84 Reproducibility - (Within run),. : - ( ), (- ) , (%). 85 n=6, S1 n=6, S2 n=6, S3 n=6, S4 n=6, S5 23,35±0,18 11,3±0,23 10,2±0,20 9,02±0,11 5,56±0,15 23,09±0,18 10,86±0,23 9,87±0,20 9,01±0,11 5,59±0,15 23,53±0,18 11,03±0,23 9,94±0,20 8,95±0,11 5,63±0,15 23,55±0,18 10,82±0,23 9,72±0,20 8,91±0,11 5,78±0,15 23,49±0,18 11,31±0,23 9,88±0,20 9,01±0,11 5,87±0,15 23,54±0,18 10,82±0,23 9,62±0,20 9,24±0,11 5,45±0,15 SD 0,18 0,23 0,20 0,11 0,15 MEAN 23,43 11,02 9,87 9,02 5,65 CV 0,77 2,10 2,02 1,27 2,71 (Within run) n=6. & (between run) 1. CV 2,8 %, n = 6,. - (between run) :,.. 22,23,24,25. 86 2 meq O 2 /kg. n=6, S1 n=6, S2 n=6, S3 n=6, S4 n=6, S5 23,02±0,20 10,76±0,16 9,54±0,23 8,74±0,23 5,27±0,14 23,34±0,20 10,57±0,16 9,76±0,23 8,91±0,23 5,32±0,14 23,12±0,20 10,79±0,16 9,65±0,23 8,67±0,23 5,41±0,14 23,09±0,20 10,87±0,16 9,33±0,23 8,78±0,23 5,23±0,14 23,45±0,20 10,45±0,16 9,22±0,23 8,72±0,23 5,37±0,14 23,49±0,20 10,65±0,16 9,78±0,23 8,45±0,23 5,23±0,14 SD 0,20 0,16 0,23 0,15 0,14 MEAN 23,25 10,68 9,55 8,71 5,31 CV 0,86 1,45 2,41 1,74 1, (between run) 2. 3 meq O 2 /kg. n=6, S1 n=6, S2 n=6, S3 n=6, S4 n=6, S5 22,65±0,15 10,45±0,14 9,43±0,15 8,65±0,24 4,99±0,13 23,03±0,15 10,52±0,14 9,65±0,15 8,53±0,24 4,74±0,13 22,87±0,15 10,32±0,14 9,32±0,15 8,24±0,24 5,06±0,13 22,91±0,15 10,49±0,14 9,51±0,15 8,95±0,24 4,87±0,13 22,77±0,15 10,39±0,14 9,31±0,15 8,72±0,24 4,83±0,13 23,02±0,15 10,74±0,14 9,27±0,15 8,49±0,24 5,05±0,13 SD 0,15 0,14 0,15 0,24 0,13 MEAN 22,88 10,49 9,42 8,60 4,92 CV 0,64 1,37 1,54 2,78 2, (between run) 3. meq O 2 /kg. 87 1 between run, 3, n = 6, n=6, n=6, n=6, n=6, n=6, S1 S2 S3 S4 S5 23,43 11,02 9,87 9,02 5, ,25 10,68 9,55 8,71 5, ,88 10,49 9,42 8,6 4,92 SD 0,28 0,27 0,23 0,22 0,36 MEAN 23,19 10,73 9,61 8,78 5,29 CV 1,22 2,52 2,44 2,49 6, (between run). meq O 2 /kg Detectability /. (LOD). 88 . N, (IUPAC) =20 LOD. LOD n= 10. ( 26 ) LOD = 0,01 meq O 2 /kg. n=10 0,31±0,1 0,25±0,1 0,40±0,1 0,36±0,1 0,15±0,1 0,29±0,1 0,18±0,1 0,12±0,1 0,24±0,1 0,15±0,1 sd 0,01 mean 0,25 cv 4, n=10 - (LOQ). LOD. 89 LOD,, LOQ. 10., LOD LOQ. LOQ = LOD x10 LOQ = 0,01 x10 LOQ = 0,10 meq O 2 /kg ,. 90 LOD = 0,25 meq O 2 /kg ,.. [1]. A Guide to the History of Clinical Chemistry, Larry J. Kricka and John Savory,Clinical Chemistry 57:8,p (2011), 91 ( last accessed at :06 ). [2]. Fundamentals of CLINICAL CHEMISTRY., Sixth edition., TIETZ,Carl A. Burtis, Ph D., Edward R. Ashwood, M.D.,David E. Runs, M.D.,Barbara G. Sawyer,Ph.D.,L.J.Kricka,DPhil.,F.A.C.B., C.Chem.,F.R.S.C., F.R.C.Path.,Jason Y.Park,M.D.,Ph.D., ISBN : ,2008.,16: [3].,.,, [4]. Harwood, J., Aparicio, R., Handbook of Olive Oil, Analysis and Properties, Aspen publishers, Maryland [5].. (2007), &, ; 4. [6]. file:///c:/users/z002786f/downloads/cxs_033e.pdf (Last accessed 23/06/2014, 20:09). [7].,.,1993,,,. [8]. 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