Practical Use Of Nir In The Feed And Food Industry

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Practical Use Of Nir In The Feed And Food Industry

  1. 1. Giovanni Campolongo NIR Seminar – Campden – October 14th 2009
  2. 2. 2004 I Italian Near Infrared • Limo •Fiber Optic on the Ham Lodi Symphosium •Baby Food Plasmon (Heinz Group) 12th International 2005 Conference on Near- • On-line Maillard reaction monitoring for food additives production (caramel) Auckland infrared Spectroscopy 2005 VII CISETA • Ice cream mixtures Cernobbio • Inorganic Integrators II Italian Near • Pectin 2006 Infrared Symphosium • SO2 • Proteolysis index in P.D.O. Cheeses Ferrara 2007 13th International • Cous cous Umeå Conference on Near- • Vanilline • Wheat flour rheological paramethers infrared Spectroscopy • On-line polymerization process 2008 III Italian Near • Licopen content in Tomatoes Lazise Infrared Symphosium • Barilla FT-NIR Network for Flour monitoring NIR Seminar – Campden – October 14th 2009
  3. 3. ! " "# $ Lab & R&D • University • State agencies for control • Private Labs INDUSTRY TECHNOLOGY PRODUCERS NEEDS • Raw material control • Analytical • Monitoring production processes • Fianl products quality control systems NIR Seminar – Campden – October 14th 2009
  4. 4. % # & ' ! ' NIR Seminar – Campden – October 14th 2009
  5. 5. # DIFFERENT RAW DIFFERENT MATERIALS PRODUCTS Wheat Flours Quality 1 Industrial BARILLA bread Quality 2 BAKERY Quality 3 Cakes Quality 4 Quality 5 Quality up to Snacks 12 NIR Seminar – Campden – October 14th 2009
  6. 6. () * "% ' TARGET: Assess Quality of incoming Wheat Flour batches • Identify Parameters to check • Identify an Analytical System able to perform quickly such checking • Define sampling methods • Collect samples and verify 12 different Wheat Flour Qualities considered 8 – 100 – 106 – 108 – 110 – 114 – 120 – 141 – 144 – 158 – 164 – 175… • Samples collected starting from March 2007 • Samples collected from different suppliers all over Italy NIR Seminar – Campden – October 14th 2009
  7. 7. ' CHEMICAL PARAMETERS REFERENCE METHODS Moisture UNI reference Protein Content Methods Falling Number Brabender RHEOLOGICAL PARAMETERS Farinograph Farinographic Baking Absorption Alveographic W P/L Chopin Alveograph NIR Seminar – Campden – October 14th 2009
  8. 8. " Spectra acquisition by diffuse reflectance using an FT-NIR Spectrometer Wavelenght range 4000 – 10000 cm-1 • 3 Sub-samples for each incoming Flour Batch (3 spectra measurment for each batch) • Each spectra as average of 64 scans having a rotating petri dish system • Samples Temperature: 20 5 C Data management • NIRCal Chemometeric software to Chemometric software develop quantitative calibration models with Evaluation Set Tecnique NIRCal 5.0 NIR Seminar – Campden – October 14th 2009
  9. 9. % # ' Sample’s spectra Measurements reports Calibrations Support PEDRIGNANO BARILLA HEADQUARTER MASTER UNIT Spectra Libraries Main Database Developement of Quantitative Calibrations Client Client Picenengo (Local database) Melfi (Local database) Client Client Novara Ascoli (Local database) Client Client (Local database) Castiglione (Local database) Rubbiano (Local database) NIR Seminar – Campden – October 14th 2009
  10. 10. " Checking incoming raw materials Why NIR? chemical composition Checking chemical composition of finished formulations NIR Seminar – Campden – October 14th 2009
  11. 11. " Raw materials are paid Example: Soy Flour according according to chemical to protein content composition Check every batch supplied means to pay the correct price To produce according to the Finished products declared composition by having an instant monitoring To obtain the same chemical Plus composition they could be used different and new raw materials, maybe cheaper NIR Seminar – Campden – October 14th 2009
  12. 12. % + , ) ! NIR Seminar – Campden – October 14th 2009
  13. 13. - WHEAT GLUTEN AND MELAMINE NIR Seminar – Campden – October 14th 2009
  14. 14. . . * Original Spectra NIRC al : C uscus _Se mo la to_ Farina_U m id ita 2311 06 26/04 /20 07 10.34.43 A dm inistra tor All Spectra Calibration Spectra Validation Spectra 0.8 R e fle c ta n c e 0.6 0.4 0.2 10000 9000 8000 7000 6000 5000 4000 Wavelengths R Samples Method Range SEP SEC C-set/V-set Umidità 210 PLS 10.00 - 16.09 0.99 / 0.99 0.12 0.12 Proteine 144 PLS 11.02 - 14.03 0.97 / 0.97 0.16 0.15 Ceneri 210 PLS 0.69 - 1.35 0.94 / 0.93 0.20 0.22 NIR Seminar – Campden – October 14th 2009
  15. 15. / ' Original Property / Predicted Property N I R C al : Lat t i er o casear i o onl i ne- aggi or nat o2. ni r Lact ose - i m pl em ent ed2 23/ 05/ 2005 9. 25. 39 cam g All Spectra Pr e d ic t e d Pr o p e r t y la c t o s e 5.5 P ro p e rty Ou t l i e r S p e c t ra V a l i d a ti o n S p e c tra f (x )= 0 .6 5 2 3 x + 1 . 7 1 3 7 r= 0 .7 7 7 8 3 8 C a l i b ra t i o n S p e c t ra f (x )= 0 . 6 5 5 3 x + 1 .6 9 9 3 r= 0 . 8 0 9 5 2 3 5.0 Original Property / Predicted Property N I R C al : Lat t i er o casear i o onl i ne- aggi or nat o2. ni r Fat A - i m pl em ent ed2 23/ 05/ 2005 8. 49. 25 cam g All Spectra P r e d ic t e d P r o p e r t y f a t A V a l i d a t i o n S p e c t ra f(x )= 0 . 9 8 8 9 x + 0 . 0 2 4 3 r= 0 .9 9 3 5 8 8 Ca l i b ra t i o n S p e c t ra f (x )= 0 . 9 8 8 2 x + 0 .0 3 6 3 r= 0 . 9 9 4 1 0 7 4.5 6 4.0 4 3.5 3.0 3.5 4.0 4.5 5.0 5.5 True Property lactose 2 0 0 2 4 6 True Property fat A C-Set V-Set Property C-Set SEE V-Set SEE Regression Regression (%) (SEC) (SEP) Coefficient Coefficient Fat 0.17 0.17 0.99 0.99 Protein 0.17 0.16 0.85 0.86 Dry matter 0.31 0.31 0.98 0.98 Lactose 0.16 0.17 0.81 0.78 NIR Seminar – Campden – October 14th 2009
  16. 16. / $ Olive grinding Olive paste Solvent Gramolatura Husk Extraction Estrazione Water Extraction Husk Separation Water Oil Filtration Extra-virgin Olive oil NIR Seminar – Campden – October 14th 2009
  17. 17. / $ Spectrometer FT-NIR NIRFlex N500 • 2 acquisition each sample • Every acquisition is tha average of 64 scan with rotating petri dish (total time< 1min) • Temperature 20° C Samples from different N IR C a l : c o p y o f M o is tu r e , 0 .8 0 5 0 , 1 - 6 ./6 , 4 6 0 0 - 1 0 0 0 0 . 2 4 /0 4 /2 0 0 8 1 4 . 3 0 .4 9 A d m in is tr a to r geograpical regions 2007 P r e d ic t e d P r o p e r t y M o is tu r e Predicted Property vs. Original Property A Spectra ll C lib tio S ectra f(x)= 73 0.73 r= 9 r2= 73 S ev(x-y)= 54 B S a ra n p 0.98 x+ 00 0.9 37 0.98 d 0.82 IA (x-y)= 0 ran e(x)= .8.. 7 n 34 g 34 1.73 = 2 V lid S ctraf(x)= .0 2x-0 18 r= 20 r2 0 84 S v(x-y)= .8 8 B S a ation pe 1 03 .1 1 0.99 = .9 0 de 0 17 IA (x-y)= .0 7 ra ge 4 .8.. 70 7 n 1 -0 64 n (x)= 1 .9 = 33 70 60 50 Standard PARAMETR Range samples errorSEP [%] 40 Moisture 0.8 34.8 – 71.7 240 30 40 50 60 70 Fat 0.9 15.8 – 31.1 200 O in P perty M isture rig al ro o NIR Seminar – Campden – October 14th 2009
  18. 18. / $ , ' Diffuse reflectance Samples from different geograpical PredictedProperty vs. Original Property regions P re d ic te d P ro p e rty F a t A Spectra ll C lib tio S e f(x)= 6 1 0 7 1 r= .98 9 r2 0 6 S e a ra n p ctra 0.9 2 x+ .1 4 0 0 = .9 21 d v(x-y)= .3 3 B S 0 8 6 IA (x-y)= 0 ra g (x)= 1.. 1 .1 n 2 6 ne 2 =9 N IR C a l : S V _ G r a s si_ 2 3 0 4 0 8 2 3 / 0 4 /2 0 0 8 1 2 . 1 7 . 3 7 A d m in ist r a t o r 12 V lid tio S e f(x)= .9 6 x+ .3 3 r= .9 2 r2 0 4 6 S e a a n p ctra 0 3 6 0 8 0 0 7 9 = .9 6 d v(x-y)= .3 7 B S -0 3 7 ra g (x)= 2.. 8.7 n 8 0 8 8 IA (x-y)= .09 9 n e =4 10 8 6 4 Standard error PARAMETR Range Samples 2 SEP [%] 0 Moisture 1.8 24.7 – 70.7 170 0 2 4 6 8 10 12 Original PropertyFat Fat 0.3 1.0 – 12.1 180 NIR Seminar – Campden – October 14th 2009
  19. 19. + 0 Fat content= 23.4% Olive Grinding Olive paste Gramolatura Fat content= 6.6% Husk Extraction Estrazione Water Constant monitoring of plant yield NIR Seminar – Campden – October 14th 2009
  20. 20. + ! & $ ' General calibrations developed thanks to refernce lab Customization according to the needs of a specific industry Predicted Property vs. Original Property All Spectra User Spectra Calibration Spectra f(x)=0.9709x+0.1267 r=0.9854 r2=0.9709 Sdev(x-y)=0.2279 BIAS(x-y)= 0 range(x)= 2 .. 8 n=181 Validation Spectra f(x)=0.9876x+0.0605 r=0.9915 r2=0.9831 Sdev(x-y)=0.1812 BIAS(x-y)=-0.006503 range(x)=2.49 .. 7.4 n=58 8 Predicted Property Grassi NIRCal : Sanse vergini grassi <8% 121207 13/05/2008 13.56.38 Administrator 6 Standard error PARAMETRO Range samples SEP [%] 4 Moisture 1.3 41.8 – 67.7 120 2 Fat 0.18 2.00 – 8.00 120 2 4 6 8 Original Property Grassi Higher measurement accuracy NIR Seminar – Campden – October 14th 2009
  21. 21. / $ 1 ' * Transflettanza Spettrometro FT-NIR NIRLab N200 Original Property / Predicted Property N IR C al : adriaoli - bas s e c onc entraz ion impurez z e.nir impurez z e bass e conc netraz ioni 0307 13/05/2008 14.25.56 c amg All Spectra P re d ic t e d P ro p e rt y Im p u re z ze Property Outlier Spectra Validation Spectra f(x)=0.9227x+0.0132 r=0.975797 Calibration Spectra f(x)=0.9487x+0.0229 r=0.974025 User Spectra 1.00 0.75 0.50 Standard error 0.25 PARAMETER Range Samples SEP [%] 0.00 Solvents 0.06 0.02 – 1.03 105 0.00 0.25 0.50 0.75 1.00 1.25 True Property Impurezze Impurities 0.07 0.02 – 0.99 105 NIR Seminar – Campden – October 14th 2009
  22. 22. Istituto Zooprofilattico 2 2/ 2 ( ) $ Sperimentale della Lombardia $ e dell'Emilia Romagna Spettrometro FT-NIR NIRFlex N-500 with liquids cell Parameters for olive oil quality evaluation Acidity Polifenol Tocoferol Perox. K232 K K270 NIR Seminar – Campden – October 14th 2009
  23. 23. Istituto Zooprofilattico 2 2/ 2 ( ) $ Sperimentale della Lombardia $ e dell'Emilia Romagna Predicted Property vs. Original Property All Spectra Calibration Spec tra f(x )=0.9959x +0.0021 r=0.9979 r2=0.9959 Sdev(x -y)=0.0400 BIAS(x-y)= 0 range(x)=0.01 .. 2.97 n=150 Validation Spectra f(x)=0.9887x+0.0130 r=0.9918 r2=0.9837 Sdev (x-y )=0.0452 BIAS(x -y )=-0.008318 range(x)=0.04 .. 1.795 n=75 1.4 Predicted Property Olio Acidità 1.2 1.0 0.8 NIRCal : Olio oliva acidità 14/05/2008 9.25.43 Administrator 0.6 0.4 0.2 0.0 -0.2 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Original Property Olio Acidità Standard Coeff. N. Spt. PARAMETEr error Range Reg. R (cp. X 3) SEP Acidity 0.04 0.99 0.01 – 2.97 228 Scores vs. Scores All Spectra Peroxide 0.2 1.8 0.97 3.2 – 43.4 237 num. 0.1 K232 0.12 0.98 1.99 – 5.27 237 NIRCal : Olio oliva acidità 14/05/2008 9.26.57 A dministrator PC 2 -0.0 k270 0.06 0.97 0.08 – 1.49 237 -0.1 0.0001 – K 0.0002 0.95 0.0555 237 -0.2 -0.2 -0.1 -0.0 0.1 0.2 0.3 Polifenol 30 0.70 139 - 300 93 PC 1 Tocoferol 21 0.95 7 - 282 129 NIR Seminar – Campden – October 14th 2009
  24. 24. / $ Dipartimento di Chimica e tecnologie ) $ Farmaceutiche e Alimentari - Univ. Genova Application of differenet multi-variate analysis techiniques to identify the geograpichal origin of olive oil 200 samples of olive oil “Near infrared spectroscopy and class modelling techniques for the geographical authentication of Ligurian extra virgin olive oil” Journal of Near Infrared Spectroscopy, November 2007 NIR Seminar – Campden – October 14th 2009
  25. 25. ! ! 3, & 4 5 ! Dry Parameter Fat Protein matter Samples 97 88 96 Method PLS PLS PLS Pretreatments ds2 dg1, nle ncl, log R C-Set 0.99 0.86 0.96 R V-SET 0.98 0.79 0.95 SEC 0.16 0.21 0.33 Pretreated spectra for SEP 0.16 0.21 0.29 “fat content” Range 1.40 – 5.30 6.80 – 8.40 15.40 – 19.90 NIR Seminar – Campden – October 14th 2009
  26. 26. ! ! ! Predicted Property vs. Original Property A Spectra ll Calibration Spectra f(x)=0.9887x+0.0421 r=0.994357 range(x)=1.32-6.32 Sdev(x-y)=0.1134 BIAS(x-y)=1.35324e-014 n=108 Validation Spectra f(x)=0.9756x+0.0837 r=0.994598 range(x)=1.49-6.03 Sdev(x-y)=0.1096 BIAS(x-y)=0.00778034 n=52 opy of O ogeniz ato gras i 040906 07/09/2006 17.23.04 buchi 6 P d te P p rty F t re ic d ro e a 4 s 2 m z 0 N C : c 0 2 4 6 IR al Original Property Fat Fat Parameter [%] Samples 80 Regres. C-set 0.99 Regres. V-set 0.99 Measurement SEE C-set 0.11 time = 15 sec. SEP V-set 0.11 Range 1.32 - 6.32 NIR Seminar – Campden – October 14th 2009
  27. 27. 6 ' + 7 PARAMETHERS Protein Total fat Saturated Fatty Acid Unsaturated Fatty Acid Lactose NIR Seminar – Campden – October 14th 2009
  28. 28. ) 8 * 2 2/ 2 Predicted Property vs. Original Property SEC/SEP Pr e dic te d P roper ty pr ote olis i TC A 1 2 % User Spectra Parameter Samples Samples Range [%] R User Spectra [%] NIR Cal : R agusano proteolis i TCA 12% 250606 13/ 05/ 2007 23.58. 49 Adm inis trat or Calibration Spectra f(x)=0.9300x+0.5710 r=0.964346 range(x)=0.43-22.04 Sdev(x-y)=1.1547 BIAS(x-y)=8.82512e-015 n=1207 Validation Spectra f(x)=0.9472x+0.4541 r=0.963864 range(x)=0.63-20.02 Sdev(x-y)=1.1610 BIAS(x-y)=-0.0124169 n=597 Property Outlier Spectra 20 15 Soluble C-set 408 0.11 – 6.84 0.96 0.31 10 nitrogen 5 TCA 12% V-Set 197 0.12 – 15.67 0.96 0.30 0 0 5 10 15 20 25 Proteolysis C-set 408 0.43 – 22.04 0.96 1.15 Original Property proteolisi TCA 12% index TCA 12% V-Set 197 0.63 – 20.02 0.96 1.16 NIR Seminar – Campden – October 14th 2009
  29. 29. - Milk Vanilla DIFFERENT MIXTURES OF Creme Yogurt ICE-CREAMS Chcocolate Only one calibration for each parameter Fat Protein Dry matter SEP = 0.4% SEP = 0.10% SEP = 0.41% NIR Seminar – Campden – October 14th 2009
  30. 30. / ! * Spectrometer FT-NIR Buchi Nirflex N-419 Original Property / Predicted Property Predicted Property Assorbimento a 610 All Spectra Validation Spectra f(x)=0.9389x+0.0292 r=0.968015 Calibration Spectra f(x)=0.9497x+0.0220 r=0.974547 0.50 N IRCal : BS111.nir ASB 610 - new 0.92* 11/03/2005 14.48.06 fer g 0.45 0.40 0.35 0.30 0.25 0.30 0.35 0.40 0.45 0.50 True Property Assorbimento a 610 Original Spectra All Spectra 0.8 0.6 NIRCal : B S111.nir A SB 610 - ne w 0.92* 11 /03/2005 14.51.44 ferg Transmittance 0.4 Parameter Samples Range R C-Set/ V-Set SEC/ SEP 0.2 550mn 125 0.325-1.133 0.98/0.97 0.04/0.04 0.0 5000 6000 7000 8000 9000 1/cm 610nm 65 0.275-0.527 0.97/0.96 0.009/0.010 Reading at a 610nm NIR Seminar – Campden – October 14th 2009
  31. 31. Parameter NaHCO3 CaHPO4 CaCO3 MgO State University of Parma Camp. 107 107 64 64 R C-Set 0.99 0.99 0.99 0.99 + R V-Set 0.99 0.98 0.99 0.99 San Marco Plant SEC 1.1 1.7 2.0 1.8 + Büchi SEP 1.0 1.8 2.0 1.8 NaHCo3 CaHPO4 NIR Seminar – Campden – October 14th 2009
  32. 32. CONSTANT MONITORING OF PRODUCTION PARAMETERS PROCESS Moisture Analisi dei campioni tal Esterification quali in uscita dalla ratio produzione OPTIMIZATION OF Galacturonic PRODUCTION Acid content PROCESS Una sola scansione tutti i parametri contemporanemante PRODUCT WITH HIGHER QUALITY NIR Seminar – Campden – October 14th 2009
  33. 33. " PARAMETER SEC SEP C-set r V-set r C-slope V-slope Moisture 1.09 1.08 0.88 0.78 0.77 0.78 NaCl 0.29 0.50 0.92 0.62 0.85 0.71 Protein 0.99 0.97 0.82 0.80 0.68 0.66 N(TCA) 0.70 0.70 0.83 0.78 0.68 0.67 Proteolysis 1.88 1.82 0.79 0.72 0.63 0.62 index NIR Seminar – Campden – October 14th 2009
  34. 34. */ 9 : Regressione con set di validazione. NIR Seminar – Campden – October 14th 2009
  35. 35. ! - $ << ; % P r e d ic t e d P r o p e r t y v s . O r ig in a l P r o p e r t y Al l S p e c tr a C a l i b ra t i o n S p e c t ra f (x )= 0 . 9 5 5 6 x + 0 . 0 7 6 5 r= 0 . 9 7 7 5 3 5 ra n g e (x )= 0 .6 4 2 -3 . 4 9 9 S d e v(x -y )= 0 . 1 1 7 7 B IA S (x -y )= -1 . 6 3 5 7 3 e -0 1 5 n = 6 0 V a l i d a t i o n S p e c t ra f (x )= 0 . 9 5 2 7 x + 0 . 0 9 3 1 r= 0 . 9 5 8 9 9 1 ra n g e (x)= 0 . 7 7 6 -2 . 3 4 5 S d e v(x -y )= 0 . 1 2 1 1 B I A S (x -y)= -0 .0 1 3 4 0 9 5 n = 2 8 Range SEC/SEP 3 Parameter Set Spectra [%] R [%] IRCal : Vanillina quantitativ 140507 14/05/2007 22.46.48Administrator Predicted Property Vanillina 2 C-set 60 0.64 – 3.50 0.97 0.12 1 Vanillin a 1 2 3 V-Set 28 0.77 – 2.34 0.96 0.12 O r ig in a l P r o p e r ty V a n illin a N NIR Seminar – Campden – October 14th 2009
  36. 36. , Transflectance analysis of samples of honey as it is NIR Seminar – Campden – October 14th 2009
  37. 37. / = ' Batch Product No. of samples Target (% alcohol) 1 Whiskey 1 12 40 2 Whiskey 2 14 40 3 Whiskey 2 15 40 4 Whiskey 2 4 40 5 Whiskey 1 15 43 Full Calibration Range (40% and 43% alcohol) Predicted Property Density Online Validation Spectra f(x)=1.0016x-0.0694 r=0.999919 Calibration Spectra f(x)=0.9998x+0.0080 r=0.999903 43 42 41 40 40 41 42 43 True Property Density Online NIR Seminar – Campden – October 14th 2009
  38. 38. ! 2! 2 NIR Seminar – Campden – October 14th 2009
  39. 39. www.nirpublications.com www.spectroscopynow.com www.nir2007.com www.buchi.com www.buchi.it campolongo.g@buchi.com # ' NIR Seminar – Campden – October 14th 2009

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