Automated semi industrial system for the NIR characterization of potato composition and quality

Transcription

1 Automated semi industrial system for the NIR characterization of potato Dr. K. Brunt, TNO Quality of Life, Groningen, The Netherlands

2 Content presentation Introduction Objectives and demands Off-line NIR feasibility study Design and construction automated NIR analysis system First tentative results Starch Coagulating protein Coming developments and investigations 2

3 Objective Development of an analytical system for the determination of the (compositional) quality of potatoes for purpose of e.g.: agricultural research industrial input quality control (e.g. starch, french fries, chips, flakes) Demands 3 The method has to fast, reliable, and robust The standard error has to be low Automation should be possible Sample to sample carry over less than 2% Relatively small samples of 3 15 kg potatoes Possible solutions Near Infrared Reflection Spectroscopy (NIRS)

4 Experimental set-up off-line NIR feasibility study Sources starch potato samples 2 different Dutch experimental farms Sandy soil Peaty soil 2 Dutch AVEBE potato starch factories Ter Apelkanaal Gasselternijveen NIR instrumentation Technicon Infralyzer 500C monochromator instrument EDAPT-1 measuring probe Spectral range nm with 2 nm intervals Data analysis Smoothing spectra by moving average Multiple scatter correction Developing NIR models by PLS-1 4

5 OFF-LINE SYSTEM NIR CHARACTERIZATION POTATOES Potato samples Washing UWW Manual rasping the potato samples 5 Data analysis Off-line NIR equipment NIR measurement homogenization

6 Starch potato NIR spectra, average composition and ranges Three year average composition industrial Dutch starch potatoes Campaign 2000/2001, 2001/2002, 2002/2003 UWW (g/5050g) Dry matter (total) Dissolved dry matter Starch Coagulating protein Average Stand. dev Maximum Minimum NIR spectra industrial Dutch starch potatoes 6

7 EFFECT RANGE OF THE CONSTITUENT ON MODEL CALCULATIONS Concentration range constituent has to be standard deviation in the reference analytical method 2. Concentration level constituent 3. Repeatability and reproducibility of reference method 7

8 Analytical performance starch determination EWERS method (ISO accreditation) Control chart starch determination in potato Septembre 2005 March 2006 Meting 21,50 21,40 21,30 21,20 21,10 21,00 20,90 20,80 20,70 20,60 20, Run -3*S -2*S Gem 2*S 3*S Meting Analytical characteristics Ewers method: Repeatability within one day : 0.09 % m/m Repeatability day-to-day : 0.13 % m/m 8

9 Analytical performance protein determination Kjeldahl method (ISO accreditation) Control chart starch determination in potato Septembre 2005 March ,70 Meting 2,65 2,60 2,55 2, Run -3*S -2*S Gem 2*S 3*S Meting Analytical characteristics Ewers method: Repeatability within one day : 0.02 % m/m Repeatability day-to-day : 0.03 % m/m 9

10 Three year average composition industrial Dutch starch potatoes NIR model starch content potatoes Calibration sample set Validation sample set Starch Average 18.7 Stand. dev. 1.4 Maximum 23.4 Minimum 14.2 Starch content Calibration Validation Summarizing statistics Range Correlation Slope Offset 0,0 1,51 10 RMSEC/RMSEP

11 NIR model coagulating protein content in potatoes Calibration sample set Validation sample set Three year average composition industrial Dutch starch potatoes Coagulating protein Average 1.14 Stand. dev Maximum 1.58 Minimum

12 NIR technology meets the demands Good results off-line NIR models for starch potatoes Off-line models developed for: Dry matter content Starch content Coagulating protein content GO-decision for the design and construction of an fully automated system 12

13 Set-up and Planning Design and construction of prototype of a fully automated system by Test phase of the prototype Development new applications 13

14 Design automated NIR analysis system a prototype for agricultural and industrial applications Fully automated sample pretreatment Sample introduction Washing Visual inspection Weighting Rasping (including CIP) NIR measurement and data analysis 3 15 kg samples Sample carry-over 2% Processing capacity 5 6 minutes per sample 14

15 Design automated NIR analysis system (prototype for industrial applications) UWW washing unit Visual inspection Rasping NIR Imaging or person Potato samples Sample composition: - dry matter - starch -protein - others automatic data analysis 15

16 Building phase 16

17 Design automated semi industrial system for NIR characterization of potato samples A = sampling crate manipulator B = bruto sample weighting C = washing machines D = inspection belt E = UWW module F = transportation screw to ultra rasp G = ultra rasp with pulp container provided with NIR sensor H = endless belt with containers for contra expertise pulped samples 17

18 Realized semi industrial automated system for NIR characterization of potato samples A = sampling crate manipulator B = bruto sample weighting C = washing machines D = inspection belt E = UWW module F = transportation screw to ultra rasp G = ultra rasp with pulp container provided with NIR sensor H = endless belt with containers for contra expertise pulped samples P = crates with potato sample 18

19 UWW module 19

20 Controle of UWW module Golf-balls can be used very well as artificial potatoes: 1. Specific gravity of golf balls is about the same as it is for potatoes 2. Size golf ball is just a little bit smaller than size of potatoes controle OWG standaard in de tijd vastgesteld OWG Series meetdatum 20

21 Potatoes in transportation screw to ultra rasp 21

22 22

23 Ultra rasp The heart of the installation: - ultra rasp - container with NIR probe and stirring device stirring device NIR probe 23

24 Carry over potato sample Expected potassium content in the potato pulp as a function of the applied standard addition KCl (75 g KCl) and the expected sample carry over (%). (potatoes with average potassium content without standard addition KCl ± % m/m) Sample code 04BORG BORG 091 *) 04BORG BORG BORG BORG 095 Weight (kg) 8,274 8,530 7,192 7,264 8,036 8,130 K-content (%m/m) Sample carry over (%) Calculated content potassium (% m/m) Sample weight (kg) and measured potassium content (% m/m in pulp) in potato samples without and with standard addition KCl *) after standard addition KCl % carry over 0,525 0,908 0,514 0,525 0,521 0,516 24

25 NIR spectra potato samples set of 126 potato samples: 9 varieties from 2 experimental farms grown with different amounts of fertilizer First derivative NIR spectra Second derivative NIR spectra 25

26 Tentative results NIR calibration dry matter in potatoes Dry matter Number samples 126 Minimum value 23.4 % Maximum value 30.1 % Mean value 27.0 % 26

27 Tentative results NIR calibration starch in potatoes Estimated starch content by NIR Estimated starch content by UWW starch content estimated by UWW 25,0 24,0 23,0 22,0 21,0 20,0 19,0 18,0 y = 1,0312x - 0,6479 R 2 = 0, ,0 17,0 18,0 19,0 20,0 21,0 22,0 23,0 24,0 starch content (polarimetric according Ewers method) 2,0 starch residue (Ewers - UWW) 1,5 1,0 0,5 0,0 17,0 18,0 19,0 20,0 21,0 22,0 23,0-0,5-1,0-1,5 starch content (polarimertic Ewers method) 27

28 Tentative results NIR calibration coagulating protein in potatoes Coagulating protein Number samples 126 Minimum value 0.69 % Maximum value 1.95 % Mean value 1.32 % 28

29 Tentative conclusions Sample carry over considerably less than 2 % Analytical capacity is high (10 12 samples/hours) Good NIR models can be obtained for at least: Dry matter Starch Coagulating protein Prediction starch content by NIR is better than by UWW System can be used for both: Industrial quality input controle Fast characterization of large amounts of field trial samples System can of course be used for investigation of other type of agricultural samples (e.g. apple, pears, carrot,... ) 29

30 Futural developments short term Development validated NIR calibrations for major constituents in potatoes as, e.g. Dry matter Starch Total raw protein Coagulating protein Sugars Mid term Development for NIR calibrations for potato quality, e.g. Baking quality Variety identification Cooking behaviour Starch quality Characterization composition/quality other crops 30