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Determination of Good Milling Quality of White Maize For

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Determination of Good Milling Quality of White Maize For THE SOUTHERN AFRICAN GRAIN LABORATORY NPC Quality is our passion DETERMINATION OF GOOD MILLING QUALITY OF WHITE MAIZE FOR HUMAN CONSUMPTION USING NIT March 2016 Wiana Louw Technical experts: Dr Corinda Erasmus Foss Grain Network Meeting Network Grain Foss Dr Paul Williams nd 22 INTRODUCTION MAIZE PRODUCTION IN SOUTH AFRICA MAIZE PROCESSING AND CONSUMPTION IN SOUTH AFRICA WHY DEVELOP A MILLING INDEX CALIBRATION PROCESS FOLLOWED TO DEVELOP THE CALIBRATION MODEL OUTLINE OF THE OF THE PRESETATION OUTLINE CONCLUSIONS AND ROLL-OUT PROCESS TOTAL RSA AREA UTILIZED FOR MAIZE PRODUCTION (11 SEASONS) 4.000.000 3.000.000 White Yellow 2.000.000 Ha Total 1.000.000 0 2013/14 2003/04 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 Season MAIZE PRODUCTION IN SOUTH AFRICA IN PRODUCTION MAIZE MAIZE PRODUCTION IN RSA (11 SEASONS) 15.000.000 14.000.000 13.000.000 12.000.000 11.000.000 10.000.000 White 9.000.000 Yellow 8.000.000 Total Tons Tons 7.000.000 6.000.000 5.000.000 4.000.000 3.000.000 2.000.000 2011/12 2003/04 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2012/13 2013/14 MAIZE PRODUCTION IN SOUTH AFRICA IN PRODUCTION MAIZE Season MAIZE YIELD IN RSA (11 SEASONS) 6,00 5,00 White Yellow 4,00 t/ha Total 3,00 2,00 2003/04 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 Season MAIZE PRODUCTION IN SOUTH AFRICA IN PRODUCTION MAIZE MAIZE IMPORTS AND EXPORTS (10 MARKETING SEASONS) 3000 2500 2000 1500 1000 Thousand Ton Thousand 500 0 04/05 05/06 06/07 07/08 08/09 09/10 10/11 11/12 12/13 13/14 MAIZE PRODUCTION IN SOUTH AFRICA IN PRODUCTION MAIZE Imports Exports MAIZE CONSUMPTION (10 MARKETING SEASONS) 4800 4600 4400 4200 4000 Thousand Ton Thousand 3800 3600 3400 3200 04/05 05/06 06/07 07/08 08/09 09/10 10/11 11/12 12/13 13/14 Human Animal MAIZE PRODUCTION IN SOUTH AFRICA IN PRODUCTION MAIZE MAIN MAIZE PRODUCTION REGIONS TOTAL WHITE MAIZE PROCESSED FOR LOCAL MARKET: 5 862 438 TONS Bio-fuel Gristing 0% 32.141 1% SEASON Animal/industrial 1.469.002 25% 2014/2015 PROCESSED Human 4.361.295 MAIZE 74% HITE W WHITE MAIZE PRODUCTS -Jul '15 - Dec '15- Total White Maize Products: 2 178 285 t Maize Meal 1.399.297 64,2% PRODUCTS Other Products 57.327 2,6% MAIZE Maize Chop HITE 637.754 29,3% W Rice, Grits, Samp 83.907 3,9% WHITE MAIZE MEAL -Jul '15 - Dec '15- Total White Maize Meal: 1 399 297 t Other Meal 23.080 1,6% PRODUCTS Special Super 233.634 MAIZE 1.142.583 16,7% 81,7% HITE W REQUEST FROM THE MAIZE MILLING INDUSTRY: ? A need for a non-destructive method capable of WHY measuring the milling quality of large numbers of samples – in a short space of time The Milling Index (MI) provides an indication of the expected milling performance of maize for the dry milling industry Milling Index (MI) of white maize cultivars is important for ALIBRATION the maize industry as it affects the yield of high quality C products and could therefore result in specific cultivars being unsuitable for future production NDEX It is an index based on the relative yield of milled products I extracted from whole maize using dry milling (no steeping or soaking) ILLING M VS White maize cultivar MILL trials Milled on both MAIZE MILL commercial and laboratory scale mills SCALE OMMERCIAL Acceptable C – correlation to continue with study LABORATORY using laboratory scale mill as reference method for ORRELATION C calibration WHAT IS MILLING PERFORMANCE (OR MILLING INDEX – MI)? It is a test of suitability It attempts to simulate true yield and quality of products in a mill (in the case of a dry mill) using a suitable laboratory or pilot plant mill Products may include flaking grits, super maize meal, polenta, flour, bran etc. PERFORMANCE Yield and quality can be defined by various means including: performance repeatability cleanness of fractions MILLING maximum extraction of a target product (e.g polenta – yellow or Special maize meal - white) MAIZE market feedback re product eating quality 35 white maize cultivars planted in three localities with three replications for milling quality and other quality parameters CALIBRATION Trials repeated over 4 production FOR seasons USED Three season’s samples used for developing the calibration and the AMPLES S fourth season’s samples used as a validation set Moisture - Datatec Moisture - NIR (5 Roff milling fractions done in duplicate on NIR) Moisture - Oven method 130°C every 15th sample to check NIR SAMPLES Test Weight – Hectolitre mass (kg/hl) Kernel size (% above 10mm sieve, above 8 mm sieve, below 8 mm sieve) THE Breakage susceptibility (%) ON Stress Cracks (%) 100 Kernel Mass (g) 1000 Kernel Mass Near Infrared Transmittance (NIT) Milling Index PERFORMED Moisture Protein Starch NALYSES Colour - Hunter-Lab Colorflex A Roff Mill - Milling Index LABORATORY SCALE MAIZE MILL B1 Meal B2 Meal B3 Meal Mill 1 Mill Mill 3 Mill B1 Grits 2 Mill B2 Grits B3 Grits B3 Chop Roff Roff B1 Chop B2 Chop Roff Total extraction: (B1 Meal + B2 Meal + B3 Meal + B3 Grits) as a % of Whole maize Each meal has different levels of starch, protein and fibre as well as different particle size, colour and cooking/eating quality 70% Extraction 70% Maize meal extraction @ R 19 507 262 600 3,9 mil tons of white R7000/ton NDUSTRY I maize processed for human consumption 30% Chop @ R 4 180 127 700 R3500/ton ILLING M 75% Extraction AIZE M 75% Maize meal extraction @ R 20 900 638 500 R7000/ton THE 3,9 mil tons of white maize processed for human consumption TO 25% Chop @ R 3 483 439 750 R3500/ton ENEFIT B R 696 687 950 = EUR 41 371 018 Different NIT calibrations were tested using whole maize: Milling Index calculated on a dry base Milling Index adjusted to 14% moisture base Grit yield – 14% moisture base Total extraction – 14% moisture base Grit yield and total extraction are expressed as a mass % while Milling Index calculations are MILLING INDEX CALIBRATIONS INDEX MILLING dimensionless index numbers (mass fraction ratios) Comparative tests to evaluate efficiency of the NIT calibration: Same samples on the same instrument before and after a 12 month storage period Same samples on two different FOSS Infratec instruments Correlations of these samples with the laboratory mill reference method THE DATA ADDITIONAL TESTS TO EVALUATE ERRORS IN ERRORS EVALUATE TO TESTS ADDITIONAL Effect of kernel size variation on Milling Index measurements To determine whether size classification of maize before milling can improve milling yield repeatability and process control Samples were classified and sieved according to different sieve sizes: KERNEL SIZE VARIATION SIZE KERNEL 10 mm – 9 mm 8 mm THE DATA THE DATA ADDITIONAL TESTS TO EVALUATE ERRORS IN ERRORS EVALUATE TO TESTS ADDITIONAL • Digital Image of maize kernels in Grayscale Inverted image - optimum contrast IMAGE ANALYSIS IMAGE Binary detection and object measurements Effect of Maize Kernel Size (mm) on NIT MI 130.0 120.0 110.0 MI Sample 1 MEASURMENTS MI Sample 2 MI Sample 3 100.0 MI Sample 4 NIT MI NIT MI Sample 5 MI Sample 6 F MAIZE SIZE CLASSIFICATION ON NIT CLASSIFICATION F MAIZE SIZE 90.0 MI Sample 7 MI Sample 8 O 80.0 70.0 >8mm >9mm >10mm Mix EFFECT EFFECT Maize size classification (round hole sieves) MOISTURE OF THE WHOLE MAIZE KERNELS DURING SCANNING ON THE NIT Three sets of samples conditioned to a different moisture level namely 11%, 15% and 18% OBJECTIVE: spectral scans of samples with same MOISTURE hardness but different moisture levels to test the – interaction between MI readings and moisture levels THE DATA THE DATA ADDITIONAL TESTS TO EVALUATE ERRORS IN ERRORS EVALUATE TO TESTS ADDITIONAL MI CLASSES USED IN INDUSTRY AS MEASURE OF MILLING QUALITY OF MAIZE: MILLING INDEX MEASUREMENT MILLING QUALITY <60 Bad milling quality 60 – 80 Acceptable milling quality 80 – 100 Good milling quality 100 – 120 Excellent milling quality INDUSTRY STANDARD INDUSTRY >120 Exceptional milling quality MILLING USEDAS AN INDEX (MI) CLASSES MOISTURE – GROUPS: SELECTED SAMPLES REPRESENTING A RANGE OF MILLING THE DATA THE DATA INDEXES Note: Moisture content may influence MI to the effect that a sample may fall into a different class - MI is variable depending on the air relative humidity ADDITIONAL TESTS TO EVALUATE ERRORS IN ERRORS EVALUATE TO TESTS ADDITIONAL 2D SCORE PLOT FOR THE FIRST TWO PRINCIPAL COMPONENTS OF THE RAW NIT SPECTRA SHOWING THE THREE DISTINCT MOISTURE GROUPS MOISTURE – THE DATA THE DATA ADDITIONAL TESTS TO EVALUATE ERRORS IN ERRORS EVALUATE TO TESTS ADDITIONAL CONCLUSION: Sample moisture strongly interacts with actual Milling Index readings on the NIT Higher moisture values will give lower Milling Index values on the same sample MOISTURE Sensitivity is high, moisture variations of as low as – 1% showed significant variations in NIT readings Discussions - practical solution required THE DATA THE DATA ADDITIONAL TESTS TO EVALUATE ERRORS IN ERRORS EVALUATE TO TESTS ADDITIONAL 3D PCA PLOT OF THE PRINCIPAL COMPONENTS OF THE SPECTRA OF THE SAMPLES STORED FOR 1 YEAR. SAMPLES DO NOT SHOW TWO SEPARATE GROUPS INDICATING RELATIVELY FEW CHANGES DURING STORAGE. A FEW OUTLIER SAMPLES CAN BE SEEN STORAGE – THE DATA THE DATA Blue – Year 1 ADDITIONAL TESTS TO EVALUATE ERRORS IN ERRORS EVALUATE TO TESTS ADDITIONAL Red – Year 2 RESULTS: Inverse linear relationship observed for moisture - can possibly be incorporated in the instrument calibration models This will improve general measurement and calibration precision for MI on the NIT Kernel size variations did not have a noticeable interaction with NIT Milling Index readings Calibrations can be developed for different applications such as hominy grits for breakfast MILLING INDEX PROJECT INDEX MILLING cereals or polenta PRIMARY OBJECTIVE: Roll-out of newly developed calibration model for use in the industry ADDITIONAL OBJECTIVES: ROLL OUT ROLL Collect outlier samples from industry – analyse to improve robustness of calibration Validate model on mixed samples Compare with different laboratory scale MILLING INDEX INDEX MILLING mill (Industry in-kind contribution) With gratitude to: The Maize Trust for financial support FOSS (Rhine Ruhr, South Africa) for assistance SAGL staff for milling and other quality testing Thank you for your attention! ACKNOWLEDGEMENTS.
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