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Rapid methods of Feed Analysis IBERS A. Foskolos 2, E. Albanell 2, M.R. Weisbjerg 3 Alejandro Belanche 1, A. Foskolos 2, E. Albanell 2, M.R. Weisbjerg.

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Presentation on theme: "Rapid methods of Feed Analysis IBERS A. Foskolos 2, E. Albanell 2, M.R. Weisbjerg 3 Alejandro Belanche 1, A. Foskolos 2, E. Albanell 2, M.R. Weisbjerg."— Presentation transcript:

1 Rapid methods of Feed Analysis IBERS A. Foskolos 2, E. Albanell 2, M.R. Weisbjerg 3 Alejandro Belanche 1, A. Foskolos 2, E. Albanell 2, M.R. Weisbjerg 3, C.J. Newbold 1 and J.M. Moorby 1 1 IBERS, Aberystwyth University (UK) 2 Universidad Autonoma Bercelona (Spain) 3 Aarhus University (Denmark) Vilnius (Lithuania), 7 th June 2013

2 Need to increase animal production Monogastrics: poultry, eggs and pigs -High efficiency of feed utilization -Food competition: -Humans -Bio-fuel

3 Efficiency of dietary N utilization 100 g N 25% Soil eutrophisacion (Nitrate) Underwater pollution (Nitrites) Greenhouse gas (N 2 O, NH 3 CH 4 ) 50% Urine 25% REduction Nitrogen EXcretion “GOOD FARMING PRACTICE” Methane emissions

4 Ruminants Use fiber and non-protein N Pastures no suitable for crops Crop residues Industrial by-products

5 Animal requirements -In: -Energy -Protein -Depend on: -Physiological stage -Animal performance -Others (BW, º C, activity) -Estimation -”Trial and error” (production) -Tables (INRA, AFRC, NRC) Feed nutritional value Optimize the ruminant nutrition

6 Static approach for feed evaluation: Chemical analysis

7 Feeding systems

8 Dynamic approach for feed evaluation: In vivo measurements “fraction a” Immediately degraded “fraction b” Degradable but not soluble 0 2 4 6 8 12 24 Incubation time 48h Disappeared (%) 0% 100% “value c” Degradation rate of b Undegraded ParameterAbbreviationMethod Water soluble CPCP WS Water Total tract CP digestibilityCP TTD Mobile bag (Duodenum-faeces) Rumen degradation pattern DM, CP and NDFa, b, c and EDIn situ or in sacco method Effective Degradability (Ørskov and McDonald 1979) ED = a + b [c / (c + k)] k= rumen outflow rate DM, CP = 5%/h (2%/h for NDF) RRT of DM, CP = 20h (50h for NDF)

9 Dynamic approach for feed evaluation: In vivo measurements Corn Corn Barley Barley Straw Straw Martín-Orúe et al., 2000 An. Feed Sci. Tech Alternatives -Rapid method for feed analysis -Accurate prediction -Simple and cost-effective

10 Infrared Spectroscopy neurologists volcanologists nutritionists

11 NIR vs. FTIR Infrared Spectroscopy

12 WAVENUMBER VIBRATION BONDS STRUCTURE 1460 nmO-HStarch 1724 nmC-OLipids 1930 nmO-H / HOHStarch, Water 2106 nmO-H / C-O / N-HStarch, Fiber, Protein 2276 nmO-H / C-OStarch, Fiber 2336 nmC-H / CH 2 Fiber, Cellulose Infrared Spectroscopy and chemical composition IR spectroscopy does not analyze the sample, simply predicts the composition according to an initially proposed equation

13  Laboratory determinations: As more accurate as better calibration  Range of measurement: Calibration samples must cover the range expected in the unknown samples  Number of samples & distribution: High number and homogeneously distributed  Sampling: Representative  Milling: Adapted to the type of feed  IR analysis: Avoid cross contamination  Data interpretation:  Typing errors  Model over fitting RULES TO GET A GOOD CALIBRATION

14 Previous findings Bruno-Soares et al., 1998 (An. Feed. Sci. Tech.) Raju et al., 2011 (An. Feed. Sci. Tech.) Green-cropsMeadow grasses

15 Objective: Evaluation the potential of IR spectrometry to predict the feed nutritional of ALL feeds used in ruminant nutrition 38 Barley-wheat forage111 Grass-clover forage39 Legume forage200 Oil by products 10 Barley whole crop 10 Winter wheat whole crop 8 Winter wheat silage 4 Barley whole crop silage 4 Green barley forage 2 Barley straw 36 Grass-clover forage 26 Grass silage 16 Grass-clover silage 14 Grass forage 7 Artificial-dry grass 8 Clover forage 2 Grass straw 2 Festulolium forage 12 Lupinus whole crop 7 Lucerne forage 5 Peas whole crop forage 4 Peas whole crop silage 4 Galega forage 4 Field beans whole crop 2 Artificial dry lucerne 1 Peas straw 112 Rapeseed 42 Soybean 25 Sunflower 12 Cotton seed 2 Soypass 2 Treated soybean meal 4 Others 18 Mill by products63 Cereal grains18 Legume seeds17 Protein products 7 Maize gluten feed 4 Maize feed meal 3 Wheat gluten feed 2 Wheat bran 2 Amyfeed 30 Barley 12 Wheat 7 Rye 5 Triticale 4 Oat 3 Maize 2 Grain mix 7 Peas 3 Soybean 3 Toasted soybean 2 Rapeseed 2 Lupinus 1 Field beans 7 Guar meal 5 Malt sprouts 3 Brewers grains 2 Potato protein 22 Maize silage32 Maize forage22 Beets35 Distillers 22 Maize silage 2 Maize silage with pulp 14 Dry sugar beet pulp 6 Fodder beets 2 Beet pulp 28 Corn distillers 5 Wheat distillers 2 Barley distillers 16 Soybean hulls127 Concentrate mix19 Total mixed ration9 Tropical feeds Dataset = 786 samples (80 different feeds)

16 FTIR analysis Sample preparation: – Dry at 60ºC and milled at 1.5 mm diameter FTIR analysis: – Equinox 55 FTIR spectrometer fitted with a Golden Gate ATR accessory – Wavelength: 500 to 4000 cm -1 (resolution 2cm -1 ) – 64 scans per sample in duplicate Raw spectra 1 st derivative & vector normalization

17 Modelling Metadata (n=663) – Mean centre scaled Spectral data – Calibration dataset (85% samples) – Validation dataset (15% samples) Prediction models – Partial Least Squares (PLS-Matlab) – Data transformation (de-trend, SNV, MSC) 1 st or 2 nd derivative Vector normalized (mean=0, variance=1SD) Mean centre scale – Outliers (high hotelling, Q residuals, >3SD) – Cross validation (“Venetian Blinds”) – Number of LV chosen to minimize RMSECV – Model accuracy (R 2 & RPD=SD/SEP) Very satisfactoryR 2 > 0.90& RPD > 3.0 Satisfactory:R 2 > 0.80& RPD > 2.5 For screening:R 2 > 0.70& RPD > 2.0 Inaccurate:R 2 < 0.70& RPD < 2.0

18 Universal model % CP (n = 655) R 2 =0.93 RPD=4.00 Very satisfactory

19 CP WS R 2 =0.82 RPD=2.26 Screening CP TTD R 2 =0.65 RPD=1.99 Inaccurate Universal models (n =655)

20 CP ED R 2 =0.69 RPD=1.56 Inaccurate CPa R 2 =0.76 RPD=1.75 Inaccurate CPb R 2 =0.74 RPD=1.65 Inaccurate R 2 =0.38 RPD=1.43 Inaccurate CPc

21 Prediction DM degradability Universal equation (n=663) CalibrationCross validationPrediction SGLVR2CR2C RMSECR 2 CV RMSECVR2PR2P RMSEPRPD DM ED 270.695.620.646.100.646.141.71 a160.776.170.746.580.657.621.78 b170.737.410.697.960.638.792.25 c, %/h160.502.580.432.750.512.392.25

22 CVA axis 1 (79% variance) MANOVA, P<0.001 CVA axis 2 (21% variance) Canonical analysis of variance (10 PCs = 90%var) ForageConcentrate Starch Protein

23 Feeds classification FORAGES Barley-wheat forage Grass-clover forage Maize forage Legume forage Total mixed ration Soybean hulls Beets ENERGY-RICH concentrates Cereal grains Mill by products Tropical feeds Concentrate mix PROTEIN-RICH concentrates Legume seeds Protein products (>30%CP) Oil by products Dried distiller grains (DGGS)

24 FORAGES (n =183) % CP Crude protein ENERGY-RICH concentrates (n =215) PROTEIN-RICH concentrates (n =266) R 2 =0.90 RPD=2.53 Satisfactory R 2 =0.93 RPD=3.50 Very satisfactory R 2 =0.92 RPD=3.20 Very satisfactory

25 CP WS Water soluble CP R 2 =0.91 RPD=2.68 Satisfactory R 2 =0.74 RPD=1.42 Inaccurate R 2 =0.76 RPD=2.40 Screening FORAGES (n =183) ENERGY-RICH concentrates (n =215) PROTEIN-RICH concentrates (n =266)

26 R 2 =0.79 RPD=2.32 Screening R 2 =0.60 RPD=2.32 Inaccurate R 2 =0.73 RPD=2.85 Screening CP TTD Total tract digestible CP FORAGES (n =183) ENERGY-RICH concentrates (n =215) PROTEIN-RICH concentrates (n =266)

27 Fraction a Immediately degradable CP R 2 =0.93 RPD=2.89 Satisfactory R 2 =0.68 RPD=1.85 Inaccurate R 2 =0.83 RPD=2.22 Screening FORAGES (n =183) ENERGY-RICH concentrates (n =215) PROTEIN-RICH concentrates (n =266)

28 R 2 =0.91 RPD=2.80 Satisfactory R 2 =0.68 RPD=1.91 Inaccurate R 2 =0.82 RPD=2.08 Screening Fraction b Degradable but not soluble CP FORAGES (n =183) ENERGY-RICH concentrates (n =215) PROTEIN-RICH concentrates (n =266)

29 R 2 =0.64 RPD=2.49 Screening R 2 =0.54 RPD=1.82 Inaccurate R 2 =0.71 RPD=1.95 Inaccurate Value c CP degradation rate of b FORAGES (n =183) ENERGY-RICH concentrates (n =215) PROTEIN-RICH concentrates (n =266)

30 CP ED Effective digestible CP R 2 =0.85 RPD=2.21 Screening R 2 =0.74 RPD=2.41 Screening R 2 =0.82 RPD=2.11 Screening FORAGES (n =183) ENERGY-RICH concentrates (n =215) PROTEIN-RICH concentrates (n =266)

31 Prediction DM digradability CalibrationCross validationPrediction SGLVR2CR2C RMSECR 2 CV RMSECVR2PR2P RMSEPRPD FOR DM ED 280.873.920.755.440.775.262.07 a180.943.610.914.550.934.343.64 b280.944.450.895.850.895.973.01 c, %/h150.751.240.671.420.521.381.96 ERC DM ED 280.843.230.674.750.705.201.78 a170.596.590.427.950.677.361.53 b170.636.480.477.940.627.761.53 c, %/h170.693.720.604.290.684.541.78 PRC DM ED 280.753.730.614.660.664.911.71 a160.704.310.624.900.655.511.85 b170.784.510.705.330.755.092.38 c, %/h180.701.200.531.520.671.141.93

32 Prediction NDF parameters R 2 =0.90 RPD=2.84 Satisfactory R 2 =0.79 RPD=2.17 Screening R 2 =0.87 RPD=2.01 Screening R 2 =0.94 RPD=2.25 Screening R 2 =0.95 RPD=2.35 Screening R 2 =0.85 RPD=1.23 Inaccurate

33 FTIR conclusions FTIR allows to classify feeds according to the nutritional value And determine: UniversalForage Protein rich concentrates Energy rich concentrates CPQuantification CP WS ScreeningQuantificationScreening CP TTD Screening CP ED Screening CP A QuantificationScreening CP B QuantificationScreening CP C Screening DM ED Quantification DM A ScreeningQuantification DM B ScreeningQuantificationScreening DM C NDFQuantification NDF ED Quantification NDF B NDF C Screening iNDF Screening Why did not work for concentrates? -BAG LEAKAGE -ACTIVE COMPOUNDS -Antimicrobial (Saponins, Flavonoids) -Protein binding (Tannins, polyphenols) -Maillard reactions

34 Near Infrared Spectroscopy (NIRS) Laser Wavelength selection Sample Detectors

35 2 nd Derivative 1 nst Derivative NIR spectra

36 NIRS Materials & Methods Group separation of feedstuffs ASugar-beet pulp14 BOils byproduct200 CByproduct98 DConcentrate152 EGrains & seed90 FForage (whole crop, straw, hay)195 TTropical9 GSilage52 TOTAL809 Byproducts Concentrates NON- FORAGES FORAGES Validation Calibration 80% 20%

37 NIRS Materials & Methods –Mathematical treatment were performed using WinISI III (v. 1.6) –Scatter correction transformations -Standard normal variate (SNV), -Detrend (D) -Standard normal variate –detrend (SNV-D) -Multiplicative scatter correction (MSC) –Calibrations were developed by the modified partial least squares (MPLS) regression technique.

38 NIRS Results & Discussion Chemical composition (ALL) Nutrient (%) Derivate treatment Scatter correctionR2R2 SECr2r2 SEPRPDRER CP2,4,4,1MSC0.990.960.991.841166.7 NDF3,10,10,1DT0.923.480.904.482.3313.2

39 CP degradability by NIRS ALLMSC3.4.4.10.850.0570.800.9062.5312.78 CP ED FORAGESNV-D3.4.4.10.890.0270.861.0122.639.76 NON-FORSNV-D3.4.4.10.880.0450.770.9522.3010.16 Nutrient (%) GroupScatter correction Derivate treatmentR2R2 SECr2r2 SEPRPDRER ALLSNV-D3.4.4.10.850.0760.770.9562.2610.20 CP AFORAGESNV-D3.4.4.10.970.0340.961.0354.5914.23 NON-FORD3.4.4.10.890.0560.820.9882.5712.15 ALLMSC3.4.4.10.830.0900.770.9922.099.30 CP BFORAGEMSC3.4.4.10.960.0420.941.0163.9412.64 NON-FORSNV-D3.4.4.10.840.0740.730.9632.048.98 ALLSNV-D3.4.4.10.420.0210.471.0012.0213.91 CP CFORAGED3.4.4.10.820.0170.840.9572.117.94 NON-FORSNV-D3.4.4.10.690.0170.550.7722.2214.61

40 ALLSNV-D3.4.4.10.970.0350.870.0372.8919.48 DM ED FORAGED3.4.4.10.930.0260.800.0372.498.89 NON-FORSNV-D3.4.4.10.890.0320.830.0402.5111.06 Nutrient (%) GroupScatter correction Derivate treatmentR2R2 SECr2r2 SEPRPDRER DM degradability by NIRS ALLD3.4.4.10.810.0560.780.0612.4213.40 DM AFORAGEMSC3.4.4.10.960.0210.900.0312.619.87 NON-FORD3.4.4.10.910.0410.870.0492.6713.66 ALLD3.4.4.10.800.0590.760.0662.8413.40 DM BFORAGEMSC3.4.4.10.960.0230.910.0322.649.56 NON-FORMSC3.4.4.10.920.0420.860.0532.5512.56 ALLMSC3.4.4.10.650.0160.650.0153.0318.30 DM CFORAGEMSC3.4.4.10.850.0090.750.0122.9810.82 NON-FORSNV-D3.4.4.10.770.0150.690.0152.3618.34

41 ALLMSC3.10.10.10.840.0540.760.0722.257.48 NDF ED FORAGESNV-D3.4.4.10.970.0240.860.0582.638.66 NDF degradability by NIRS Nutrient (%) GroupScatter correction Derivate treatmentR2R2 SECr2r2 SEPRPDRER ALLD3.10.10.10.820.0620.700.0951.825.94 NDF BFORAGED3.4.4.10.850.0710.740.0821.866.20 ALLMSC3.10.10.10.640.0100.530.0191.675.95 NDF CFORAGED1.10.10.10.740.0130.680.0141.576.28

42 GENERAL CONCLUSIONS Universal equations can be used to predict chemical structure However, group separation of samples improved predictions of degradability data (except C) IR spectroscopy can be incorporated as a field tool to determine dynamic parameters of feed evaluation models (FORAGES) Current feeding evaluation systems must combine the traditional equations with IR data in order to improve the prediction of: – Feed nutritional value – Animal performance

43 ADVENTAGES - Quick analysis - No destructive technique - Low cost per sample - Minimun sample preparation - Easy to use - Environmentally friendly (no waste) - Multianlysis (several parmeters) - Simultaneous prediction of static and dynamic parameters DISADVENTAGES - Indirect method (calibration) - Technical support - Calibration updating - Dependence on the ref. method - Analysis can be affected by: -Particle size -Temperature -Humidity - High investment in equipment - Difficulty to compare between different equipment CONCLUSIONS TO KEEP IN MIND

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