Quality of oilseeds, protein crops and fibre plants

Transcription

1 Aspects of Product Quality in Plant Production ASPECTS OF PRODUCT QUALITY IN PLANT PRODUCTION Oil and protein analytics (Practical experiments) J. Vollmann, November Glucosinolates 2. NIRS for oil / protein / carbohydrate content ASPECTS OF PRODUCT QUALITY IN PLANT PRODUCTION Quality of oilseeds, protein crops and fibre plants crop species genetics agronomy analytical aspects Analytical methods for crop quality determination Pre-requisites large sample numbers to be managed in a short period of time (plant breeding, silo...) small sample size (e.g. single plant harvest) whole seed measurement (non-destructive measurement) in plant breeding sufficient accuracy (depending on goal of measurement) adequate sampling (problems of sample disintegration, single plant samples etc.) Analytical methods for crop quality determination Universal methods Kjeldahl nitrogen determination (protein) Soxhlet extraction (oil) GC (gas chromatography), HPLC (high performance liquid chromatography) e.g. for fatty acids, amino acids and many other separable constituents such as agrochemical residues, toxins etc. NIRS (near-infrared reflectance spectroscopy) for different organic constituents Element analyzer (e.g. CN analyzer) Analytical methods for crop quality determination Specific methods (a few examples) sedimentation farinogram extensiogram amylogram falling number vitrousness carotinoid content polarisation glucosinolate tests image analysis iodine value paper chromatography for erucic acid thiobarbituric test for linolenic acid electrophoresis (SDS- PAGE) for specific protein patterns DNA markers 1

2 Determination of glucosinolate content Determination of glucosinolate content DIRECT DETERMINATION METHODS USING DEGRADATION PRODUCTS 1. determination of glucose 2. formation of coloured complexes 3. determination of sulphate Glucosinolates: Present in all Brassicaceae plants S-D-thioglucose, sulfate ester, R = organic side chain. 116 different glucosinolates according to side chain. Some glucosinolates are toxic or mutagenic and used as biocides to sterilize soils, other glucosinolates are known to have health benefits in human nutrition. METHODS BASED ON DETERMINATION AND SUMMATION OF INDIVIDUAL GLUCOSINOLATES 1. Gas chromatography of trimethylsilylated desulphoglucosinolates 2. HPLC methods of desulphoglucosinolates or intact glucosinolates 3. Capillary electrophoresis INDIRECT METHODS (NON-DESTRUCTIVE) 1. X-ray fluorescence 2. Near IR spectroscopy Determination of glucosinolate content Method 1: determination of sulphate Determination of glucosinolate content Method 2: determination of glucose glucosinolate is hydrolized by the enzyme myrosinase (present in cells, active when cells are destroyed), glucose is determined using a glucose testing strip with o-tolidine 1 mole glucose = 1 mole of glucosinolate high glucosinolate low glucosinolate Image analysis Analysis of digital images: Numerical information on color, intensity, distance, area, perimeter, shape and other characteristics can be obtained from digital images. Sample applications: Medicine, remote sensing, quality control, agriculture, biometrics etc. 2

3 Image analysis applications in agriculture Image analysis (color) Quality control: seed coloration, sample purity etc. Quality determination: yellow pigmentation of durum wheat, cotton grades (color), fibre content of flax from stem cross-section micrographs Microscopic image analysis of quality features in cereal grains and flours (wheat, barley, oat) Agronomy: Nitrogen fertilization control (leaf color), irrigation control, plant density measurement, leaf area, weed density etc. Yield: yield estimation from satellite images or aerial pictures original Image analysis (color) overlay Overlay area measurement Image analysis (intensity) Image analysis (intensity) 3

4 Image analysis (pore size of bread samples) Image analysis (pore size of bread samples) Pores in bw-mode Image analysis (pore size of bread samples) Image analysis (leaf size, chlorophyll content, nitrogen fixation) Pore analysis results F 6 -lines in the field Image analysis (leaf size, chlorophyll content, nitrogen fixation) Image analysis (nitrogen fixation) seed protein content (g/kg) r = Col 2 vs Col 3 Col 6 vs Col Plot 1 Regr 0,50 0,55 0,60 0,65 0,70 0,75 0,80 0,85 saturation (HSB leaf image analysis) nod + nod - 4

5 Thiobarbituric acid test for linolenic acid Testing principle: linolenic acid from seed is oxidized using UV and / or acids, thiobarbituric acid (TBA) + oxidized linolenic acid forms a complex of intense red color. Applications: Selection for low or high linolenic acid in oilseeds, control of lipid oxidation in food control TBA test during selection for linolenic acid NIRS as an example of a physical analytical method NIRS: Near infrared reflectance spectroscoopy near-infrared: nm mid-infrared far-infrared micro-waves Principle: NIRS uses reflections/absorption of light in the range of nm wavelength to determine the content of organic components of a sample Calibration of NIRS machine / Validation Measurement (scanning) of samples Prediction of content Advantages / problems over other methods 5

6 Analytical methods for crop quality determination absorbance NIRS (near-infrared reflectance spectroscopy) machines wave number (cm -1 ) NIRS spectra Examples of NIRS absorbance areas Chemical wavelength (nm) -CH water 1450 C=O 1450 urea NH 1500 protein 1520 starch 1540 cellulose 1780 water 1790 protein 2055 oil 2070 starch 2100 protein 2180 oil 2310 NIRS principles Spectroscopic absorbance peaks at different wavelengths are due to different constituents, e.g. water peak at 1450 nm. Molecule stretching etc.... Vis-NIR spectra for zeaxanthin (black), lutein (blue), and lutein mixtures (maize; Brenner & Berardo, 2004) 6

7 NIRS principles absorbance Spectroscopic absorbance peaks at different wavelengths are due to different constituents, e.g. water peak at 1450 nm. The Beer-Lambert law describes the linear relationship between absorbance and concentration of an absorber of electromagnetic radiation. wave number (cm -1 ) NIRS spectra NIRS procedures: - calibration (PLSR, MLR) using a learning set - validaton of calibration with known samples - routine prediction of unknown samples predicted weight a predicted weight b 1.6 calibration set validation set r = n = 69 S.E.E. = r = n = 40 S.E.P. = actual weight (g per 1000 seeds) NIRS applications in plant breeding Quantitative predictions (examples): oil, protein, starch, fibre, sugars, moisture fatty acids, amino acids, glucosinolates carotinoides, isoflavones, beta-glucane breadmaking parameters, brewing parameters grain hardiness, particel size, digestibility, botanical composition (hay) etc. Qualitative applications Diversity analysis, sample/genotype discrimination, metabolic analysis... (mutant identification?) Prediction of seed protein content in soybean (Bruker Matrix I / OPUS Quant validation) 7

8 Prediction of malt extract content in brewing barley NIRS developments: Genotyping Cultivar identification Real time measurement 8

9 Analytical methods for crop quality determination Kunitz trypsin inhibitor of soybean PCR-based DNAmarkers (microsatellites) SDS-PAGE type electrophoresis of proteins (wheat) Examples of molecular genetic techniques Kunitz trypsin inhibitor of soybean Genetic markers 48 soybean genotypes differing in an SSR (simple sequence repeat = microsatellite) marker Soybean Kunitz - trypsin inhibitor in SDS PAGE type of electrophoresis (absence of 21.5 kda-protein in lanes 4, 6 and 8) 9

10 Genetic markers Genetic markers Merlin, Gallec: Allele for high Cd 48 soybean genotypes differing in an SSR (simple sequence repeat = microsatellite) marker OAC Erin, ES Mentor: Allele for low Cd SSR marker SacK149 associated with low (lo) or high (hi) cadmium (Cd) accumulation in soybean seed Seed cadmium content in four soybean genotypes at three Cd rates in the soil Quality of oilseeds, protein crops and fibre plants crop species genetics agronomy analytical aspects 10