Progress report on the use of NIR to predict rice functionality

Transcription

1 Progress report on the use of NIR to predict rice functionality Jean-François Meullenet Terry Siebenmorgen Mohammed Saleh Rusty Bautista UofA Rice Processing Program

2 Outline 1. Basic principles of NIR 2. Applications in rice Composition (slc, protein, amylose) Functional properties (RVA, texture, color) UARPP efforts 3. Thoughts for the future

3 Electromagnetic Spectrum Range of all possible electromagnetic radiations (color, sounds) Visible to the human eye ( nm) NIR ( nm) IR ( nm)

4

5 Basic Principles NIR techniques allow the measurement of the absorption of different NIR frequencies by a sample The term absorption refers to the physical process of absorbing light, while absorbance refers to the mathematical quantity. Different functional groups absorb characteristic frequencies 1.3 = Log10 Absorbance Wavelength (nm) I I A 0 Milled Rice Absorbance Spectra

6 Basic Principles Lamp illuminates sample with white light. Some light absorbed (depending on the composition of the sample) and the rest reflected Reflected light hits a grating, separates the light by wavelength. Instead of white light, we now have a "rainbow". Absorbance for each wavelength measured and absorbance spectra collected Absorbance Wavelength (nm) Courtesy, Perten Instruments

7 Arkansas Rice Performance Trials (Location, Cultivar, HMC) Cooperative Extension Service Research Verification Program Others? DD-50? USDA NRRC? Sample source Ricetec Quality Lab Validation UA Rice Processing Program Property Database/ NIR Calibration Development Validation Riceland Foods Variety Testing Program Publications: (Wells Research Series) Breeders Industry Users NIR Calibrations

8 NIRS Applications in Rice Apparent amylose, rice flour (Delwiche et al 1995) Apparent amylose, milled rice (Delwiche et al 1996) Amino acids (Wu et al 2002) Protein content (Sohn et al 2004) Fat acidity, rough rice (Sheng and Shaw et al 1997) Surface lipid, milled rice (Chen et al 1997; Wang et al 2006) Moisture content (Iwamoto 1987) Degree of starch gelatinization (Onda et al 1994) Cooked rice texture (Windham et al 1997; Champagne et al 2001; Bao et al., 2001; Meullenet et al 2002) Rapid Visco Analyzer parameters (Delwiche 1996; Meadows and Barton 2002) Rice starch quality parameters (AAC, RVA, DSC, Gel Texture) (Bao et al 2001)

9 Appearance related Delwiche et al 1996 Chen et al 1997 Natsuga & Kawamura 2006 SLC Whiteness Transparency Milling Degree L* a* b* R 2 = SEP=0.6 R 2 = SEP=0.15 R 2 = SEP= Measured on a Satake MM-1B 2 Measured on whiteness meter SEP= standard error of prediction R 2 =0.99 SEP=0.05 R 2 = SEC=0.26 R 2 =0.95 SEC=5.95 R 2 =0.99 SEC=0.05 R 2 =0.99 SEC=0.03 R 2 =0.99 SEC=0.01

10 Surface Lipid Content R 2 = 0.94 SEP=0.06 N=960 Predicted SLC (%) Measured SLC (%)

11 Predicted a* R 2 = 0.89 SEP= Measured a* green (-) to red (+) Color Predicted L R 2 = 0.93 SEP= Measured L Predicted b* R 2 = 0.88 SEP= Measured b* blue (-) to yellow (+)

12 Apparent Amylose Sample type Grain Type N R2 SEP Delwiche et al1995 Flour medium long Delwiche et al1996 Milled short medium long Bao et al 2001 Flour short medium Long Sohn et al 2004 Flour short medium long 90 (1996) 128 (1999)? 1.0 Wu & Shi 2004 Brown short 59 cultivars Rough medium long 474 Individual seeds Natsuga & Kawamura 2006 Milled short

13 Amylose Content R 2 = 0.72 SEP=1.79 n=158 Predicted (%) Measured (%) Milled Rice Rice Flour Predicted (%) R 2 = 0.77 SEP=1.92 n= Measured (%)

14 Protein Content Sample type Grain Type N R2 SEP Delwiche et al1995 Flour medium long Delwiche et al1996 Milled short medium long Sohn et al 2004 Flour short medium long 90 (1996) 128 (1999)? 0.24 Natsuga & Kawamura 2006 Milled short

15 Protein Content Milled Rice Predicted (%) R 2 = 0.87 SEP=0.41 n= Measured (%)

16 RVA Peak Viscosity Sample type Grain Type N R2 SEP Delwiche et al1996 Milled short medium long Bao et al 2001 Flour short Medium Long Natsuga & Kawamura 2006 Milled short Viscosity (Pa.s) Breakdown Peak Viscosity Final Viscosity Trough Viscosity Setback Time (min)

17 Peak Viscosity Predicted (RVAU) y = x R 2 = 0.57 SEP=25.3 n= Measured (RVAU)

18 Final Viscosity Predicted (RVAU) R 2 = 0.36 SEP=30.9 n=959 Delwiche R 2 = 0.42 SEP= Measured (RVAU)

19 Trough Predicted (RVAU) R 2 = 0.38 SEP=18.7 n=959 Delwiche R 2 =0.74 SEP= Measured (RVAU)

20 Breakdown Predicted (RVAU) R 2 = 0.71 SEP=15.6 n=959 Delwiche R 2 = 0.72 SEP= Measured (RVAU)

21 Setback 250 Predicted (RVAU) R 2 = 0.73 SEP=22.3 n=959 Delwiche R 2 =0.74 SEP= Measured (RVAU)

22 Sensory Hardness 6 Predicted Hardness R 2 = 0.64 SEP=0.75 N= Measured Hardness

23 Measured RVA PCA FRNS XP723 FRNS FRNS CYBT WLLS CYBT FRNS CYBT FRNS FRNS CYBT WLLS WLLS WLLS CYBT WLLS WLLS XP723 XP723 CYBT CYBT CYBT CYBT FRNS FRNS CYBT WLLS -50 XP723 JUPITER JUPITER 0 50 JUPITER FRNS WLLS 100 JUPITER XP723 JUPITER JUPITER XP723 FRNS JUPITER JUPITER XP723 XP723 WLLS Good discrimination of medium grain rice

24 NIR Predicted RVA PCA 60 JUPITER JUPITER JUPITER 20 JUPITER JUPITER JUPITER JUPITER JUPITER XP723 XP XP723 CYBT FRNS WLLS CYBT FRNS FRNS WLLS CYBT CYBT CYBT WLLS CYBT CYBT FRNS FRNS WLLSWLLS FRNS CYBT WLLS XP723 FRNS FRNS WLLS XP723 CYBT XP723 CYBT WLLS -40 XP723 WLLS -60 XP723 FRNS FRNS -80 Similar map, < discrimination for medium grain

25 A passing Grade? Characteristics Grade SLC A Protein A Amylose B Color B+ RVA B- Peak C+ Trough D Final C Breakdown B Setback B Texture B- GPA B

26 Final Thoughts Time savings 30s (6s) versus 120min per sample Can we afford imperfection? Significant variability of replicates Form Milled ~ Flour, Brown? Quantity 5g ~ individual kernel

27 The NIR Fingerprint? Absorbance Wavelength

28 Special Thanks Arkansas Rice Research and Promotion Board and The Rice Foundation (research support) Perten Instruments (UARPP Sponsor) Zack Nehus, Riceland Foods; Karen Moldenhauer and James Gibbons, UA rice breeders (sharing of samples and data)

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