determining ripeness in white sturgeon females to maximize
TRANSCRIPT
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Determining Ripeness in White Sturgeon Females to Maximize Yield and Quality
of Caviar
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Molly Webb, Montana State University, USFWS
Principal Investigators and Institutions
Serge Doroshov, University of California, Davis
Barbara Rasco, Washington State University
Anna Cavinato, Eastern Oregon University
Wendy Sealey, Montana State University, USFWS
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Outreach: Gary Fornshell, University of Idaho Extension
Principal Investigators and Institutions
Industry Advisor: Peter Struffenegger Sterling Caviar, LLC
Technical Advisor: Fred Conte, UC Davis
Industry Participants: Leo Ray, Fish Breeders of Idaho Linda Lemmon, Blind Canyon Aquaranch Inc.
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Long-Term Project Goal Develop a less invasive, faster, and better
predictor of maturity to optimize caviar yield and quality from white sturgeon at harvest
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B
A
PI=A/B
Replace Oocyte Polarization Index (PI)
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Proposed Tools
Short Wavelength Near Infrared Spectroscopy
Fourier Transform Infrared Spectroscopy
Radioimmunoassay
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Ideal Approach to Sorting Step 1
Fall
Low PI (< 0.18) Early
High PI (> 0.26)
Late
Medium PI (0.19-0.26)
Middle
Harvest
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Ideal Approach to Sorting Step 2
Harvest Low PI (< 0.13)
Harvest
Medium High PI (> 0.13)
Don’t Harvest
Atretic
Don’t Harvest
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Caviar 2009 Study California
November (n=160)
Low PI (0.162 + 0.034)
January
Medium PI (0.242 + 0.008)
March
High PI (0.301 + 0.016)
May
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Caviar 2009: Oocyte PI at Harvest �
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Low PI Medium PI High PI
PI Groups
Polar
izatio
n In
dex
*
*
SortedControl
�
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Caviar 2009: Caviar Yield (% Body Wt) �
0
5
10
15
Low PI Medium PI High PI
PI Groups
Cavia
r Yiel
d (%
Bod
y Weig
ht)
*
SortedControl
�
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Ovarian Adiposity
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0.0
5.0
10.0
15.0
Low Fat Medium Fat High Fat
Fat Categories
8 year olds
7 year olds
Cavia
r Yiel
d (%
Body
Weig
ht)
aa
b b
c
c
�
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Model Development for Prediction of Oocyte PI
SW-NIR FT-IR
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SW-NIR Model for Fall Sorting by Abdominal Scan
Classified PI Actual Low PI (%)
PI Medium and High PI (%)
Low PI (< 0.18)
73 (947)
34 (198)
Medium (0.19-0.26) High PI (> 0.26)
27 (343)
66 (391)
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SW-NIR Model for Fall Sorting by Egg Scan
Classified PI Actual Low PI (%)
PI Medium and High PI (%)
Low PI (< 0.18)
84 (938)
31 (185)
Medium (0.19-0.26) High PI (> 0.26)
16 (184)
69 (418)
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SW-NIR Model for Harvest by Abdominal Scan
Classified PI Actual Low PI (%)
PI Medium and High PI (%)
Low PI (< 0.13)
76 (710)
32 (301)
High PI (> 0.13)
24 (227)
68 (641)
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SW-NIR Model for Harvest by Egg Scan
Classified PI Actual Low PI (%)
PI Medium and High PI (%)
Low PI (< 0.13)
81 (605)
30 (295)
High PI (> 0.13)
19 (140)
70 (685)
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Classification No. Correctly Classified Spectra
% Correctly Classified Spectra
0.05 < PI < 0.10 278 92.67
0.10 < PI < 0.15 281 93.67
0.15 < PI < 0.20 269 89.67
0.20 < PI < 0.25
271 90.33
PI > 0.25 292 97.33
FT-IR Model using Plasma
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Fall 2009 - Spring 2010
Segregated females using FT-IR at both California and Idaho farms
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Regression Analysis Using Model Developed from 2008-2009 Data
0.10
0.15
0.20
0.25
0.30
Pred
icte
d PI
(200
8-20
09 D
ata)
0.10 0.15 0.20 0.25 0.30
Actual PI
R2=0.235
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Regression Analysis Using Model Developed from 2008-2009 Data Plus 2010 Fish
0.10
0.15
0.20
0.25
0.30
Pred
icte
d PI
(200
8-20
09 D
ata
Plus
201
0 Su
bset
)
0.10 0.15 0.20 0.25 0.30
Actual PI
R2=0.718
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Actual PI Predicted PI using 2008-2009
model
Predicted PI using 2008-2009 model plus 2010
0.133 ± 0.021 0.245 ± 0.006 0.150 ± 0.007
0.155 ± 0.035 0.230 ± 0.019 0.172 ± 0.017
0.182 ± 0.019 0.201 ± 0.011 0.175 ± 0.007
0.225 ± 0.020 0.180 ± 0.013 0.242 ± 0.011
0.248 ± 0.018 0.256 ± 0.017 0.251 ± 0.012
PI Comparisons
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Fall 2010-Spring 2011 Segregate females using FT-IR with multiple year classes
Refine/optimize both FT-IR and SW-NIR models
Explore SW-NIR probe design
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Accomplishments to Date Plasma sex steroids, SW-NIR, and FT-IR can
be used to predict early atresia with 70-95% accuracy
Oocyte PI may be predicted from SW-NIR and
FT-IR spectra; model development continues Highly variable caviar yield appears to be caused
by ovarian adiposity
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Outreach Activities to Date
11 Presentations at national meetings 1 Manuscript published (J. Agric. Food Chem.) 3 Manuscripts accepted (Aquaculture) 5 Manuscripts in prep 2 Extension publications in prep
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