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Ashby, K. R. (1957). The effect of steroid hormones on the brown trout (Salmo trutta L.) during the period of gonadal differentiation. Journal of Embryology and Experimental Morphology 5, 225–249. Barnes, M. E., Carreiro, J. M. & Cordes, R. J. (2001). Hermaphroditism observed in captive fall Chinnok salmon broodstock. North American Journal of Aquaculture 63, 262–264. Baroiller, J. F., Guiguen, T. & Fostier, A. (1999). Endocrine and environmental aspects of sex differentiation in fish. Cellular and Molecular Life Sciences 55, 910–931. de Beer, G. R. (1924). Note on a hermaphrodite trout. Anatomical Record 27, 61–62. Benson, N. G. (1958). Hermaphroditism in the cutthroat trout. Copeia 1958, 239–240. 1150 E. QUILLET ET AL. Chevassus, B. & Krieg, F. (1992). Effect of the concentration and duration of methyltestosterone treatment on masculinization rate in the brown trout (Salmo trutta). Aquatic Living Resources 5, 325–328. Chevassus, B., Devaux, A., Chourrout, D. & Jalabert, B. (1988). Production of YY rainbow trout males by self-fertilization of induced hermaphrodites. Journal of Heredity 79, 89–92. Crawford, D. R. (1927). Notice of hermaphroditism in silver salmon, Oncorhynchus kisutch. Copeia 1927, 34. Fraser, D. (1997). A hermaphrodite Arctic charr from Loch Rannoch, Scotland. Journal of Fish Biology 50, 1358–1359. doi: 10.1006/jfbi.1997.0392. Gercken, J. & Sordyl, H. (2002). Intersex in feral marine and freshwater fish from northeastern Germany. Marine Environmental Research 54, 651–655. doi: 10.1016/ S0141-1136(02)00156-3. Gibbs, E. D. (1956). A bisexual steelhead. California Fish and Game 42, 229–231. Hikita, T. & Hashimoto, S. (1978). A hermaphroditic chum salmon, Oncorhynchus keta, from the Chitose River with an example of its self-fertilization. Scientific Reports of the Hokkaido Salmon Hatchery 32, 61–64. Hitron, J. W. & Bonham, K. (1977). Hermaphroditism in a chum salmon (O. keta). Copeia 1977, 591–592. Komen, J., de Boer, P. & Richter, C. J. J. (1992a). Male sex reversal in gynogenetic XX females of common carp (Cyprinus carpio L.) by a recessive mutation in a sex-determining gene. Journal of Heredity 83, 431–434. Komen, J., Wiegertjes, G. F., Van Ginneken, V. J. T., Eding, E. H. & Richter, C. J. J. (1992b). Gynogenesis in common carp (Cyprinus carpio L.). III. The effect of inbreeding on gonadal development of heterozygous and homozygous offspring. Aquaculture 104, 51–66. Luksiene, D., Sandstro¨ m, O., Lounasheimo, L. & Andersson, J. (2000). The effects of thermal effluent exposure on the gametogenesis of female fish. Journal of Fish Biology 56, 37–50. doi: 10.1006/jfbi.1999.1139. Lyndon, A. (2002). Intersex fish found in U.K. estuaries. Marine Pollution Bulletin 44, 722. Mrsic, W. (1923). Die Spa¨ tbefruchtung und deren Einfluك auf Entwicklung und Geschlectsbildung, experimentell nachgepru¨ ft an der Regenbogenforelle. Archiv fu¨er mikroskopische Anatomie 98, 129–209. Quillet, E., Aubard, G. & Que´ au, I. (2002). Mutation in a sex-determining gene in rainbow trout: detection and genetic analysis. Journal of Heredity 93, 91–99. Ross, A. J., Yasutake, W. T. & White, G. R. (1963). Hermaphroditism in rainbow trout. Transactions of the American Fisheries Society 92, 313–314. Stewart, C. (1891). On a hermaphrodite trout, Salmo fario. Journal of the Linnean Society of London (Zoology) 24, 69–70. Turner, C. L. (1946). A case of hermaphroditism in the cutthroat trout. The Chicago Academy of Sciences, Natural History Miscellenea 1, 1–2. Uzmann, J. R. & Hesselholt, M. N. (1958). Teratological hermaphroditism in the chum salmon, Oncorhynchus keta (Walbaum). Progressive Fish-Culturist 20, 191–192. Vigano, L., Arillo, A., Massari, A. & Mandich, A. (2001). First observation of intersex cyprinids in the Po River (Italy). The Science of the Total Environment 269, 189–194. doi: 10.1016/S0048-9697(00)00821-4. INTERSEX IN RAINBOW TROUT 1151


# 2004 The Fisheries Society of the British Isles, Journal of Fish Biology 2004, 64, 1147–1151

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Near infrared refectance spectroscopy in the prediction

D. COZZOLINO1, I. MURRAY1 & J.R. SCAIFE2
1 Animal Biology, SAC Aberdeen, Scottish Agricultural College, Aberdeen, Scotland, UK; 2 Department of Agriculture,
MacRobert Building, University of Aberdeen, Aberdeen, Scotland, UK

Abstract

Near infrared refectance spectroscopy in the prediction
of chemical characteristics of minced raw ®sh
D. COZZOLINO1, I. MURRAY1 & J.R. SCAIFE2
1 Animal Biology, SAC Aberdeen, Scottish Agricultural College, Aberdeen, Scotland, UK; 2 Department of Agriculture,
MacRobert Building, University of Aberdeen, Aberdeen, Scotland, UK
Abstract
Near infrared re¯ectance spectroscopy (NIRS) was applied
to predict chemical composition in minced raw ®sh samples
used to make ®shmeal. The coecients of determination
(R2
calibration) and standard error in cross validation (SECV)
were 0.99 (3.86) and 0.96 (8.01) in g kg±1 for moisture and
oil, respectively. Total volatile nitrogen (TVN) gave R2
calibration
and SECV of 0.96 (3.51) in mg g±1. Temperature also was
predicted by NIRS, yielding R2
calibration: 0.98 and SECV:
1.07 °C. We conclude that NIRS can be used successfully to
assess the chemical composition and storage conditions in
minced raw ®sh used by the ®shmeal industry.
KEY WORDS : moisture, near infrared spectroscopy, oil, raw
®sh, temperature, total volatile nitrogen
Received 10 July 2000, accepted 12 December 2000
Correspondence: D. Cozzolino, National Institute for Agricultural Re-
search, INIA La Estanzuela. Ruta 50 km 11, CC 39173, Colonia, Uruguay.
E-mail: cozolino@inia.org.uy
Introduction
The quality of raw ®sh is highly variable in many properties
(moisture, oil, protein and volatile nitrogen from protein
breakdown). This variability arises from di.erent ®sh species,
®sh processing systems and seasonal variations. Recent
studies have shown that deteriorative changes in proteins
and lipids occur also during the storage of ®sh (Borquez &
Speek 1994; Raghunath et al. 1995), increasing variability.
Fish quality has traditionally been evaluated through sensory
assessments (Freeman & Hearnsberger 1994). However,
sensory assessments are subjective and a trained taste panel
is needed to carry out this evaluation. In the case of ®sh
waste from ®lleting operations, the use of a taste panel is not
appropriate. The search for a simple objective method to
assess chemical and physical characteristics has been receiv-
ing increasing attention during the past years (Zhang & Lee
1997). The lack of simple, reliable and nondestructive
methods for the determination of carcass composition in
®sh and ®sh by-products, has been one of the main obstacles
for the development of quality control in the ®sh industry.
Conventional methods involve time consuming, laborious
and costly procedures, including dissection and chemical
analysis. During recent years, new developments have
resulted in rapid methods relating multivariate physical
records of investigating samples to content of speci®c
chemical constituents. Consequently, the demand for tradi-
tional analysis using chemical reagents is reduced (Rye 1991).
Near infrared re¯ectance spectroscopy (NIRS) is a physical
and nondestructive technique. The NIR region of the
electromagnetic spectrum lies between the visible and infra-
red region (750±3000 nm), while the spectra appear as
smooth, but they consist of many overlapped bands.
The re¯ectance spectrum of a sample is the summation of
the spectra of its major chemical components (Deaville &
Flinn 2000). In raw ®sh, NIRS has successfully been applied
for determination of carcass composition in rainbow trout
(Gjerde & Martens 1987; Valdes et al. 1989), salmon (Lee
et al. 1992), freshwater ®sh (Mathias et al. 1987), ®sh tissue
using mid-infrared transmission (Darwish et al. 1989) and
near infrared transmittance (Solberg 1995). The total UK
production of ®shmeal in 1997 was 45 000 tonnes [mostly
from the trimming from food ®sh (UFP 1996; FIN 1998)]. A
large proportion of this production came from industrial ®sh
processing. Raw ®sh is ®lleted, skinned and trimmed to
produce ®llets, and these by-products are used to make the
®shmeal by the industry. To be able to control and optimize
the processing of the ®shmeal, it is important to measure and
analyse the chemical composition of the raw material. It
would be valuable to determine key parameters such as the
chemical composition (moisture, oil and protein) and other
parameters such as total volatile nitrogen (TVN) or salt
1
Aquaculture Nutrition 2002 8;1^6
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Ó 2002 Blackwell Science Ltd
content related with both storage and conservation of the
material before entry into the ®shmeal factory. Several
chemical compounds have been found related to the change
of ®sh quality such as ATP degradation products (e.g.
hypoxanthine, trimethylamine, total volatile base, free fatty
acids). The methods for determination of those compounds
generally are colorimetric or chromatographic measure-
ments. These methods also required a lot of sample prepar-
ation and involve many chemical manipulations (Zhang &
Lee 1997). The objectives of this paper were (1) to report the
main absorption bands in the near infrared region used to
assess the chemical composition of minced raw ®sh and (2) to
predict moisture, oil, TVN and temperature in the samples
for quality control in the ®shmeal industry.
Materials and methods
Samples
One hundred and ®ve (n . 105) minced raw ®sh samples
from an industrial manufacturing plant (UFP, Tullos,
Aberdeen, UK) were collected from October 1996 to August
1997. Minced raw ®sh samples came from di.erent ®sh
species such as mackerel (n . 15) (Scomber scombrus),
herring (n . 25) (Clupea harengus), salmon (n . 15) (Salmo
salar), bluewhiting (n . 20) (Micromesistius poutassau) and
other ®sh species (n . 30). These samples were mainly
produced as a by-product of ®lleting ®sh for human
consumption, except bluewhiting. Whole bluewhiting sam-
ples were eviscerated and skinless, after homogenization. The
samples were homogenized fresh in a food blender, (Moul-
inex, France) and scanned fresh.
Chemical analysis
Moisture content was determinated by oven drying the
sample at 105 °C for 4 h (AOAC 1990), oil was extracted by
Soxhlet (Det, Gras, Selecta, Spain) using petroleum ether
(BP 40±60°) (AOAC 1990). The TVN was measured by
alkaline distillation and titration using 0.1 N NaOH until the
indicator turns from purple to green (AOAC 1990). Tem-
perature was measured in the fresh material using a digital
thermocouple.
NIR analysis
Aliquots (25±50 g) of minced raw ®sh were scanned fresh
from 1100 to 2500 nm in re¯ectance mode at 4 nm intervals
in a scanning monochromator NIRS 5000 (NIRSystems,
Silver Spring, MD, USA).
Computer operation and data was manipulated using ISI
version 3.1 software (InfraSoft International, Port Matilda,
PA, USA). The samples were placed in a large rectangular
quartz cup (Part Number IH-0314). Two pairs of lead
sulphide detectors collected the re¯ectance spectra. The
absorbance spectrum was recorded as log (1/R; R: re¯ect-
ance) for each minced raw ®sh sample. Re¯ected energy
readings were referenced to corresponding readings from a
ceramic disk. A reference scan was collected and stored to
computer memory before each sample was scanned. The
spectrum of each sample was the average of 32 successive
scans. Prediction models were developed using modi®ed
partial least squares regression (MPLS) (Shenk & Wester-
haus 1993) with cross validation and scatter correction by
standard normal variate (SNV) and detrend (Barnes et al.
1989). Because NIR spectra are a.ected by particle size and
light scatter (re¯ectance) and path-length variation (trans-
mission), pretreatment of the spectral data improve calibra-
tion accuracy (Deaville & Flinn 2000). Application of SNV
and detrend transformation to the spectral data results in
spectra which have reduced amounts of variation because of
physical e.ects (Sanderson et al. 1997). Cross validation was
used to avoid over®tting of the equations. Cross validation
estimates the prediction error by splitting the calibration
samples into groups (four in this study). One group was
reserved for validation and the remaining groups were used
for calibration. The process was repeated until all groups
have been used for validation at once (Shenk & Westerhaus
1993). After cross validation, the calibration is performed on
all samples using the number of factors that gave the
minimum standard error in cross validation (SECV). The
math treatment applied was 1, 4, 4, 1, where the ®rst number
is the order of the derivative (1 is ®rst derivative of log 1/R),
the second number is the gap in nm over which the derivative
is calculated, the third number is the number of nm used in
the ®rst smoothing and the fourth number refers to the
number of nm over which the second smoothing is applied.
Calibration statistics calculated include the standard error
of calibration (SEC), the coecient of multidetermination in
calibration (R2
calibration), the SECV and the coecient of
determination in cross validation (R2
validation) (Shenk &
Westerhaus 1993). The optimum calibrations were selected
on the basis of minimising the SECV. The SEC and SECV
were calculated as follows:
SEC and SECV . ..Y ÿ Yi.2=.n ÿ t ÿ 1..1=2
D. Cozzolino et al.
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Ó 2002 Blackwell Science Ltd Aquaculture Nutrition8;1^6
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where SEC and SECV are standard error of calibration and
standard error in cross-validation, respectively; Y and Yi are
the predicted and observed values for sample i (TVN, oil;
moisture); n is the number of samples used to build the
calibration models; t is the number of partial least squares
(PLS) factors in the model (Shenk & Westerhaus 1993).
Principal components (PC) were computed on spectra, in
order to rede®ne the optical properties of ®sh samples. The
Mahalanobis distance of each spectrum with respect to the
average spectra was calculated. In this way a structuring of
samples according to spectral features is possible and each
sample can be graphically placed in a three-dimensional plot
de®ned by any three PC scores (Shenk & Westerhaus 1993).
The CENTER program ranks spectra in a ®le according to
their Mahalanobis distance (H statistics) from the average
spectra of the ®le using PC scores (NIRS 21995). In order to
visualize the relative spectral position of samples from
di.erent ®sh species, samples were graphically displayed by
means of the ®rst two (one and two) or second two (two and
three), with the SYMMETRY program of the same software
(ISI, 3.01).
Results and discussion
Spectra characterization
The mean spectrum of minced raw ®sh samples is shown in
Fig. 1. The mean spectrum showed absorption bands at
1200 nm related to carbon-hydrogen (CH) stretch second
overtone, at 1456 nm related to oxygen-hydrogen (OH)
stretch ®rst overtone, at 1730 nm related to CH stretch ®rst
overtone, at 1947 nm related to OH absorption because of
water content and at 2310 nm to CH combinations (Murray
1986). According to Osborne et al. (1993) second derivative
spectra have a trough corresponding to each peak in the
original spectra. Figure 2 showed the second derivative of the
mean spectrum. The second derivative of the mean spectrum
had absorption bands at 1166 nm related to CH stretch
second overtone, at 1332 nm related to CH overtones, at
1418 nm related to OH stretch ®rst overtone, at 1712 and
1780 nm related to CH stretch ®rst overtone, at 1940 nm to
water, at 2054 and 2178 nm related to protein and at
2304 nm to CH combinations (Murray 1986; Osborne et al.
1993). In the second derivative, the main SDs occurred in the
water region (1456 and 1947 nm). That means water is the
domain component, which a.ected the mean spectrum of
minced raw ®sh samples.
NIRS calibration statistics for raw minced fish
Table 1 showed the NIRS calibration statistics in minced raw
®sh for the chemical parameters analysed. The R2
calibration and
SECV were 0.99 (3.86), 0.96 (3.51), 0.99 (8.01) and 0.98 (1.07)
for moisture (M), TVN and oil in g kg±1, and temperature
(T) in °C, respectively. Gjerde & Martens (1987) reported
that NIR can be used to determine fat, moisture and protein
in freeze dried rainbow trout (Oncorhynchus mykiss).
They used a 19 ®lter NIR instrument, in the wavelength
range 1445±2350 nm and the reported prediction errors were
4.5, 3.5 and 5.0 g kg±1 for fat, moisture and protein,
respectively. Rasco et al. (1991) analysed cross sections of
frozen and thawed rainbow trout by NIRS re¯ectance
between 900 and 1800 nm and found prediction errors of
10, 3.7 and 18 g kg±1 for fat, moisture and protein, respect-
ively. Sollid & Solberg (1992) measured homogenized Atlan-
tic salmon paste of 23 mm thickness, by near infra red
transmittance (NIT) and found prediction errors of 7 g kg±1
for fat. Isaksson et al. (1995) analysed intact salmon ®llets
Figure 1 NIRS mean spectrum (line) and SD (dotted) of minced raw
®sh samples.
Figure 2 Second derivative of NIRS mean spectrum (line) and SD
(dotted line) of minced raw ®sh samples.
NIRS in the prediction of chemical characteristics in fish
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Ó 2002 Blackwell Science Ltd Aquaculture Nutrition8;1^6
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(Salmo salar) by NIRS re¯ectance using remote and on line
®bre optic probe, either on frozen or thawed ®llets. They
found SEs for prediction of 12.8 and 11.6 in g kg±1 for fat
and moisture, respectively, worked in the spectral range of
1100±2500 nm. The calibration statistics obtained in this
work on raw ®sh were consistent with those of the previous
authors. An acceptable calibration for TVN was observed.
This parameter is a measure of nitrogen degradation during
storage and protein breakdown in raw ®sh (Borquez & Speek
1994; Borquez et al. 1994; Raghunath et al. 1995) and is used
to determine the quality (freshness) of the raw ®sh, previous
to its entry into the ®shmeal factory. The results found in this
work showed that NIRS could be used to assess this
parameter under industrial conditions satisfactorily. Oil
content in the sample was also well predicted by NIRS.
Although good calibrations were obtained for oil, a high
number of outlier samples were observed (n . 50). The
presentation of the samples to the instrument played an
important role to obtain good NIRS calibration statistics.
The nonadequate device used to present the samples to the
monochromator could explain the number of sample outliers
observed on the calibration models for oil. This was
especially veri®ed for very liquid samples, like salmon.
The cuvette was placed vertically and this may cause oil
separation in the sample during scan collection. Secondly,
di.erent species were also used to perform the calibration
models; ®sh samples with di.erent oil content because of
processing and storage conditions, seasonal variations,
determining di.erent optical characteristics. Figure 3 plotted
the clusters related to di.erent species used on the calibra-
tion. Four clusters were observed, corresponding to salmon,
bluewhiting, herring and other ®sh species. Individual NIRS
calibrations for each raw ®sh species were not explored,
because it escaped the objectives of this paper. Sample
temperature was well measured by NIRS. Temperature of the
minced raw sample was another parameter used to reject
samples by the industry. The sensing of temperature with
NIRS may come as a by-product of other measurements.
Any time we collect a spectrum which includes water as one
of the components, we have the possibility to measure the
temperature of that water (Norris 1988). This is true because
the water absorption bands are sensitive to temperature
(Isaksson et al. 1989). The performance of the calibration for
moisture, TVN, oils and temperature in the minced raw ®sh
was evaluated by using the SECV/SD ratio. When the error
in calibration (SEC or SECV) exceeds one-third of the SD of
the population, regression models can be misleading (Murray
1993). As none of the values for this ratio are >0.33 the
calibration models for moisture (SECV/SD: 0.06); TVN
(SECV/SD: 0.10) and oil (SECV/SD: 0.18) were classi®ed as
good (see Table 2). Figures 4 & 5 show the NIRS predicted
Mean SD SEC R2
calibration SECV R2
validation n T
M 723.0 58.3 3.03 0.99 3.86 0.99 82 7
TVN 33.3 7.2 1.37 0.96 3.51 0.83 83 10
oil 111.1 43.1 1.11 0.99 8.01 0.96 50 5
Temp. 12.5 3.7 0.38 0.98 1.07 0.92 60 13
SD: Standard deviation, SEC: standard error of calibration, R2
calibration: coe¤cient of multidetermination in
calibration, SECV: standard error of cross validation, R2
validation: coe¤cient of determination in cross
validation, T: number of terms used to perform the calibration model, TVN: Total volatile nitrogen
(mg g)1), M: moisture (g kg)1), oil: (g kg)1),Temp: temperature (°C).
Table 1 NIRS calibration and cross
validation statistics for moisture (M),
oil, TVN and temperature in minced raw
®sh
Figure 3 Score plots of minced raw ®sh samples. BW: Bluewhiting;
HE: herring; Salmon; other species (mackerel).
Table 2 Performance of calibration for raw ®sh samples
R2
calibration SD SECV CV (%) SECV/SD
M 0.997 58.3 3.86 0.53 0.06
TVN 0.996 33.3 3.51 10.5 0.10
oil 0.991 43.1 8.01 7.2 0.18
Temp. 0.989 3.7 1.07 8.6 0.29
SD: Standard deviation, R2
calibration: coe¤cient of multidetermination in
calibration, SECV: standard error of cross validation, CV percentage:
(SECV/mean) ´ 100; TVN: Total volatile nitrogen (mg g)1), M: moisture
(g kg)1), oil: (g kg)1),Temp.: temperature (°C).
D. Cozzolino et al.
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Ó 2002 Blackwell Science Ltd Aquaculture Nutrition8;1^6
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data vs. chemical data for moisture and TVN. Moisture on
bluewhiting samples tends to be overestimated by NIRS.
Sample characteristics could explain these results (whole
samples) such as surface moisture, freshness. Besides TVN
were underestimated on salmon samples (Fig. 5).
Conclusions
This work demonstrates that NIRS is a simple and easy
technique that can be used to successfully monitor the quality
of raw ®sh used to make ®shmeal. The operational meas-
urements described were adequately determined by NIRS
and improve speed of reporting and assist in decision-making
processes of management and process optimization. On the
other hand, NIRS does not completely replace all reference
analytical methods for oil quality and it is important to
maintain skill in reference analysis by lab sta.. The industry
would like to get more speci®c chemical information related
to the freshness of the sample, which still requires to be
checked periodically. Installation of NIR leads to release of
lab sta. time from routine quality control analysis which
allows more e.ort to be directed towards the establishment
of more sophisticated chemical and physical information on
the raw ®sh (origin, species, salt, adulterants) which may in
future be analysed by NIRS.
Acknowledgements
Ms A. Chree and K. Kreshaw (UFP, Tullos, Aberdeen, UK)
are thanked for providing the samples and technical assist-
ance.
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NIRS in the prediction of chemical characteristics in fish
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دوستان عزيز اگر انتقاد يا پيشنهادي در رابطه با اين وبلاگ داريد خواهش مي کنم فقط به ادرس زير ارسال نماييد.
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food and nutrition

Aquaculture Magazine Sep/Oct 2001
Volume 27, Number 5
© 2001 Aquaculture Magazine
FISH, FEED & NUTRITION
Ronald W. Hardy
Professor and Director
Hagerman Fish Culture Experiment Station
University of Idaho
August 20, 2001
One Size Doesn’t Fit All
Dr. Randy MacMillan, President of
the National Aquaculture Association
and VP of Research and Environmental
Affairs for Clear Springs Foods, Inc., is
famous in Southern Idaho for two
statements applied to the process of
establishing limits on nutrient levels in
trout farm discharge water. First,
MacMillan suggested that regulating
agencies could create a model to
establish optimum operating parameters
to reduce effluent phosphorus levels in
trout farms and that he expected such a
model to follow the dictum of “garbage
in, gospel out.” Second, concerning the
proposal to develop universal guidelines
for Idaho trout farms and effluent
management, MacMillan was quoted in
the local press as saying that “One size
doesn’t fit all.” MacMillan’s pithy
remarks use only four and five words,
respectively, but they say a great deal
about the proposals. Entire university
fisheries’ programs now focus on
computer modeling for setting standards
for aquaculture and as a result a
generation of students in these programs,
are diligently working on
computer tans. Modeling is a valuable
fisheries management tool but unless
models are developed by people who
get real tans spending time in the field
or on vessels gaining hands-on experience
about the variability of data that
goes into models, the odds of these
computer models being used widely is
remote. Concerning the second
observation that one size doesn’t fit all,
MacMillan is dead on, especially in
connection with nutritional requirements
of fish, and fish feed formulations.
Feed Formulation For Closely
Related Farmed Species
People in this business are said to
fall into two general groups those that
want to consolidate requirements for
like species, and those that want to
separate nutrition standards for each
individual species. Aquaculture
nutritionists may fall into either group.
For example, some have proposed that
the dietary requirements of salmon and
trout are basically the same, and thus
feed formulations should be the same,
including semi-purified diets used in
research. This group is the ‘one size
fits all group’. Others believe that each
species of fish, even those that are
closely related, have slightly different
dietary requirements, at least for some
essential nutrients. I fall into the latter
group, especially since I began rearing
wild cutthroat trout in our laboratory.
Cutthroat trout (Oncorhynchus
clarkii) are closely related to rainbow
trout (O. mykiss), being in the same
genus and being native to waters west
of the continental divide in North
America (Behnke, 1992). The Lewis
and Clark expedition first recorded
cutthroat trout in 1805, hence the
species name clarkii. There are two
divergent subspecies, the westslope
cutthroat (O. c. lewisi) associated with
the Columbia River, and the
Yellowstone cutthroat (O. c. bouvieri),
found in the upper Snake River drainage.
Several years ago, the Hagerman
Station was asked to conduct a feeding
study with cutthroat trout using fish of
wild origin. We collected wild cutthroat
from the Blackfoot River in
eastern Idaho (a tributary of the Snake
River) with the assistance of the Idaho
Department of Fish and Game, collected
gametes from mature females
and males, and brought them back to
the Station for fertilization and incubation.
When the fish were at the first
feeding stage, we offered them trout
starter feed that we routinely use with
rainbow trout. The cutthroat fry readily
consumed the feed, and all went well
for several weeks. Then a small
number of fish in each tank began to die
each day. The number rose over the
next few days. By chance, Professor
Ron Roberts, the world authority on
fish diseases (and columnist for this
magazine) was visiting, and he advised
in diagnosing the problem. We checked
the fish for bacterial and viral diseases
and found nothing. We checked water
quality and everything was fine.
Finally, Roberts remarked that the fish
seemed to be exhibiting signs of
vitamin deficiency, specifically pyridoxine
deficiency. I confirmed this in a
heartbeat, and we immediately began
feeding fresh beef liver. The fish
improved noticeably in a day, and
completely recovered in three days. We
switched to feed produced by another
company and resumed using rainbow
trout feed. Within a week, the fish
again showed signs of deficiency. Back
to liver and recovery. We then made
our own feed, doubling the vitamin
premix, and resumed feeding this to the
cutthroat trout fry. This time the fish
flourished. After checking with state
and federal fish hatcheries in the region
with experience raising wild cutthroat
Aquaculture Magazine Sep/Oct 2001
Volume 27, Number 5
.
trout, we found that our experience was
common. All hatcheries reported
having significant problems using
rainbow trout starter feeds with wild
cutthroat, and they supplemented
commercial feed with liver until the fish
reached the fingerling stage, at which
time they seemed to do well on rainbow
trout feeds. My fish are now two years
old and average 2-3 lbs., and have been
fed diets containing elevated levels of
vitamins compared to the recommended
levels specified for rainbow trout diets.
What could be different between
rainbow and cutthroat trout fry that
might influence their dietary requirement
for vitamins? They are both
native to the lakes and streams of
western North America. Their diets are
similar, at least during the fry stages.
Yellowstone cutthroat are native to the
western slopes of the Rocky Mountains,
whereas westslope cutthroat are found
from the western slopes of the Rocky
Mountains to coastal areas, similar to
rainbow trout. Thus, in general, they
are found in areas lower in elevation.
Cutthroat and rainbow trout are closely
related, and can even hybridize to
produce rainbow-cutthroat crosses.
Could the dietary requirements for
certain vitamins of Yellowstone
cutthroat trout really be different from
that of westslope cutthroat or rainbow
trout? Could our observations be the
result of using fish of wild origin?
Cutthroat trout from hatchery
stocks may have already undergone a
form of genetic modification as
compared to fish that could not survive
when fed feeds formulated for rainbow
trout fry. The answers to these questions
await studies by fish nutritionists.
But clearly MacMillan’s statement that
one size doesn’t fit all applies to the
formulation of starter feeds for cutthroat
and rainbow trout.
This observation has important
implications in the context of restoration
efforts being undertaken by
agencies to rebuild stocks of cutthroat
trout and other species in areas where
their numbers have been reduced,
especially efforts to rebuild fish
populations using captive broodstock
techniques with wild fish as the founder
stocks.
New York Times And Salmon
Aquaculture In Scotland
The New York Times ran a story on
July 17 on salmon aquaculture and
salmon sport fishing in Scotland,
written by Alan Cowell, a reporter from
London. The focus of the story was
that salmon fishing in Scotland has
diminished greatly as a result of weak
stocks. Many people blame salmon
farming for the reduction in wild
salmon stock abundance. I spoke to
Mr. Cowell for this article about salmon
aquaculture and found him to be an
open-minded, bright man who was
interested in getting a range of perspectives
on the pros and cons of salmon
farming. One issue we discussed at
length was the suggestion that increased
production of salmon by the aquaculture
industry is resulting or will result
in higher rates of harvest of wild, forage
fish to make fish meal for salmon feeds.
This is a common allegation by nongovernment
organizations (NGOs) that
oppose fish farming. I explained to Mr.
Cowell that fish meal production has
not increased as a result of salmon
farming, at least on a global basis.
However, fish feed producers are
purchasing a larger proportion of annual
global production of fish meal than they
did a decade ago because they are
willing to pay more for fish meal than
other major users, namely poultry and
swine feed producers. I also said that
given the fact fish meal is a finite
resource, this trend of a higher proportion
of annual global fish meal being
used to make fish feeds can only go on
so long. I explained that people in the
aquaculture industry were well aware of © 2001 Aquaculture Magazine

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fizalia

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