34 
Measurements of resistance and reactance 
in fish with the use of bioelectricai impedance 
analysis: sources of error 
M. Keith Cox (contact author ) 1 
Ron Heintz 1 
Kyle Hartman 2 
Email address for contact author: Keith.Cox@noaa.gov 
1 National Marine Fisheries Service 
Alaska Fisheries Science Center 
Auke Bay Laboratories 
1 1 305 Glacier Hwy 
Juneau, Alaska 99801 
2 West Virginia University 
Davis College of Agriculture, Forestry & Consumer Sciences 
1170 Agricultural Sciences Building 
Morgantown, West Virginia 26506-6010 
Abstract — New technologies can be 
riddled with unforeseen sources of 
error, jeopardizing the validity and 
application of their advancement. 
Bioelectricai impedance analysis 
(BIA) is a new technology in fisheries 
research that is capable of estimat- 
ing proximate composition, condition, 
and energy content in fish quickly, 
cheaply, and (after calibration) with- 
out the need to sacrifice fish. Before 
BIA can be widely accepted in fish- 
eries science, it is necessary to iden- 
tify sources of error and determine a 
means to minimize potential errors 
with this analysis. We conducted 
controlled laboratory experiments 
to identify sources of errors within 
BIA measurements. We concluded 
that electrode needle location, pro- 
cedure deviations, user experience, 
time after death, and temperature 
can affect resistance and reactance 
measurements. Sensitivity analy- 
ses showed that errors in predictive 
estimates of composition can be large 
(>50%) when these errors are experi- 
enced. Adherence to a strict protocol 
can help avoid these sources of error 
and provide BIA estimates that are 
both accurate and precise in a field 
or laboratory setting. 
Manuscript submitted 25 February 2010. 
Manuscript accepted 30 September 2010. 
Fish. Bull. 109:34-47 (2011). 
The views and opinions expressed 
or implied in this article are those of the 
author (or authors) and do not necessarily 
reflect the position of the National Marine 
Fisheries Service, NOAA. 
Successful application of promis- 
ing new technologies is predicated 
on understanding and controlling 
sources of errors. The need to identify 
sources of error with the development 
of new fisheries technologies is docu- 
mented in genetic studies (Blanca 
et ah, 2009), in mark and recapture 
studies (Curtis, 2006), in the tracking 
of vessels with global position sys- 
tems (GPS) (Palmer, 2008) and in 
measuring water clarity with beam 
transmissometers (Larson et al., 
2007). To identify sources of errors 
for new technologies, measurements 
are often compared with those from 
established technologies (Larson et 
al., 2007), simulated theoretical ones 
(Palmer, 2008), or known measure- 
ments (Curtis, 2006). Regardless of 
the process, the desired end result is 
to identify and reduce sources of error 
to increase the accuracy of measure- 
ments, thereby enhancing technol- 
ogy to provide accurate and reliable 
results within the fields of fisheries 
research and management. 
Bioelectricai impedance analy- 
sis (BIA) has the potential for wide 
application in fisheries as a tool to 
quickly and accurately perform a 
number of physiologically important 
field measurements. The BIA method 
is capable of estimating proximate 
composition, fish condition, and en- 
ergy content in fish quickly, cheaply, 
and (after calibration) without the 
need to sacrifice fish (Cox and Hart- 
man, 2005). Bioelectricai impedance 
analysis has been found to be ac- 
curate for measuring compositional 
mass (i.e., measured in grams), (Cox 
and Hartman, 2005), but not so ac- 
curate for measuring estimates of 
percentages or energy per wet weight 
(Pothoven et al., 2008). Bioelectricai 
impedance analysis involves measur- 
ing the impedance, resistance (R), 
and reactance (X c ) of fish tissues to 
an electrical current, and relating 
those measurements to the proximate 
composition, condition, or energy con- 
tent of the fish. Linear models re- 
lating impedance to compositional 
components are highly significant 
(PcO.001) with coefficients of deter- 
mination (r 2 )>0.96 (Cox and Hart- 
man, 2005). Relationships between 
observed and predicted values have 
slopes equal to one and intercepts 
that do not differ from zero. Estima- 
tions of body composition, condition, 
and energy content with BIA may 
be an asset to a variety of fisheries- 
related research and management 
projects by increasing the number of 
observations taken in the field and 
providing a means to take repeated 
measurements on individuals. 
Physiological parameters are esti- 
mated from measured resistance ( R ) 
and reactance (X c ) values. Resistance 
