Fig 1.
Washout problem in flow through respirometry systems.
When the animal changes the concentration of a metabolic gas inside the respirometry chamber with a certain shape, it appears in a different pattern in the outlet.
Fig 2.
Experimental setup to determine the impulse response of a flow through respirometry system.
The impulse response of each respirometry setup was found by infusing a pulse of CO2 with the duration of 100 ms by means of the picospritzer, roughly in the location where an animal would be. The recorded output was normalized to find the impulse response.
Fig 3.
Experimental setup to evaluate the methods.
A high-speed valve switches the inlet flow between pure air and 100 ppm CO2. The concentration of CO2 in the outlet gas is measured in the gas analyzer.
Fig 4.
Impulse responses of three respirometry chambers with five different flow rates.
None of the impulse responses are in the form of pure exponential decay.
Fig 5.
The effect of fans on the impulse response of the system.
Even after using a fan the impulse responses do not approach exponential decay. In this experiment the impulse responses for a 28 mL respirometry chamber in different flow rates is determined when the embedded fans within the chamber are on and off.
Fig 6.
Fitting different curves to the impulse response.
The curves with new equations fit more accurately to experimentally determined impulse response than the exponential decaying curve (black).
Table 1.
Parameters of the impulse responses.
Experimentally-determined impulse responses of three respirometry chambers in five different flow rates were modeled in four ways. In the first three models, αtme−βt has been fitted to the data. Here m, α, and β are constant parameters that are needed to fit this curve (αtme−βt) to the experimental data (see text for details). In the first one, m is considered as a real number. In the second, one we restricted the m to be an integer and then found the parameters. In the third one, we decreased m to the lowest integer number without having more than 10% ITAE (). In the last one, we forced m to be zero in order to recover the ZT model.
Table 2.
Estimated parameters of ZT and EZT methods using Eqs 2 and 11 and from experimental data.
Fig 7.
Comparing different methods for recovering the CO2 input.
CO2 was infused with rectangular pulses with different frequency and duration into the respirometry chamber in two flow rates of 250 and 500 mL/min and the output was recorded (A). ZT, EZT, and GZT methods were used to recover the CO2 inputs from the recorded data (B and C). The results indicate that the precision of GZT method is significantly higher than the ZT and EZT methods and the EZT is more precise than the ZT method.
Table 3.
Normalized ITAE to the area of the input signal of the recovered signals in different frequencies.
Fig 8.
Comparison of ZT, EZT, and GZT methods.
The injected input of 100 ppm CO2 with duration of 200 ms into the 28 mL respirometry chamber is estimated from the output signal using different methods.
Fig 9.
Recovery of the actual metabolic rate of a beetle.
ZT, EZT, and GZT methods were applied on the recorded respirometry data of a Zophobas morio adult and the recovered instantaneous signals compared with a threshold line to find the open and closed phase of the spiracles.
Table 4.
Calculated opening phase based on different recovered respiratory data.