OA−ICOS−Based Oxygen and Carbon Dioxide Sensors for Field Applications in Gas Reflux Chicken Coops

Abstract

To facilitate the effective assessment of respiratory entropy during poultry breeding, a novel oxygen (O₂) and carbon dioxide (CO₂) sensor was developed based on the off−axis integrated cavity output spectroscopy technique, featuring effective absorption optical paths of 15.5 m and 8.5 m, respectively. The sensor employs integrated environmental control technology, substantially enhancing detection precision. To improve the instrument’s response speed, the miniaturization of the cavity and structural optimization were implemented, achieving a rapid response time of merely 6.22 s, addressing the stringent requirements for quick responsiveness in poultry respiration thermometry research. A signal processing model tailored for on−site applications was designed, boosting the system’s signal−to−noise ratio by 4.7 times under complex environmental noise conditions. Utilizing Allan variance analysis, the sensor’s detection limits for O₂ and CO₂ were ascertained to be 2.9 ppm and 7.4 ppb, respectively. A 24−h field application test conducted in Gongzhuling demonstrated that the sensor’s results align with the respiratory characteristics of poultry under normal physiological conditions, validating its extensive potential for application in respiratory analysis, environmental monitoring, and industrial sectors.

1. Introduction

Poultry, as a crucial food source for humans, has consistently been the focus of research aimed at enhancing both yield and quality. However, the assessment of the absorption and conversion processes of essential nutritional elements such as carbon and nitrogen in poultry is challenging due to the limitations of current observation methods in avian husbandry. Presently, the measurement of gas production and consumption during feeding, combined with the calculation of the respiratory quotient through indirect calorimetry, provides insights into the nutritional intake and digestive status of poultry, playing a vital role in observing the process of nutrient conversion and promoting increased and improved production [1,2,3].

Among the various gas detection methods, electrochemical [4], gas chromatography [5], and optical techniques are commonly employed [6]. In

Table 1. Performance and cost comparison of different detection methods for O₂ concentration.

Testing Method	Measurement Range	Accuracy	Response Time (T90)	Stability	Cost (RMB)
Electrochemical	0~100%	±3%FS	<60 s	one week	10,000
Gas Chromatography	0~30%	±1%FS	<120 s	one month	500,000
CRDS	5~25%	<2 ppm	<5 s	three months	1,950,000
OA−ICOS	0~25%	<100 ppm	<5 s	three months	100,000

, the performance parameters and prices of the four gas measurement methods are compared. The electrochemical method is predominantly used in poultry husbandry observations; however, issues such as zero−drift, limited precision, and gas poisoning have constrained its application [7]. In contrast, the G2207i O₂ analyzer developed by Picarro, utilizing Cavity Ring−Down Spectroscopy (CRDS) [8], offers significantly enhanced precision in concentration detection, but its high cost renders it impractical for agricultural research. Indirect measurement methods are limited by a short measurement duration of only 20 min in the metabolic process of poultry. In practical applications, not only is precision crucial, but also a rapid response time. Consequently, OA−ICOS technology has emerged as a more advantageous detection technique, given its ability to meet the criteria of precision, response time, and cost−effectiveness.

Compared to CO₂ measurement, research on O₂ measurement is relatively scarce as it faces the challenges of wide range and high precision detection. The sensors commonly used in the field still primarily rely on electrochemical methods. In 1989, Dr. Bradley Joseph and colleagues designed an O₂ sensor array based on semiconductor manufacturing using the electrochemical approach [9]. While these sensors are cost−effective and compact, the widespread issues of significant offset and long response times associated with electrochemical sensors make them inadequate for certain demands. In optical measurement methods, recent research, including that of Qixin He [10], has achieved a measurement precision of 199 ppm and a response time of 30 s using the OA−ICOS gas concentration detection method and pressure calibration with the PSO−SVM algorithm. Nevertheless, this falls short of the precision and response time required for agricultural detection. In light of the current research, the design and implementation of a low−cost, high−precision, and rapid−response O₂ and CO₂ sensor is an urgent issue that needs resolution.

This study addresses the core requirements of high precision, rapid response, and cost−effectiveness in gas sensors for the poultry industry. It introduces a dual−gas sensor based on OA−ICOS technology, which significantly improves the measurement accuracy of O₂ and CO₂ concentrations, achieving 2.9 ppm and 7.4 ppb (Allan variance), respectively, through structural, adjustment, and environmental control and filter comparisons. Moreover, a compact integral cavity with a length of 6 cm and a diameter of 2 cm was designed to provide a rapid response time of 6.22 s. The sensor utilizes DFB butterfly lasers at 760.9 nm and 2004 nm, simulates the absorption cross−sections of the two gases using the HITRAN database, and identifies the absorption spectral lines for O₂ and CO₂ at 760.654 nm and 2004 nm, respectively. An off−axis integral cavity with effective optical paths of 15.5 m and 8.5 m is designed. The sensor employs wavelength modulation spectroscopy (WMS−2f) for signal amplification. The calibration of the sensor is performed using a gas mixer, and it is applied within a novel multi−chamber parallel open−loop poultry respiratory calorimetry device to measure the O₂ consumption and CO₂ production of different chicken breeds. These data are then used to assess physiological activity and feeding information through the respiratory quotient calculated using indirect calorimetry [11].

2. Materials and Methods

2.1. Sensor Solutions

The sensor scheme is exemplified by an O₂ single−channel measurement system, with Figure 1 illustrating its detection principle. This system employs a 14−pin butterfly−packaged Distributed Feedback (DFB) laser diode as the light source, utilizing Tunable Diode Laser Absorption Spectroscopy (TDLAS) in the vicinity of 760.9 nm for wavelength scanning. An optical collimator (FC/APC−780, Thorlabs, Newton, NJ, USA) is utilized at the input end of the integrating cavity for beam collimation. The high−precision integrated cavity, approximately 6.2 cm in length, consists of two concave high−reflection mirrors with a reflectivity of 99.6%, achieving an effective absorption path length of 15.5 m. To ensure the coaxial alignment of the internal mirrors and to minimize the impact of temperature on the cavity’s stability, indium steel is specifically chosen as the cavity material. At the cavity’s output end, a 100 mm focal length lens focuses the beam onto the photosensitive surface of a photodiode (PDAPC1, Thorlabs), with data acquisition and driving equipment (DAQ, USB−6211, National Instrument, Austin, TX, USA) used for data collection. To mitigate the impact of environmental factors on the device’s performance, a dual mass flowmeter is adopted for pressure and flow stabilization of the measured gas, while the gas cavity structure is maintained at a constant temperature via a thermoelectric cooler (TEC, SiRui, Zhongshan, China) [12,13,14].

2.2. Absorption Spectra and Modulation

For practical application considerations, this scheme focuses on spectral line intensity, the absence of interference from other gases, and compatibility with mature laser technology. Based on the HITRAN database [15], a simulation analysis of the absorption spectral lines of O₂ and CO₂ under standard temperature and pressure conditions is conducted (Figure 2). Common interfering gases such as water vapor (H₂O) and methane (CH₄) present minimal interference in the 13,142.2 cm⁻¹ waveband for O₂ detection, and meet the requirements for extensive O₂ concentration measurements; interference from water vapor (H₂O) on CO₂ is relatively weak, with its absorbance at least three times lower than that of CO₂, allowing for elimination through subsequent data processing. Therefore, the chosen absorption wavebands are 13,142.2 cm⁻¹ (760.89 nm) for O₂ and 4990 cm⁻¹ (2004 nm) for CO₂.

Regarding laser wavelength modulation, the center current for the O₂ laser diode is set at 26.5 mA, with a temperature of 26 °C, a modulation frequency of 2000 Hz, and a modulation depth of 1 mA; for the CO₂ laser diode, the center current is 61.5 mA, the temperature is 22 °C, and the modulation frequency and depth are 2000 Hz and 5 mA, respectively (Figure 3) [16,17,18].

2.3. Light Path Analysis and Integrating Cavity Setting

In the practical testing of optical path analysis and integrated cavity adjustment, the reflectivity of concave lenses and their assembly precision play a decisive role in the effective optical path of the integral cavity [19]. To ensure measurement accuracy, this scheme strives to conduct measurements within a linear range, thus safeguarding sensor performance. The reflectivity was determined to be 99.6% for O₂ and 99.27% for CO₂, with a spherical lens of 1000 mm curvature utilized to construct an integral cavity structure of 6.2 cm. According to the optical path calculation method from the literature [20], the effective absorption optical paths measured were 15.5 m and 8.5 m, respectively. In terms of assembly, the key lies in ensuring that the two concave mirrors are relatively parallel in the same plane along the same axis [21,22]. Compared to traditional visible laser integrator cavity assembly methods, this proposal advocates the use of precision prisms to establish a three−dimensional structural coordinate system and employs a theodolite to adjust the integrator cavity mirror seats, thus quantifying the relative position of the two−dimensional mirrors with an accuracy of up to 30 arc seconds (Figure 4).

2.4. Temperature and Pressure Control

Temperature and pressure are critical factors affecting detection accuracy. In this study, a specialized temperature and pressure control unit was designed. An independent constant temperature cavity was designed within the temperature control section (Figure 5), with the most thermally sensitive cavity placed inside a constant temperature box as the primary control target. The constant temperature box is made of a thermally less sensitive EEP (expanded polypropylene) material and is doubly wrapped with aluminum alloy metal films to minimize the impact of external temperature fluctuations on performance. The temperature control unit utilizes temperature and pressure sensors within the cavity for closed−loop control feedback. Temperature regulation is achieved by controlling the direction and magnitude of current flow through Thermoelectric Coolers (TECs) for heating or cooling the air inside the constant temperature box. A looped unidirectional heat flow exchange method is adopted to avoid turbulence within the temperature−controlled cavity, and key positions within the cavity employ multi−point temperature measurement combined with weighted temperature control to ensure precision as much as possible. The temperature control accuracy of critical positions within the cavity can reach ±0.005 °C, as demonstrated by the actual measurement results shown in the Figure 6.

For pressure control, the pressure sensor is connected to the measurement gas chamber, converting pressure signals into digital signals and transmitting them to the signal processing module. The flow control module feeds back current flow information to the signal processing module and works in conjunction with the PID control module. Based on the comparison between the measured pressure and the set pressure values, it outputs the corresponding flow control information. Through digital−to−analog conversion, a proportional solenoid valve controls the opening flow, achieving precise control of the cavity pressure. The pressure inside the cavity is adjusted by mass flow meters at the inlet and outlet ends of the cavity. The temperature and pressure control unit developed in this study can achieve a stability of ±0.005 °C and ±1.5 pa (Figure 7).

3. Results

3.1. Gas Preparation

To enhance the accuracy of the sensor measurements, a gas preparation system was configured to produce multiple concentration gas samples with a stable output flow rate. Standard gases of 50% O₂ and 3% CO₂ concentrations have an uncertainty of ≤1%. A gas mixer (SX−7100, LNI Swissgas, Versoy, France) was used to thoroughly mix pure nitrogen with standard gases, preparing multi−gradient standard gases. The accuracy of the gas mixer is better than 0.5%, and its repeatability exceeds 0.2%.

3.2. Calibration and Data Fitting

Multiple standard concentrations of O₂ and CO₂ samples were tested to establish the relationship between the 2f amplitude and gas concentration. Sampling intervals of 5 min were used to obtain an average, and concentration calibration was conducted using linear fitting.

As shown in Figure 8, the fitting equation for O₂ is Y = 6.806 × 10⁻⁷ × X − 15.59, with a correlation coefficient R² = 0.9973; for CO₂, the fitting equation is C = 0.002697 × U + 242.7, with a correlation coefficient R² = 0.9998. After fitting, residuals were calculated and the maximum deviation between the two was estimated. The relative concentration deviation of O₂ was approximately 0.6%, and that of CO₂ was 0.7%.

3.3. Allan Variance Estimation

After ensuring that the temperature and pressure conditions were met, the Allan variance method was employed to evaluate the long−term stability and detection limits [23]. By allowing 99.99% nitrogen to flow continuously into the gas chamber for at least 15 min, the relationship between the Allan variance and integration time was observed.

Figure 9a shows the detection situation for O₂, where the minimum detection limit reaches 24.6 ppm at an integration time of 1 s, and the optimal detection limit is 2.9 ppm at 622 s. In comparison, the measurement range for CO₂ is smaller, but with higher precision; the detection limit is approximately 3.37 ppm at 1 s and as low as 7.4 ppb at 350 s.

3.4. Response Time

The system’s response time is primarily governed by the volume of the gas chamber and the intake flow rate. To measure the response time, O₂ or CO₂ of varying concentrations was introduced into the integrating cavity. As illustrated, the Figure 10 and Figure 11 presents cross−sectional fluid simulations of different intake methods for two types of integrating cavity structures. The conventional intake structure of the integrating cavity, as shown in Figure 10, features an internal cavity length of 6.2 cm, a mirror size of 5.08 cm, and an air inlet positioned at the top of the cavity, with a gas flow rate set at 1 L/min. The simulation results indicate that, due to the influence of intake methods and structural characteristics, rapid air exchange within the cavity in less than 10 s leads to the formation of eddies and dead zones, which hinders fast response and precise measurement.

Hence, structural optimizations were made to the integrating cavity by eliminating the larger−sized high−reflection mirror (≥5.08 cm) commonly used in systems to reduce the size of the off−axis integrating cavity and thereby enhance the response time. In this study, a compact off−axis integrating cavity was designed, utilizing a 1−inch high−reflection mirror, with an overall cavity length of 7.6 cm, an inner diameter of 1.8 cm, and a distance of 6.2 cm between the two mirror surfaces. To mitigate the dead zone phenomenon during the air ventilation of the optical cavity, the mirror mounts were internally chamfered, and the intake method was altered to reduce the impact of eddies on the detection accuracy without affecting the reflection of the off−axis light beam. At a gas flow rate of 1 L/min, the theoretical calculation determined that it takes 2.873 s to fill the gas chamber. Using a 20% O₂ concentration, the equilibrium time within the cavity was calculated at a flow rate of 1 L/min. The simulation results revealed that the airflow within the integrating cavity became gradually stable after 0.6 s, and the O₂ concentration reached equilibrium after 2.7 s.

The actual test results, as shown Figure 12, set the flow rate of the gas distributor at 1 L/min, initially introducing 10 min of low−concentration O₂ (17%), with the average of the last 2 min taken as C₁, followed by switching to high−concentration O₂ (19%) for 10 min and the average of the last 2 min taken as C₂. During the switching process, the response times for T₉₀ were calculated as 6.62 s (T₂−T₁). The test results demonstrate the instrument’s excellent rapid response performance, enabling quick responses to changes in the concentration of the measured gas [24].

3.5. Filter Optimization

3.5.1. Conventional Phase−Locked Amplification

Noise is a pivotal factor affecting the sensitivity and detection limit of gas detection. The traditional phase−locked amplifier is effective in extracting the target frequency signal while reducing the impact of noise. In gas detection, the amplitude of the secondary harmonic signal after gas absorption is directly proportional to the concentration of the gas being measured. Consequently, by mixing the input signal with a double−frequency reference signal and passing the output through a low−pass filter, the relative concentration of the gas can be obtained. Despite the minimization of circuit noise by the conventional phase−locked amplifier, variables such as external temperature, pressure fluctuations, and laser stability still introduce uncertainties that affect measurement accuracy.

3.5.2. Windowed Fourier Transform (WFT)

Following the phase−locked amplifier, a low−pass filter is employed to demodulate the original signal. Traditional signal processing utilizes the Fourier transform, which lacks the ability to capture local characteristics and fails to analyze the relationship between frequency and time for non−stationary signals. In contrast, the Windowed Fourier Transform (WFT) divides the time−domain signal into equal short time intervals and performs Fourier transformations on each segment, thus obtaining frequency information across different time periods. However, Short−Time Fourier Transform does have limitations when selecting the window width.

3.5.3. Wavelet Transform

Wavelet transform differs from Fourier transform by employing finite and decaying wavelet bases instead of infinite triangular function bases. The wavelet bases can be dilated and translated, providing both frequency information and temporal localization. Therefore, for the 2f signals detected using OA−ICOS technology, wavelet transform is particularly suitable for processing the signal amplitude in the time domain.

$$(\mathrm{W}\mathsf{\phi }\mathrm{f})(\mathrm{a},\mathrm{b})={\left|\mathrm{a}\right|}^{-{\displaystyle \frac{1}{2}}}{\int }_{-\mathrm{\infty }}^{+\mathrm{\infty }}\mathrm{f}(\mathrm{t})\mathsf{\phi }\left({\displaystyle \frac{\mathrm{t}-\mathrm{b}}{\mathrm{a}}}\right)\mathrm{d}\mathrm{t}$$

(1)

Here, Wφ represents the base function, f is the signal function, a is the scale factor, and b is the translation factor. By introducing a window length factor, the scale becomes variable, with a controlling the dilation of the base wavelet and b determining the time of signal analysis. The following Figure 13 illustrates the comparative results of the three filters. The signal fluctuation, after wavelet transform processing, intersected with the traditional phase−locked amplifier and windowed Fourier transform, showing reductions of 4.93 times and 2.83 times, respectively, with the standard deviation improved from 5.33 ppm to 1.21 ppm.

4. Field Application

Animals, after ingesting feed, undergo digestion and absorption to introduce nutrients into their bodies, which are then utilized in energy metabolism through biological oxidation−reduction reactions. This process essentially involves the animal inhaling O₂, which enters the bloodstream through the lungs and is transported to various tissue cells, participating in oxidation−reduction reactions of nutrients and resulting in the production of CO₂ and water. The CO₂ is subsequently transported through the bloodstream to the lungs and exhaled. By measuring the amount of O₂ consumed and CO₂ exhaled by animals during this gas exchange process, we can indirectly calculate their heat production. The ratio of the amount of CO₂ exhaled to the amount of O₂ consumed, known as the respiratory quotient (RQ), can be used to assess the physiological state of the animal.

RQ = Emissions of CO₂ (L)/Consumption of O₂ (L)

(2)

This study employed an open recirculation device with sensors to evaluate the concentrations of O₂ and CO₂ in the breath of poultry. Yellow chickens from Dehui City, Jilin Province, were reared in one group (Coop A) and another group (Coop B), respectively, and provided with ample feed. Continuous 24−h gas concentration monitoring was conducted using the natural feeding method. The gases were introduced in a series distribution, with the atmosphere first serving as the background gas for CO₂ and O₂ concentration calculations, followed by the extraction of the gas post−metabolism by the yellow chickens, which was filtered and dust−removed before entering the sensors. Since the intake flow rate of the sensors was constant, the mean value of the last minute with stable concentration values was taken as the measured concentration. Among the measured values there are four concentrations: C_coops is the concentration of CO₂ in the chicken coops, C_atmosphere is the concentration of CO₂ in the atmosphere, O_atmosphere is the concentration of O₂ in the atmosphere, and O_coops is the concentration of O₂ in the chicken coops. The respiratory entropy of chicken coops A and B at different times of the day were calculated separately using Equation (3).

RQ = (C_coops − C_atmosphere)/(O_atmosphere − O_coops)

(3)

Figure 14 presents the measured data of O₂ and CO₂ concentrations within chicken coops from 6:00 to 8:00, across five experimental groups. The concentrations were assessed at 24−min intervals for the ambient air in Coop A and Coop B. One yellow chicken was placed in Coop A, while two were placed in Coop B. Continuous 24−h monitoring was conducted to track the gas concentration variations within these two coops. In the Figure 15, graphs a and b depict the 24−h patterns of gas concentration fluctuations for Coops A and B, respectively. The respiratory quotient (RQ) for both groups of chicks was calculated over the 24−h period using the respiratory quotient calculation formula. The results, as presented in the Figure 16, indicate that the RQ values align with the physiological activity patterns during feeding (RQ = 0.95–1.05) and fasting (RQ = 0.6–0.8) stages in chicks. This demonstrates the instrument’s suitability for the indirect calorimetry measurement of the avian respiratory quotient.

5. Conclusions

In this study, a self−developed dual gas sensor based on off−axis integrated cavity enhanced absorption spectroscopy was designed and measured the concentrations of O₂ and CO₂ for poultry RQ assessment. An innovative temperature and pressure regulation control module was developed to minimize the effects of environmental changes on gas measurement accuracy. To meet the demand for rapid response, a novel counterflow integral cavity structure was devised, effectively reducing the response time to 6.62 s (T₉₀). The detection system achieved a minimum detection limit for O₂ of 2.9 ppm at 622 s and carbon CO₂ of 7.4 ppb at 350 s. The system underwent a 24−h field test in an open recirculating poultry house, where its reliability and stability were validated through theoretical computations based on poultry respiration entropy. This design offers a new detection method for rapid response and high−precision detection in the livestock industry.