Development of a Tuneable NDIR Optical Electronic Nose

sensors

Article Development of a Tuneable NDIR Optical Electronic Nose

Siavash Esfahani , Akira Tiele , Samuel O. Agbroko and James A. Covington * School of Engineering, University of Warwick, Coventry CV4 7AL, UK; [email protected] (S.E.); [email protected] (A.T.); [email protected] (S.O.A.) * Correspondence: [email protected]; Tel.: +44-(0)-24-765-74494

Received: 2 November 2020; Accepted: 27 November 2020; Published: 1 December 2020

Abstract: Electronic nose (E-nose) technology provides an easy and inexpensive way to analyse chemical samples. In recent years, there has been increasing demand for E-noses in applications such as food safety, environmental monitoring and medical diagnostics. Currently, the majority of E-noses utilise an array of metal oxide (MOX) or conducting polymer (CP) gas sensors. However, these sensing technologies can suffer from sensor drift, poor repeatability and temperature and humidity effects. Optical gas sensors have the potential to overcome these issues. This paper reports on the development of an optical non-dispersive infrared (NDIR) E-nose, which consists of an array of four tuneable detectors, able to scan a range of wavelengths (3.1–10.5 µm). The functionality of the device was demonstrated in a series of experiments, involving gas rig tests for individual chemicals (CO2 and CH4), at different concentrations, and discriminating between chemical standards and complex mixtures. The optical gas sensor responses were shown to be linear to polynomial for different concentrations of CO2 and CH4. Good discrimination was achieved between sample groups. Optical E-nose technology therefore demonstrates significant potential as a portable and low-cost solution for a number of E-nose applications.

Keywords: electronic nose; tuneable optical sensor; non-dispersive infrared; odour ﬁngerprints; chemical analysis; gas rig testing

1. Introduction The electronic nose (E-nose) has been in continuous development since its conception in the early 1980s by Persaud and Dodd [1]. The term E-nose describes an instrument consisting of an array of cross-sensitive gas sensors, coupled with a pattern recognition approach. This operating principle attempts to mimic the function of biological olfactory receptors by analysing samples as a whole (so-called “odour fingerprints” [2]), rather than individual chemicals. In the past, E-noses were mainly used in the food safety or food quality, for example, for the determination of tea quality, adulteration of olive oil, fruit ripening and the rancidity of meat [3]. More recently, two other major application areas for E-noses have emerged; specifically, environmental monitoring (e.g., detecting pollutants, hazardous chemicals and/or explosives) [4] and biomedical purposes (e.g., monitoring and diagnosing diseases) [5]. These applications rely on the ability of E-noses to generate a holistic analysis of a gas-phase mixture, which are made up of volatile organic compounds (VOCs) and some non-VOCs, such as inorganic gases. VOCs can be broadly defined as carbon-based compounds (C2–C30), which include a diverse group of compounds such as hydrocarbons, esters, alcohols, ketones and aldehydes, with high vapour pressures and low boiling points (50–260 ◦C) [6]. In gas-phase chemical sensing, the gold standard is generally considered to be gas chromatography mass spectrometry (GC-MS) [7]. While this method is highly reproducible and accurate, it is also very expensive, requires highly trained staff and lacks portability. The key advantage of E-noses is that

Sensors 2020, 20, 6875; doi:10.3390/s20236875 www.mdpi.com/journal/sensors Sensors 2020, 20, 6875 2 of 16 their relatively simple operating principle and construction enables far more flexibility in the tailoring of the design and technical specification (e.g. ease of use, battery life, durability, number of sensors, sensing technology and sampling technique). There are several types of sensor technologies suitable for constructing E-nose arrays. An in-depth summary can be found in the review by Dospinescu et al. [8]. The most commonly used sensors in E-noses are metal oxide (MOX) and conducting polymers (CP). Both types of sensors operate through a change in conductivity when exposed to a target gas [9,10]. Examples of commercial E-noses, based on MOX and CP technology, include PEN3 [11] (AIRSENSE Analytics, Schwerin, Germany), FOX4000 [12] (AlphaMOS, Toulouse, France) and Cyranose 320 [13] (Sensigent, Baldwin Park, CA, USA). While these E-noses have been successfully deployed in a number of studies [14], a limitation is that they can suffer from drift and poor repeatability. MOX sensor responses can drift significantly, due to ambient temperature, pressure changes and material aging [15], and CP sensors often demonstrate poor repeatability and reproducibility, due to the random nature of the polymer [16]. The effects of sensor drift can be mitigated by utilising multi-sensor arrays (e.g., 6–18 sensors), to reduce the relative drift of individual sensors within the array, and by using pattern recognition techniques to account for material drift and environmental changes. Repeatability issues can be addressed by calibrating the E-nose to a number of standardised gas exposures. While these methods are relatively well-established, other sensing technologies are available that are fundamentally less susceptible to these effects, such as optical gas sensors. Optical gas sensors measure the modulation of light properties or characteristics, such as changes in absorbance, polarisation, fluorescence or other optical properties [17]. Since this method does not relate to chemical reactions, such as those for MOX and CP sensors, environmental changes have less effect on the response of optical sensors, with the exception of pressure. In general, optical gas sensors therefore require less frequent calibration. Moreover, with regard to sensor drift, single, or much smaller numbers of optical gas sensors, could perform similarly to larger MOX or CP sensor arrays and would also be less reliant on pattern recognition techniques to account for environmental changes. Further advantages of this sensing mechanism are that it is highly sensitive to a wide range of VOCs, often have good selectivity (especially to gases such as CO2 and CH4), good response/recovery times and longer sensor life than other gas sensor types [18,19]. The disadvantages of optical sensors are that they are typically more expensive and can have lower portability, due to the delicate optics and electronics [4]. Moreover, they are difficult to miniaturise and may require complex electrical circuits, since they do not rely on measuring a simple transduction mechanism, such as electrical resistance [19]. Despite some of these limitations, it has been argued that optical sensors are fundamentally among those best suited for E-nose applications [5]. Compared to the other gas sensor technologies, optical sensors have had less commercial success, likely due to cost and level of sensitivity, with the exception of targeted CO2 and CH4 detection [20]. Review papers evaluating the application of E-noses in food safety focus entirely on MOX and CP sensor-based system and merely mention optical technology in a long list of potential sensing methods [21,22]. Similar trends are observed in review articles that focus on the application of E-noses for environmental monitoring [4,23] and medical diagnostics [24,25]. These reviews indicate that E-noses are currently under-utilised in research and that there is still significant scope to evaluate the potential of this technology. In the commercial domain, there is a small selection of gas analysers, which utilise optical technology, such as the IRIS 4100 mid-IR and MIRAN SapphIRe (Thermo Fisher Scientific, Waltham, MA, USA). The former uses laser absorption spectroscopy to measure CO2, while the latter uses a single beam infrared (IR) spectrophotometer for the monitoring of ambient air samples. The MIRAN SapphIRe unit is portable and can detect the presence of up to 121 gases, but is also expensive (upwards of $30,000). Further examples of portable gas analysers include the GT5000 Terra, DX4015 and DX4000 (Gasmet Technologies, Vantaa, Finland). These operate based on Fourier Transform IR (FTIR) spectroscopy and can measure up to 50 gases simultaneously, but they cost upwards of $40,000. These instruments exemplify the current state of commercial optical sensors, in that the devices are too costly or specialised. Sensors 2020, 20, 6875 3 of 16 Sensors 2020, 20, x FOR PEER REVIEW 3 of 16

The growing growing demand demand for for Internet-of-things Internet-of-things (IoT) (IoT) enabled enabled environmental environmental monitors monitors [26], point- [26], point-of-careof-care diagnostic diagnostic devices devices [24] and [24] real-time and real-time food food quality quality sampling sampling equipment equipment [27] [ 27calls] calls for fornovel novel E- E-nosenose designs designs and and concepts. concepts. These These should should enable enable E- E-nosesnoses to to operate operate in in environmentally environmentally challenging conditions, withwith continuous continuous temperature temperature and and humidity humidity ﬂuctuations. fluctuations. While While current current MOX MOX and CP-based and CP- gasbased sensors gas sensors may struggle may struggle in these in situations, these situations optical, optical sensing sensing technology technology has the has potential the potential to thrive. to Thethrive. aim The of this aim work of this is thereforework is therefore to develop to adevelop novel optical-based a novel optical-based E-nose, which E-nose, is portable which andis portable can be deployedand can be in deployed a wide variety in a wide of typical variety E-noses of typical applications. E-noses applications.

2. Materials and Methods The proposed proposed optical optical E-nose E-nose is isco comprisedmprised of four of four emitters, emitters, paired paired with withtuneable tuneable detectors. detectors. Each Eachemitter–detector emitter–detector pair is pair individually is individually encapsulated encapsulated in a inheated a heated sensor sensor chamber. chamber. The The sample sample inlet inlet is iscontrolled controlled using using a avalve valve and and the the system system operates operates a a negative-pressure negative-pressure system, system, using using a a pump at the exhaust. The system is controlled by the user usingusing aa laptop,laptop, viavia wiredwired USBUSB connection.connection. A systemsystem diagram of thethe opticaloptical E-noseE-nose isis shownshown inin FigureFigure1 1..

Figure 1. Optical electronic nosenose system diagram.

The following sectionsection discussesdiscusses thethe individualindividual sub-systemssub-systems ofof thethe devicedevice inin moremore detail.detail.

2.1. Optical Gas Sensors A nondispersive infrared (NDIR) sensor system consistsconsists of three main parts: emitter (IR source), gas flowflow pathpath andand IRIR detectordetector (with(with filter)filter) (Figure(Figure2 2).). TheThe operatingoperating principleprinciple isis basedbased onon molecularmolecular absorption spectrometry.spectrometry. When When a a gas gas is is inside inside the the chamber, chamber, molecules molecules absorb absorb the the radiation radiation from from the the IR source.IR source. The The filter filter on theon the optical optical sensor sensor will will only only let thelet the radiation radiation wavelength wavelength related related to the to targetthe target gas pass.gas pass. Molecular Molecular absorption absorption of IRof IR is measuredis measured to to detect detect a falla fall in in signal, signal, which which can can produce produce aa uniqueunique odour fingerprintfingerprint [[28].28]. Several VOCs relevant for food safety and biomedical monitoring applications are knownknown toto havehave absorption absorption frequencies frequencies in in the the range range of 2.8–5.2of 2.8–5.2µm, µm, such such as acetone, as acetone, 2-butanone 2-butanone and isopropanoland isopropanol [29,30 ].[29,30]. It is worth It is noting worth that noting the number that the of photonsnumber absorbedof photons is directly absorbed proportional is directly to theproportional power of theto photonthe power beam of from the thephoton emitter beam and from thus thethe amount emitter of and the gasthus/vapour the amount detected. of Itthe is thereforegas/vapour possible detected. to determine It is therefore the concentrationpossible to determine of the measured the concentration molecule. of There the measured are two advantages molecule. associatedThere are two with advantages IR radiation. associated Firstly, IR with radiation IR radiation. scatters Firstly, less than IR visibleradiation radiation scatters in less the than presence visible of steam,radiation mist in orthe smoke, presence due of to steam, its longer mist wavelength or smoke, due [31]. to Secondly, its longer the wavelength miniaturisation [31]. Secondly, of IR sources the andminiaturisation detectors is of possible IR sources using and the detectors latest MEMS is possible technology. using the latest MEMS technology. The optical E-nose utilises commercially available emitters and detectors. The emitters are high emissivity thermal IR emitters (EMIRS200, Axetris, Kägiswil, Switzerland). The tuneable detectors were developed by InfraTec (Dresden, Germany) and are summarised in Table1.

Sensors 2020, 20, 6875 4 of 16 Sensors 2020, 20, x FOR PEER REVIEW 4 of 16

FigureFigure 2. 2. OpticalOptical gas gas sensing sensing principle. principle. Table 1. List of sensors used in the optical electronic nose. The optical E-nose utilises commercially available emitters and detectors. The emitters are high emissivity thermal IR Manufactureremitters (EMIRS200, Axetris, Type Kägiswil, Optical Switzerland). Wavelength The tuneable detectors were developed by InfraTecInfraTec (Dresden, Germany) LFP-3144C-337 and are summarised 3.1–4.4 inµ Tablem 1. InfraTec LFP-3850C-337 3.8–5.0 µm TableInfraTec 1. List of sensors LFP-5580C-337 used in the optical electronic 5.5–8.0 nose.µm InfraTec LFP-80105C-337 8.0–10.5 µm Manufacturer Type Optical Wavelength InfraTec LFP-3144C-337 3.1–4.4 µm These detectors have the ability to scan the mid- and long-wave IR range (3.1–10.5 µm) [32], InfraTec LFP-3850C-337 3.8–5.0 µm in steps of 20 nm. This should be considered the wavelength resolution of the optical E-nose. Due to InfraTec LFP-5580C-337 5.5–8.0 µm this feature, any fixed frequency measurements can be considered as “virtual sensors”. Conventional InfraTec LFP-80105C-337 8.0–10.5 µm IR detectors use expensive precision filters to select specific frequencies [33]. Modern IR detectors use a filtering technique based on micromachined Fabry–Pérot interferometer (FPI), which involves an These detectors have the ability to scan the mid- and long-wave IR range (3.1–10.5 µm) [32], in optical cavity with two parallel reflecting surfaces, which act as a half-wave resonator [34]. By changing steps of 20 nm. This should be considered the wavelength resolution of the optical E-nose. Due to this the plate separation, the central wavelength can be tuned [35]. This can be achieved by simply adjusting feature, any fixed frequency measurements can be considered as “virtual sensors”. Conventional IR the voltage across a control pin. Since gases/VOCs have a unique infrared absorption frequency [36], detectors use expensive precision filters to select specific frequencies [33]. Modern IR detectors use a this optical E-nose can be used to identify individual gases/VOCs in a complex sample, as well as filtering technique based on micromachined Fabry–Pérot interferometer (FPI), which involves an analysing the odour as a whole without identification of each chemical [37]. optical cavity with two parallel reflecting surfaces, which act as a half-wave resonator [34]. By changing the2.2. plate Sensor separation, Chamber Designthe central and wavelength Heating can be tuned [35]. This can be achieved by simply adjusting the voltage across a control pin. Since gases/VOCs have a unique infrared absorption frequency [36], this opticalThe emitter–detector E-nose can be pairsused areto identify encapsulated individual in individual, gases/VOCs heated in chambers.a complex Gassample, chambers as well for as IR analysingsensors can the be odour designed as a inwhole two diwithoutfferent id ways,entification intended of toeach either chemical maximise [37]. resolution or signal-to-noise ratio (SNR). Since most applications of E-noses require the detection of low concentrations of VOCs 2.2.(ppm Sensor to ppb Chamber range), Design the high-resolution and Heating design was chosen. The length of the chamber is a critical feature of this design. According to Beer–Lambert law, increasing the optical path will increase sensitivity,The emitter–detector but this decreases pairs the are SNR encapsulated from emitter in to individual, detector. Di heatedfferent chambers. sensor chamber Gas chambers lengths were for IRevaluated sensors tocan determine be designed the optimalin two different length of ways, the selected intended sensors. to either Lengths maximise under resolution consideration or signal- were to-noise10, 20, 30, ratio 40 and(SNR). 50cm. Since The most results applications from these of experiments E-noses require demonstrate the detection that theof low 30 cm concentrations chamber was of VOCs (ppm to ppb range), the high-resolution design was chosen. The length of the chamber is a associated with the highest differential voltage sensor response, using CO2 as the test gas. The term critical“differential feature voltage of this sensor design. response” According refers to theBeer–Lambert difference between law, increasing the baseline the (ambient optical air)path sensor will increaseresponse sensitivity, vs. signal whenbut this exposed decreases to an the analyte—as SNR from measuredemitter to indetector. Volts (or Different milli-Volts). sensor The chamber voltage lengths were evaluated to determine the optimal length of the selected sensors. Lengths under drops in the presence of 1000 ppm CO2 (absorption frequency set to 4.2 µm) are summarised in Table2. consideration were 10, 20, 30, 40 and 50 cm. The results from these experiments demonstrate that the 30 cm chamber wasTable associated 2. Differential with the voltage highest sensor differential response to voltage 1000 ppm sensor carbon response, dioxide. using CO2 as the test gas. The term “differential voltage sensor response” refers to the difference between the baseline (ambient air) sensorGas response Chamber vs. Lengthsignal when ex Diposedfferential to an Voltage analyte—as Sensor measured Response in Volts (or milli- Volts). The voltage drops in10 the cm presence of 1000 ppm CO2 (absorption 55.4 mV frequency set to 4.2 µm) are summarised in Table 2. 20 cm 77.9 mV 30 cm 103.3 mV 40 cm 58.0 mV

SensorsSensors 2020 2020, 20, 20, x, xFOR FOR PEER PEER REVIEW REVIEW 5 5of of 16 16

TableTable 2. 2. Differential Differential voltage voltage sensor sensor response response to to 1000 1000 ppm ppm carbon carbon dioxide. dioxide.

GasGas Chamber Chamber Length Length Differential Differential Voltage Voltage Sensor Sensor Response Response 1010 cm cm 55.4 55.4 mV mV 2020 cm cm 77.9 77.9 mV mV 3030 cm cm 103.3 103.3 mV mV Sensors 2020, 20, 6875 5 of 16 4040 cm cm 58.0 58.0 mV mV

TheThe sensor sensor chamber chamber length length was was manufactured manufactured to to a alength length of of 30 30 cm. cm. The The four four chambers chambers were were arrangedarranged in inin a a 22 2× 2 ×2 formation, 2formation, formation, with with with mounting mounting mounting plates plates plates on either on on either end,either as end, end, shown as as inshown shown Figure in 3in. Figure AnFigure alternative 3. 3. An An × alternativealternativeapproach approach could approach involve could could ﬁtting involve involve all fitting four fitting emitters all all four four andem emitters detectorsitters and and detectors intodetectors a single into into widera asingle single chamber; wider wider chamber; chamber; however, however,however,this is likely this this tois is likely result likely to in to result poor result SNR. in in poor poor SNR. SNR.

(a()a ) (b(b) )

Figure 3. Optical electronic nose 2 2 gas sensor chamber formation: (a) 3D model; FigureFigure 3. 3. Optical Optical electronic electronic nose nose 2 2× ×2 2gas ×gas sensor sensor chamber chamber formation: formation: ( a()a )3D 3D model; model; and and ( b(b) ) manufacturedmanufacturedand (b) manufactured prototype. prototype. prototype. The sensor chambers are made of stainless-steel tubes. The internal surface was polished, TheThe sensor sensor chambers chambers are are made made of of stainless-steel stainless-steel tubes. tubes. The The internal internal surface surface was was polished, polished, which which which results in more reflections. The material is also relatively easy to clean and has good thermal resultsresults in in more more reflections. reflections. The The material material is is also also relatively relatively easy easy to to clean clean and and has has good good thermal thermal characteristics to implement an effective chamber heating system. A heating system is necessary in characteristicscharacteristics to to implement implement an an effective effective chamber chamber heating heating system. system. A A heating heating system system is is necessary necessary in in order to avoid condensation forming inside the chambers, thereby reducing environmental effects orderorder to to avoid avoid condensation condensation forming forming inside inside the the ch chambers,ambers, thereby thereby reducing reducing environmental environmental effects effects on on on the sensors. By having a fixed temperature above ambient, thermal effects of different sample thethe sensors. sensors. By By having having a afixed fixed temperature temperature above above ambient, ambient, thermal thermal effects effects of of different different sample sample temperatures is reduced. However, this also reduces the SNR. temperaturestemperatures is is reduced. reduced. However, However, this this also also reduces reduces the the SNR. SNR. Nichrome (NiCr) wire was used as a heating element, since it is low-cost and simple to implement NichromeNichrome (NiCr) (NiCr) wire wire was was used used as as a aheating heating element, element, since since it it is is low-cost low-cost and and simple simple to to and control. NiCr wire was coiled around each of the chambers and wrapped in high temperature implementimplement and and control. control. NiCr NiCr wire wire was was coiled coiled arou aroundnd each each of of the the chambers chambers and and wrapped wrapped in in high high resistant Kapton tape (436-2778, RS, Corby, UK) to provide some insulation and prevent short circuits. temperaturetemperature resistant resistant Kapton Kapton tape tape (436-2778, (436-2778, RS, RS, Co Corby,rby, UK) UK) to to provide provide some some insulation insulation and and prevent prevent The NiCr wire’s heat dissipation is in direct relation to the amount of voltage and current applied shortshort circuits. circuits. The The NiCr NiCr wire’s wire’s heat heat dissipation dissipation is is in in direct direct relation relation to to the the amount amount of of voltage voltage and and to it. The temperature inside the chamber is automatically regulated using a 24 V supply and a PID currentcurrent applied applied to to it. it. The The temperature temperature inside inside th the echamber chamber is is automatically automatically regulated regulated using using a a24 24 V V controller algorithm (proportional, integral and differential). The temperature set-point is selected by supplysupply and and a aPID PID controller controller algorithm algorithm (proportional, (proportional, integral integral and and differential). differential). The The temperature temperature set- set- the user (e.g., 35 C) and feedback from inside the chamber is provided by a SHT75 temperature and pointpoint is is selected selected by ◦by the the user user (e.g., (e.g., 35 35 °C) °C) and and feedback feedback from from inside inside the the chamber chamber is is provided provided by by a a humidity sensor (Sensirion, Stäfa, Switzerland). The maximum temperature should not exceed 65 C SHT75SHT75 temperature temperature and and humidity humidity sensor sensor (Sensirion (Sensirion, Stäfa,, Stäfa, Switzerland). Switzerland). The The maximum maximum temperature temperature◦ so as not to damage the sensors. The heating procedure is initiated during start up and takes round shouldshould not not exceed exceed 65 65 °C °C so so as as not not to to damage damage the the sensors. sensors. The The heating heating procedure procedure is is initiated initiated during during 5 min, as shown in Figure4. startstart up up and and takes takes round round 5 5min, min, as as shown shown in in Figure Figure 4. 4.

FigureFigure 4. 4. Internal InternalInternal chamber chamber chamber temperature temperature temperature increasing increasing increasing to to to the the the user-defined user-defined user-deﬁned set-point set-point of of 35 35 °C. ◦°C.C.

2.3. Electronic Design and Software This system can be controlled by a tablet or PC, via wired USB connection. A custom app was created using Universal Windows Platform (UWP) to view the sensor responses in real-time and change system properties (e.g., chamber temperature). The UWP app communicates with a central Sensors 2020, 20, x FOR PEER REVIEW 6 of 16

2.3. Electronic Design and Software This system can be controlled by a tablet or PC, via wired USB connection. A custom app was Sensors 2020, 20, 6875 6 of 16 created using Universal Windows Platform (UWP) to view the sensor responses in real-time and change system properties (e.g., chamber temperature). The UWP app communicates with a central microcontroller, aa TeensyTeensy 3.63.6 (PJRC,(PJRC, Portland,Portland, OR,OR, USA)USA) developmentdevelopment board.board. This is used to interface with the the emitter emitter and and detector detector boards boards (using (using UART), UART), as aswell well as the as therest restof the of control the control electronics. electronics. The Theelectronic electronic design design requirements requirements for the for optical the optical E-nose E-nose are divided are divided into four into segments: four segments: (1) sensor (1) sensordrive; (2)drive; emitter (2) emitter drive; drive;(3) heater (3) heatersystem; system; and (4) and control (4) control electronics. electronics. The sensor drive was designed to run all four em emitteritter/detector/detector pairs. Th Thee sensors have different different supply voltage requirements:requirements: 3.3,3.3, ±5,5, andand 1212 V.TheV. The electronic electronic drive drive board board was was designed designed to to work work with with 24 ± V24 andV and regulated regulated to lower to lower and negative and negative voltages voltages using linear using voltage linear regulators. voltage regulators. In addition, In depending addition, dependingon the sensor, on athe voltage sensor, of a 30–90 voltage V wasof 30–90 required V was for required controlling for thecontrolling central wavelength;the central wavelength; specifically, specifically,30 V for LFP3144C-337, 30 V for LFP3144C-337, 45 V for LFP-3850C-337, 45 V for LFP-3850C-337, 60 V for LFP-8850-337 60 V for LFP-8850-337 and 90 V for and LFP-80105C-337. 90 V for LFP- The80105C-337. voltage regulatorsThe voltage used regulators to generate used 12, to 5, generate 3 and 512, V were5, 3 and LM2937IMP, −5 V were L78L05ABD,LM2937IMP, LD1117S33TR L78L05ABD, − LD1117S33TRand L79L05ABD, and respectively. L79L05ABD, To respectively. generate 90 V To from generate 12 V, a LT1372HVCS890 V from 12 V, high a LT1372HVCS8 frequency switching high frequencyregulator was switching used in regulator combination was withused ain flyback combination transformer. with a flyback transformer. The emitter drive implemented a power-regulated circuit. This approach can compensate the inherent variationvariation in in source source parameters parameters and and is moreis more effi cientefficient for DC for voltages DC voltages with lowwith frequency low frequency pulses. Apulses. square-pulse A square-pulse wave from wave the from Teensy the 3.6 Teensy was used 3.6 towas turn used on andto turn off theon emitters,and off the at aemitters, frequency at ofa 10frequency Hz. The of heater 10 Hz. system The heater regulates system the temperature regulates the of thetemperature sensor chambers. of the sensor A simple chambers. switching A simple circuit switchingturns the voltagecircuit onturns/off theacross voltag thee NiCr on/off wire. across A digital the NiCr control wire. signal A digital from thecontrol Teensy signal 3.6 isfrom used the to Teensyturn on /3.6off isa MOSFET,used to turn which on/off allows a MOSFET, current towhic passh allows through current the NiCr to pass wire, through when the the temperature NiCr wire, whenreading the falls temperature below a predetermined reading falls below threshold. a predeter The controlmined electronicsthreshold. The provide control 24 Velectronics to the four provide heated 24sensor V to chambers,the four heated sensor sensor circuitry chambers, and 12 sensor V to the circuitry pump andand valve.12 V to Two the pump input powerand valve. adaptors Two input were powerused to adaptors separate thewere heating used inputto separate power the from heating the control input circuitry. power from This isthe because control of circuitry. the high currentThis is becauserequired of for the the high heater current system. required An overview for the ofheat theer optical system. E-nose An overview communication of the /controloptical linesE-nose is communication/controlshown in Figure5. lines is shown in Figure 5.

Figure 5. OverviewOverview of of the the optical optical electronic electronic nose communication/control communication/control lines. 2.4. Mechanical Design 2.4. Mechanical Design Mechanical elements of the optical E-nose include the internal flow path configuration and Mechanical elements of the optical E-nose include the internal flow path configuration and mounting of components inside an electronics enclosure. The flow path within the optical E-nose mounting of components inside an electronics enclosure. The flow path within the optical E-nose can can be configured in two ways, as shown in Figure6. The sample flow can pass through the sensor be configured in two ways, as shown in Figure 6. The sample flow can pass through the sensor chambers either in series or in parallel. Both versions have their advantages and disadvantages. In both chambers either in series or in parallel. Both versions have their advantages and disadvantages. In variants, a valve controls the sample inlet and a pump pulls the sample through the device, to create a both variants, a valve controls the sample inlet and a pump pulls the sample through the device, to negative pressure system. In the series version, the pump flowrate is crucial for the sensor readings. create a negative pressure system. In the series version, the pump flowrate is crucial for the sensor If the sample passes through the system too quickly, the sensors may not be able to produce an accurate readings. If the sample passes through the system too quickly, the sensors may not be able to produce and reproducible reading. The advantage of this configuration is that the total amount of VOCs is an accurate and reproducible reading. The advantage of this configuration is that the total amount of present across all chambers during analysis. In the parallel configuration, the sample will be split.

Since the sample is divided by the number of chambers (in this case four), dilution and marginal pressure differences may occur from chamber to chamber. The advantage of this version is that all Sensors 2020, 20, x FOR PEER REVIEW 7 of 16 Sensors 2020, 20, x FOR PEER REVIEW 7 of 16 VOCs is present across all chambers during analysis. In the parallel configuration, the sample will be split.VOCs Since is present the sample across is all divided chambers by the during number analysis. of chambers In the parallel (in this configuration,case four), dilution the sample and marginal will be Sensors 2020, 20, 6875 7 of 16 pressuresplit. Since differences the sample may is divided occur from by the chamber number to of chamchambersber. The (in this advantage case four), of thisdilution version and ismarginal that all sensorspressure will differences have sufficient may occur time fromto detect chamber the VOCs to cham in theber. sample. The advantage The current of this version version of the is thatoptical all sensors will have sufficient time to detect the VOCs in the sample. The current version of the optical eNosesensors utilises will have the parallel sufficient flow time path to detectconfiguration. the VOCs in the sample. The current version of the optical eNose utilises the parallel flow path configuration. eNose utilises the parallel flow path configuration.

(b) (a) (b) (a) Figure 6. Optical electronic nose airflow pathways: (a) series configuration; and (b) parallel Figure 6. Optical electronic nose airﬂow pathways: (a) series configuration; and (b) parallel configuration. configuration.Figure 6. Optical electronic nose airflow pathways: (a) series configuration; and (b) parallel configuration. The optical E-nose was designed and constructed to match the form factor of traditional E-noses. TheThe completed optical E-nose system was weighs designed around and 12 constructed kg and the to dimensions match the form of the factor white of electronicstraditional enclosureE-noses. The are The optical E-nose was designed and constructed to match the form factor of traditional E-noses. The completed48 cm 23 system cm weighs47 cm. around The circuit 12 kg boards and the and dimensions sensor chamber of the white were electronics mounted insideenclosure the are enclosure 48 cm completed× system× weighs around 12 kg and the dimensions of the white electronics enclosure are 48 cm ×using 23 cm 3× mm47 cm. acrylic The circuit sheet supportboards and structures sensor cham (434-295,ber were RS, Corby, mounted UK). inside Internal the enclosure and external using views 3 mm of × 23 cm × 47 cm. The circuit boards and sensor chamber were mounted inside the enclosure using 3 mm acrylicthe unit sheet are shownsupport in structures Figure7. A(434-295, screenshot RS, ofCorby, the graphical UK). Internal user interfaceand external (GUI) views is shown of the in Figureunit are8. shownacrylic insheet Figure support 7. A screenshot structures of(434-295, the graphica RS, Corby,l user interface UK). Internal (GUI) isan shownd external in Figure views 8. of the unit are shown in Figure 7. A screenshot of the graphical user interface (GUI) is shown in Figure 8.

(a) (b ) (a) (b) FigureFigure 7. 7. OpticalOptical electronic electronic nose: nose: (a ()a internal) internal view; view; and and (b (b) external) external view, view, with with laptop. laptop. Figure 7. Optical electronic nose: (a) internal view; and (b) external view, with laptop.

Sensors 2020, 20, 6875 8 of 16

Sensors 2020, 20, x FOR PEER REVIEW 8 of 16

FigureFigure 8. 8.Optical Optical electronicelectronic nose gr graphicalaphical user user interface. interface.

3. Results3. Results TwoTwo sets sets of of experiments experiments were were conducted conducted toto evaluateevaluate the the developed developed innovative innovative optical optical E-nose. E-nose. TheThe first first used used was was a gas a gas rig test,rig test, using using individual individual gases, gases, whereby whereby the concentrationthe concentration of the ofgas theis gas altered is inaltered order to in observe order to the observe sensitivity the ofsensitivity the sensors. of the The sensors. second The was asecond sample was group a sample discrimination group test,discrimination whereby di test,fferent whereby simple different chemical simple standards chemical and standards complex and odour complex mixtures odour mixtures were analysed. were Theanalysed. first set The of tests first wasset of intended tests was to intended develop to an de understandingvelop an understanding of how theof how optical the sensorsoptical sensors respond to direspondfferent to concentration different concentration changes. The changes. second The set ofsecond tests replicateset of tests the replicate most common the most application common of E-noses—utilisingapplication of E-noses—utilising a pattern recognition a pattern approach recognition to distinguish approach between to distinguish different between sample different groups. sample groups. 3.1. Gas Rig Testing 3.1. Gas Rig Testing The optical E-nose was evaluated using a gas rig to test the response to single gases, at different The optical E-nose was evaluated using a gas rig to test the response to single gases, at different concentrations. CO2 and CH4 were chosen for these experiments, since they are the most commonly concentrations. CO2 and CH4 were chosen for these experiments, since they are the most commonly targeted gases for optical sensing applications. NDIR sensors are generally used for these gases due to targeted gases for optical sensing applications. NDIR sensors are generally used for these gases due their relatively large molar absorption coefficients [38]. Gas cylinders with nominal concentrations of to their relatively large molar absorption coefficients [38]. Gas cylinders with nominal concentrations 1000 ppm CO2 and 100 ppm CH4 were used. The gases were diluted with zero-air from a zero-air of 1000 ppm CO2 and 100 ppm CH4 were used. The gases were diluted with zero-air from a zero-air generator (HPZA-7000-220, Parker, Warwick, UK). The total flow rate of the diluted gas was set to generator (HPZA-7000-220, Parker, Warwick, UK). The total flow rate of the diluted gas was set to 300 mL/min. For the CO2 test, the concentration of CO2 was increased linearly from 25 to 250 ppm, 300 mL/min. For the CO2 test, the concentration of CO2 was increased linearly from 25 to 250 ppm, in in steps of 25 ppm. Additionally, concentrations of 500, 750 and 1000 ppm were tested. For the CH test, steps of 25 ppm. Additionally, concentrations of 500, 750 and 1000 ppm were tested. For the CH4 test,4 thethe concentration concentration of CHof 4 CHwas4 increasedwas increased linearly linearly from 2.5 from to 25 2.5 ppm, to in25 steps ppm, of 2.5in ppm.steps Concentrationsof 2.5 ppm. ofConcentrations 50, 75 and 100 of ppm 50, 75 were and also 100 tested.ppm were For also both tested. tests, For the both exposure tests, the and exposure recovery and periods recovery were 25periods min between were 25 concentration min between steps.concentration CO2 has steps. an absorption CO2 has an frequency absorption of frequency 4.2 µm, while of 4.2CH µm,4 haswhile two absorptionCH4 has two frequencies absorption in frequencies the IR spectrum in the (3.4IR spectr and 7.8umµ (3.4m). and Zero-air 7.8 µm). was Zero-air used during was used recovery during for E-noserecovery responses for E-nose to return responses to baseline to return levels. to baseline levels. TheThe changes changes in in concentration concentration of of CO CO22 vs.vs. sensor response are are shown shown in in Figure Figure 9.9 .The The sampling sampling raterate of theof the optical optical E-nose E-nose was was set set to to 1 1 Hz Hz (1 (1 sample sample per per second). second). SensorSensor responses are are presented presented using using a 100a data-point100 data-point moving-average. moving-average. This This indicates indicates that thethat sensor the sensor response response is linear is linear to polynomial to polynomial (second degree)(second for degree) changes for in changes CO2 concentration. in CO2 concentration. The plot in The Figure plot9 insuggests Figure that9 suggests the limit that of the detection limit of for detection for CO2, using this technology is in the low ppm range. CO2, using this technology is in the low ppm range.

Sensors 2020, 20, 6875 9 of 16 SensorsSensors 2020 2020, 20, 20, x, FORx FOR PEER PEER REVIEW REVIEW 9 of9 16of 16

(a) (b) (a) (b)

Figure 9. Sensor response to CO2 gas with different concentrations, from 25 to 1000 ppm, at 4.2 µm: Figure 9. Sensor response to CO2 gas with diﬀerent concentrations, from 25 to 1000 ppm, at 4.2 µm: Figure 9. Sensor response to CO2 gas with different concentrations, from 25 to 1000 ppm, at 4.2 µm: (a)(a sensor) sensor response; response; and and ( b(b)) polynomial polynomial ﬁtfit (R-Square(R-Square = 0.999).0.999). (a) sensor response; and (b) polynomial fit (R-Square = 0.999).

The changes in concentration of CH4 vs. sensor response are shown in Figures 10 and 11, for the The changes in concentration of CH4 vs. sensor response are shown in Figures 10 and 11, for the The changes in concentration of CH4 vs. sensor response are shown in Figures 10 and 11, for the twotwo frequencies. frequencies. These These results results werewere gathered simultaneously, simultaneously, thus thus demonstrating demonstrating the the system’s system’s ability ability twoto frequencies.measure multiple These resultssensor wereoutputs gathered concurrently. simultaneously, Like CO2 ,thus the demonstratingsensor responses the show system’s that abilitythe to measure multiple sensor outputs concurrently. Like CO2, the sensor responses show that the to responsemeasure is multiple linear to polynomialsensor outputs (second concurrently. degree) for changesLike CO in2 ,CH the4 concentration.sensor responses The plot show in Figurethat the response is linear to polynomial (second degree) for changes in CH4 concentration. The plot in response11 suggests is linear that tothe polynomial projected sensitivity (second degree) to CH4 for, as changesindicated in by CH the4 concentration. drop in sensor The response plot in from Figure Figure 11 suggests that the projected sensitivity to CH4, as indicated by the drop in sensor response 11 baselinesuggests levels that priorthe projected to the first sensitivity CH4 reading, to CH is less4, as than indicated 1 ppm. byThe the limit drop of detection in sensor for response CH4 using from from baseline levels prior to the ﬁrst CH4 reading, is less than 1 ppm. The limit of detection for CH4 baselinethis technology levels prior must to thereforethe first CHalso4 bereading, below is1 ppm.less than 1 ppm. The limit of detection for CH4 using using this technology must therefore also be below 1 ppm. this technology must therefore also be below 1 ppm.

(a) (b)

Figure 10. Sensor response to CH gas with diﬀerent concentrations, from 2.5 ppm to 100 ppm, Figure 10. Sensor response(a) to CH4 gas4 with different concentrations, from 2.5 ppm(b) to 100 ppm, at 3.4 atµm: 3.4 µ (m:a) sensor (a) sensor response; response; and (b and) polynomial (b) polynomial fit (R-Square ﬁt (R-Square = 0.995).= 0.995).

Figure 10. Sensor response to CH4 gas with different concentrations, from 2.5 ppm to 100 ppm, at 3.4 µm: (a) sensor response; and (b) polynomial fit (R-Square = 0.995).

SensorsSensors2020 2020, 20, ,20 6875, x FOR PEER REVIEW 10 of10 16 of 16

(a) (b)

Figure 11. Sensor response to CH gas with different concentrations, from 2.5 ppm to 100 ppm, Figure 11. Sensor response to CH4 gas4 with different concentrations, from 2.5 ppm to 100 ppm, at 7.8 at 7.8µm:µ (m:a) sensor (a) sensor response; response; and ( andb) polynomial (b) polynomial fit (R-Square fit (R-Square = 0.997).= 0.997). 3.2. Simple and Complex Chemical Testing 3.2. Simple and Complex Chemical Testing While CO2 and CH4 have value in environmental, agricultural and medical applications [39,40], While CO2 and CH4 have value in environmental, agricultural and medical applications [39,40], therethere are are also also other other compounds, compounds, such such asas acetone,acetone, ethanol and and isopropanol, isopropanol, which which are are more more relevant relevant inin the the food food quality quality and and biomedical biomedical domain domain [[21,4121,41––43].43]. These These compounds compounds are are associated associated with with known known absorptionabsorption frequencies, frequencies, which fall fall within within the the range range of the of optical the optical E-nose E-nosesystem. system.For example, For acetone example, acetoneand ethanol and ethanol both produce both produce four distinct four distinct absorption absorption peaks within peaks 3.1–10.5 within 3.1–10.5 µm. All µfourm. Alldetectors four detectors could couldtherefore therefore simultaneously simultaneously measure measure these theseVOCs, VOCs, at different at diff wavelengths.erent wavelengths. TheThe first first set set of of these these experiments experiments involvedinvolved a si singlengle chemical; chemical; specifically, specifically, 1 1mL mL acetone acetone (99% (99% puritypurity from from Sigma-Aldrich, Sigma-Aldrich, Dorest, Dorest, UK)UK) mixedmixed with 4 mL of of water. water. The The 5 5mL mL sample sample was was aliquoted aliquoted intointo a 20a 20 mL mL glass glass vial vial and and heated heated forfor 1010 minmin atat 40 ± 0.10.1 °CC in in aa heater heater block block (DB-2D (DB-2D Dri-Block, Dri-Block, ± ◦ TechneTechne/Cole-Parmer,/Cole-Parmer, Stone, Stone, UK). UK). TheThe samplesample headspaceheadspace was was collected collected using using a a10 10 mL mL syringe syringe and and injectedinjected into into the the inlet inlet port port of of the the E-nose E-nose system.system. These These concentrations concentrations were were used used as asthey they are are the the calibrationcalibration standards standards recommended recommended by by electronic electronic nosenose manufacturers. TheThe sensor sensor response response of of diluted diluted acetone acetone waswas compared to to an an ambient ambient air air sample, sample, across across the the entire entire IRSensorsIR range, range, 2020 as, as shown20, shownx FOR in PEER in Figure Figure REVIEW 12 12a.a. Ambient Ambient air air was was used used as as a a reference reference sample, sample, since since this this represents represents11 of 16 the baselinethe baseline sensor sensor response. response. This resultThis result clearly clearly demonstrates demonstrates that that the responsethe response of the of opticalthe optical E-nose E-nose array the acetone sample. Some of the chosen sensor wavelengths were based on the known absorption canarray differentiate can differentiate between between the acetone the andacetone air sample.and air Thesample. diff erencesThe differences are mainly are observed mainly observed within the wavelengthwithin the absorption of the single frequencies chemicals, 3.16–3.70 while those and for 5.56–9.00 complex µm. chemical were set to cover the full range absorption frequencies 3.16–3.70 and 5.56–9.00 µm. of wavelengths,As shown insince Figure they 12b,are notthe associatedresults from with the a specificoptical E-noseabsorption closely frequency. match the NIST database reference absorption profile. Since the optical E-nose has slightly different baseline values, the data from the experiments were normalised to a scale of 0–1. Sensor 1 covers the range 3.1–4.4 µm and detected acetone at 3.34 µm, which is similar to the database reference. Wavelength of Sensors 1 and 2 overlap within 3.8–4.4 µm. The absorption peak for both sensors was 4.28 µm. This is due to ambient CO2 gas not being removed by the zero-air generator. Sensor 3, which covers the wavelength range of 5.5–8.0 µm, shows two absorption peaks around 5.88 and 7.44 µm. This is close to absorption frequencies of the NIST database. Sensor 4, which covers the wavelength range 8.0–10.5 µm, detects acetone around 8.1 µm. In summary, the results in Figure 12b demonstrate, to a satisfactory degree, that the sensor array is able to detect acetone at all expected wavelengths. Following these experiments, more complex mixtures were also analysed. Figure 13 shows the radar plot of the raw responses of sensors at different wavelengths for various chemicals. In addition to acetone, ethanol and isopropanol, coffee, cola and orange juice were tested. The ethanol and isopropanol samples were diluted using the same method used for acetone, while the cola and orange juice samples were pure.(a) The coffee sample was prepared by dissolving( 1b )g of instant-coffee powder in 4 mL of water. All samples were heated and injected using the previously described method for FigureFigure 12. 12.Optical Optical electronicelectronic nosenose responses: (a (a) )ambient ambient air air and and acetone acetone in inwater, water, across across the the entire entire

infraredinfrared range range (3.1–10.5 (3.1–10.5µ µm);m); andand ((bb)) acetoneacetone absorption frequencies. frequencies.

Figure 13. Radar plot of raw values of sensor responses for chemical standards and complex mixtures, across the entire infrared range (3.1–10.5 µm).

Most applications of E-noses use classification analysis to distinguish between two or more groups and ultimately attempt to build and train a model that can be used as a discriminatory tool (e.g., ripe vs. unripe fruit in food quality control). Common classification techniques for E-nose datasets include principal component analysis (PCA) and linear discriminant analysis (LDA). PCA is an unsupervised linear method, which reduces the dimensionality (e.g., number of features) of the data, by selecting a small number of linearly uncorrelated principal components (PC) that explain the majority of the variation in the data [44]. LDA is a supervised linear method that tries to find a linear discriminant function to separate between datasets [45]. The commercial software package MultiSense Analyzer (JLM Innovation, Tübingen, Germany) was used to conduct the PCA and LDA analysis, as shown in Figure 14. Feature extraction was completed using the “maximum value” approach. This value can be easily calculated by subtracting the baseline from the maximum sensor response, for a given sample (previously referred to as: differential voltage sensor response).

Sensors 2020, 20, 6875 11 of 16 Sensors 2020, 20, x FOR PEER REVIEW 11 of 16

the Asacetone shown sample. in Figure Some 12 ofb, the the chosen results sensor from wavelengths the optical E-nose were based closely on matchthe known the NIST absorption database referencewavelength absorption of the single profile. chemicals, Since the while optical those E-nose for complex has slightly chemical different were baseline set to cover values, the thefull datarange from theof experimentswavelengths, weresince normalisedthey are not toassociated a scale of with 0–1. a Sensorspecific 1 absorption covers the frequency. range 3.1–4.4 µm and detected acetone at 3.34 µm, which is similar to the database reference. Wavelength of Sensors 1 and 2 overlap within 3.8–4.4 µm. The absorption peak for both sensors was 4.28 µm. This is due to ambient CO2 gas not being removed by the zero-air generator. Sensor 3, which covers the wavelength range of 5.5–8.0 µm, shows two absorption peaks around 5.88 and 7.44 µm. This is close to absorption frequencies of the NIST database. Sensor 4, which covers the wavelength range 8.0–10.5 µm, detects acetone around 8.1 µm. In summary, the results in Figure 12b demonstrate, to a satisfactory degree, that the sensor array is able to detect acetone at all expected wavelengths. Following these experiments, more complex mixtures were also analysed. Figure 13 shows the radar plot of the raw responses of sensors at different wavelengths for various chemicals. In addition to acetone, ethanol and isopropanol, coffee, cola and orange juice were tested. The ethanol and isopropanol samples were diluted using the same method used for acetone, while the cola and orange juice samples were pure. The coffee sample was prepared by dissolving 1 g of instant-coffee powder in 4 mL of water. All samples were heated and injected using the previously described method for the acetone sample. Some(a) of the chosen sensor wavelengths were based(b) on the known absorption wavelength of the single chemicals, while those for complex chemical were set to cover the full range Figure 12. Optical electronic nose responses: (a) ambient air and acetone in water, across the entire of wavelengths, since they are not associated with a specific absorption frequency. infrared range (3.1–10.5 µm); and (b) acetone absorption frequencies.

FigureFigure 13. 13. Radar plot of of raw raw values values of of sensor sensor responses responses for for chemical chemical standards standards and and complex complex mixtures, mixtures, acrossacross the the entireentire infraredinfrared range (3.1–10.5 µm).µm).

MostMost applications applications of of E-noses E-noses use use classification classification analysis analysis to distinguishto distinguish between between two two or moreor more groups andgroups ultimately and ultimately attempt toattempt build andto build train and a model train thata model can bethat used can asbe a used discriminatory as a discriminatory tool (e.g., tool ripe vs. unripe(e.g., ripe fruit vs. in foodunripe quality fruit in control). food quality Common control). classification Common techniquesclassification for techniques E-nose datasets for E-nose include principaldatasets componentinclude principal analysis component (PCA) and analysis linear (PCA) discriminant and linear analysis discriminant (LDA). analysis PCA is (LDA). an unsupervised PCA is linearan unsupervised method, which linear reduces method, the which dimensionality reduces the (e.g.,dimensionality number of (e.g., features) number of theof features) data, by of selecting the adata, small by number selecting of a linearlysmall number uncorrelated of linearly principal uncorrelated components principal (PC) components that explain (PC) thethat majority explain the of the variationmajority inof the variation data [44]. in LDA the data is a [44]. supervised LDA is a linear supervised method linear that method tries to that find tries a linear to find discriminant a linear functiondiscriminant to separate function between to separate datasets between [45]. The da commercialtasets [45]. softwareThe commercial package software MultiSense package Analyzer (JLMMultiSense Innovation, Analyzer Tübingen, (JLM Innovation, Germany) Tübingen, was used Germany) to conduct was the used PCA to and conduct LDA the analysis, PCA and as shown LDA in analysis, as shown in Figure 14. Feature extraction was completed using the “maximum value” Figure 14. Feature extraction was completed using the “maximum value” approach. This value can be approach. This value can be easily calculated by subtracting the baseline from the maximum sensor easily calculated by subtracting the baseline from the maximum sensor response, for a given sample response, for a given sample (previously referred to as: differential voltage sensor response). (previously referred to as: differential voltage sensor response).

SensorsSensors 20202020, ,2020, ,x 6875 FOR PEER REVIEW 1212 of of 16 16

(a) (b)

Figure 14. Classification analysis for testing of chemical standards and complex mixtures: (a) principal Figure 14. Classification analysis for testing of chemical standards and complex mixtures: (a) principal component analysis; and (b) linear discriminant analysis (DF—Discriminant Function). component analysis; and (b) linear discriminant analysis (DF—Discriminant Function). The results in Figure 14 demonstrate that, for both analyses, distinct sample clusters with little The results in Figure 14 demonstrate that, for both analyses, distinct sample clusters with little to no overlap were created. This indicates that there is good separation between the sample groups. to no overlap were created. This indicates that there is go-od separation between the sample groups. Isopropanol and ethanol are from the same class of chemicals and are therefore close to each other in Isopropanol and ethanol are from the same class of chemicals and are therefore close to each other in both sets of analysis. In the future, these experiments could be repeated using at least three analyte both sets of analysis. In the future, these experiments could be repeated using at least three analyte concentrations to verify the device performance (regarding analyte discrimination), independent of concentrations to verify the device performance (regarding analyte discrimination), independent of the concentration. the concentration. 4. Discussion 4. Discussion Unlike classical gas sensor technologies, which are based on transduction principles (e.g., electrical resistance,Unlike potentialclassical andgas sensor current, technologies, frequency), opticalwhich sensorsare based measure on transduction changes in principles light properties. (e.g., electricalThe key advantageresistance, ofpotential this method and current, is that it freq is ableuency), to overcome optical sensors some of measure the limitations changes associated in light properties.with typical The MOX key advantage and CP sensor-based of this method E-noses. is thatTo it is the able best to ofovercome our knowledge, some of the the limitations developed associatedoptical E-nose with instrument typical MOX is the and first CP device sensor-based that operates E-noses. based To on the FPI best sensors of our with knowledge, tuneable filters. the developedThis technology optical is knownE-nose toinstrument be less susceptible is the first to influencesdevice that from operates environmental based on parameters, FPI sensors such with as tuneablehumidity filters. and temperature. This technology However, is known further to be gas less rig susceptible testing is required to influences to characterise from environmental these effects parameters,on the sensors such deployed as humidity in the E-nose.and temperature. Other sensor However, parameters, further such gas as sensitivity,rig testing detectionis required limits, to characteriserepeatability, these response effects and on recovery the sensors times, deployed are also in worth the E-nose. further investigating.Other sensor parameters, Due to the operating such as sensitivity,principle of detection the optical limits, E-nose, repeatability, the response response and recovery and recovery times are times, very rapid are also (less worth than a second).further investigating.Therefore, the Due response to the timeoperating is that principle of the system of the optical and of E-nose, the sample the response rate, not ofand the recovery sensor. times Since arethe very addition rapid/removal (less than of thea second). sample Therefore, is controlled the using response the valve-pumptime is that negative-pressureof the system and system,of the samplethe effects rate, of not altering of the thesensor. pump Since flow the rate addition/r could beemoval investigated. of the sample is controlled using the valve- pumpTraditional negative-pressure E-noses usesystem, an array the effects of sensors of alteri to analyseng the a pump chemically flow complexrate could sample. be investigated. In our system, not onlyTraditional do we E-noses have an use array an of array four of optical sensors sensors, to analyse but these a chemically sensors can complex scan across sample. a range In our of system,frequencies. not only In use,do we we have can usean array specific of four frequencies optical sensors, (or a range but these of frequencies) sensors can to scan behave across as “virtual”a range ofsensors frequencies. to form In arrays use, ofwe di ffcanerent use sizes. specific Furthermore, frequencies diff (orerent a combinationsrange of frequencies) of these virtualto behave sensors as “virtual”can be used sensors for specific to form applications, arrays of different making the sizes. instrument Furthermore, more flexibledifferent than combinations a traditional of E-nose. these virtualThe sensors unit could can be also used be usedfor specific as a traditional applications, gas analyser, making but the it instrument would be necessary more flexible to determine than a traditional E-nose. the detection limits for a number of potentially relevant compounds, such as CO2, CH4, ozone, carbonThe monoxide, unit could oxygen, also be ammoniaused as a andtraditional sulphur gas dioxide analyser, [46]. but However, it would this be isnecessary not necessarily to determine needed thefor detection E-nose applications, limits for a number since these of potentially focus on analysing relevant complexcompounds, mixtures. such as An CO alternative2, CH4, ozone, use iscarbon to use monoxide,this E-nose oxygen, to produce ammonia a so-called and totalsulphur VOC dioxide (TVOC) [46] reading.. However, The concept this is not of TVOC necessarily attempts needed to report for E-nosethe combined applications, effect ofsince diff erentthese VOCs, focus whichon analysing may not complex be otherwise mixtures. captured, An alternative due to low use concentration is to use thislevels E-nose of some to produce contributing a so-called VOCs total [47]. VOC In this (TVOC) case, the reading. E-nose The would concept be set of to TVOC scan theattempts widest to possible report the combined effect of different VOCs, which may not be otherwise captured, due to low concentration levels of some contributing VOCs [47]. In this case, the E-nose would be set to scan the

Sensors 2020, 20, 6875 13 of 16 range of wavelengths and use these measurements to calculate a single value reading for air quality. To the best of our knowledge, an optical sensor has not been used to establish TVOC readings. In the case of traditional quality assurance and quality control (QA-QC) applications, E-noses are likely to be in a controlled or possibly even laboratory environment to assure product uniformity in manufacturing is maintained. However, E-noses are being increasingly required to operate in uncontrolled conditions, such as open-air for environmental monitoring [48] and patient wards for point-of-care testing [49]. Food quality control protocols are also moving towards “spot-checks” which are conducted during transit or upon delivery of good [50]. For these applications, the sensor array must have low sensitivity to a wide variety of environmental parameters, but in particular to temperature and humidity [17]. In these situations, the benefits of utilising optical gas sensing technology is likely to be greatest. However, these environments also bring rise to new challenges. There are some other limitations and drawbacks associated with the developed unit. In its current implementation, the sample vials need to be connected to the device inlet manually for analysis. This is both time-consuming and vulnerable to human error. An automatic injection system could be added to address this drawback. There are commercially available autosamplers, suitable for sample headspace analysis, which include agitator functionalities and allow the method parameters to be configured. In addition to these modifications, there may be some opportunities to further miniaturise the sensor configuration by implementing a more compact gas chamber design [51]. This could reduce the overall dimensions of the system and allow the internal components to be housed in a more portable enclosure. Further scope for improvement relates to the transmission of sample data. The system currently connects to a laptop or PC via wired USB connection. In order for the device to be more practical in an outdoor or clinical setting, and keep up with current technological trends, the optical E-nose could be fitted with a Wi-Fi and/or Bluetooth compatible microcontroller to interface with a smartphone app or transmit data directly to a cloud-based computing platform for data analysis [52]. A previous publication has demonstrated how E-nose devices can be designed or upgraded to be Internet-of-things (IoT) enabled [53]. The appropriate data protection measures would also have to be implemented in order to ensure secure transmission and storage of data. Lastly, further work is also needed to ensure the environmental tolerance of the unit, in harsh conditions, and the addition of data processing capabilities to remove background signals.

5. Conclusions In this paper, we show the development and testing of a novel optical-based E-nose, with an array of NDIR detectors. The optical E-nose is comprised of four emitter–detector pairs, which are encapsulated in individual heated sensor chambers. The inlet is controlled using a valve and a pump is used to create a negative-pressure system. Real-time data is presented to the user on a laptop. The functionality of the system was demonstrated in three sets of experiments. The first involved the testing of individual gases (CO2 and CH4), at different concentrations. The sensor responses showed that the response is linear to polynomial (second degree) for changes in CO2 and CH4 for the tested concentration range. Moreover, the results from this test indicate that the sensitivity for CO2 is below 25 ppm, and below 1 ppm for CH4. The second set of experiments involved testing chemical standards, as well as some complex mixtures and attempting to discriminate between these. PCA and LDA analysis demonstrated that the developed system was able to distinguish between the odour fingerprints of acetone, ethanol, isopropanol, coffee, cola and orange juice. The developed system has shown potential to serve as a discriminating tool for individual chemicals and groups. Further work will focus on determining the detection limits for different compounds and evaluating how this novel technology performs in different E-nose applications, including air quality monitoring and the analysis of food and biological samples.

Author Contributions: J.A.C. and S.E. conceptualised the project. S.E. and S.O.A. designed and configured the device. Samples were prepared and analysed by S.E. Original draft preparation and review and editing of the Sensors 2020, 20, 6875 14 of 16 manuscript were completed by S.E., A.T. and J.A.C. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Persaud, K.; Dodd, G. Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature 1982, 299, 352–355. [CrossRef] 2. Bonah, E.; Huang, X.; Aheto, J.H.; Osae, R. Application of electronic nose as a non-invasive technique for odor fingerprinting and detection of bacterial foodborne pathogens: A review. J. Food Sci. Technol. 2020, 57, 1977–1990. [CrossRef] 3. Persaud, K. Electronic Noses and Tongues in the Food Industry. In Electronic Noses and Tongues in Food Science; Elsevier: Amsterdam, The Netherlands, 2016; pp. 1–12. ISBN 978-0-12-800243-8. 4. Wilson, A.D. Review of Electronic-nose Technologies and Algorithms to Detect Hazardous Chemicals in the Environment. Procedia Technol. 2012, 1, 453–463. [CrossRef] 5. Wilson, A.D.; Baietto, M. Advances in Electronic-Nose Technologies Developed for Biomedical Applications. Sensors 2011, 11, 1105–1176. [CrossRef][PubMed] 6. Mansurova, M.; Ebert, B.E.; Blank, L.M.; Ibáñez, A.J. A breath of information: The volatilome. Curr. Genet. 2018, 64, 959–964. [CrossRef][PubMed] 7. Miekisch, W.; Schubert, J. From highly sophisticated analytical techniques to life-saving diagnostics: Technical developments in breath analysis. TrAC Trends Anal. Chem. 2006, 25, 665–673. [CrossRef] 8. Dospinescu, V.-M.; Tiele, A.; Covington, J.A. Sniffing Out Urinary Tract Infection—Diagnosis Based on Volatile Organic Compounds and Smell Profile. Biosensors 2020, 10, 83. [CrossRef][PubMed] 9. Madrolle, S.; Grangeat, P.; Jutten, C. A Linear-Quadratic Model for the Quantification of a Mixture of Two Diluted Gases with a Single Metal Oxide Sensor. Sensors 2018, 18, 1785. [CrossRef][PubMed] 10. Persaud, K.C. Polymers for chemical sensing. Mater. Today 2005, 8, 38–44. [CrossRef] 11. Berna, A. Metal Oxide Sensors for Electronic Noses and Their Application to Food Analysis. Sensors 2010, 10, 3882–3910. [CrossRef] 12. Covington, J.; Westenbrink, E.; Ouaret, N.; Harbord, R.; Bailey, C.; O’Connell, N.; Cullis, J.; Williams, N.; Nwokolo, C.; Bardhan, K.; et al. Application of a Novel Tool for Diagnosing Bile Acid Diarrhoea. Sensors 2013, 13, 11899–11912. [CrossRef][PubMed] 13. Sánchez, C.; Santos, J.; Lozano, J. Use of Electronic Noses for Diagnosis of Digestive and Respiratory Diseases through the Breath. Biosensors 2019, 9, 35. [CrossRef][PubMed] 14. Wilson, A.D. Applications of Electronic-Nose Technologies for Noninvasive Early Detection of Plant, Animal and Human Diseases. Chemosensors 2018, 6, 45. [CrossRef] 15. Abidin, M.Z.; Asmat, A.; Hamidon, M.N. Comparative Study of Drift Compensation Methods for Environmental Gas Sensors. IOP Conf. Ser. Earth Environ. Sci. 2018, 117, 012031. [CrossRef] 16. Wilson, A. Advances in Electronic-Nose Technologies for the Detection of Volatile Biomarker Metabolites in the Human Breath. Metabolites 2015, 5, 140–163. [CrossRef] 17. Wilson, A.; Baietto, M. Applications and Advances in Electronic-Nose Technologies. Sensors 2009, 9, 5099–5148. [CrossRef] 18. Korotcenkov, G. Comparative Analysis of Gas Sensors. In Handbook of Gas Sensor Materials: Properties, Advantages and Shortcomings for Applications Volume 1: Conventional Approaches; Integrated Analytical Systems; Springer: New York, NY, USA, 2013; ISBN 978-1-4614-7164-6. 19. Liu, X.; Cheng, S.; Liu, H.; Hu, S.; Zhang, D.; Ning, H. A Survey on Gas Sensing Technology. Sensors 2012, 12, 9635–9665. [CrossRef] 20. Yasuda, T.; Yonemura, S.; Tani, A. Comparison of the Characteristics of Small Commercial NDIR CO2 Sensor Models and Development of a Portable CO2 Measurement Device. Sensors 2012, 12, 3641–3655. [CrossRef] 21. Loutfi, A.; Coradeschi, S.; Mani, G.K.; Shankar, P.; Rayappan, J.B.B. Electronic noses for food quality: A review. J. Food Eng. 2015, 144, 103–111. [CrossRef] 22. Tan, J.; Xu, J. Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review. Artif. Intell. Agric. 2020, 4, 104–115. [CrossRef] Sensors 2020, 20, 6875 15 of 16

23. Capelli, L.; Sironi, S.; Del Rosso, R. Electronic Noses for Environmental Monitoring Applications. Sensors 2014, 14, 19979–20007. [CrossRef][PubMed] 24. Farraia, M.V.; Cavaleiro Rufo, J.; Paciência, I.; Mendes, F.; Delgado, L.; Moreira, A. The electronic nose technology in clinical diagnosis: A systematic review. Porto Biomed. J. 2019, 4, e42. [CrossRef] 25. Wojnowski, W.; Dymerski, T.; G˛ebicki, J.; Namie´snik, J. Electronic Noses in Medical Diagnostics. Curr. Med. Chem. 2019, 26, 197–215. [CrossRef] 26. García-Orellana, C.J.; Macías-Macías, M.; González-Velasco, H.M.; García-Manso, A.; Gallardo-Caballero, R. Low-Power and Low-Cost Environmental IoT Electronic Nose Using Initial Action Period Measurements. Sensors 2019, 19, 3183. [CrossRef] 27. Gliszczyńska-Swigło,´ A.; Chmielewski, J. Electronic Nose as a Tool for Monitoring the Authenticity of Food. A Review. Food Anal. Methods 2017, 10, 1800–1816. [CrossRef] 28. Esfahani, S. Electronic Nose Instrumentation for Biomedical Applications. Ph.D. Thesis, University of Warwick, Coventry, UK, 2018. 29. Pan, D.D.; Wu, Z.; Peng, T.; Zeng, X.Q.; Li, H. Volatile organic compounds profile during milk fermentation by Lactobacillus pentosus and correlations between volatiles flavor and carbohydrate metabolism. J. Dairy Sci. 2014, 97, 624–631. [CrossRef][PubMed] 30. Chatterjee, S.; Castro, M.; Feller, J.F. An e-nose made of carbon nanotube based quantum resistive sensors for the detection of eighteen polar/nonpolar VOC biomarkers of lung cancer. J. Mater. Chem. B 2013, 1, 4563. [CrossRef][PubMed] 31. Baetz, W.; Kroll, A.; Bonow, G. Mobile robots with active IR-optical sensing for remote gas detection and source localization. In Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan, 12–17 May 2009; pp. 2773–2778. 32. Neumann, N. Tunable infrared detector with integrated micromachined Fabry-Perot filter. J. MicroNanolithogr. MEMS MOEMS 2008, 7, 021004. [CrossRef] 33. Intelligent Opto Sensors. In Smart Sensors and MEMS; Yurish, S.Y.; Gomes, M.T.S.R., Eds.; NATO Science Series; Springer: Dordrecht, The Netherlands, 2004; Volume 181, ISBN 978-1-4020-2928-8. 34. Hariharan, P. The Fabry–Perot Interferometer. In Basics of Interferometry; Elsevier: Amsterdam, The Netherlands, 1992; pp. 37–43. ISBN 978-0-08-091861-7. 35. InfraTecFPI Detectors—Pyroelectric Detectors with Spectrometer Functionality; InfraTec GmbH: Dresden, Germany, 2015; pp. 1–8. 36. Gordon, I.E.; Rothman, L.S.; Hill, C.; Kochanov,R.V.; Tan, Y.; Bernath, P.F.; Birk, M.; Boudon, V.; Campargue, A.; Chance, K.V.; et al. The HITRAN2016 molecular spectroscopic database. J. Quant. Spectrosc. Radiat. Transf. 2017, 203, 3–69. [CrossRef] 37. Persaud, K.C. Electronic gas and odour detectors that mimic chemoreception in animals. TrAC Trends Anal. Chem. 1992, 11, 61–67. [CrossRef] 38. Honeycutt, W.T.; Ley, M.T.; Materer, N.F. Precision and Limits of Detection for Selected Commercially Available, Low-Cost Carbon Dioxide and Methane Gas Sensors. Sensors 2019, 19, 3157. [CrossRef][PubMed] 39. Das, S.; Pal, M. Review—Non-Invasive Monitoring of Human Health by Exhaled Breath Analysis: A Comprehensive Review. J. Electrochem. Soc. 2020, 167, 037562. [CrossRef] 40. Huttmann, S.E.; Windisch, W.; Storre, J.H. Techniques for the Measurement and Monitoring of Carbon Dioxide in the Blood. Ann. Am. Thorac. Soc. 2014, 11, 645–652. [CrossRef][PubMed] 41. Jia, Z.; Patra, A.; Kutty, V.; Venkatesan, T. Critical Review of Volatile Organic Compound Analysis in Breath and In Vitro Cell Culture for Detection of Lung Cancer. Metabolites 2019, 9, 52. [CrossRef][PubMed] 42. Trefz, P.; Obermeier, J.; Lehbrink, R.; Schubert, J.K.; Miekisch, W.; Fischer, D.-C. Exhaled volatile substances in children suffering from type 1 diabetes mellitus: Results from a cross-sectional study. Sci. Rep. 2019, 9, 15707. [CrossRef] 43. Rondanelli, M.; Perdoni, F.; Infantino, V.; Faliva, M.A.; Peroni, G.; Iannello, G.; Nichetti, M.; Alalwan, T.A.; Perna, S.; Cocuzza, C. VolatileOrganic Compounds as Biomarkers of Gastrointestinal Diseases and Nutritional Status. J. Anal. Methods Chem. 2019, 2019, 1–14. [CrossRef][PubMed] 44. Wlodzimirow, K.A.; Abu-Hanna, A.; Schultz, M.J.; Maas, M.A.W.; Bos, L.D.J.; Sterk, P.J.; Knobel, H.H.; Soers, R.J.T.; Chamuleau, R.A.F.M. Exhaled breath analysis with electronic nose technology for detection of acute liver failure in rats. Biosens. Bioelectron. 2014, 53, 129–134. [CrossRef][PubMed] Sensors 2020, 20, 6875 16 of 16

45. Smolinska, A.; Hauschild, A.-C.; Fijten, R.R.R.; Dallinga, J.W.; Baumbach, J.; van Schooten, F.J. Current breathomics—A review on data pre-processing techniques and machine learning in metabolomics breath analysis. J. Breath Res. 2014, 8, 027105. [CrossRef][PubMed] 46. Pénard-Morand, C.; Annesi-Maesano, I. Air pollution: From sources of emissions to health effects. Breathe 2004, 1, 108–119. [CrossRef] 47. Meˇciarová, L’.; Vilˇceková, S.; Krídlová Burdová, E.; Kiselák, J. Factors Effecting the Total Volatile Organic Compound (TVOC) Concentrations in Slovak Households. Int. J. Environ. Res. Public. Health 2017, 14, 1443. [CrossRef] 48. Pobkrut, T.; Siyang, S.; Thepodum, T.; Kerdcharoen, T. The Applications of Portable Electronic Nose for Indoor and Outdoor Air Quality Assessments. In Proceedings of the 2019 IEEE International Conference on Consumer Electronics—Taiwan (ICCE-TW), Yilan, Taiwan, 20–22 May 2019; pp. 1–2. 49. Coronel Teixeira, R.; Rodríguez, M.; Jiménez de Romero, N.; Bruins, M.; Gómez, R.; Yntema, J.B.; Chaparro Abente, G.; Gerritsen, J.W.; Wiegerinck, W.; Pérez Bejerano, D.; et al. The potential of a portable, point-of-care electronic nose to diagnose tuberculosis. J. Infect. 2017, 75, 441–447. [CrossRef][PubMed] 50. Rodríguez, J.; Durán, C.; Reyes, A. Electronic Nose for Quality Control of Colombian Coffee through the Detection of Defects in “Cup Tests”. Sensors 2009, 10, 36–46. [CrossRef][PubMed] 51. Liu, H.; Shi, Y.; Wang, T. Design of a six-gas NDIR gas sensor using an integrated optical gas chamber. Opt. Express 2020, 28, 11451. [CrossRef][PubMed] 52. Dimitrov, D.V. Medical Internet of Things and Big Data in Healthcare. Healthc. Inform. Res. 2016, 22, 156. [CrossRef] 53. Tiele, A.; Wicaksono, A.; Ayyala, S.K.; Covington, J.A. Development of a Compact, IoT-Enabled Electronic Nose for Breath Analysis. Electronics 2020, 9, 84. [CrossRef]

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional aﬃliations.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).