Development of a Mobile Device for Odor Identification and Optimization

sensors

Article Development of a Mobile Device for Odor Identiﬁcation and Optimization of Its Measurement Protocol Based on the Free-Hand Measurement

Gaku Imamura 1,* and Genki Yoshikawa 2,3

1 International Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), World Premier International Research Center Initiative (WPI), 1-1 Namiki, Tsukuba 305-0044, Japan 2 Center for Functional Sensor & Actuator (CFSN), Research Center for Functional Materials, National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba 305-0044, Japan; [email protected] 3 Materials Science and Engineering, Graduate School of Pure and Applied Science, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8571, Japan * Correspondence: [email protected]; Tel.: +81-0-29-860-4988

Received: 30 September 2020; Accepted: 28 October 2020; Published: 30 October 2020

Abstract: Practical applications of machine olfaction have been eagerly awaited. A free-hand measurement, in which a measurement device is manually exposed to sample odors, is expected to be a key technology to realize practical machine olfaction. To implement odor identification systems based on the free-hand measurement, the comprehensive development of a measurement system including hardware, measurement protocols, and data analysis is necessary. In this study, we developed palm-size wireless odor measurement devices equipped with Membrane-type Surface stress Sensors (MSS) and investigated the effect of measurement protocols and feature selection on odor identification. By using the device, we measured vapors of liquids as odor samples through the free-hand measurement in different protocols. From the measurement data obtained with these protocols, datasets of transfer function ratios (TFRs) were created and analyzed by clustering and machine learning classification. It has been revealed that TFRs in the low-frequency range below 1 Hz notably contributed to vapor identification because the frequency components in that range reflect the dynamics of the detection mechanism of MSS. We also showed the optimal measurement protocol for accurate classification. This study has shown a guideline of the free-hand measurement and will contribute to the practical implementation of machine olfaction in society.

Keywords: machine olfaction; Membrane-type Surface stress Sensor (MSS); Transfer Function Ratio (TFR); free-hand measurement

1. Introduction Among the five human senses (i.e., sight (vision), hearing (audition), touch (somatosensation), taste (gustation), and smell (olfaction)), machine olfaction and gustation applications have not been widely implemented in society. Compared to gustation, it is more challenging to realize machine olfaction because there are no “basic smells” for olfaction whereas taste can be sensed by detecting the five basic tastes (i.e., sweet, sour, salty, umami, and bitter) [1,2]. As first demonstrated by Persaud et al. in 1982 [3], machine olfaction—gas sensor systems for identifying odors—has been extensively studied by many researchers [4,5]. Owing to the advancement in sensing technologies including the development of sensor elements, fabrication techniques, and signal analysis methods, some products have already been available in the market. However, for practical application, there are still many technical issues in sensitivity, selectivity, usability, commercial availability, and so on. Considering the

Sensors 2020, 20, 6190; doi:10.3390/s20216190 www.mdpi.com/journal/sensors Sensors 2020, 20, 6190 2 of 14 recent evolution of the Internet of Things (IoT), the realization of practical machine olfaction is eagerly awaited in various fields such as food, cosmetics, housing, and medical fields [6]. One of the issues for practical applications of machine olfaction is the development of mobile measurement devices. Although the miniaturization of the sensor elements has been achieved owing to the advancement in the microelectromechanical systems (MEMS) technology [7,8], it is still challenging to make a small measurement device that includes whole measurement elements. This is mainly due to gas flow lines including pumps; gas flow rates of odor samples need to be strictly controlled in many cases because signal features characteristic to odors are difficult to be extracted without precise gas flow control [9]. For such reasons, current odor measurement devices tend to be bulky and power-consuming. To resolve this issue, the development of new sensing methods without such gas flow control is required. Nimsuk et al. developed gas classification systems for gas flow in which the concentration of samples dynamically changed. They focused on the frequency components of the sensing signals and demonstrated gas classification based on them [10,11]. Trincavelli et al. developed gas discrimination robot systems in an open-sampling condition, in which the gas flow of sample to sensors was not controlled [12,13]. In their system, gases were identified by focusing on the transient responses of segmented sensing signals obtained with metal oxide (MOX) sensors. Vergara et al. also developed a MOX sensor-based gas identification system in an open-sampling condition. The system detected the turbulent plume of sample gases in a wind tunnel facility and identified the gas species by the static responses of arrayed MOX sensors [14]. In 2019, a new measurement protocol was proposed based on transfer function ratios (TFRs), which are intrinsically independent of gas flow control and can be calculated only from sensing signals [15]. On the basis of data analysis methods using TFRs, we demonstrated odor identification through “free-hand measurement,” in which a small sensor array is manually brought close to target odors. The free-hand measurement could be achieved owing to the novel characteristics of Membrane-type Surface stress Sensors (MSS) [16]. MSS are small sensors fabricated through a MEMS process [17,18]. MSS have high sensitivity and high chemical versatility, which are advantageous to the sensor element of machine olfaction. Another merit of using MSS is its relatively high resistance against mechanical vibration and shock. Compared to gas sensors that detect shifts in resonant frequencies caused by gas sorption (e.g., quartz crystal microbalance, microcantilever-type sensors in the dynamic mode [19–21]), sensing responses of MSS are rather robust against physical shocks like touching or moving measurement devices owing to a full-Wheatstone bridge configuration with a relatively small mass of the sensing part. The small footprint of MSS also enables it to configure a small array of sensor channels, which can be considered to be spatially equivalent for the gas input, satisfying the intrinsic requirement to calculate TFRs. Thus, the combination of MSS and the data analysis method based on TFRs makes it possible to identify odors through the free-hand measurements. There is, however, still room for improving the protocol of the free-hand measurement. As the free-hand measurement is based on the data analysis methods using TFRs, the input pattern of the protocol—the motion of a hand holding a measurement device—should contain a wide range of frequency components. For that reason, an ideal free-hand measurement protocol should be done by totally randomly moving a measurement device near a sample for a long time period, which is not suitable for practical operation. Although TFRs are independent of the input pattern in principle, protocols of the free-hand measurement are restricted by considering the practical operation. For example, measurement time within 10 s is favored for prompt identification of a target odor. As “randomly” is unclear and confusing, specific instructions on how to expose a measurement device to samples will be helpful to operators without professional expertise in sensor technologies. To popularize odor identification based on the free-hand measurements, optimization of measurement protocols under such restricted conditions is desired. The data analysis methods, especially the feature selection—the appropriate selection of signal features for the subsequent data analysis—of TFRs, also needs to be improved accordingly. In this study, we have first developed a palm-size wireless measurement device equipped with MSS for the free-hand measurement (Figure1). The aim of this study is to optimize the measurement Sensors 2020, 20, 6190 3 of 14 Sensors 2020, 20, x FOR PEER REVIEW 3 of 13 protocol of thethe free-handfree-hand measurementmeasurement and provideprovide guidelinesguidelines for developingdeveloping accurateaccurate machine learning classificationclassification models models from from TFRs TFRs datasets. datasets. By using By theusing wireless the wireless measurement measurement device equipped device withequipped a 4-channel with a MSS4-channel chip, MSS we measuredchip, we vaporsmeasured of fourvapors liquids of four (i.e., liquids water, (i.e., hexane, water, methanol, hexane, andmethanol, acetone) and through acetone) the through free-hand the measurementfree-hand meas withurement different with protocols. different Datasetsprotocols. of Datasets TFRs were of createdTFRs were from created the measurement from the measurement data and analyzed data an byd clusteringanalyzed by and clustering machine and learning machine classification. learning Onclassification. the basis of On the the result basis of dataof th analysis,e result weof discussdata analysis, the effect we of discuss measurement the effect protocols of measurement and feature selectionprotocols onand the feature vapor selection identification. on the vapor identification.

Figure 1. Picture of wireless measurement devicedevice for the free-hand measurement. 2. Materials and Methods 2. Materials and Methods 2.1. MSS 2.1. MSS An MSS is a silicon (Si)-based sensor fabricated through a MEMS process [16–18]. A thin membrane An MSS is a silicon (Si)-based sensor fabricated through a MEMS process [16–18]. A thin is suspended by four beams, in which piezoresistors (R1, R2, R3, and R4) are embedded. A receptor membrane is suspended by four beams, in which piezoresistors (R1, R2, R3, and R4) are embedded. A material, which absorbs gas species, is coated on the membrane. According to this unique structure, receptor material, which absorbs gas species, is coated on the membrane. According to this unique surface stress caused by the expansion/shrinkage of the receptor material accumulates in the four beams, structure, surface stress caused by the expansion/shrinkage of the receptor material accumulates in resulting in resistance change of the piezoresistors. As the piezoresistors consists a full Wheatstone the four beams, resulting in resistance change of the piezoresistors. As the piezoresistors consists a bridge circuit, the signal output (Vout) is described as the following equation: full Wheatstone bridge circuit, the signal output (Vout) is described as the following formula: ! V𝑉B ∆Δ𝑅R1 ∆Δ𝑅R2 ∆RΔ𝑅3 ∆RΔ𝑅4 Vout = + (1) 𝑉 = 4 R1 −−R2 +R3 − −R4 (1) 4 𝑅 𝑅 𝑅 𝑅 where VB isis the the bridge voltage applied to the circuit. In In this this study, study, we used 4-channel4-channel MSS chipschips provided by NanoWorld AGAG (Neuch(Neuchâtel,âtel, Switzerland). Receptor materials were coated on MSS by inkjet spotting. In this study, we used four polymers as receptor receptor materials: materials: poly(4-vinylphenol), poly(4-vinylphenol), polyepichlorohydrin, polyepichlorohydrin, poly(styrene-co-allyl poly(styrene-co-allyl alcohol), alcohol), and andpoly(methyl poly(methyl methacrylate) methacrylate) for channel for channel (Ch.) Ch.1, 1, Ch.2 Ch.2,, Ch.3, Ch.3, and and Ch.4, Ch.4, respectively. respectively. The The polymers polymers were dissolved in N,N-dimethylformamide (DMF)(DMF) atat a concentrationconcentration of 1.0 g/L. g/L. The solution was delivered onto each MSS channelchannel withwith LaboJet-500SPLaboJet-500SP (MICROJET(MICROJET Corporation,Corporation, Shiojiri, Japan). The stage on which thethe MSSMSS chip chip was was set set during during the the inkjet inkjet spotting spotting was was heated heated to 80 to◦ C.80 Two°C. Two hundred hundred droplets droplets were shotwere onto shot Ch.1onto while Ch.1 300while droplets 300 droplets were shot were onto shot Ch.2, onto Ch.3, Ch.2, and Ch.3, Ch.4. and The Ch.4. MSS The chip MSS coated chip with coated the 4with polymers the 4 polymers were observed were observed by an upright by an microscopeupright microscope (Eclipse (Eclipse Ni, Nikon Ni, Corporation, Nikon Corporation, Tokyo, Japan).Tokyo, Japan).The basic sensing properties of the MSS chip was evaluated by a gas-sensing measurement systemThe equipped basic sensing with twoproperties mass flow of the controllers MSS chip (MFCs) was evaluated which provided by a gas-sensing dried nitrogen. measurement The same measurementsystem equipped system withwas two usedmass inflow our controllers previous studies(MFCs) [ 22which–24]. provided One MFC dried (MFC1) nitrogen. was connectedThe same tomeasurement a glass vial whichsystem contains was used a samplein our previous liquid; dried studies nitrogen [22–24]. from One MFC1 MFC delivered(MFC1) was the connected vapor of the to a glass vial which contains a sample liquid; dried nitrogen from MFC1 delivered the vapor of the sample liquid. The nitrogen almost saturated with the sample vapor was then diluted by dried Sensors 2020, 20, 6190 4 of 14 sample liquid. The nitrogen almost saturated with the sample vapor was then diluted by dried nitrogen provided by the other MFC (MFC2). The diluted sample vapor was delivered to a sensor chamber in which the MSS chip was set. To evaluate the sensing properties of the MSS chip, sample vapors and dried nitrogen were alternately delivered to the MSS chip. First, the MSS chip was purged with dried nitrogen for 30 s (the gas flow rates of MFC1 and MFC2 were 0 and 100 standard cubic centimeters per minute (sccm), respectively). Then, a sample vapor was injected (the gas flow rates of MFC1 and MFC2 were 20 and 80 sccm, respectively) for 30 s, followed by the dried nitrogen purge for 30 s. This cycle of the sample vapor injection and the dried nitrogen purge was repeated 4 times. In this study, we used water, hexane, methanol, and acetone as sample liquids. Before each measurement, the MSS was “washed” by water vapor for approximately 60 s. The signal output was recorded at a sampling rate of 20 Hz with an analog-to-digital converter (NI-9214, NI, Austin, TX, USA). Bridge voltage V of 0.5 V B − was applied with a digital-to-analog converter module (NI-9269, NI, Austin, TX, USA). The gas flow of the two MFCs were controlled with a custom program based on LabVIEW (NI, Austin, TX, USA). Hexane and DMF were purchased from Wako Pure Chemical Industries (Osaka, Japan) while methanol and acetone from Kanto Chemical Co., Inc. (Tokyo, Japan) and Sigma-Aldrich (St. Louis, MO, USA), respectively. Ultrapure water was prepared with Direct-Q UV3 (Merck & Co., Inc., Kenilworth, NJ, USA). All the polymers were purchased from Sigma-Aldrich (St. Louis, MO, USA).

2.2. Free-Hand Measurements Gas sensing measurements were done with the wireless measurement device as shown in Figure1. The dimensions of the device are 70 mm 22 mm 14 mm, and it weighs 20 g. Power was supplied × × by a lithium-ion battery. The 4-channel MSS chip was set at the top of the device. A bridge voltage of 1.0 V was applied to each channel. The signal output of each channel was recorded at a sampling − rate of 20 Hz and 24-bit resolution, and the data were transmitted to a laptop by Bluetooth 4.2 (Bluetooth Low Energy). The power consumption of the device was less than 70 mW. Free-hand measurements were performed on the 4 sample liquids in glass beakers: water, hexane, methanol, and acetone. The vapors were measured by manually bringing the wireless measurement device into the headspace of the beakers. In this study, three types of measurement protocols were performed; the device was brought into the headspace for 1, 3, and 5 times in 10 s. Hereafter, these protocols are described as “Protocol-1”, “Protocol-3”, and “Protocol-5”, (see Supplementary Materials, Video S1). Thirty measurement data were obtained for each liquid sample; the sample size of measurement data was 120 for each protocol. To avoid data bias caused by measurement conditions, measurement order for the samples was randomized. Furthermore, measurements were taken over more than two days for each measurement protocol. As the beginning and the end parts of the measurements tended to be disturbed, the time region from 2 to 9 s were used for analysis.

2.3. TFRs A sensor is regarded as a control system in which gas concentration c(t) and a sensing signal y(t) are an input and an output, respectively. For a multichannel sensor system in which all the channels are assembled in close proximity, input c(t) is considered to be the same for each channel. The output signal of the i-th channel yi(t) can be described as a convolution of its transfer function hi(t) and an input c(t): Z ∞ yi(τ) = hi(τ)c(t τ)dτ (2) 0 − In the frequency domain, Equation (2) is written as the following formula:

Yi( f ) = Hi( f )C( f ) (3) Sensors 2020, 20, 6190 5 of 14

where Yi(f ), Hi(f ), and C(f ) refer to yi(t), hi(t), and c(t) in the frequency domain, respectively. As C(f ) is theSensors same 2020 for, 20 all, x theFOR channels, PEER REVIEW the following equation holds between the m-th and the n-th channels.5 of 13

Ym( f ) = Hm( f )C( f ) (4) 𝑌𝑓 =𝐻𝑓𝐶𝑓 (4)

Yn( f ) = Hn( f )C( f ) (5) 𝑌𝑓 =𝐻𝑓𝐶𝑓 (5) Thus, the transfer function ratio (TFR) between the m-th and the n-th channels deﬁned as Thus, the transfer function ratio (TFR) between the m-th and the n-th channels defined as Km,n(f) Km,n(f ) = Hm(f )/Hn(f ) is represented as the following form: = Hm(f)/Hn(f) is represented as the following form:

Hm𝐻(f )𝑓Ym(𝑌f) 𝑓 Km,n( f) = = (6)(6) 𝐾, 𝑓 =H ( f ) Y=( f ) n𝐻𝑓 n 𝑌𝑓

GasGas species species can can be be identified identified by byK Km,nm,n((ff) because because KKm,nm,n(f()f is) isspecific specific to togas gas species. species. As As Km,nK(m,nf) does(f ) does not notcontain contain C(Cf),(f gas), gas species species can can be be identified identified only only from from sensing sensing signals signals at at each each channel; channel; controlling controlling or monitoringmonitoring the the gas gas flow flow of of a a sample sample gas gas is is not not required. required. InIn this this study, study, TFRs TFRs were were calculated calculated from from the the time-series time-series data data fromfrom thethe 44 channels.channels. To transform thethe time-domain time-domain data data toto thethe frequency domain, domain, the the fast fast Fourier Fourier transform transform (FFT) (FFT) was was applied applied to the to thetime-series time-series data. data. Before Before FFT, FFT, the themean mean value value of time-series of time-series data data was wassubtracted subtracted from from the data, the data, and andHanning Hanning window window was was applied. applied. From From the the frequency-domain frequency-domain data, data, 6 6TFRs TFRs were were calculated calculated according toto Equation Equation (6): (6): namely, namely,K K1,21,2((ff), K1,31,3(f(),f ), KK1,41,4(f),( fK),2,3K(f2,3), (Kf 2,4),(Kf),2,4 K(3,4f ),(f).K 3,4As( fthe). As length the lengthof a time-series of a time-series data is data8 s, isthe 8 s,minimum the minimum unit of unit frequency of frequency is 0.125 is Hz. 0.125 According Hz. According to the Nyquist–Shannon to the Nyquist–Shannon sampling sampling theorem, theorem,the maximum the maximum frequency frequency component component of the data of is the10 Hz data, as isthe 10 data Hz, were as the obtained data were at a sampling obtained rate at a samplingof 20 Hz. rate In this of 20study, Hz. the In thisoffset study, value the (0 Hz off setcomponent) value (0 Hzwas component) not used. As wasTFRs not are used. complex As TFRsnumbers, are complexTFRs were numbers, divided TFRs into were absolute divided values into and absolute argument valuess; logarithms and arguments; of the absolute logarithms values of the were absolute used valuesas features were usedbecause as featuresthey vary because in a wide they range. vary Fi ingure a wide 2 shows range. an Figure example2 shows of TFRs an exampleshown in of graphs. TFRs shownFinally, in graphs.the calculated Finally, TFRs the calculated(arguments TFRs and (argumentslogarithms of and absolute logarithms values) of absolutewere concatenated values) were to concatenatedform 960-dimensional to form 960-dimensional data for each measurement. data for each measurement.

FigureFigure 2. 2.Transfer Transfer function function ratios ratios (TFRs) (TFRs) of of the the data data obtained obtained through through Protocol-5.Protocol-5. MeanMean valuesvalues and

standardstandard deviations deviations of of log log||Km,nKm,n| |and and arg argKm,nKm,nare are plottedplotted asas squaressquares andand shaded areas, respectively. WaterWater was was used used as as a sample.a sample.

2.4. Cluster Analysis Principal component analysis (PCA) was performed on the dataset of the TFRs to visualize the dataset. PCA is one of the most common dimensionality reduction algorithms—projecting the original data to new coordinates consisting of principal components. From a multidimensional dataset, PCA finds a direction that maximizes the variance of the dataset (PC1). The second principal component (PC2) is the direction that maximizes variance among all directions orthogonal to PC1. The further components are determined in the same manner. To evaluate the quality of clusters, we employed the Davies–Bouldin (DB) index [25]. DB index is defined as the following equation:   Xn  σ + σ  1  i j  DB = max  (7) n i,j   i d ci, cj where n is the number of clusters. ci and cj are the centroids of cluster i and j, respectively, and d(ci, cj) is the distance between ci and cj. σi and σi denote the mean distance of all elements in cluster i and j from ci and cj, respectively. According to the definition, DB index becomes small for the clusters which are well-separated with each other.

2.5. Machine Learning Classification We developed classification models based on logistic regression and random forests. For developing classification models based on logistic regression, TFRs datasets from the measurements were first standardized, and the dimensions were reduced by PCA. The logistic regression classifier was then trained with the datasets. In contrast, classification models based on random forests were developed without dimensionality reduction. The models were optimized and validated through a 5 5 × nested cross-validation process. First, all the data were randomly divided into 5 subsets. One subset was used as a test dataset while the other 4 subsets were for a training dataset. A classification model was developed from the training dataset, and its performance was evaluated by the test dataset. By changing the combination of the 5 subsets for training and test datasets (“outer” cross-validation), both the mean accuracy and the standard deviation of classification models were evaluated. In the model building, the training dataset was further split into 5 subsets; one subset was for validation and the other 4 subsets were for model building. In this “inner” cross-validation, the hyperparameters of classification models were optimized by grid search. The hyperparameters of the models are summarized in Table1. To develop classification models, scikit-learn was used in this study.

Table 1. Hyperparameters of the models.

Classiﬁcation Models Based on Classiﬁcation Models Based on Logistic Regression Random Forests

Number of principal component • Hyperparameters analysis (PCA) components Number of estimators (Name of the parameter in the (n_components) • (n_estimators) scikit-learn library) Regularization parameter (C) •

Note that this machine learning model is applicable only to the samples used in the training of the supervised learning. Unused samples or a mixture of used samples cannot be identiﬁed because the model classify any sample into the learned categories. Sensors 2020, 20, 6190 7 of 14

3. Results

3.1. Basic Properties of the MSS Figure3a shows the optical microscope image of the MSS chip. The receptor materials were coated on the 4 channels. The coatings did not deform or peel off from the membrane after the gas-sensing measurements with the gas flow line. Figure3b–e shows the results of the gas-sensing measurements. Baseline offset values were subtracted from the signals. Each channel responded to the sample gases differentlySensors 2020 from, 20, eachx FOR other.PEER REVIEW 7 of 13 Sensors 2020, 20, x FOR PEER REVIEW 7 of 13

FigureFigure 3. 3. ((a) Microscope Microscope image image of the of Membrane-type the Membrane-type Surface Surfacestress Sensors stress (MSS) Sensors chip. (MSS)Poly(4- chip. Poly(4-vinylphenol),Figurevinylphenol), 3. (a) Microscope polyepichlorohydrin, polyepichlorohydrin, image of the poly(styrene-co-allyl Membrane-type poly(styrene-co-allyl Surface alcohol) alcohol), stress, and Sensors andpoly(methyl poly(methyl (MSS) methacrylate)chip. methacrylate) Poly(4- vinylphenol), polyepichlorohydrin, poly(styrene-co-allyl alcohol), and poly(methyl methacrylate) werewere coated coated on Ch.1,on Ch.1, Ch.2, Ch.2, Ch.3, Ch.3, and and Ch.4, Ch.4, respectively.respectively. Results Results of of the the gas-sensing gas-sensing measurements measurements for for were coated on Ch.1, Ch.2, Ch.3, and Ch.4, respectively. Results of the gas-sensing measurements for (b) Ch.1,(b) Ch.1, (c) Ch.2, (c) Ch.2, (d) Ch.3,(d) Ch.3, and and (e) ( Ch.4.e) Ch.4. (b) Ch.1, (c) Ch.2, (d) Ch.3, and (e) Ch.4. FigureFigure4 shows 4 shows examples examples of of the the sensing sensing signals signals obtainedobtained through through the the free-hand free-hand measurement; measurement; Figure 4 shows examples of the sensing signals obtained through the free-hand measurement; the measurementsthe measurements were were performed performe by Protocol-1,d by Protocol-1, Protocol-3, Protocol-3, and Protocol-5 and Protocol-5 for Figure for4 a–c,Figure respectively. 4a–c, therespectively. measurements Peaks werecan beperforme clearly dseen by Protocol-1,for each measurement Protocol-3, anprotocol,d Protocol-5 reflecting for Figurethe different 4a–c, Peaks can be clearly seen for each measurement protocol, reﬂecting the diﬀerent concentration of the respectively.concentration Peaks of the canvapor be inside clearly and seen outside for eachthe beaker. measurement protocol, reflecting the different vaporconcentration inside and of outside the vapor the inside beaker. and outside the beaker.

Figure 4. Examples of sensing signals for each protocol; the measurement device was brought into FigureFigurethe 4.headspaceExamples 4. Examples of the of of sensingbeaker sensing (a signals) signalsonce (Protocol-1), forfor eacheach protoc protocol; (b) 3 ol;times the the (Protocol-3), measurement measurement and devi (c device)ce 5 timeswas was brought (Protocol-5). brought into into the headspacetheWater headspace was ofused theof asthe beaker a beakersample. (a ()a ) once once (Protocol-1), (Protocol-1), ( (bb)) 3 3 times times (Protocol-3), (Protocol-3), and and (c) 5 ( ctimes) 5 times (Protocol-5). (Protocol-5). WaterWater was was used used as aas sample. a sample. 3.2. Cluster Analysis and Machine Learning Classification 3.2. Cluster Analysis and Machine Learning Classification The results of PCA on the dataset of TFRs are shown in Figure 5. Data were obtained by Protocol- 1, Protocol-3,The results and of PCAProtocol-5 on the for dataset Figure of TFRs5a–c, arerespecti showvely.n in FigureIt is clear 5. Data from were Figure obtained 5 that by data Protocol- points 1,from Protocol-3, the same and samples Protocol-5 form clustersfor Figure on the5a–c, PCA respecti scorevely. plot, Itshowing is clear that from the Figure TFRs reflect5 that interactionsdata points frombetween the samereceptor samples materials form andclusters sample on the vapors. PCA scorHowee plot,ver, showingthe quality that of the cluster TFRs separation reflect interactions seems to betweendiffer between receptor the materials three protocols; and sample the cluster vapors. of Howe waterver, totally the qualityoverlaps of on cluster that ofseparation hexane for seems Figure to differ5a, while between they arethe separatedthree protocols; from eachthe cluster other inof Figure water totally5c. It seems overlaps that oncluster that ofseparation hexane for improves Figure 5a,as thewhile number they are of separatedexposures fromin a protocoleach other increase in Figures. This 5c. trendIt seems can that be quantitativelycluster separation confirmed improves by as the number of exposures in a protocol increases. This trend can be quantitatively confirmed by Sensors 2020, 20, 6190 8 of 14

3.2. Cluster Analysis and Machine Learning Classification The results of PCA on the dataset of TFRs are shown in Figure5. Data were obtained by Protocol-1, Protocol-3, and Protocol-5 for Figure5a–c, respectively. It is clear from Figure5 that data points from the same samples form clusters on the PCA score plot, showing that the TFRs reflect interactions between receptor materials and sample vapors. However, the quality of cluster separation seems to differ between the three protocols; the cluster of water totally overlaps on that of hexane for Figure5a, Sensors 2020, 20, x FOR PEER REVIEW 8 of 13 whileSensors they 2020,are 20, x separated FOR PEER REVIEW from each other in Figure5c. It seems that cluster separation improves as8 of the 13 number of exposures in a protocol increases. This trend can be quantitatively confirmed by focusing focusing on the DB index. DB indices for the TFRs datasets obtained by Protocol-1, Protocol-3, and onfocusing the DB on index. the DB DB index. indices DB for indices the TFRs for datasetsthe TFRs obtained datasets byobtained Protocol-1, by Protocol-1, Protocol-3, Protocol-3, and Protocol-5 and Protocol-5 are 4.87, 4.79, and 3.96, respectively. As a small DB index is expected for a dataset with areProtocol-5 4.87, 4.79 are and 4.87, 3.96, 4.79, respectively. and 3.96, respectively. As a small DB As indexa small is expectedDB index foris expected a dataset for with a dataset good cluster with good cluster separation, Protocol-5 achieved the best cluster quality among the three protocols. separation,good cluster Protocol-5 separation, achieved Protocol-5 the bestachieved cluster the quality best cluster among quality the three among protocols. the three protocols.

Figure 5. PCA score plots for TFRs datasets. Measurement data were obtained by (a) Protocol-1, (b) FigureFigure 5.5. PCA score score plots plots for for TFRs TFRs datasets. datasets. Measurement Measurement data data were were obtained obtained by (a by) Protocol-1, (a) Protocol-1, (b) Protocol-3,(bProtocol-3,) Protocol-3, and and and( c(c) )Protocol-5. (Protocol-5.c) Protocol-5.

TheTheThe accuraciesaccuracies accuracies of ofof the thethe machine machinemachine learning learninglearning classification classiclassificationfication models models are shownare shownshown in Figure inin FigureFigure6. As the 6.6. modelsAsAs thethe modelsweremodels developed were were developed developed by nested by by cross-validation,nested nested cross-validation, cross-validation, error bars erroerro (standardrr bars (standard deviations) deviations) of the accuracies ofof thethe accuraciesaccuracies are also areshown.are also also shown. For shown. both For For two both both classifiers, two two classifiers,classifiers, vapors could vaporsvapors be coco identifiedulduld be identified with a mean with accuracya meanmean accuracyaccuracy of more of thanof moremore 0.8, thanshowingthan 0.8, 0.8, thatshowing showing classification thatthat classificationclassification accuracy of accuracyaccuracy about 0.8 ofof could aboutabout be 0.80.8 achieved could be with achieved the free-hand withwith thethe measurement free-handfree-hand measurementwithoutmeasurement further without without optimization further further in optimization measurementoptimization inin protocols measmeasurementurement and feature protocols selection. and feature For the selection. logisticselection. regression ForFor the the logisticclassifier,logistic regression regression the classification classifier,classifier, model thethe builtclassificationclassification from the model Protocol-3model builtbuilt dataset from exhibitedthe Protocol-3 slightly datasetdataset higher exhibitedexhibited accuracy slightlythanslightly the higher onehigher built accuracy accuracy from the thanthan Protocol-1 thethe oneone dataset. builtbuilt fromfrom In contrast,ththee Protocol-1 accuracies dataset. of classification In contrast,contrast, modelsaccuraciesaccuracies based ofof classificationonclassification random forests models models are basedbased not significantly onon randrandomom diforestsforestsfferent. areare The notnot random significantly forest-based different. model TheThe developedrandomrandom forest-forest- from basedProtocol-5based model model TFRs developed developed dataset exhibitedfrom from Protocol-5 Protocol-5 the highest TFRsTFRs accuracy datasedatasett exhibitedexhibited (0.96 0.03). the highest accuracyaccuracy (0.96(0.96 ±± 0.03).0.03). ±

FigureFigureFigure 6. 6.6. Summary Summary ofof thethe classificationclassification classiﬁcation models. models.

3.3.3.3. Dependence Dependence on on Frequency Frequency Range Range AsAs the the protocols protocols of of the the free-hand free-hand measurementsmeasurements affectffect the cluster quality ofof TFRs’TFRs’ datasetsdatasets and and thethe accuracy accuracy of of classifica classificationtion models, models, itit isis expectedexpected thatthat therethere is an optimal frequencyfrequency rangerange inin TFRsTFRs forfor vapor vapor identification. identification. To To investigate investigate such such dependencedependence onon the frequency range,range, wewe preparedprepared 4 4 TFRs TFRs datasetsdatasets that that contain contain different different frequency frequency componencomponents:ts: 0.125–2.5000.125–2.500 Hz (Data I),I), 2.625–5.0002.625–5.000 HzHz (Data(Data II),II), 5.125–7.500 5.125–7.500 Hz Hz (Data (Data III), III), and and 7.625–10.0007.625–10.000 HzHz (D(Data IV). Figure 7 shows thethe resultsresults ofof PCAPCA onon thesethese datasets.datasets. InIn anyany protocol,protocol, thethe clustersclusters collcollapseapse as the frequency region shiftsshifts toto higherhigher frequenciesfrequencies (left (left to to right right in in Figure Figure 7).7). AlthoughAlthough ththee dependencedependence of cluster separationparation onon protocolsprotocols is is not clear from Figure 7, it seems that the dataset obtained by Protocol-5 shows better cluster not clear from Figure 7, it seems that the dataset obtained by Protocol-5 shows better cluster separation than the ones obtained by Protocol-1 and Protocol-3. For example, in Data I (Figure 7a,e,i), separation than the ones obtained by Protocol-1 and Protocol-3. For example, in Data I (Figure 7a,e,i), Sensors 2020, 20, 6190 9 of 14

3.3. Dependence on Frequency Range As the protocols of the free-hand measurements affect the cluster quality of TFRs’ datasets and the accuracy of classification models, it is expected that there is an optimal frequency range in TFRs for vapor identification. To investigate such dependence on the frequency range, we prepared 4 TFRs datasets that contain different frequency components: 0.125–2.500 Hz (Data I), 2.625–5.000 Hz (Data II), 5.125–7.500 Hz (Data III), and 7.625–10.000 Hz (Data IV). Figure7 shows the results of PCA on these datasets. In any protocol, the clusters collapse as the frequency region shifts to higher frequencies (left to right in Figure7). Although the dependence of cluster separation on protocols is not clear fromSensors Figure 2020, 207, x it FOR seems PEER that REVIEW the dataset obtained by Protocol-5 shows better cluster separation9 thanof 13 the ones obtained by Protocol-1 and Protocol-3. For example, in Data I (Figure7a,e,i), three clusters (water,three clusters hexane, (water, and acetone) hexane, overlap and acetone) for Protocol-1 overlap (Figurefor Protocol-17a) and (Figure Protocol-3 7a) and (Figure Protocol-36e) while (Figure only two6e) clusterswhile only (water two andclusters acetone) (water overlap and acetone) for Protocol-5 overlap (Figure for Protocol-57i) except (Figure two outliers. 7i) except The two DB outliers. indices forThe the DB datasets indices are for summarized the datasets in are Figure summarized8. As we havein Figure seen in8. FigureAs we7 have, the DBseen index in Figure increases 7, the as theDB frequencyindex increases components as the containedfrequency in components the datasets contain shift toed higher in the frequencies; datasets shift cluster to higher quality frequencies; deteriorates forcluster TFRs’ quality datasets deteriorates which contain for TFRs’ only datasets high-frequency which contain components. only high-frequency This trend can components. be seen for all This the protocols.trend can Inbeany seen frequency for all the region, protocols. datasets In any from frequency Protocol-5 region, exhibited datasets better from cluster Protocol-5 quality exhibited than the onesbetter from cluster Protocol-1 quality andthan Protocol-3. the ones from Protocol-1 and Protocol-3.

FigureFigure 7. 7.PCA PCA score score plots plots for for TFRs TFRs datasets. datasets. (a )–((a)–(d)d Data) Data I, DataI, Data II, DataII, Data III, andIII, and Data Data IV from IV from the TFRs the datasetsTFRs datasets obtained obtained by Protocol-1; by Protocol-1; (e)–(h) ( Datae)–(h) I, Data Data I, II, Data Data II, III, Data and III, Data and IV Data from IV the from TFRs the datasets TFRs obtaineddatasets byobtained Protocol-3; by Protocol-3; (i)–(l) Data (i)–( I,l Data) Data II, I, Data Data III, II, andData Data III, and IV fromData theIV from TFRs the datasets TFRs obtaineddatasets byobtained Protocol-5. by Protocol-5.

Figure 8. Colormap of Davies–Bouldin (DB) indices for the TFRs datasets.

Classification models were also developed for each dataset. The accuracies of the classification models are summarized in Figure 9. Similar to the DB index, classification accuracy tends to deteriorate as the frequency range contained in a dataset becomes high. No significant difference is observed among the three protocols. Of all the classification models, the random forest-based Sensors 2020, 20, x FOR PEER REVIEW 9 of 13 three clusters (water, hexane, and acetone) overlap for Protocol-1 (Figure 7a) and Protocol-3 (Figure 6e) while only two clusters (water and acetone) overlap for Protocol-5 (Figure 7i) except two outliers. The DB indices for the datasets are summarized in Figure 8. As we have seen in Figure 7, the DB index increases as the frequency components contained in the datasets shift to higher frequencies; cluster quality deteriorates for TFRs’ datasets which contain only high-frequency components. This trend can be seen for all the protocols. In any frequency region, datasets from Protocol-5 exhibited better cluster quality than the ones from Protocol-1 and Protocol-3.

Figure 7. PCA score plots for TFRs datasets. (a)–(d) Data I, Data II, Data III, and Data IV from the TFRs datasets obtained by Protocol-1; (e)–(h) Data I, Data II, Data III, and Data IV from the TFRs

Sensorsdatasets2020, 20 ,obtained 6190 by Protocol-3; (i)–(l) Data I, Data II, Data III, and Data IV from the TFRs datasets10 of 14 obtained by Protocol-5.

Figure 8. Colormap of Davies–Bouldin (DB) indices for the TFRs datasets. Figure 8. Colormap of Davies–Bouldin (DB) indices for the TFRs datasets. Classification models were also developed for each dataset. The accuracies of the classification Classification models were also developed for each dataset. The accuracies of the classification models are summarized in Figure9. Similar to the DB index, classification accuracy tends to deteriorate models are summarized in Figure 9. Similar to the DB index, classification accuracy tends to asSensors the 2020 frequency, 20, x FOR range PEER REVIEW contained in a dataset becomes high. No significant difference is observed10 of 13 deteriorate as the frequency range contained in a dataset becomes high. No significant difference is among the three protocols. Of all the classification models, the random forest-based classification model observed among the three protocols. Of all the classification models, the random forest-based developedclassification from model Data developed I of the Protocol-5 from Data TFRs I of dataset the Protocol-5 exhibited TFRs the highestdataset classificationexhibited the accuracy highest (classification0.98 0.03), whichaccuracy is slightly(0.98 ± better0.03), which than that is slightly of the random better than forest-based that of the model random built forest-based from all the ± frequencymodel built components from all the of frequency the Protocol-5 components TFRs dataset of the (0.96Protocol-50.03). TFRs dataset (0.96 ± 0.03). ±

Figure 9.9. SummarySummary of of the the classification classification models. models. Accuracies Accuracies of classification of classification models models based onbased (a) logisticon (a) regressionlogistic regression and (b) randomand (b) random forests. forests. 4. Discussion 4. Discussion On the basis of the results of the cluster analysis and the machine learning classification, it is On the basis of the results of the cluster analysis and the machine learning classification, it is evident that high-frequency components of TFRs do not represent the interaction between sample evident that high-frequency components of TFRs do not represent the interaction between sample vapors and receptor materials. As such a high-frequency region contains more electrical noises than a vapors and receptor materials. As such a high-frequency region contains more electrical noises than low-frequency region does, using the high-frequency components as features is not effective for vapor a low-frequency region does, using the high-frequency components as features is not effective for identification. This is also confirmed from the statistical investigation of TFRs; for most of log|Km,n| and vapor identification. This is also confirmed from the statistical investigation of TFRs; for most of argKm,n, high-frequency components exhibit higher standard deviation than low-frequency components log|Km,n| and argKm,n, high-frequency components exhibit higher standard deviation than low- (see Figure S1 in Supplementary Materials). Low-frequency components, on the other hand, seem to frequency components (see Figure S1 in Supplementary Materials). Low-frequency components, on reflect the dynamics of the sensing mechanism. According to the viscoelastic model proposed by the other hand, seem to reflect the dynamics of the sensing mechanism. According to the viscoelastic Wenzel et al. [26,27], the dynamics of nanomechanical sensors, including MSS, can be expressed by model proposed by Wenzel et al. [26,27], the dynamics of nanomechanical sensors, including MSS, two time constants: gas diffusion constant τs and time constant for relaxation modulus τr. As a rough can be expressed by two time constants: gas diffusion constant τs and time constant for relaxation estimation, the inverse of the time constants corresponds to the frequency components. Previous studies modulus τr. As a rough estimation, the inverse of the time constants corresponds to the frequency have shown that these time constants range from 1 to 100 s [27,28]; hence frequency components of components. Previous studies have shown that these time constants range from 1 to 100 s [27,28]; 0.01 to 1 Hz correspond to the time constants. To precisely evaluate the TFRs of such a frequency hence frequency components of 0.01 to 1 Hz correspond to the time constants. To precisely evaluate the TFRs of such a frequency range, the input to the sensor system (c(t) and C(f) in Equations (2) and (3), respectively) should contain sufficient frequency components of that range as well. To investigate the input frequency components that each protocol contains, we simulated the inputs c(t) of the protocols as shown in Figure 10a. The signals were simulated as binary waveforms; the values alternately took 0 and 1, which correspond to the vapor concentration c(t) outside and inside the beaker, respectively. The time interval of 0 and 1 domains was 8/3 (=2.67), 8/7 (=1.14), 8/11 (=0.72) s for Protocol-1, Protocol-3, Protocol-5, respectively. A waveform consists of 160 data points, which is equivalent to a measurement with a sampling rate of 20 Hz for 8 s. Figure 10b shows the result of FFT to the simulated waveforms; the absolute values of the frequency components of the waveforms are plotted for the protocols. In the frequency region below 2.5 Hz, the first peak shifts to a higher frequency as the number of waves in the input waveform increases. Another feature is that Protocol- 5 covers more frequency components than Protocol-1 or Protocol-3 in the frequency region below 1 Hz; the sums of the absolute values of the frequency components in the range from 0.125 to 1 Hz are 98.54, 103.27, 116.96 for Protocol-1, Protocol-3, and Protocol-5, respectively. As we have discussed, a frequency range below 1 Hz contains more information of sensing dynamics than other ranges; protocols that cover the entire frequency components in that range are preferable for vapor identification based on TFRs. Thus, the TFRs dataset obtained by Protocol-5 exhibited the best cluster quality and achieved the most accurate classification model among the three TFRs datasets. Sensors 2020, 20, 6190 11 of 14 range, the input to the sensor system (c(t) and C(f ) in Equations (2) and (3), respectively) should contain sufficient frequency components of that range as well. To investigate the input frequency components that each protocol contains, we simulated the inputs c(t) of the protocols as shown in Figure 10a. The signals were simulated as binary waveforms; the values alternately took 0 and 1, which correspond to the vapor concentration c(t) outside and inside the beaker, respectively. The time interval of 0 and 1 domains was 8/3 (=2.67), 8/7 (=1.14), 8/11 (=0.72) s for Protocol-1, Protocol-3, Protocol-5, respectively. A waveform consists of 160 data points, which is equivalent to a measurement with a sampling rate of 20 Hz for 8 s. Figure 10b shows the result of FFT to the simulated waveforms; the absolute values of the frequency components of the waveforms are plotted for the protocols. In the frequency region below 2.5 Hz, the first peak shifts to a higher frequency as the number of waves in the input waveform increases. Another feature is that Protocol-5 covers more frequency components than Protocol-1 or Protocol-3 in the frequency region below 1 Hz; the sums of the absolute values of the frequency components in the range from 0.125 to 1 Hz are 98.54, 103.27, 116.96 for Protocol-1, Protocol-3, and Protocol-5, respectively. As we have discussed, a frequency range below 1 Hz contains more information of sensing dynamics than other ranges; protocols that cover the entire frequency components in that range are preferable for vapor identification based on TFRs. Thus, the TFRs dataset obtained by Protocol-5 exhibited the best cluster quality and achieved the most accurate classification modelSensors among2020, 20, x the FOR three PEER TFRs REVIEW datasets. 11 of 13

FigureFigure 10.10. (a()a Simulated) Simulated waveform. waveform. (b) Absolute (b) Absolute value valueof the frequency of the frequency components components for the simulated for the simulatedwaveforms. waveforms.

AlthoughAlthough the the di differencefference in in classification classification accuraciesaccuracies betweenbetween thethe two classifiers classifiers are not not significant, significant, classificationclassification models models based based on on randomrandom forestsforests scoredscored higherhigher mean accuracies than than the the ones ones based based on on logisticlogistic regression regression for for every every dataset dataset (except(except DataData IVIV ofof Protocol-3). As As the the logistic logistic regression regression classifier classifier waswas trained trained by by TFRs TFRs datasetsdatasets ofof whichwhich thethe dimensionalitydimensionality was reduced by PCA, the the learning learning was was moremore contaminated contaminated byby thethe noisenoise componentscomponents than thethe random forests classifier, classifier, which which is is a a kind kind of of ensembleensemble learning learning algorithm. algorithm. ThisThis isis becausebecause PCAPCA determines the principal components components on on the the basis basis ofof variance, variance, which which is is intrinsically intrinsically notnot relatedrelated toto thethe importance of each feature. If If classification classification models models werewere developed developed without without dimensionality dimensionality reduction, reduction, training training of logistic of logistic regression regression classifier classifier would would suffer fromsuffer the from “curse the of“curse dimensionality” of dimensionality” [29], resulting [29], resulting in low in accuracies. low accuracies. In contrast, In contrast, random random forest-based forest- modelsbased canmodels be trained can be with trained low biaswith andlow low bias variance and low owing variance to its owing learning to procedure;its learning a randomprocedure; forest a classifierrandom isforest developed classifier by is aggregatingdeveloped by multiple aggregat decisioning multiple tree modelsdecision developed tree models from developed data subsets from data subsets sampled by bootstrapping, leading to high applicability to high dimensional datasets. Thus, a random forest is more preferable as a classifier for developing classification models from high-dimensional TFRs datasets than logistic regression. Toward a practical implementation of machine olfaction based on the free-hand measurement, it is also important to evaluate the dependence on gas concentration. In principle, TFRs do not depend on the concentration of gases as long as the responses of the sensors are regarded to be linear. For a low concentration range, however, the classification accuracy would be worsened because the signal to noise ratio becomes lowered. To obtain effective sensing signals, the measurement device needs to be exposed to the samples for a longer time than for the case of headspace gases of the liquid samples—high concentration samples.

5. Conclusions In this study, we have developed palm-size wireless odor measurement devices equipped with MSS chips for the free-hand measurement. Although the free-hand measurement is independent of the input pattern in principle, roughly fixed measurement protocols are favored from a viewpoint of practical use. To explore the optimal measurement protocol in a practical condition with the measurement time of 10 s, we have examined three types of measurement protocols that are easy to perform with the wireless measurement device and investigated their effects on odor identification. From the TFRs datasets created from the measurement data obtained with such protocols, features (i.e., frequency components) used for developing machine learning classification models were Sensors 2020, 20, 6190 12 of 14 sampled by bootstrapping, leading to high applicability to high dimensional datasets. Thus, a random forest is more preferable as a classifier for developing classification models from high-dimensional TFRs datasets than logistic regression. Toward a practical implementation of machine olfaction based on the free-hand measurement, it is also important to evaluate the dependence on gas concentration. In principle, TFRs do not depend on the concentration of gases as long as the responses of the sensors are regarded to be linear. For a low concentration range, however, the classification accuracy would be worsened because the signal to noise ratio becomes lowered. To obtain effective sensing signals, the measurement device needs to be exposed to the samples for a longer time than for the case of headspace gases of the liquid samples—high concentration samples.

5. Conclusions In this study, we have developed palm-size wireless odor measurement devices equipped with MSS chips for the free-hand measurement. Although the free-hand measurement is independent of the input pattern in principle, roughly fixed measurement protocols are favored from a viewpoint of practical use. To explore the optimal measurement protocol in a practical condition with the measurement time of 10 s, we have examined three types of measurement protocols that are easy to perform with the wireless measurement device and investigated their effects on odor identification. From the TFRs datasets created from the measurement data obtained with such protocols, features (i.e., frequency components) used for developing machine learning classification models were optimized. It has been clarified that high-frequency components of TFRs did not effectively contribute to vapor identification because of noise components, whereas classification models developed from TFRs datasets with low-frequency components yielded high accuracies. In addition, for accurate vapor identification, we have demonstrated that measurement should be performed in such a way that an MSS chip is exposed to the sample vapor several times in ten seconds. In such a measurement protocol, the input waveform—the time variation in concentration of a sample vapor—covers sufficient frequency components below 1 Hz, which are related to the sensing dynamics of MSS. The utilization of other receptor materials with quicker dynamics than the typical ones might be useful for collecting effective data from other frequency ranges, especially above 1 Hz. As the next step toward a practical application of this method, the repeatability between measurement devices needs to be evaluated. Considering the compactness and its usability, the present odor measurement device has great potential in mobile use and IoT applications. The present study will contribute to practical applications of machine olfaction and its implementation in society.

Supplementary Materials: The following are available online at http://www.mdpi.com/1424-8220/20/21/6190/s1, Figure S1: TFRs of data obtained through Protocol-5. Video S1: Movie of the three measurement protocols (i.e., Protocol-1, Protocol-3, and Protocol-5) with the wireless measurement device. Author Contributions: Conceptualization, G.I.; methodology, G.I. and G.Y.; software, G.I.; validation, G.I. and G.Y.; formal analysis, G.I.; investigation, G.I. and G.Y.; resources, G.I. and G.Y.; data curation, G.I.; writing—original draft preparation, G.I.; writing—review and editing, G.I. and G.Y.; visualization, G.I.; supervision, G.I. and G.Y.; project administration, G.I. and G.Y.; funding acquisition, G.I. and G.Y. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Leading Initiative for Excellent Young Researchers, Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan; a Grant-in-Aid for Scientific Research (C) [20K05345, MEXT, Japan]; the Iketani Science and Technology Foundation, Japan; the Murata Science Foundation, Japan; a Grant-in-Aid for Scientific Research (A) [18H04168, MEXT, Japan]; the Public/Private R&D Investment Strategic Expansion Program (PRISM), Cabinet Office, Japan; the World Premier International Research Center Initiative (WPI) on Materials Nanoarchitectonics (MANA), NIMS; the Center for Functional Sensor & Actuator (CFSN), NIMS; and the MSS Forum. Acknowledgments: We thank Fusako Hidaka (MANA, NIMS) for the preparation of MSS and the gas-sensing measurements. Conflicts of Interest: The authors declare no conflict of interest associated with this manuscript. Sensors 2020, 20, 6190 13 of 14

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