九州大学大学院総合理工学府報告第 43 巻 第 2 号 42-48 頁 令和 4 年 2 月

Engineering Sciences Reports, Kyushu University
Vol. 43, No. 2, pp. 42-48, FEB, 2022

Discrimination of Complex Odors
with Gas Chromatography-Mass Spectrometry Data
by Texture Image Analysis and Machine Learning
Chaiyanut JIRAYUPAT*1,2 Kazuki NAGASHIMA*2,3† Takuro HOSOMI*2,3
Tsunaki TAKAHASHI*2,3 Wataru TANAKA*2
Masaki KANAI*4 and Takeshi YANAGIDA*2,4†
E-mail of corresponding author: kazu-n@g.ecc.u-tokyo.ac.jp, yanagida@cm.kyushu-u.ac.jp†(Received January 26, 2022, accepted February 1, 2022)
Conventional odor discrimination is generally performed by gas chromatography–massspectrometry (GC–MS) that identifies specific marker molecules. Such marker identification process
is, however, labor-intensive, and the limited number of identified marker molecules is often
insufficient to discriminate complex odors. In this study, we have demonstrated a facile method fordiscriminating complex odors with GC–MS data by combining texture image analysis (TIA) andmachine learning (ML). We extracted various texture features (i.e., contrast, energy, homogeneity,
correlation, dissimilarity and angular second moment) of two-dimensional (2D) MS maps by TIA, andused them as datasets for ML. Each texture feature contains a lot of molecular information appearedin 2D MS maps, and thus serves as an effective parameter for discriminating complex odors. Basedon this method, we successfully performed the discrimination of breath samples collected from thepersons of different blood glucose levels with higher performances and reliability than theconventional approach.
Key words: Odor discrimination, texture image analysis, machine learning, 2D MS map, GLCM

1. Introduction
Odor analysis is a promising technique for
non-invasively characterizing a subject basedon the species and the concentrations ofcontained volatile chemical compounds. This
type of analysis has recently attracted muchattention in various scientific and industrial
fields such as pathology,1-3) pharmacology,4,5)
healthcare,6-12) food industry,13-15) fragrance
and perfume industry,16-18) environmental
conservation,19-21) agriculture22-24) and soon.25-27) The odor analysis is performed by
two-step process consisting of i) a marker
molecules
identification
and
ii)
a
discrimination or classification of odors basedon the identified specific markers. In odor
Department of Molecular and Material Sciences,
Interdisciplinary Graduate School of Engineering
Sciences, Kyushu University, Graduate Student*2 Department of Applied Chemistry, Graduate Schoolof Engineering, The University of Tokyo*3 Japan Science and Technology Agency, PRESTO*4 Division of Integrated Materials, Institute forMaterials Chemistry and Engineering, Kyushu
University*1

analysis, GC–MS is conventionally employed
for identifying marker molecules.28-33) However,themarkeridentification
processislabor-intensive, and limited number ofidentified
markermolecules
isoften
insufficient to discriminate complex odors.
The texture image analysis (TIA) is a usefulway to effectively collect large amount of
information in an image.34,35) TIA has recently
been applied to medical image analyses andsuccessfully demonstrated its performances on
the tumor identification and the radiotherapy
beyond the sense of human eyes.36,37) Despite
the advantage of TIA, it has rarely been
applied to odor discrimination. Thesebackgrounds motivated us to investigate theapplicability of TIA to the discrimination ofcomplex odors.
In this study, we demonstrated thediscrimination of human breath samples with
GC–MS data by combining texture image
analysis and machine learning (TIA–ML). In
this method, various texture features wereextracted from two-dimensional (2D) MS maps.
Each texture feature contains a lot of令和 3 年度

九州大学大学院総合理工学報告

molecular information appeared in 2D MS
maps, and thus serves as an effective
parameter for discriminating complex odors.
Based on this method, we successfully
performed the discrimination of breath
samples collected from persons with different
blood glucose levels. The performance andreliability of the TIA–ML method werediscussed in comparison with those of aconventional marker identification approach.

2. Experimental
2.1 Collections of Breath Samples and Blood
Glucose Data
The human breath samples were collectedfrom healthy volunteers under fasting
condition (8–10 h). To control the blood glucose
levels, the volunteers took a 150 mL aqueous
solution of 50g glucose (TRELAN-G50, AY
Pharmaceuticals). The blood glucose level ofvolunteers was measured by a glucometer withconventional fingerstick method and a flash
glucose monitoring system (FreeStyle Libre,
Abbott). Each 50 breath samples werecollected from the persons with high blood
glucose level (HBG, ≥ 125 mg/dL) and low
blood glucose level (LBG, < 120 mg/dL). The
exhaled breath was collected using a 10 L gas
sample bag (Smart bag PA, GL Sciences). The
500 mL of collected breath was thentransferred to an adsorbent-filled tube (Packed
Liner with Tenax GR, mesh 80/100 #2414-1021,
GL Science Inc.) using an air pump at thepumping rate of 50 mL/min. The sample tubes
were sealed and stored in a refrigerator at–18 °C until they were used for the GC–MS
measurements.
2.2

Breath Component Analysis by GC–MS

Total ion current (TIC) chromatograms andMS chromatograms of the breath samples
were obtained by GC–MS (Shimadzu,
GCMS-QP2020) equipped with an inlet
temperature control unit (OPTIC). A InertCap
5MS/NP capillary column (60 m length, 0.25
mm inner diameter, 1 μm thickness, GL
Sciences) was used, and the temperature
profile of GC oven was set as follows: (i) held at
40 °C for 5 min, (ii) elevating to 280 °C at a
rate of 5 °C/min, and (iii) held at 280 °C for 5
min. The inlet temperature was set at 300 °C
with split-less mode. The temperatures of theion source and the GC-to-MS junction wereboth set at 200 °C. The vacuum pressure in theionization chamber was 9.9 × 10−5 Pa. He

第 43 巻 第 2 号

43(99.9999% pure) was used as a carrier gas incolumn and a purge gas, and the flow rates
were set at 1 mL/min and 5 mL/min,
respectively. The MS measurements werecarried out with a single quadrupole MSanalyzer in a mode of electron ionization withpositive ion analysis and the full scan dataacquisition. A mass to charge ratio (m/z) wascharacterized in the range of 35–300. The
obtained data were analyzed by GCMS
Solution ver. 4.45 SP1.
2.3 Texture Image Analysis and Machine
Learning of 2D GC–MS Data
GC–MS data was analyzed by the TIA–ML
method and the conventional marker
identification approach. The workflows of the
TIA–ML method and the conventional marker
identification approach are shown in Figs.1(a)and (b). For the TIA–ML method, firstly, all
MS chromatograms, i.e., the series of retention
time–signal abundance data, were combined
and converted into a 2D MS map as thefunctions of m/z (x-axis) and retention time
(y-axis). The range of m/z and retention timeused for analysis were 35–300 and 3–58 min,respectively. The image resolution of 2D MS
map was set to be 1300 × 3700 pixels. The
intensity of 2D MS map was scaled by a powerlaw ( = 0.5), displayed by 256 colors, andnormalized via the highest peak using
Matplotlib ver. 3.5.1. To investigate therobustness of the TIA-ML method, theinfluences of position alignment and noise
reduction in 2D MS map were examined. The
details of such image processing for 2D MS
map can be seen in our previous study.38)
To extract texture features of the 2D MS
map, TIA was performed with gray-levelco-occurrence
matrix(GLCM)39)
using
Scikit-image ver. 0.19.1. The GLCM functions
characterize the textures of an image by
calculating the number of pairs of pixels withspecific values in a specified spatial distance,
creating GLCM maps, and then extracting
statistical texture feature values from thematrix of GLCM map. In this study, thedistance of 1 pixel and the angle of 45° wereused. GLCM maps of contrast, energy,
homogeneity, correlation, dissimilarity, andangular second moment (ASM) were createdfrom 2D MS map using the formulas shown in
Table 1. Then the texture feature was obtainedby a summation of the feature values of all
pixels in a GLCM map. The extracted texture
features were normalized and used as datasets44

Discrimination of Complex Odor with GC-MS Data by Texture Image Analysis and Machine Learning

Fig.1

Graphical workflows of (a) texture image analysis (TIA) and (b) conventional marker identification approach fordiscriminating complex odors in GC–MS data.

for ML.
For conventional marker identification
approach, peaks were detected in TIC
chromatograms and their intensities wereused as datasets for ML. The peak detection
was performed by CentWave method with theparametersof
ppm=10,peak
width
minimum=1,
peak
width
maximum=2,
snthresh=100,
mzdiff=6,
prefilterscan
number=0.01, prefilter scan abundance=3, andbw=100, which were optimized by using themethod reported by Manier et al.40) In thisstudy, for the simplicity, all detected peaks
were used as the marker molecules for thediscrimination of human breath samples,while the marker molecules are identified bycarefully screening the detected peaks in theconventional odor discrimination study.
ML was conducted by a neural network
algorithm. For ML, the datasets were divided
into training data and testing data with a ratioof 70% and 30%, respectively. For enriching
the training datasets while preventing
overfitting,thedata
augmentation
technique38) was employed. In this technique,the intensity of 2D MS maps was randomly
modulated in the range of 0.0–10.0%.
Consequently, the number of data increased by
100 datasets. The two-levels classification ofbreath samples (i.e., HBG and LBG) was
performed with a multilayer perceptron, which
is a class of feedforward artificial neuralnetwork, using Scikit-learn ver. 1.0.2. The
classifiers
wereoptimizedbythehyper-parameters and operated with theparameters of hidden_layer_sizes = (128, 128),
activation = 'relu', solver = 'adam', alpha = 1,
max_iter = 1000 for the TIA-ML method andhidden_layer_sizes = (256, 512), activation ='relu', solver = 'adam', alpha = 1, max_iter =
3000
for
the
conventional
markeridentification approach.
The odor discrimination performances in the
TIA–ML method and the conventional marker
identification approach were evaluated by
calculating and comparing their classification
accuracy, sensitivity, and specificity. The
averaged area under the curve of receiver
operating characteristic curve (AUC–ROC)
was utilized to evaluate the reliability of令和 3 年度

九州大学大学院総合理工学報告第 43 巻 第 2 号

45

Table 1 Texture features and formulas
Texture feature

FormulaContrast

EnergyHomogeneity

Correlation

Dissimilarity

ASM(Angular second moment)

classifier. The significance of each feature forthe discrimination of human breath samples
was evaluated with the p-value obtained int-test.

3.

Results & Discussion

3.1 2D MS Map and Texture Features of
Human Breath Samples
Figs.2(a) and (b) show the representative
TIC chromatograms and 2D MS maps ofbreath samples collected from the persons
with two different blood glucose levels ( i.e.,
HBG, ≥ 125 mg/dL and LBG, < 120 mg/dL).
TIC chromatogram is a primary form of
GC–MS data used to identify marker
molecules in the conventional approach. Eachpeak in the TIC chromatogram corresponds to
a component molecule species in the tested
breath, and each bright spot in the 2D MS map
corresponds to a fragment peak of a component
molecule species. Contrary to the TIC
chromatograms, where the peak intensity canbe quantitatively compared, the 2D MS maps
were hardly distinguishable to the humaneyes.
Next, we extracted the features of GC–MS
data. The texture features of the 2D MS maps
were extracted by TIA with GLCM. Fig.3
shows the GLCM maps for the breath samples
collected from the persons of HBG and LBG.
Each texture feature was then obtained by asummation of feature values of all pixels in a
GLCM map. The extracted texture features

Fig.2(a) TIC chromatograms and (b) 2D MS maps ofbreath samples collected from the persons withhigh blood glucose level (HBG, ≥ 125 mg/dL) andlow blood glucose level (LBG, < 120 mg/dL),
respectively. For visibility, the 2D maps areshown in the restricted range (m/z: 30−210,
retention time: 3−43 min).

were normalized and used as datasets for ML.
We created a classifier by ML with a neuralnetwork algorithm. As a comparison, we also
created a classifier by the conventional marker
identification approach. For this purpose, we
identified the peaks of marker molecules in the
TIC chromatograms, and the peak intensities
were used as datasets for ML. By using theclassifiers, we calculated the classification
accuracies for the test breath samples.
3.2 Classification Performance of TIA-ML
Method for Human Breath Samples
Fig.4(a) shows the classification accuracy ofbreath samples of HBG and LBG, plotted as afunction of the number of features employed
for creating the classifier. The employed
features were arranged in ascending order ofthe p-values. In the conventional marker
identification approach, the classification
accuracy was as low as 20.0% when employing
a single feature. It tended to increase withincreasing the number of employed featuresand reached to 100% with 50 features. On theother hand, in the TIA–ML method, theclassification accuracy was 83.3% with a single
feature, and reached to 100% with two
features. These results clearly indicated thatthat the TIA–ML method provided a higher
classification accuracy with fewer features
than the conventional marker identification
approach. Note that both of the specificity andsensitivity of the TIA–ML method reached to

46

Fig.3

Discrimination of Complex Odor with GC-MS Data by Texture Image Analysis and Machine Learning

Fig.4(a) The classification accuracy of the breath
samples and (b) the averaged AUC-ROC for the
TIA–ML method and the conventional marker
identification approach, plotted as a function of
the number of features employed for creating theclassifier.

Fig.5

Radar charts for the feature values used in (a) the
TIA and (b) the conventional marker
identification approach, respectively. In thesecharts, the mean feature values of breath samples
collected from the persons of HBG and LBG areplotted.

Fig.6

Box-and-whisker plots for the texture features ofthe breath samples collected from the persons of
HBG and LBG.

GLCM maps for texture features of contrast,
energy, homogeneity, correlation, dissimilarity,and ASM.

100% with two features. Such excellent
discrimination performance of the TIA–ML
method can be interpreted by the fact thateach texture feature contains a lot ofmolecular information.
3.3 Reliability of TIA-ML Method
To confirm the validity of above-mentioned
classification performance, we evaluated thereliability of classifiers. Fig.4(b) shows theaveraged AUC–ROC for the TIA–ML method
and the conventional marker identification
approach, plotted as a function of the numberof features employed for creating the classifier.
As well as the trends of classification accuracy
in Fig.4(a), the AUC–ROC tended to increaseby accompanying with the increase of thenumber of employed features. The AUC–ROC
in the TIA–ML method reached to 1.00 with
two features while that in the conventional
marker identification approach was as low as
0.47. These results highlighted that the
TIA–ML method showed better performancesin both the accuracy and reliability for thediscrimination of the human breath samples.
3.4 Advantage of Texture Feature
Here we discuss the contribution of eachfeature on the classification results in Fig.4(a).

In the conventional marker identification
approach,theclassification
accuracy
decreased with increasing number of featuresdue to a so-called overlearning effect, in which
the performance of classifier deteriorates bylearning disturbing features. Interestingly,
such an overlearning effect did not occur at allin the TIA–ML method. This indicates that all令和 3 年度

Fig.7

九州大学大学院総合理工学報告第 43 巻 第 2 号

47

Box-and-whisker plots for the representative texture features (correlation and homogeneity) of the breath samples
collected from the persons of HBG and LBG when performing the image processing (position alignment and noise
reduction) with various combinations.

texture features positively contributed to theclassification.
To gain an in-depth understanding as to
the role of extracted texture features, wequantitatively compared their feature values
on the human breath samples of HBG and
LBG. Figs.5(a) and (b) show the radar chartsof the feature values for the TIA–ML method
and the conventional marker identification
approach, respectively. In these charts, themean feature values are plotted. Note that thefeatures are arranged in ascending order of thep-values and displayed clockwise on the charts.
We found that there was no clear relationshipbetween the substantial difference in thefeature values and the arrangement order ofthe features in the TIA–ML method. A similar
trend was also found in the chart of theconventional marker identification approach.
Fig.6 shows the box-and-whisker plots for the
texture features in the human breath samplesof HBG and LBG. The results showed that the
distributions of feature values more clearly
separated for the features of earlier order. On
the other hand, the distributions of feature
values overlapped in some texture features
such as dissimilarity, contrast, energy andASM. Considering the fact that no overlapping
occurred in either the classification accuracy
or the AUC–ROC, the classification error ineach texture feature could be negligible in theassembled features (i.e., combination of
texture features). Such a beneficial role oftexture feature might be available only whenmore maker molecules than contaminantmolecules occupy the analyte odors.
3.5 Robustness of TIA–ML Method
Finally, we investigated the robustness of
the TIA–ML method by examining theinfluences of position shift and noise in 2D MS
maps. The position shift of spots occurs when aliquid phase of GC column deteriorates overtime. The noise is caused by the deteriorationof GC column and/or the contaminantmolecules (i.e., non-marker molecules) inanalytes. Fig.7 shows the box-and-whisker
plots for representative texture features of thehuman breath samples of HBG and LBG when
performing the image processing. As examples,the effects of position alignment and noise
reduction in correlation and homogeneity are
shown. The essential importance of positionalignment
andnoise
reduction
wasdemonstrated in our previous study for theidentification of marker molecules by image
analysis.38) We found that the significance ofeach texture feature (i.e., p-value) was in thealmost same order, regardless of the positionalignment and/or the noise reduction. These
results are reasonably interpreted by theprinciple of TIA, where the spatial relationship

48

Discrimination of Complex Odor with GC-MS Data by Texture Image Analysis and Machine Learningof pixels is emphasized in the texture features.
It should be worth describing that therobustness to position shift and noise in TIC
chromatograms is not available in theconventional marker identification approach.
As such, the data analysis process in the
TIA–ML method can be simpler than that intheconventional
markeridentification
approach and thus the high-throughput
discrimination of complex odors would be
expected by the TIA–ML method.

4. Summary and Conclusion
We demonstrated a facile method fordiscriminating complex odors with GC–MS
data by combining texture image analysis andmachine learning (i.e., TIA–ML method). In
the proposed method, various texture features(i.e., contrast, energy, homogeneity, correlation,
dissimilarity and ASM) of 2D MS maps wereextracted by TIA with GLCM and used asdatasets for ML. Contrary to the conventional
marker identification approach, which relieson the limited number of marker molecules,
each texture feature contains a lot ofmolecular information appeared in the 2D MS
map, and thus served as an effective
parameter for discriminating complex odors.
By the TIA–ML method, we successfully
performed the discrimination of breath
samples collected from the persons of different
blood glucose levels with higher performancesand reliability than the conventional marker
identification approach. While this study was
limited to a two-levels classification, theTIA–ML method is essentially applicable to a
multilevel classification. Thus, we believe that
the TIA–ML method paves a novel avenue incomplex odor analysis.
Acknowledgments
This work was supported by KAKENHI (No.
JP18H05243) and PRESTO Program (No.
JPMJPR19J7)
of
Japan
ScienceandTechnology
Corporation(JST).
K.N.
acknowledges JACI. This work was partly
supported by the Cooperative Research
Programs of “Dynamic Alliance for Open
Innovation Bridging Human, Environment
and Materials in Network Joint ResearchCenter for Materials and Devices”, “Network
Joint Research Center for Materials andDevices”. This study was performed with theapproval of the Research Ethics Committee.

We acknowledge Prof. Takao Yasui and Prof.
Yoshinobu Baba in Nagoya University for theirtechnical advices on human experiments.
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