pubs.acs.org/ac

ArticleImage Processing and Machine Learning for Automated
Identiﬁcation of Chemo-/Biomarkers in Chromatography−Mass
Spectrometry
Chaiyanut Jirayupat, Kazuki Nagashima,* Takuro Hosomi, Tsunaki Takahashi, Wataru Tanaka,
Benjarong Samransuksamer, Guozhu Zhang, Jiangyang Liu, Masaki Kanai, and Takeshi Yanagida*

Downloaded via UNIV OF TOKYO on October 27, 2021 at 17:13:43 (UTC).
See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

Cite This: https://doi.org/10.1021/acs.analchem.1c03163

ACCESS

Metrics & More

Read Online

Article Recommendations

sı Supporting Information*

ABSTRACT: We present a method named NPFimg, which
automatically identiﬁes multivariate chemo-/biomarker featuresof analytes in chromatography−mass spectrometry (MS) data by
combining image processing and machine learning. NPFimg
processes a two-dimensional MS map (m/z vs retention time) todiscriminate analytes and identify and visualize the marker features.
Our approach allows us to comprehensively characterize thesignals in MS data without the conventional peak picking process,which suﬀers from false peak detections. The feasibility of marker
identiﬁcation is successfully demonstrated in case studies of aroma
odor and human breath on gas chromatography−mass spectrometry (GC−MS) even at the parts per billion level. Comparison with the widely used XCMS shows the excellent reliability of NPFimg,
in that it has lower error rates of signal acquisition and marker identiﬁcation. In addition, we show the potential applicability of
NPFimg to the untargeted metabolomics of human breath. While this study shows the limited applications, NPFimg is potentiallyapplicable to data processing in diverse metabolomics/chemometrics using GC−MS and liquid chromatography−MS. NPFimg isavailable as open source on GitHub (http://github.com/poomcj/NPFimg) under the MIT license.

■

INTRODUCTION
Untargeted metabolomics/chemometrics have gained muchattention in diverse ﬁelds including pathology, microbiology,
pharmacology, food industry, environmental evaluation, andhealthcare.1−11 In these studies, mass spectrometry (MS)coupled with gas chromatography (GC−MS) or liquid
chromatography (LC−MS) is widely used, and the featuresof chemo-/biomarker molecules in analytes are identiﬁed fromtheir mass chromatogram data. The goal of untargeted
metabolomics/chemometrics is to comprehensively characterize the chemo-/biomarker molecules in analytes. Analytes inbiology and healthcare ﬁelds usually consist of a huge numberof chemical components with various concentrations. Also, thereliable chemo-/biomarker characterization needs the examination of many samples. In this respect, a reliable sample
characterization technique and a data analysis method forautomated marker identiﬁcation are strongly desired. To date,most eﬀorts have been devoted to suﬃciently extract themarker features in MS data. Recent development in MS
technology allows for signal detection of important molecules
in analytes at an ultratrace level.12−16 This development ofanalytical hardware substantially expands the applicable ﬁeld of
research in metabolomics/chemometrics. On the other hand,the data processing in raw MS data remains a challenging issue.
In general, there are two major tasks in the raw GC− or LC−© XXXX American Chemical Society

MS data processing including peak picking and subsequent
pairwise peak list comparison.17−25 Various software resources
including XCMS,17,18 MZmine,19,20 TracMass,21 KPIC,22 andothers23−25 have been developed to perform these tasks.
However, they often suﬀer from insuﬃcient peak picking
performance. The peak picking algorithm in the abovementioned software resources is based on a binary (“peak”
or “noise)” output method, in which the chromatographic
peaks with a satisfactory shape (e.g., Gaussian), signal-to-noise
ratio, and peak width are extracted by a thresholding approach.
Such a thresholding approach usually causes many false
positive/false negative peak detections. For example, lessrestricted threshold setting increases the number of false
positive peaks, while the larger number of features can beextracted. Contrary, highly restricted threshold setting yields
false negative peaks, while the ﬁdelity of extracted peaks can be
improved. These false detections in the peak picking process
Received: July 26, 2021
Accepted: October 18, 2021

A

https://doi.org/10.1021/acs.analchem.1c03163
Anal. Chem. XXXX, XXX, XXX−XXX

Analytical Chemistrypubs.acs.org/ac

ArticleTable 1. Summary of Molecule Additives to Aroma Odor Samples and Human Breath Samples, Which Serve as Chemo-/
Biomarkers in This Study
samplesaroma#1

aroma#2

aroma#3

breathmolecule additives

1-butanol
2-pentanone
1-hexanol

heptanal
3-octanone
decane3-decanone

1-pentyn-3-ol
1-hexanol
heptanal
3-octanone
3-decanone

heptanal
nonanaldecaneundecanebenzaldehydewith the vaporized aroma odors. The vial bottle has two
separated ports; one port was connected to an adsorbent-ﬁlled
tube (Packed Liner with Tenax GR, mesh 80/100 #2414−
1021, GL Science Inc.), and the other port was connected to anitrogen gas cylinder (99.997% pure). The other side of theadsorbent-ﬁlled tube was connected to an automatic air
sampling pump (GSP-400FT, GASTEC Corp.). Then, 100 mLof the aroma odor was transferred from the headspace of thevial bottle to the adsorbent-ﬁlled tube at the pumping/nitrogenﬂow rates of 50 mL/min. For the human breath samples, wecollected the exhaled breath of 10 L from a healthy humanusing a gas sampling bag (Smart Bag PA CEK-10, GL ScienceInc.). Then, the sampling bag was connected to an adsorbentﬁlled tube, and 500 mL of the collected breath was transferredto the adsorbent-ﬁlled tube at the pumping rate of 50 mL/min.
For preparing the samples containing chemo-/biomarker
molecules, we intentionally introduced the molecule additives
including 1-butanol, 2-pentanone, and 1-hexanol for aroma#1,
heptanal, 3-octanone, decane, and 3-decanone for aroma#2, 1pentyn-3-ol, 1-hexanol, heptanal, 3-octanone, and 3-decanone
for aroma#3, and heptanal, nonanal, decane, undecane, andbenzaldehyde for human breath (as summarized in Table 1). A
total of 2 μL of liquid concentrate for each molecule additive
was taken in a vial bottle, and the vaporized species wascollected together with aroma odor and human breath by using
an adsorbent-ﬁlled tube. Twenty diﬀerent samples wereprepared for each condition (aroma#1, aroma#2, aroma#3,
human breath, and their molecule additive-containing
samples). For the human breath samples, we collected theexhaled breath at once from the same donor and divided it into
several portions to make sure the reliability of biomarkers
without the interference of unexpected biological variations.
The sample tubes were sealed and stored in a refrigerator at 4
°C until they were used for GC−MS measurements.

leads to wrong scientiﬁc discoveries and interferes with theinterpretation of the correct ones.
To solve the problem in the peak picking process, various
machine learning-assisted techniques have recently beendeveloped, which are based on support vector machine,26
Bayesian optimization,27,28 deep learning,29,30 and others.31
The former one automates the optimization of thresholdparameter settings in the conventional software, for example,
XCMS, and the latter two improve the peak/noise discrimination performance via recognizing the peak shape incomputer vision. Such machine learning-assisted techniques
successfully improved the peak picking performance comparedwith conventional software resources. However, these methods
are complex and time-consuming because peak shape needs tobe trained in advance by creating a original database. In
addition, a peak/noise discrimination for trace-level molecules
is a challenging issue because the shape recognition of a peakof low signal-to-noise ratio is diﬃcult. Especially, theautomated characterization of trace-level molecules in complex
analytes (e.g., human breath), in which both high concentration and low concentration molecules coexist, is diﬃcult.
Thus, an automated data processing tool, capable of characterization of numerous molecules including trace-level ones, isstrongly desired in untargeted metabolomics/chemometrics.
In this work, we present a method named NPFimg, which
automatically identiﬁes multivariate chemo-/biomarker features of analytes in chromatography−MS data by combining
image processing and machine learning. NPFimg processes a
two-dimensional (2D) MS map to comprehensively characterize MS data, discriminate analytes, and identify and visualize
marker features without the conventional peak picking process.
The feasibility of chemo-/biomarker characterization is
successfully demonstrated in case studies of aroma odors andhuman breath at various molecular concentration ranges[down to parts per billion (ppb) level]. The reliability of
NPFimg is discussed by comparing it with the widely used
XCMS. Furthermore, the applicability of NPFimg to untargeted
metabolomics is examined via the human breath samples.

■■

GC−MS MEASUREMENT

Mass chromatogram data of the aroma odor samples and thehuman breath samples were obtained by GC−MS (GCMSQP2020, Shimadzu) using an inlet temperature control unit
(OPTIC4). For the aroma odor samples, a SLB-IL60 capillarycolumn (30 m length, 0.25 mm inner diameter, 0.2 μm
thickness, Sigma-Aldrich) was used, and the GC oventemperature proﬁle was set as follows: (i) kept constant at
40 °C for 5 min, (ii) increasing to 200 °C at a rate of 10 °C/
min, and (iii) kept at 200 °C for 5 min. For the human breath
samples, an InertCap FFAP capillary column (60 m length,0.25 mm inner diameter, 0.5 μm thickness, GL Science) wasused, and the GC oven temperature proﬁle was set as follows:(i) kept constant at 40 °C for 3 min, (ii) increasing to 200 °C
at a rate of 5 °C/min, and (iii) kept at 200 °C for 5 min. The
inlet temperature was increased to 300 °C with a split ﬂow of
He at a rate of 5 mL/min for the aroma odor samples and 2
mL/min for the human breath samples. MS measurements

EXPERIMENTAL SECTION

Sample Preparation. We evaluated the performance of
NPFimg to identify the chemo-/biomarker features in analytes
by using aroma odor samples and human breath samples. In
this study, the samples containing chemo-/biomarker molecules were prepared by adding the marker molecules to theoriginal aroma odor/breath samples. For the aroma odor
samples, we employed three types of commercial aroma oil
including bergamot organic essential oil (aroma#1, Neal’s Yard
Remedies Inc.), lavender essential oil (aroma#2, Neal’s Yard
Remedies Inc.), and blended essential oil (aroma#3, Ryohin
Keikaku Co., Ltd.). To collect the aroma odors, 50 μL of thearoma oil was ﬁrst taken in a 20 mL vial bottle and it was left
for 10 min at room temperature for fulﬁlling the vial bottle
B

https://doi.org/10.1021/acs.analchem.1c03163
Anal. Chem. XXXX, XXX, XXX−XXX

Analytical Chemistrypubs.acs.org/ac

ArticleFigure 1. Graphical workﬂow of NPFimg for visualizing chemo-/biomarker signals from raw gas chromatography−mass spectrometry (GC−MS)
data. Starting from (a) series of raw MS data, the workﬂow follows (b) creation of a primary two-dimensional (2D) MS map (m/z vs retentiontime) with power-law scale intensity, (c) creation of a regulated 2D MS map by noise reduction and position alignment, (d) image segmentationand machine learning in each segment, (e) feature score calculation, and (f) creation of a 2D feature score map.

were conducted by electron ionization mode and positive ion
analysis. The ion source temperature and the interface
temperature at the GC-to-MS junction were 200 °C and 230°C, respectively. The vacuum pressure was 9.9 × 10−5 Pa. An
MS analyzer of single quadrupole and the full scan dataacquisition mode were used. Data were analyzed by GCMS
Solution ver. 4.45 SP1. The concentrations of chemo-/
biomarker molecules were estimated by calibration curves
created using tracer molecules.
Data Analysis. Raw MS data were treated and analyzed by
the following protocols in NPFimg. The workﬂow of NPFimg isshown in Figures 1 and S1 and S2. The source codes weredeveloped in Python ver.3.7.7 and are provided on GitHub(http://github.com/poomcj/NPFimg) under the MIT license.
First, all MS data, that is, the series of retention time-signal
abundance data (.CDF: computable document format)(Figure 1a) were merged and converted into a 2D MS map(.PNG: portable network graphic) as the functions of m/z (xaxis) and retention time (y-axis). The range of m/z andretention time used for analysis were 35−300 (m/z) and 3−26
min (retention time) for aroma odor and 35−300 (m/z) and3−48 min (retention time) for human breath, respectively.
The resolutions of m/z and retention time in raw GC−MS
data were 1 and 0.02 s, respectively. The image size of the 2D
MS map was 1350 × 3750 pixels, where they correspond to theresolutions of ca. 0.20 in m/z and ca. 0.37 s in retention time
for aroma odors and ca. 0.20 in m/z and ca. 0.72 s in retention
time for human breath, respectively.
For the image processing, the intensity of the 2D MS map(i.e., signal abundance in raw MS data) was scaled by a powerlaw (γ = 0.5), represented by 256 colors, and normalized via
the highest peak using Matplotlib ver.3.2.2 (primary 2D MS
map, Figure 1b). A Gaussian ﬁlter (SciPy ver1.5.2) based on
the dilation method was applied for the noise reduction of the

2D MS map. The position alignment of the 2D MS maps wasthen performed by identifying the reference peak of external
standard−cyclohexene, 1-methyl-4-(1-methyletheny)-(R)using the blob detection technique32 and moving window
technique,33 followed by adjusting the reference peak position
to be the same in all 2D MS maps (regulated 2D MS map,
Figure 1c). After the position alignment, the eﬀective image
area of the 2D MS map was 1300 × 3700 pixels. For machinelearning, the 2D MS map was divided into the small segments
consisting of 1 × 1 or 2 × 2 pixels, and the average intensity ofeach segment was extracted. Intensity data for the segmentswith the same address was collected in all 2D MS maps andused as a data set (Figure 1d).
In the machine learning process, the data set was divided
into training data, validation data, and testing data with theratio of 50, 25, and 25%, respectively. To enrich the training
data set while preventing overﬁtting, we employed the data
augmentation technique. The intensity of the 2D MS maps was
randomly modulated in the range of 1.0−10.0% with diﬀerent
interpolation methods including bilinear, hanning, hermite,gaussian, and sinc. Consequently, the number of training data
increased by ﬁve times the primary ones. The discrimination of
the original aroma odor/breath samples and the molecule
additive-containing samples and the calculation of the featurescore for each data set were performed by the logistic
regression model (Figure 1e).34 Machine learning was
performed to optimize the following equation: log p/(1 − p)
= β0+ x1β1 + x2β2 + x3β3 + ... + xnβn, where p is the probabilityof which the data sets can be classiﬁed, xn is the intensity ofeach segment in the 2D MS map, βn (n ≥ 1) is the model’s
learned weight (i.e., feature score), and β0 is the bias. The
validation data were used to tune the hyperparameters. Afterobtaining the feature score for all segments in a 2D MS map, a
2D feature score map was created by reconstructing the 2DChttps://doi.org/10.1021/acs.analchem.1c03163
Anal. Chem. XXXX, XXX, XXX−XXX

Analytical Chemistrypubs.acs.org/ac

ArticleFigure 2. (a,d,g) Regulated 2D MS maps, (b,e,h) 2D feature score maps, and (c,f,i) receiver operating characteristic (ROC) curves of classiﬁers for(a−c) bergamot organic essential oil―aroma#1, (d−f) lavender essential oil―aroma#2, and (g−i) blend essential oil―aroma#3 incomparison with those with chemomarker molecule additives. For the regulated 2D MS maps, the one of original aroma odor is shown in the left
and the other with molecule additives is shown in the right. For the visibility, the 2D maps are shown in the restricted range (m/z: 30−180,
retention time: 3−18 min). The molecule additives in each sample are summarized in Table 1.

MS maps are shown in the restricted range (m/z: 30−180,
retention time: 3−18 min). The full range 2D MS maps areshown in Figure S3. The clear diﬀerence can be seen in the twomaps. Figure 2b shows the 2D feature score map of molecularfragment signals of chemomarkers for discriminating aroma#1
and aroma#1 with molecule additives. For machine learning,the 2D MS map was divided into the segments with the 2 × 2
pixels size because the image quality of the resultant 2D featurescore map was comparable to the one with the higher
resolution analysis using the segment size of 1 × 1 pixel.
Contrary to the 2D MS maps, the 2D feature score map
exhibits only the limited number of molecular fragment signals.
We conﬁrmed that the addresses of the observed molecularfragment signals on the 2D feature score maps (m/z, retentiontime) are in good agreement with those of the molecular
additives on the 2D MS maps (Figure S4). Figure 2c shows thereceiver operating characteristic (ROC) curve of the classiﬁer.
The values of area under the curve (AUC), sensitivity andspeciﬁcity of the classiﬁer are 1.00, 1.00, and 1.00, showing thesuﬃcient reliability of the classiﬁer. Figure 2d−i shows (d,g)
the 2D MS maps, (e,h) the 2D feature score maps, and (f,i) theROC curves for (d,e,f) aroma#2 and aroma#2 with fourmolecule additives and (g,h,i) aroma#3 and aroma#3 with ﬁvemolecule additives, respectively. Compared to aroma#1, thelarger number of molecular fragment signals is seen in the 2D
MS maps of aroma#2, aroma#3, and the ones with molecule
additives. We found that the reliable 2D feature score maps
(the detailed validation is shown in Figures S5 and S6) andROC curves were also obtained even when the analytes
become more complex (AUC, sensitivity, and speciﬁcity were1.00, 1.00, and 1.00 for aroma#2 and 0.99, 0.97, and 0.98 foraroma#3). With respect to the time cost, the feature

image with the calculated feature scores at each address(feature score f: 0 ≤ f ≤ 1) (Figure 1f). The signals in a 2D
feature map were then extracted with their m/z and retention
time by blob detection and compared with the MS spectra
database (NIST14).
In order to conﬁrm the reliability of data analysis in NPFimg,
the data analysis was also performed by XCMS and the results
were compared. For data analysis by XCMS, peak detection
was performed by the CentWave method with the optimized
parameter settings.35 The details of parameter settings are
given in Table S1. To evaluate the feature detection
performance in XCMS, we counted the number of featuresby varying the alpha level and optimizing sensitivity andprecision. The initially examined alpha level was determined by
dividing the highest p-value obtained in t-test of the detectedpeaks with the number of examined samples.

■

RESULTS AND DISCUSSION
The performance of NPFimg in terms of the identiﬁcation ofmultivariate chemomarker features and its time cost is ﬁrst
validated in a case study of aroma odors, which consist of at
most 10 species of volatile molecules. Here, we employed threearoma odor samples including bergamot organic essential
oil―aroma#1, lavender essential oil―aroma#2, and blend
essential oil―aroma#3. We intentionally introduced themolecule additives listed in Table 1 into the original aroma
odor samples at the tens parts per million (ppm) order ofconcentration as the chemomarkers and examined theidentiﬁcation of these molecule additives by comparing themwith the original aroma odor samples. Figure 2a shows the 2D
MS maps for aroma#1 (i.e., left map) and aroma#1 with threemolecule additives (i.e., right map). For the visibility, the 2DDhttps://doi.org/10.1021/acs.analchem.1c03163
Anal. Chem. XXXX, XXX, XXX−XXX

Analytical Chemistrypubs.acs.org/ac

ArticleFigure 3. (a) Regulated 2D MS maps, (b) 2D feature score map, and (c) ROC curve of classiﬁers for breath vs breath + molecule additives,
respectively. For the regulated 2D MS maps, the one of original breath is shown in the left and the other with molecule additives is shown in theright. (d) Comparisons in concentrations of biomarker molecules in breath and breath + molecule additives. (e) Relationship between
concentration ratio [(breath + molecule additives)/breath] and feature score for the biomarker molecules. For the visibility, the 2D maps areshown in the restricted range (m/z: 30−180, retention time: 3−40 min). The molecule additives are summarized in Table 1.

identiﬁcation of chemomarkers in NPFimg was completed
within 5 min. The time cost in NPFimg was almost unchanged
even when the analytes became complex. Thus, these results
clearly validated the performance of NPFimg for the immediate
identiﬁcation of multivariate chemomarker features in analytes.
To evaluate the applicability of NPFimg to more complex
analytes, next we examined the ultratrace level biomarker
analysis in human breath, which contains over hundreds or
thousands of chemical compounds with various concentrations
from ppb to ppm orders.36 We intentionally introduced ﬁvemolecular additives, including heptanal, nonanal, decane,undecane, and benzaldehyde, into the original exhaled sample
at the ppb level as biomarkers. The selected molecules are wellknown lung cancer biomarkers in exhaled breath.37−40 In order
to eliminate the biological variation that inﬂuences thereliability of the selected biomarkers, the breath sample wascollected from the same donor at once in this study. Figure 3ashows the 2D MS maps for the human breath sample (i.e., leftmap) and the human breath sample with ﬁve molecule
additives (i.e., right map). For the visibility, the 2D maps areshown in the restricted range (m/z: 30−180, retention time:
3−40 min). The full range 2D MS maps are shown in Figure
S7. The identiﬁcation of the diﬀerences in the two maps israther diﬃcult due to their complexities. On the other hand,the 2D feature score map exhibited the limited number ofmolecular fragment signals, as shown in Figure 3b. We
conﬁrmed that the addresses of the molecular fragment signals
on the 2D feature score maps are in good agreement withthose of the molecular additives on the 2D MS maps (FigureS8). Figure 3c shows the ROC curve of the classiﬁer. The
values of AUC, sensitivity, and speciﬁcity of the classiﬁer are
0.98, 0.98, and 0.97, respectively, showing the suﬃcientreliability of the classiﬁer. The quantitative analysis showedthat the concentrations of biomarkers in the analytes were in a
few ppb to several tens of ppb level, as shown in Figure 3d.
Also, we found that the feature score for each biomarker iscritically governed by the concentration ratio in analytes rather
than the absolute concentration diﬀerence (Figure 3e). This
principle allows us to reliably extract the feature of low

concentration molecules under the coexistence of highconcentration molecules. Thus, these results highlight theapplicability of NPFimg to the ultratrace level biomarker
analysis in complex analytes, in which both high concentrationand low concentration molecules coexist.
We discuss the reliability of MS data processing in NPFimg
by comparing it with a widely used analysis software―XCMS. In most of the conventional MS data processing
algorithms for characterizing the chemo-/biomarkers, there are
two major processes including the peak picking process in the
raw MS spectra and the subsequent pairwise peak list
comparison process.17−25 We compared NPFimg and XCMS
in terms of the performances of signal acquisition in raw MS
data and feature identiﬁcation (Figure S9). The signal
acquisition for NPFimg was conducted by the blob detection
technique using the regulated MS maps. Totally, 88, 160, and131 of the molecular fragment peaks were extracted fromaroma#1 + three molecule additives, aroma#2 + four molecule
additives, and aroma#3 + ﬁve molecule additives, respectively,
by NPFimg. On the contrary, only 79, 133, and 99 peaks weredetected by XCMS. These results are consistent with theprevious report, which stated that XCMS produced many false
negative peaks (i.e., missing peaks) during its peak picking
process.20 The false negative peaks in XCMS would strongly
inﬂuence the following feature identiﬁcation process. For thefeature identiﬁcation, contrary to our expectation, a similar
number of or more features were identiﬁed by XCMS, while itproduced false negative peaks during the peak picking process.
Sensitivity and precision for feature identiﬁcation are on
average 0.90 and 0.99 in NPFimg and 0.80 and 0.41 in XCMS,
respectively, showing the higher ratios of both false positiveand false negative features in XCMS. The detailed analysis
revealed that the observed false positive and false negative
features are caused by the missing peaks during the peak
picking process and the batch-to-batch variation of signal
intensity in analytes (i.e., batch eﬀect),41,42 respectively. In
order to conﬁrm that these problems are addressed in NPFimg,
we evaluated the performance of chemomarker feature
identiﬁcation by varying the functions of NPFimg (Figures
Ehttps://doi.org/10.1021/acs.analchem.1c03163
Anal. Chem. XXXX, XXX, XXX−XXX

Analytical Chemistrypubs.acs.org/ac

S9 and S10). We found that the number of false negative
features increased when using the 2D MS map with a linearscale plot, that is, only the limited number of signals can beseen in the map. On the other hand, the number of false
positive features increased when removing the intensity
normalization process, that is, in case that the signal intensity
varies in each batch. These results validated that the problemsof missing peaks and the batch eﬀect are successfully addressedby the power-law scale intensity plot and intensity normalization in NPFimg. Nevertheless, the batch eﬀects inuntargeted metabolomics/chemometrics need to be carefully
corrected by involving other techniques43 because the intensity
normalization used in this study is based on an internal
standard, which can be applicable only to quality-controlled
biological/chemical replicates. We also found that the false
positive features are also produced when the random forest
algorithm was used instead of the logistic regression algorithm
for machine learning. In this case, false identiﬁcation of noise
as a chemomarker occurred due to its feature identiﬁcation
principle (Figure S11). Thus, the abovementioned results
highlight that the functions employed in image processing andthe logistic regression algorithm employed in machine learning
make NPFimg reliable compared with the conventional peak
picking-based data processing approach.
Finally, we demonstrate the applicability of NPFimg to
untargeted metabolomics for analyzing the concentration
variations of metabolites in analytes (Figure S12). Afterobtaining the 2D feature score map, the features of biomarkers
are extracted by the blob detection technique. The addresses offeatures are fed back to the regulated 2D MS map with thelinear-scale intensity, and the peak area/intensity of themarkers is compared among analytes. The MS spectra of thebiomarkers (decane, undecane, heptanal, nonanal, andbenzaldehyde) showed that the variations of their peak area/
intensity among analytes are successfully observed. Thus, theseresults demonstrated the feasibility of NPFimg for untargeted
metabolomics in complex analytes.

Articleanalysis of various complex analytes would be expected byuploading the data ﬁle on Cloud space.

■

ASSOCIATED CONTENT* Supporting Information
sı

The Supporting Information is available free of charge athttps://pubs.acs.org/doi/10.1021/acs.analchem.1c03163.
Workﬂow details of NPFimg; eﬀect of Gaussian ﬁlteringon signal intensity; details of position alignment and thestandard deviation of the peak positions on 2D MS map;
full scale 2D MS map; comparison of the molecularfragment signals in raw MS data and 2D feature score
map; optimization of the XCMS parameter setting;performances of NPFimg and XCMS on signal
acquisition and feature identiﬁcation; 2D feature score
map based on the random forest algorithm; andapplicability of NPFimg to untargeted metabolomics
(PDF)

■

AUTHOR INFORMATION

Corresponding Authors

Kazuki Nagashima − Department of Applied Chemistry,
Graduate School of Engineering, The University of Tokyo,
Tokyo 113-8656, Japan; Japan Science and Technology
Agency (JST), PRESTO, Saitama 332-0012, Japan;
orcid.org/0000-0003-0180-816X; Email: kazu-n@g.ecc.u-tokyo.ac.jp
Takeshi Yanagida − Department of Applied Chemistry,
Graduate School of Engineering, The University of Tokyo,
Tokyo 113-8656, Japan; Interdisciplinary Graduate Schoolof Engineering Sciences and Institute for Materials Chemistry
and Engineering, Kyushu University, Fukuoka 816-8580,
Japan; orcid.org/0000-0003-4837-5701;
Email: yanagida@g.ecc.u-tokyo.ac.jp■

Authors

CONCLUSIONSIn conclusion, we presented a method named NPFimg, which
automatically identiﬁes multivariate chemo-/biomarkers feature of analytes in chromatography−MS data without the peak
picking process, which had been a crucial bottleneck for dataprocessing of raw MS data. NPFimg combines image
processing and machine learning and processes a 2D MS
map to discriminate analytes and identify and visualize marker
features. Our approach allows us to comprehensively characterize the signals in MS data without employing the conventional
peak picking process, which suﬀers from the false peakdetections. The feasibility of chemo-/biomarker characterization was successfully demonstrated in case studies of aroma
odor and human breath on GC−MS even at the ppb level.
Comparison with the widely used XCMS showed the excellent
reliability of NPFimg, in that it had lower error rates of thesignal acquisition and the feature identiﬁcation of chemo-/
biomarkers. In addition, we showed the potential applicabilityof NPFimg to the untargeted metabolomics of human breath.
While this study showed the limited applications, NPFimg ispotentially applicable to data processing in diverse metabolomics/chemometrics using GC− and LC−MS. Because time
cost in NPFimg is much shorter than the peak picking-based
conventional approaches, the high throughput online MS data

Chaiyanut Jirayupat − Department of Applied Chemistry,
Graduate School of Engineering, The University of Tokyo,
Tokyo 113-8656, Japan; Interdisciplinary Graduate Schoolof Engineering Sciences, Kyushu University, Fukuoka 8168580, Japan
Takuro Hosomi − Department of Applied Chemistry,
Graduate School of Engineering, The University of Tokyo,
Tokyo 113-8656, Japan; Japan Science and Technology
Agency (JST), PRESTO, Saitama 332-0012, Japan;
orcid.org/0000-0002-5649-6696
Tsunaki Takahashi − Department of Applied Chemistry,
Graduate School of Engineering, The University of Tokyo,
Tokyo 113-8656, Japan; Japan Science and Technology
Agency (JST), PRESTO, Saitama 332-0012, Japan;
orcid.org/0000-0002-2840-8038
Wataru Tanaka − Department of Applied Chemistry,
Graduate School of Engineering, The University of Tokyo,
Tokyo 113-8656, Japan
Benjarong Samransuksamer − Department of Applied
Chemistry, Graduate School of Engineering, The University ofTokyo, Tokyo 113-8656, Japan
Guozhu Zhang − Department of Applied Chemistry, Graduate
School of Engineering, The University of Tokyo, Tokyo 1138656, JapanF

https://doi.org/10.1021/acs.analchem.1c03163
Anal. Chem. XXXX, XXX, XXX−XXX

Analytical Chemistrypubs.acs.org/ac

Jiangyang Liu − Department of Applied Chemistry, Graduate
School of Engineering, The University of Tokyo, Tokyo 1138656, Japan
Masaki Kanai − Institute for Materials Chemistry andEngineering, Kyushu University, Fukuoka 816-8580, Japan(13) Thiele, C.; Wunderling, K.; Leyendecker, P. Nat. Methods 2019,16, 1123−1130.
(14) Wang, Z.; Cui, B.; Zhang, F.; Yang, Y.; Shen, X.; Li, Z.; Zhao,
W.; Zhang, Y.; Deng, K.; Rong, Z.; Yang, K.; Yu, X.; Li, K.; Han, P.;
Zhu, Z. J. Anal. Chem. 2019, 91, 2401−2408.
(15) Tsugawa, H.; Nakabayashi, R.; Mori, T.; Yamada, Y.;
Takahashi, M.; Rai, A.; Sugiyama, R.; Yamamoto, H.; Nakaya, T.;
Yamazaki, M.; Kooke, R.; Bac-Molenaar, J. A.; Oztolan-Erol, N.;
Keurentjes, J. J. B.; Arita, M.; Saito, K. Nat. Methods 2019, 16, 295−
298.
(16) Taylor, M. J.; Lukowski, J. K.; Anderton, C. R. J. Am. Soc. Mass
Spectrom. 2021, 32, 872−894.
(17) Smith, C. A.; Want, E. J.; O’Maille, G.; Abagyan, R.; Siuzdak, G.
Anal. Chem. 2006, 78, 779−787.
(18) Forsberg, E. M.; Huan, T.; Rinehart, D.; Benton, H. P.; Warth,B.; Hilmers, B.; Siuzdak, G. Nat. Protoc. 2018, 13, 633−651.
(19) Pluskal, T.; Castillo, S.; Villar-Briones, A.; Orešič, M. BMC
Bioinf. 2010, 11, 395.
(20) Myers, O. D.; Sumner, S. J.; Li, S.; Barnes, S.; Du, X. Anal.
Chem. 2017, 89, 8689−8695.
(21) Tengstrand, E.; Lindberg, J.; Åberg, K. M. Anal. Chem. 2014,
86, 3435−3442.
(22) Ji, H.; Zeng, F.; Xu, Y.; Lu, H.; Zhang, Z. Anal. Chem. 2017, 89,
7631−7640.
(23) Wanichthanarak, K.; Fan, S.; Grapov, D.; Barupal, D. K.; Fiehn,
O. PLoS One 2017, 12, No. e0171046.
(24) Tsugawa, H.; Cajka, T.; Kind, T.; Ma, Y.; Higgins, B.; Ikeda, K.;
Kanazawa, M.; Vandergheynst, J.; Fiehn, O.; Arita, M. Nat. Methods
2015, 12, 523−526.
(25) O’Shea, K.; Misra, B. B. Metabolomics 2020, 16, 36.
(26) Borgsmüller, N.; Gloaguen, Y.; Opialla, T.; Blanc, E.; Sicard, E.;
Royer, A.-L.; Bizec, B. L.; Durand, S.; Mingné, C.; Pétéra, M.; PujosGuillot, E.; Giacomoni, F.; Guitton, Y.; Beule, D.; Kirwan, J.
Metabolites 2019, 9, 171.
(27) Woldegebriel, M.; Vivó-Truyols, G. Anal. Chem. 2015, 87,7345−7355.
(28) Wiczling, P.; Kamedulska, A.; Kubik, Ł. Anal. Chem. 2021, 93,6961−6971.
(29) Liu, Z.; Portero, E. P.; Jian, Y.; Zhao, Y.; Onjiko, R. M.; Zeng,
C.; Nemes, P. Anal. Chem. 2019, 91, 5768−5776.
(30) Melnikov, A. D.; Tsentalovich, Y. P.; Yanshole, V. V. Anal.
Chem. 2020, 92, 588−592.
(31) Liebal, U. W.; Phan, A. N. T.; Sudhakar, M.; Raman, K.; Blank,
L. M. Metabolites 2020, 10, 243.
(32) Marsh, B. P.; Chada, N.; Sanganna Gari, R. R.; Sigdel, K. P.;
King, G. M. Sci. Rep. 2018, 8, 978.
(33) Fu, J.; Chu, W.; Dixson, R.; Orji, G.; Vorburger, T. AIP Conf.
Proc. 2009, 1173, 280−284.
(34) Chai, H.; Liang, Y.; Wang, S.; Shen, H.-W. Sci. Rep. 2018, 8,13009.
(35) Manier, S. K.; Keller, A.; Meyer, M. R. Drug Test. Anal. 2019,11, 752−761.
(36) Chan, L. W.; Anahtar, M. N.; Ong, T. H.; Hern, K. E.; Kunz, R.
R.; Bhatia, S. N. Nat. Nanotechnol. 2020, 15, 792−800.
(37) Fuchs, P.; Loeseken, C.; Schubert, J. K.; Miekisch, W. Int. J.
Cancer 2010, 126, 2663−2670.
(38) Chen, X.; Xu, F.; Wang, Y.; Pan, Y.; Lu, D.; Wang, P.; Ying, K.;
Chen, E.; Zhang, W. Cancer 2007, 110, 835−844.
(39) Bouza, M.; Gonzalez-Soto, J.; Pereiro, R.; De Vicente, J. C.;
Sanz-Medel, A. J. Breath Res. 2017, 11, No. 016015.
(40) Peng, G.; Hakim, M.; Broza, Y. Y.; Billan, S.; Abdah-Bortnyak,R.; Kuten, A.; Tisch, U.; Haick, H. Br. J. Cancer 2010, 103, 542−551.
(41) Rong, Z.; Tan, Q.; Cao, L.; Zhang, L.; Deng, K.; Huang, Y.;
Zhu, Z. J.; Li, Z.; Li, K. Anal. Chem. 2020, 92, 5082−5090.
(42) Liu, Q.; Walker, D.; Uppal, K.; Liu, Z.; Ma, C.; Tran, V.; Li, S.;
Jones, D. P.; Yu, T. Sci. Rep. 2020, 10, 13856.
(43) Wehrens, R.; Hageman, J. A.; van Euwijk, F.; Kooke, R.; Flood,
P. J.; Wijnker, E.; Keurentjes, J. J. B.; Lommen, A.; van Eekelen, H. D.

Complete contact information is available at:https://pubs.acs.org/10.1021/acs.analchem.1c03163
Author Contributions

C.J. and K.N. designed this work and performed sample
preparation/characterization. C.J. constructed the algorithmsof NPFimg and improved by discussing with K.N., T.H., T.T.,
W.T., B.S., G.Z., J.L., M.K., and T.Y. C.J. and K.N. wrote thismanuscript, and T.H., T.T., W.T., M.K., and T.Y. improved themanuscript. All authors approved the ﬁnal version of themanuscript.
Notes

The authors declare no competing ﬁnancial interest.

■

ACKNOWLEDGMENTS
This work was supported by PRESTO Program of Japan
Science and Technology Corporation (JST) (No.
JPMJPR19J7) and KAKENHI (Nos. JP18H01831,
JP18KK0112, JP18H05243, JP20H02208, and JP20F20048).
K.N., T.H., T.T., and Y.T. were supported by CREST (No.
JPMJCR19I2) and JST Mirai R&D. K.N. was supported by the
Research Program for CORE lab of Dynamic Alliance forOpen Innovation Bridging Human, Environment and Materials
in Network Joint Research Center for Materials and Devices.
K.N. acknowledges JACI Prize for Encouraging Young
Researcher. This work was partly performed under the
Cooperative Research Program of “Network Joint ResearchCenter for Materials and Devices” and the MEXT Project of“Integrated Research Consortium on Chemical Sciences.”■

ArticleREFERENCES(1) Eloh, K.; Sasanelli, N.; Maxia, A.; Caboni, P. J. Agric. Food Chem.
2016, 64, 5963−5968.
(2) Leite, V. S. A.; Reis, M. R.; Pinto, F. G. ACS Food Sci. Technol.
2021, 1, 242−248.
(3) Wang, J.; Jayaprakasha, G. K.; Patil, B. S. ACS Food Sci. Technol.
2021, 1, 77−87.
(4) Yao, C. H.; Wang, L.; Stancliffe, E.; Sindelar, M.; Cho, K.; Yin,W.; Wang, Y.; Patti, G. J. Anal. Chem. 2020, 92, 1856−1864.
(5) Wikoff, W. R.; Nagle, M. A.; Kouznetsova, V. L.; Tsigelny, I. F.;
Nigam, S. K. J. Proteome Res. 2011, 10, 2842−2851.
(6) Zhang, W. X.; Li, H. Q.; Xu, Z. D.; Dou, J. J. RSC Adv. 2020, 10,3092−3104.
(7) Meister, I.; Zhang, P.; Sinha, A.; Sköld, C. M.; Wheelock, Å. M.;
Izumi, T.; Chaleckis, R.; Wheelock, C. E. Anal. Chem. 2021, 93,5248−5258.
(8) Edmands, W. M. B.; Ferrari, P.; Scalbert, A. Anal. Chem. 2014,
86, 10925−10931.
(9) Alkhalifah, Y.; Phillips, I.; Soltoggio, A.; Darnley, K.; Nailon, W.
H.; McLaren, D.; Eddleston, M.; Thomas, C. L. P.; Salman, D. Anal.
Chem. 2020, 92, 2937−2945.
(10) Bruderer, T.; Gaisl, T.; Gaugg, T. G.; Nowak, N.; Streckenbach,B.; Müller, S.; Moeller, A.; Kohler, M.; Zenobi, R. Chem. Rev. 2019,
119, 10803−10828.
(11) Dührkop, K.; Nothias, L. F.; Fleischauer, M.; Reher, R.;
Ludwig, M.; Hoffmann, A. M.; Petras, D.; Gerwick, H. W.; Rousu, J.;
Dorrestein, C. P.; Bocker, S. Nat. Biotechnol. 2021, 39, 462−471.
(12) García-Cañaveras, J. C.; Donato, M. T.; Castell, J. V.; Lahoz, A.
J. Proteome Res. 2011, 10, 4825−4834.
G

https://doi.org/10.1021/acs.analchem.1c03163
Anal. Chem. XXXX, XXX, XXX−XXX

Analytical Chemistrypubs.acs.org/ac

ArticleL. M.; Hall, R. D.; Munn, R.; de Vos, R. C. H. Metabolomics 2016, 12,
88.

■

NOTE ADDED AFTER ASAP PUBLICATIONThis paper was published on October 27, 2021. Due to
production error, some incorrect words were left in the Resultsand Discussion section. The corrected version was reposted on
October 27, 2021.

H

https://doi.org/10.1021/acs.analchem.1c03163
Anal. Chem. XXXX, XXX, XXX−XXX