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Characterization of Botanical Parts of Erythrina crista-galli Using Pyrolysis-Gas Chromatography/Mass Spectrometry and Multivariate Analysis Abd.
Wahid Rizaldi Akili1.
Ari Hardianto1.
Jalifah binti Latip2.
Maya Ismayati3, and Tati Herlina1* Department of Chemistry.
Faculty of Mathematics and Natural Sciences.
Universitas Padjajaran.
Jl.
Raya Bandung Sumedang Km 21 Jatinangor.
Sumedang 45363.
Indonesia School of Chemical Sciences and Food Technology.
Faculty of Science and Technology.
Universiti Kebangsaan Malaysia.
Selangor 46300.
Malaysia Research Center for Biomass and Bioproducts.
National Research and Innovation Agency (BRIN).
Jl.
Raya Bogor Km 46.
Cibinong 16911.
Indonesia * Corresponding author:
email: tati.
herlina@unpad.
Received: August 27, 2022 Accepted: April 20, 2023 DOI: 10.
22146/ijc.
Abstract: Erythrina crista-galli is commonly used in folk medicines for its pharmacological properties which are associated with the bioactive compounds.
Profiling botanical parts of E.
crista-galli is an exciting topic and essential to uncover the similarity and clustering based on their chemical content.
The botanical parts of E.
crista-galli, including bark, flowers, leaves, roots, and twigs, were subjected to pyrolysis-gas chromatography/mass spectrometry.
The samples were pyrolyzed using a multi-shot The relative abundance of the pyrolysate was subjected to multivariate analysis, i.
, principal component analysis (PCA) and hierarchical cluster analysis (HCA).
The scree plot for PC.
PC.
2, and PC.
3 accounted for 36.
5%, 27.
2%, and 20.
Together, the first three PCs explain 84% of the total variance.
The PCA allows characterizing the roots of E.
crista-galli by the highest relative abundance of lignin G, followed by the twigs, bark, and leaves, while the flowers had the least relative abundance of lignin G.
The HCA allows to cluster the botanical parts of E.
crista-galli into three different clusters based on their chemical component similarity, i.
, flowersleaves, twigs, and roots-bark.
In conclusion.
Py-GC/MS analysis can be used in conjunction with multivariate data analysis to characterize the botanical parts of E.
crista-galli.
Keywords: E.
crista-galli.
pyrolysis-GC/MS.
multivariate analysis.
principal component hierarchical clustering analysis n INTRODUCTION Erythrina (Fabacea.
is a large genus comprising around 200 species .
They are commonly used in folk medicines in Asian.
African, and South American countries due to their pharmacological properties.
One of the Erythrina species.
crista-galli, was traditionally used as a wound healing and sedative.
Meanwhile, people in Indonesia used E.
crista-galli for malaria treatment by stewing the leaves and barks .
Additionally.
cristagalli was also reported to have laxative, hypertensive, and diuretic activities.
The botanical parts of E.
crista-galli Abd.
Wahid Rizaldi Akili et al.
have various bioactivity.
for example, the aerial parts of crista-galli have analgesic and anti-inflammatory the root has antibacterial and antifungal activities, the bark has antibacterial, antimycobacterial, and antifungal activities.
the leaves have antibacterial, antifungal, antivirus, animal repellent, and cytotoxic while the flowers show antimutagenic activity .
These efficacies are associated with the metabolites constituents, which may unevenly spread within the botanical parts of E.
crista-galli, as reported for some other species .
Thus, profiling the botanical parts of crista-galli is an exciting topic and essential to uncover Indones.
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the similarity and the clustering of every botanical part based on their chemical content.
Various methods can be used for metabolite profiling, such as gas chromatography (GC) .
, highperformance liquid chromatography (HPLC) .
, gas chromatography coupled to mass spectrometry (GC-MS) .
, gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) .
, ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry (UPLC-TOFMS) .
, and pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) .
Among these methods.
Py-GC/MS has the advantage that it is a fast analysis method, it requires simple sample preparation and a small amount of sample.
Py-GC/MS can analyze diverse metabolite species, including high molecular weight metabolites, which in turn provides the opportunity to analyze the whole compound including primary and other metabolites .
Pyrolysis works by applying heat greater than the energy of specific bonds so that the molecule will fragment in a reproducible way.
The fragments produced are then separated by the capillary column of the GC to produce the pyrogram.
The interpretations of resulting pyrograms require detailed knowledge of the pyrolysis behavior of the desired compounds.
This poses extreme difficulty for the global elucidation of metabolites, but since the PyGC/MS of complex matrices results in a complex mixture of volatile fragments of the original sample, the resulting pyrogram can be used very effectively as a fingerprint of that particular sample.
The analysis of the fingerprint pattern of these samples is often accomplished by the use of multivariate statistical techniques, which can be used to reveal relationships between samples and correlations between variables .
Two of the most used multivariate techniques to explore similarities and hidden patterns among samples are principal component analysis (PCA) and hierarchical cluster analysis (HCA) .
When the variables in a data set are highly correlated, which suggests data redundancy.
PCA is extremely beneficial.
PCA can be used to reduce the original variables into a smaller number of new variables called principal components that explain the majority of the variance in the original variable due to this Abd.
Wahid Rizaldi Akili et al.
PCA can also provide visualization to look for grouping in a data set.
However, this method does not explicitly define clusters, and this is where the HCA method comes in .
HCA is a method to determine the underlying structure of observations by repeating a procedure that associates or dissociates each object until they are all processed wholly and equally.
This method divides samples from a data set into groups that are related to one another .
Therefore, in our study, we use HCA in addition to PCA to explore similarities and hidden patterns among different parts of crista-galli.
In this study.
Py-GC/MS was applied to characterize the botanical parts .
ark, flowers, leaves, roots, and twig.
of E.
crista-galli.
The result obtained from Py-GC/MS was then subjected to PCA and HCA multivariate analysis to distinguish between parts of E.
crista-galli based on their whole chemical component.
The PCA and HCA analyses were performed in the R programming language.
To the best of our knowledge, this is the first study that aimed to characterize five different parts of the E.
crista-galli plant based on their whole metabolites using Py-GC/MS and to cluster these different parts of E.
crista-galli based on their metabolite fingerprint similarity.
n EXPERIMENTAL SECTION Materials Materials used were the botanical parts of E.
cristagalli, including bark, flowers, leaves, roots, and twigs, that were collected from Bandung.
West Java.
Indonesia.
These plant materials have been determined at the Laboratory of Agricultural Production Technology & Services.
Agricultural Cultivation Department.
Faculty of Agriculture.
Universitas Padjajaran, under voucher specimen number 1020.
Instrumentation The equipment used in this study was eco-cup SF PY1-EC50F, glass wool, multi-shot pyrolyzer (EGA/PY3030D) interfaced with GC/MS system QP-2020 NX (Shimadzu.
Japa.
equipped with an SH-Rxi-5Sil MS column with electron impact of 70 eV.
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Procedure Gs .
A Pyrolysis-GC/MS measurement Py-GC/MS was performed on several botanical parts of E.
crista-galli plants .
, bark, flowers, leaves, roots, and twig.
About 500 g of samples were analyzed by PyGC/MS.
It was put in eco-cup SF PY1-EC50F and covered by glass wool.
Furthermore, the eco-cup was pyrolyzed at 500 AC for 6 s using a multi-shot pyrolyzer (EGA/PY3030D) which was interfaced .
nterface temperature 280 AC) with a GC/MS system QP-2020 NX (Shimadzu.
Japa.
equipped with an SH-Rxi-5Sil MS column .
m y 0.
25 mm i.
film thickness 0.
, with electron impact of 70 eV and helium as a carrier gas.
The pressure was 20.
0 kPa .
9 mL/min, column flow 61 mL/mi.
The temperature profile for GC was as follows: 50 AC held for 1 min.
Then the temperature increased until 280 AC .
AC/mi.
, and 13 min at 280 AC.
Products resulting from the pyrolysis were identified by comparing their retention times and mass spectra data with NIST LIBRARY 2017.
The identified pyrolysates were further compared with the literature .
Multivariate analysis In this study, we performed two multivariate analyses.
PCA which was followed by agglomerative hierarchical clustering or Hierarchical Clustering on Principal Components (HCPC.
Pyrograms of the botanical parts were assigned a matrix .
ow i, column .
The botanical parts were assigned as observations .
, whereas pyrolysis products were as descriptors .
Mean centering and scaling were applied to the matrix during the preprocessing stage.
The mean centering procedure was performed to maintain the important variation.
The scaling step was employed due to the different scales of pyrolysis products.
An orthogonal linear transformation was applied to the matrix to produce principal components .
Fs .
indicates the coordinate vectors of the samples .
pyrolysis product.
, which can be expressed as Eq.
Fs .
A Eu xik m k Gs .
As k Abd.
Wahid Rizaldi Akili et al.
Eu x ik pi Fs .
As k whereas Fs.
and Gs.
represent the coordinates of the botanical part i and pyrolysis product k on the axis s.
Notation s is the eigenvalue corresponding to the axis s.
Notations of mk and pi are the weights associated with pyrolysis product k and the botanical part i, respectively, whereas xik refers to the matrix .
ow i, column .
The first PCs responsible for at least 80% variance were retained and subjected to agglomerative hierarchical The most similar individual observations i were agglomerated iteratively based on the pairwise distance of WardAos criterion.
The number of clusters was selected according to the hierarchical tree.
PCA and HCPC were computed in the R programming language environment using FactoMineR .
The results were visualized using factoextra .
or ggplot2 .
Leaveone-out cross-validation (LOOCV) computation for PCA was performed using chemometrics .
n RESULTS AND DISCUSSION Pyrolysis Products of the Botanical Parts of E.
crista-galli The chemical compositions of bark, flowers, leaves, roots, and twigs of E.
crista-galli were analyzed by PyGC/MS.
This analysis method produces a pyrogram that plots retention time to its relative intensity.
The resulting pyrograms from the analysis of botanical parts of E.
crista-galli are given in Fig.
According to the resulting pyrogram (Fig.
, 93
pyrolysis products .
were identified by comparing their retention times with mass spectra data with NIST LIBRARY 2017.
Table 1 shows pyrolysates and their relative intensities in each sample.
The most abundant pyrolysates belong to polysaccharides, followed by lignins and extractives.
This finding is unsurprising since polysaccharides and lignins are the main constituents of plant materials .
In softwood, polysaccharides such as cellulose and hemicellulose compose 41Ae50 and 11Ae33% of the biomass, respectively, while lignin constitutes 19Ae30%.
The cellulose and Indones.
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Fig 1.
Pyrogram comparison of 5 botanical parts of E.
crista-galli Table 1.
Pyrolysis products and their relative abundance Pyrolysis product .
a (%)b ammonium carbamate 2-oxopropanal 2-methylpropanal butane-2,3-dione 3-methylbutanoic acid acetic acid 2,5-dihydrofuran 1-hydroxypropan-2-one 1-hydroxypropan-2-one 2-oxobutyl acetate 2,3-dihydro-1,4-dioxine 1-methylpyrrole 3-methylpenta-1,4-diene 1-nitropropan-2-one methyl 2-oxopropanoate 5-.
pyrrolidin-2-one furan-2-carbaldehyde 2-hydroxycyclohexyl acetate 2-oxopropyl acetate 4,4-dimethyl-5-oxopentanenitrile 2H-furan-5-one cyclopentane-1,2-dione Abd.
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Molecular
Roots Polysaccharide CH6N2O2
C 3H 4O 2
C4H8O
C4H6O2
C5H10O2
C2H4O2
C4H6O
C3H6O2
C3H6O2
C6H10O3
C4H6O2
C5H7N
C6H10
C3H5NO3
C4H6O3
C11H19NO
C5H4O2
C8H14O3
C5H8O3
C7H11NO
C4H4O2
C5H6O2
Relative abundance (%) Flowers Leaves Bark Twigs Indones.
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a Pyrolysis product 4,5-dimethyloctane ethenyl propanoate 5-methylfuran-2-carbaldehyde 3-methylcyclopent-2-en-1-one 2-hydroxy-3-methylcyclopent-2-en-1one 2-hydroxy-3-methylcyclopent-2-en-1one 4-methoxyphenol .
-creso.
3-methylbutyl 2-methylpropanoate 3-hydroxy-2-methylpyran-4-one 3-ethyl-2-hydroxycyclopent-2-en-1one 1,4-dioxaspiro.
heptan-5-one 7-methyl-1,4-dioxaspiro.
heptan-5one 1,4:3,6-dianhydro--D-glucopyranose 2,3-dihydro-1-benzofuran 2,3-anhydro-D-mannosan 6,7-dimethoxy-1-[(E)-2phenyletheny.
-1,2,3,4tetrahydroisoquinoline 3H-.
,2-.
pyrimidin-4one tetradecanoic acid 7,11,15-trimethyl-3methylidenehexadec-1-ene 7,11,15-trimethyl-3methylidenehexadec-1-ene (E)-octadec-6-enyl acetate hexadecenoic acid octadecanoic acid Total 4-methylguaiacol 4-ethylguaiacol 4-vinylguaiacol 4-propylguaiacol cis-isoeugenol Abd.
Wahid Rizaldi Akili et al.
(%)b Molecular
C10H22
C5H8O2
C6H6O2
C6H8O
C6H8O2
Roots C6H8O2
C7H8O2
C4H8O
C9H18O2
C6H6O3
C7H10O2
C5H6O3
C6H8O3
C6H8O4
C8H8O
C6H8O4
C19H21NO2
C10H6N2O2
C14H28O2
C20H38
C20H38
C20H38O2
C16H32O2
C18H36O2
C32H66
C40H82
C32H66
Lignin G
C7H8O2
C8H10O2
C9H12O2
C9H10O2
C10H12O2
C10H14O2
C10H12O2
Relative abundance (%) Flowers Leaves Bark Twigs .
a Indones.
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Pyrolysis product trans-isoeugenol (E)-4-.
-hydroxyprop-1-en-1-y.
-2methoxyphenol 973 (E)-4-.
-hydroxyprop-1-en-1-y.
-2methoxyphenol Total
731 phenol
901 2-methylphenol
642 p-cresol
(%)b Molecular
C10H12O2
C9H10O3
C10H12O3
C10H12O3
Roots C10H12O3
C8H7N
C9H9N
C12H16O8
C15H32O
C20H40
C18H32O2
C16H28O2
C18H32O2
C20H40O
C18H30O2
C16H26O
C16H33NO
C17H17NO2
C30H54O2
Total Abd.
Wahid Rizaldi Akili et al.
380 o-xylene 169 2,2-diethyl-3-methyl-1,3-oxazolidine 419 2-.
-2-nitropropane1,3-diol 262 1H-indole 897 3-methyl-1H-indole 275 3,4-diacetyloxy-6,8dioxabicyclo.
octan-2-yl acetate 026 pentadecan-1-ol 85 3,7,11,15-tetramethylhexadec-2-ene 833 (Z)-18-octadec-9-enolide 963 .
Z)-1-oxacycloheptadec-8-en-2-one 188 (Z)-18-octadec-9-enolide 401 phytol .
Z,12Z,15Z)-octadeca-9,12,15trienoic acid
Z,10Z,13Z)-hexadeca-7,10,13-trienal 88 hexadecanamide 976 6,7-dimethoxy-1-phenyl-3,4dihydroisoquinoline 688 2,6,10,15,19,23-pentamethyl-2,6,18,22tetracosatetraen-10,15-diol 4-methylsyringol
4-vinylsyringol
cis-4-propenylsyringol
trans-4-propenylsyringol
Twigs Lignin H
C6H6O
C7H8O
C7H8O
Lignin S C8H10O3
C9H12O3
C11H14O4
C11H14O3
C9H10O4
C11H14O3
C12H16O4
Extractive and others C8H10
C8H17NO
C4H9NO5
Total Relative abundance (%) Flowers Leaves Bark Indones.
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Pyrolysis product
a 224 (E)-3,3'-dimethoxy-4,4'dihydroxystilbene 284 methyl 2-phenylquinoline-7carboxylate 421 clionasterol
815 squalene 404 heptacosyl heptafluorobutyrate Total
(%)b Molecular
C16H16O4
Roots C17H13NO2
C29H50O
C30H50
C31H55F7O2
Relative abundance (%) Flowers Leaves Bark Twigs SI (%) = Similarity index based on NIST 2017 library (%) tR .
= retention time in minutes hemicellulose contents in the hardwood are 39Ae53 and 19Ae36%, respectively, while lignin is 17Ae24%.
Meanwhile, the percentages of cellulose and hemicellulose in the herbaceous plants are 24Ae50 and 12Ae38%, respectively, whereas lignin is 6Ae29% .
Based on Table 1, carbohydrates generate several classes of compounds during pyrolysis, such as anhydrous sugars, carbonyls, lactones, furans, pyrans, carboxylic acids, and esters .
The examples of anhydrous sugars are 1,4:3,6-dianhydro--D-glucopyranose and 2,3anhydro-D-mannosan, whereas those of carbonyls are 2oxopropanal and butane-2,3-dione.
During the Py-GCMS analysis, lignin is fragmented into its monomers: H .
-hydroxyphenyl uni.
G .
uaiacyl uni.
, and S .
yringyl uni.
The concentration of lignin and its monomeric composition change between plant species, tissues, cell types, and different cell wall layers during development .
Based on the relative abundance (%) of lignin monomers, among the 5 botanical parts of E.
crista-galli, the twigs have the highest total lignin content .
59%), followed by roots .
56%),
26%), and leaves .
03%), while flowers have the lowest total lignin content .
56%).
Lignin accumulates in the cell walls of specialized cell types to enable plants to stand upright and conduct water and minerals .
Twigs provide mechanical support and transport water, carbohydrates, and nutrients .
This explains why the twigs have the highest total lignin content among the other botanical parts of E.
crista-galli.
Py-GC/MS provides a complete overview of global metabolite fingerprints to characterize botanical parts of Abd.
Wahid Rizaldi Akili et al.
crista-galli.
Through pattern recognition analysis, the multivariate data obtained from Py-GC/MS analysis can be useful to provide information on how each botanical part of E.
crista-galli is different from one another based on the metabolite fingerprint.
Therefore, we coupled the Py-GC/MS results with multivariate analysis in the next Multivariate analysis is concerned with datasets having several response variables for each observational or experimental.
The commonly used multivariate data analysis for pattern recognition are PCA and HCA.
These are examples of unsupervised learning techniques in which the objective is to identify previously unknown structures in the data set, as well as to identify clusters in a given dataset without using class membership information in the calculations .
Multivariate Analysis Multivariate analysis with all pyrolysis products PCA is a statistical method that can be used to visualize information in a data set by describing how each sample differs from another, which variables contribute significantly to this difference, as well as to identify sample patterns.
In our research, in order to easily identify which metabolite contribute to the similarity or differences between 5 botanical parts of E.
crista-galli based, we use the relative abundance data of metabolites as variables for PCA analysis, as done by several previous studies .
PCA minimizes the data dimension by creating the so-called principal components (PC.
, which are linear combinations of the variables in the data set to Indones.
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summarize the data .
Fig.
shows the scree plot, which is a line plot of the principal components along with the percentage of explained variance from the principal component analysis of the data set.
Crossvalidation was subjected to the data set in order to determine the number of PCs that should be retained in order to account for most of the data variability.
The result from cross-validation suggests that at least the first three PCs should be retained to fulfill a variance of 80% (Fig.
Fig.
3 shows the score plot of the botanical parts of crista-galli on PC.
PC.
2, and PC.
PC.
1 accounts for 5% of the total variance, while PC.
2% and the PC.
Together, the first three PCs explain 84% of the total variance.
Each PC can be described by the origin variables (Rt.
Variables described the best in each PC can be identified by the correlation coefficient and the coordinates of the botanical parts on a PC.
Correlation coefficients are calculated for all the variables, followed by testing the significance of each correlation coefficient and sorting the variables from the most to the less The most significant variables then describe each PC.
such a method is beneficial for interpreting the dimensions with many variables .
Table 2 shows a list of significantly correlated variables to PC.
1, 2, and 3 from the PCA.
According to Table 2, eugenol, 4-ethylguaiacol, trans-isoeugenol, and (E)-4-.
-hydroxyprop-1-en-1y.
-2-methoxyphenol are pyrolysates that have a positive correlation to PC.
Therefore, samples with a high score Fig 2.
Scree plot and .
box plot of cumulative variances resulted from leave-one-out cross-validation Fig 3.
PCA score plot of botanical parts of E.
crista-galli on PC.
PC.
2, and PC.
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Table 2.
List of significantly correlated variables to PC.
PC.
2, and PC.
Corr.
p-value Pyrolysis product PC.
4-ethylguaiacol trans-isoeugenol (E)-4-.
-hydroxyprop-1-en-1-y.
-2-methoxyphenol PC.
-2-propanone PC.
4-vinylsyringol 3-methylbutyl 2-methylpropanoate on PC.
1 will have a high relative abundance of these pyrolysis products.
On the other hand, phenol has a negative correlation to PC.
Thus, any sample with a high score on PC.
1 will have a low relative abundance of that pyrolysis product.
For PC.
2, only 1-.
-2propanone has a significant positive correlation to the second latent variable.
Meanwhile, for PC.
3, 4vinylsyringol and 3-methylbutyl 2-methylpropanoate are positively and negatively correlated to the third latent variable, respectively.
The PCA score plot (Fig.
shows that roots have the highest score on PC.
1, followed by twigs, bark, leaves, and flowers.
Revering to Table 2, most of the significantly correlated variables on PC.
1 come from the pyrolysis products of lignin G.
Roots have the highest score on PC.
1, while flowers have the lowest score.
Thus, roots are characterized by high lignin G content, whereas flowers are low lignin G content, which is also confirmed by Table Similarly, since the bark owns a high score on PC.
2, it has the highest relative abundance of 1-.
-2propanone, whereas twigs have the lowest relative abundance of this pyrolysis product.
Visualization provided by PCA score plots may facilitate clustering in pyrolysis product data.
Nonetheless.
PCA does not explicitly define clusters.
More formal approaches can be used by clustering methods.
Cluster analysis divides observations into groups that are related to one another.
In terms of specific characteristics, each group or cluster is homogeneous and should be distinct from others.
The closeness of two objects is expressed by Abd.
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Origin Lignin-G Lignin-G Lignin-G Lignin G Lignin-H Linear ketone derivatives Lignin-S Linear ketone derivatives similarity or dissimilarity, which can be computed by mathematical methods, and eventually displayed in a dendrogram based on the features of individual objects .
HCPC is a clustering approach that allows to combine principal component method, hierarchical clustering, and partitioning clustering method to identify clusters within a data set.
The combination of the principal component method along with the clustering method is useful in a situation where the data set contains multiple continuous variables.
The PCA can be used to reduce the dimension of the data, and then clustering can be performed on the PCA result .
From the PCA and LOOCV analysis, at least the first three PCs should be retained to cover 80% of the variance (Fig.
Therefore, we performed the HCPC analysis from the first three .
% total varianc.
and four principal components .
% total varianc.
Fig.
shows the dendrogram of botanical parts of E.
crista-galli resulting from HCPC analysis.
HCPC analysis from the first three and four principal components shows that the botanical parts of crista-galli are divided into three different clusters.
Fig.
4 show that in HCPC analysis with the first three PCs, cluster 1 consists of flower, cluster 2 consists of leaves and bark, and cluster 3 consists of twig and root, whereas in HCPC analysis with four PCs.
Cluster 1 consists of flowers and leaves, cluster 2 consists of twigs and cluster 3 consists of roots and bark.
Since the first four PCs cover 100% variability.
HCPC analysis from the first four PCs is used to cluster botanical parts of E.
crista-galli.
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Fig 4.
Hierarchical clustering on the factor map of botanical parts of Erythrina crista-galli with .
three PCs and .
four PCs.
Clusters 1, 2, and 3 are denoted by pink, green, and yellow, respectively Table 3.
Variables that describe the most each cluster .
Mean in Overall p-value Pyrolysis product Origin Cluster 1 Cluster 2 heptacosyl heptafluorobutyrate 2,6,10,15,19,23-pentamethyl-2,6,18,22tetracosatetraen-10,15-diol tetradecanoic acid 2-methylphenol 2,5-dihydrofuran p-cresol 2,3-butanedione Cluster 3 7-methyl-1,4-dioxaspiro.
heptan-5-one .
S,3R,6R,7R,9R)-2,5,8trioxatricyclo.
nonan-9-ol methyl 2-oxopropanoate 5-methylfuran-2-carbaldehyde Table 3 shows a list of variables that describe the most exact cluster.
Variables that are significantly associated with specific clusters have higher mean category values than the overall mean.
Thus, it could be said that cluster one .
, flowers and leave.
is characterized by the higher content of 1H-indole 1H-indole is assumed to be a minor pyrolysis product that originated from protein .
or extractive as an alkaloid after fragmentation of the pyrolysis process and was detected by Py-GC/MS .
1H-Indole is produced by the pyrolysis of the amino acid tryptophan.
Abd.
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Unknown 1H-indole Extractive/Unknown? Unknown Lignin-S Linear ketone derivatives Lignin-H Furan derivatives Lignin-H Linear ketone derivatives Lactone derivatives Anhydro sugars Linear ketone derivatives Cyclopentenone derivatives It undergoes thermal degradation at a temperature above 800 AC.
Three main pyrolysates of indole are phenylacetonitrile, 2- methylbenzonitrile, and 3methylbenzonitrile which formed due to the opening of the pyrrole ring .
Since, in our research, the pyrolysis was performed at the temperature of 500 AC, the indole might not undergo a pyrolytic reaction.
ThatAos why 1Hindole .
etention time, tR = 19.
267 mi.
and 3-methyl-1H-indole .
R = 21.
897 mi.
pyrolysate are still detected.
Those pyrolysates might also indicate the presence of indole alkaloids such as 1H-indole-3-propanamide.
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abrine, and hypaphorine (Fig.
that has been identified in Erythrina genus .
Since indole pyrolysate is associated with the presence of indole alkaloids, the flowers and leaves contain a higher amount of indole alkaloids compared to the other clusters.
Other pyrolysates that could indicate the presence of 6,7-dimethoxy-1-phenyl-3,4dihydroisoquinoline .
R = 44.
976 mi.
and methyl 2phenylquinoline-7-carboxylate .
R = 48.
284 mi.
Isoquinoline is one compound that is very stable at elevated temperatures.
It undergoes pyrolysis at a temperature above 900 AC to produce benzene, toluene, naphthalene, phenanthrene, and anthracene, as well as the isomer of the other quinoline, indole, and several nitriles, including benzonitrile, and several isomers of cyanostyrene and cyanonaphthalene .
Cluster 2 .
, twig.
is characterized by a higher relative abundance of heptacosyl heptafluorobutyrate, 2,6,10,15,19,23-pentamethyl-2,6,18,22-tetracosatetraen10,15-diol, syringylacetone, tetradecanoic acid, 2methylphenol, 2,5-dihydrofuran, p-cresol, and 2,3butanedione pyrolysate.
Heptacosyl heptafluorobutyrate 2,6,10,15,19,23-pentamethyl-2,6,18,22tetracosatetraen-10,15-diol were detected at the end of pyrogram as minor pyrolysis products from amino acids of lignocellulose biomass samples .
Cluster 3 .
roots and bark.
is characterized by a higher relative abundance of 7-methyl-1,4-dioxaspiro.
heptan-5-one, .
S,3R,6R,7R,9R)-2,5,8-trioxatricyclo.
nonan9-ol, methyl 2-oxopropanoate, and 5-methylfuran-2carbaldehyde pyrolysate.
Tables 2 and 3 show that the distribution of samples in the score plot of PC.
PC.
2, and PC.
3, as well as the clustering, are mainly influenced by the polysaccharide and lignin content in those samples.
This is mainly true since polysaccharides and lignin are relatively abundant compared to extractives in higher plants, whether in softwood, hardwood, or even in herbaceous plants .
Multivariate analysis with only extractive pyrolisate The second principal component analysis was performed on the relative abundance (%) of extractive Fig.
6 shows the score plot of the samples for the second PCA.
For the second principal component analysis.
PC.
and PC.
2 account for 37.
8 and 31.
9% of the total variance, respectively, while PC.
3 contributes to 20.
of the total variance.
Together the first three PCs account 9% of the total variance.
The analysis shows that phytol .
R = 38.
401 mi.
is the variable that significantly correlated to PC.
= 0.
938, p val.
= 0.
Since bark has the highest score on PC.
1, therefore it has the highest relative abundance of phytol.
Indole .
R = 19.
262 mi.
is the pyrolysate that is significantly correlated to PC.
= Oe0.
949, p-value = 0.
and the correlation of indole with PC.
2 is Thus, samples with the smallest score in PC.
, leaves and flower.
have the highest relative abundance of this pyrolysate.
(Z)-18-Octadec-9-enolide, .
Z)-1-oxacycloheptadec-8-en-2-one, and (Z)-18Octadec-9-enolide are pyrolysates that significantly correlated to PC.
3 with correlation value of Oe0.
Since the correlation value is negative, indicating samples that have a positive value on PC.
3 will have a small relative abundance of those pyrolysates.
Fig 5.
Indole alkaloids identified in Erythrina Abd.
Wahid Rizaldi Akili et al.
Indones.
Chem.
, 2023, 23 .
, 899 - 912
Fig 6.
Score plot of botanical parts of E.
crista-galli for the second principal component analysis n CONCLUSION Py-GC/MS analysis can be used in conjunction with multivariate data analysis to characterize the botanical parts of E.
crista-galli.
The Py-GC/MS shows that most pyrolysis products or pyrolysate are originated from polysaccharides and lignin.
PCA shows that the roots of crista-galli is characterized by the highest relative abundance of lignin G, while the flowers have the least relative abundance of lignin G.
Hierarchical cluster analysis shows that the botanical parts of E.
crista-galli are clustered in three different clusters based on their Cluster 1 consists of flowers and leaves and is characterized by the higher content of indole pyrolysate.
Cluster 2 consist of twigs and characterized by higher relative abundance of heptacosyl heptafluorobutyrate, 2,6,10,15,19,23-pentamethyl-2,6,18,22-tetracosatetraen10,15-diol, syringylacetone, tetradecanoic acid, 2methylphenol, 2,5-dihydrofuran, p-cresol, and 2,3butanedione pyrolysate, and cluster 3 consist of roots and barks is characterized with higher relative abundance of 7-methyl-1,4-dioxaspiro.
heptan-5-one, .
S,3R,6R,7R,9R)-2,5,8-trioxatricyclo.
nonan9-ol, methyl 2-oxopropanoate, and 5-methylfuran-2carbaldehyde pyrolysate.
Abd.
Wahid Rizaldi Akili et al.
n ACKNOWLEDGMENTS The authors are grateful to the Universitas Padjadjaran for providing funds through the Outstanding Padjadjaran Postgraduate Scholarships (BUPP) scheme and Academic Leader Grant (ALG) by Tati Herlina (No.
1959/UN6.
1/PT.
00/2.
n AUTHOR CONTRIBUTIONS Conceptualization.
Tati Herlina and Ari Hardianto.
Data curation.
Abd.
Wahid Rizaldi Akili and Maya Ismayati.
Formal analysis.
Ari Hardianto.
Abd.
Wahid Rizaldi Akili.
Jalifah binti Latip.
Maya Ismayati.
Tati Herlina.
Funding acquisition.
Tati Herlina.
Investigation.
Maya Ismayati.
Abd.
Wahid Rizaldi Akili.
Methodology.
Ari Hardianto.
Tati Herlina, and Maya Ismayati.
Software.
Abd.
Wahid Rizaldi Akili and Ari Hardianto.
Validation.
Maya Ismayati.
Ari Hardianto, and Tati Herlina.
Visualization.
Abd.
Wahid Rizaldi Akili and Ari Hardianto.
Writing Ae original draft.
Abd.
Wahid Rizaldi Akili and Ari Hardianto.
Writing Ae review & editing.
Tati Herlina.
Jalifah binti Latip and Ari Hardianto.
n REFERENCES