Indonesian Journal of Forestry Research Vol. 11 No. 2, October 2024, 299-306

ISSN 2355-7079/E-ISSN 2406-8195

IDENTIFICATION OF LIGNOCELLULOSE-LIKE MATERIAL
USING SPECTROSCOPY ANALYSIS
Danang S Adi1* , Widya Fatriasari2, Narto2, Dimas Triwibowo2, Teguh Darmawan2, Yusup Amin2,
Imran A Sofianto1, Rohmah Pari1, Dyah A Agustiningrum1, RGH Rahmanto1, Listya M Dewi1,
S Khoirul Himmi3, Djarwanto1, Ratih Damayanti4, Wahyu Dwianto2
Research Center for Applied Botany-National Research and Innovation Agency, Bogor,
West Java 16911, Indonesia
2
Research Center for Biomass and Bioproducts-National Research and Innovation Agency,
Bogor, West Java, 16911, Indonesia
3
Research Center for Applied Zoology-National Research and Innovation Agency, Bogor,
West Java, 16911, Indonesia
4
Directorate of Scientific Collection Management- National Research and Innovation Agency,
Bogor, West Java, 16911, Indonesia
1

Received: 01 November 2023, Revised: 04 June 2024, Accepted: 19 June 2024
IDENTIFICATION OF LIGNOCELLULOSE-LIKE MATERIAL USING SPECTROSCOPY
ANALYSIS. Lignocellulose materials, such as bamboo, rattan, and wood, have been largely used for furniture
and crafts. On the other hand, the utilization of lignocellulose-like materials, which have a similar texture
and appearance to those from nature, has been increasing recently due to their superior durability. This
research aimed to identify the lignocellulose-like material using spectroscopy analysis, such as Raman and
Near Infrared (NIR), which is well-known as a non-destructive, quick, and accurate approach for material
identification. We investigated four types of lignocellulose-like materials that were provided by Dewan
Serat Indonesia (The Indonesian Fiber Council) from an industry that produces them. The NIR analysis
was performed at wavenumbers 10,000-4,000 cm-1. The natural lignocellulose (bamboo and wood) and the
polymers (polyethylene and polypropylene) were used as standards. Raman analysis was further employed
to identify the composition of selected lignocellulose-like materials by comparing their spectra with the
library software. The results showed that the original NIR spectra of lignocellulose-like and those natural
materials were different, indicating that the NIR analysis can differentiate those materials. The NIR spectra
of lignocellulose-like materials were similar to those of polyethylene spectra. Those lignocellulose-like were
also identified as polyethylene due to the similarity of the Raman spectra and their library spectra.
Keywords: Near Infrared spectroscopy, Raman spectroscopy, lignocellulose, lignocellulose-like materials,
polyethylene
IDENTIFIKASI BAHAN SERUPA LIGNOSELULOSA MENGGUNAKAN ANALISIS
SPEKTROSKOPI. Material lignoselulosa seperti bambu, rotan, dan kayu telah banyak digunakan untuk mebel dan
kerajinan. Disisi lain, pemanfaatan material tiruan, dimana bahan tersebut mempunyai kesamaan tekstur dan penampilannya
dengan lignoselulosa dari alam semakin meningkat. Hal ini juga didorong oleh sifat keawetannya yang sangat tinggi sehingga
menjadi salah satu bahan pengganti yang potensial. Tujuan dari penelitian ini adalah untuk mengidentifikasi material
tiruan lignoselulosa dengan analisa spektroskopi Near Infrared dan Raman, dimana kedua alat ini mempunyai keunggulan
karena cepat, tidak merusak, dan memiliki keakuratan tinggi untuk melakukan identifikasi. Penelitian ini menggunakan
empat jenis material tiruan lignoselusa yang diperoleh dari Dewan Serat Indonesia. Bahan ini merupakan salah satu produk
yang dihasilkan oleh Perusahaan luar negeri. Analisa Near Infrared dilakukan dengan perekaman spektra pada nomor
gelombang 10.000-4.000 cm-1. Sebagai pembanding, digunakan material lignoselulosa alami dan juga polimer seperti bambu,
kayu, poli etilen, dan poli propilen. Sedangkan analisa dengan Raman dilakukan dengan membandingkan spektra Raman
dari material tersebut dengan spektra yang ada pada software Raman spektroskopi. Hasil penelitian menunjukkan bahwa
spektra original Near Infrared dari bahan tersebut sangat berbeda dengan bahan lignoselulosa alami, yang mengindikasikan
bahwa analisa tersebut dapat digunakan untuk membedakan bahan lignoselulosa alami dan tiruan. Selain itu, spektra
*

Corresponding author: danang.sudarwoko.adi@ brin.go.id

©2024 IJFR. Open access under CC BY-NC-SA license. doi:10.59465/ijfr.2024.11.2.299-306

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ISSN: 2355-7079/E-ISSN: 2406-8195

Near Infrared dari bahan tiruan mempunyai kemiripan dengan polimer poli etilen. Hal ini juga dikuatkan dengan hasil dari
analisa Raman, dimana spektranya sama dengan spektra poli etilen pada data base software Raman.
Kata kunci: Near Infrared, Raman spektroskopi, . lignoselulosa, lignoselulosa tiruan, poli etilen

I. INTRODUCTION
Indonesia is well known for its mega
biodiversity in its natural resources, including
natural fibers or lignocellulosic materials, such
as wood, bamboo, and rattan. For instance,
almost 11.5% of all bamboo species in the world
are grown in Indonesia, which consists of 161
species from 12 families (Widjaja et al., 2014).
In addition, Indonesia has 4,000 wood species,
and hundreds of those species have been traded
with high commercial values (Damayanti et al.,
2019). The abundance of natural resources
plays an important role in helping developing
countries maximize the potential utilization
of those resources for their economic growth.
Furthermore, natural resources are known
as bio-renewable and eco-friendly materials
(Karimah et al., 2021), thus their utilization can
reduce the risk of environmental issues.
Lignocellulose materials have a wide range of
applications, including structural uses in housing
and building materials, furniture, and crafts.
Moreover, the utilization of bamboo and wood
as raw materials for crafts has been increasing
and becoming one of the fastest-growing
micro, small, and medium enterprises of the
creative economic sector in Indonesia, which
contributes to 60% of Gross Domestic Products
(GDP) (Christine, Dachyar, & Nurcahyo, 2019;
Herliana, Crestofel Lantu, Rosmiati, Chaerudin,
& Lawiyah, 2022). As an organic material,
lignocellulose has issues related to its durability,
stability, and performance against weather and
organisms (Himmi et al., 2017; Ismayati et al.,
2011). On the other hand, the development of
lignocellulose-like materials is also rising, which
have similar texture, surface, and appearance to
natural lignocellulose. One of the producers
of lignocellulose-like materials claimed that

300

their products are very resistant and durable
against weather, moisture, mold, and termites,
thus indicating the product is superior to the
natural one. However, the increasing awareness
of people toward their environment has led
to the utilization of natural resources as the
main raw materials for replacing hazardous and
synthetic materials (Karimah et al., 2021). Thus,
material identification to differentiate natural
and lignocellulose-like materials is needed.
Direct investigation of the sample is usually
destructive and requires a large sample, labor
intensives, and time-consuming. Therefore,
rapid analysis, non-destructive, and accurate
method is urgently required to identify the
natural and lignocellulose-like materials.
Spectroscopy analysis using Near Infrared
(NIR) and Raman spectroscopy might be able
to meet all those requirements (Ayanleye &
Avramidis, 2021; Deneva et al., 2020).
NIR is a region of the electromagnetic
spectrum at the wave number 12,820-4,000 cm-1
(wavelength 780-2,500 nm) (Schwanninger,
Rodrigues, & Fackler, 2011). The combination
of chemometrics analysis and NIR spectroscopy
is very useful as qualitative and quantitative
methods (Tsuchikawa & Kobori, 2015). As
a qualitative analysis, the principle of using
NIR is comparing spectra of unknown and
known materials (Wang, Xiang, Tang, Chen,
& Xu, 2022), which could be used to identify
some wood species (Adi et al., 2020; Hwang,
Horikawa, Lee, & Sugiyama, 2016). The NIR
range is suitable for measuring organic materials
containing high moisture due to the smaller
molar absorptivity compared to the infrared
(IR) range (Tsuchikawa & Kobori, 2015). In
addition, NIR spectroscopy is less strong than IR
spectroscopy, which enables the thick polymer

Identification of Lignocellulose-Like Material Using Spectroscopy Analysis ...................(Danang S Adi et al.)

samples to be analyzed without suffering from
the light. By using NIR spectroscopy, the
absorption is very weak, making it possible to
measure the NIR transmittance or reflectance
without any pretreatment (Shinzawa et al.,
2021). Moreover, analyzing the sort of plastic
could be processed by NIR with high speed
measurements, radiation penetration depth, and
signal-to-noise ratio (Xia, Huang, Li, Xiong,
& Min, 2021). On the other hand, Raman
spectroscopy is popular as a non-destructive
test for woody materials (Yamauchi, Iijima,
& Doi, 2005). Raman spectroscopy is useful
for detecting the vibrations of symmetrical
structures and is very sensitive to polarizability
(Gierlinger, 2018). In addition, it has important
information regarding the structural and
chemical composition (Amjad, Ullah, Khan,
Bilal, & Khan, 2018). Raman spectroscopy
is not only used to identify organic materials,
but it is also used to identify polymers (Kida,
Sasaki, Hiejima, Maeda, & Nitta, 2020; Zhou,
Liu, Hao, & Liu, 2021). Therefore, this
study was conducted to identify natural and
lignocellulose-like materials by comparing their
NIR and Raman spectra.
II. MATERIAL AND METHOD
A. Materials
This study investigated four types of
lignocellulose-like materials, which have similar
appearance and texture to wood and bamboo.
These materials were obtained from Dewan

Serat Indonesia (The Indonesian Fiber Council)
in the form of raw materials in the square form,
not as finished products (Figure 1. A-D). The
samples were produced by a foreign company
that it seems made from some plastic polymers.
Several types of wood and bamboo species were
selected at random and subsequently analyzed
as the standard for natural lignocellulose. Eight
commercial wood species from Indonesia were
used for comparison, namely: Balau, heavy
Red Meranti, White Meranti (obtained from
commercial market around Bogor-Indonesia);
Old Teak, Platinum Teak, Jati Plus (fast-growing
Teak) (obtained from the National Research and
Innovation Agency of Indonesia-BRIN); Shorea
bracteolata, and Shorea parvifolia (obtained from
Xylarium Bogoriense). Furthermore, four bamboo
species from the bamboo collection field at the
BRIN in Cibinong, such as Dendrocalamus asper,
Bambusa vulgaris, Giganthocloa atter, and Bambusa
maculata Widjaja, were also investigated. In
addition, low-density polyethylene (PE) and
polypropylene (PP) were also analyzed as
standards for plastics.
B. Near Infrared and Raman Spectroscopy
Methods
The samples were analyzed directly in
the NIR machine (Frontier, PerkinElmer,
Massachusetts, USA) to get the spectra at
wavenumber 10,000-4,000 cm-1, with 32 scans
and a resolution of 16 cm-1. The NIR spectra
were set to provide a graph with absorbance
and wavenumber. The determination of

Figure 1. The samples of lignocellulose-like materials: A&D the shape is rectangular with different
colours; B&C the shape is rounded with different colours

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natural and lignocellulose-like materials was
carried out by comparing the original spectra
of those materials and the spectra of PE
and PP. However, additional classification
procedures would be applied if the original
spectra exhibited comparable shapes in both
natural and lignocellulose-like materials. Raman
Spectroscopy analysis (LabRam HR Evolution
Confocal Raman Microscope, Horiba France
SAS, Longjumeau, France) was conducted at the
Integrated Laboratory of Bioproducts (iLab)BRIN, with the laser 532 nm and grating 600.
The other parameters on the Raman analysis
were ND filter 50%, objective 100x visible,
acquisition time 12 seconds, and accumulation
10. Then, an integrated identifier software
in Raman software was used to identify the
composition of all samples by comparing their
spectra to the database (Bio-Rad Laboratories).

ISSN: 2355-7079/E-ISSN: 2406-8195

III. RESULT AND DISCUSSION
A. Near Infrared Spectroscopy
A graphical representation of the NIR
original spectra of natural lignocellulose samples
of 8 wood species (A) and 4 bamboo species
(B) is visualized in Figure 2. The graphs indicate
that there were identical shapes and bands for
all-natural lignocellulose. The discrepancies of
the original spectra are only for the absorbance
value, where the wood species has a big variation
compared to those bamboo species. Adi et al.
(2020) revealed the relationship between density
and their NIR absorbance values shows that the
high density tends to have a positive correlation
with absorbance, for instance, Balau (0.76 g/
cm3) has a higher absorbance value than red
Meranti (0.64g/cm3). This result is also similar
to that of Belangke bamboo, in which the upper

Figure 2. The NIR original spectra at wavenumber 10,000-4,000 cm-1 of wood species (A)
and bamboo species (B)

Figure 3 The average of NIR original spectra at wavenumber 10,000-4,000 cm-1 of wood
and bamboo species (A); polymer standard for PP and PE (B)

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Identification of Lignocellulose-Like Material Using Spectroscopy Analysis ...................(Danang S Adi et al.)

part has higher absorbance than the other part
since the top part has the highest density value
(Iswanto et al., 2022). However, this study used
several wood and bamboo species as a standard
only.
The mean spectra for wood and bamboo
species revealed that their typical spectra were in
a similar shape (Figure 3A), although there were
different values of absorbance, baseline shift,
and some flat curves. This phenomenon usually
occurs in the original spectra, which makes the
interpretation of the bands very difficult. The
chemical and structural characteristics of the
cell walls have an impact on NIR spectroscopy
because it is sensitive to functional groups
(Horikawa, Mizuno-Tazuru, & Sugiyama,
2015). The spectra of plastic materials, such

as PE and PP, are completely different from
those of organic materials (Figure 3B). The
basic concept of NIRS qualitative analysis is
comparing the spectra of unidentified materials
with those of known samples spectra (Wang et
al., 2022). Consequently, it had been acceptable
to easily distinguish between lignocellulose and
plastic materials by just comparing the original
spectra directly. The spectra of polymers
contain broad overtones of stretching and
bending vibrational bands due to the light
reflectance of the polymers and the NIR light
source (Michel et al., 2020). PE and PP have
different bands in the region of 6,500 cm-1
and 5,500-5,000 cm-1, which might be useful
information for differentiating both materials.
Some treatments, like the first derivative and

Figure 4. The average of NIR original spectra at wavenumber 10,000-4,000 cm-1 of 4 types
of lignocellulose-like materials

Figure 5. The Raman spectra of 4 types of lignocellulose-like materials

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Savitzky-Golay filter, could make the bands
more clearly visible (Duan & Li, 2021).
Figure 4 shows the mean of original NIR
spectra of 4 types of lignocellulose-like
material at 10,000-4,000 cm-1. At first glance,
the absorbance values of all samples were
identical, and it seems the graphs overlap. It
was presumed that the raw materials used in
all samples were comparable. The classification
of unknown materials in optical spectroscopy
usually uses visual inspection of the spectrum
(Tuncer, Dogu, & Akdeniz, 2021). By comparing
the spectra of lignocellulose-like material to
the standard in Figure 3A, it was clearly seen
that the raw material of lignocellulose-like was
not made from the natural material. According
to Figure 3B, the spectra of the 4 samples
were compared to the PE spectra due to the
appearance of some bands in wavenumber
6,490 cm-1 and the region around 5,500-5,000
cm-1; thus, it was supposed that all the samples
were made of PE plastic.
B. Raman Spectroscopy
Raman spectra of all lignocellulose samples
are presented in Figure 5. In general, the spectra
showed similar shapes, although there were
some noise peaks on specific Raman shifts.
Getting smooth spectra in Raman spectroscopy
is challenging. The fluorescence interference
has influenced the variation of baseline
modeling; thus, choosing the appropriate
window for polynomial fitting was required to
minimize the noise (Amigo, Babamoradi, &
Elcoroaristizabal, 2015). Furthermore, setting
the proper accumulation and acquisition
time is also needed in the analysis of Raman
spectroscopy. This study found that there
were at least 5 peaks in the Raman spectra,
with different settings of acquisition times and
accumulation. These peaks were at Raman shift
1,100 cm-1, 1,250 cm-1, 1,450 cm-1, 2,700 cm-1,
and 2,850 cm-1.
Micro Confocal Hyperspectral 3D Raman
Spectroscopy in this study was completed
with software to determine the spectra based
on comparison with the spectra database from

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Bio-Rad Laboratories. The results revealed that
4 types of synthetic lignocellulose materials
had different compositions. The sample 1
was identified as High-Density Polyethylene
(HDPE), while sample 2, sample 3, and sample
4 were Low-Density Polyethylene (LDPE),
LDPE + acrylic acid, and LDPE, respectively.
PE was a base structure for that synthetic
lignocellulose, even though 2 samples have
different PE compositions. These findings were
in accordance with those from the NIR spectra.
Kniggendorf, Wetzel, & Roth (2019) reported
that PE has Raman bands at 2,850 cm-1 and
2,884 cm-1. All samples in this study also had
bands at 2,850 cm-1, which was the highest
peak and contained -CH2 and -CH3 stretching
vibrations (Gopanna, Mandapati, Thomas,
Rajan, & Chavali, 2019).
Several factors might have contributed to
the variances in PE structure in these samples,
such as treatment processes to the Raman
spectra, the surface of the sample, and some
additives or colors to the samples. Selecting the
proper window for polynomial fitting plays a
crucial role. For instance, using a small window
has the effect of generating noise, which means
new bands appear in the spectrum. In contrast,
choosing a wide window could eliminate
some bands (Amigo et al., 2015). The surface
characteristics have an effect on adding noise
to the signal (Duan & Li, 2021). Adding some
color and additives could change the spectrum
(Michel et al., 2020). LDPE and HDPE have
similar molecular structures (Duan & Li, 2021),
so differentiating those materials by molecular
vibrations is challenging.
IV. CONCLUSION
Determination between natural and
lignocellulose-like materials was successfully
introduced by using NIR and Raman
spectroscopy. These techniques were nondestructive, fast, reliable, and traceable. The
original spectra of natural and lignocelluloselike were very different, which indicated that
the lignocellulose-like was made from synthetic

Identification of Lignocellulose-Like Material Using Spectroscopy Analysis ...................(Danang S Adi et al.)

material. By comparing the standard spectra
of plastics, the composition of these materials
was identified as LDPE. The Raman spectra
corresponded to the NIR analysis since the
spectra were similar to the LDPE spectra
reference, even though Sample 1 was identical
with HDPE and Sample 3 consisted of LDPE
and acrylic acid.
ACKNOWLEDGEMENT
The authors thank Dewan Serat Indonesia for
providing lignocellulose-like material samples,
and Xylarium Bogoriense for supplying samples
of various wood species.. This study is partially
supported by the National Research and
Innovation Agency (BRIN). The publication is
partially supported by JSPS Alumni Association
of Indonesia (JAAI).
REFERENCES
Adi, D. S., Hwang, S., Pramasari, D. A., Amin,
Y., Cipta, H., Damayanti, R., Dwianto, W.,
Sugiyama, J. (2020). Anatomical properties
and near infrared spectra characteristics
of four shorea species from Indonesia.
HAYATI Journal of Biosciences, 27(3), 247–257.
doi://10.4308/hjb.27.3.247.
Amigo, J. M., Babamoradi, H., & Elcoroaristizabal,
S. (2015). Hyperspectral image analysis. A
tutorial. Analytica Chimica Acta, 896, 34–51.
doi://10.1016/j.aca.2015.09.030.
Amjad, A., Ullah, R., Khan, S., Bilal, M., & Khan,
A. (2018). Raman spectroscopy based analysis
of milk using random forest classification.
Vibrational
Spectroscopy,
99,
124–129.
doi://10.1016/j.vibspec.2018.09.003.
Ayanleye, S., & Avramidis, S. (2021). Predictive
capacity of some wood properties by nearinfrared spectroscopy. International Wood
Products Journal, 12(2), 83–94. doi://10.1080/
20426445.2020.1834312.
Christine, M. G. F., Dachyar, M., & Nurcahyo, R.
(2019). Product segmentation of wooden
handicraft micro, small and medium enterprises
(msmes) in indonesia. IOP Conference Series:
Materials Science and Engineering, 598(1).
doi://10.1088/1757-899X/598/1/012063.

chr, R., Prakasa, E., Krisdianto, Dewi, L. M.,
Wardoyo, R., Sugiarto, B., Pardede, H. F.,
Riyanto, Y., Astutiputri, V. F., Panjaitan, G. R.,
Hadiwidjaya, M. L., Maulana, Y. H., Mutaqin,
I. N. (2019). Lignoindo: image database of
indonesian commercial timber. IOP Conference
Series: Earth and Environmental Science, 374(1).
doi://10.1088/1755-1315/374/1/012057.
Deneva, V., Bakardzhiyski, I., Bambalov, K.,
Antonova, D., Tsobanova, D., Bambalov, V.,
Cozzzolino, D., Antonov, L. (2020). Using
raman spectroscopy as a fast tool to classify
and analyze bulgarian wines—a feasibility
study. Molecules, 25(1), 1–10. doi://10.3390/
molecules25010170.
Duan, Q., & Li, J. (2021). Classification of common
household plastic wastes combining multiple
methods based on near-infrared spectroscopy.
ACS ES&T Engineering, 1(7), 1065–1073.
doi://10.1021/acsestengg.0c00183.
Gierlinger, Notburga. (2018). New insights into plant
cell walls by vibrational microspectroscopy.
Applied Spectroscopy Reviews, 53(7), 517–551. do
i://10.1080/05704928.2017.1363052.
Gopanna, A., Mandapati, R. N., Thomas, S. P.,
Rajan, K., & Chavali, M. (2019). Fourier
transform infrared spectroscopy (ftir), Raman
spectroscopy and wide-angle X-ray scattering
(waxs) of polypropylene (pp)/cyclic olefin
copolymer (coc) blends for qualitative and
quantitative analysis. Polymer Bulletin, 76(8),
4259–4274. doi://10.1007/s00289-018-25990.
Herliana, S., Crestofel Lantu, D., Rosmiati, M.,
Chaerudin, R., & Lawiyah, N. (2022). Analysis
of potential of indonesian craft exports.
JASAE, 18(11), 1317–1325.
Himmi, S. K., Yoshimura, T., Yanase, Y., Torigoe,
T., Akada, M., Ikeda, M., & Imazu, S. (2017).
Volume visualization of hidden gallery
system of drywood termite using computed
tomography: a new approach on monitoring
of termite infestation. In B. McLellan (Ed.),
Sustainable Future for Human Security: Environment
and Resources, VI, 61–68. doi://10.1007/978981-10-5430-3_6.
Horikawa, Y., Mizuno-Tazuru, S., & Sugiyama,
J. (2015). Near-infrared spectroscopy as
a potential method for identification of
anatomically similar Japanese diploxylons.
Journal of Wood Science, 61, 251–261.
doi://10.1007/s10086-015-1462-2.

305

Indonesian Journal of Forestry Research Vol. 11 No. 2, October 2024, 299-306

Hwang, S. W., Horikawa, Y., Lee, W. H., & Sugiyama,
J. (2016). Identification of Pinus species
related to historic architecture in Korea using
NIR chemometric approaches. Journal of
Wood Science, 62(2), 156–167. doi://10.1007/
s10086-016-1540-0.
Ismayati, M., Setiawan, K. H., Tarmadi, D.,
Zulfiana, D., Yusuf, S., & Santoso, B. (2011).
The efficacy of organo-complex-based wood
preservative formula against dry-wood termite
cryptotermes cynocephalus light. Insects, 2(4),
491–498. doi://10.3390/insects2040491.
Iswanto, A. H., Madyaratri, E. W., Hutabarat, N.
S., Zunaedi, E. R., Darwis, A., Hidayat, W.,
Susilowati, A., Adi, D. S., Lubis, M. A. R.,
Sucipto, T., Fatriasari, W., Antov, P., Savov,
V., Hua, L. S. (2022). Chemical, physical , and
mechanical properties of belangke bamboo
(Gigantochloa pruriens) and its application
as a reinforcing material in particleboard
manufacturing. Polymers, 14(3111), 1–26.
doi://10.3390/polym14153111.
Karimah, A., Ridho, M. R., Munawar, S. S., Adi,
D. S., Ismadi, Damayanti, R., Subiyanto, B.,
Fatriasari, W., Fudholi, A. (2021). A review
on natural fibers for development of ecofriendly bio-composite: characteristics, and
utilizations. Journal of Materials Research and
Technology. Elsevier Ltd. doi://10.1016/j.
jmrt.2021.06.014.
Kida, T., Sasaki, T., Hiejima, Y., Maeda, S.,
& Nitta, K. hei. (2020). Rheo-Raman
spectroscopic study of plasticity and
elasticity transformation in poly(etherblock-amide) thermoplastic elastomers.
Polymer, 189(November 2019), 122128.
doi://10.1016/j.polymer.2019.122128.
Kniggendorf, A. K., Wetzel, C., & Roth, B. (2019).
Microplastics detection in streaming tap water
with raman spectroscopy. Sensors (Switzerland),
19(8), 12–14. doi://10.3390/s19081839.
Michel, A. P. M., Morrison, A. E., Preston, V. L.,
Marx, C. T., Colson, B. C., & White, H. K.
(2020). Rapid identification of marine plastic
debris via spectroscopic techniques and
machine learning classifiers. Environmental
Science and Technology, 54(17), 10630–10637.
doi://10.1021/acs.est.0c02099.

306

ISSN: 2355-7079/E-ISSN: 2406-8195

Schwanninger, M., Rodrigues, J. C., & Fackler,
K. (2011). A review of band assignments
in near infrared spectra of wood and
wood components. Journal of Near Infrared
Spectroscopy. doi://10.1255/jnirs.955.
Shinzawa, H., Watanabe, R., Yamane, S.,
Koga, M., Hagihara, H., & Mizukado, J.
(2021). Aging of polypropylene probed
by near infrared spectroscopy. Journal of
Near Infrared Spectroscopy, 29(5), 259–268.
doi://10.1177/0967033521999115.
Tsuchikawa, S., & Kobori, H. (2015). A review
of recent application of near infrared
spectroscopy to wood science and technology.
Journal of Wood Science, 61(3), 213–220.
doi://10.1007/s10086-015-1467-x.
Tuncer, F. D., Dogu, D., & Akdeniz, E. (2021).
Efficiency of preprocessing methods for
discrimination of anatomically similar pine
species by NIR spectroscopy. Wood Material
Science & Engineering, 0(0), 212–221. doi://10.
1080/17480272.2021.2012821.
Wang, Y., Xiang, J., Tang, Y., Chen, W., & Xu, Y.
(2022). A review of the application of nearinfrared spectroscopy (NIRS) in forestry.
Applied Spectroscopy Reviews, 57(4), 300–317. do
i://10.1080/05704928.2021.1875481.
Widjaja, E. A., Rahayunigsih, Y., Rahajoie, J. S.,
Ubaidillah, R., Maryanto, I., Walujo, E. B., &
Semiadi, G. (2014). Kekinian Keanekaragaman
Hayati Indonesia 2014. Jakarta: LIPI Press.
Xia, J., Huang, Y., Li, Q., Xiong, Y., & Min, S.
(2021). Convolutional neural network
with near-infrared spectroscopy for plastic
discrimination. Environmental Chemistry Letters,
19(5), 3547–3555. doi://10.1007/s10311021-01240-9.
Yamauchi, S., Iijima, Y., & Doi, S. (2005).
Spectrochemical characterization by ftraman spectroscopy of wood heat-treated
at low temperatures: Japanese larch and
beech. Journal of Wood Science, 51(5), 498–506.
doi://10.1007/s10086-004-0691-6.
Zhou, X. X., Liu, R., Hao, L. T., & Liu, J. F. (2021).
Identification of polystyrene nanoplastics
using surface enhanced raman spectroscopy.
Talanta,
221(August
2020),
121552.
doi://10.1016/j.talanta.2020.121552.