Jurnal Ilmu Farmasi dan Farmasi Klinik
(Journal of Pharmaceutical Science and Clinical Pharmacy)
ISSN 1693-7899 (print) - 2716-3814 (online)
DOI: 10.31942/jiffk.v21i1.9529

Analysis of Black Cumin Oil Adulteration Using a Combination
of UV-Vis Spectrophotometry and Chemometric Methods
Triyadi Hendra Wijaya*, Yuni Nur Aeni, Muhamad Salman Fareza , Nialiana Endah
Endriastuti , Masita Wulandari Suryo Putri
Department of Pharmacy, Faculty of Health Sciences, Jenderal Soedirman University, Purwokerto, Central Java,
Indonesia

ABSTRACT: Black cumin oil is a vegetable oil known for its health benefits, but its high price makes
it susceptible to adulteration. To address this issue, a combination of UV-vis spectrophotometric and
chemometric methods was developed to distinguish black cumin oil from other oils. This research
specifically aims to evaluate the effectiveness of UV-vis spectrophotometry and principal component
analysis (PCA) and hierarchical cluster analysis (HCA) in differentiating black cumin oil from corn
and soybean oil. Samples were analyzed for absorption across a wavelength range of 220-290 nm.
The UV-vis spectral data was subsequently processed using PCA and HCA to classify the samples. The
results indicate that the PCA model effectively differentiates between black cumin, corn, and soybean
oils. Minitab software yielded the most significant PCA results, showing a data variation distribution
of 85.7% for Principal Component 1 and 10% for Principal Component 2. Furthermore, the HCA
analysis corroborated the PCA findings, revealing similar sample groupings. The dendrogram
produced by Minitab showcased the most distinct sample groupings. Overall, the combination of UVvis spectrophotometry and PCA can effectively differentiate black cumin oil from corn and soybean
oil.
Keywords: Chemometrics; Black Cumin Oil; Uv-Vis Spectrophotometry

*Corresponding author:

Name
: Triyadi Hendra Wijaya
Email
: triyadihendrawijaya@unsoed.ac.id
Address : Faculty of Health Sciences, Jenderal Soedirman University, Purwokerto, Central Java, Indonesia

137
Submitted: 10-02-2025; Received in Revised Form: 03-05-2025; Accepted: 03-06-2025

Wijaya et al.

INTRODUCTION
Currently, numerous health issues frequently arise, leading to an increasing
consideration of vegetable oil as a potential solution due to its effectiveness in addressing
various diseases (Calugar, Grozea, and Butnairu, 2024). However, the adulterating of
vegetable oil has become a significant problem in recent years (Bian et al., 2022). A common
practice in Indonesia involves substituting high-quality vegetable oils with lower-cost
alternatives that are more readily available (Zulmi et al., 2022). One such oil that is often
adulterated due to its relatively high price is black cumin oil, which can reach Rp1120/ml,
making it expensive for just 1 ml. The adulteration of black cumin oil with other vegetable
oils can diminish its health benefits. Black cumin oil is known for its various health
advantages, including antitumor, anti-inflammatory, central nervous system depressant,
analgesic, and hypoglycemic effects. Despite this, the analysis of black cumin oil
adulterating has not been extensively studied using UV-Vis spectrophotometry. Previous
analyses have been conducted by Rohman & Ariani (2013) utilizing a combination of FTIR
spectroscopy and PLS chemometrics methods, as well as using the UHPLC-Q-TOF–MS/MS
method (Kotecka-Majchrzak et al., 2020). Therefore, there is a pressing need for a more
rapid and sensitive instrumental method to analyze vegetable oil adulteration (Bian et al.,
2022).
Vegetable oil adulterating analysis has been the subject of extensive research. One
effective approach involves the integration of spectroscopy with chemometric methods.
Notably, studies by Ali et al. (2018) explored the combination of Fluorescence Spectroscopy
and Principal Component Analysis (PCA). Additionally, research conducted by Han et al.
(2020), Dou et al. (2023), and Sufriadi et al. (2023) employed a combination of Fourier
Transform Infrared (FTIR) spectroscopy and chemometric techniques, demonstrating
successful application in the classification and quantification of adulterate oils.
Furthermore, Balbino et al. (2022) utilized Near-Infrared (NIR) spectroscopy alongside
chemometric methods, showing that this combination effectively distinguished pure oil
from alduterate oil. The analysis of oil adulterating has also incorporated the combination
of Gas Chromatography-Mass Spectrometry (GC-MS), phytosterol profile determination,
and chemometrics, with findings indicating that these methods are applicable for
adulterating analysis (Yang et al., 2024).
Research on the analysis of vegetable oil adulterated has often relied on
instruments that are not widely available in all laboratories due to equipment limitations
(Andriyani et al., 2019). Therefore, there is a need to develop a more accessible method for
analyzing black cumin oil adulterating. One promising approach is the use of UV-Vis
spectrophotometry (Jiang et al., 2015). However, when analyzing mixtures of different
vegetable oils with UV-Vis spectrophotometry, overlapping spectra can occur because they
share the same ultraviolet-active group (Bian et al., 2022). To address this issue,
multivariate chemometric analysis is necessary to interpret the UV-Vis spectra accurately.
Principal Component Analysis (PCA) and Hierrarchical Cluster Analysis (HCA) is one such
chemometric technique that can be effectively employed in the study of vegetable oil
adulterating(Jiang et al., 2015 ; Granato et al., 2017).
Principal Component Analysis (PCA) is a statistical technique utilized to simplify
complex data by grouping samples (Damayanti et al., 2020; Guntarti et al., 2020). This
method has been effectively integrated with UV-Vis spectrophotometry to analyze olive oil
adulterating, yielding results that successfully differentiate oil mixtures based on the first
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Analysis of Black Cumin Oil...

three principal components (Jiang et al., 2015). Hierarchical Cluster Analysis (HCA) further
enhances data interpretation by grouping samples according to their similarities, creating
a hierarchical structure illustrated in a dendrogram. This dendrogram elucidates the
relationships among samples and the groups formed. HCA can classify oil samples based on
their chemical composition, distinguish between different oil types, and identify patterns
within oil composition (Granato et al., 2017). In this study, the integration of these
methodologies is expected to effectively differentiate between black cumin oil, corn oil, and
soybean oil, thereby providing optimal results for identifying instances of black cumin oil
adulterating.
METHODS
Sampling
The sampling method employed in this study is consecutive sampling. This
approach continues until the required number of samples is achieved. A total of 8 samples
of black cumin oil from various brands available in Purwokerto and/or through electronic
commerce were collected. Additionally, the standard oils used in this study include black
cumin oil, corn oil, and soybean oil, all of which possess a Certificate of Analysis (CoA).
Sample Characteristics Observation
Observation of sample characteristics involves assessing color, odor, texture, and
free fatty acid content. Black cumin oil, corn oil, and soybean oil were evaluated
organoleptically for their respective color, odor, and texture. The observed characteristics
were then compared to reference values for each oil. To determine the acid number,
soybean oil samples were analyzed. A 3-gram sample was weighed and placed in a 250 mL
Erlenmeyer flask, followed by the addition of 50 mL of 95% ethanol. After mixing, 3 to 5
drops of phenolphthalein (PP) indicator were added. The resulting solution was titrated
with a 0.1 N sodium hydroxide standard solution until a pink color appeared. Following the
titration, the free fatty acid content was calculated using the formula from Badan
Standarisasi Nasional (1998).
Free fatty acids (mg KOH/g) = (V × T × 56,1)/m
Details :
V = volume of NaOH required for imaging, in mL
T = normality of NaOH
m = sample weight, in grams
Absorbance Measurement and Wavelength Spectrum Profile Reading of 100% Oil
Black cumin, corn, and soybean oils, each weighing up to 20 mg, were dissolved in
100 mL of n-Hexane to achieve a concentration of 200 ppm. The resulting solution was then
measured for absorbance using UV-Vis spectrophotometry over a wavelength range of 200
to 800 nm (Amereih et al., 2014).

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Making Oil Mixtures
The vegetable oil mixture was prepared at concentrations of 20%, 33%, 40%, and
66% (Amereih et al., 2014), using the following formula for comparison:
No.
Sample
1
2
3
4
5
6
7
8
9

Table 1. Comparison of Oil Mixtures
Concentration
Black Cumin Oil
Corn Oil
(%)
(mL)
(mL)
Black Cumin 66% (A)
10
5
Black Cumin 66% (B)
10
0
Corn 66%
5
10
Corn 40%
10
10
Soybean 40%
10
5
Black Cumin 33%
5
5
Corn 33%
0
5
Soybean 33%
0
10
Black Cumin 20%
5
10

Soybean Oil
(mL)
0
5
0
5
10
5
10
5
10

Wavelength Spectrum Measurement of Oil and Sample Mixtures
Black cumin oil samples collected in Purwokerto were measured at 10 mg and
dissolved in 25 mL of n-Hexane, resulting in a solution with a concentration of 400 ppm.
Additionally, a Quality Control solution was prepared by mixing the market sample with
the previously created oil mixtures. Two Quality Control solutions were developed: Quality
Control 1, which contained 8 mL of market sample E and 8 mL of 66% black cumin oil (A),
and Quality Control 2, which included 8 mL of market sample E and 8 mL of 40% soybean
oil. The sample solution with a concentration of 400 ppm, along with the Quality Control
solutions and oil mixtures at concentrations of 20%, 33%, 40%, and 66%, were analyzed
for absorbance using UV-Vis spectrophotometry across a wavelength range of 200 - 800
nm (Amereih et al., 2014).
Chemometric Analysis
The absorption measurement data from all samples, obtained through UV-Vis
spectrophotometry at wavelengths ranging from 200 to 300 nm, were converted into CSV
format. This CSV file was subsequently utilized for multivariate analysis employing
Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) with Minitab
22 Trial, R Studio 4.3.3, and Metaboanalyst 6.0 software. The incorporation of three
different software platforms in this study aims to evaluate the similarities and differences
in the analysis results across these tools.
RESULT AND DISCUSSION
Sample Characteristics
The results of observations of sample characteristics can be seen in Table 2. Black
cumin oil is yellowish-brown, has a distinctive fresh cumin aroma, and features a slightly
thick texture. These observations regarding color, odor, and texture align with findings
from Azzahra et al. (2018). The results are consistent with those reported by both Azzahra
et al. (2018) and Farhan et al. (2021). However, there are variations in the acid number:
Azzahra et al. (2018) report it as 15.69 mg KOH/g, while Farhan et al. (2021) state it as 14.3
mg KOH/g. The acid number of black cumin oil, according to the Certificate of Analysis, is
14.18 mg KOH/g.
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Table 2. Sample Characteristics
Characteristic
No

Sample

1
2
3

Color

Black Cumin Oil
Corn Oil
Soybean Oil

Tawny
Yellow
Clear
yellowish

Smell

Texture

Fresh cumin flavor
Typical weak corn
Soybean specialty

A little thick
A little thick
Liquid

Acid Number
(mg KOH/g)
14.18
0.16
2.46

Corn oil is characterized by a light yellow color, a faint corn aroma, and a slightly
thick texture, consistent with SNI 01-3394-1998 standards. According to the Certificate of
Analysis, the acid number of corn oil is 0.16 mg KOH/g. In contrast, soybean oil is yellow,
has a distinct soybean scent, and a liquid texture, with an acid number of 2.46 mg KOH/g.
These findings align with Puspita (2016), who noted that the maximum acceptable acid
number is 3 mg KOH/g.
Black Cumin, Corn and Soybean Oil Spectrum Profile Analysis 100%
Black cumin oil exhibits two characteristic peaks at wavelengths of 250 and 258
nm, as shown in Figure 1 (a). In contrast, corn oil displays two peaks at wavelengths of 268
and 279 nm, depicted in Figure 1 (b). Additionally, soybean oil has a single characteristic
peak at a wavelength of 230 nm, illustrated in Figure 1 (c).
2,728

2,000

2,000

Abs.

Abs.

2,728

1,000

1,000

0,000

0,000
-0,248
200,00

-0,248
200,00

400,00

600,00

400,00

600,00

800,00

nm.

800,00

nm.

(a)

(b)
0,186

0,150

Abs.

0,100

0,050

0,000

-0,050
-0,060
213,63

240,00

260,00
nm.

280,00

299,62

(c)
Figure 1. Spectrum Profile of 100% Oil (a) Black Cumin Oil (b) Corn Oil (c) Soybean Oil

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Oil Mixture Spectrum Profile Analysis
Almost all samples exhibit two typical peaks in the wavelength range of 250-260
nm, similar to the absorption peaks of 100% black cumin oil, except for the 33% corn and
soybean oils. This discrepancy arises because these two concentration series do not contain
100% black cumin oil. For the other oil concentration series, the peaks primarily differ in
intensity. At wavelengths of 220-240 nm, both the 33% corn and soybean oils show typical
peaks characteristic of 100% soybean oil. The spectrum profile of the oil mixture does not
reveal any typical peaks associated with 100% corn oil at 265-270 nm. However, a peak
resembling that of 100% corn oil appears in the 250-260 nm range. This variation in the
typical peak is likely due to the oil mixture containing not only 100% corn oil but also 100%
black cumin and soybean oils, consistent with the formulated mixture (Uncu & Ozen, 2019).
The spectrum profile of the oil mixture is illustrated in Figure 2.
2,534

Details :

Abs.

2,000

1,000

0,000
-0,161
200,00

220,00

240,00

260,00

280,00

300,00

nm.

Figure 2. Oil Mixture Spectrum Profile

Analysis of Black Cumin Oil Spectrum Profile in the Market
The black cumin oil samples available on the market, as well as those used for
quality control (QC), exhibit a diverse range of spectral shapes and intensities in the
wavelength range of 200-800 nm, as shown in Figure 3. Market samples A, B, C, and D
demonstrate peak absorption between 220-310 nm, while market sample E peaks at 240280 nm. Market samples F, G, and H show peak absorption at 230-300 nm. In contrast, QC
samples 1 and 2 have peak absorption at 250-260 nm. Variations in peak positions and the
potential absence of a discernible absorption peak may result from differences in quality,
variety, and geographic origin of the samples analyzed (Uncu & Ozen, 2019).
3,316

Details :

3,000

Abs.

2,000

1,000

0,000

-0,501
200,00

220,00

240,00

260,00

280,00

300,00

nm.

Figure 3. Spectrum Profile of Black Cumin Oil in the Market

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Chemometric Analysis
Wavelength Selection
The optimal wavelength selection process should consider the 100% oil profile
(Figure 1) and the solvent's UV cut-off (Figure 4). To ensure accurate data, absorbance
values recorded between 200 and 210 nm must be excluded (Raja and Barron 2024). The
selected wavelengths indicate that absorbance values in the 220-290 nm range can yield
effective grouping data.
1,636
1,500

Abs.

1,000

0,500

0,000
-0,109
200,00

400,00

600,00

800,00

nm.

Figure 4. UV-Cut off N-hexane

Principal Component Analysis
Principal Component Analysis (PCA) is an effective method for reducing data
dimensionality by identifying principal components that encapsulate the greatest
variability of the original dataset. This PCA procedure employs an operation matrix to
transform the data into a series of new components that are mutually orthogonal. The
outcomes of the PCA are represented in a score plot, which delineates the similarities and
differences among samples, and a loading plot, which elucidates the variables that drive the
observed differences. In the context of oil mixture analysis, PCA can be utilized to identify
patterns in oil composition, differentiate between various oil types, and evaluate other
parameters within the oil composition (Arina, Shiyan, and Suprayetno, 2022; Andriansyah
et al., 2022; Beattie and Esmonde-White, 2021).
In this study, Principal Components 1 and 2 were selected, accounting for 95.7% of
the data variability. The results illustrated in Figure 5 depict the sample replication points.
The score plot indicates distinct groupings based on the concentration of the oil mixture
and its components. Notably, the 100% black cumin oil sample is distinguishably separated
from the oil mixture group, as well as from the 100% corn and soybean oil samples.
Conversely, the 100% corn and soybean oils occupy overlapping areas within the plot,
making visual differentiation challenging. This difficulty is likely attributable to the similar
physical and chemical properties of corn and soybean oils (Rohman et al., 2021). A
significant factor influencing the results of the PCA analysis is the concentration of free fatty
acids. The findings indicate that the free fatty acid values of corn and soybean oils are
closely aligned, resulting in their proximity on the score plot. In contrast, black cumin oil is
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positioned distinctly away from the other two. These results underscore the considerable
impact of chemical characteristics, such as free fatty acids, on chemometric analysis
(Baskara, Lelono, and Widodo, 2016).

(a)

(b)

(c)
Figure 5. Score Plot of PCA Analysis on Software (a) Minitab (b) R Studio (c) Metaboanalyst

In the oil mixture sample, the location of each sample plot corresponds closely to
the dominant component of each mixture. The plot for the 66% black cumin oil sample is
situated near the 100% black cumin oil point, as 100% black cumin oil is the predominant
component in its formulation. Conversely, the 33% soybean and corn oil plot is positioned
near the 100% soybean and corn oil point, reflecting its composition, which is primarily
influenced by 100% soybean or corn oil. The plots for the 40% corn and soybean oil, 66%
corn, and 33% black cumin oil are located between the groups of black cumin, corn, and
100% soybean oil, indicating that none of these compositions are significantly dominant.
The market samples exhibited no discernible grouping. Market sample B is
identified as the closest to 100% black cumin oil, based on its similar physical and chemical
properties. In contrast, market sample A belongs to the oil mixture group, positioned
adjacent to the sample containing 40% corn and soybean oil. It is estimated that market
sample A shares comparable physical and chemical properties with the 40% corn and
soybean oil. Meanwhile, market samples C, D, E, F, G, and H are located in regions that are
not adjacent to either the oil mixture group or the 100% black cumin oil. Consequently, the
oil content of samples other than A and B remains unknown, as indicated by the score plot,
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which does not position them near the oil mixture group, 100% black cumin oil, or 100%
corn and soybean oil. In the quality control (QC) sample, a grouping is observed with 66%
black cumin oil samples A and B, attributed to the QC sample's composition of 66% black
cumin oil, resulting in similarities in their physical and chemical properties (Rafi et al.,
2018; Batubara et al., 2022; Rohman et al., 2021). Therefore, the developed PCA model can
effectively differentiate between groups of oil mixtures, including black cumin oil, corn, and
100% soybean oil.
The findings of the study indicated that there were no significant differences in the
PCA analysis results among the three software programs utilized. Variations were observed
primarily in the presentation of the score plot data. In Minitab, the separation between
samples is readily discernible visually, aided by the inclusion of distinct symbols for each
sample, which facilitates result interpretation. Conversely, in R Studio, the symbols for each
sample are less visually accessible, making analysis more challenging. In Metaboanalyst,
the color coding of samples does not exhibit substantial differences, potentially leading to
inaccuracies in visual analysis of the PCA results.
Hierarchial Clustering Analysis
Hierarchical Cluster Analysis (HCA) is a statistical method that groups samples
according to their similarity, resulting in a hierarchical structure represented by a
dendrogram. This dendrogram illustrates the relationships among samples and the
resulting groups. HCA can be employed to classify oil samples based on their chemical
composition, differentiate various types of oil, or identify patterns in oil composition
(Granato et al., 2017).
The HCA clustering results presented in Figure 6 effectively distinguished between
the oil mixture cluster and the 100% oil category. Among the eight market samples
analyzed, this method identified that market sample B exhibited a higher similarity to
100% black cumin oil, while market sample A showed a notable similarity to the 40% corn
and soybean oil mixture. One of the chemical components influencing the HCA analysis is
the concentration of free fatty acids. The results of this study indicate that the free fatty acid
values of corn and soybean oils are relatively similar, which is reflected in their close
positioning on the dendrogram. In contrast, black cumin oil is positioned significantly
further away from these two oils. These findings suggest that chemical characteristics, such
as free fatty acids, have a considerable impact on chemometric analysis (Baskara, Lelono,
and Widodo, 2016).
Details :

(a)

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(b)

(c)
Figure 6. HCA Analysis Dendogram in Software (a) Minitab (b) R Studio (c) Metaboanalyst

The results of the HCA analysis demonstrated significant differences among the
three software programs utilized. Specifically, there were instances of multiple replications
in samples that were not grouped. This was particularly evident in the Metaboanalyst
software, which revealed four samples where the three replications remained ungrouped.
In contrast, the Minitab and R Studio software did not exhibit similar findings.
This study was conducted with qualitative chemometric analysis using Principal
Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to see the grouping of
oil mixtures between black cumin oil, corn oil and soybean oil. Chemometric analysis using
PCA and HCA is appropriate, but other chemometric analysis can be used to complement
the results of this study. Other chemometric analysis include LDA (Linear Discriminant
Analysis), SVM (Support Vector Machines and SIMCA (Soft Independent Modeling of Class
Analyze).
CONCLUSION
The analysis in this study concluded that combining UV-vis spectrophotometry with
Principal Component Analysis (PCA) effectively distinguishes black cumin oil from corn
and soybean oil. This is evidenced by the results of chemometric analysis, which
demonstrate distinct groupings of oil samples. These groupings are based on the
similarities in their physical and chemical properties. Additionally, the three software
programs used for the chemometric analysis did not reveal significant differences in the
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PCA results, as illustrated in the scree plot and score plot curves. The only variation
observed was in the presentation of the score plot data.
ACKNOWLEDGMENT
The author would like to express his gratitude for the funds provided by LPPM Jenderal
Soedirman University through the Competency Improvement Research Scheme with
Contract Number 26.712 /UN23.35.5/PT.01/II/2024
AUTHOR CONTRIBUTION
THW: Concepts or ideas; design; definition of intellectual content; literature search;
experimental studies; data analysis; manuscript preparation.
YNA: literature search; experimental studies; data analysis; manuscript preparation.
MSF: Concepts or ideas; design; definition of intellectual content; literature search;
experimental studies; data analysis; manuscript preparation.
NEE : Concepts or ideas; design; definition of intellectual content; literature search.
MWSP : Concepts or ideas; design; definition of intellectual content; literature search.
ETHICS APPROVAL
None
CONFLICT OF INTEREST (If any)
None to declare
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