Statistical Analysis of Agarwood Oil Compounds based on GC-MS Data

Haron, Mohamad Hushnie; Taib, Mohd Nasir; Ismail, Nurlaila; Ali, Nor Azah Mohd; Tajuddin, Saıful Nizam

doi:10.1109/icsgrc.2018.8657571

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…The targeted species are focused to only Aquilaria Malaccensis species but with varies oil volume, origin and age. FRIM and BARCE use gas chromatography-mass spectrometry (GC-MS) apparatus to obtain their chemical substances [9]. There are 660 samples starting from low, medium low, medium high and high different qualities of Agarwood oils.…”

Section: Sample Acquisitionmentioning

confidence: 99%

Pre-processing technique of Aquilaria species from Malaysia for four different qualities

Mohd Huzir,

Hasnu Al-Hadi,

Zaidi

et al. 2024

Bulletin EEI

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The paper interprets data distribution by using boxplot pre-processing in classify the quality of Agarwood oil for eleven chemical substances into four different qualities. The varieties usage of Agarwood oil makes it considered as an expensive and valuable product on the essential oil market. Perfumes, fragrances, incense, aromatherapy, and traditional medicine are the most popular Agarwood oil applications. However, the classification of Agarwood oil grades does not yet have standard grading method. This because it has been graded manually into different qualities by using human sensory evaluation. Boxplot analysis involving eleven chemical subtances that will be focusing in this study by concerned the quality for low, medium low, medium high and high. ɤ-eudesmol, ar-curcumene, β-dihydro agarofuran, ϒ-cadinene, α-agarofuran, allo aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin compounds are the selected significant compounds that represent the input for boxplot. Agarwood oil consist 660 data samples from low, medium low, medium high, and high quality. The result in this study showed that the four selected significant compounds (ɤ-eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin) are important as a marker for Agarwood oil quality classification. The identification of chemical substances on high quality done as reference for future research studies.

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Section: Sample Acquisitionmentioning

confidence: 99%

Pre-processing technique of Aquilaria species from Malaysia for four different qualities

Mohd Huzir,

Hasnu Al-Hadi,

Zaidi

et al. 2024

Bulletin EEI

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“…Collaborative efforts involving academia and industry partners have sought to identify crucial marker compounds responsible for Agarwood's unique aroma. Research findings advocate for the quality classification of Agarwood or Aquilaria based on their chemical compounds abundance with a primary focus on φ-eudesmol, α-agarofuran, β-agarofuran and 10-epi-φ-eudesmol known as the key contributors to its robust woody aroma [13], [14].…”

Section: Introductionmentioning

confidence: 99%

“…Agarwood oil's market value is determined by its inherent quality with higherquality variants commanding premium prices [17], [18]. As previously stated, human sensory panels are used to evaluate the quality of Agarwood oil, a practice fraught with inefficiencies, fatigue and time consumption [13], [14], [19]. Consequently, doubts may arise and the grading process varies between exporting countries [7], [20].…”

Section: Introductionmentioning

confidence: 99%

Agarwood oil quality identification using artificial neural network modelling for five grades

Mohd Huzir,

Tajuddin,

Mohd Yusoff

et al. 2024

IJECE

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Agarwood (Aquilaria Malaccensis) oil stands out as one of the most valuable and highly sought-after oils with a hefty price tag due to its widespread use of fragrances, incense, perfumes, ceremonial practices, medicinal applications and as a symbol of luxury. However, nowadays the conventional method that rely on color alone has its limitations as it yields varying results depending on individual panelists' experiences. Hence, the quality identification system of Agarwood oil using its chemical compounds had been proposed in this study to enhance the precision of the Agarwood oil grades thus addressing the shortcomings of traditional methods. This study indicates that the primary chemical compounds of Agarwood oil encompass ɤ-Eudesmol, ar-curcumene, β-dihydroagarofuran, ϒ-cadinene, α-agarofuran, allo-aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran and dihydrocollumellarin. This study employed artificial neural network analysis with the implementation of Levenberg-Marquardt algorithm to identify the Agarwood oil grades. The study's findings revealed that this modeling system of five grades got 100% accuracies with mean square error of 0.14338×10-08. Notably, this lowest mean square error (MSE) value falls within the best hidden neuron 3. These study outcomes play a pivotal role in highlighting the Levenberg Marquardt- artificial neural network (LM-ANN) modeling that contribute to the successful of Agarwood oil quality identification using its chemical compounds.

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“…Agarwood is resin-impregnated heartwood. All parts of the agarwood plant has its own use [4]. For example, tree trunks and branches can produce quality powder and in addition the essential oils produce from the stems [4].…”

Section: Introductionmentioning

confidence: 99%

“…All parts of the agarwood plant has its own use [4]. For example, tree trunks and branches can produce quality powder and in addition the essential oils produce from the stems [4]. Agarwood is used in incense, perfumery, medicine and ceremonies by numerous cultures and religious [3], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Modeling of agarwood oil compounds based on linear regression and ANN for oil quality classification

Mahabob

Yusoff

Amidon

et al. 2021

IJECE

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<span>Agarwood oil is in increasing demand in Malaysia throughout the world for use in incense, traditional medicine, and perfumes. However, there is still no standardized grading method for agarwood oil. It is vital to grade agarwood oil into high and low quality so that both qualities can be properly differentiated. In the present study, data were obtained from the Forest Research Institute Malaysia (FRIM), Selangor Malaysia and Bioaromatic Research Centre of Excellence (BARCE), Universiti Malaysia Pahang (UMP). The work involves the data from a previous researcher. As a part of on-going research, the stepwise linear regression and multilayer perceptron have been proposed for grading agarwood oil. The output features of the stepwise regression were the input features for modeling agarwood oil in a multilayer perceptron (MLP) network. A three layer MLP with 10 hidden neurons was used with three different training algorithms, namely resilient backpropagation (RBP), levenberg marquardt (LM) and scaled-conjugate gradient (SCG). All analytical work was performed using MATLAB software version R2017a. It was found that one hidden neuron in LM algorithm performed the most <span>accurate result in the classification of agarwood oil with the lowest mean squared error (MSE) as compared to SCG and RBP algorithms. The findings in this research will be a</span> benefit for future works of agarwood oil research areas, especially in terms of oil quality classification.</span>

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Statistical Analysis of Agarwood Oil Compounds based on GC-MS Data

Cited by 7 publications

References 6 publications

Pre-processing technique of Aquilaria species from Malaysia for four different qualities

Pre-processing technique of Aquilaria species from Malaysia for four different qualities

Agarwood oil quality identification using artificial neural network modelling for five grades

Modeling of agarwood oil compounds based on linear regression and ANN for oil quality classification

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