Agarwood Oil Quality Research Articles

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

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.

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

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.

Accurate Agarwood Oil Quality Determination: A Breakthrough With Artificial Neural Networks and the Levenberg- Marquardt Algorithm

Accurate Agarwood Oil Quality Determination: A Breakthrough With Artificial Neural Networks and the Levenberg- Marquardt Algorithm

Stepwise regression of agarwood oil significant chemical compounds into four quality differentiation

<span>This paper gives precise summary on the application of stepwise regression model based upon the pre-process analysis of boxplot for four chemical compounds into four different qualities of agarwood oil. In the global market, agarwood oil is acknowledged as a pricey and valuable nature product owing to its benefits. Unfortunately, there is no standard grading method for agarwood oil grade classification. Intelligent model in grading the quality of agarwood oil is crucial as one of the efforts to classify the agarwood quality. The main model chosen in this study is stepwise regression by concerned specific parameter which is the value of correlation coefficient, R2. To achieve this goal, four out of eleven significant compounds of agarwood oil that consist of 660 data samples from low, medium low, medium high and high quality are representing the input. The independent variables are X1, X2, X3 and X4 which refer to the ɤ-Eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran and dihydrocollumellarin compounds, respectively. MATLAB software version r2015a has been chosen as the simulation platform for this research work. The result showed that the stepwise regression model has a correlation coefficient of 0.756 and p-value less than 0.05 significance level which successfully passed the performance criteria toward regression value.</span>

The k-Nearest Neighbor modelling by varying Mahalanobis and Correlation in distance metric for agarwood oil quality classification

Agarwood oil is well known for its unique scent and has many usages; as an incense, as ingredient in perfume, is burnt during religious ceremonies and is used in traditional medical preparation. Therefore, agarwood oil has high demand and is traded at different price based on its quality. Basically, the oil quality is classified by using physical properties (odor and color) and this technique has several problems: not consistent in term of accuracy. Thus, this study presented a new technique to classify the quality of agarwood oil based on chemical properties. The work focused on the k-Nearest Neighbor (k-NN) modelling by varying Mahalanobis and Correlation in distance metric for agarwood oil quality classification. It involved of 96 samples of agarwood oil, data pre-processing (data randomization, data normalization, and data division to testing and training datasets) and the development of k-NN model. The training dataset is used to train the k-NN model, and the testing dataset is used to test the developed model. During the model development, Mahalanobis and Correlation are varied in k-NN distance metric. The k-NN values are ranging from 1 to 10. Several performance criteria including resubstitution error (closs), cross-validation error (kloss) and accuracy were applied to measure the performance of the built k-NN model. All the analytical work was performed via MATLAB software version R2020a. The result showed that the accuracy of Mahalanobis distance metric has a better performance compared to Correlation from k=1 to k=5 with the value of 100.00%. This finding is important as it proved the capabilities of k-NN modelling in classifying the agarwood oil quality. Not limited to that, it also contributed to the agarwood oil research area as well as its industry.

A Ppreliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading

Essential <span>oils extracted from trees has various usages like perfumes, incense, aromatherapy and traditional medicine which increase their popularity in global market. In Malaysia, the recognition system for identifying the essential oil quality still does not reach its standard since mostly graded by using human sensory evaluation. However, previous researchers discovered new modern techniques to present the quality of essential oils by analyse the chemical compounds. Agarwood essential oil had been chosen for the proposed integrated intelligent models with the implementation of k-nearest neighbor (k-NN) due to the high demand and an expensive natural raw world resource. k-NN with Euclidean distance metrics had better performance in terms of its confusion matrix, sensitivity, precision accuracy and specificity. This paper presents an overview of essential oils as well as their previous analysis technique. The review on k-NN is done to prove the technique is compatible for future research studies based on its performance.</span>

The significance of artificial intelligent technique in classifying various grades of agarwood oil

Agarwood oil quality is often separated into two or three categories. This makes classifying agarwood oil quality using current methods difficult. Current approaches rely solely on human perception to determine the quality of agarwood, whether in raw material or oil. This technique has other undesirable implications. It can affect the human sensory system, particularly the eyes and nose. Categorization takes time, which is a considerable expense to succeed in this method. As a result, a new classification system should be devised. The chemical components in agarwood oil are used to classify it in this study. In this study, samples with preprocessing data from two to five quality levels were used. The purpose is to categorize this data based on its qualities and analyze whether this new quality group is acceptable. The K-nearest neighbours (KNN) approach was used to classify all samples and their properties for this dataset. All samples may be correctly classified by grade without any errors. This shows the chemical compound-based classification of agarwood oil can be retained. With these findings, future agarwood oil research may focus on building a new classification.

Determination of Substantial Chemical Compounds of Agarwood Oil for Quality Grading

Agarwood is a resin saturated heartwood producing its ownessential oil. This oil comprises of a complex mixture of chromone derivatives, oxygenated sesquiterpenes and sesquiterpene hydrocarbons. This mixture has a heavy woody scentand is one of the contributors to the Agarwood oil quality. In this paper, a study that focuses on the approach to select the substantial chemical compounds for Agarwood quality grading was carried out. GC-MS analysis was used to extract the chemical compounds from the Agarwood oil. The data were then preprocessed using techniques such as missing values ratio, natural logarithm and min. max. normalization. Next, synthetic data were generated using MUNGE to fulfil the passing condition of sampling adequacy test. To determine the substantial compounds, PCA and Pearson’s correlation were used. This approach was successful in determining three substantial compounds namely β-agarofuran, αagarofuran and 10-epi-γ-eudesmol. These substantial chemical compounds will be used later to predict the quality of Agarwood oil.

Agarwood Oil Quality Classification based on Aromatic Alkane using Portable FTIR Spectroscopy Technique

Agarwood Oil Quality Classification based on Aromatic Alkane using Portable FTIR Spectroscopy Technique

SVM Modelling of Agarwood Oil Quality Grading using Radial Basis Function (RBF) and Sequential Minimal Optimization (SMO) Learning Algorithms

Agarwood Oil also known as Gaharu Oil is an expensive oil with extreme demand in the world trading especially in Japan, China and the Middle East countries. Currently, the grading of the agarwood oil quality only can be done by trained human graders by physically checked the color, odour and the resin content. However, this technique is limited due to human body limitation and not too accurate as it is supposed to be. To improve the problem faced by the existing method, the grading technique using Sequential Minimal Optimization (SMO) and Radial Basis Function (RBF) in Support Vector Machine (SVM) was conducted. The works involved of data collection, data pre-processing, SVM model development and testing of the developed SVM model. The finding showed that both RBF and SMO successfully can grade the agarwood oil quality due to their accuracies is above than 80 % and error rate or MSE is close to 0. Thus, the technique presented in this paper proved its capability in overcoming the constraint of human trained grader and benefit as well as contribute to the agarwood oil industry especially its oil quality grading.

Grading Of Agarwood Oil Quality Based On Its Chemical Compounds Using Self Organizing Map (SOM)

Grading Of Agarwood Oil Quality Based On Its Chemical Compounds Using Self Organizing Map (SOM)

Incubation of Aquilaria subintegra with Microbial Culture Supernatants Enhances Production of Volatile Compounds and Improves Quality of Agarwood Oil

Incubation with microbial culture supernatants improved essential oil yield from Aquilaria subintegra woodchips. The harvested woodchips were incubated with de man, rogosa and sharpe (MRS) agar, yeast mold (YM) agar medium and six different microbial culture supernatants obtained from Lactobacillus bulgaricus, L. acidophilus, Streptococcus thermophilus, Lactococcus lactis, Saccharomyces carlsbergensis and S. cerevisiae prior to hydrodistillation. Incubation with lactic acid bacteria supernatants provided higher yield of agarwood oil (0.45% w/w) than that obtained from yeast (0.25% w/w), agar media (0.23% w/w) and water (0.22% w/w). The composition of agarwood oil from all media and microbial supernatant incubations was investigated by using gas chromatography-mass spectrometry. Overall, three major volatile profiles were obtained, which corresponded to water soaking (control), as well as, both YM and MRS media, lactic acid bacteria, and yeast supernatant incubations. Sesquiterpenes and their oxygenated derivatives were key components of agarwood oil. Fifty-two volatile components were tentatively identified in all samples. Beta-agarofuran, α-eudesmol, karanone, α-agarofuran and agarospirol were major components present in most of the incubated samples, while S. cerevisiae-incubated A. subintegra provided higher amount of phenyl acetaldehyde. Microbial culture supernatant incubation numerically provided the highest yield of agarwood oil compared to water soaking traditional method, possibly resulting from activity of extracellular enzymes produced by the microbes. Incubation of agarwood with lactic acid bacteria supernatant significantly enhanced oil yields without changing volatile profile/composition of agarwood essential oil, thus this is a promising method for future use.

Statistical analysis of agarwood oil compounds in discriminating the quality of agarwood oil

Agarwood oil is one of the plant’s primary metabolites. Products produced by agarwood oil are very valuable and have a great demand in many fragrance as well as aromatic industries. Enhancing and improving the discrimination technique is the main aim to determine or grade the good quality of agarwood oil. In this paper, all statistical works were performed via SPSS software. Two parameters involved are abundance of compound (%) and quality of the agarwood oil either low or high quality. The result showed that, there is clear significant normality test depends on the parameters through the boxplot and Quantile-Quantile (Q-Q) plot for data distribution. Then, Kolmogorov-Smirnov (K-S), non-parameter test and hypothesis were covered in normality test. The techniques proved their capabilities in statistical analysis for agarwood oil compounds and confirmed that the data is suitable for further work especially for classification.

Agarwood oil quality classifier using machine learning

Agarwood oil is known as one of the most expensive and precious oils being traded. It is widely used in traditional ceremonies and religious prayers. Its quality plays an important role on the market price that it can be traded. This paper proposes on a proper classification method of the agarwood oil quality using machine learning model k-nearest neighbour (k-NN). The chemical compounds of the agarwood oil from high and low quality are used to train and build the k-NN classifier model. Correlation reduce the dimension of the data before it is being fed into the model. The results show a very high accuracy (100%) model trained and can be used to classify the agarwood oil quality accurately. Keywords: agarwood oil; k-nearest neighbours; quality; machine learning.

A Study on the Application of Electronic Nose Coupled with DFA and Statistical Analysis for Evaluating the Relationship between Sample Volumes versus Sensor Intensity of Agarwood Essential Oils Blending Ratio

The exquisite agarwood oils are primary used for perfumery industries either as pure essential oils or in a perfume base. Commonly, Agarwood oils are extracted from low grade 100% agarwood chips via distillation processes and the extracted oil is called as pure agarwood essential oil which containing 100% of extracted material. In perfumery industry, the agarwood pure oils are often blend with other essential oils such as geranium, sandalwood, gurjum balsam, jasmine and Ylang ylang to create rich, complex and pleasant oils compared to pure Agarwood oils smell alone that may not suit all users preferences. To dates, agarwood oil quality assessment is typically carried out manually via human olfactory system which produces different results and inconsistency from traders and buyers. From the results, multiple linear regression analysis used to run the multiple regression prediction models using combination of 11 sensors shown better results by increasing the R2 value from 0.674 to 0.915 and the RMSE value from 14.65% to 6.80% compared to single regression prediction models using sensor LY2/G. The sensors intensity values from multiple sensors are showing a strong correlation to the volume of the B1 in the blended samples (M11~M20) as the ratio of B1 is increased.

Major Volatile Chemical Compounds of Agarwood Oils from Malaysia Based on Z-Score Technique

Agarwood oil has been widely used in a number of different applications, including its use as a perfumery ingredient in incense products and a therapeutic agent in traditional medicines [1–8]. The oil itself can be obtained from the scented wood of plants belonging to the Aquilaria species (Thymelaeaceae) [1]. Agarwood oil consists of a complex mixture of chemicals, including sesquiterpenes, oxygenated sesquiterpenes, and chromone derivatives 7–9�. These compounds can be used to differentiate agarwood oil into different grades and therefore have a significant impact on its quality and commercial value [8]. Many factors can influence the quality of agarwood oil, but the two most important are the chemical composition of the oil and the temperature used during the extraction of the oil from plant material [8, 10].

Mathematical modeling for extraction of essential oil from Aquilaria crassna by hydrodistillation and quality of agarwood oil

Agarwood oil is widely used in medicine, perfume and cosmetic industries due to its unique properties and high medicinal value. However, knowledge on distillation process and standard for quality of the oil are far from sufficient. In this work, agarwood oil was hydrodistilled at various temperatures in order to study the distillation kinetics and quality of the oil. Higher oil yield and better quality of the oil were obtained at higher operating temperature. The mechanism of oil hydrodistillation consisted of two stages: washing stage and diffusion stage. The diffusion stage was the controlling step of oil hydrodistillation. Both mass transfer kinetic coefficients increased with increasing distillation temperatures. The oil gain at a higher temperature had a darker color and seemed to stick longer to the human skin. The different physical properties of the oil were a result of different chemical constituents exhibited in GC-MS profiles.