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

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 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 benefit for future works of agarwood oil research areas, especially in terms of oil quality classification

A novel application of artificial neural network for classifying agarwood essential oil quality

This work studies the agarwood oil classification into high and low quality by using two different techniques. Initially, the Forest Research Institute Malaysia (FRIM) and Universiti Malaysia Pahang (UMP) are where the sample preparation and compound extraction of agarwood oil is collected. The data collections were done from the previous researcher consists of 96 samples from seven significant agarwood oil compounds. The artificial neural network (ANN) and the proposed stepwise regression technique were used in this study. The stepwise regression was done the feature selection and successfully reduced agarwood oil compounds from seven to four. Then, the ANN technique was used to classify agarwood oil into high and low using input from seven and four compounds separately. The performance of ANN with different inputs is compared (ANN with seven inputs compared with ANN with four inputs). All the experimental work was performed using the MATLAB R2017b using the “patternet” implemented Levenberg Marquardt algorithm and ten hidden neurons. It was found that the ANN technique using seven compounds obtained the best performance according to high accuracy and lower mean square error (MSE) value. Finally, 1 hidden neuron in ANN with seven inputs selected as the best neuron for grading the agarwood oil compounds

Kesahan dan kebolehpercayaan soal selidik ‘Pengalaman Pembelajaran Berbantukan Rakan’ versi bahasa Melayu untuk pembelajaran teknik Lampu Celah Biomikroskopi

Pembelajaran Berbantukan Rakan (PBR) merupakan kaedah pembelajaran berpusatkan pelajar dan memerlukan soal selidik yang mempunyai kesahan dan kebolehpercayaan yang tinggi untuk mengetahui keberkesanan kaedah tersebut. Soal selidik yang sedia ada untuk mengukur keberkesanan PBR adalah dalam Bahasa Inggeris dan untuk bidang Kejururawatan. Justeru, kajian ini dijalankan untuk menentukan kesahan dan kebolehpercayaan soal selidik ‘Pengalaman Pembelajaran Berbantukan Rakan’ bagi teknik Lampu Celah Biomikroskopi versi Bahasa Melayu. Soal selidik Peer Teaching Experience Questionnaire (PTEQ) dan Clinical Teaching Preference Questionnaire (CTPQ) dalam Bahasa Inggeris diterjemahkan ke versi Bahasa Melayu menggunakan kaedah penterjemahan forward-backward dan sesi pengharmonian. Soal selidik tersebut disahkan oleh pakar bahasa sebelum digunakan. Kajian dijalankan untuk menguji kesahan dan kebolehpercayaan soal selidik ini pada subjek kajian yang pernah terlibat dalam sesi PBR bagi teknik Lampu Celah Biomikroskopi iaitu pelajar Tahun 3 dan Tahun 4 Program Optometri, Universiti Kebangsaan Malaysia. Hasil kajian menunjukkan soal selidik mempunyai nilai Alpha Cronbach yang tinggi iaitu 0.86 (PTEQ) dan 0.87 (CTPQ) versi Bahasa Melayu. Kedua-dua soal selidik mempunyai kesahan dan kebolehpercayaan yang tinggi serta mempunyai kepelbagaian dimensi untuk diguna pakai bagi menguji keberkesanan kaedah pembelajaran PBR. Soal selidik versi baharu yang dihasilkan adalah setara dengan versi asal iaitu Bahasa Inggeris dan sah digunapakai. Penterjemahan soal selidik PTEQ dan CTPQ ke versi Bahasa Melayu telah menghasilkan peralatan baharu yang mempunyai ciri psikometri untuk mengukur keberkesanan kaedah pembelajaran PBR pada masa akan datang yang menepati konteks dan norma di Malaysia

Self-tuning fuzzy PID controller using online method in essential oil extraction process

During the extraction process, the temperature plays the major effect on the quality and output yield.Numerous studies on this domain mention that excessive heat during the extraction process will degrade the quality of oil and produced poor output.Recently, researchers take efforts to fix this problem by develop intelligent control technique in order to regulate the temperature. This study proposed the self-tuning fuzzy PID (STFPID) controller using online method. The STFPID controller will regulate the steam temperature below 100OC where very little publication so far discussed on that range.The robustness of STFPID controller was test using load disturbance and set point tracking.The performance effectiveness was evaluated based on rise time, settling time, percent overshoot, and steady stare error.From the simulation result, the STFPID controller shows good performance in both transient and steady state dynamics.The STFPID controller also has the ability to track the set point change and curb the load disturbance

k-nearest neighbor modelling of agarwood oil samples available in capital of Malaysia market

Agarwood oil is consumed during traditional ceremonies and even in medicinal purposes due to its effective therapeutic characteristic. As a part of ongoing research on agarwood oil, this paper presented a k-nearest neighbor (k-NN) modelling of agarwood oil samples available in the capital of Malaysia market. The work involved agarwood oil samples from three sources which are lab, local manufacturer and market. The inputs are the chemical compounds and the output is the oil's resources. The input-output was divided into training and testing dataset with the ratio of 80% to 20%, respectively, before they were fed to the k-NN for model development as well as model validation. During the model development, the k-value was varied from 1 to 5, and their accuracy was observed. The result showed that the k=1 and k=2 shared the similar accuracy for training and testing datasets, which are 98.63% and 100.00%, respectively. This study revealed the capabilities of the k-NN model in classifying the agarwood oil samples to the three sources: lab, local manufacturer and market. It was a significant study and contributed to further work especially those related to agarwood oil research area

Picoseconds Dark Pulse Zirconia-Yttria-Aluminium-Erbium-doped Fiber Laser

A 3.4 picoseconds dark pulse fiber laser was successfully generated using Zirconia-YttriaAlumina-Erbium-doped fiber laser (Zr-EDF) cavity with graphene oxide as saturable absorber.The laser cavity was 11.5 m long with the group delay dispersion of -0.04 ps2. Themultiwavelength optical spectrum provides 1 MHz repetition rate and 67.8 dB optical signal tonoise, defines the high stability of the dark pulse. A strong nonlinearity and high birefringenceof the Zr-EDF and GOSA cause the pulse turn down to dark region. An ultrafast dark pulse is demand due to low interference and excellent stability in the existence of noise that are requiredfor high efficiency and accuracy demanding by biomedical devices

Preliminary study on agarwood essential oil and its classification techniques using machine learning

Using essential oils derived from trees for pharmaceutical purposes, incense, aromatherapy, and other areas has expanded its popularity on the international market. However, since human sensory evaluation is still the primary technique used to grade essential oils in Malaysia, the classification technique for determining their grade is still below standard. Nonetheless, prior studies established new approaches for classifying the grade of essential oils by studying their chemical compounds. Therefore, agarwood essential oil was selected for the suggested model due to the increasing demand and the high cost of the world's natural raw materials. The support vector machine (SVM) using one versus all (OVA) approach was selected as the classifier for agarwood essential oil. This study provides an overview of essential oils and their prior research techniques. In addition, a review of SVM is conducted to demonstrate that the technique is appropriate for future studies

A study on ann performance towards three significant compounds of high quality agarwood oil

This study demonstrated the application and the performance of the artificial neural network (ANN) as classification tool for luxury oil which is agarwood essential oil. For the scope of this research, the compounds of agarwood essential oil were obtained from FRIM and BARCE (UMP). The 103 compounds data is pre-processed through a pre-processing technique known as principal component analysis (PCA) and Pearson's correlation. It was found that three compounds were significant and they were high quality; -Agarofuran, α-Agarofuran, and 10-epi-eudesmol. The significant compounds were continued to be fed into ANN as input data meanwhile the output data categorized as low and high quality of the agarwood essential oil. The Scaled Conjugate Gradient (SCG) was employed as the default classifier algorithm during network training. Three layers of ANN architecture were used and 1 to 10 hidden neurons were varied in a hidden layer. The performance of the ANN was measured using the mean squared error (MSE), epochs and their execution time and the confusion matrix. The work was performed using Matlab R2017a. The finding shows that SCG-ANN successfully classified agarwood essential oil with the best performance at 3 hidden neurons. This research is significant for future work, especially on the classification of the agarwood essential oil field

Boxplot analysis of 4 grade agarwood essential oil for various grades

Agarwood essential oil is used in most perfumery ingredients, as an incense and in traditional medical preparations. Agarwood essential oil, called "Black Gold," is extremely valued to the global community due to its numerous benefits. As of now, there is still no standard technique of grading different grades of agarwood essential oil. The current grading technique is inefficient since the agarwood essential oil is graded by using human sensory panel. Different people might have different perspective on grading the agarwood essential oil hence, the technique is not practical to adapt it globally. Due to the current technology, numerous intelligent techniques for verifying the grades of agarwood essential oil have been proposed and implemented. The study has conducted a statistical analysis on 4 grade agarwood essential oil using boxplot. Boxplot analysis summarizes the abundances for each chemical compounds from four different grades of agarwood essential oil with a high grade as a reference. This study shows the analysis of boxplot investigated 10-epi-δ-eudesmol, α-agarofuran, βagarofuran, δ-eudesmol and dihydrocollumellarin as most important chemical compounds in high grade of agarwood essential oil. The chemical compounds that have been identified in high grade of agarwood essential oil can be a reference for future research studies