Intelligent grading of kaffir lime oil quality using non-linear support vector machine

This paper presents kaffir lime oil quality grading using the intelligent system classification method, a non-linear support vector machine (NSVM). This method classifies the quality kaffir lime oil into two groups: high and low quality, based on their significant chemical compounds. The 90 data of kaffir lime oil were used in this project from high to low quality. The abundance (%) of significant chemical compounds will act as the input and high or low quality as an output. The 90 data will be divided into two sets: training and testing data sets with a ratio of 8:2. The radial basis function (RBF) optimization kernel parameters in NSVM. Using the implementation of MATLAB software version R2020a, all data and analysis work was performed automatically. The results showed that the NSVM model met all performance criteria for 100% accuracy, sensitivity, specificity, and precision

Evaluation of energy consumption in small-scale agarwood distillation pot based on averaged control signal simulation

Water temperature of a hydro distillation process are represented by using first order plus dead time (FOPDT) model by performing a step test. From the model obtained, a PID controller have been implemented based on several tuning method includes Ziegler-Nichols, Cohen-Coon and Integral Square Error (ISE)-Load to enhance the performance of the system. In this study, a setpoint was set to 80°C and the comparative performance of PID controller with several tuning rules was evaluate and analyse via simulation. The analysis of the performance was depend on settling time, percentage of overshoot and rise time. Moreover, in this study, the average amount of control signal have been evaluated based on several tuning rules by using an integral control signal. The simulation result shows the ISE-Load that completed with minimum percentage of overshoot could result in best temperature control for hydro distillation process. However, in term of energy consumption, PID ZN gives lower energy usage

Observation on SPME different headspace fiber coupled with GC-MS in extracting high quality agarwood chipwood

Agarwood is well known as one of the expensive woods in the world. It has a unique scent which brings it to have wide usages especially in perfumery ingredient, as incense, in traditional medical preparation, and as symbol of wealth. Due to that, this paper presents the analysis on chemical profiles of agarwood chipwood, as a part of agarwood grading system. The work involved of Solid Phase Microextraction (SPME) coupled with Gas Chromatography - Mass Spectrometry (GC-MS) GC-MS in extracting high quality. Three headspace fibers; PDMS-DVB, CAR-PDMS and DVB-CAR-PDMS were used during the extraction to identify the compounds with the sampling time of 60 minutes. The result showed that high quality agarwood chipwood is made up of terpene group which are monoterpene hydrocarbon, sesquiterpene hydrocarbon and oxygenated sesquiterpene. The relative peak areas (%) for compounds are tabulated and plotted. The finding in this study confirmed that the difference in compounds extracted and their relative peak area (%) are due to different fiber's polarity and absorbent, Thus, it is significant and benefit especially in agarwood oil quality grading and its related area

Evaluation on energy consumption in compact hydro distillation process between MPC and PID control

This paper presents the comparison of the MPC and PID control in compact hydro distillation process. Both of PID controllers and MPC undergone the performances of controller tests such as set point, set point change and load disturbances. The comparative performances of MPC and PID controllers (PIDCC and PIDZN) were evaluated and analysed based on transient responses performance and also in term of energy consumption via simulation. The simulation results show that MPC gives good performances in term of transient responses such as settling time, rise time and percentage of overshoot. Moreover, in term of energy consumption, the integral absolute control signal (IACS) has been used to simulate the energy that have been consumed. The result indicates that, MPC produces lower IACS compared to both PID controllers

A Review Study of Agarwood Oil and Its Quality Analysis

This paper presents an overview of analysis agarwood oil and its quality grading. The review suggested agarwood oil can be graded according to their chemical properties and so that there is a common standard recognized worldwide on grading the agarwood oil. Analysis based on chemical profiles is required to ensure that agarwood oil can be classified based on their respective classes or grades where the accurate results can be measured. Conventionally, the grading of agarwood oil is performed by trained human graders (sensory panels) depends on its physical appearance such as color, odor, high fixative and consumer perception. However, this method is limited due to human nose cannot accept many samples in one time and easily get fatigues especially when dealing with continuous production. The human sensory panel also limited in terms of subjectivity, poor reproducibility, time consumption and large labour expense. These are constraining factors in increasing agarwood oil trade and market penetration

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

Quadratic tuned kernel parameter in non-linear support vector machine (SVM) for agarwood oil compounds quality classification

This paper presents the analysis of agarwood oil compounds quality classification by tuning quadratic kernel parameter in Support Vector Machine (SVM). The experimental work involved of agarwood oil samples from low and high qualities. The input is abundances (%) of the agarwood oil compounds and the output is the quality of the oil either high or low. The input and output data were processed by following tasks; i) data processing which covers normalization, randomization and data splitting into two parts in which training and testing database (ratio of 80%:20%), and ii) data analysis which covers SVM development by tuning quadratic kernel parameter. The training dataset was used to be train the SVM model and the testing dataset was used to test the developed SVM model. All the analytical works are performed via MATLAB software version R2013a. The result showed that, quadratic tuned kernel parameter in SVM model was successful since it passed all the performance criteria’s in which accuracy, precision, confusion matrix, sensitivity and specificity. The finding obtained in this paper is vital to the agarwood oil and its research area especially to the agarwood oil compounds classification system

Analysis of algorithms variation in multilayer perceptron neural network for agarwood oil qualities classification

This study investigates the performance of the Multilayer Perceptron (MLP) classifier in discriminating the qualities of agarwood oil significant compounds by different qualities based on three training algorithms namely Scaled Conjugate Gradient (SCG), Levernbergh-Marquardt (LM) and Resilient Backpropagation (RP) Neural Network by using Matlab version 2013a. The dataset used in this study were obtained at Forest Research Institute Malaysia (FRIM) and University Malaysia Pahang (UMP). Further, the areas (abundances, %) of chemical compounds is set as an input and the quality represented (high or low) as an output. The MLP performance was examined with different number of hidden neurons which is in the ranged of 1 to 10. Their performances were observed to accurately found the best technique of optimization to apply to the model. It was found that the LM is effective in reducing the error by enhancing the number of hidden neurons during the network development. The MSE of LM is the smallest among SCG and RP. Besides that, the accuracy of training, validation and testing of LM performed the best accuracy (100%)

Pattern classifier of chemical compounds in different qualities of agarwood oil parameter using scale conjugate gradient algorithm in MLP

This paper presents the modelling of agarwood oil (AO) significant compounds by different qualities using Scaled Conjugate Gradient (SCG) algorithm. This technique involved of data collection from Gas Chromatography-Mass Spectrometry (GC-MS) for compound extraction. The development of Multilayer perceptron (MLP) is used to discriminate the qualities of AO chemical compounds to the high and low quality. The input and output data was transferred to the MATLAB version R2013a for extended analysis. The input is the abundances of significant compounds (%) and the output is the oil quality either high or low. This involved of identification, selection and optimization of a MLP as classifiers to identify and classify the agarwood oil quality. The result showed that MLP as pattern classifier is successful classify agarwood oil quality using SCG algorithm with 100% accuracy. This finding is important in agarwood oil area especially in grading system