This recent work describes the data pre-processing method of FT-NIR spectroscopy datasets of cooking oil and its quality parameters with chemometrics method. Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modelling. Hence, this work is dedicated to investigate the utility and effectiveness of preprocessing algorithms namely row scaling, column scaling and single scaling process with Standard Normal Variate (SNV). The combinations of these scaling methods have impact on exploratory analysis and classification via Principle Component Analysis plot (PCA). The samples were divided into palm oil and non-palm cooking oil. The classification model was build using FT-NIR cooking oil spectra datasets in absorbance mode at the range of 4000cm -1 -14000cm -1 . Savitzky Golay derivative was applied before developing the classification model. Then, the data was separated into two sets which were training set and test set by using Duplex method. The number of each class was kept equal to 2/3 of the class that has the minimum number of sample. Then, the sample was employed t-statistic as variable selection method in order to select which variable is significant towards the classification models. The evaluation of data pre-processing were looking at value of modified silhouette width (mSW), PCA and also Percentage Correctly Classified (%CC). The results show that different data processing strategies resulting to substantial amount of model performances quality. The effects of several data pre-processing i.e. row scaling, column standardisation and single scaling process with Standard Normal Variate indicated by mSW and %CC. At two PCs model, all five classifier gave high %CC except Quadratic Distance Analysis.

The ability to decorate silicate surface with different organoalkoxysilanes creates powerful new capabilities for catalyst, adsorbents and chemical separation. Mesopororus silica, MCM-41 was modified by grafting of amino and mercaptopropyl functional group. The structures of these materials were characterized by using Fourier Transform Infrared Spectroscopy (FT-IR), and X-Ray diffraction (XRD). The samples were found to exhibit structural properties similar to those reported earlier. Significant functional groups of the modified mesoporous silicates were found in the spectrum of FT-IR. Standard structure of mesoporous silicates were found to be preserved at planar [100] of XRD difractogram of mesoporous silicates. Adsorption of Cu (II) ions were done under different temperatures, initial concentrations and pH. Adsorption process also was determined from kinetic point of view and was found to be better fitted to pseudo second order of kinetic model.

This paper outlines the application of chemometrics and pattern recognition tools to classify palm oil using Fourier Transform Mid Infrared spectroscopy (FT-MIR). FT-MIR spectroscopy is used as an effective analytical tool in order to categorise the oil into the category of unused palm oil and used palm oil for frying. The samples used in this study consist of 28 types of pure palm oil, and 28 types of frying palm oils. FT-MIR spectral was obtained in absorbance mode at the spectral range from 650 cm·1 to 4000 cm·1 using FT-MIR-ATR sample handling. The aim of this work is to develop fast method in discriminating the palm oil by implementing Partial Least Square Discriminant Analysis (PLS-DA), Leaming Vector Quantisation (LVQ) and Support Vector Machine (SVM). Raw FT-MIR spectra were subjected to Savitzky-Golay smoothing and standardised before developing the classification models. The classification model was validated by finding the value of percentage correctly classified using test set for every model in order to show which classifier provided the best classification. In order to improve the performance of the classification model, variable selection method known as /-statistic method was applied. The significant variable in developing classification model was selected through this method. The result revealed that PLS-DA classifier of the standardised data with application of t-statistic showed the best performance with highest percentage correctly classified among the classifiers.

This paper shows the determination of iodine value (IV) of pure and frying palm oils using Partial Least Squares (PLS) regression with application of variable selection. A total of 28 samples consisting of pure and frying palm oils which acquired from markets. Seven of them were considered as high-priced palm oils while the remaining was low-priced. PLS regression models were developed for the determination of IV using Fourier Transform Infrared (FTIR) spectra data in absorbance mode in the range from 650 cm-1 to 4000 cm-1. Savitzky Golay derivative was applied before developing the prediction models. The models were constructed using wavelength selected in the FTIR region by adopting selectivity ratio (SR) plot and correlation coefficient to the IV parameter. Each model was validated through Root Mean Square Error Cross Validation, RMSECV and cross validation correlation coefficient, R2cv. The best model using SR plot was the model with mean centring for pure sample and model with a combination of row scaling and standardization of frying sample. The best model with the application of the correlation coefficient variable selection was the model with a combination of row scaling and standardization of pure sample and model with mean centering data pre-processing for frying sample. It is not necessary to row scaled the variables to develop the model since the effect of row scaling on model quality is insignificant.

This paper outlines the application of chemometrics and pattern recognition tools to classify palm oil using Fourier Transform Mid Infrared spectroscopy (FT-MIR). FT-MIR spectroscopy is used as an effective analytical tool in order to categorise the oil into the category of unused palm oil and used palm oil for frying. The samples used in this study consist of 28 types of pure palm oil, and 28 types of frying palm oils. FT-MIR spectral was obtained in absorbance mode at the spectral range from 650 cm-1 to 4000 cm-1 using FT-MIR-ATR sample handling. The aim of this work is to develop fast method in discriminating the palm oil by implementing Partial Least Square Discriminant Analysis (PLS-DA), Leaming Vector Quantisation (LVQ) and Support Vector Machine (SVM). Raw FT-MIR spectra were subjected to Savitzky-Golay smoothing and standardised before developing the classification models. The classification model was validated by finding the value of percentage correctly classified using test set for every model in order to show which classifier provided the best classification. In order to improve the performance of the classification model, variable selection method known as /-statistic method was applied. The significant variable in developing classification model was selected through this method. The result revealed that PLS-DA classifier of the standardised data with application of t-statistic showed the best performance with highest percentage correctly classified among the classifiers.