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This paper reports the water quality of Murum Hydroelectric Reservoir, eight months after impoundment of the dam. A total of nine stations were selected in the reservoir. Temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD5), chlorophyll-a and total suspended solids (TSS) were studied. The results showed that the water was warmer at 0.5 m depth. As the depth increased, temperature in the reservoir decreased in the range of 4.40°C to 7.35°C from 0.5 to 20 m depth. The concentration of DO dropped to zero at 4 - 6 m depth leading to anoxic level. pH at 0.5 m depth ranged from 6.40 to 7.20 and they were significantly higher (p

Proteins are natural heteropolymers and are most vital nutrients essential for human survival and life. Proteins generally exist in nature either in the form of fibrous proteins (water insoluble) or in the form of globular proteins (water soluble). Proteins are also available abundant in nature and are biodegradable. Proteins provide a broad spectrum of functional and structural properties because of the presence of polar and nonpolar amino acids and are therefore ideal raw materials for the production of bioplastics used for packaging materials. At present, a number of protein-based films are produced for the purpose of packaging of food. The protein based films possess some unique characteristics such as; excellent optical properties (gloss and transparency), are good fat barriers, at low and intermediate humidity possess an excellent oxygen and organic vapor barrier and have moderate mechanical properties. The contents of this chapter are as follows: introduction, proteins for packaging materials, processing methods, shaping agents, and properties.

The chemical characteristics of humic substances (HS) extracted from several samples of peat soils of Sarawak, Malaysia, were determined by size exclusion chromatography, FTIR, UV–Vis, acid–base titration, and elemental and total hydrolysable carbohydrate analyses. The results showed that the total acidity of these HS ranged between 5.8 and 8.3 meq/g, with a clear indication that the fulvic acid component contained more carboxylic groups compared to humic acids. A comparison with the literature data for HS occurring elsewhere showed that the tropical peat of Sarawak has higher carboxylic and phenolic contents, is richer in carbohydrate moiety and also is more oxygenated. The UV–Vis analyses gave a relatively high E 4 / E 6 ratio, implying the prominence of aliphatic components. The relatively lower range of M w further suggested the dominance of smaller-sized humic molecules. These structural attributes suggest that the local HS may exhibit a strong complexing capability and greater susceptibility to electrophilic cleavage by trihalomethane-forming agents.

In the palm oil industry, iodine value (IV) has become an important parameter in quality control that measures the degree of unsaturation of the oils. However, it is difficult to obtain the IV chemically. In other hand, the use of instrumental analysis in IV determination accurately needs suitable data pre-processing. In this study, we proposed the strategy for pre-processing the FT-NIR and FTIR spectra data in analyzing the IV of non-fried and fried palm oils. The utility and effectiveness of four data pre-processing which are column standardization, mean centre and combination of row scaling with column standardization and mean centre were applied. The effect of data splitting methods which are duplex and kenstone was also investigated in the Partial Least Squares (PLS) regression model of palm oils. From the result, the use of different data pre-processing provides different quality of prediction model. Either the application of the row scaling and column scaling individually or combination of both methods may improve the quality of the model. It is concluded that the data pre-processing is context dependent which is depend on the nature of the dataset and there can be no single method for general use.

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 pre-processing 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.