In order to classify unknown gelatin into their species of origin, a simple and rapid method for the qualitative determination was developed using Fourier transform infrared (FTIR) in combination with attenuated total reflectance (ATR) and discriminant analysis. The spectra were analysed using a chemometric method, principal component analysis (PCA), to classify and characterise gelatin compounds using regions of the FTIR spectra in the range of 3290-3280 cm1 and 1660-1200 cm1 as calibration models. Results from PCA, which were subsequently represented by the Cooman's plot showed a clear distinction between gelatin samples of bovine and porcine origins. This qualitative approach, besides providing a rapid determination of the source of gelatin, may also be established based on a second derivative study of the FTIR spectrum to alleviate any doubt of the gelatin source for applications in the food and pharmaceutical industries.

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The optimum formulation for production of a Malaysian traditional baked cassava cake was determined using response surface methodology (RSM). Effects of amount of ingredients such as sugar (10-30%) and coconut milk (15-35%) on the textural characteristics (hardness and chewiness) and sensory qualities (colour, firmness, cassava flavour and overall acceptability) of cakes were investigated. Significant regression models which explained the effects of different percentages of sugar and coconut milk on all response variables were determined. The coefficients of determination, R 2 of all the response variables were higher than 0.8. Based on the response surface and superimposed plots, the basic formulation for production of Malaysian traditional baked cassava cake with desired sensory quality was obtained by incorporating with 25% of sugar and 20% of coconut milk.

The volatile compounds of pork, other meats and meat products were studied using an electronic nose and gas chromatography mass spectrometer with headspace analyzer (GCMS-HS) for halal verification. The zNose™ was successfully employed for identification and differentiation of pork and pork sausages from beef, mutton and chicken meats and sausages which were achieved using a visual odor pattern called VaporPrint™, derived from the frequency of the surface acoustic wave (SAW) detector of the electronic nose. GCMS-HS was employed to separate and analyze the headspace gasses from samples into peaks corresponding to individual compounds for the purpose of identification. Principal component analysis (PCA) was applied for data interpretation. Analysis by PCA was able to cluster and discriminate pork from other types of meats and sausages. It was shown that PCA could provide a good separation of the samples with 67% of the total variance accounted by PC1.

Headspace solid phase microextraction (SPME) coupled to fast gas chromatography-mass spectrometry (GC-MS) was applied to analyze the volatile compounds of durian (Durio zibethinus) varieties D2, D24, and D101 from Malaysia. Sampling sensitivity was improved by evaluation of sample matrix, sampling size, headspace volume, salt addition and sampling duration. A total of 39 volatile compounds were identified including 22 esters, 9 sulphur-containing alkanes, 3 thioacetals, 2 thioesters, 2 thiolanes and 1 alcohol. The relative amount of volatiles estimated using 1 ppm internal standard (IS) revealed the differences in the volatile composition among varieties. Further classification and characterization of each durian variety was successfully conducted using principal component analysis (PCA).

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