Page 70 - rise 2017

DSC and chemometric analysis. Principal Component Analysis (PCA) was used to classify the
differences and similarities of different mixture of non-halal recycled cooking oil.

Materials and Methods
Materials
Palm oil (Saji brand, Delima Oil Products Sdn Bhd), beef (Aust BF Knucle blk True Aussie Best) and
pork were purchased from the local retail shop. Chicken breasts were purchased from the local wet
market. The stove used was an electric stove with temperature indicator.

Sample preparation
Chicken breasts, beef and pork were cut into little pieces with 1cm x 1cm dimension. Six hundred
milliliter of palm oil was pre-heated for 5 minutes at 180°C. Subsequently, 100 g of samples (chicken,
beef and pork), were deep-fried in the palm oil for 5 minutes at 180°C separately. The used cooking
oils were then filtered using kitchen towel prior to deposit in clean container.

Preparation of blends
A set of experimental samples of fried-pork oil (P) and fried-chicken oil (C) was prepared by adding P
oil in a proportion ranging from 0.5 % to 50.0 % (v/v), to C and M oils separately. These sample
mixtures were then subjected to DSC analysis.

Differential scanning calorimetry
Thermal analysis method was conducted as described by Yanty, Marikkar, Man, and Long [29]. DSC
was carried out using a Mettler Toledo differential scanning calorimeter (DSC 823 Model) equipped
with a thermal analysis data station (STARe software, Version 9.0x, Schwerzenbach, Switzerland).
The purge gas used was Nitrogen (99.999 % purity) at a rate of ~20 mL/min. Subsequently,
approximately 4-8 mg of sample was placed in a standard DSC aluminum pan and hermetically
sealed. The reference used was an empty hermetically-sealed DSC aluminum pan. The samples were
subjected to the following temperature program: 70°C isotherm for 1 min, cooled at 5°C/min to -
o
70°C. The samples were held at -70°C isotherm for 1 min, and heated at 5 C/min to reach 70°C.

Statistical analysis
Principal component analysis (PCA) was used to classify the differences and similarities of different
mixture of non-halal recycled cooking oil. The PCA was run using Unscrambler 9.7 (Camo, USA)
software. PCA is a technique that reduces the original data to acquire a new smaller set of data called
principal components (PC) [31]. There are usually two outcomes of PCA: (i) the loading plot and (ii)
the score plot. The loading plot infers to the relationships between the variables, while the score plot
indicates the sample patterns, grouping differences and similarities [30]. Exothermic and endothermic
regions of spectrums consist of five variables each were used as variables.

Results and Discussion

Principal component analysis
There are two components, PC1 and PC2, in the PCA which define as the first two biggest variance of
data compiled in the PCA [28]. PC1 and PC2 are used to build the PCA distribution chart to
distinguish between the groups of samples. Figure 1 shows the PCA distribution chart of fried-pork
oil (P), fried-chicken oil (C) and fried-beef oil (M) labeled as pork, chicken and meat, respectively.
Control sample was 600 mL of palm oil heated for 10 min at 180°C. PC1 and PC2 accounted for 83%
and 11% of the variation, respectively; thus 94% of the variance was accounted for the first two PCs.
The differences of all samples can be seen clearly from the chart, with chicken, meat and control were
in the positive side and located far apart from each other. While pork is located in the negative side.
This result indicates that it is possible to differentiate between those samples.