The present study may serve as a preliminary reference for the selection of optimum extract in further C. nutans in vivo anti-inflammatory study.

Increasing levels of antibiotic resistance by Staphylococcus aureus have posed a need to search for non-antibiotic alternatives. This study aimed to assess the inhibitory effects of crude and fractionated cell-free supernatants (CFS) of locally isolated lactic acid bacteria (LAB) against a clinical strain of S. aureus. A total of 42 LAB strains were isolated and identified from fresh vegetables, fresh fruits and fermented products prior to evaluation of inhibitory activities. CFS of LAB strains exhibiting a stronger inhibitive effect against S. aureus were fractionated into crude protein, polysaccharide and lipid fractions. Crude protein fractions showed greater inhibition against S. aureus compared to polysaccharide and lipid fractions, with a more prevalent effect from Lactobacillus plantarum 8513 and L. plantarum BT8513. Crude protein, polysaccharide and lipid fractions were also characterised with glycine, mannose and oleic acid being detected as the major component of each fraction, respectively. Scanning electron microscopy revealed roughed and wrinkled membrane morphology of S. aureus upon treatment with crude protein fractions of LAB, suggesting an inhibitory effect via the destruction of cellular membrane. This research illustrated the potential application of fractionated extracts from LAB to inhibit S. aureus for use in the food and health industry.

The detection of 3-and 2-MCPD ester and glycidyl ester was transformed from selected ion monitoring (SIM) mode to multiple reaction monitoring (MRM) mode by gas chromatography triple quadrupole spectrometry. The derivatization process was adapted from AOCS method Cd 29a-13. The results showed that the coefficient of determination (R2) of all detected compounds obtained from both detection mode was comparable, which falls between 0.997 and 0.999. The limit of detection and quantification (LOD and LOQ) were improved in MRM mode as compared to SIM mode. In MRM mode, the LOD of 3-and 2-MCPD ester was achieved 0.01 mg/kg while the LOQ was 0.05 mg/kg. Besides, LOD and LOQ of glycidyl ester were 0.024 and 0.06 mg/kg respectively. A blank spiked with MCPD esters (0.03, 0.10 and 0.50 mg/kg) and GE (0.06, 0.24 and 1.20 mg/kg) were chosen for repeatability and recovery tests. MRM mode showed better repeatability in area ratio and recovery with relative standard deviation (RSD %) < 5% for 2-, 3-MCPD ester at 0.5 mg/kg and GE at 1.2 mg/kg. Quantification of 22 food samples from different category were performed by repeated injections in both detection modes. Briefly, the contaminants from crude palm oil, mustard and olive oil were present in minute amount which below the LOD or LOQ in both detection modes. Sample from chocolate and infant formula products showed certain level of MCPD esters and GE, and their detection was more precisely quantitated based on MRM mode. Besides, margarine products showed a higher level of contaminations due to the high fat content in these products. MRM mode detection was proven to provide precise data with low RSD % in different food matrices. MRM mode detection was robust and selective for MCPD esters and GE analyses, it should be applied to determine the concentration of MCPD esters and GE contaminations in food.

The technique of Fourier transform infrared spectroscopy is widely used to generate spectral data for use in the detection of food contaminants. Monochloropropanediol (MCPD) is a refining process-induced contaminant that is found in palm-based fats and oils. In this study, a chemometric approach was used to evaluate the relationship between the FTIR spectra and the total MCPD content of a palm-based cooking oil. A total of 156 samples were used to develop partial least squares regression (PLSR), artificial neural network (nnet), average artificial neural network (avNNET), random forest (RF) and cubist models. In addition, a consensus approach was used to generate fusion result consisted from all the model mentioned above. All the models were evaluated based on validation performed using training and testing datasets. In addition, the box plot of coefficient of determination (R²), root mean square error (RMSE), slopes and intercepts by 100 times randomization was also compared. Evaluation of performance based on the testing R² and RMSE suggested that the cubist model predicted total MCPD content with the highest accuracy, followed by the RF, avNNET, nnet and PLSR models. The overfitting tendency was assessed based on differences in R² and RMSE in the training and testing calibrations. The observations showed that the cubist and avNNET models possessed a certain degree of overfitting. However, the accuracy of these models in predicting the total MCPD content was high. Results of the consensus model showed that it slightly improved the accuracy of prediction as well as significantly reduced its uncertainty. The important variables derived from the cubist and RF models suggested that the wavenumbers corresponding to the MCPDs originated from the -CH=CH₂ or CH=CH (990-900 cm⁻¹) and C-Cl stretch (800-700 cm⁻¹) regions of the FTIR spectrum data. In short, chemometrics in combination with FTIR analysis especially for the consensus model represent a potential and flexible technique for estimating the total MCPD content of refined vegetable oils.

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