I. Amin

Vicilin-class globulins and their degradation during cocoa fermentation

Cocoa beans were fermented for 144 h using shallow wooden boxes at ambient temperature. Two major polypeptides were found to consist of the storage protein and an albumin fraction. The storage protein comprises two vicilin fractions with molecular weights of 47.1 and 39.2 kDa, and the albumin fraction has a molecular weight of 21.1 kDa. The degradation of vicilin fractions during the course of fermentation was visually detected by sodium docecyl sulphate-polyacrylamide gel electrophoresis. The albumin fraction was found to be the most resistant to proteolysis during fermentation. At the end of fermentation, the 39.2 kDa polypeptide was completely degraded but the 47.1 kDa polypeptide was still present at low intensity. The protein concentrations of 47.1 and 39.2 kDa polypeptides decreased from 1.74 to 0.03 μg and from 0.93 to 0.02 μg, respectively. The protein concentration of 46 and 46.5 kDa polypeptides increased from 0.06 to 0.34 μg and from 0.03 to 0.23 μg, respectively. This could be due to the result of the degradation products of the 47.1 kDa polypeptide.

LC-QTOF-MS identification of porcine-specific peptide in heat treated pork identifies candidate markers for meat species determination.

The purpose of this study was to identify porcine-specific peptide markers from thermally processed meat that could differentiate pork from beef, chevon and chicken meat. In the initial stage, markers from tryptic digested protein of chilled, boiled and autoclaved pork were identified using LC-QTOF-MS. An MRM method was then established for verification. A thorough investigation of LC-QTOF-MS data showed that only seven porcine-specific peptides were consistently detected. Among these peptides, two were derived from lactate dehydrogenase, one from creatine kinase, and four from serum albumin protein. However, MRM could only detect four peptides (EVTEFAK, LVVITAGAR, FVIER and TVLGNFAAFVQK) that were consistently present in pork samples. In conclusion, meat species determination through a tandem mass spectrometry platform shows high potential in providing scientifically valid and reliable results even at peptide level. Besides, the specificity and selectivity offered by the proteomics approach also provide a robust platform for Halal authentication.

Use of principal component analysis for differentiation of gelatine sources based on polypeptide molecular weights

The study was aimed to differentiate between porcine and bovine gelatines in adulterated samples by utilising sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) combined with principal component analysis (PCA). The distinct polypeptide patterns of 6 porcine type A and 6 bovine type B gelatines at molecular weight ranged from 50 to 220 kDa were studied. Experimental samples of raw gelatine were prepared by adding porcine gelatine in a proportion ranging from 5% to 50% (v/v) to bovine gelatine and vice versa. The method used was able to detect 5% porcine gelatine added to the bovine gelatine. There were no differences in the electrophoretic profiles of the jelly samples when the proteins were extracted with an acetone precipitation method. The simple approach employing SDS-PAGE and PCA reported in this paper may provide a useful tool for food authenticity issues concerning gelatine.

RP-HPLC method using 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate incorporated with normalization technique in principal component analysis to differentiate the bovine, porcine and fish gelatins

The amino acid compositions of bovine, porcine and fish gelatin were determined by amino acid analysis using 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate as derivatization reagent. Sixteen amino acids were identified with similar spectral chromatograms. Data pre-treatment via centering and transformation of data by normalization were performed to provide data that are more suitable for analysis and easier to be interpreted. Principal component analysis (PCA) transformed the original data matrix into a number of principal components (PCs). Three principal components (PCs) described 96.5% of the total variance, and 2 PCs (91%) explained the highest variances. The PCA model demonstrated the relationships among amino acids in the correlation loadings plot to the group of gelatins in the scores plot. Fish gelatin was correlated to threonine, serine and methionine on the positive side of PC1; bovine gelatin was correlated to the non-polar side chains amino acids that were proline, hydroxyproline, leucine, isoleucine and valine on the negative side of PC1 and porcine gelatin was correlated to the polar side chains amino acids that were aspartate, glutamic acid, lysine and tyrosine on the negative side of PC2. Verification on the database using 12 samples from commercial products gelatin-based had confirmed the grouping patterns and the variables correlations. Therefore, this quantitative method is very useful as a screening method to determine gelatin from various sources.

Advances in Differential Scanning Calorimetry for Food Authenticity Testing

Differential scanning calorimetry (DSC) is a thermoanalytical technique that is commonly used for studying thermal effects. In food science, it is used to study physical behavior during storage and processing of fat and oils, carbohydrates, water or alcohol, proteins, and food packaging. Furthermore, it is well known as a simple, rapid, and environmentally friendly technique. It was reported that DSC has been successful in authenticating several food products such as fats and oils. This chapter therefore briefly reviews DSC applications in foods, with an emphasis on authenticity issues. The principles of this technique together with examples of current uses, limitations, and prospects are discussed. Continuing development should focus on the potential of this technique in combination with chemometric analysis.