A Review of Gelatin Source Authentication Methods <s> Chromatographic-Chemometric Method <s> A series of monomeric and polymeric alkyl phases of different phase lengths was prepared by reaction with appropriate mono- and trichlorosilanes. Selectivity changes with alkyl phase length were examined for phases ranging in length from C/sub 8/ to C/sub 30/. The retention behavior of polycyclic aromatic hydrocarbons (PAH) was observed to vary significantly with alkyl length. By use of PAH solute probes, phase selectivity of long-chain-length monomeric phases was found to approach that observed for C/sub 18/ polymeric phases. Retention behavior is discussed in terms of schematic representation of bonded phase behavior. <s> BIB001 </s> A Review of Gelatin Source Authentication Methods <s> Chromatographic-Chemometric Method <s> Gelatin is a collagen derivative, which has a large application in the pharmaceutical, food and adhesive industries as well as photography. The large similarity in structure and properties of gelatins from different origins makes their differentiation difficult. Certain chemometric methods, such as principal component analysis (PCA), can help to classify and characterize gelatin components. In this study 14 bovine and 5 porcine gelatins were examined. The analysis procedure involved complete hydrolysis of samples by classic acid hydrolysis in order to release their amino acid residues. Separation and determination of amino acids was achieved by reversed-phase (RP) HPLC following pre-column derivatisation. Orthophtaldialdehyde (OPA) and 4-chloro-7-nitro benzofurazane (NBD-Cl) were used as derivatisation reagents. From the 20 peaks detected by HPLC analysis, one was very typical in bovine gelatin. Peak height, area, area percentage and width were used to make matrixes. Principal component analysis with the MATLAB program was used to differentiate these gelatins. PCA on matrix of height, width and total matrix were resulted in good differentiation between bovine and porcine gelatins. <s> BIB002 </s> A Review of Gelatin Source Authentication Methods <s> Chromatographic-Chemometric Method <s> 1. Introduction 1.1 Analytical problems 1.2 Errors in qunatitative analysis 1.3 Types of error 1.4 Random and systematic errors in titrimetric analysis 1.5 Handling systematic errors 1.6 Planning and design of experiments 1.7 Calculators and computers in statistical calculations 2. Statistics of Repeated Measurements 2.1 Mean and standard deviation 2.2 The distribution of repeated measurements 2.3 Log-normal distribution 2.4 Definition of a 'sample' 2.5 The sampling distribution of the mean 2.6 Confidence limits of the mean for large samples 2.7 Confidence limits of the mean for small samples 2.8 Presentation of results 2.9 Other uses of confidence limits 2.10 Confidence limits of the geometric mean for a log-normal distribution 2.11 Propagation of random errors 2.12 Propagation of systematic errors 3. Significance Tests 3.1 Introduction 3.2 Comparison of an experimental mean with a known value 3.3 Comparison of two experimental means 3.4 Paired t-test 3.5 One-sided and two-sided tests 3.6 F-test for the comparison of standard deviations 3.7 Outliers 3.8 Analysis of variance 3.9 Comparison of several means 3.10 The arithmetic of ANOVA calculations 3.11 The chi-squared test 3.12 Testing for normality of distribution 3.13 Conclusions from significance tests 3.14 Bayesian Statistics 4. The Quality of Analytical Measurements 4.1 Introduction 4.2 Sampling 4.3 Separation and estimation of variances using ANOVA 4.4 Sampling strategy 4.5 Quality control methods - Introduction 4.6 Stewhart charts for mean values 4.7 Stewhart charts for ranges 4.8 Establishing the process capability 4.9 Average run length: cusum charts 4.10 Zone control charts (J-charts) 4.11 Proficiency testing schemes 4.12 Method performance studies (collaborative trials) 4.13 Uncertainty 4.14 Acceptable sampling 4.15 Method validation 5. Calibration Methods in Instumental Analysis 5.1 Introduction: instrumentational analysis 5.2 Calibration graphs in instrumental analysis 5.3 The product-moment correlation coefficient 5.4 The line of regression of y on x 5.5 Errors in the slope and intercept of the regression line 5.6 Calculation of a concentration and its random error 5.7 Limits of detection 5.8 The method of standard additions 5.9 Use of regression lines for comparing analytical methods 5.10 Weighted regression lines 5.11 Intersection of two straight lines 5.12 ANOVA and regression calculations 5.13 Curvilinear regression methods - Introduction 5.14 Curve fitting 5.15 Outliers in regression 6. Non-parametric and Robust Methods 6.1 Introduction 6.2 The median: initial data analysis 6.3 The sign test 6.4 The Wald-Wolfowitz runs test 6.5 The Wilcoxon signed rank test 6.6 Simple tests for two independent samples 6.7 Non-parametric tests for more than two samples 6.8 Rank correlation 6.9 Non-parametric regression methods 6.10 Robust methods: introduction 6.11 Simple robust methods: trimming and winsorization 6.12 Further robust estimates of location and spread 6.13 Robust ANOVA 6.14 Robust regression methods 6.15 Re-sampling statistics 6.16 Conclusions 7. Experiimental Design and Optimization 7.1 Introduction 7.2 Randomization and blocking 7.3 Two-way ANOVA 7.4 Latin squares and other designs 7.5 Interactions 7.6 Identifying the important factors: factorial designs 7.7 Fractional factorial designs 7.8 Optimization: basic principles and univariate methods 7.9 Optimization using the alternating variable search method 7.10 The method of steepest ascent 7.11 Simplex optimization 7.12 Simulated annealing 8. Multivariate Analysis 8.1 Introduction 8.2 Initial analysis 8.3 Prinicipal component analysis 8.4 Cluster analysis 8.5 Discriminant analysis 8.6 K-nearest neighbour method 8.7 Disjoint class modelling 8.8 Regression methods 8.9 Multiple linear regression 8.10 Principal component regression 8.11 Partial least squares regression 8.12 Natural computation methods artificial neural networks 8.13 Conclusions Solutions to Exercises Appendix 1 Commonly used statistical significance tests Appendix 2 Statistical tables Index <s> BIB003 </s> A Review of Gelatin Source Authentication Methods <s> Chromatographic-Chemometric Method <s> Gelatine is a collagen derivative obtained from bones and hides/skin mainly from bovine and pigs. As a consequence of the outbreak of bovine spongiform encephalopathy (BSE), the use of bovine gelatine in feed, food and pharmaceutical products has been restricted by regulatory authorities. However, no method was presently available for its specific detection. The large similarity in amino-acid sequences of collagens from different species make their immunochemical differentiation difficult when using polyclonal antibodies raised against the whole molecule [A. Venien, D. Levieux, J. Immunoassay Immunochem., in press]. To obtain bovine-specific antibodies, we immunized rabbits against putative species-specific sequences of the bovine collagen alpha 1(I) chain. Using these antibodies, an indirect ELISA was developed to allow a quick and easy differentiation between bovine and porcine gelatines. Moreover, a competitive indirect ELISA was found suitable to detect bovine gelatine in porcine gelatine purchased from laboratory chemicals suppliers down to a dilution of 2-4 parts per 1000 with CVs ranging from 5.7 to 7.7%. When testing mixtures of the largest possible range of industrial batches of bovine and porcine gelatines (skin/hides or bones origin, acid or alkaline processes, high or low Bloom) the detection limit was down to a dilution of 8 parts per 100 bovine gelatine in porcine gelatine. These ELISAs could be routinely used by pharmaceutical and food manufacturers to secure their supply chain. <s> BIB004 </s> A Review of Gelatin Source Authentication Methods <s> Chromatographic-Chemometric Method <s> Gelatin used for capsule shells can be derived from porcine origin. Some religion like Islam prohibited their followers to consume any products containing porcine derivatives including porcine gelatin in the products. Consequently, some rapid and reliable techniques are continuously developed for detection of porcine gelatin in some pharmaceutical products like capsule shells. The aim of this study was to evaluate the possibility to use amino acid profile in combination with chemometrics of principal component analysis for detection and classification between gelatin in capsule derived from porcine and bovine. Based on score plot of first principal components (PC1) and second principal components (PC2), porcine and bovine gelatins in capsule could be apparently distinguished. Key words: bovine gelatin; porcine gelatin; amino acid profile; principal component analysis <s> BIB005 </s> A Review of Gelatin Source Authentication Methods <s> Chromatographic-Chemometric Method <s> Gelatin is widely used in some food products, including soft candy (one of food products preferred by children) . Most of the gelatin available in the market derived from pigs . Some religions like Islam and Jews prohibited their followers to consume any food products containing pig derivatives, including porcine gelatin. Therefore, it is necessary to develop some rapid and reliable methods for detection of porcine gelatin in soft candy . The purpose of this study was to differentiate and classification the gelatin sources (porcine or bovine) in soft candy based on amino acid profiles combined with chemometrics of p rincipal component analysis (PCA) . Separation and determination of amino acid was conducted by reversed-phase HPLC using a fluorescent detector, after being derivatized with o rtho -phtalaldehyde in 2-mercaptoethanol ( OPA/2-MCE). Parameters of peak height percentage of each amino acids from each sample were analyzed by PCA . Based on PC1 and PC2, porcine and bovine gelatins in soft candy could be apparently distinguished. 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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. <s> BIB007
High-performance liquid chromatography (HPLC) has become a very useful analytical technique for separation of biomolecules based on differences in their structure and/or composition  . Reverse phase HPLC (RP-HPLC) is the most commonly used because it is easier to use, suitable for wide range of molecules and readily controlled. For high sensitivity, detection with fluorometer is more suitable for amino acid analysis with RP-HPLC. Therefore, most HPLC methods required derivatisation of amino acids to make them fluorescence BIB001 . Chemometric which is also known as Principal component analysis (PCA) is the unsupervised pattern recognition technique for classification and differentiation of objects BIB003 . PCA facilitate data restructuring by reducing the dimension of data to separate samples from different groups. PCA is based on the mathematical and statistical operations to distinguish the variables of analogous profiles. Prior to running PCA task, data are pretreated using Normalisation method to improve data sets that contained high variation BIB007 . The combination of the chromatographic and chemometric method has been reportedly used for source authentication of gelatin. BIB002 investigated 14 porcine and five bovine gelatins using PCA of amino acid profile obtained through (RP) HPLC-fluorescence analysis of acid hydrolysis of gelatin. About 20 peaks were reportedly detected by HPLC analysis, of which one was very peculiar for bovine gelatin. PCA of peak height, area, area percentage and width was successfully used to differentiate gelatin based source. It was concluded that PCA of matrix height, width, and total matrix were capable of differentiation of bovine and porcine gelatins. Table 3 showed previous studies on ChromatographicChemometric analysis for differentiation of gelatin source. In order to differentiate between capsule shells containing bovine and porcine gelatin, BIB005 developed a method that involves chemometric analysis of HPLC-fluorescence detection profile of amino acids. Amino acids of gelatin in capsule shells were obtained by HCL-hydrolysis. The quantity of amino acids was dependent on the source of gelatin and PCA score plot of PC1 and PC2, respectively described 64.4% and 15.7%, variations between porcine and bovine gelatins. Hence, PCA of HPLC amino acid profile could be suitable for specie-specific gelatin authentication. BIB006 studied differentiation of bovine and porcine gelatins in soft candy based on amino acid profiles and chemometrics. Separation of HCL derived amino acids was achieved by (RP) HPLC-fluorescent detector. Percentage of the peak height of the amino acids was analysed by PCA. PC1 and PC2 could be used to distinguish between porcine and bovine gelatins in soft candy. BIB007 investigated chromatographic combined with a chemometric method to differentiate the bovine, porcine and fish gelatins. RP-HPLC-fluorescence method was used for separation of derivatives of HCL-hydrolysed gelatin samples. The PCA model demonstrated the relationships among amino acids in the correlation loadings plot to the group of gelatins in the scores plot. The method was stated to be very suitable for determination of gelatin from various sources. In spite of all the above achievements, chromatographic-chemometric method has not been demonstrated yet to be capable for detection of contamination in gelatin containing mixture of different sources BIB004 . Also, the possibility of transformation of amino acid through Maillard reaction during food processing might hinder the application of chromatographic-chemometric method for gelatin authentication.