DEVELOPMENT OF HALAL TESTING METHOD TO DIFFERENTIATE THE GELATIN FROM DIFFERENT SOURCES USING AN RP-HPLC INCORPORATED

WITH PRINCIPAL COMPONENT ANALYSIS

Presented by;AZILAWATI MOHD ISMAILFOOD TECHNOLOGISTMALAYSIA HALAL ANALYSIS CENTRE (MYHAC),HALAL HUB DIVISION, JAKIM

WHAT IS HALAL? (LAWFUL)

Halal is an Arabic term meaning “lawful” or “permissible” according to Islamic law (shariahcompliant)

Thoyyiba– good or wholesome (quality, safety, hygiene, clean, nutritious, secure)

Halal products must not involve the use of haram (prohibited) ingredients and are notharmful or intended for harmful use (toyyiban compliant).

The products should comply with following requirements:

Does not contain elements not allowed according to Islamic law

Has not been in contact with prohibited/not allowed substances during production,transportation and storage

Is not stored in facilities or premises or transported using transportation vehicleswhich are not allowed

Unlawful(haram) things are prohibited to everyone alike.

Basically, all food products are permitted except those that are explicitly forbiddenaccording to islamic dietary laws including:

i. Swine/pork/porcine and its by-products

ii. Alcohol and intoxicants

iii. Blood and blood by-products

iv. Meat from cadavers and meat of animals that have not been slaughtered accordingto islamic rules

v. Foods contaminated with any of the above products

WHAT IS HARAM? (UNLAWFUL)

MASHBOOH“HALAL IS CLEAR AND HARAM IS CLEAR;

IN BETWEEN THESE TWO ARE CERTAIN THINGS THAT ARE SHUBHAH (SUSPECTED).

MANY PEOPLE MAY NOT KNOW WHETHER THOSE ITEMS ARE HALAL OR HARAM.

WHOSOEVER LEAVES THEM, HE IS INNOCENT TOWARDS HIS RELIGION AND HIS CONSCIENCE.

HE IS, THEREFORE, SAFE.

ANYONE WHO GETS INVOLVED IN ANY OF THESE SUSPECTED ITEMS, HE MAY FALL INTO THE UNLAWFUL AND THE PROHIBITION.

THIS CASE IS SIMILAR TO THE ONE WHO WISHES TO RAISE HIS ANIMALS NEXT TO A RESTRICTED AREA,

HE MAY STEP INTO IT.

INDEED FOR EVERY LANDLORD THERE IS A RESTRICTED AREA.

INDEED THE RESTRICTIONS OF ALLAH ARE THE UNLAWFUL (HARAM).”

HADITH BUKHARI AND MUSLIM

3 MAIN COMPONENTS PRIOR TO GET HALAL PRODUCTS CERTIFIED

DOCUMENTATION SUBMISSION

AUDIT FIELDSAMPLING FOR LABORATORY

ANALYSIS

HALAL TESTING METHODS

ALCOHOL

FAT AND OIL - EMULSIFIER

PROTEIN AND GELATIN

MEAT SPECIATION

GENETICALLY MODIFIED ORGANISM

BRISTLE AND LEATHER

CHALLENGES IN HALAL PRODUCTS TESTING

i. Lack of sensitive test methods

ii. High cost for method development

iii. Products are complex and/or highly processed

iv. Low traceability as limited amount of halal/non-Halal ingredient is

used in certain products

v. Economically Motivated Adulteration products (EMA) – involving

the replacement of high cost ingredients with lower grade and

cheaper substitues

Gelatin is a product of thermal denaturation or disintegration of insoluble collagen by

partial acid or alkaline hydrolysis process.

Gelatin is only derived from sources rich in Type I collagen that generally contains no

Cys.

Consist of high molecular weight polypeptide with repetition of Gly-Pro-Hyp

A mixture of water-soluble protein (85 to 92 % of protein , mineral salts and moisture)

Type of sources – mainly derived from bones, hides, skin and cartilages

Bovine

Porcine

Marine - cold and warm water fish (scale and bone)

Poultry - chicken

Others - donkeys and horses

GELATIN

DENATURATION HYDROLYSIS PROCESS

GELATIN

Raw materials for industrial-scale manufacture are slaughter by-products andbyproduct of the fish-processing industry, available in sufficient quantities at aneconomical price

Animals that have been officially declared fit for human consumption.

2 main process:

i. Acid process

Limited to the tissue of younger animals

(calf skin : 2 – 3 years , pig skin : up to 18 months)

The collagen have a lesser degree of covalent bonding

Type A gelatin – IEP : 7 – 9, nitrogen content : 18.5%

ii. Alkaline process Bovine hides or bones Not suitable for pig skin because it leads to

saponification of the fat content, making furtherprocessing very difficult

Type B gelatin – IEP : 4.6 – 5.4, nitrogen content : 18 %

Fish gelatin can be conditioned using both acid and alkali process.

GELATIN

2 MAIN FUNCTIONAL PROPERTIES:

GELLING PROPERTIES

SURFACES EFFECTS PROPERTIES

APPLICATION AREA:

FOOD AND BEVERAGE , 29%

NEUTRACEUTICALS, 25.80%

PHARMACEUTICALS, 21%

PHOTOGRAPHY, 13.50%

COSMETICS, 5.50%

OTHERS, 6 %

GELATIN

Food and beverage, 29%

Neutraceuticals, 25.80%

Pharmaceuticals, 21%

Photography, 13.50%

Cosmetics, 5.50%

Others, 6.10%

AMINO ACIDS ANALYSIS

AMINO ACIDS

Containing an amine group, a carboxylic acid group, and a side-chain that

is specific to each amino acid.

Basic elements are carbon, hydrogen, oxygen, and nitrogen

The side-chain make an amino acid a weak acid or a weak base, a

hydrophilic if the side-chain is polar or a hydrophobic if it is non polar.

Serve as the building blocks of proteins

20 amino acids are naturally incorporated into polypeptides and are called

proteinogenic or standard amino acids and are encoded by the universal

genetic code.

9 standard amino acids are called "essential" for humans

SAMPLE WEIGHT, 0.18 G

MIX WITH 5ML OF 6N HCL

(HEAT AT 110OC, 25 HRS)

COOLING DOWN THE MIXTURE

ADD IN 4 ML OF 2.5MM AABA (INTERNAL STD)

DILUTE TO 100 ML WITH DISTILLED WATER

FILTER 2 ML OF THE TEST SOLUTION USING

0.45 µM CELLULOSE ACETATE MEMBRANE

TAKE 10 µL OF THE ALIQUOT FOR DERIVATIZATION

(70 UL OF BORATE BUFFER &

20 UL OF ACCQ REAGENT)

HEAT SAMPLE AT 550C, FOR 10 MIN

INJECT 10 UL OF SAMPLE TO HPLC

EQUIPPED WITH FLUORESCENCE DETECTOR

AMINO ACID ANALYSIS

15

INSTRUMENT CONDITIONS :

Equipment - Waters® Alliance System (2695 separation module)Waters® 2475 Multi-λ Fluorescence detector(250 nm excitation, 395 nm emissions)

HPLC Column – Waters AccQ•Tag amino acids analysis( 3.9 mm X 150 mm i.d, 4 µm)

Column temperature – 36OC Injection volume – 10 µl Flow rate – 1 ml/min

Gradient Elution : (A) AccQ•TagTM Eluent A, concentrate (B) Deionised water(C) Acetonitrile

Dilution factor – 0.01 Data acquisition – Waters EmpowerTM Pro software

DIPEPTIDEHCl

OTHER DERIVATIZATION REAGENTS

Derivative reagents Effects

Phenylisothiocyanate (PITC) Rapid with high performance analysis but consists of multiple steps, time

consuming

Orthophthalaldehyde (OPA) Reacts only on primary amino acids

Dabsyl chloride Has large interfering peaks due to excess reagent

Dansyl chloride & fluoreny methy

chloroformate (FMOC-CL)

Can form multiple derivatives with selected amino acids

Accq fluor reagent Reacts with primary and secondary amines in a few seconds with little matrix

interference.

Both AMQ and AQC-derivatives amines have the same excitation maximum but

different in emission maximum which allowed for the selective detection of the

AQC-derivatives in the presence of a large excess of AMQ.

The optimized chromatographic conditions can be evaluated at sub-picomolar

detection limits within sub-microgram sample levels

Detection on primary and secondary amines

METHOD VALIDATION AND MEASUREMENT OF

UNCERTAINTY

METHOD DEVELOPMENT

Method is Specific

• Difference in RT <0.2

• Difference in % peak area

<1.5%

• Rs > 1.5 (most AA)

Amino acid Calibration in aqueous solution r2 Calibration in matrix solution r2

Hyp Y = 0.0033x - 0.0081 1.0000 Y = 0.0030x + 0.0262 0.9990

Asp Y = 0.0030x + 0.0262 0.9999 Y = 0.0026x + 0.0521 1.0000

Ser Y = 0.0045x + 0.0146 1.0000 Y = 0.0042x + 0.0115 1.0000

Glu Y = 0.0031x + 0.0625 0.9977 Y = 0.0031x + 0.0825 0.9999

Gly Y = 0.0049x - 0.0514 0.9973 Y = 0.0042x + 0.0715 0.9996

His Y = 0.0076x - 0.0264 0.9998 Y = 0.0068x + 0.0591 1.0000

Arg Y = 0.0073x - 0.0609 0.9997 Y = 0.0067x + 0.0518 0.9996

Thr Y = 0.0078x + 0.0132 0.9996 Y = 0.0086x - 0.0233 0.9997

Ala Y = 0.0076x - 0.0822 0.9969 Y = 0.0094x - 0.1149 0.9991

Pro Y = 0.0037x + 0.0174 0.9998 Y = 0.0035x + 0.0290 1.0000

Cys Y = 0.0008x + 0.0066 0.9999 Y = 0.0008x + 0.0043 1.0000

Tyr Y = 0.0076x - 0.0245 0.9981 Y = 0.0073x + 0.0026 0.9999

Val Y = 0.0115x + 0.0858 0.9997 Y = 0.0107x + 0.1141 0.9998

Met Y = 0.0112x + 0.0308 0.9994 Y = 0.0111x + 0.0317 0.9990

Lys Y = 0.0045x + 0.0879 0.9981 Y = 0.0053x + 0.0581 0.9988

Ile Y = 0.0160x + 0.1251 0.9996 Y = 0.0137x + 0.2119 1.0000

Leu Y = 0.0180x + 0.0602 0.9994 Y = 0.0180x + 0.0732 0.9996

Phe Y = 0.0212x + 0.0449 0.9998 Y = 0.0218x + 0.0793 1.0000

F-test: Residual variance are not different

t-test: The slopes are not different

23

VALIDATION : DETECTION AND QUANTITATION LIMIT

8 concentrations level (pmol/μl): 37.5, 50, 100, 250, 500, 1000, 1500 & 2000

result (OLSM method) –a) Regression acceptedb) non linear curvec) working range unaccepted

Action – Discard outliers.

-2.00

-1.50

-1.00

-0.50

0.00

0.50

0 500 1000 1500 2000

Are

a r

ati

o

Concentration (pmol/ul)

ASP

Upper limit Lower limit yi -ý

OUTLIERS

-0.25-0.20-0.15-0.10-0.050.000.050.100.150.200.25

0 200 400 600 800 1000 1200Are

a r

ati

o

Concentration (pmol/ul)

ASP

Upper limit Lower limit yi -ý

6 concentrations level (pmol/μl): 37.5, 50, 100, 250, 500, & 1000

result (OLSM method) –a) Regression acceptedb) Linearity acceptedc) working range accepted

Action – Develop calibration curve

25

Amino acid Calibration equations r2

Hyp Y = 0.00453x - 0.1769 0.98

Asp Y = 0.00257x + 0.0879 0.99

Ser Y = 0.00486x - 0.0531 0.99

Glu Y = 0.0031x + 0.0538 0.99

Gly Y = 0.00635x - 0.2645 0.97

His Y = 0.00936x - 0.2810 0.99

Arg Y = 0.00841x - 0.1206 1.00

Thr Y = 0.00861x - 0.1080 0.99

Ala Y = 0.0071x + 0.0137 0.98

Pro Y = 0.0037x + 0.0061 1.00

Cys Y = 0.0009x - 0.0155 0.99

Tyr Y = 0.0095x - 0.3066 0.98

Val Y = 0.0118x + 0.0236 0.99

Met Y = 0.0125x - 0.1791 0.99

Lys Y = 0.0039x + 0.1709 0.98

Ile Y = 0.0165x + 0.0114 0.99

Leu Y = 0.0184x - 0.0090 0.99

Phe Y = 0.0284x - 1.0510 0.98

y = 0.0026x + 0.0893

r² = 0.985

0.00

0.50

1.00

1.50

2.00

2.50

3.00

-100 100 300 500 700 900 1100

Area r

atio

Concentration (pmol/µl)

ASP

Working range : 37.5 – 1000 pmol/μl

26

Method precision : CV < 10%

r value :difference between 2 values should be lower than or equal to r

Method trueness (recovery) : average 99 % determined: range ≈ 64 – 111 %: IQC spiking ≈ 250 pmol/µl

27

ESTIMATING UNCERTAINTIES FOR AMINO ACIDS IN GELATIN

CHEMOMETRICS

‘The science of relating measurements made on a chemical system or process to the state ofthe system via application of mathematical or statistical methods.’

(International Chemometrics Society)

‘The chemical discipline that uses mathematical and statistical methods, (a) to design or selectoptimal measurement procedures and experiments, and (b) to provide maximum chemicalinformation by analyzing chemical data.’

Journal of Chemometrics (Wiley) andChemometrics and Intelligent Laboratory Systems (Elsevier).

• Was coined by Svante Wold (Swede) and Bruce R. Kowalski (American) in 1972.

• Early applications involving multivariate classification of analytical chemical

datasets.

• Current developments :–

a) involving very complex datasets (metabolomics or proteomics).

b) new application that are biologically driven and emerging a new interface

between chemometrics and bioinformatics

c) forensics (the use of chemical and spectroscopy information to determine

the origins of samples)

d) pharmaceuticals ( multivariate image analysis)

e) chemical engineering

f) thermal analysis (materials)

Basic Statistics, Signal Processing, Factorial Design, Calibration, Curve Fitting,

Factor Analysis, Detection, Pattern Recognition and Neural Networks

PATTERN RECOGNITION (PR)

Exploratory Data Analysis

(EDA)

Principal Components

Analysis (PCA)

Factor Analysis (FA)

Unsupervised PR

(detect similarities)

Cluster Analysis

Supervised PR (Classification)

Discriminant Analysis

SIMCA

PLS

K Nearest Neighbours

Multiway PR

Tucker3

Models

PARAFAC

Unfolding

• Is a subset to an exploratory data analysis (EDA) that aims to determine

underlying information from multivariate raw data.

• It is a technique that will reduce the dimensionality of a data set consisting

of a large number of interrelated variables and transform it to a new set of

uncorrelated variables called principal components (PCs).

• The variations present in the original data were retained as much as possible

to build up groups of orthogonal axes representing the PCs.

• Data pre-treatment such as centering and normalization technique was

performed to facilitate the process of differentiation among samples by

reducing the variation of the variables in the data.

The raw data were imported to Unscrambler X software version 9.7. Data matrix (X) is in the form of an (m x n) containing the responses for the n variables in

each of the m samples. Concepts in PCA:

i. rank the data matrix - identify the amino acids that are significantly present in all gelatins (n variables)

ii. PCA transforms the original data matrix into a number of principal components (PCs) or a new co-ordinate system (axes)

iii. The axes are located in the centre of the data points.iv. The first PC lies along the direction of the maximum variances of the data while the

second PC lies along the direction of the second highest variances and the process continues up to certain PCs where the total variances have been accounted.

v. The linear function of new variables constructed by separate PC is uncorrelated and having an orthogonal properties.

vi. The variation is expressed in percentage under a number of successive PCs.vii. The remaining percentage number is usually represented by error or noise.

• In matrix terms (chemical factors) : X = C.S + E • In PCA terms : X = T .P + E

X is the original data matrix

S = p is a matrix consisting of the spectra of each compound ; LoadingsC =T is a matrix consisting of the elution

profiles of each compound; Scores

E is an error matrix (the same size as X)

Each scores matrix consists of a series of column vectors and each loadings matrix consist a series of row vectors

S

A

M

P

L

E

S

,

m

VARIABLES, n

The PCA will decompose the variation of the data matrix (X) into scores (T), loadings (P) and a residuals matrix (E)

i. Eigenvalue - The amount of variation explained by each PC. Expressed as a percentage of theoverall sum of squares of the entire data matrix.

ii. Eigenvector – provides the weight to the new variables and defined the direction on to which data can be projected.

iii. Hotelling’s T2 ellipse - identify the accepted data points within 95% of confidence limits. These data points are lying inside the ellipse. The remaining 5% are the rejected data that lie outside the ellipse

iv. Scores plot - identify the samples groupings, outliers and other strong patterns in the datav. Loading plot - interprets the relationships among variables that contribute to the effects of

sample grouping in the score plots.vi. Correlation loadings plot - consisting of two ellipse, explaining the 50% (inner circle) and 100%

(outer circle) of explained variance limits. vii. Influence plot – measure the distance of each point (sample) from the centre data point (a

grouping data) or the PC model. Detect outliers. viii. Explained variance plot - measures the distance of variables from its mean value and cause

variation in the data. The variation is expressed in percentage under a number of successive PCs.

Porcine

Bovine

Fish

Porcine

Bovine

Fish

at 0.1, 1, 5, 10, 30, 40% (w/w)

Porcine

Bovine

Porcine

Fish

EXPECTATION

CO

NT

AM

INA

TIO

N :

0.1

% (

W/W

)

CO

NT

AM

INA

TIO

N :

0.1

, 5,1

0, 3

0 &

40

% (

w/w

) Bovine gelatin

Porcine gelatin

Fish gelatin

PUBLICATIONS

PUBLICATIONS

Accepted by JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS –on 8th July 2016

Estimation Of Uncertainty From Method Validation Data: Application To A Reverse-phase High-performance Liquid ChromatographyMethod For The Determination Of Amino Acids In Gelatin Using 6-aminoquinolyl-N-hydroxysuccinimidyl Carbamate Reagent

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