PROF.

DR.

ART

EMİS

KARA

ALİ

Ista

nbul T

echn

ical u

nive

rsity

Food

Eng

inee

ring

Dep

artm

ent

This

cou

rse

cove

rs t

he p

rinc

iple

s of

ch

emic

al a

nd in

stru

men

tal m

etho

ds f

or

the

qual

itat

ive

and

quan

tita

tive

ana

lyse

s of

foo

d co

mpo

nent

s (m

oist

ure,

pro

tein

, ca

rboh

ydra

te, l

ipid

s, m

iner

als

and

vita

min

s), l

inki

ng t

hem

to

the

basi

c ch

emic

al s

truc

ture

s an

d pr

oper

ties

of

moi

stur

e, p

rote

in, c

arbo

hydr

ate,

lipi

ds,

min

eral

s an

d vi

tam

ins

and

thei

r ro

les

in

food

sys

tem

s .

COURS

E OBJ

ECTI

VES:

-Pro

vide

a k

nowl

edge

on

anal

ytic

al m

etho

ds d

evel

oped

fo

r el

ucid

atin

g t

he c

ompo

siti

on o

f fo

ods,

the

ir

chem

ical

and

phy

sica

l pro

pert

ies

as w

ell a

s fo

r in

vest

igat

ing

foo

d a

uthe

ntic

ity;

-App

ly t

his

know

ledg

e wi

th p

arti

cula

r em

phas

is o

n ap

plic

atio

n of

ana

lyti

cal c

hem

istr

y to

foo

ds b

y pr

ovid

ing

vari

ous

sele

cted

exa

mpl

es f

or in

divi

dual

fo

od c

omm

odit

y ty

pes.

It is

inte

nded

to

give

som

e ba

ckgr

ound

in

form

atio

n on

pri

ncip

les

of o

f m

etho

ds

deve

lope

d fo

r fo

od a

naly

sis.

Fund

amen

tal p

rinc

iple

s an

d ap

plic

atio

ns

will

be s

tres

sed

rath

er t

han

deta

ils o

f pr

oced

ures

.

Not

hing

can

rep

lace

act

ual h

ands

-on

labo

rato

ry e

xper

ienc

e as

a le

arni

ng t

ool;

howe

ver,

thi

s co

urse

is a

ttem

ptin

g to

m

ake

a sy

nops

is o

f bo

th c

onve

ntio

nal a

nd

nove

l tec

hniq

ues

and

the

theo

ries

un

derl

ying

the

m.

CO

UR

SE O

UTL

INE

1.In

trod

uctio

n :

Bod

ies

enga

ged

in

food

an

alys

es,

stan

dard

s us

ed,

legi

slat

ive

aspe

cts

2.Sa

mpl

ing

and

tech

niqu

es

: R

elat

ed

stat

istic

al

conc

epts

, ty

pes

of

sam

ples

an

d sa

mpl

ing,

an

d sa

mpl

e ha

ndlin

g

3.Pr

oxim

ate

anal

ysis

fo

r nu

triti

on

labe

lling

: A

com

pend

ium

of

all

avai

labl

e m

etho

ds f

or m

oist

ure,

pr

otei

n , l

ipid

, car

bohy

drat

e an

alys

es, a

s w

ell a

s th

e an

alys

es

for

quan

tifyi

ng

and

char

acte

rizin

g in

divi

dual

co

mpo

nent

s of

pr

otei

ns,

lipid

s an

d ca

rboh

ydra

tes,

the

resp

ectiv

e pr

inci

ples

, pro

cedu

res,

ap

plic

atio

ns,

caut

ions

, ad

vant

ages

an

d di

sadv

anta

ges

of

indi

vidu

al

met

hods

4. W

et a

nd d

ry a

shin

g, lo

w te

mpe

ratu

re p

lasm

a as

hing

, m

icro

wav

e as

hing

, prin

cipl

es a

nd in

stru

men

tatio

n,

appl

icat

ions

, pos

t ash

ing

proc

edur

es (a

lkal

inity

and

so

lubi

lity

of a

sh)

5. M

iner

al a

naly

ses

on a

sh: g

ravi

met

ric, t

itrim

etric

, co

lrim

etric

met

hods

, ion

-sel

ectiv

e el

ectr

odes

, ato

mic

ab

sorp

tion

and

atom

ic e

mis

sion

spe

ctro

scop

ies,

ICP-

AES

6. A

naly

sis

of v

itam

ins

(bio

assa

ys, m

icro

biol

ogic

al a

ssay

s,

phys

ico-

chem

ical

met

hods

, UV-

vIS

spec

trop

hoto

met

ry,

fluor

omet

ry, l

iqui

d ch

rom

atog

raph

y)

7. E

nzym

es: D

eter

min

atio

n of

enz

ymic

activ

ity,

appl

icat

ions

of e

nzym

es in

oth

er a

naly

ses

as a

naly

tical

ai

ds

Stud

ent p

rese

ntat

ions

on

sele

cted

topi

cs:

�8.

Con

tam

inan

ts :

Myc

otox

ins,

pes

ticid

es,

anim

aldr

ugs

�9.

Foo

d ad

ditiv

es:

sulp

hurd

ioxi

de,

food

dy

es

(qua

litat

ive

and

quan

titat

ive)

�10

.Ana

lyse

s fo

r aut

hent

icity

test

ing

�11

. An

alys

is

of

sens

ory

attr

ibut

es:

Obj

ectiv

e m

etho

ds f

or c

olor

and

tex

ture

an

alys

es

�CO

UR

SE E

VALU

ATIO

N C

RIT

ERIA

Midte

rm E

xaminat

ion:

30%

Fina

l Ex

aminat

ion

: 50

%Ass

ignm

ent

:

20%

�TE

XTB

OO

K:

��F

ood

Anal

ysis

, S. N

iels

en, 1

998.

Refe

renc

e Bo

ok:

Food

Ana

lysis,

The

ory

and

Prac

tice

, Y.

Pom

eran

zan

d C.

E. M

eloa

n, 1

987.

I. I

NTR

ODUCT

ION

Why

Foo

ds A

re A

nalyze

d?

1. F

or r

outine

con

trols

(Off

icial Sur

veillan

ce) on

co

mmod

ities

on t

he f

ood

mar

ket,

co

nduc

ted

for:

a)Hea

lth

reas

ons-

vita

l co

mmod

ities,

af

fect

hea

lth

dire

ctly;

to s

ee t

hat

they

are

who

leso

me

and

hea

lthy

b)Ec

onom

ical

reas

ons-

for

trad

e

and

quality

aspe

cts

c) N

utrition

al L

abellin

g

Def

inition

of "

Adu

lter

ation"

:

All

kind

s of

che

ating:

i.e

.re

mov

al

of a

con

stitue

nt t

hat

shou

ld b

e pr

esen

t; a

ddition

of a

sub

stan

ce t

o incr

ease

bulk

or w

eigh

t, t

o mak

e it

appe

ar b

ette

r th

an it

really is,

to

redu

ce its

str

engt

h; p

artial o

r co

mplet

e su

bstitu

tion

of

one

prod

uct

for

anot

her,

or

conc

ealm

ent

of a

n infe

rior

or

damag

ed p

rodu

ct.

Obj

ective

s ar

e:

-Pr

otec

ting

co

nsum

ers

from

ad

ulte

ration

-Pr

otec

ting

pr

oduc

ers

from

dish

ones

t an

d un

fair

compe

tition

.In

dividu

al

exam

ples

of

fo

od

adulte

ration

includ

e us

e of

un

perm

itte

dad

ditive

s an

d ca

ses

wher

e a

valuab

le c

onst

itue

nt i

s de

liber

ately

remov

ed

2. F

or

quality

cont

rols

by

indu

strial f

irms.

3. R

esea

rch

purp

oses

4. F

or ind

ustr

ial de

velopm

ent

(bot

h t

o im

prov

e th

e pr

oduc

t qu

ality

and

to f

ormulat

e no

vel

prod

ucts

).

FOOD S

AFE

TY:

Only

way

to k

now

for

sure

tha

t a

food

is s

afe

to e

at is

to r

un

phys

ical,c

hemical,

biolog

ical

and

sens

ory

analys

is.

This

requ

ires

a "t

ripo

d" s

yste

m:

1. Y

ou h

ave

to h

ave

official

stan

dard

s an

d sp

ecificat

ions

, wh

ere

iden

tity

an

d qu

ality

criter

ia

of

food

s ar

e clea

rly

define

d an

d th

e an

alyz

ed s

amples

hav

e to

con

form

to

thes

e. (

TSE

, IS

O,

Code

x Alim

enta

rius

Sta

ndar

ds

and

somet

imes

bu

yer

tech

nica

l sp

ecificat

ions

.)

2. O

fficial met

hods

of

analys

is:

(Int

erna

tion

al

and

Nat

iona

l St

anda

rdized

M

etho

ds)

The

met

hods

of

th

e fo

llowi

ng

inte

rnat

iona

l as

sociat

ions

form

th

e ba

sis

of Tu

rkish

official

met

hods

given

in

TGKY

and

TS�

s:AOAC-

Ass

ociation

of

Off

icial Ana

lytica

l Ch

emists

AACC

-Ass

ociation

of

Cere

al C

hemists

AOCS

-Ass

ociation

of

Oil

Chem

ist

Societ

yIU

PAC-

Inte

rnat

iona

l Union

of

Pu

re

and

App

lied

Chem

ists

, Fo

od C

hemicals

Code

x

AO

AC

= A

ssoc

iati

on o

f O

ffic

ial A

naly

tica

l Ch

emis

ts.

�Pu

blis

hes

Off

icia

l Met

hods

of

Ana

lysi

s an

d th

e pr

esti

giou

s pe

riod

ical

� J.

of

AO

AC�

.

�O

rgan

ized

in 1

884

by U

SSta

te &

Fed

eral

Che

mis

ts.

�In

itia

lly c

over

ed f

erti

lizer

ana

lyse

s on

ly; n

owco

ver

food

s, d

rugs

, cos

met

ican

d ag

ricu

ltur

alpr

oduc

ts.

Ever

y f

ive

year

s th

ese

are

revi

sed,

by

incl

udin

g n

ewly

de

velo

ped

met

hods

, di

scar

ding

pla

usib

le o

nes.

Now

the

ir

onlin

e ve

rsio

ns a

re a

vaila

ble

upon

pur

chas

e.

3. L

egislation

s an

d of

ficially

auth

orized

bo

dies

to a

pply t

hese

.Fo

r ex

ample,

in

Euro

pean

co

mmun

ity,

all

expo

rts

and

impo

rts

have

to

co

nfor

m to

EC

regu

lation

s an

d st

anda

rds

. In

USA

the

re a

re

the

Code

of

Fe

dera

l Re

gulation

s,

USD

A

Stan

dard

s, EP

A Pe

sticide

Regu

lation

s, Fo

od

Add

itives

Amen

dmen

t et

c.In

Tur

key

we h

ave

the

TGKY

.Fu

rthe

rmor

e,

ther

e is

need

fo

r also

du

ly

accr

edited

labo

rato

ries

for

the

resu

lts

of

analys

es t

o be

off

icially

rec

ognise

d.

In T

urke

y, w

e ha

ve r

elev

ant

Turk

ish

stan

dard

s, T

urkish

Foo

d Co

dex.

(Pr

evious

ly G

MT-

Turk

ish

Food

Act

).

Aut

horize

d go

vern

men

tal or

ganiza

tion

s ar

e:

�M

inistr

ies

of A

gricultu

re a

nd o

f Hea

lth

�M

unicipalities

SOURC

ES O

F IN

FORM

ATI

ON

Somet

imes

th

e st

anda

rds

men

tion

ed

abov

e

are

not

suff

icient

and

you

have

to

dev

elop

you

r ow

n met

hod.

Fo

r th

is,

you

have

to

sear

ch f

urth

er i

n sc

ient

ific

liter

atur

e.

In s

earc

hing

liter

atur

e, f

irst

of

all

you

have

to

de

fine

ke

ywor

dsfo

r th

e indi

vidu

al su

bjec

t. Th

en,

proc

eed

with

th

e fo

llowi

ng s

ourc

es:

A.

Gene

ral Sou

rces

1. A

n ad

vanc

ed t

extb

ook

--th

eory

and

ref

eren

ces

allowi

ng y

ou t

o ge

t ev

en m

ore

dept

h an

d info

rmat

ion,

or

a h

andb

ook.

2.Fo

od

orga

niza

tion

s pu

blish

stan

dard

met

hods

sp

ecific t

o ce

rtain

type

s of

foo

ds.

a.St

anda

rd M

etho

ds f

or E

xaminat

ion

of D

airy

Pr

oduc

ts pu

blishe

d by

th

e Amer

ican

Pu

blic Hea

lth

Ass

ociation

b.Amer

ican

Ass

ociation

of

Cere

al C

hemist�s

"A

ppro

ved

Met

hods

of

Ana

lysis"

for

stan

dard

met

hods

relat

ing

to d

airy

and

cer

eal pr

oduc

ts.

c.Amer

ican

Oil

Chem

ists

Soc

iety

also

publishe

s st

anda

rd m

etho

ds f

or t

esting

oil

and

oil ba

sed

prod

ucts

.

B. C

ompu

terize

d Libr

ary

Sear

ches

Now

aday

s th

ere

are

mod

ern

info

rmat

ion

retr

ieva

l sy

stem

s; e

xamples

inc

lude

:

1. F

STA:

Food

Scien

ce a

nd T

echn

olog

yAbs

trac

tson

CD

-ROM

S,

which

star

t fr

om 19

69an

d co

ver

mor

e th

an12

00

jour

nals pu

blishe

d in 50

co

untr

ies.

Tu

rkish

Jour

nals,

Gõda

, an

dGõ

daTek

nolojis

iar

e includ

ed in

this s

ourc

e.

2. C

ompr

ehen

sive

Online

Dat

abas

es o

f:1)

USD

A:

Unite

d St

ates

of

Dep

artm

ent

of

Agr

icultu

re2)

Agr

icola

: In

clud

e indi

vidu

al r

epor

ts3)

FAO

:P

hDan

d mas

ter

thes

is o

n f

ood

-relet

adissu

es.

Gene

ral ar

ticles

in

the

area

-wi

ll co

ntain

met

hodo

logy

Abs

trac

ts f

rom C

hemical,

Biolog

ical, an

d Agr

icultu

ral jo

urna

ls -

on-l

ine

thro

ugh

FSTA

an

d re

quire

only a

unive

rsity

email ac

coun

t (u

se y

our

user

name

and

pass

word

to

log

on). Y

ou c

an a

lso

go t

o Ce

ntra

l Libr

ary

and

sear

ch v

arious

dat

abas

es u

sing

the

CD-

ROM

equ

ipmen

t av

ailable

in t

he r

efer

ence

are

a.

*The

sese

ldom

go b

ack

furt

her

that

199

0 ,

but

do n

ot f

orge

t th

at t

hese

sea

rche

s m

iss

the

logica

l de

velopm

ent

asso

ciat

ed w

ith

read

ing

whole

articles

(wi

th c

itat

ions

), a

nd

miss

read

ing

the

field

�yo

u ge

t on

ly y

our

key

word

ret

urns

.

C. R

eviews

You

might

con

sult r

eviews

(i.e.

Critical r

eviews

in

food

sc

ienc

e,

surv

eys,

or

"Adv

ance

s in

food

sc

ienc

e",

"Pro

gres

s in f

ood

scienc

e")

which

desc

ribe

th

e cu

rren

t av

ailable

know

ledg

e in th

e pa

rticular

field.

The

se c

an b

e as

sess

ed v

ia:

1. Ch

emical Abs

trac

ts-

This

publicat

ion

comes

ou

t an

nually

and

give

s a

bibliogr

aphy

of

all

chem

ical

review

s.2.

"Ana

lytica

l Ch

emistr

y"-

ever

y Apr

il -

broa

d an

d co

mplet

e co

vera

ge o

f an

alyt

ical a

reas

of

chem

istr

y.

3. "

Adv

ance

s in F

ood

Rese

arch

�-"C

ritica

l Re

view

s in

Food

and

Nut

rition

", "

Prog

ress

in

Food

and

Nut

rition

Sc

ienc

e",

and

"Rec

ent

Adv

ance

s in F

ood

Scienc

e" a

re

good

sou

rces

of

review

inf

ormat

ion.

D.

Abs

trac

ts1.

Ch

emical Abs

trac

ts-

publ

ish

over

20

0,00

0 ab

stra

cts

per

year

fro

m a

bout

10,

000

jour

nals.

2. Cu

rren

t Co

nten

ts-

lists

mor

e th

an 1,

000

jour

nals in

Agr

icultu

re a

nd r

elat

ed a

reas

.3.

"F

ood

Tec

hnolog

y�

is

publ

ishe

d by

th

e In

stitut

e of

Fo

od

Tec

hnolog

ists

an

d of

ten

cont

ains

info

rmat

ion

that

is ve

ry us

eful to

th

e fo

od a

nalyst

.4.

Dairy

Sc

ienc

e Abs

trac

tspu

blishe

s ab

stra

cts

relate

d to

dairy

pro

duct

s.

E.The

ses

The

sis

stud

ies

cond

ucte

d on

the

subj

ect

are

cove

red

in

"Disse

rtat

ion

Abs

trac

ts".

Gene

rally

con

tain ve

ry g

ood

liter

atur

e re

view

and

mor

e de

taile

d ap

proa

ches

to

expe

rimen

ts t

han

liter

atur

e pu

blishe

d in s

cien

tific

jour

nals.

"Che

mical A

bstr

acts

" ca

n be

use

d to

sea

rch

thes

es b

y su

bjec

t.

F.

Sympo

sia

Proc

eeding

s of

sym

posia

cont

ain

individu

al a

rticles

that

may

ha

ve d

etailed

met

hods

sec

tion

;bu

t o

ften

are

of s

pace

-va

riab

le q

ualit

y an

d ar

e no

t co

mplet

e in t

heir c

over

age

of a

su

bjec

t.

G. T

rade

Pub

licat

ions

e.g.

�F

ood

Ana

lysis"

"F

ood

Prod

uct

Test

ing"

"A

mer

ican

La

bora

tory

" -

may

co

ntain

some

good

info

rmat

ion

on

adve

rtisem

ents

in

thes

e pe

riod

icals

�th

ough

be s

kept

ic w

hen

read

ingth

eclaims

QU

ALI

TY A

SSU

RAN

CE A

ND

Q

UA

LITY

CO

NTR

OL

IN

LABO

RATO

RY E

XPE

RIM

ENTS

TS E

N I

SO/I

EC 1

7025

(200

0)

�Den

eyve

Kalib

rasy

onLa

bora

tuva

rlarõnõn

Yete

rlili

giiç

inGe

nelŞ

artl

ar�

Stan

dard

Lab

orat

ory

Prac

tice

s

�Sa

mpl

e ha

ndlin

g be

fore

the

labo

rato

ry�

Sam

ple

hand

ling

in th

e la

bora

tory

�In

stru

men

tatio

n�

Rea

gent

s an

d st

anda

rds

�An

alyt

ical

pro

cedu

res

�St

anda

rd o

pera

ting

pro

cedu

res

Sam

ple

Han

dlin

g Be

fore

th

e La

bora

tory

�Co

llect

ion

proc

edur

es�

Repr

esen

tati

vene

ssof

sam

ple

colle

ctio

n�

Colle

ctio

n de

vice

s, e

.g.,

pum

ps�

Cont

aine

rs, e

.g.,

acid

-was

hed

bott

les

�Pr

eser

vati

ves,

e.g

., ac

id, c

old

room

, fr

eezi

ng�

Tran

spor

tati

on o

f sa

mpl

es�

Chai

n of

cus

tody

Sam

ple

Han

dlin

g in

the

La

bora

tory

�La

b ID

num

ber

�St

orag

e lo

catio

n�

Hol

ding

tim

es�

Dis

posa

l

Inst

rum

enta

tion

�Is

it a

ppro

pria

te fo

r the

task

?�

Is it

cal

ibra

ted?

�Is

it m

aint

aine

d?�

Is it

cle

an b

efor

e an

d af

ter u

se?

Reag

ents

and

Sta

ndar

ds

�Pu

rity

�Pr

epar

atio

n�

Stan

dard

izat

ion

�La

belin

g�

Shel

f life

�D

ispo

sal

Ana

lyti

cal P

roce

dure

s�

Wat

chon

e, d

oon

e, te

ach

one

�Pl

an a

head

�St

art s

mal

l

�R

ead

the

SOP

and

unde

rsta

nd th

e pr

inci

ples

�D

o no

t tak

e sh

ortc

uts.

�D

ocum

ent e

very

thin

g .

Stan

dard

Ope

rati

ng P

roce

dure

s�

Det

aile

d in

stru

ctio

ns�

Spec

ific

for a

giv

en la

b�

Haz

ards

are

kno

wn,

und

erst

ood,

and

re

spec

ted

�R

egul

ar m

aint

enan

ce a

nd u

pdat

ing

or

revi

ew�

Prom

ote

unifo

rmity

of p

roce

dure

to

min

imiz

e ra

ndom

var

iatio

ns a

nd

max

imiz

e co

mpa

rabi

lity

of d

ata

QU

ALI

TY A

SSES

SMEN

T TE

CHN

IQU

ES�

Rep

licat

es�

Ran

dom

dup

licat

es�

Spik

ed s

ampl

es�

Stan

dard

ana

lyte

s�

Inte

rnal

test

sam

ples

�In

terla

bora

tory

com

paris

on�

Con

trol c

harts

Repl

icat

es

�R

epet

itive

mea

sure

men

t on

the

sam

e un

know

n sa

mpl

e�

Goo

d as

sess

men

t of p

reci

sion

�R

epor

t ave

rage

of r

esul

ts�

Tim

e-co

nsum

ing

and

cost

ly

Rand

om D

uplic

ates

�R

ando

mly

sel

ect o

ne s

ampl

e pe

r an

alyt

ical

bat

ch

�Fo

r exa

mpl

e, e

very

10-

15 s

ampl

es

Spik

ed S

ampl

es

�Kn

own

addi

tion

of a

naly

teto

a re

al

sam

ple

�M

easu

re s

pike

d sa

mpl

e an

d un

spik

edsa

mpl

es�

Com

pute

% re

cove

ry�

Hel

ps to

acc

ount

for m

atrix

effe

cts

�H

elps

to a

sses

s bo

th a

ccur

acy

and

prec

isio

n

Stan

dard

Ana

lyte

s

�Pu

re m

ater

ials

mad

e in

to s

tand

ards

to

use

regu

larly

�U

sed

to c

alib

rate

inst

rum

enta

tion

�As

sess

men

t of a

ccur

acy

�C

ontro

l cha

rt

Inte

rnal

Tes

t Sa

mpl

es

�R

eal m

ater

ial

�C

ompo

sitio

n si

mila

r to

unkn

owns

�W

ell-c

hara

cter

ized

mat

eria

l�

Con

trol c

hart

data

Met

hod

Valid

atio

nIn

terl

abor

ator

yCo

mpa

riso

ns

Cont

rol C

hart

s�

Whe

n to

thro

w o

ut a

n ob

serv

atio

n if

ther

e is

no

know

n so

urce

of e

rror?

A c

ontro

l cha

rt ca

n of

fer a

rela

tivel

y un

bias

ed w

ay o

f dec

idin

g if

som

ethi

ng is

real

ly w

rong

in th

e an

alys

is.

�Th

e ch

arts

doc

umen

t the

pre

cisi

on o

f an

anal

ysis

�Th

ey a

llow

us

to id

entif

y tre

nds

in th

e da

ta

Plot

ting

Con

trol

Cha

rts

�x-

axis

tem

pora

l sca

le: d

ate,

tim

e se

quen

ce�

y-ax

is: a

naly

tical

resu

lts, d

iffer

ence

be

twee

n re

sults

, dev

iatio

n fro

m tr

ue

valu

e, %

reco

very

of t

heor

etic

al v

alue

�U

se lo

ng-te

rm m

eans

and

cal

cula

ted

stan

dard

dev

iatio

ns fo

r a s

tand

ard

sam

ple.

�W

arni

ng le

vels

: e.g

., 1

stan

dard

de

viat

ion

�R

ejec

tion

leve

ls: e

.g.,

3 st

anda

rd

devi

atio

ns

War

ning

and

Rej

ecti

on

Leve

ls

Reje

ctio

n Cr

iter

ia (t

ypic

al)

�an

y po

int o

utsi

de 3

s

�tw

o po

ints

in a

row

out

side

2s

�fo

ur in

a ro

w o

utsi

de 1

s

�tw

o in

a ro

w w

ith a

rang

e of

>4s

�te

n in

a ro

w o

n th

e sa

me

side

of t

he

mea

n

Out

-of-

Cont

rol D

ata

�R

ejec

t the

dat

a se

t tha

t is

run

with

that

co

ntro

l

�C

orre

ct th

e pr

oble

m

�R

epea

t, if

poss

ible

�U

se w

ith d

iscl

aim

er, i

f nec

essa

ry

Compo

nent

s of

Met

hod

Relia

bilit

y(p.

57-6

1)Th

e se

lect

ed m

etho

d sh

ould p

osse

ss:

1. R

epro

ducibi

lity

: de

pend

son

diffe

renc

es a

mon

g diff

eren

t labo

rato

ries

wh

ich

perf

orm t

he s

ame

analys

is2.

Rep

eata

bilit

y :

depe

nds

on d

iffe

renc

es w

ithin

the

same

labo

rato

ry3.

Fre

edom

fr

om s

yste

mat

ic e

rror

4. S

pecificity

for

ana

lyze

d pr

oper

ty5.

Neg

ligib

le lim

it o

f er

rors

Acc

urac

y : Th

e de

gree

to

which

a mea

n es

timat

e ap

proa

ches

the

tru

e va

lue.

Prec

ision

: Th

e de

gree

to

which

two

dete

rminat

ion

yield

cons

iste

nt r

esults

.

Sens

itivity

: Ra

tio

betw

een

mag

nitu

de o

f inst

rumen

tal

resp

onse

and

amou

nt o

f an

alyt

e (smallest

mea

sura

ble

diff

eren

ce b

etwe

en t

wo s

amples

)

For

dete

rmining

thes

e pr

oper

ties

, kn

own

amou

nts

of

the

analyt

e at

thr

ee d

iffe

rent

(low,

int

ermed

iate

, high

) co

ncen

trat

ions

are

spike

d to

7 r

eplic

ates

eac

h on

blan

k sa

mples

, an

d an

alys

ed. ¯X

, ra

nges

of

stan

dard

dev

iation

s an

d co

efficien

ts o

f va

riat

ions

are

de

term

ined

for

eac

h leve

l to

yield v

alue

s fo

r ac

cura

cy, pr

ecision,

and

sen

sitivity

I.

Acc

urac

y:Po

or P

reci

sion

:Poo

r

3.A

ccur

acy:

Good

Prec

isio

n:Po

or

2.

Acc

urac

y:Po

or P

reci

sion

:Goo

d

4.

Acc

urat

e P

reci

se

Acc

urac

y an

d Pr

ecis

ion

(Cha

pter

4)

�Th

e �E

RRORS

�(an

y de

viat

ion

from

acc

urac

y) m

ight

be

eith

er:

�"r

ando

m-n

onre

prod

ucib

le, un

cont

rolla

ble"

�

Or

�"s

yste

mat

ic-r

epea

ts its

elf,

and

can

be

unde

r co

ntro

l".

�Fo

r ex

ample,

if

resu

lts

of a

nalyse

s ar

e alwa

ys t

oo h

igh,

th

at c

ould b

e be

caus

e th

e st

anda

rds

are

of lo

w po

tenc

y.

Such

err

ors

might

also

come

from

ana

lyst

's w

ay o

f ca

lculat

ion

or w

eigh

ing,

or

from

rea

gent

s an

d eq

uipm

ents

, or

en

viro

nmen

tal co

nditions

-brigh

t su

nlight

, high

hum

idity,

etc.

So

the

analys

t sh

ould be

cap

able o

f

thinking

critica

lly in

asse

ssing

thes

e situ

ations

(Rea

d ne

xt s

lides

in T

urkish

for

lear

ning

mor

e ab

out

erro

rs).

Reco

rd-K

eeping

and

Dat

a Pr

oces

sing

�K

eep

orig

inal

fiel

d no

tes

�D

ata

tran

sfer

�Sa

mpl

e id

entit

y

�C

ompu

ter p

roce

ssin

g

Compu

ter

Proc

essing

�Sa

ve ra

w d

ata

and

inte

rmed

iate

ste

ps

�Sa

ve p

rogr

ams

and

mac

ros

(to re

trace

ste

ps)

�D

ocum

ent d

ata

files

, pro

gram

s, m

acro

s, e

tc.

�Ba

ck-u

p fre

quen

tly

�St

ore

data

in tw

o pl

aces

Field

Not

eboo

ks�

Sepa

rate

not

eboo

ks fo

r di

ffere

nt p

arts

of p

roje

ct1.

raw

vs.

pro

cess

ed d

ata

2. s

epar

ate

by ty

pe o

f mea

sure

men

t�

Loos

e-le

af, r

emov

e ch

rono

logi

cal d

ata

shee

t to

take

to fi

eld

�D

ocum

ent r

eam

s of

aut

omat

ed d

ata

(inst

ead

of in

clud

ing

in n

oteb

ook)

ERRO

R A

SSES

SMEN

T

�Sy

stem

atic

Erro

rs

�R

ando

m E

rrors

�Er

ror A

naly

sis

and

Sign

ifica

nt F

igur

es(M

easu

rem

ent o

f Unc

erta

inty

:Ölç

üm B

elirs

izliğ

i)

Syst

emat

ic E

rror

s

�O

pera

tor e

rror

�In

stru

men

t erro

r

Rand

om E

rror

s

�O

pera

tor e

rror

�N

oise

and

drif

t in

elec

troni

c ci

rcui

ts

�Te

mpe

ratu

re

varia

tions

�Bu

ildin

g vi

brat

ions

�Pa

ssin

g tra

ffic

�H

umid

ity

20

True

val

ue

+3σ

-3σ

+2σ

-2σ

+σ-σ

Mea

sure

men

t res

ults

serie

s di

strib

utio

n

Con

fiden

ce in

terv

al o

f 68

,27%

(k=1

), 99

,73%

(k=3

)95

,45%

(k=2

),

Arit

hmet

ic m

ean

valu

e

Ran

dom

erro

rEr

ror

Syst

emat

ic e

rror

Met

rolo

gica

l ter

ms

Ger

çek

Değ

erA

ritm

etik

Orta

lam

a

Ras

tgel

eH

ata

Hat

aSist

emat

ik H

ata

II.SA

MPL

ING

and

Sample

Prep

arat

õon

(Cha

pter

5)

SAM

PLE

The

sam

ple

shou

ld b

e re

pres

enta

tive

of

the

whol

e m

ater

ial i

f th

e an

alys

is o

f th

e sa

mpl

e is

to

have

any

mea

ning

.

Sam

ple

shou

ld b

ea

com

posi

te t

hat

adeq

uate

ly s

ampl

es f

rom

eac

h of

the

po

pula

tion

s wi

thin

the

pro

duct

to

be

anal

yzed

.

SAM

PLIN

GSam

pling

is t

he p

roce

ss o

f dr

awing

or s

elec

ting

con

tainer

s or

sa

mple

units

from

a lot

or

prod

uction

. As

a re

sult o

f sa

mpling,

inf

ormat

ion

is o

btaine

d by

which

an

estimat

e ca

n be

mad

e to

acc

ept,

rej

ect

orne

gotiat

e th

e mer

chan

dise

in

ques

tion

. Sam

pling

proc

edur

es w

hich

con

tain b

oth

sample

size

and

acce

ptan

ce c

rite

ria

are

common

ly r

efer

red

to a

s �a

ccep

tanc

e sa

mpling�

.

The

re a

re m

any

type

s of

acc

epta

nce

sampling

syst

ems

in u

se

toda

y. A

plan

that

is

suitab

le f

or o

nepr

oduc

t or

typ

e of

insp

ection

may

be

entire

ly u

nsuita

ble

for

anot

her

prod

uct

or

insp

ection

sys

tem. The

plan

selec

ted

is d

eter

mined

to

a larg

e ex

tent

by

the

degr

ee t

o wh

ich

it s

atisfies

the

need

s of

the

use

r.

�In

deve

loping

acc

epta

nce

sampling

plan

s, initial

cons

ider

ation

shou

ld b

e give

n to

qua

lity

evalua

tion

of t

he

end

prod

uct.

This

requ

ires

ope

ning

of

cont

aine

rs w

ith

resu

ltan

t loss

of

prod

ucts

. Th

is t

ype

ofinsp

ection

is

refe

rred

to

as �de

stru

ctive

sampling�

. Not

only

is t

he los

s of

pro

duct

an

impo

rtan

tco

nsid

erat

ion,

but

also

dest

ruct

ive

sampling

is g

ener

ally q

uite

tim

e co

nsum

ing,

�Co

nseq

uent

ly,

both

insp

ection

tim

e an

d ec

onom

ic los

s of

pr

oduc

t th

roug

h de

stru

ctive

insp

ection

are

significa

nt

limiting

fact

ors

in d

evelop

ing

sampling

plan

s fo

r qu

ality

evalua

tion

of

proc

esse

d fo

ods.

Sam

ple

size

mus

tne

cess

arily

be

relat

ively

small in o

rder

to

mak

e th

e plan

pra

ctical in

applicat

ion.

RISKS

The

aim

of

any

sampling

plan

sho

uld

be t

o ac

cept

mor

e �g

ood�

lot

s an

d re

ject

mor

e �b

ad�lot

s. S

ince

prob

ability

an

d ch

ance

are

inv

olve

d, d

ecisions

will,

of n

eces

sity

, invo

lve

an e

lemen

t of

risk.

This

risk

fact

or h

as t

o be

ac

cept

ed a

s a

part

of

any

sampling

proc

edur

e. O

ne m

etho

d of

red

ucing

the

buye

r�s

risk

of a

ccep

ting

deliver

ies

of n

on-

conf

orming

quality

is t

o incr

ease

sam

ple

size

. In

oth

er

word

s, t

he lar

ger

the

sample,

the

les

s risk

inv

olve

d in

acce

pting

�bad

� lots

. �I

nspe

ction

leve

l�is t

he t

erm ind

icat

ing

the

relative

amou

nt

of s

ampling

and

insp

ection

per

form

ed o

n lots

of

a give

n pr

oduc

t or

class

of

prod

ucts

.

The

valid

ity

of co

nclusion

s dr

awn

from

an

alys

is

depe

nds

very

highly

on s

ampling.

Popu

lation

: A

ny f

inite

grou

p or

collect

ion

having

a

prop

erty

th

at

dist

ingu

ishe

s item

s wh

ich

belong

fr

om

which

don'

t be

long

.Sa

mple:

A p

iece

or

item

tha

t sh

ows

the

quality

of

the

popu

lation

fro

m w

hich

it

was

take

n.Sa

mpling:

Select

ion

from

a p

opulat

ion

of a

finite

numbe

r of

sam

ples

for

ana

lysis.

Aliq

uot

(Tes

t po

rtion)

: Th

at qu

antity

of

sa

mple

suff

icient

for

mea

suring

the

pro

pert

y of

int

eres

t.

Step

s in s

ampling

are:

1. I

dent

ificat

ion

of p

opulat

ion

2. I

dent

ificat

ion

of s

ampling

sche

me

3. C

ollect

ion

of .-r

epre

sent

ative

samples

4.

Red

uction

to

aliquo

t size

In o

rder

to

be t

ruly r

epre

sent

ative,

sam

pling

shou

ld c

onfo

rm t

o st

atistica

l no

rms.

How

ever

, a

gene

ral ru

le is:

n=c√

N

Her

e,

n: N

umbe

r of

sam

ples

tak

en f

rom p

opulat

ion,

N:

The

popu

lation

(#

of

indi

vidu

al un

its

in

popu

lation

)Fo

r ex

ample,

if th

ere

are

2200

bo

xes

each

co

ntaining

24

sa

usag

es,

that

mak

es

5280

0 un

its.

The

re a

re t

ables

for

each

spe

cific

type

of

com

mod

ity.

(i.e.

if we

ight

of un

it

is<1

kg,

take

22

9 sa

mples

, if w

>1kg

, ta

ke 4

8 sa

mples

. )

C:A

fact

or

that

is

<1

for

homog

enou

s pr

oper

ties

, an

d c>

1 fo

r he

tero

gene

ous

samples

. I

n less

tha

n 28

obs

erva

tion

s we

can

co

ver

the

rang

e "±

3s".

Off

icial

Sampling

Plan

s :

Thes

e includ

e:

1. I

nspe

ction

Leve

ls ;Fo

r no

rmal t

rading

pur

pose

s Le

vel I

is

reco

mmen

ded.

In

the

case

of

disp

ute

or c

ontr

over

sy,

i.e.

for

Co

dex

refe

ree

purp

oses

, Le

vel II

is

reco

mmen

ded.

2. S

ample

Size

s in r

elat

ion

to lot

size

and

cont

aine

r size

; an

d

3. A

ccep

tanc

e Num

bers

.A s

ample

is d

rawn

at

rand

om f

rom t

he lot

acc

ording

to

the

appr

opriat

e sc

hedu

le in

the

Sampling

Plan

s.Ea

ch s

ample

unit is

exam

ined

acc

ording

to

the

requ

irem

ents

of

the

individu

al C

odex

Sta

ndar

d an

dclas

sified

as

eith

er

�acc

epta

ble�

or

as �de

fect

ive�

. Ba

sed

on t

he t

otal n

umbe

r of

�d

efec

tive

s � in t

hesa

mple,

the

lot

eithe

r �m

eets

� or

�fa

ils� th

ere

quirem

ents

of

the

Code

x st

anda

rd.

�Pla

ns a

pply

, acc

ordi

ng t

o th

e fo

llowi

ng c

rite

ria:

-M

eets

if t

he n

umbe

r of

�def

ecti

ves�

is e

qual

to,

or

less

tha

n,

the

acce

ptan

ce n

umbe

r of

the

appr

opri

ate

plan

.-

Fails

if t

he n

umbe

r of

�def

ecti

ves�

exc

eeds

the

acc

epta

nce

num

ber

of t

he a

ppro

pria

tepl

an.

�Nex

t s

lides

give

a ta

bular

pres

enta

tion

app

ropr

iate

for

acce

ptan

ce s

ampling

of p

repa

ckag

ed f

oods

whe

re a

n AQ

L*of

6.5

ha

s be

en a

ccep

ted

for

cert

ain

prod

uct

char

acte

rist

ics.

�A

QL*

:Acc

epta

ble

Qua

lity

Leve

l. Th

is c

hara

cter

isti

c is

de

fine

d as

�the

max

imum

perc

ent

defe

ctiv

e un

its

in lo

ts t

hat

will

be a

ccep

ted

mos

t of

the

tim

e (a

ppro

xim

atel

y 95

perc

ent

of

the

tim

e)�.

Lots

or

prod

ucti

on c

onta

inin

g m

ore

defe

ctiv

e m

ater

ial w

ill b

e ac

cept

ed le

ss o

ften

-th

e ra

tio

of r

ejec

tion

to

acce

ptan

ce in

crea

sing

as

the

sam

ple

size

incr

ease

s an

d as

the

pe

rcen

t de

fect

ive

mat

eria

l in

the

lot

incr

ease

s.

Summar

y of

The

Tur

kish

Foo

d Co

dex

Sampling

Plans

A.(Fo

r ro

utine

cont

rols-

LEVE

L I)

A.1

. IF

NET

WEI

GHT I

S 1

KG o

r LE

SS:

(N)*

,(n

)**

(c)*

**<4

800

6 1

4801

-240

0013

2 24

001-

4800

021

3 48

001-

8400

0 29

4 84

001-

1440

00 48

6 14

4001

-240

000

84 9

>240

000

126

13

Her

e,*

N: Th

e po

pulation

**

n=Num

ber

of s

amples

tak

en f

rom p

opulat

ion

***c

= ac

cept

able n

umbe

r of

non

conf

orming

samples

A.2

.IF

NET

WEI

GHT

IS >

1 KG

,bu

t <

4,5

KG :

(N) (n

) (c

)<2

400

6 1

2401

-150

00 1

3 2

1500

1-24

000

21 3

24

001-

4200

0 29

4

4200

1-72

000

48 6

72

001-

1200

00 8

4 9

>120

000

126

13

A.3

.IF

NET

WEI

GHT is

>4,5

:(N

) (n

) (c

) <6

00 6

160

1-20

00 1

3 2

2001

-720

0 21

3

7201

-150

00 2

9 4

1500

1-24

000

48 6

24

001-

4200

0 84

9

>420

00 1

26 1

3

B. (Fo

r su

spicious

sam

ples

)LEV

EL 2

B.1.

IF

NET

WEI

GHT I

S 1K

G or

LES

S:

(N) (n

) (c

)<4

800

13 2

48

01-2

4000

21

3 24

001-

4800

0 29

4

4800

1-84

000

48 6

84

001-

1440

00 8

4 9

1440

01-2

4000

0 12

6 13

>2

4000

020

0 19

B.2.

IF

NET

WEI

GHT

IS >

1 KG

but<

4,5

KG :

(N) (n

) (c

)<2

400

13 2

24

01-1

5000

21

3 15

001-

2400

0 29

4

2400

1-42

000

48 6

42

001-

7200

0 84

9

7200

1-12

0000

126

13

>120

000

200

19

B.3.

IF N

ET W

EIGH

T IS

>4,

5 KG

:(N

) (n

) (c

) <6

00 13

260

1-20

00 2

1 3

2001

-720

0 29

4

7201

-150

00 4

8 6

1500

1-24

000

84 9

24

001-

4200

0 12

6 13

>4

2000

200

19

In u

sing

the

Sam

pling

Plan

s ,

the

follo

wing

inf

ormat

ion

shall be

kno

wn:

a Co

ntaine

r size

(ne

t we

ight

in

kg o

r lb

)b

Insp

ection

Lev

el

c Lo

t size

(N)

d Re

quirem

ents

of

the

Code

x St

anda

rd w

ith

resp

ect

to

prod

uct

quality

(i.e.

class

ificat

ion

of d

efec

tive

s an

d re

quirem

ents

for

acc

epta

nce

of t

he lot

).

The

follo

wing

ste

ps a

re t

aken

in I

NSP

ECTI

ON

a The

app

ropr

iate

ins

pect

ion

leve

l is s

elec

ted

as f

ollows

:In

spec

tion

Lev

el I

-Nor

mal s

ampling

Insp

ection

Lev

el I

I -

Dispu

tes

(Cod

ex r

efer

ee p

urpo

ses

sample

size

), e

nfor

cemen

t or

need

for

bet

ter

lot

estimat

e.

�b D

eter

mine

the

lot

size

(N), i.e

. nu

mbe

r of

primar

y co

ntaine

rs o

r sa

mple

units.

�c D

eter

mine

the

numbe

r of

sam

ple

units

(sam

ple

size

(n)

) to

be

draw

n fr

om t

he ins

pect

ion

lot,

cons

ider

ation

being

giving

to

cont

aine

r size

, lot

size

, an

d insp

ection

lev

el.

�d D

raw

at r

ando

m t

he r

equire

d nu

mbe

r of

sam

ple

units

from

the

lot

giving

pro

per

cons

ider

ationt

oco

de o

r ot

her

iden

tify

ing

mar

ks in

select

ion

of t

he s

ample

.

�e E

xamine

the

prod

uct

in a

ccor

danc

e wi

th t

he r

equire

men

ts o

f th

eCo

dex

Stan

dard

. Cl

assify

any

cont

aine

ror

sam

ple

unit w

hich

fails

to m

eet

the

spec

ified

quality

leve

l of

the

sta

ndar

d as

ade

fect

ive

on t

he b

asis o

f th

e clas

sifica

tion

of

defe

ctives

con

tained

in

the

Code

x St

anda

rd.

�f R

efer

to

the

appr

opriat

e Sam

pling

Plan

It is

not

nece

ssar

y to

res

trict

the

sample

size

to

the

minim

um c

orre

spon

ding

to

the

appr

opriat

e lot

size

and

Insp

ection

Lev

el. In

all

case

s a

larg

er s

ample

may

of

cour

se b

e dr

awn.

Stat

istica

l co

ncep

ts

are

influe

nced

gr

eatly

by

part

icle s

ize

and

homog

eneity

. So

met

imes

, fo

od

cont

aminat

ion

may

be

ve

ry

hete

roge

nic.

The

at

trib

ute

is i

n so

me

case

s un

even

ly d

istr

ibut

ed i

n fo

od t

hrou

ghou

t th

e sa

mple.

Mac

rohe

tero

genity

implies

diffe

ring

con

cent

ration

s am

ong

units

of lot

s

Micro

hete

roge

nity

implies

diff

ering

conc

entr

ations

am

ong

part

s of

units

OJE

CTIV

ES O

F SA

MPL

ING

Food

sam

ples

are

collect

ed f

or:

1.Co

mmer

cial tr

ansa

ctions

lik

e im

port

s, ex

port

s or

loca

l pu

rcha

ses

2.Ro

utine

quality

cont

rols-b

oth

official

and

auto

co

ntro

ls:

-by

official

auth

orities-

to

see

that

th

ey

conf

orm t

o pr

edet

ermined

spe

cifica

tion

s.-b

y plan

t pe

rson

nel fo

r au

to-c

ontr

ols

(or

for

surv

eilla

nce

of p

roce

sses

).3.

Complaint

sam

ples

fro

m c

usto

mer

s/co

nsum

ers

4. In

vest

igat

ing

compe

tito

r's

samples

5.Re

sear

ch an

d de

velopm

ent

-sho

uld

be ba

sed

on

stat

istica

l ex

perimen

tal de

sign

s

The

info

rmat

ion

on

the

follo

wing

sh

ould

be

includ

ed in

an e

xper

imen

tal or

a s

ampling

plan

:

-the

ob

ject

ive

of

the

inve

stigat

ion

includ

ing

info

rmat

ion

on t

he c

ompo

nent

s or

org

anisms

to b

e de

term

ined

;-t

he

part

ies

invo

lved

(c

lient

, pe

rson

ta

king

th

e sa

mple,

lab

orat

ory,

etc

.)-t

he na

ture

of

th

e sa

mple,

sa

mpling

loca

tion

an

d time

of s

ampling;

-the

num

ber

of s

amples

, th

e wa

y wh

ich

they

are

to

be t

aken

, pa

cked

and

tra

nspo

rted

(re

quirem

ents

on

ster

ility

, co

ntaine

rs a

nd e

quipmen

t, s

ampling

mod

el,

etc.

);

-any

req

uire

men

ts r

egar

ding

the

pre

-tre

atmen

t of

sa

mples

and

the

selec

tion

of

analyt

ical m

etho

ds;

-the

tim

e an

d co

st r

equire

men

ts(f

or t

he e

ntire

inve

stigat

ion,

for

the

per

son

taking

the

sam

ple,

fo

r th

e labo

rato

ry);

-any

pos

sible

lega

l re

quirem

ents

and

int

erna

tion

al

agre

emen

ts w

hich

hav

e to

be

obse

rved

;-t

he r

equire

men

ts o

n do

cumen

tation

(on

sam

pling,

re

port

s fr

om t

he lab

orat

ory,

the

clie

nt�s o

wn

summar

y); an

d -t

he q

ualit

y as

sura

nce

aspe

cts

of t

he inv

estiga

tion

(the

clie

nt�s o

wn a

ctivities,

any

req

uire

men

ts o

n th

e pe

rson

tak

ing

the

sample,

the

lab

orat

ory

and

any

othe

rs inv

olve

d).

Sampling

shou

ld b

e ca

rried

out

aiming

at e

nsur

ing

that

th

e sa

mple

is r

epre

sent

ative

of t

he c

onsign

men

t to

be

inve

stigat

ed.

Esta

blishe

d info

rmat

ion

which

will

help

in

the

cons

ider

ation

of t

he d

evelop

men

t of

sam

pling

plan

s an

d pr

oced

ures

ar

e th

e fo

llowi

ng:

1.The

inst

ruct

ions

on

Sa

mpling

for

the

Code

x Co

mmod

ity

Committe

es

on

the

App

licat

ion

of

the

Gene

ral Pr

inciples

of

Code

x Sa

mpling

Proc

edur

es.

This

docu

men

t wa

s de

velope

d by

the

Cod

ex C

ommitte

e on

M

etho

ds of

Ana

lysis

and

Sampling

and

is cu

rren

tly

unde

rgoing

rev

ision

in t

he l

ight

of

the

new

role a

nd

impo

rtan

ce o

f th

e Co

dex

Alim

enta

rius

Commission

.2.

The

Co

de of

Pr

actice

on

Sa

mpling

for

Ana

lysis

or

Exam

inat

ion

prep

ared

und

er t

he U

K Fo

od S

afet

y Act

19

90.

3.In

tern

ationa

l Co

mmission

on

M

icro

biolog

ical

Spec

ificat

ions

for

Foo

ds.

1. R

ando

m S

ampling:

The

bulk

of t

he s

ample

mat

erial

is

divide

d into

a

numbe

r of

se

gmen

ts

and

samples

ar

e se

lect

ed

acco

rding

to a

pr

edet

ermined

pat

tern

. Fo

r ex

ample,

sam

pling

plan

may

be

bas

ed o

n ge

nera

ted

rand

om nu

mbe

rs.

You

have

to

give

eq

ual

chan

ce

for

each

ba

tch

for

select

ion

(i.e.

yo

u eith

er

select

fr

om

Rand

om nu

mbe

rs or

by

dr

awing

numbe

rsfr

om a

bag

or

taking

ev

ery

n'th

sample-

i.e.

for

official q

ualit

y co

ntro

ls)

TYPE

S OF

SAM

PLIN

G

�2.

Syst

emat

ic S

ampling:

You

shou

ld h

ave

a

pred

eter

mined

pat

tern

to

base

you

r ex

perimen

tal de

sign

. App

licat

ions

are

In

inve

stigat

ing

cert

ain

prop

erty

as

effe

cted

by

cert

ain

proc

essing

par

amet

ers.

You

have

to

draw

sam

ples

fro

m v

arious

point

s of

pro

cess

to

test

cha

nges

in

compo

sition

with

chan

ges

in

para

met

ers

of p

roce

ss (mos

tly

rese

arch

and

de

velopm

ent

purp

oses

). Y

ou c

an b

ase

your

sa

mples

on

pres

et d

esign

type

s lik

e "c

entr

al

compo

site

des

ign"

.

Subs

ampling:

With

larg

e initial sa

mple

size

,you

will

have

to

decr

ease

the

sample-

size

till y

ou r

each

th

e re

quired

aliq

uot

sample

size

.

�Fo

r th

is p

urpo

se, af

ter

spre

ading

the

whole

sample

on a

wid

e su

rfac

e, u

se q

uart

ering

with

a s

traigh

t ed

ge a

nd m

ixing

only t

he t

wo o

f th

e op

posite

qu

arte

rs.

�So

met

imes

, sa

mples

fro

m d

iffe

rent

bat

ches

can

be

admixed

to

for

m a

�co

mpo

site

-sam

ple�

, wh

ich(

in

Tur

kish

"pa

çal") is a

n ad

mix

ture

pre

pare

d by

ad

ding

one

sub

sample

to a

noth

er (of

2 o

r mor

e po

rtions

of s

ame

size

, we

ight

or

volume)

"Lab

orat

ory

Samples

":In

gen

eral g

lass

and

met

al c

onta

iner

s ar

e pr

efer

red

to p

last

ic a

nd p

aper

con

tainer

s. P

last

ic a

nd p

aper

co

ntaine

rs d

o no

t pe

rmit t

rans

fer

of w

ater

vap

our

or a

ir in

or o

ut.Her

met

ically s

ealed

jars

are

far

be

tter

.So

me

samples

are

:La

bile: Tha

t wh

ich

is e

asily

dec

ompo

sed

The

rmolab

ile: de

stro

yed

easily b

y he

at(e

spec

ially

vita

mins,

enz

ymes

and

fat

ty a

cids

).Fo

r th

ese,

you

have

to

minim

ize

the

time

of

expe

rimen

t. S

omet

imes

you

may

hav

e to

put

the

m

in r

efrige

rate

d co

ntaine

rs o

ra

free

zer.

Ide

ally

you

shou

ld f

reez

e all th

e sa

mples

if y

ou w

ont

analyz

e th

em immed

iate

ly,

but

this is

quite

expe

nsive.

Labo

rato

ry S

ample

Size

s

Ave

rage

or o

ptim

al fo

od s

ample

size

is

250

grfo

r un

pack

ed f

oods

, bu

t th

is is v

ery

muc

h co

rrelat

ed w

ith

the

part

icle

size

of

units

of

popu

lation

.Fo

r ex

ample:

-Spice

sam

ples

<10

0 gr

-Fru

its

(app

les) =

1 k

g-H

azelnu

ts=

250

gr

Also,

for

micro

biolog

ical a

nalysis

ofhe

tero

gene

ously

cont

aminat

ed s

amples

(i.e.

M

oulds)>5

00 g

of

sample

is r

equire

d.

Sample

Prep

arat

ion

for

Nut

rition

al L

abellin

g

�Mos

t an

alyt

ical

exp

erim

ents

are

ver

y ex

pens

ive.

You

shou

ld m

inim

ize

the

num

ber

of s

ampl

es y

ou t

ake

and

take

car

e th

at n

o ch

ange

s oc

cur

from

sam

plin

g to

an

alys

isst

age

�The

idea

l sam

ples

for

Nut

rien

t A

naly

sis

shou

ldco

nsis

t of

a

com

posi

te o

f 12

sub

-sam

ples

(con

sum

er u

nits

) ra

ndom

ly c

hose

n -

1 fr

om e

ach

of 1

2 ca

ses.

Guid

elines

for

sam

ple

hand

ling

1 -Pr

oper

Sam

ple

iden

tifica

tion

(Dat

e, w

ho t

ook

the

sample-

the

insp

ecto

r or

the

ana

lyst

-, t

he

popu

lation

it

repr

esen

ts, te

mpe

ratu

re s

hould

be

spec

ified

in a

distinc

tive

man

ner.

)2-

Rese

rving

duplicat

e sa

mples

(şa

hit

numun

e)3-

Secu

ring

sea

ls f

or s

amples

of

official s

ignf

ican

ce,

so t

hat

the

cont

aine

r ca

nnot

be

open

ed w

itho

ut

brea

king

the

sea

l.

4-Se

curing

agains

t pr

obab

le d

amag

e (ie

, inse

cts,

mou

lds,

etc

) by

fum

igat

ion.

5-Se

curing

agains

t ch

emical &

micro

biolog

ical s

poila

geof

lab

ile c

ompo

nent

s(Lo

w To

or f

roze

n st

orag

e fo

r th

ermolab

iles,

at

low

moist

ure,

inda

rk p

lace

s, u

nder

N²

gas,

with

enzy

me

inac

tiva

tion

)

Pret

reat

men

ts f

or P

rese

rving

Samples

:1.

Clean

to

remov

e an

y su

rfac

e co

ntam

inat

ion

.2.

Dry

for

enz

yme

inac

tiva

tion

; by

hea

t or

by

chem

icals

Ex:

to s

hift

to

the

optimal p

H,

add

(NH4)

2SO4

.3.

Sto

ring

in

herm

etic c

onta

iner

s-glas

s an

d met

als

are

pref

erre

d to

pap

er a

nd p

last

ics.

Fo

r av

oiding

any

con

tact

with

air,

kee

p un

der

N2

or

diss

olve

in

solven

ts t

o cu

t of

f O2,

and

ad

d an

tiox

idan

ts o

r pr

eser

vative

s fo

r ad

dition

al p

reca

ution.

For

sam

ples

sus

cept

ible

to m

icro

biolog

ical a

ttac

k, u

se p

rest

erilize

d"a

scep

tic"

sam

ple

cont

aine

rs,

and

carr

y in ice

-ba

gs(0

-10°

C) .

4. F

reez

e or

use

dry

ice

wr

appe

d in a

pap

er a

nd

plac

ed in

cham

ber

for

ther

molab

ileco

mpo

nent

s (the

rmal t

reat

men

ts) In

cas

e of

fre

ezing,

car

e sh

ould b

e ta

ken

in t

hawi

ng v

ery

slow

ly,

and

not

losing

awa

y an

y th

awed

liquids

. 5.

Pes

ticide

app

licat

ions

may

help

agains

t risk

of

inse

ct inf

esta

tion

. In

som

e ca

ses

wher

e fo

od

mat

erials m

ay d

eter

iora

te d

ue t

o pa

rasite

s,yo

u ha

ve t

o ap

ply

pest

icid

es (firs

t on

a b

lott

ing

pape

r,

then

in

a clos

ed c

onta

iner

) . Fu

migan

tsmay

also

be

used

for

avo

iding

inse

ct inf

esta

tion

, (C

HCl

3 or

pa

radich

loro

benz

ene)

.6.

Grind

to

desire

d pa

rticle s

ize

Hom

ogen

izat

ion

of L

abor

ator

y Sa

mples

You

have

to

homog

enize

samples

bef

ore

analys

es.

For

this

purp

ose,

firs

tof

all

you

have

to

decide

on

the

basis

of r

epor

ting

you

r re

sults.

Is

it

going

to b

e on

th

e ed

ible p

ortion

(ha

zelnut

→on

ly inn

er p

art)?

If s

o, t

hen

get

rid o

f ined

ible

port

ion.

How

ever

, if y

ou a

re g

oing

to

analys

eap

ples

for

pes

ticide

s, y

ou s

hould

not

remov

e th

e sk

in p

art,

bec

ause

the

pest

icid

es a

re o

n th

e ou

ter

part

and

apples

may

be

cons

umed

with

skins.

Also

of h

elp

for

homog

enizing

are

appa

ratu

s fo

r:

-Mec

hanica

l gr

indi

ng (mills)

-Blend

ing;

gra

ting

; st

irring

etc

.

Idea

l pa

rticle s

ize

for

mos

t an

alys

is a

re (0.

5-1

mm) or

in

mes

hsize

(20

/40

mes

h).

-20

mes

h mea

ns t

here

are

20

open

ings

per

1lin

ear

inch

of a

scr

een,

this

corr

espo

nding

to

~o.1

mm p

article

or d

iamet

er.

-40

mes

h co

rres

pond

s to

40 o

pening

s pe

r lin

ear

inch

or

~0.0

6mm

part

icle d

iamet

er.

20/4

0 mes

h mea

ns t

hat

sample

pass

es t

hrou

gh s

ieve

wi

th m

esh

size

20,

but

is

reta

ined

on

siev

e wi

th

mes

h size

40.

In m

ost

case

s, o

ptim

um p

article

size

is

indi

cate

d in

the

analys

is m

etho

d.

GENER

AL

ANALY

TIC

AL

STAGE

S

�Ana

lysis

might

or

might

not

des

troy

the

initial

compo

sition

.

�Bo

th d

estr

uctive

and

non

-des

truc

tive

tec

hnique

s ar

e av

ailable

for

mos

t an

alys

esI.

CLE

AN-U

P: R

emov

al o

f ot

her

inte

rfer

ing

subs

tanc

es.

II. CO

NCE

NTRA

TIO

Nor

Dõlu

tion

to w

ithin

dete

ction

limits:

Som

etim

es y

ouha

ve t

o br

ing

in

dete

ction

limits

by d

irec

t dilution

or b

y co

ncen

trat

ion.

III.

DET

ERM

INATIO

N

I.

CLEA

NUP:

Man

y se

para

tion

clean

-up

tech

niqu

esar

e av

ailabl

e. Th

ese

are:

1. E

xtra

ction-

Part

itioning

of

mat

erials b

etwe

en t

wo

phas

es d

epen

ding

on

relative

solub

ilities

of a

nalyte

in

the

two

phas

es. E

xamples

: So

lid-l

iquid

and

Liqu

id-

liquid

Extr

action

s .I

n ch

oosing

the

solve

nt s

yste

m,

the

solven

t se

lect

ivity

and

relative

solub

ility

is

impo

rtan

t.

In s

uper

critical g

as e

xtra

ctions

, Su

perc

ritica

l CO

2 ha

s incr

ease

d so

lubi

lity

at low

er t

empe

ratu

res;

It is

pref

erre

d be

caus

e it is

a no

ntox

ic a

nd p

ract

ical g

as

with

low

boilin

g po

int(th

us p

rodu

cing

no

heat

dam

age)

an

d is v

ery

enviro

nmen

tal fr

iend

ly. It

is

being

used

fo

r re

cove

ring

ant

ioxi

dant

s an

d ve

geta

ble

oils.

Within

some

analyt

ical m

etho

ds, we

also

app

ly

sepa

rato

ryfu

nnels

(ayõrm

ahu

nisi) fo

r liq

uid

liquid

extr

action

s.

2. D

istilla

tion

-Re

dist

ribu

tes

molec

ules

bet

ween

ph

ases

. All

liquids

hav

e a

spec

ific v

apor

pre

ssur

e th

at is

cons

tant

at

a give

n te

mpe

ratu

re.

Whe

n th

e te

mpe

ratu

re (T°

) is r

aise

d to

tha

t po

int

so

that

vap

our

pres

sure

of

subs

tanc

e eq

uals t

he

exte

rnal p

ress

ure,

the

sub

stan

ce b

oils.

Exam

ples

are

"F

ract

iona

l dist

illat

ion"

for

sam

ples

wi

th r

athe

r na

rrow

boilin

g po

int

rang

e, a

nd

"Ste

am d

istilla

tion

" wh

ich

are

applied

espe

cially

for

hete

roge

neou

s liq

uid

mixtu

res

cons

isting

of

subs

tanc

es n

ot s

olub

le in

each

oth

er like

wate

r.

i.e.

volat

ile w

ater

-ins

olub

le m

ater

ials c

an b

e th

us s

epar

ated

fro

m n

onvo

latiles.

3. A

dsor

ption:

Dep

ends

on

the

affinity

or

prop

erty

of

bind

ing

on s

olid s

urfa

ces.

Ex

ample

isCo

lumn

chro

mat

ogra

phy,

inv

olving

selec

tive

de

sorb

ing

by p

rope

rly

select

ed s

olve

nt

syst

ems

(gas

es in

liquid

in

GC,

liquids

in

liquid

in

HPL

C).

All

chro

mat

ogra

phic s

yste

ms

apply

adso

rption

/des

orpt

ion

principles

.

Oth

erclea

n-up

tec

hnique

s includ

e�C

ryst

alliz

ation�

,�F

iltra

tion

�,

�Der

ivat

izat

ion�

.

II.

CONCE

NTR

ATIO

N t

o W

ithin

Det

ection

Lim

its

�In

mos

t ca

ses

it is

done

by

evap

orat

ing

in ro

tary

vac

uuev

apor

ator

s. T

he h

igh

tempe

ratu

res

might

be

harm

ful fo

ther

molab

ileco

mpo

nent

s; t

hen

you

have

to

mak

e it u

nde

vacu

um a

nd a

t lowe

r te

mpe

ratu

res.

III.

DET

ERM

INATI

ON

�Q

uant

ificat

ion

is r

ealiz

ed b

y titr

ation,

or

grav

imet

ry, or

by c

ompu

ter

cont

rolle

d inte

grat

ion.

But

the

met

hod

used

has

to b

e va

lidat

ed a

nd c

onfirm

ed b

y re

cove

ry t

rials,

wh

ere

a kn

own

amou

nt o

f re

fere

nce

grad

e st

anda

rd is

adde

d to

the

blank

and

rep

licat

e an

alys

es a

re p

erfo

rme d

İnau

tomat

ed m

etho

ds.

Use

r-pr

ogra

mmab

le lab

orat

ory

robo

ts a

re a

vaila

ble

now

Supe

rvisor

y minicom

pute

rs a

re u

sed

for

data

base

man

agem

ent

and

repo

rt g

ener

ation.

Good

Lab

orat

ory

Prac

tice

(İyi L

abor

atua

r Uyg

ulam

asõ)

�La

bora

tuva

rlar

õn

test

yap

arke

n uy

malar

õ ge

reke

n ko

şulla

rõ v

e or

ganiza

syon

u be

lirleye

n yö

netim

sist

emid

ir.

�Amac

õ:

Yapõlan

analitik ö

lçüm

lerin

kalit

esini gü

venc

e altõna

alm

ak

GLP

Ana

Pre

nsipler-

2�

Stan

dard

Çalõşma

Pros

edür

lerine

gör

e ça

lõşma

( SO

P)�

Test

ve

kont

rol ko

nularõnõn

tanõmlanm

õş

olmas

õ�

Yazõlõ

ve

onay

lõ pr

otok

ollara

gör

e ça

lõşma

�Ölçüm

son

uçlarõnõn

rapo

r olar

ak s

unulmas

õ�

Ölçüm

son

uçlarõna

ist

endiğind

e er

işeb

ilme

Ölçüm

Doğ

ruluğu

(Acc

urac

y of

Mea

sure

men

t)Ölçüm

son

ucu

ile ö

lçülen

büy

üklüğü

n ge

rçek

değ

eri ar

asõnda

ki ya

kõnlõk

dere

cesi.

Ölçüm

Doğ

ruluğu

(x

-τ

)

x=ö

lçüm

son

ucu

τ =

gerç

ek d

eğer

� Ge

rçek

lik +

Kes

inlik

�

Hat

a(E

rror)

Ölç

üm s

onuc

unda

n, ö

lçül

en b

üyük

lüğe

ait

gerç

ek

değe

rin çõk

artõl

masõ i

le e

lde

edile

n değe

r.H

ata

= x

-τ

x=ö

lçüm

son

ucu

τ =

gerç

ek d

eğer

Hat

a Çe

şitler

i

�Pr

osed

ür h

atalar

õTar

tõm, Titra

syon

da s

on n

okta

nõn

tam

anlaşõlamam

asõ, t

am ç

özem

eme,

emisyo

n sp

ektr

osko

pisind

e ka

ynak

tan

gelen

osila

syon

lar,

inte

rfer

ans,

vs

�Ölçüm

hat

alar

õ�Si

stem

atik h

atalar

�Ra

stge

le h

atalar

Rast

gele H

ata

(Ran

dom e

rror

)

Tekr

arlana

bilir

lik k

oşullarõ a

ltõnda

ayn

õ ölçü

m s

onsu

z sa

yõda

yap

õldõğõnda

, he

r bi

r ölçü

m d

eğer

inin s

onsu

z sa

yõda

ki

ölçü

mün

ort

alam

asõnda

n çõkar

tõlm

asõ ile

elde

edi

len

değe

rdir.

Rasg

ele

Hat

a =

Xi-

Xor

t

Sist

emat

ik H

ata

(Sys

temat

ic e

rror

)

Tekr

arlana

bilir

lik k

oşullarõ a

ltõnda

ger

çekleş

tirilen,

ay

nõ ö

lçülen

büy

üklüğe

ait b

irbi

rini izley

en s

onsu

z sa

yõda

ki ö

lçüm

ün o

rtalam

asõnda

n, ö

lçülen

büy

üklüğü

n ge

rçek

değ

erinin çõkar

tõlm

asõ ile

elde

edile

n de

ğerd

ir. Ge

nelde

belir

sizlikle ifa

de e

dilir

.

Sist

emat

ik H

ata

= X

ort-

τ

x ort

=ölçüm

ün o

rtalam

asõ τ

= ger

çek

değe

r

16

Bağõ

l Hat

a(R

elat

ive

Erro

r)

Ölç

üm h

atasõnõn

ölç

ülen

büy

üklü

gün

gerç

ek d

eğer

ine

bölü

nmes

i ile

eld

e ed

ilen

değe

r.

Eğilim

(Bias)

Ölçüm

met

odun

un s

iste

mat

ik h

atas

õdõr,

ulaş

õlmak

ist

enen

değ

erde

n sa

pmay

õ gö

ster

ir.

Sist

emin o

ffse

t�idi

r.

Uyg

un b

ir R

efer

ans

Mad

desi ile b

elirlene

bilir

.�

Met

od�

Labo

ratu

ar�

Pers

onel

�M

atriks

15

Ger

çek

Değ

er(T

rue

valu

e)

Ele

alõn

an b

elli

bir b

üyük

lüğü

n ta

nõmõn

a ka

rşõlõ

k ge

len

ve

anca

k id

eal b

ir öl

çüm

ile

elde

edi

lece

k bi

r hip

otet

ik d

eğer

dir.

x =

τ +

∆ +

δ =

µ +

δ

x=ö

lçüm

son

ucu

τ =

gerç

ek d

eğer

∆ =

eğili

m,b

ias

δ =

rast

gele

hat

aµ

=bek

lene

n değe

r

Kesinlik

(Pre

cision

)

Ayn

õ ölçü

m k

oşullarõnd

a ya

põlan

ölçü

m

sonu

çlar

õnõn b

irbi

rine

yak

õnlõğ

õdõr v

e sist

emin t

ekra

rlan

abilirliğinin b

ir

ölçü

südü

r.

Tekr

arlana

bilir

lik(R

epea

tabi

lity)

Ayn

õ ölçü

m k

oşullarõ a

ltõnda

ge

rçek

leşt

irile

n, a

ynõ ölçü

len

büyü

klüğ

e ait

birb

irini izleye

n ölçü

m s

onuç

larõ a

rasõnd

aki

yakõnlõk d

erec

esi.

Tekr

ar

Gerç

ekleşt

irile

bilir

lik(R

epro

ducibi

lity)

Fark

lõ ölçü

m k

oşullarõ a

ltõnda

ge

rçek

leşt

irile

n, a

ynõ ölçü

len

büyü

klüğ

e ait

biribi

rini izley

en ö

lçüm

son

uçlarõ

aras

õnda

ki y

akõnlõk

der

eces

i.

Duy

arlõlõk

(Has

sasiye

t)(S

ensivity

)

Bir

ölçü

m c

ihaz

õnõn e

lekt

ronik

yanõt

siny

alinde

ki d

eğişim

in

bunu

yar

atan

et

ki s

inya

linde

ki d

eğişim

e or

anõ.

Seçicilik

(Spe

cificity

)

Met

odun

sa

dece

ölçü

mü

amaç

lana

n mad

deyi

ölçm

e ye

tene

ği

Met

odun

Sağ

lamlõğ

õ(R

ugge

dnes

s-Ro

bust

ness

)

Bir

met

odu

uygu

lark

en y

apõla

n uf

ak

tefe

k op

eras

yone

l de

ğişikliklerin

sonu

cu e

tkile

me

duru

mud

ur.

Ölçüm

Belirsizliği

(Unc

erta

inty

)Ölçüm

son

ucu

ile b

erab

er y

er a

lan

ve

ölçü

len

büyü

klüğ

e mak

ul b

ir ş

ekild

e ka

rşõlõk

gelebi

lece

k de

ğerler

in d

ağõlõmõnõ

kara

kter

ize

eden

par

amet

redi

r.

Ölçüm

hat

alar

õ ge

nelde

belir

sizlik içind

e ifad

e ed

ilir.

Belir

sizliğin N

eden

leri

�Num

une

alma

(tem

sili

numun

e)

�Num

une

alma

veya

num

une

hazõrlam

a sõra

sõnd

aki

bulaşm

alar

�Ek

stra

ksiyon

ver

imi

�M

atriks

etk

ileri v

e et

kileşimler

�Çe

vres

el k

oşullarõn

ölçü

m işlem

ine

etkilerinin

bilin

mem

esi

�Vo

lümet

rik

ekipman

larõn,

ter

azile

rin

belir

sizliği

�Ci

hazlar

õn ç

özün

ürlüğü

�Ana

log

ciha

zlar

õn o

kunm

asõ

�Ölçüm

met

odun

daki y

uvar

lama

ve v

arsa

yõmlar

Kalib

rasy

on(C

alib

ration

)

Belli

koşu

llard

a ölçü

m

sist

eminin

göst

erdiği de

ğer

ile ölçü

len

büyü

klüğ

ün

gerç

ek

değe

ri

aras

õnda

ki

bağõntõyõ

bulm

ak için

yapõlan

işlemlerd

ir.

Kalib

rasy

on E

ğrisi

(Calib

ration

Cur

ve)

Ölçüm

siny

alinin

ölçü

mü

ya

põlan

büyü

klüğ

e ka

rşõ

değ

erinin g

rafiks

el

göst

erim

idir.

Tayin

Limiti

(Lim

it o

f Det

erminat

ion-

Qua

ntitat

ion)

Bir

ölçü

m s

iste

mi ile

an

alite

ait

belir

lene

bilen

en d

üşük

değ

erdi

r.

Kalib

rasy

on a

ralõğ

õnõn e

n dü

şük

değe

ridi

r.

Kör

örne

kle

yapõlan

ölçü

mlerin

stan

dart

sa

pmas

õnõn

6 ka

tõ a

lõnar

ak h

esap

lanõr.

.

Belir

leme

Limiti

(Lim

it o

f de

tect

ion)

Ölçüm

siste

mi ile

belli

bir

örne

k içer

isinde

be

lirlene

bilen

en

düşü

k mikta

rdõr.

Kör

örne

kle

yapõlan

ölçü

mlerin

stan

dard

sa

pmas

õnõn 3

kat

õ alõnar

ak b

ulun

ur.

Valid

asyo

n(V

alidat

ion)

Bir

sist

emin b

elirlene

n

özel

amaç

lara

uy

gunluğ

unun

ob

jekt

if

olar

ak t

est

edile

rek

onay

lanm

asõdõr.

Met

od V

alid

asyo

nu:

Bir

met

odun

pe

rfor

man

sõnõ b

elirlemek

için

yapõlan

sist

emat

ik t

est

/ ölçü

m v

e ista

tist

ik

değe

rlen

dirm

e ça

lõşmalar

õdõr.

Belir

sizliğin N

eden

leri

�Num

une

alma

(tem

sili

numun

e)

�Num

une

alma

veya

num

une

hazõrlam

a sõra

sõnd

aki

bulaşm

alar

�Ek

stra

ksiyon

ver

imi

�M

atriks

etk

ileri v

e et

kileşimler

�Çe

vres

el k

oşullarõn

ölçü

m işlem

ine

etkilerinin

bilin

mem

esi

�Vo

lümet

rik

ekipman

larõn,

ter

azile

rin

belir

sizliği

�Ci

hazlar

õn ç

özün

ürlüğü

�Ana

log

ciha

zlar

õn o

kunm

asõ

�Ölçüm

met

odun

daki y

uvar

lama

ve v

arsa

yõmlar

Kalib

rasy

on(C

alib

ration

)

Belli

koşu

llard

a ölçü

m

sist

eminin

göst

erdiği de

ğer

ile ölçü

len

büyü

klüğ

ün

gerç

ek

değe

ri

aras

õnda

ki

bağõntõyõ

bulm

ak için

yapõlan

işlemlerd

ir.

Kalib

rasy

on E

ğrisi

(Calib

ration

Cur

ve)

Ölçüm

siny

alinin

ölçü

mü

ya

põlan

büyü

klüğ

e ka

rşõ

değ

erinin g

rafiks

el

göst

erim

idir.

Tayin

Limiti

(Lim

it o

f Det

erminat

ion-

Qua

ntitat

ion)

Bir

ölçü

m s

iste

mi ile

an

alite

ait

belir

lene

bilen

en d

üşük

değ

erdi

r.

Kalib

rasy

on a

ralõğ

õnõn e

n dü

şük

değe

ridi

r.

Kör

örne

kle

yapõlan

ölçü

mlerin

stan

dart

sa

pmas

õnõn

6 ka

tõ a

lõnar

ak h

esap

lanõr.

.

Belir

leme

Limiti

(Lim

it o

f de

tect

ion)

Ölçüm

siste

mi ile

belli

bir

örne

k içer

isinde

be

lirlene

bilen

en

düşü

k mikta

rdõr.

Kör

örne

kle

yapõlan

ölçü

mlerin

stan

dard

sa

pmas

õnõn 3

kat

õ alõnar

ak b

ulun

ur.

Valid

asyo

n(V

alidat

ion)

Bir

sist

emin b

elirlene

n

özel

amaç

lara

uy

gunluğ

unun

ob

jekt

if

olar

ak t

est

edile

rek

onay

lanm

asõdõr.

Met

od V

alid

asyo

nu:

Bir

met

odun

pe

rfor

man

sõnõ b

elirlemek

için

yapõlan

sist

emat

ik t

est

/ ölçü

m v

e ista

tist

ik

değe

rlen

dirm

e ça

lõşmalar

õdõr.