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Detection of milk adulteration Marlene Ransborg Pedersen Per Waaben Hansen Steve Holroyd Arla Foods GCO R&D Foss Analytical A/S Fonterra Research Centre June 2012

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Detection of milk adulteration

Marlene Ransborg Pedersen Per Waaben Hansen Steve Holroyd

Arla Foods GCO R&D Foss Analytical A/S Fonterra Research Centre

June 2012

2 Outline

The economic adulteration of milk/risks

Targeted and untargeted FTIR models for detecting

adulteration

Describe project

Results and conclusions

3

Milk processing….

4 The risks of milk adulteration

• By country

• Milk collection procedure

• By availability of potential adulterants

• Financial incentives

5 Economic adulteration in liquid milk

6 Economic adulteration in liquid milk

7 Economic adulteration in liquid milk

8 Potential milk adulterants

Chemicals with high nitrogen content

– Soluble, tasteless, colourless and odourless

– Increase value of milk

Cheap substitutes

– Nitrogen containing compounds

– Fat or protein from an alternative source

Adulteration may often be with a complex chemical

“cocktail”

Any adulteration poses a significant food safety risk

Melamine

9 Important factors in detecting adulteration

• Optimum sampling protocol location

– Cost, ability to sample, impact on customers and

business

• Best technique

– Speed, cost, accuracy, sensitivity, reliability

• Response to detection

– Business decision based on risk

“Infrared spectroscopy has strong potential”

10

Where infrared spectroscopy fits in….

Final product

grading

In process

Before processing

For payment

11 Where to screen for adulteration?

Hypot het ical adult erant concent rat ion ( ppm adult erant ) wit h t ime f o r milk powder p rocess

0

200

400

600

800

1000

1200

1 3 5 7 9 11 13 15 17 19 21 2 25 27 2 31 3 35 37 3 41 4 45 47 4

FTIR (MIR)

NIR

12

FTIR using mid IR is already used

widely for rapid compositional analysis

of liquid dairy products

High through put

- Up to 600 samples/hour

- 1000s of instruments in place

globally

-Can recognise unknown

adulterants

FTIR for routine milk screening

13 Milk and water FTIR spectra

14 Milk — water difference spectrum

Fat

Protein

Lactose

15 Difference spectrum — melamine

10001100120013001400150016000

0.05

0.1

wavenumber

abso

rban

ce d

iffer

ence

Melamine dissolved in milk - FT6000

0 ppm

250 ppm

500 ppm

1000 ppm

1500 ppm

2000 ppm

2500 ppm

3000 ppm

16 Project scope

Create an FTIR-based tool that can:

– Be capable of detecting economic

adulteration of milk supply with a

range of known and currently

unknown adulterants

– Non-dairy fat and protein not in

scope

– Understand performance in

different areas of the world

– Be part of a wider system

17 Project timeline

2008 melamine crisis in China

2009 presentation in Sochi, start of

collaborative project

2010 Preliminary proof of concept

2011-12 Commercial trials

18 Targeted and untargeted analyses

Traditional FTIR applications are quantitative – they “target” specific compounds such

as fat ands protein

Based on spiking experiments we can construct such calibrations for a range of

potential adulterants

An additional approach is to create qualitative calibrations to create “untargeted

models” capable of detecting an adulterant to a certain concentration

19 Results - targeted analysis

C:\valhalla\MSCFT2\PCA\DK\New Folder\pre0

0 100 200 300 400 500 600 700 800 900 1000

0

100

200

300

400

500

600

700

800

900

1000

RMSEP=28.111 SEP=28.158 SEPCorr=28.237 SDrep=12.757 Mean=338.09

Predicted (Melamine F/C=9/22)

Act

ual

57 samples in 3 replicates

%CV: RMSEP=8.31 SEP=8.33 SEPCorr=8.35 SDrep=3.77

Slope: 1.0013

Intcpt: -1.8636

r: 0.9969

Bias : -1.4116

FTIR predicted melamine (ppm)

Refe

ren

ce t

est

mela

min

e (

pp

m)

20 Untargeted analysis

Only information from ”normal” unadulterated milk is

needed to create a model

An “adulteration score” is calculated for each

sample that is higher as the sample becomes more

adulterated

With the current generation of FTIR instruments, a

melamine LoD (Limit of Detection) that is sufficient

to detect potential adulterants at economic levels is

obtained

21 Results - untargeted analysis

22 The link between LoD and false positives

0

200

400

600

800

1000

1200

0 1 2 3 4 5

Lim

it of

Det

ectio

n (p

pm)

% Acceptable false positives

Melamine

Ammonium sulphate

Urea

• The LoD is dependent on

the level of false positives

• The choice of false

positive rate must be based

on a risk assessment

23 Targeted vs. untargeted screening

Targeted

Specific information on adulterants present

Not able to detect new adulterants

Low LoD: 40-75 ppm for melamine

(1 % false positives)

Untargeted

No information on the nature of the

adulteration

Detects any adulterant affecting the MIR

spectrum

High LoD: 200-450 ppm for melamine

(1 % false positives)

A combined approach is the

optimal solution

24

Combining untargeted and targeted models

Does the untargeted model

indicate a deviating sample?

Do any targeted models indicate

the nature of the deviation?

The sample is most likely

adulterated

Confirm the result with

certified wet chemistry

Do any targeted models indicate

adulteration with a specific

adulterant?

No Yes

Yes Yes

The deviating sample

must be investigated

further

The sample is normal

No No

25 FTIR spectra of non-adulterated milk

0.0

0.1

0.2

0.3

0.4

1000 1100 1200 1300 1400 1500 1600

wavenumber

ab

so

rba

nce

26 Spectral detail

27 Results - summary

Successfully detect economic adulteration of milk

– Both untargeted and targeted models are used

sequentially

Untargeted models have a threshold value that

determines performance

– Can detect previously unknown adulterants

Targeted models detect specific potential

adulterants

28 Outcomes

High through-put (1000+ samples/day)

Sensitivity

Applicability

Ability to make decisions rapidly

29 Current status

Both targeted and untargeted models have been created based on over 10,000

samples of farm milk samples from Scandinavia, New Zealand and China

Selective spiking experiments and other detection technologies (LC/MS/MS) have

been used to both develop and validate the FTIR models performance

The models are currently undergoing test trials in several locations globally

30

Month Year

Questions