Application of PTR-QiTOF in food authenticity

Including PTR-TOF data handling

12 December 2017, Martin Alewijn (RIKILT)

Typical PTR/TOF output:

Ion counts in 199488 time bins per second

~10MB raw data per minute (1-5GB/day)

Hierarchical Data Format (HDF5), includes instrumental info

Data handling

2

65000 70000 75000 80000

010

000

2000

030

000

4000

050

00

Typical basic workflow:

Peak integration

Convert to mass and mass-align across the experiment

Quantification

Further processing/modelling

PTR-MS viewer

Made by Ionicon

Limited functionality

Requires predefined mass-list

Software (1)

3

PTR-TOF Data Analyzer

By M. Müller (Univ. Innsbruck)

Requires fixed calibration peaks

Unifies mass list across samples

Most advanced peak detection and integration

Commercial use prohibited (!)

Software (2)

4

PTRwid

By R. Holzinger (UU)

No parameters

Brute force calibration

Unifies masslist, ppb calculation

Software (3)

5

Software (4): by Franco Biasioli... soon

Intentionally and for economic gain:

substituting, adding of food (ingredients)

or making false statements about a food’s properties

Food fraud introduction

6

Composition

species/varieties, addition of product-foreign material or product-own material, addition of water, protein,

fat, other constituents, counterfeiting

Processing/storage

freezing, heating, artisanal production, storage (sell-

by-date)

Production system

organic, animal welfare, sustainable, wild/farmed,

fair trade

Geographical origin

from region to continent-level, specialty products,

origin image

Chemical profiling as authentication tool

Chemical profiles are influenced by many factors:

Metabolic profiles possibly carry information on “natural variation”

Possibly usable to detect food fraud!

• Variety/breed • Production system • Climate/weather • Various stress responses

• Maturation/ripeness• Processing • (Ingredients used)• Etc.

Relatively expensive, origin, processing: vulnerable for fraud

ents 2016-2017

Olive oil fraud

8

Sample numberstraining set

EVOO 136ROO 52POO 17

Vegetable oil 67test setEVOO "claimed" 100

Mixed OO 20validation set

EVOO 23ROO 7POO 1

Vegetable oil 9

Dates of analysis

11-28 July 2016 4-6 Oct 2016

15-16 Feb 2017

4-19 May 2017

Aim: authentic extra virgin olive oil(EVOO) or not?

(work by Jing Yan)

(preliminary) Results

9

total Correct Incorrect %correcttraining set

EVOO 136 135 1 99%ROO 52 48 4 92%POO 17 15 2 88%

Vegetable oil 67 67 0 100%test set

EVOO "claimed" 100 90 10 90%Mixed OO 20 9 11 45%

validation setEVOO 23 19 4 83%

ROO 7 7 0 100%POO 1 1 0 100%

Vegetable oil 9 9 0 100%

EVOOROOPOOVegetable oil

(66%)

(8%)

(7%)

PCA scores plot PLS-DA classification result

PTR-TOF can yield long-term results after corrections

Profiles are sufficient to discriminate EVOO and other (olive) oils

Mass calibration (PTRwid) and literature data allows identification of most masses found

Formal validation & accreditation of this method is foreseen early 2018

Wrap-up

10

End slide or section heading

Questions?

11