Is Honey of real interest for European Customs?

No?

That’s why it is exciting!

F. Guyon (SCL-33)

C. Bizet, L. Fino, M. Gaudefroy, B. Hoareau, M. Landuré (SCL-13)

E. Chavez Da Costa, L. Gaillard, E. Logodin, A. Morin, S. Rosset, JP Rosec, A. Vigouroux (SCL-33)

C. Bonnot, C. Chérubin, M. Genoud, V. Joly (SCL-59)

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Who are we? Service Commun des Laboratoires (SCL)

French Anti-Frauds

DGCCRF

French Customs

DGDDI

Service Commun des Laboratoires

SCL Labs (01/01/2007)

Labs LabsSamples

Respond to analysis

requests and expertise

Technical and scientific

advices and support

Methods development

Scientific cooperation

SCL :

11 Laboratories

390 people

Labs‘ specialization

For honey control :

SCL-13 : Marseille Lab

SCL-33 : Bordeaux Lab

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Why a Custom’s interest in honey?

In the Tariff classification: 0409 : Natural Honey

1712 : Honey substituteLimited interest

What’s Honey in popular

Consciousness and reality?

natural, rare

Bees’ Work, good for environment

handwork product

“local” production

mono Floral specifications

Good for health (medicinal Virtues)

2/3 of honey imported in France (2014)

mixture of geographical origins

mixture of floral origins

Honeys adulterations

……

Our customs’ missions

Protecting EU financial interest

Supporting legitimate business

Combatting Fraud

Product authenticity

Tracking Forgeries

Ensuring security and safety

Consumer health protection

Control of dangerous substances

Environmental protection

Honey is a multifaceted interest for customs

its control needs European harmonization

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Which Kinds of frauds?

Forgeries on :

- geographical origin (national, regional to local)

- floral origin

- Honey substitutes (sugars …)

In 2016Requests for an increase of

laboratory analytical capacities

for honey controls

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SCL’s action plan

Partnership

with French honey cooperatives

Mono-floral + geographical origins

SCL-Marseille

Sensorial and pollen analysis (authenticity)

Chemical analysis (HMF, pH, sugars…)

Mineral analysis Stable isotope (13C) analysis (Honey and proteins)

Spectrophotometry : near-IR

SCL-Lille

Ethanol isotope analysis (honey fermentation, 13C and 2H)

Spectrophotometry : Mid-IR

Stable isotope analysis (proteins 15N, HPLC-co-IRMS)

NMR analysis (1H and 13C )

SCL-Bordeaux

~ 400 samples ; 600 at the end of 2019

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Mono-dimensional- concentration limit, presence/absence of compounds

Bi-dimensional- d13C = f(2H) for ethanol

Multi-dimensional

- IR models

- NMR 1H and 13C models

How to detect forgeries ?

Different ways for analytical data treatment

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Honey supplementation

Sugars concentration by HPLC-refractometry and IC-amperometry

- main sugars : glucose, fructose, sucrose

- small sugars: turanose, melesitose, panose…

Stable isotope by IRMS- comparison between d13C of honey and extracted proteins

Glucose

référence

Glucose

Fructose

Disaccharides

Oligosaccharides

Milestone :1H NMR quantification

Stable isotope by HPLC-co-IRMS

- detection of oligosaccharides

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Honey supplementation and floral origin

Ethanol stable isotope quantification - Fermentation, Distillation

- 2H NMR and 13C IRMS

- Mono-floral adulteration

&- Sugar adulteration

Acacia

Lavender

Chestnut

Adulterated

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Necessity of multivariate data treatment

Introduction of artificial intelligence in food control ! Construction of a model based on the mathematical treatment

of analytical data issued from various sources.

3 parts spreadsheet

- identification of the samples

- variables

- classes attribution

1

2

3

..

..

n

Variables :

combination or not of

Isotopic data

Mineral concentration

GC / HPLC concentration1H NMR spectra

IR spectra

Classes(floral

Geographical)

composition

Critical point for models accuracy: Samples authenticity !

Elaboration of a mathematical model

controlled

Samples data

Classification prediction

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Honey Mid-IR spectra

ATR diamond

No specific honey preparation

Pre-treatment : - 2nde derivative

- autoscaleTreatment : SIMCA

France Romania %

France 53 0 100

Romania 0 50 100

Multivariate analysis of IR spectra

application to geographical recognition

Mid-IR : - Potential not enough used

- Interest for customs : portable devices

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New Sample preparation SCL-33- honey (0,5 g) in buffered solution (5 mL, oxalate pH 4,3)

- agitation 30 min, centrifugation 10 min - 0,5 mL + 0,05 mL of « lock » solution

- 15 min analysis with Honeyscreener®

Untargeted analysisEntire spectrum

No molecules identification

Data TreatmentPre-treatment « Range Scale »

SIMCA

Main sugars (glucose and Fructose):

no discriminating power for floral origin classification!

Multivariate analysis of 1H NMR spectra

application to botanical recognition

1H NMR spectrum

1H NMR spectrum

Modelling power7 floral origins

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P_Aca P_Che P_Scr P_Hon P_Rap P_Lav P_Sun No match %

Acacia 5 100

Chestnut 10 100

Scrub 6 100

Honeydew 5 100

Rapeseed 0 100

Lavender 12 100

Sunflower 0 100

Out model

Predictions on an excluded set of data

Good predictions of the model

Limitation :

the botanical origin needs to be in the model

Multivariate analysis of 1H NMR spectra

application to botanical recognition

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Types of Climate in France

1

2

3

4

5

6

8

7

Pred-1 Pred-2 Pred-3 Pred-4 Pred-5 Pred-6 Pred-7 Pred-8 %

1 17 100

2 7 100

3 45 100

4 32 100

5 34 100

6 49 100

7 49 100

8 111 100

Very encouraging results

The impact of floral origin on classification needs to be checked

Method LOOCV (leave one out cross validation)

Prospective work 13C NMR spectra

application to regional classification

13C RMN quantitative in specific conditions (pb. relaxation time)

as all honeys analyzed in similar conditions spectra comparison possible!

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Conclusion / Perspectives

This study

Results highlights showed great potential for honey control by custom laboratories

From laboratory classical technique (HPLC, IR) to specific techniques (NMR, IRMS)

Development of methods and prediction tools available to non-experts

Application to floral and geographical origin of French honeys

What is missing in the model?

representative sets - of many Floral origins

- of European countries

Perspectives

- Develop a European Customs collaboration for a European Honey data bank

- Honey (traditional product) an open door in the new millennium?

How artificial intelligence will revolutionize food controls!

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Thank you for your attention