Fighting for authenticity against food crimes

HONAS

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Naziv projekta / Project: Inovacije tvrtke Spectrolabas KK.03.2.2.04.0126

Kratki opis projekta / Short description of the project:
Glavni problem koji se adresira ovim projektom je nedovoljna konkurentnost tvrtke Spectrolabas d.o.o., te potreba prilagodbe usluga analize meda koja trenutno ne odgovara dokumentiranim zahtjevima tržišta. Glavne ciljane skupine projekta su: tvrtka Spectrolabas, svi budući zaposlenici u projektu (5), te budući novozaposleni (3) - ukupno 8. Aktivnosti su: 1 Prilagodba usluge zahtjevima tržišta, 2 Priprema lansiranja usluge, 3 Upravljanje projektom i 4 Promidžba i vidljivost. /
Main problem addressed through this project is insufficient competitiveness of company Spectrolabas and need of adjustment of honey analysis which currently is not responding to documented requirements of the market. Main target groups of the project are: Company Spectrolabas, of future project employees (5) and future newly employed persons (3) – total 8. Activities of the project are: 1 Adjustment od the service to market needs, 2 Preparation of service launching, 3 Project management and 4 Publicity and visibility.

Ciljevi i očekivani rezultati projekta / Objectives and expected results of the project:
Uspješno prilagođena i lansirana inovacija na tržište s potencijalom rasta i izvoza/
Successfully adjusted and launched innovation with potential of growth and export on the market.

Naziv korisnika / User: Spectrolabas d.o.o.

Ukupna vrijednost projekta / Total value: 1.891.073,14 HRK

EU sufinanciranje projekta / EU financing: 1.399.722,89 HRK

Razdoblje provedbe projekta / Project duration: 1/9/2019. do 1/3/2021

Kontakt osoba / Contact person: Laurynas Jukna laurynas.jukna@gmail.com
www.strukturnifondovi.hr

DO WE KNOW WHAT WE EAT?

This question plagues our contemporary society.

HONAS is on a mission to create a solution to fight honey counterfeit crimes for Governments, Laboratories and in General public.

PROBLEM

Honey counterfeiting, product adulteration, ingredient substitution, package mislabeling or outright fraud – there are many ways honey can be tampered with. Honey fraud is damaging to public health, customer interests, food producers and product brands around the world.

  • Honey is third most faked foods in the world
  • Foreign sugars were found 1.4 times in every 10 honey samples tested by the European Joint Research Centre
  • Around 20% of honey either declared as blends of EU honey or unblended honey bearing a geographical reference related to a member state were found to be suspicious of containing added sugar.

IMPACT TO SOCIETY:

  • Health hazards
  • Disruption of local production
  • National heritage loss
  • Consumer expectation

HONEY COUNTERFEIT CRIME IS GLOBAL

Honey counterfeit crime is a vast area of activities that can range from seemingly innocuous regulatory non-compliance to organized crime operating industrial scale and It can happen in any stage, processing, distribution or retail.

Processing

Distribution

Retail

HOW IT WORKS

Reliable product analysis with a simple point-and-click procedure

  • 1. Scan product

    Scan a questionable product with a Raman spectrometer

  • 2. Submit data

    Upload scan data into the HONAS

  • 3. Data analysis

    Data is processed and analysed through the Neural Network in a matter of minutes

  • 4. Receive results

    Receive results on product composition, authenticity, safety and quality                                                                                                                        

THE TECHNOLOGY

HONAS solution is enabled by cross-disciplinary technological integration

Raman Spectrometry

Continued advances in hardware engineering are set to make handheld Raman spectrometers ubiquitous throughout the food industry in the coming 5 years

Artificial Intelligence

Applications of machine learning and neural networking methods allow to make sense of complex spectrometric data from honey samples

HONAS is developing unique algorithms for differentiating honey products and their quality indicators using various machine learning and advanced statistical analysis methods.

Working technology use-case – authentic honey recognition:

  • >95% accuracy in differentiating natural honey from diluted honey;
  • >92% accuracy when estimating key honey purity indicators.

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