Food fraud: Assessing fraud vulnerability in the extra virgin olive oil supply chain

Yan, Jun; Erasmus, Sara W.; Aguilera-Toro, Miguel; Huang, H.; Ruth, Saskia M. van

doi:10.1016/j.foodcont.2019.107081

Cited by 63 publications

(37 citation statements)

References 27 publications

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“…It is worth to note that the algorithm attributes some non-negligible importance to some weaker spectral features, such as the NH 336. 3 www.nature.com/scientificreports/ emissions, and the emissions of the C 2 (Δν = 0, -1) bands at 516.5 and 563.5 nm. Very interestingly, the contribution of the continuum background is almost eliminated using this algorithm, and practically the spectral lines' emissions are mainly contributing in the discrimination procedure.…”

Section: Classification Results From the Linear Discriminant Analysismentioning

confidence: 99%

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Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

Gyftokostas

Stefas

Kokkinos

et al. 2021

Sci Rep

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Olive oil is a basic element of the Mediterranean diet and a key product for the economies of the Mediterranean countries. Thus, there is an added incentive in the olive oil business for fraud through practices like adulteration and mislabeling. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) assisted by machine learning is used for the classification of 139 virgin olive oils in terms of their geographical origin. The LIBS spectra of these olive oil samples were used to train different machine learning algorithms, namely LDA, ERTC, RFC, XGBoost, and to assess their classification performance. In addition, the variable importance of the spectral features was calculated, for the identification of the most important ones for the classification performance and to reduce their number for the algorithmic training. The algorithmic training was evaluated and tested by means of classification reports, confusion matrices and by external validation procedure as well. The present results demonstrate that machine learning aided LIBS can be a powerful and efficient tool for the rapid authentication of the geographic origin of virgin olive oil.

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Section: Classification Results From the Linear Discriminant Analysismentioning

confidence: 99%

“…All these resulted to a steadily increasing olive oil demand and value. Inevitably, this situation makes olive oil prone to adulteration and to other illegal and dangerous for the health activities 3 . Therefore, the necessity to ensure the quality of olive oil becomes of high importance.…”

Section: Introductionmentioning

confidence: 99%

Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

Gyftokostas

Stefas

Kokkinos

et al. 2021

Sci Rep

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“…In Morocco, olive oil was adulterated with lubricating oil used in jet engines, which made 10,000 people sick. These incidents of fraud with olive oil resulted in risks and uncontrolled public health (Yan et al, 2020).…”

Section: Fraud In Extra Virgin Olive Oilmentioning

confidence: 99%

Fraud in Food Produts

Santos

Shinohara

Macedo

et al. 2020

RSD

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In the context of food quality, Food Fraud has been identified as an emerging risk for the food industry and a significant concern for consumers and official supervisory bodies. Fraud refers to changes in a food product, always for profit, disrespecting the consumer's right in addition to representing a risk to human health. These frauds have the ability to diminish consumer confidence in their food supply and interfere with the reputation of honest food business operators. Thus, this review aimed to point out the opportunities for fraud and adulteration in some foods, showing the different ways of carrying them out, and also, some cases that have occurred throughout history. While pointing out the need for investment in specific and effective analyzes of production processes by the industries, in addition to control measures, actions aimed at reducing or eliminating possible hazards, which contributed to ensuring the quality and safety of products.

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“…speed of transactions), animal welfare, waste reduction, environmental impact, having an auditable system, trusted information, (improved) supervision and management and support for small farmers and growers (Kamilaris et al 2019;Galvez et al 2018). Various technologies could be used in combination with a Blockchain system including radio-frequency ID (RFID) based systems (Musa et al 2014;Shin & Eksioglu, 2015) to reduce the risk of fraudulent behaviour (Yan et al 2020). Several large retail and manufacturing chains have been active in Blockchain developments for the supply chain, notably Walmart, Unilever, Nestle, Cargill, Kroger and Coca Cola (Kamilaris et al 2019).…”

Section: Take In Figurementioning

confidence: 99%

Food provenance: assuring product integrity and identity

Wallace

Manning

2020

CABI Reviews

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Food supply chains are highly complex and involve numerous actors who influence food safety and the integrity of products and processes, both at individual points in the supply chain and more holistically throughout the chain as a whole. Provenance can relate to a particular source or origin of a food and its individual ingredients and/or relate to claims on how the product is produced and what marketing claims have been attached to the product. The aim of this review is to consider the recent advances in developing transparent data systems to demonstrate food provenance. One technological development is the use of Blockchain, a data handling structure which provides a secure network of information that cannot be changed or destroyed, distributed between supply chain actors. Other developments in information systems that can be used to monitor a range of criteria include geographic information systems (GIS) which can be linked with, for example, stable isotope analysis to provide an indication of provenance for a given product or ingredient. This technology is used as a case study in this paper to demonstrate the opportunities and limitations to such technological approaches. The review reflects on aspects of provenance and the actions that can be taken at organisational and supply chain level to demonstrate transparency so that consumers can have trust in those procuring, processing and supplying food.

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Food fraud: Assessing fraud vulnerability in the extra virgin olive oil supply chain

Cited by 63 publications

References 27 publications

Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

Fraud in Food Produts

Food provenance: assuring product integrity and identity

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