Facing the challenges of food fraud in the global food system

Sammut, Jesmond; Gopi, Karthik; Saintilan, Neil; Mazumder, Debashish

doi:10.1016/b978-0-12-821104-5.00009-x

Cited by 4 publications

(4 citation statements)

References 97 publications

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“…Food fraud represents a global economic and food safety issue for consumers, industry, and governments (Spink et al, 2016;Spink et al, 2019).This situation arises when food products, their components, or packaging are deliberately added, altered, or tampered with for the purpose of economic gain (Spink and Moyer, 2011a ;Manning and Soon,2016) . Food fraud is perpetrated to increase the apparent value of the product or reduce production costs and can lead to acute health consequences, including allergies, illnesses, or even deaths resulting from mislabeling (Pointing et al, 2020 ;Sammut et al, 2021 ;Visciano and Schirone, 2021;Spink and Moyer, 2011b). The prevalence and lack of control over adulteration in the milk and dairy products sector lead to the introduction of lowquality products into the market (Thangaraju, Modupalli, and Natarajan, 2021 ;Nacul and Revoredo-Giha, 2022).Quick detection of adulteration in these products is of critical importance.…”

Section: Introductionmentioning

confidence: 99%

Classification of Starch Adulteration in Milk Using Spectroscopic Data and Machine Learning

DURGUN

2023

UMAG

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In this comprehensive research, an in-depth evaluation of several machine learning algorithms, including Multilayer Perceptron, IBk, KStar, M5Rules, and RandomForest, is conducted to ascertain their effectiveness in detecting adulteration in milk products using spectroscopic data. The algorithms were rigorously deployed and assessed through a series of controlled experiments involving both raw and adulterated milk samples. Notably, IBk and KStar algorithms emerged with a perfect accuracy rate of 100% in identifying adulteration, highlighting their superior capability in this domain. Additionally, the Decision Table algorithm also performed exceptionally well, achieving a remarkable correlation coefficient of 0.9871. These promising results emphasize the undeniable potential of machine learning algorithms as reliable and precise tools for detecting adulteration in milk. Such technological interventions play a critical role in elevating the safety and quality standards of milk and milk-based products in the market. Moreover, the deployment of these advanced machine-learning techniques provides an invaluable layer of consumer protection, plays a significant role in combating widespread fraudulent practices in the milk industry, and ensures compliance with stringent food safety standards. These methodologies could be indispensable for both industry players and regulatory bodies, significantly contributing to the safeguarding of public health.

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Section: Introductionmentioning

confidence: 99%

Classification of Starch Adulteration in Milk Using Spectroscopic Data and Machine Learning

DURGUN

2023

UMAG

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“…This was propagated by the availability of these products through unregulated sources, particularly 'the internet'. This put the consumer at high risk [8][9][10][11][12], raising an apprehension about the quality of the available supplements on the market and necessitating their regulation and authentication.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Chromatographic Fingerprinting Combined with Chemometrics for the Identification of Regulated Plants in Suspicious Plant Food Supplements

2023

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The popularity of plant food supplements has seen explosive growth all over the world, making them susceptible to adulteration and fraud. This necessitates a screening approach for the detection of regulated plants in plant food supplements, which are usually composed of complex plant mixtures, thus making the approach not so straightforward. This paper aims to tackle this problem by developing a multidimensional chromatographic fingerprinting method aided by chemometrics. To render more specificity to the chromatogram, a multidimensional fingerprint (absorbance × wavelength × retention time) was considered. This was achieved by selecting several wavelengths through a correlation analysis. The data were recorded using ultra-high-performance liquid chromatography (UHPLC) coupled with diode array detection (DAD). Chemometric modelling was performed by partial least squares–discriminant analysis (PLS-DA) through (a) binary modelling and (b) multiclass modelling. The correct classification rates (ccr%) by cross-validation, modelling, and external test set validation were satisfactory for both approaches, but upon further comparison, binary models were preferred. As a proof of concept, the models were applied to twelve samples for the detection of four regulated plants. Overall, it was revealed that the combination of multidimensional fingerprinting data with chemometrics was feasible for the identification of regulated plants in complex botanical matrices.

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“…Profiling assists in food quality control measures and ensures consumer safety by focusing on the heavy metal readings of the profile [2] (A profile is unique and may act as a product signature or fingerprint, which can then serve in authenticating products and detecting food fraud along the supply chain. [3]). The conventional methods for SEP are numerous and typically use hyphenated inductively coupled plasma mass spectrometry (ICP-MS) techniques [4][5][6], stable isotope analysis (SIA), and fatty acid profiling [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Application of Portable Handheld X-ray Fluorescence (XRF) Scanner for Determining Seafood Provenance: A Case Study on Penaeus monodon

Malo,

Mazumder,

Crawford

et al. 2023

Foods

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Seafood elemental profiling (SEP) is the quantification of a range of elements in seafood products and may serve in addressing questions of seafood provenance and quality. Traditional methods for analyzing soft tissue present several limitations for the industry-level use of SEP. Portable handheld X-ray fluorescence (pXRF) analysis is a promising alternative to conventional methods; however, its application for biological analysis has not been fully established. Intact giant tiger prawn (Penaeus monodon) abdomens were analyzed with a Vanta M series XRF portable analyzer following a novel soft tissue protocol. Exploratory statistics (principal component analysis, nonmetric multidimensional scaling, and canonical discriminant analysis), as well as random forest models, have been implemented with pXRF profiles, yielding 81% accuracy when assigning the geographical origin of P. monodon. The results of this study highlight that SEP via pXRF is a viable industry-level analysis, and its application will depend on improved instrument calibration to account for fluctuating wetness factors that are influenced by cooking, storage, and other pre- and post-harvest treatments.

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Facing the challenges of food fraud in the global food system

Cited by 4 publications

References 97 publications

Classification of Starch Adulteration in Milk Using Spectroscopic Data and Machine Learning

Classification of Starch Adulteration in Milk Using Spectroscopic Data and Machine Learning

Multidimensional Chromatographic Fingerprinting Combined with Chemometrics for the Identification of Regulated Plants in Suspicious Plant Food Supplements

Evaluating the Application of Portable Handheld X-ray Fluorescence (XRF) Scanner for Determining Seafood Provenance: A Case Study on Penaeus monodon

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