Pattern recognition techniques in food quality and authenticity: A guide on how to process multivariate data in food analysis

Gomes, A. A.; Azcarate, Silvana M.; S̆pánik, Ivan; Khvalbota, Liudmyla; Goicoechea, Héctor C.

doi:10.1016/j.trac.2023.117105

Cited by 18 publications

(6 citation statements)

References 246 publications

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“…As the recommended level for diastase activity is not less than 8 Schade units [1,2], four values (obtained for two acacia honey samples from the Central region, 2022, and Belgrade, 2018, and for two polyfloral honey samples from Belgrade, 2018) were not in the line. The level of diastase activity is influenced by the floral sources, the nectar collection period, and environmental conditions, as well as the bees themselves [9,18,34]. Low diastase activity also indicates a low content of nectar and therefore possible heat treatment, i.e., adulteration [26].…”

Section: Diastase Activitymentioning

confidence: 99%

“…Physicochemical techniques yield extensive and reproducible datasets comprising precise numerical values for numerous samples. Recent studies have investigated the utility of cANN using physicochemical data [18]. Mathematical models that capture the associations between physicochemically derived descriptors and diverse sample types can be constructed using a range of machine learning algorithms [19].…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Impact of Botanical Origins, Harvest Years, and Geographical Variability on the Physicochemical Quality of Serbian Honey

Tasić,

Pezo,

Lončar

et al. 2024

Foods

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This study summarized the physicochemical analysis of 609 honey samples originating from the Republic of Serbia. Variations among honey samples from different botanical origins, regions of collections, and harvest years were exposed to descriptive statistics and correlation analysis that differentiated honey samples. Furthermore, most of the observed physicochemical parameters (glucose, fructose, sucrose content, 5-hydroxymethylfurfural (5-HMF) levels, acidity, and electrical conductivity) varied significantly among different types of honey, years, and regions. At the same time, no noticeable difference was found in diastase activity, moisture content, and insoluble matter. Based on the obtained results, 22 honey samples could be considered adulterated, due to the irregular content of sucrose, 5-HMF, acidity, and diastase activity. In addition, 64 honey samples were suspected to be adulterated. Adulterated and non-compliant samples present a relatively low percentage (14.1%) of the total number of investigated samples. Consequently, a considerable number of honey samples met the required standards for honey quality. Overall, these findings provide insights into compositional and quality differences among various types of honey, aiding in understanding their characteristics and potential applications.

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Section: Diastase Activitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the Impact of Botanical Origins, Harvest Years, and Geographical Variability on the Physicochemical Quality of Serbian Honey

Tasić,

Pezo,

Lončar

et al. 2024

Foods

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“…Figure 1 shows the NSR RAMAN fingerprint of edible vegetable oils analysed for this study before and after the pre-processing step. Unsupervised and supervised pattern recognition techniques were explored using PLS_Toolbox, version 8.6.1 (Eigenvector Research Inc., Manson, WA, USA) [38].…”

Section: Data Treatmentmentioning

confidence: 99%

Discrimination/Classification of Edible Vegetable Oils from Raman Spatially Solved Fingerprints Obtained on Portable Instrumentation

Jiménez-Hernández,

Ortega-Gavilán,

Bagur-González

et al. 2024

Foods

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Currently, the combination of fingerprinting methodology and environmentally friendly and economical analytical instrumentation is becoming increasingly relevant in the food sector. In this study, a highly versatile portable analyser based on Spatially Offset Raman Spectroscopy (SORS) obtained fingerprints of edible vegetable oils (sunflower and olive oils), and the capability of such fingerprints (obtained quickly, reliably and without any sample treatment) to discriminate/classify the analysed samples was evaluated. After data treatment, not only unsupervised pattern recognition techniques (as HCA and PCA), but also supervised pattern recognition techniques (such as SVM, kNN and SIMCA), showed that the main effect on discrimination/classification was associated with those regions of the Raman fingerprint related to free fatty acid content, especially oleic and linoleic acid. These facts allowed the discernment of the original raw material used in the oil’s production. In all the models established, reliable qualimetric parameters were obtained.

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“…A food matrix is a complex multi-compositional material system; therefore, the verification of its authenticity (in the quality scenario) should be carried out after the application of a nontargeted approach, in which 'the whole set' of intrinsic characteristics of the food is considered, but 'what' or 'how much' of these characteristics are present is not identified. 10 Therefore, in the food quality and authenticity scenario, the targeted approach is not the most suitable approach to be applied as it implies a waste of resources, use of reagents and solvents, and waste generation; in short, it is not an environmentally sustainable/green approach. Therefore, the application of a non-targeted approach would be the most appropriate for quality control and the targeted approach should be used in the food safety scenario, focused on the detection of chemical compounds that may cause a problem for human health.…”

Section: Introductionmentioning

confidence: 99%

Innovative non‐targeted liquid chromatography fingerprinting approach for authenticating tigernuts under Protected Designation of Origin quality seal

Medina‐García,

Jiménez‐Carvelo,

Bagur‐González

et al. 2023

J Sci Food Agric

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BackgroundTigernut is a typical foodstuff from a specific region of Valencia (Spain) "L'Horta Nord", where it is commercialized under a Protected Designation of Origin (PDO) Chufa de Valencia ("Valencia's tigernut"). PDO‐recognised tigernuts present unique characteristics associated with their particular production region. The increasing in their demand and, therefore, the expansion of its cultivation area has made necessary an exhaustive quality control to check the geographical origin and quality seal.ResultsIn this work, a new multivariate analytical method capable of authenticating the PDO quality seal of tigernut samples was developed. For it, tigernut fat fraction was extracted under optimal conditions estimated applying the methodology of design of experiments. The analytical method combined fingerprinting methodology and chemometrics tools to observe the natural grouping of the samples using exploratory analysis method (PCA) and, to develop classification models (PLS‐DA) to discriminate between two samples categories: (i) PDO tigernuts; and (ii) NON‐PDO tigernuts.ConclusionThe built PLS‐DA model demonstrates 100% accuracy, high sensitivity, and specificity revealing that tigernuts fat fraction can be applied to authenticate of the PDO quality seal.This article is protected by copyright. All rights reserved.

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Pattern recognition techniques in food quality and authenticity: A guide on how to process multivariate data in food analysis

Cited by 18 publications

References 246 publications

Assessing the Impact of Botanical Origins, Harvest Years, and Geographical Variability on the Physicochemical Quality of Serbian Honey

Assessing the Impact of Botanical Origins, Harvest Years, and Geographical Variability on the Physicochemical Quality of Serbian Honey

Discrimination/Classification of Edible Vegetable Oils from Raman Spatially Solved Fingerprints Obtained on Portable Instrumentation

Innovative non‐targeted liquid chromatography fingerprinting approach for authenticating tigernuts under Protected Designation of Origin quality seal

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