Detection of Gluten in Gluten-Free Foods of Plant Origin

Výrostková, Jana; Regecová, Ivana; Zigo, František; Marcinčák, Slavomí­r; Kožárová, I.; Kováčová, Mariana; Bertová, Daniela

doi:10.3390/foods11142011

Cited by 7 publications

(4 citation statements)

References 29 publications

(31 reference statements)

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“…In terms of the complexity of the experiment, our IoT prototype portable solution also has a great ease-of-use advantage, because the user only needs the kit (sensor, Raspberry Pi 4, DLPNIRNANOEVM sensor, 3D Mechanical system, and collecting plate) and a device with an internet connection (mobile, tablet or pc), and to put the samples into the collecting plate and press a few clicks to make the prediction. Instead, other analytical methods take significantly more time, counting from the sample preparation until the data recovery; around 0.5-2.5 h for ELISA [13]; ≥0.5 h for HPLC [49]; and > 3 h for PCR [50]. Other studies have achieved very good results while decreasing the detection time, to illustrate, a recent study designed an optical nanosensor for rapid detection of the gluten content of samples containing wheat.…”

Section: Discussionmentioning

confidence: 99%

IoT System for Gluten Prediction in Flour Samples Using NIRS Technology, Deep and Machine Learning Techniques

et al. 2023

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Gluten is a natural complex protein present in a variety of cereal grains, including species of wheat, barley, rye, triticale, and oat cultivars. When someone suffering from celiac disease ingests it, the immune system starts attacking its own tissues. Prevalence studies suggest that approximately 1% of the population may have gluten-related disorders during their lifetime, thus, the scientific community has tried to study different methods to detect this protein. There are multiple commercial quantitative methods for gluten detection, such as enzyme-linked immunosorbent assays (ELISAs), polymerase chain reactions, and advanced proteomic methods. ELISA-based methods are the most widely used; but despite being reliable, they also have certain constraints, such as the long periods they take to detect the protein. This study focuses on developing a novel, rapid, and budget-friendly IoT system using Near-infrared spectroscopy technology, Deep and Machine Learning algorithms to predict the presence or absence of gluten in flour samples. 12,053 samples were collected from 3 different types of flour (rye, corn, and oats) using an IoT prototype portable solution composed of a Raspberry Pi 4 and the DLPNIRNANOEVM infrared sensor. The proposed solution can collect, store, and predict new samples and is connected by using a real-time serverless architecture designed in the Amazon Web services. The results showed that the XGBoost classifier reached an Accuracy of 94.52% and an F2-score of 92.87%, whereas the Deep Neural network had an Accuracy of 91.77% and an F2-score of 96.06%. The findings also showed that it is possible to achieve high-performance results by only using the 1452–1583 nm wavelength range. The IoT prototype portable solution presented in this study not only provides a valuable contribution to the state of the art in the use of the NIRS + Artificial Intelligence in the food industry, but it also represents a first step towards the development of technologies that can improve the quality of life of people with food intolerances.

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Section: Discussionmentioning

confidence: 99%

IoT System for Gluten Prediction in Flour Samples Using NIRS Technology, Deep and Machine Learning Techniques

et al. 2023

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“…Just like other allergens, wheat allergenic reaction does not have a cure; the best cure is prevention, and it is imperative that consumers are able to make an informed choice when selecting their diet to ensure they are wheat/gluten-free. The conventional method for wheat and gluten detection includes using the polymerase chain reaction (PCR) and ELISA methods [34,35], which are accurate and reliable but come with the drudgery of analysis, need for trained personnel, and waste that pollutes the environment.…”

Section: Examples Of Common Food Allergens 141 Wheat and Gluten Aller...mentioning

confidence: 99%

The Magnitude and Impact of Food Allergens and the Potential of AI-Based Non-Destructive Testing Methods in Their Detection and Quantification

Adedeji,

Priyesh,

Odugbemi

2024

Foods

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Reaction to food allergens is on the increase and so is the attending cost on consumers, the food industry, and society at large. According to FDA, the “big-eight” allergens found in foods include wheat (gluten), peanuts, egg, shellfish, milk, tree nuts, fish, and soybeans. Sesame was added to the list in 2023, making the target allergen list nine instead of eight. These allergenic foods are major ingredients in many food products that can cause severe reactions in those allergic to them if found at a dose that can elicit a reaction. Defining the level of contamination that can elicit sensitivity is a work in progress. The first step in preventing an allergic reaction is reliable detection, then an effective quantification method. These are critical steps in keeping contaminated foods out of the supply chain of foods with allergen-free labels. The conventional methods of chemical assay, DNA-PCR, and enzyme protocols like enzyme-linked immunosorbent assay are effective in allergen detection but slow in providing a response. Most of these methods are incapable of quantifying the level of allergen contamination. There are emerging non-destructive methods that combine the power of sensors and machine learning to provide reliable detection and quantification. This review paper highlights some of the critical information on the types of prevalent food allergens, the mechanism of an allergic reaction in humans, the measure of allergenic sensitivity and eliciting doses, and the conventional and emerging AI-based methods of detection and quantification—the merits and downsides of each type.

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“…This level is the lowest limit of detection for gluten in foods using scientifically validated analytical methods. As methods evolve and new methods are developed to detect gluten and/or wheat in complex food matrices [12][13][14][15][16], this level may be revised. Gluten-free food was defined in the Codex Alimentarius by the FAO and WHO in the 'Standard for Foods for Special Dietary Use for persons intolerant to gluten', first adopted in 1979, revised in 2008, and amended in 1983 and 2015 [17].…”

Section: Introductionmentioning

confidence: 99%

Gluten-Free Product Recalls and Their Impact on Consumer Trust

Liu,

El Khoury,

Joye

2023

Nutrients

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The range of gluten-free food products available to consumers is steadily expanding. In recent years, recalls of food products have highlighted the importance of accurate labeling of food products for the presence of wheat, other gluten-containing cereals, or gluten itself as refined ingredient. The purpose of this study was to gain more insights into recent food recalls related to undeclared gluten/wheat contamination and consumer experiences with these recalls. Recalls of products triggered by gluten contamination are relatively scarce and are not often triggered by a consumer complaint. The impact of these recalls on consumer trust was evaluated through an online survey that was distributed among supporters of Celiac Canada (CCA) and covered (i) strategies to adhere to a gluten-free diet, (ii) experiences with gluten-free recalls and their impact on consumer trust, and (iii) demographic information. Consumer concern regarding gluten-free product recalls is significant, but the concern regarding recalls is not heightened after experiencing a recall. Companies pursuing transparency in the process, identification of the source of contamination, and mitigation strategies going forward are likely to retain consumer trust in their product and brand. Based on the survey results, further efforts focusing on consumer education regarding interpreting nutrient labels, identifying sources of information on product recalls, and understanding procedures to follow upon suspected gluten contamination of a gluten-free product are recommended.

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Detection of Gluten in Gluten-Free Foods of Plant Origin

Cited by 7 publications

References 29 publications

IoT System for Gluten Prediction in Flour Samples Using NIRS Technology, Deep and Machine Learning Techniques

IoT System for Gluten Prediction in Flour Samples Using NIRS Technology, Deep and Machine Learning Techniques

The Magnitude and Impact of Food Allergens and the Potential of AI-Based Non-Destructive Testing Methods in Their Detection and Quantification

Gluten-Free Product Recalls and Their Impact on Consumer Trust

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