Simple detection of gluten in wheat-containing food samples of celiac diets with a novel fluorescent nanosensor made of folic acid-based carbon dots through molecularly imprinted technique

Karamdoust, Sanaz; Milani-Hosseini, Mohammad-Reza; Faridbod, Farnoush

doi:10.1016/j.foodchem.2022.135383

Cited by 13 publications

(3 citation statements)

References 32 publications

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“…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. They obtained results for the determination coefficient (R2) with 0.995 for folic acid-based-carbon dots molecularly imprinted polymer (FA-CDs-MIP) and 0.903 for FA-CDs none-imprinting polymer (FA-CDs-NIP), within a range of gluten detection of 0.36 to 2.20 µM [51]. They reported less than 4 min as the response time for the florescent nanosensor.…”

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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“…136 Recently, a "TURN-ON" fluorescent sensor was reported based on molecularly imprinted polymer (MIP) conjugated CDs for the detection of gluten in flour samples. 137 An increase in the fluorescence of MIP@CDs was observed in the presence of gluten in the concentration ranging from 0.36 to 2.20 µM. The developed gluten sensing kit demonstrated a response time of 4 minutes with a detection limit of 0.10 µM and hence can be used for commercial applications as well.…”

Section: Fluorescent Sensorsmentioning

confidence: 95%

Recent advancements in nanomaterial based optical detection of food additives: a review

Bhardwaj,

Deep,

Bhardwaj

et al. 2023

Analyst

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“…Gluten is a structural protein found primarily in grains and is recognized as a food allergen. In very recent work, a CD‐based MIP sensor was successfully developed for the rapid detection of gluten in bread and flour samples, which showed a linear detection range of 0.36–2.20 µM with a LOD of 0.1 µM (Karamdoust et al., 2023). This fluorescent probe was applied to form a selective response toward template gluten molecules by the inhibition of PET.…”

Section: Applications Of Qds In Food Quality and Safetymentioning

confidence: 99%

Quantum dots as advanced nanomaterials for food quality and safety applications: A comprehensive review and future perspectives

Zou,

Shi,

Wang

et al. 2024

Comp Rev Food Sci Food Safe

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The importance of food quality and safety lies in ensuring the best product quality to meet consumer demands and public health. Advanced technologies play a crucial role in minimizing the risk of foodborne illnesses, contamination, drug residue, and other potential hazards in food. Significant materials and technological advancements have been made throughout the food supply chain. Among them, quantum dots (QDs), as a class of advanced nanomaterials with unique physicochemical properties, are progressively demonstrating their value in the field of food quality and safety. This review aims to explore cutting‐edge research on the different applications of QDs in food quality and safety, including encapsulation of bioactive compounds, detection of food analytes, food preservation and packaging, and intelligent food freshness indicators. Moreover, the modification strategies and potential toxicities of diverse QDs are outlined, which can affect performance and hinder applications in the food industry. The findings suggested that QDs are mainly used in analyte detection and active/intelligent food packaging. Various food analytes can be detected using QD‐based sensors, including heavy metal ions, pesticides, antibiotics, microorganisms, additives, and functional components. Moreover, QD incorporation aided in improving the antibacterial and antioxidant activities of film/coatings, resulting in extended shelf life for packaged food. Finally, the perspectives and critical challenges for the productivity, toxicity, and practical application of QDs are also summarized. By consolidating these essential aspects into this review, the way for developing high‐performance QD‐based nanomaterials is presented for researchers and food technologists to better capitalize upon this technology in food applications.

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Simple detection of gluten in wheat-containing food samples of celiac diets with a novel fluorescent nanosensor made of folic acid-based carbon dots through molecularly imprinted technique

Cited by 13 publications

References 32 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

Recent advancements in nanomaterial based optical detection of food additives: a review

Quantum dots as advanced nanomaterials for food quality and safety applications: A comprehensive review and future perspectives

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