A Review on Identified Major Food Allergens: Characteristics and Role in Food Allergy

Arya, Chhavi; Jantwal, Chetna

doi:10.21048/ijnd.2017.54.3.11626

Cited by 3 publications

(3 citation statements)

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“…Based on these insights, we recommend that the authors of future position statement papers adopt a more scientifically balanced approach when addressing EPA and DHA nutrients within VN diets. In addition, future position statements regarding the suitability of VN diets for children should also include information on the increased occurrence of food allergies related to specific plant-based foods, the risks associated with the consumption of ultra-processed VN products, and guidance on dietary modifications to ensure continued nutritional adequacy [140][141][142]. For VN diets, it is often stated that the cost is high, and only individuals with higher economic status can afford to follow them.…”

Section: Additional Considerationsmentioning

confidence: 99%

Vegan Diets for Children: A Narrative Review of Position Papers Published by Relevant Associations

Jakše,

Fras,

Fidler Mis

2023

Nutrients

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The scientific discourse on vegan diets for children and adolescents primarily involves referencing position statement papers from different scientific and professional organizations, including paediatric associations. Over the past two decades, specialized associations have issued official statements and published position papers about adopting well-designed vegan diets during crucial life stages, including pregnancy and lactation, infancy, and childhood. A subset of these associations firmly supports the notion that a well-designed vegan diet can indeed be healthy and support normal growth and development during particularly delicate life stages, emphasizing careful planning, vitamin B12 supplementation, and regular supervised medical and dietetics oversight. In contrast, specific paediatric associations caution against vegan diets for children and adolescents, citing potential harm and the lack of adequate substantiation. These criticisms in position papers frequently point to lower-quality studies and/or outdated studies. Additionally, concerns extend to comparing vegan and omnivorous diets, considering public health issues such as obesity and early stages of cardiovascular disease as well as the risk of prediabetes and type 2 diabetes. Notably, some scepticism stems from studies where children’s adherence to a well-designed vegan diet is incomplete. Scientific rigor suggests performing a comparable assessment of omnivorous and vegan diets. This narrative review highlights the need for a comprehensive, up-to-date literature review to inform balanced perspectives on vegan diets for children and adolescents. Researchers and decision-makers should aim to actively improve the design and consistent implementation of both diet types.

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Section: Additional Considerationsmentioning

confidence: 99%

Vegan Diets for Children: A Narrative Review of Position Papers Published by Relevant Associations

Jakše,

Fras,

Fidler Mis

2023

Nutrients

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“…According to the report by the United Nations Food and Agriculture Organization, more than 90% of the food allergens have existed in eight types of food: Fish, eggs, milk, crustaceans, soybeans, peanuts, nuts, and wheat [ 7 ]. Although more than 180 kinds of foods have been identified as containing allergens, there are still many food allergens that have not been discovered.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins

Wang

Niu

Zhao

et al. 2021

Foods

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Traditional food allergen identification mainly relies on in vivo and in vitro experiments, which often needs a long period and high cost. The artificial intelligence (AI)-driven rapid food allergen identification method has solved the above mentioned some drawbacks and is becoming an efficient auxiliary tool. Aiming to overcome the limitations of lower accuracy of traditional machine learning models in predicting the allergenicity of food proteins, this work proposed to introduce deep learning model—transformer with self-attention mechanism, ensemble learning models (representative as Light Gradient Boosting Machine (LightGBM) eXtreme Gradient Boosting (XGBoost)) to solve the problem. In order to highlight the superiority of the proposed novel method, the study also selected various commonly used machine learning models as the baseline classifiers. The results of 5-fold cross-validation showed that the area under the receiver operating characteristic curve (AUC) of the deep model was the highest (0.9578), which was better than the ensemble learning and baseline algorithms. But the deep model need to be pre-trained, and the training time is the longest. By comparing the characteristics of the transformer model and boosting models, it can be analyzed that, each model has its own advantage, which provides novel clues and inspiration for the rapid prediction of food allergens in the future.

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“…According to the report by the United Nations Food and Agriculture Organization, more than 0.9000 of food allergens are existed in eight types of food: fish, eggs, milk, crustaceans, soybeans, peanuts, nuts and wheat [5]. Although more than 180 kinds of foods have been identified as containing allergens, there are still many food allergens that have not been discovered.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins

Wang

Niu

Zhao

et al. 2021

Preprint

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Traditional food allergen identification mainly relies on in vivo and in vitro experiments, which often needs a long period and high cost. The artificial intelligence (AI)-driven rapid food allergen identification method has solved the two drawbacks and is becoming an efficient auxiliary tool. Aiming to overcome the limitations of lower accuracy of traditional machine learning models in predicting the allergenicity of food allergens, this work proposed to introduce transformer deep learning model with self-attention mechanism and ensemble learning model (representative as Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting (XGBoost)) to solve the problem. In order to highlight the superiority of the proposed novel method, the study also selected various commonly used machine learning models as the baseline classifiers. The results of 5-fold cross-validation found that the AUC of the deep model was the highest (0.9400), which was better than the ensemble learning and baseline algorithms. But it needed to be pre-trained, and the training cost was highest. By comparing the characteristics of transformer model and boosting models, it can be analyzed that the two types of models have their own advantages, which provides novel clues and inspiration for the rapid prediction of food allergens in the future.

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A Review on Identified Major Food Allergens: Characteristics and Role in Food Allergy

Cited by 3 publications

References 0 publications

Vegan Diets for Children: A Narrative Review of Position Papers Published by Relevant Associations

Vegan Diets for Children: A Narrative Review of Position Papers Published by Relevant Associations

A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins

A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins

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