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This study aims to determine what consumers take into consideration while buying food and to increase awareness. We also demonstrated food additives knowledge, and the association between food additive consumption and illness.

An online survey was used to collect data from respondents (n = 433).

Gender and knowledge of food additives and E numbers were found to be statistically different, as were education status and knowledge of food additives (p < 0.05). When purchasing foods, 40.0% of the respondents seldom read labels and also 34.9% were reading for each buy who verified the product’s expiration date (94.2%), followed by brand name (84.8%). Sucralose, Acesulfame potassium (Ace-K) and aspartame consumption were associated with type II diabetes mellitus (T2DM). Additionally, sulfite consumption was linked to diarrhea/constipation.

This study has some limitations. First, the study was cross-sectional, which does not allow the establishment of causal relationships for the associations found here. Second, the study was limited to one city in Turkey. Therefore, the study's findings cannot be extrapolated to Turkey.

Nutrition education should be given by the experts, and the policies should be implemented so that food labels may be used more effectively. Furthermore, nutritional education and policies can increase the general public's awareness of food additives.

Nutrition education should be given by the experts, and the policies should be implemented so that food labels may be used more effectively. Furthermore, nutritional education and policies can increase the general public's awareness of food additives.

Consumers must be knowledgeable about food additives and E numbers. However, the findings revealed that the majority of Turkish consumers seldom read product labels, and the use of several food additives resulted in negative health repercussions. Therefore, professionals should provide nutrition education, and legislation should be put in place so that food labels may be used more effectively.

This study aims to assess the effectiveness of different AI models in accurately aggregating information about the protein quality (PQ) content of food items using four artificial intelligence (AI) models -– ChatGPT 3.5, ChatGPT 4, Bard AI and Bing Chat.

A total of 22 food items, curated from the Food and Agriculture Organisation (FAO) of the United Nations (UN) report, were input into each model. These items were characterised by their PQ content according to the Digestible Indispensable Amino Acid Score (DIAAS).

Bing Chat was the most accurate AI assistant with a mean accuracy rate of 63.6% for all analyses, followed by ChatGPT 4 with 60.6%. ChatGPT 4 (Cohen’s kappa: 0.718, p < 0.001) and ChatGPT 3.5 (Cohen’s kappa: 0.636, p: 0.002) showed substantial agreement between baseline and 2nd analysis, whereas they showed a moderate agreement between baseline and 3rd analysis (Cohen’s kappa: 0.538, p: 0.011 for ChatGPT 4 and Cohen’s kappa: 0.455, p: 0.030 for ChatGPT 3.5).

This study provides an initial insight into how emerging AI models assess and classify nutrient content pertinent to nutritional knowledge. Further research into the real-world implementation of AI for nutritional advice is essential as the technology develops.