Since the amount of food can affect the copper content, the copper content of different amounts of food (except foods without copper) is different. The copper content of some foods is inappropriately calculated per 100 kcal, 100 g or 100 mL, or the reference amount customarily consumed (RACC). Thus, making some food choices based on these calculations to achieve adequate copper intake may increase the risk of some chronic diseases. Calculating the copper content and determining appropriate copper levels (to achieve adequate copper intake) based on U.S. Food and Drug Administration (FDA), Codex Alimentarius Commission (CAC), and the proposed method were performed in 7,379 food items. Making some food choices based on the FDA and CAC per serving (the serving is derived from the RACC) or CAC per 100 g or 100 mL to achieve adequate copper intake exceeded energy needs, which could lead to overweight or obesity. Making some food choices based on the CAC per 100 kcal or CAC per 100 g or 100 mL to achieve adequate copper intake did not meet copper requirements, which could lead to copper deficiency. Some foods that met copper requirements were not appropriate food choices based on the CAC per 100 g or 100 mL or CAC per serving to achieve adequate copper intake. On the basis of the proposed method, calculating the copper content and determining appropriate copper levels in foods are performed by considering RACCs and the energy content of foods. Thus, making food choices based on the proposed method met copper requirements and did not exceed energy needs.

The prediction of biological targets of bioactive molecules from machine-readable materials can be routinely performed by computational target prediction tools (CTPTs). However, the prediction of biological targets of bioactive molecules from non-digital materials (e.g., printed or handwritten documents) has not been possible due to the complex nature of bioactive molecules and impossibility of employing computations. Improving the target prediction accuracy is the most important challenge for computational target prediction. A minimum structure is identified for each group of neighbor molecules in the proposed method. Each group of neighbor molecules represents a distinct structural class of molecules with the same function in relation to the target. The minimum structure is employed as a query to search for molecules that perfectly satisfy the minimum structure of what is guessed crucial for the targeted activity. The proposed method is based on chemical similarity, but only molecules that perfectly satisfy the minimum structure are considered. Structurally related bioactive molecules found with the same minimum structure were considered as neighbor molecules of the query molecule. The known target of the neighbor molecule is used as a reference for predicting the target of the neighbor molecule with an unknown target. A lot of information is needed to identify the minimum structure, because it is necessary to know which part(s) of the bioactive molecule determines the precise target or targets responsible for the observed phenotype. Therefore, the predicted target based on the minimum structure without employing the statistical significance is considered as a reliable prediction. Since only molecules that perfectly (and not partly) satisfy the minimum structure are considered, the minimum structure can be used without similarity calculations in non-digital materials and with similarity calculations (perfect similarity) in machine-readable materials. Nine tools (PASS online, PPB, SEA, TargetHunter, PharmMapper, ChemProt, HitPick, SuperPred, and SPiDER), which can be used for computational target prediction, are compared with the proposed method for 550 target predictions. The proposed method, SEA, PPB, and PASS online, showed the best quality and quantity for the accurate predictions.

Computing the food component (nutrient) amount in 100 kilocalories, 100 grams or 100 milliliters, the reference amount customarily consumed (RACC), or 50 grams of food demonstrates the food component amount of some foods unsuitably. So, selecting some foods based on them may elevate the hazards of some chronic diseases. Computing the food component amount and assessing suitable levels of food components and the nutritional quality according to the Codex Alimentarius Commission (CAC), the United States Food and Drug Administration (FDA), and the suggested procedure were implemented on 8,596 food cases, 29 food components, and 25 food categories. Selecting some foods under the FDA and CAC to reach sufficient intakes of positive food components surpassed energy demands. Selecting some foods under the CAC did not satisfy the demands of positive food components. Some foods that satisfied the demands of positive food components were not suitable food selections under the CAC. Selecting some foods under the FDA or CAC surpassed the demands of negative food components (including cholesterol, energy, fat, saturated fat, and sodium). Some foods that did not surpass the demands of negative food components were not suitable food selections under the CAC or FDA. Due to the vulnerabilities of selecting foods on the basis of the reference amounts of food, fast foods under the CAC and FDA in serving size (the serving size or serving is obtained from the RACC), spices and herbs under the CAC in 100 grams or 100 milliliters, and vegetables and vegetable products under the CAC in 100 kilocalories obtained the highest average scores for nutritional quality based on positive food components (including vitamins, protein, dietary fiber, and minerals, excluding sodium) among food categories for children aged four years and older and adults. Graphical Abstract

Background: Calculating the fat content per 100 g or 100 mL, 50 g, or the reference amount customarily consumed (RACC) shows the fat content of some foods inappropriately. Therefore, making some food choices based on them to limit fat intake may increase the risks of some chronic diseases. Methods: We calculated the fat content and determining appropriate fat levels (to limit fat intake) based on U.S. Food and Drug Administration (FDA), Codex Alimentarius Commission (CAC), and the proposed method were performed on 8,596 food items during July 2018 to June 2020 in Tehran, Iran. Information on food and fat profiles was provided from the USDA National Nutrient Database for Standard Reference, released in 2016. Results: Making some food choices based on the CAC per 100 g or 100 mL or FDA per serving (the serving is derived from the RACC, 100 g, or 50 g) to limit fat intake exceeded fat needs, which may increase the risks of some chronic diseases. Some foods that did not exceed fat needs were not appropriate food choices based on the CAC per 100 g or 100 mL or FDA per serving to limit fat intake. Some foods were free of, but not low in, fat, based on the FDA per serving. Conclusion: Making food choices based on the proposed method to limit fat intake did not exceed fat needs and the appropriate percentage of energy from fat. Also, foods that did not exceed fat needs were appropriate food choices based on the proposed method to limit fat intake.