Precision Food Composition Data as a Tool to Decipher the Riddle of Ultra-Processed Foods and Nutritional Quality.

BACKGROUND: Epidemiology supports a link between ultra-processed foods (UPFs) and health, mediated mainly through the clustering of foods with suboptimal nutrient profiles within UPFs. However, successful NOVA categorization requires access to a food's ingredient list, which we hypothesized can impact both UPF identification and the link between processing and composition. METHODS: Foods (n = 4851) in the HelTH branded food composition database were classified as NOVA1-4, with or without using the ingredient lists (generic and branded approach, respectively), to identify differences in NOVA classification (chi-square test) and the estimated average nutritional composition of each NOVA group (Kruskal-Willis U test). RESULTS: Using the ingredients list increased UPF identification by 30%. More than 30% of foods commonly assumed to be minimally processed (NOVA1-plain dairy, frozen vegetables, etc.) were reclassified as UPFs when using ingredient lists. These reclassified foods, however, had nutritional compositions comparable to NOVA1 foods and better than UPFs for energy, fat, sugars, and sodium (p < 0.001). In fact, UPFs did not show a uniform nutritional composition covering foods from Nutri-Score A (~10%) to Nutri-Score E (~20%). CONCLUSIONS: The assumption that all UPFs have the same unfavorable nutritional composition is challenged when NOVA is applied using the appropriate branded food composition database.

How Nutritious Are French Beans (Phaseolus vulgaris L.) from the Citizen Science Experiment?

French beans are tender, immature, edible pods that are harvested early in the plant's growth cycle and are usually eaten cooked. The growth habits of French beans were studied for the first time in a Citizen Science experiment, and 19 pod samples were collected for further nutritional analysis. Various macronutrients (e.g., protein, ash, fat, carbohydrates, amino acids) and multi-element profiles were determined. A survey of their growing habits revealed that beans are usually planted once or twice a year in May and June at a length of 5-10 m, with a predominance of dwarf beans cultivation over climbing varieties, and pest resistance and stringless pods are the most important characteristics when deciding on a bean. Homogenised freeze-dried pod samples contained 16.1-23.1% protein, 4.5-8.2% ash, 0.1-1.1% fat, and 62.0-70.6% carbohydrates and had a caloric value of 337-363 kcal/100 g. Of the 17 free amino acids identified, 8 were essential (histidine, threonine, methionine, valine, lysine, isoleucine, leucine, phenylalanine) and 9 were non-essential (cysteine, aspartic acid, serine, glutamic acid, glycine, arginine, alanine, proline, tyrosine); meanwhile, of the 12 elements, 5 were macroelements and 7 were microelements. The predominant free amino acids were aspartic acid, glutamic acid, and serine. In the multiple comparisons (Box and Whisker plot), the parameters caloric value and iron showed the strongest response. A very strong positive significant Pearson correlation (≥0.95) was found for five pairs of variables within the free amino acids. Comparison of the nutrient data obtained in the pods showed near-perfect or high complementarity (85.2-103.4%) with the food composition databases for half of the parameters, suggesting that the home-grown French beans from the Citizen Science experiment are a highly nutritious vegetable.

Accelerating the Classification of NOVA Food Processing Levels Using a Fine-Tuned Language Model: A Multi-Country Study.

The consumption and availability of ultra-processed foods (UPFs), which are associated with an increased risk of noncommunicable diseases, have increased in most countries. While many countries have or are planning to incorporate UPF recommendations in their national dietary guidelines, the classification of food processing levels relies on expertise-based manual categorization, which is labor-intensive and time-consuming. Our study utilized transformer-based language models to automate the classification of food processing levels according to the NOVA classification system in the Canada, Argentina, and US national food databases. We showed that fine-tuned language models using the ingredient list text found on food labels as inputs achieved a high overall accuracy (F1 score of 0.979) in predicting the food processing levels of Canadian food products, outperforming traditional machine learning models using structured nutrient data and bag-of-words. Most of the food categories reached a prediction accuracy of 0.98 using a fined-tuned language model, especially for predicting processed foods and ultra-processed foods. Our automation strategy was also effective and generalizable for classifying food products in the Argentina and US databases, providing a cost-effective approach for policymakers to monitor and regulate the UPFs in the global food supply.

Determining classes of food items for health requirements and nutrition guidelines using Gaussian mixture models.

Introduction: The identification of classes of nutritionally similar food items is important for creating food exchange lists to meet health requirements and for informing nutrition guidelines and campaigns. Cluster analysis methods can assign food items into classes based on the similarity in their nutrient contents. Finite mixture models use probabilistic classification with the advantage of taking into account the uncertainty of class thresholds. Methods: This paper uses univariate Gaussian mixture models to determine the probabilistic classification of food items in the South African Food Composition Database (SAFCDB) based on nutrient content. Results: Classifying food items by animal protein, fatty acid, available carbohydrate, total fibre, sodium, iron, vitamin A, thiamin and riboflavin contents produced data-driven classes with differing means and estimates of variability and could be clearly ranked on a low to high nutrient contents scale. Classifying food items by their sodium content resulted in five classes with the class means ranging from 1.57 to 706.27 mg per 100 g. Four classes were identified based on available carbohydrate content with the highest carbohydrate class having a mean content of 59.15 g per 100 g. Food items clustered into two classes when examining their fatty acid content. Foods with a high iron content had a mean of 1.46 mg per 100 g and was one of three classes identified for iron. Classes containing nutrient-rich food items that exhibited extreme nutrient values were also identified for several vitamins and minerals. Discussion: The overlap between classes was evident and supports the use of probabilistic classification methods. Food items in each of the identified classes were comparable to allowed food lists developed for therapeutic diets. This data-driven ranking of nutritionally similar classes could be considered for diet planning for medical conditions and individuals with dietary restrictions.

Food Composition Databases (FCDBs): A Bibliometric Analysis.

Food composition databases (FCDBs) are important tools that provide information on the nutritional content of foods. Previously, it was largely unclear what nutritional contents and which FCDBs were involved in highly cited papers. The bibliometric study aimed to identify the most productive authors, institutions, and journals. The chemicals/chemical compounds with high averaged citations and FCDBs used by highly cited papers were identified. In July 2023, the online database Web of Science Core Collection (WoSCC) was queried to identify papers related to FCDBs. A total of 803 papers were identified and analyzed. The first paper indexed in WoSCC was published in 1992 by Pennington, which described the usefulness of FCDB for researchers to identify core foods for their own studies. In that paper, the FCDB described was the USDA 1987-88 NFCS (the United States Department of Agriculture 1987-88 Nationwide Food Consumption Survey). The most productive author was Dr. Paul M. Finglas, the Head of the Food Databanks National Capability at the Quadram Institute (Norwich, UK) and the Managing Director of EuroFIR. His most cited paper among this dataset was about the development of an online Irish food composition database together with EuroFIR. The most productive institutions were the USDA and the World Health Organization (WHO) instead of universities. Flavonoid was the most recurring chemical class among the highly cited ones. The anti-oxidative properties and protective effects against heart disease and cancer of flavonoids might be some of the reasons for their popularity in research. Among the highly cited papers, the most heavily used FCDBs were the USDA database for the flavonoid content of selected foods, Fineli, the USDA National Nutrient Database for Standard Reference (USNDB), EuroFIR eBASIS-Bioactive Substances in Food Information Systems, and Phenol-Explorer. High-quality national and international FCDBs should be promoted and made more accessible to the research and public communities to promote better nutrition and public health on a global scale.

Food Allergens in Ultra-Processed Foods According to the NOVA Classification System: A Greek Branded Food Level Analysis.

Ultra-processed foods' (UPFs') consumption has been positively linked to the presence of allergic symptoms, but it is yet unknown whether this is linked to their nutritional composition or allergen load. This study used the ingredient lists available in the Greek Branded Food Composition Database, HelTH, to classify foods (n = 4587) into four grades of food processing (NOVA1-4) according to the NOVA System. Associations between NOVA grades and the presence of allergens (as an ingredient or trace) were studied. Overall, UPFs (NOVA4) were more likely to contain allergens than unprocessed foods, NOVA1 (76.1% vs. 58.0%). However, nested analyses among similar foods showed that in >90% of cases, processing degree was not linked to allergens' presence. Recipe/matrix complexity was more strongly linked to allergens' presence with NOVA4 foods declaring 1.3 allergenic ingredients vs. 0.4 allergenic ingredients in NOVA1 foods (p < 0.01). Exposure to trace allergens was more common for NOVA4 than NOVA1 foods (45.4% vs. 28.7%), but the extent of contamination was similar (2.3 vs. 2.8 trace allergens). Overall, UPFs are more complex mixtures with higher numbers of allergens per food and are more prone to cross-contamination. However, indicating a food's degree of processing is not sufficient to help identify allergen-free choices within the same subcategory.

Systematic Review of the Literature to Inform the Development of a South African Dietary Polyphenol Composition Database.

Comprehensively compiled dietary polyphenol data is required to compare polyphenol content between foods, calculate polyphenol intake and study its association with health and disease. The purpose of this review was to identify data on the presence and content of polyphenolic components in South African foods, with the aim of compiling the data into a database. An electronic literature search was conducted up until January 2020 using multiple databases. Additional literature was sourced from South African university repositories. A total of 7051 potentially eligible references were identified, of which 384 met the inclusion criteria. These studies provided information on food item name, geographical distribution, polyphenol type, quantity, and quantification method. Data for 1070 foods were identified, amounting to 4994 polyphenols. Spectrophotometry was the main method used for quantification of gross phenolic content in various assays such as total phenolic content (Folin-Ciocalteu assay), total flavonoid content (AlCl3 assay) and condensed tannin content (vanillin-HCl assay). Phenolic acids and flavonoids were the main polyphenol classes identified. This review highlights that South Africa has abundant information on the polyphenol content of foods, which could be utilised within a food composition database for the estimation of polyphenol intake for South Africa.

Expansion of an Australian food composition database to estimate plant and animal intakes.

Despite evidence for favourable health outcomes associated with plant-based diets, a database containing the plant and animal content of all foods eaten is required to undertake a reliable assessment of plant-based diets within a population. This study aimed to expand an existing Australian food database to include the plant and animal content of all whole foods, beverages, multi-ingredient products and mixed dishes. Twenty-three plant- and animal-based food group classifications were first defined. The food servings per 100 g of each product were then systematically calculated using either a recipe-based approach, a food label-based approach, estimates based on similar products or online recipes. Overall, 4687 (83·5 %) foods and beverages were identified as plant or plant-containing products, and 3701 (65·9 %) were animal or animal-containing products. Results highlighted the versatility of plant and animal ingredients as they were found in various foods across many food categories, including savoury and sweet foods, as well as discretionary and core foods. For example, over 97 % of animal fat-containing foods were found in major food groups outside the AUSNUT 2011-2013 'fats and oils' group. Surprisingly, fruits, nuts and seeds were present in a greater percentage of discretionary products than in core foods and beverages. This article describes a systematic approach that is suitable for the development of other novel food databases. This database allows more accurate quantitative estimates of plant and animal intakes, which is significant for future epidemiological and clinical research aiming to investigate plant-based diets and their related health outcomes.

Development of a food composition database of different food contaminants CONT11 and estimation of dietary exposure in children of southern Spain.

Increasing food security is one of the Sustainable Development Goals. One of the main risks in food is the increase in food contaminants. Processing methods, such as the addition of additives or heat treatment, influence contaminant generation and increase their levels in food. The aim of the present study was to create a database using a methodology similar to that of food composition databases but with a focus on potential food contaminants. CONT11 collects information on 11 contaminants: hydroxymethyl-2-furfural, pyrraline, Amadori compounds, furosine, acrylamide, furan, polycyclic aromatic hydrocarbons, benzopyrene, nitrates, nitrites and nitrosamines. This is collected for more than 220 foods obtained from 35 different data sources. A food frequency questionnaire validated for use with children was used to validate the database. Contaminant intake and exposure in 114 children aged 10-11 years were estimated. Outcomes were within the range of values described by other studies, confirming the usefulness of CONT11. This database will allow nutrition researchers to go a step further in assessing dietary exposure to some food components and the association of this with disease, whilst also informing strategies to reduce exposure.

Validation of the AOAC method for analyzing fatty acids in meat by-products for the Korean Food Composition Database.

The constituents of meat and meat by-product are susceptible to degradation processes, especially, the control of oxidative processes is critical. The main factors that influence their lipid oxidation are fat content and fatty acid composition. The aim of this study was to validate AOAC method 996.06 for analysis of fatty acid in meat by-products and to provide their accurate, precise, and reliable information in the Korean Food Composition Database. A total of 19 fatty acids were identified. The head, small intestine, stomach, and heart of pigs and the feet of chickens contained higher contents of total fatty acids, whereas the blood of cow had the lowest content. In particular, meat by-products other than the liver, blood, and stomach of cows and the stomach and small intestine of pigs contained higher contents of unsaturated fatty acids. Their main fatty acids were oleic acid, palmitic acid, linoleic acid, and stearic acid. Supplementary Information: The online version contains supplementary material available at 10.1007/s10068-022-01223-8.

Estimation of Free Sugars in the Filipino Food Composition Table and Evaluation of Population-Level Intake.

Recommendations to reduce intake of free sugars are included in some national dietary guidelines. However, as the content of free sugars is absent from most of the food composition tables, the adherence to such recommendations is hard to monitor. We developed a novel method to estimate the free sugar content in the Philippines food composition table, based on a data-driven algorithm that enabled automated annotation. We then used these estimates to analyze the free sugar intake of 66,016 Filipinos aged 4 years and over. The average free sugar consumption was 19 g/day, accounting for an average of 3% of the total caloric intake. Snacks and breakfast were the meals with the highest content of free sugars. Intake of free sugars, in grams per day and as % of energy, was positively associated with wealth status. The same pattern was observed for the consumption of sugar-sweetened beverages.

Natural language processing and machine learning approaches for food categorization and nutrition quality prediction compared with traditional methods.

BACKGROUND: Food categorization and nutrient profiling are labor intensive, time consuming, and costly tasks, given the number of products and labels in large food composition databases and the dynamic food supply. OBJECTIVES: This study used a pretrained language model and supervised machine learning to automate food category classification and nutrition quality score prediction based on manually coded and validated data, and compared prediction results with models using bag-of-words and structured nutrition facts as inputs for predictions. METHODS: Food product information from University of Toronto Food Label Information and Price Database 2017 (n = 17,448) and University of Toronto Food Label Information and Price Database 2020 (n = 74,445) databases were used. Health Canada's Table of Reference Amounts (TRA) (24 categories and 172 subcategories) was used for food categorization and the Food Standards of Australia and New Zealand (FSANZ) nutrient profiling system was used for nutrition quality score evaluation. TRA categories and FSANZ scores were manually coded and validated by trained nutrition researchers. A modified pretrained sentence-Bidirectional Encoder Representations from Transformers model was used to encode unstructured text from food labels into lower-dimensional vector representations, followed by supervised machine learning algorithms (i.e., elastic net, k-Nearest Neighbors, and XGBoost) for multiclass classification and regression tasks. RESULTS: Pretrained language model representations utilized by the XGBoost multiclass classification algorithm reached overall accuracy scores of 0.98 and 0.96 in predicting food TRA major and subcategories, outperforming bag-of-words methods. For FSANZ score prediction, our proposed method reached a similar prediction accuracy (R2: 0.87 and MSE: 14.4) compared with bag-of-words methods (R2: 0.72-0.84; MSE: 30.3-17.6), whereas structured nutrition facts machine learning model performed the best (R2: 0.98; MSE: 2.5). The pretrained language model had a higher generalizable ability on the external test datasets than bag-of-words methods. CONCLUSIONS: Our automation achieved high accuracy in classifying food categories and predicting nutrition quality scores using text information found on food labels. This approach is effective and generalizable in a dynamic food environment, where large amounts of food label data can be obtained from websites.

Potassium, sodium, calcium and magnesium levels of commonly consumed foods and estimates of dietary intakes of selected Nigerian adults.

Micronutrient deficiency is a public health challenge globally, as it affects both people and the economy. In Nigeria, most micronutrients, especially minerals are lost during food processing. The study was carried out to determine the potassium, sodium, calcium and magnesium composition of foods commonly eaten by Nigerian adults and to estimate the average adults' daily intake of these macrominerals. The contents of these minerals in 141 food items collected 'as consumed' from 10 locations in Federal Capital Territory, Abuja and Ogun State, Nigeria, were quantified by digesting the foods through dry ashing and analysed using flame atomic absorption spectrometer. The levels (mg/100 g fresh weight) of potassium, sodium, calcium and magnesium in the various foods ranged from 2.92 to 1520, 1.46 to 30,700, 1.35 to 1280 and 1.16 to 416, respectively. Recovery values were within the range of 95-110%. Adults' mean mineral intakes (mg/person/day) of the analysed foods were 1970 ± 780, 2750 ± 1100, 423 ± 300 and 389 ± 130 for potassium, sodium, calcium and magnesium, respectively. Mean sodium intake was higher while potassium and calcium intakes were lower compared with international recommendations (mg/person/day) of 1500, 2300-3400 and 1000-1300, respectively; indicating the need to enlighten consumers. The snapshot data from this study are useful to update the Nigerian Food Composition Database.

The relation of whole grain surrogate estimates and food definition to total whole grain intake in the Finnish adult population.

OBJECTIVES: Challenges in estimating total whole grain intake have led to the use of surrogate estimates, of which accuracy has not been assessed. We examined the suitability of five potential surrogates (dietary fiber; bread; rye bread; rye, oat and barley combined; rye) and a whole grain food definition to measure total whole grain intake in the Finnish adult population. METHODS: Our data comprised 5094 Finnish adults participating in the national FinHealth 2017 Study. Dietary intake was assessed by a validated FFQ. Food and nutrient intakes, including total whole grain, were calculated utilizing the Finnish Food Composition Database. The Healthgrain Forum whole grain food definition was applied to examine definition-based whole grain intake. Spearman correlations and quintile cross-classifications were calculated. RESULTS: Definition-based whole grain intake and consumption of rye, oat and barley combined had consistently the strongest correspondence with total whole grain intake. Rye and rye bread consumption also corresponded well with total whole grain intake. The correspondences of dietary fiber and bread with total whole grain were lower and more affected by the exclusion of energy under-reporters. Furthermore, their correlations with total whole grain intake varied the most between population subgroups. CONCLUSIONS: Rye-based estimates, especially rye, oat and barley combined, and definition-based whole grain intake appeared suitable surrogate estimates of total whole grain intake for epidemiological research of Finnish adults. The variation between surrogate estimates in their correspondence with total whole grain intake demonstrated the need for further evaluation of their accuracy in different populations and regarding specific health outcomes.

Intake and sources of dietary fibre and dietary fibre fractions in Finnish children.

The current definition of dietary fibre was adopted by the Codex Alimentarius Commission in 2009, but implementation requires updating food composition databases with values based on appropriate analysis methods. Previous data on population intakes of dietary fibre fractions are sparse. We studied the intake and sources of total dietary fibre (TDF) and dietary fibre fractions insoluble dietary fibre (IDF), dietary fibre soluble in water but insoluble in 76 % aqueous ethanol (SDFP) and dietary fibre soluble in water and soluble in 76 % aqueous ethanol (SDFS) in Finnish children based on new CODEX-compliant values of the Finnish National Food Composition Database Fineli. Our sample included 5193 children at increased genetic risk of type 1 diabetes from the Type 1 Diabetes Prediction and Prevention birth cohort, born between 1996 and 2004. We assessed the intake and sources based on 3-day food records collected at the ages of 6 months, 1, 3 and 6 years. Both absolute and energy-adjusted intakes of TDF were associated with age, sex and breast-feeding status of the child. Children of older parents, parents with a higher level of education, non-smoking mothers and children with no older siblings had higher energy-adjusted TDF intake. IDF was the major dietary fibre fraction in non-breastfed children, followed by SDFP and SDFS. Cereal products, fruits and berries, potatoes and vegetables were major food sources of dietary fibre. Breast milk was a major source of dietary fibre in 6-month-olds due to its human milk oligosaccharide content and resulted in high SDFS intakes in breastfed children.

Nutritional Quality of Plant-Based Meat and Dairy Imitation Products and Comparison with Animal-Based Counterparts.

While consumers are increasingly adopting plant-based meat and dairy imitation products, the nutritional quality and adequacy of those foods to act as a substitute is still under discussion. The Greek Branded Food Composition Database (HelTH) was expanded to map currently available meat and dairy imitations in Greece. Their main ingredient used, nutritional composition, and promotion as a healthy, nutritious food were described, and their overall nutritional quality using the Nutri-Score algorithm was evaluated. A total of n = 421 plant-based imitations were analyzed, made primarily of wheat or wheat mixes (83.5%) for meat imitations and grain (19.8%) or vegetable oil (17.1%) for dairy imitations. All meat imitations were high in protein and fiber, while, for dairy, only yogurts carried a protein content claim (80.9%). Imitation sausages, milk, and yogurt products had lower total fat and saturated fat content compared to their animal-based counterparts. All dairy imitations had lower protein content than animal-based dairy. The nutritional quality of imitation cheeses was graded as D-E, under the Nutri-Score system, compared to A-C for the animal-based cheese. Plant-based imitations have variable composition based on their main ingredient, and the substitution of specific food groups with plant-based alternatives may not support an equivalent or improved diet.

The Update of the Italian Food Composition Database of Gluten-Free Products and Its Application in Food-Based Dietary Guidelines Menus.

Complete food composition databases (FCDBs) on gluten-free (GF) foods are needed to assess the nutrient intakes of celiac disease patients. The aim of the present work was to update the previously developed version of the Italian GF-FCDB and to apply it to a theoretical GF diet. The updated GF-FCDB includes the composition of 108 GF cereal-based foods, as sold, in terms of energy and macro- and micro-nutrients, imputed using food label information combined with the standard recipe approach. Three scenarios (i.e., refined, mixed, and wholegrain cereals) of the weekly guideline menu for the general Italian population were analyzed for energy and nutrient content in a theoretical dietary assessment using traditional gluten-containing (GC) foods and the corresponding GF substitutes. All GF menus were higher than the corresponding GC menus in polyunsaturated fatty acids, linoleic acid, and vitamin E. Zinc was lower in GF than in GC menus only in the wholegrain-cereal scenario. Thanks to the application of the updated GF-FCDB including a comprehensive list of micronutrients, we observed that it is possible for celiac disease patients to meet nutrient requirements by simply substituting GC with GF cereal-based products following recommendations for the general population.

Evaluation of the New Individual Fatty Acid Dataset for UK Biobank: Analysis of Intakes and Sources in 207,997 Participants.

The Oxford WebQ is an online 24 h dietary assessment tool used by several large prospective studies. This study describes the creation of the new individual fatty acid (FA) dataset for the Oxford WebQ and reports intakes and sources of dietary individual FAs in the UK Biobank. Participants who completed ≥1 (maximum of five) 24 h dietary assessments were included (n = 207,997). Nutrient intakes were obtained from the average of all completed 24 h dietary assessments. Nutrient data from the UK McCance and Widdowson's The Composition of Foods and the US Department of Agriculture food composition tables were used to calculate intakes of 21 individual FAs. The individual FA dataset included 10 saturated fatty acids (SFAs), 4 monounsaturated fatty acids (MUFAs), and 7 polyunsaturated fatty acids (PUFAs; including alpha-linolenic (18:3), eicosapentaenoic (20:5), and docosahexaenoic (22:6) acids). Palmitic (16:0; mean ± standard deviation (SD): 13.5 ± 5.7 g/d) and stearic (18:0; 5.2 ± 2.5) acids were the main contributors to SFAs, and the main sources of these were cereals and cereal products (mostly desserts/cakes/pastries), milk and milk products (mostly cheese and milk), and meat and meat products. Oleic acid (18:1; 24.2 ± 9.8) was the main MUFA, derived mainly from cereals and cereal products, and meat and meat products. Linoleic acid (18:2; 9.7 ± 4.3) was the main PUFA, derived mostly from cereals and cereal products, and vegetables (including potatoes) and vegetable dishes. The individual FA dataset for the Oxford WebQ will allow future investigations on individual FAs and disease risk.

Development of a Canadian Food Composition Database of Gluten-Free Products.

Country-specific food composition data are needed for gluten-free (GF) food products to assess nutritional adequacy and diet quality. This research aimed to develop a comprehensive GF food composition database for key GF foods consumed in Canada. Average nutrient data from 167 products were estimated from Nutrition Fact Panel labels and the commercial ingredient list, using an iterative and systematic approach. The database reports mean values for energy and 29 nutrients per 100 g for 33 GF commercial grain-based foods. Nutrient values were evaluated with Health Canada's nutrient content claims per standard reference serving. On average, GF products were, at minimum, a source of thiamin (73%), riboflavin (70%), niacin (58%), iron (58%), fibre (55%), magnesium (48%), folate (36%), zinc (19%), and calcium (15%). Most GF products were low in saturated fat (85%) and cholesterol (64%) but only 15% were low in total fat and 6% were free of sugar. Micronutrient enrichment and the use of nutrient-dense whole grain flours, legume flours, oil seed husks, and functional fibre ingredients varied within and between categories and brands but appeared to contribute to nutrient content. This database provides a new tool to enhance GF diet assessment in individuals or populations in Canada.

Comparative analysis of dietary fibre determination by AOAC 991.43 and AOAC 2011.25 for frequently consumed foods in Slovenia.

The definition of dietary fibre was modified by Codex Alimentarius commission in 2009, and now includes low molecular weight soluble dietary fibre. This prompted the development of new methods for determination of dietary fibre, such as AOAC 2009.01 and AOAC 2011.25. We compared the dietary fibre contents of 45 different foods from six food groups, as sources of dietary fibre frequently consumed in Slovenia. Dietary fibre was determined with methods AOAC 991.43 and AOAC 2011.25, and the results were compared. Majority of results show significantly higher dietary fibres when determined using AOAC 2011.25. Differences can be explained by low molecular weight soluble dietary fibre, which is measured by AOAC 2011.25. New methods for dietary fibre determination have impacts on national food composition databases, which remain mainly based on AOAC 991.43; defining the need to update food composition databases with data that include low molecular weight soluble dietary fibre.