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Condiments

Condiments are a diverse class of food items used to enhance the flavor or appearance of other dishes.
This includes a wide range of sauces, seasonings, and garnishes such as mustard, ketchup, relish, salt, pepper, and spices.
Condiments can be derived from a variety of plant and animal sources, and often play an important role in culinary traditions around the world.
Reserach on condiments may focus on their chemical composition, production methods, nutritional properties, or cultural/historical significane.
Proper use and storage of condiments is also an important considerstion for food safety and quality.

Most cited protocols related to «Condiments»

Comprehensive Dietary Assessment Protocol

The FFQ was designed to assess habitual diet over the past year, with emphasis on fish consumption and a traditional diet in the study population. Questions were asked about the intake of milk, coffee, orange juice, soft drinks, yoghurt, breakfast cereal, bread, fat on bread, toppings for open sandwiches (jam, cheeses, meat and fish products), fruit, vegetables, potatoes, rice, pasta, rice porridge, fish and fish products, shellfish, condiments and sauces for fish, meat and poultry, eggs, ice cream, cakes, desserts, chocolate, snacks, alcoholic beverages, and dietary supplements. Similar items were grouped together in blocks with question headings. The response options were predefined and listed in increasing order with check-boxes to facilitate completion and optical reading. For example, the items listed under the question "How often do you eat fruit?" were "apples/pears", "oranges", "bananas", and "other fruit" with the following options: "never/rarely", "1–3 per month", "1 per week", "2–4 per week", "5–6 per week", "1 per day", and "2+ per day". The first alternative for consumption frequencies was always "never/rarely", but the number of options ranged from 4 to 7 depending on the food. When convenient, the questions were phrased in terms of natural units, such as glasses (milk, fruit juice, soft drinks, and wine), cups (coffee), slices (bread), or number (eggs and potatoes). Separate questions about the usual amounts consumed were included for fat on bread, vegetables, fish and fish products, sauces and condiments for fish, meat and meat products, ice cream, chocolate, and cod liver oil supplements. The number of response options ranged from 3 to 5 with units in pieces, slices, decilitres, florets (broccoli and cauliflower), or spoonfuls. The dietary intake computations included a total of 132 questions in the FFQ (consumption frequencies = 91, types of fat used on bread = 7, amounts = 28, and time of year for the consumption of different species of fish = 6). A detailed list of the food items, including a specification of those with a separate amount question, can be found in Additional file 1. The original version of the test-retest FFQ is shown in Additional file 2.

Parr C.L., Veierød M.B., Laake P., Lund E, & Hjartåker A. (2006). Test-retest reproducibility of a food frequency questionnaire (FFQ) and estimated effects on disease risk in the Norwegian Women and Cancer Study (NOWAC). Nutrition Journal, 5, 4.

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Publication 2006

Alcoholic Beverages Banana Bread Broccoli Cacao Cauliflower Cereals Cheese Coffee Condiments Diet Dietary Supplements Eggs Eyeglasses Fishes Fish Products Food Fowls, Domestic Fruit Fruit Juices Ice Cream Meat Meat Products Milk, Cow's Oil, Cod Liver Oryza sativa Paste Pears Potato Shellfish Snacks Soft Drinks Vegetables Vision Wine Yogurt

Gluten Intake and Coronary Heart Disease Risk

In both cohorts, diet was assessed in 1986, 1990, 1994, 1998, 2002, 2006, and 2010. For each food item, participants were asked about the frequency with which they consumed a commonly used portion size for each food over the previous year; available responses ranged from never or less than once a month to six or more times a day. We calculated nutrients by using the Harvard T. H. Chan School of Public Health nutrient database, which was updated every two to four years during the period of food frequency questionnaire distribution.19 We used year specific nutrient tables for ingredient level foods. Previous validation studies have shown that the derivation of nutrient values correlates highly with nutrient intake as measured by one week food diaries in women and men.20 (link)
21 (link)
For each of these two cohorts, we derived the quantity of gluten consumed. We calculated the quantity of gluten on the basis of the protein content of wheat, rye, and barley based on recipe ingredient lists from product labels provided by manufacturers or cookbooks in the case of home prepared items. Previous studies have used conversion factors of 75% or 80% when calculating the proportion of protein content that comprises gluten; we used the more conservative estimate of 75%.22 (link)
23 (link)
24 (link) Although gluten’s proportion of total protein may be more variable for rye and barley than for wheat,25 (link) we used the same conversion factor for all three grains, consistent with previous studies.22 (link)
23 (link) Although trace amounts of gluten can be present in oats and in condiments (for example, soy sauce), we did not calculate gluten on the basis of these items as the quantity of gluten is much lower than that in cereals and grains and the contribution to total gluten intake would be negligible.26 (link)
In 1986 the five largest contributors to gluten in both cohorts were dark bread, pasta, cold cereal, white bread, and pizza (supplementary table A). Previous validation studies within these cohorts found that the Pearson correlation coefficients between the number of servings of these items reported on food frequency questionnaires and that reported on seven day dietary records ranged from 0.35 (pasta) to 0.79 (cold cereal) for women and from 0.37 (dark bread) to 0.86 (cold cereal) for men.27 (link)
28 (link) A separate validation study of this food frequency questionnaire found that this method of measuring vegetable (that is, plant based) protein intake, of which gluten is the major contributor, correlated highly with that measured in seven day dietary records (Spearman correlation coefficient 0.66).29
We divided cohort participants into fifths of estimated gluten consumption, according to energy adjusted grams of gluten per day. We obtained energy adjusted values by regression using the residual method, as described previously.30 (link) To quantify long term dietary habits, we used cumulative averages through the questionnaires preceding the diagnosis of coronary heart disease, death, or the end of follow-up.31 (link) For example, we calculated cumulative average estimated gluten intake in 1994 by averaging the daily consumption of gluten reported in 1986, 1990, and 1994. We treated cumulative average estimated gluten intake as a time varying covariate. For participants with missing dietary data, we used the most recent previous dietary response on record. Because the development of a significant illness may cause a major change in dietary habits, and so as to reduce the possibility of reverse causality, we suspended updating dietary response data for participants who developed diabetes, cardiovascular disease (including stroke, angioplasty, or coronary artery bypass graft surgery), or cancer. For such patients, the cumulative average dietary gluten value before the development of this diagnosis was carried forward until the end of follow-up.32 (link)
The primary outcome of incident coronary heart disease consisted of a composite outcome of non-fatal myocardial infarction or fatal myocardial infarction. For all participants who recorded such a diagnosis, we requested and reviewed medical records. We classified myocardial infarctions meeting World Health Organization criteria, which require typical symptoms plus either diagnostic electrocardiographic findings or elevated cardiac enzyme concentrations, as definite, and we considered myocardial infarctions requiring hospital admission and corroborated by phone interview or letter only as probable. Deaths were identified from state vital records and the National Death Index or reported by participants’ next of kin. We classified coronary heart disease deaths by examining autopsy reports, hospital records, or death certificates. Fatal coronary heart disease was confirmed via medical records or autopsy reports or if coronary heart disease was listed as the cause of death on the death certificate and there was previous evidence of coronary heart disease in the medical records. We designated as probable those cases in which coronary heart disease was the underlying cause on the death certificate but no previous knowledge of coronary heart disease was indicated and medical records concerning the death were unavailable. We considered definite and probable myocardial infarction together as our primary outcome, as we have previously found that results were similar when probable cases were excluded.33 (link)

Lebwohl B., Cao Y., Zong G., Hu F.B., Green P.H., Neugut A.I., Rimm E.B., Sampson L., Dougherty L.W., Giovannucci E., Willett W.C., Sun Q, & Chan A.T. (2017). Long term gluten consumption in adults without celiac disease and risk of coronary heart disease: prospective cohort study. The BMJ, 357, j1892.

Publication 2017

Angioplasty Autopsy Bread Cardiovascular Diseases Cereals Cerebrovascular Accident Common Cold Condiments Coronary Artery Bypass Surgery Diabetes Mellitus Diagnosis Diet Electrocardiography Enzymes Fatal Outcome Food Food Ingredients Gluten Heart Heart Disease, Coronary Heart Diseases Hordeum vulgare Malignant Neoplasms Myocardial Infarction Nutrient Intake Nutrients Oats Pastes Patients Plants Proteins Protein S Soy Sauce Triticum aestivum Vegetables Woman

Household Food Consumption Measurement

Household food consumption was determined on a daily basis by calculating changes in the home food inventory. All foods and condiments purchased from markets and picked from gardens were carefully recorded and measured with Chinese balance scales (graduation: 10 grams [g]) before the year 2004 and with digital diet and kitchen scales (graduation: 1 g) thereafter at the start and end of each survey. All foods in stock at the initiation of the survey (including edible oils, sugar, and salt), foods purchased and/or produced at home during the survey period, and food preparation waste (including spoiled rice or food fed to animals) were weighed and considered in the calculation of household food consumption. This was the only method that was used by the Institute of Nutrition and Food Safety for national and large-scale surveys prior to the CHNS, and it was continued with the addition of three consecutive 24-hour recalls with the initiation of the CHNS in 1989.

Zhai F., Du S., Wang Z., Zhang J., Du W, & Popkin B. (2014). Dynamics of the Chinese Diet and the Role of Urbanicity, 1991–2011. Obesity reviews : an official journal of the International Association for the Study of Obesity, 15(0 1), 10.1111/obr.12124.

Publication 2013

Carbohydrates Chinese Condiments Diet Food Mental Recall Oils Oryza sativa Sodium Chloride, Dietary

Fully Automated Dietary Recall: DietDay

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Publication 2010

Condiments Diet Dietary Supplements Food Households Hyperostosis, Diffuse Idiopathic Skeletal Mental Recall Nutrients Snacks Vision

Meal Intake and Mood States in Binge Eating

All analyses were performed with SPSS 14.0 (31 ). Data were screened for univariate and multivariate normality. There were no influential outliers. Logarithmic transformations were made for total energy intake and fat-free mass, and arcsine transformations were conducted for the percentage of macronutrient content (fat, protein, and carbohydrate) intake. Pre- and post-meal mood states, as assessed by the BRUMS, were mean rank transformed to achieve normality.
Primary hypotheses were examined with a series of linear mixed models with repeated measures. The dependent variables were total energy intake (kcal), percentage of energy intake from protein, carbohydrate, and fat, food groups (dairy, desserts/snacks, meats, vegetables, fruits, bread, condiments, drinks), duration of eating (minutes), post-meal state Anxiety, and post-meal state Anger, Confusion, Depression, Fatigue, Tension, and Vigor. The fixed-factor main effects in the model were LOC (presence or absence of at least one episode in the month preceding assessment) and meal type (normal, binge). We also tested the two-way interaction between LOC and meal type to determine whether any observed effects of LOC significantly varied between the normal meal and the binge meal. Each model included sex, age, race (coded non-Hispanic Caucasian and all other racial/ethnic minorities), fat-free mass, and percent fat mass as covariates. For the models predicting duration of eating and post-meal state anxiety and mood, total energy intake also was included as a covariate. Pre-test meal anxiety or mood state was included in the models predicting post-test meal anxiety and mood states. Randomization order was also considered as a covariate in all analyses, but was removed because it did not significantly contribute to any model. Reported means and standard errors are adjusted for all variables included in each model. Differences were considered significant when p values were ≤0.05. All tests were two-tailed.

Tanofsky-Kraff M., McDuffie J.R., Yanovski S.Z., Kozlosky M., Schvey N.A., Shomaker L.B., Salaita C, & Yanovski J.A. (2009). Laboratory assessment of the food intake of children and adolescents with loss of control eating. The American Journal of Clinical Nutrition, 89(3), 738-745.

Publication 2008

Anger Anxiety Bread Carbohydrates Caucasoid Races Condiments Fatigue Food Fruit Hispanics Macronutrient Meat Mood Neuroses, Anxiety Proteins Racial Minorities Snacks Test Anxiety Vegetables

Most recents protocols related to «Condiments»

Regional Dietary Cost Estimation

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Publication 2023

Coffee Condiments Food Spices

Assessing Impact of Dietary Diversity on Adolescents

Data entry was done by using Microsoft Access and data analysis was done by STATA (version 14). Dietary diversity score was determined by adding the number of food categories consumed by each participant over the previous 24 hours. The questionnaire contained 16 food groups. The 16 food groups were—cereals; white roots and tubers; vitamin A-rich vegetables and tubers; dark green leafy vegetables; other vegetables; vitamin A-rich fruits, other fruits; organ meat; flesh meats; eggs; fish and seafood; legumes; nuts and seeds; milk and milk products; oils and fats; sweets, spices, condiments, and beverages. We combined certain food groups into a single food group for analytical purposes. We analyzed 9 food groups for measuring individual dietary diversity score. Dietary diversity score ranged from 0 to 9. According to the guideline, when we aggregated two food groups into one, any of the food group’s “Yes” answer is considered as “Yes” for that aggregated group and numbered as “1” for the respected group. According to the guidelines, there is no established cut-off point in terms of food groups to indicate adequate or inadequate dietary diversity [27 ]. For this, we calculated the mean dietary diversity score for analytical purpose.
Categorical variables were presented as frequency and percentage. Continuous variables were presented as mean with standard deviation. To see the relationship with the study group, we performed t-test for normally distributed data and Mann-Whitney test for skewed data. As the dietary diversity score was incomparable between the control and intervention arm at baseline, difference-in-difference (DID) analysis was performed to assess the effect of the intervention. During performing the DID we adjusted for adolescent’s age, adolescent girls’ father’s age, years of schooling of caregiver, adolescents’ father’s years of schooling, household’s monthly income, household head’s occupation and asset index.

Nyma Z., Rahman M., Das S., Alam M.A., Haque E, & Ahmed T. (2023). Dietary diversity modification through school-based nutrition education among Bangladeshi adolescent girls: A cluster randomized controlled trial. PLOS ONE, 18(3), e0282407.

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Publication 2023

Adolescent Adolescent Fathers Adolescents, Female Beverages Cereals Condiments Diet Eggs Fabaceae Fats Fishes Food Fruit Head of Household Households Meat Milk, Cow's Nuts Oils Plant Embryos Plant Leaves Plant Roots Plant Tubers Seafood Spices Vegetables Vitamin A Woman

Dietary Quality Assessment Using DBI-16

The DBI-16 is an effort to evaluate dietary quality more rationally under the guidance of the Dietary Guidelines for Chinese Residents published in 2016. It contains 8 food indicators, namely (range of values) (1) Cereals (−12–12) (2) Fruits and vegetables (vegetables 6-0, fruits 6-0); (3) Dairy and soybeans (dairy 6-0, soybeans 6-0); (4) Animal foods (meat 4–4, fish 4-0, eggs 4–4); (5) Pure energy foods (cooking oil 0–6, alcohol 0–6); (6) Condiments (sugar 0–6, salt (0–6); (7) Varied diet (−12–0); and (8) water (−12–0). If data on water consumption are lacking, they could be ignored in the evaluation. A varied diet was calculated based on 12 food groupings: rice, pasta, coarse grains and potatoes, dark vegetables (≥500 μg carotenoids per 100 g of vegetables), light vegetables (<500 μg carotenoids per 100 g of vegetables), fruits, soy products, dairy products, livestock, poultry, eggs, and fish and shrimp.
The low bound score (LBS) of the scale is the sum of the absolute values of all negatively assigned index scores, reflecting inadequate dietary intake, and ranges from 0 to 60. The high bound score (HBS) of the scale is the sum of all positively assigned index scores, reflecting excessive dietary intake, and ranges from 0 to 38. The dietary quality distance (DQD) is the sum of the absolute values of each index, reflecting dietary imbalance. A score of 0 indicates good dietary intake (Suitable), a score that is below 20% of the total score indicates good dietary intake (More suitable), and a score that is 20–40% of the total score indicates acceptable dietary intake (Low level), a score that is 40–60% of the total score indicates poor dietary intake (Medium level), and a score that is more than 60% of the total score indicates the worst dietary intake (High level), also defined as poor dietary quality [30 (link)]. Data on water and sugar were lacking, so scores for added sugar and drinking water were not accounted for [31 (link)].

Zhang H., Shi K., Yuan L., Guan X., Yin H., Zhao W., Zhou X, & Chu A. (2023). Association Between Dietary Quality and Serum Cystatin C in Kidney Transplant Recipients Based on Chinese Diet Balance Index 2016 (DBI-16). Annals of Transplantation, 28, e939149-1-e939149-9.

Publication 2023

Carbohydrates Carotenoids Cereals Chinese Condiments Dairy Products Diet Eggs Ethanol Feeds, Animal Fishes Food Fowls, Domestic Fruit Light Livestock Meat Oryza sativa Pastes Potato Sodium Chloride, Dietary Soybeans Therapy, Diet Vegetables Water Consumption

Comprehensive Food Security Assessment

We used household and child dietary diversity score (DDS) and the reduced coping strategies index (rCSI) to assess food security. We applied a 24-hour food recall to a checklist of 12 food groups for estimating the household DDS, as recommended by the Food and Agriculture Organisation of the United Nations [23 (link)]. The household DDS food groups were as follows: (1) cereals; (2) white tubers and roots; (3) vegetables; (4) fruits; (5) meat; (6) eggs; (7) fish and seafood; (8) pulses, nuts, and seeds; (9) dairy products; (10) oils and fats; (11) sweets; and (12) spices, condiments, and beverages. The household DDS has a range of 0 to 12. Similarly, we applied a 24-hour food recall to a checklist of 7 food groups to estimate child DDS, as recommended by WHO [24 ]. The child DDS food groups were as follows: (1) grains, roots and tubers; (2) legumes and nuts; (3) dairy products; (4) flesh foods; (5) eggs; (6) vitamin A-rich fruits and vegetables; and (7) other fruits and vegetables. The child DDS has a range of 0 to 7. The rCSI is a simple tool applied in different contexts that assesses the frequency, in days within a 7-day period, and the severity of 5 coping strategies commonly used by households, when they cannot access enough food [25 ]. The 5 coping strategies are consuming less preferred foods, borrowing food, reducing meals, reducing portion sizes, and restricting adult’s food consumption to preserve children’s food consumption. As per recommendations, we weighted and summed the frequency responses to these strategies to create an index where higher scores indicate greater food insecurity. The rCSI range is 0 to 56. Rations of prepared food were distributed in this setting, so we asked how many days, within a 7-day period, the household relied on these wet rations. We asked how many meals were consumed in the household in the past 24-hour period.

Grijalva-Eternod C.S., Jelle M., Mohamed H., Waller K., Osman Hussein B., Barasa E., Solomon A., Mehjabeen S., Copas A., Fottrell E, & Seal A.J. (2023). Evaluation of conditional cash transfers and mHealth audio messaging in reduction of risk factors for childhood malnutrition in internally displaced persons camps in Somalia: A 2 × 2 factorial cluster-randomised controlled trial. PLOS Medicine, 20(2), e1004180.

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Publication 2023

Adult Beverages Cereals Child Condiments Dairy Products Diet Eggs Fabaceae Fats Fishes Food Fruit Households Meat Mental Recall Nuts Oils Plant Embryos Plant Roots Plant Tubers Pulses Seafood Spices Vegetables Vitamin A

Dietary Assessment for Colorectal Cancer Risk

The dietary intake of each subject was assessed using a semi-quantitative food frequency questionnaire (FFQ), which was an adapted version of the FFQ used in the National Health and Morbidity Survey (NHMS) 2014 [35 ]. The NHMS was a nationally representative food consumption survey conducted in both Peninsular Malaysia and East Malaysia. The FFQ applied in this study was modified to include food sources associated with CRC development. Details on the validation and reliability study of this FFQ are provided elsewhere [36 (link)]. The finalised FFQ used in this study includes 142 food items from 13 food groups: cereal products, meats, fish and seafood, eggs, vegetables, nuts and legumes, milk and dairy products, condiments, bread spread, fruits, confectionaries, fast food, and sugar-sweetened drinks.
The FFQ was administered through a face-to-face interview in which subjects were requested to provide the type of normally consumed food items, the frequency of intake of each food item based on the standard serving size, and the number of serving sizes consumed in the preceding year prior to diagnosis/interview using five response categories (“never,” “per day,” “per week,” “per month,” or “per year”), guided by a trained dietitian. Each food item in the FFQ was assigned a portion size using common household units, such as spoons, bowls, cups, bowls, matchbox sizes, glasses, and plates, to estimate the serving size of the food eaten. To minimise errors while assessing the dietary intake of subjects, the interview process was started by asking the subject to recall all the food items that were typically consumed daily, and the recall process was built up over weeks and months. Further, the completed FFQs were cross-checked by a dietitian for completeness and accuracy in terms of portion size and ingredients recorded.
Based on the FFQ data, reported food intakes in grams (g) per day were calculated for each subject. Nutrient and total energy intakes per day were estimated using the Nutrient Composition of Malaysian Foods, Malaysian Atlas of Food Exchanges and Portion Sizes, Album Makanan Malaysia, and Malaysian Food Composition Database (MyFCD, 2020) [37 ,38 ,39 ,40 ]. For food items that were not available in the published literature mentioned above, nutritional food labels and recipes from websites were used as references. The amount of daily food intake was calculated from the FFQ according to the following formula: {frequency of intake (the conversion factor) × serving size × total number of servings × weight of food in one serving (g)} [41 (link)]. From the values of the amount of food consumed per day, the detailed analysis of the intake of nutrients was calculated using the Nutritionist Pro™ Diet Analysis Software version 7.8.0 (Axxya Systems, version 2020, Redmond, WA, USA) to obtain energy and nutrient values for each subject.

Shafiee N.H., Razalli N.H., Shahril M.R., Muhammad Nawawi K.N., Mohd Mokhtar N., Abd Rashid A.A., Ashari L.S., Jan Mohamed H.J, & Raja Ali R.A. (2023). Dietary Inflammatory Index, Obesity, and the Incidence of Colorectal Cancer: Findings from a Hospital-Based Case-Control Study in Malaysia. Nutrients, 15(4), 982.

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Publication 2023

Bread Carbohydrates Cereals Chryseobacterium bernardetii Condiments Dairy Products Diagnosis Diet Dietitian Eating Eggs Eyeglasses Fabaceae Face Fast Foods Fishes Food Food Labeling Fruit Households Meat Mental Recall Milk, Cow's Nutrient Intake Nutrients Nutritionist Nuts Seafood Sweetened Drinks Vegetables

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More about "Condiments"

Condiments are a diverse category of food items used to enhance the flavor, aroma, or appearance of other dishes.
This includes a wide array of sauces, seasonings, and garnishes such as mustard, ketchup, relish, salt, pepper, and various spices.
Condiments can be derived from a variety of plant and animal sources, and often play a crucial role in culinary traditions around the globe.
Research on condiments may focus on their chemical composition, production methods, nutritional properties, or cultural/historical significance.
Understanding the proper use and storage of condiments is also an important consideration for food safety and quality.
Condiment research may involve techniques like Mouse BreederDiet 8626, LD101A, and C57BL/6 mouse models, as well as the use of compounds like Fumaric acid, Alkaline peptone water (APW), Sodium citrate buffer solution, and SIMCA-P version 11.5 software for data analysis.
The versatility and global importance of condiments have led to a growing body of research on topics such as their impact on flavor profiles, interactions with other food components, and potential health benefits or risks.
Analytical methods like Ethanol extraction, Ninhydrin solution, and Sulfuric acid testing may be employed to characterize the chemical properties and composition of different condiment products.
By leveraging AI-driven protocol optimization and seamless access to the latest literature, pre-prints, and patents, researchers can elevate their condiment development and take their work to new heights.