Subjects – CANDAT – Food Research

Foods that we eat and their relationship to health

Main menu

Week-weighted Averages in CANDAT
A typical Candat scenario

The navigation shown below is a good example of the navigation available throughout CANDAT. You will get used to using the function keys, particularly the keys “F6”, “F5”, “F1” and “Esc”.

Modules and tasks

The first to appear once Candat is started is the TASK MODULES screen. It contains a list of the modules available in CANDAT. Within each module is a list of tasks, each prefixed with a task number. For example, in module 3. Subject files… you will get a series of 300 level tasks. These tasks are related to subject entry, validation, reporting, etc… but all tasks that have to do with subjects.

You will see 300 related tasks in module 5. Questionnaire definition and input. That is because some of those tasks work with questionnaire data once it has been converted to subject files. Just an added convenience if you happen to be working with questionnaire data.

For now, choose the Subject module so that we can enter a simple recall to see how CANDAT works.

Subject files maintenance and reports

Subject file maintenance

This is where subject data is entered, on a per day, per meal basis. You can also configure general information about this subject file here.

Once you have started the task you may choose a user food file to activate. As you enter food codes this will allow CANDAT to automatically validate foods that may not be in the Master food file or Institute food file.

The next step allows you to select an existing subject file or create a new one.

We have chosen to create a new subject file which we have called “demo”, for the purposes of this tutorial. Please note that subject files can hold an unlimited number of subjects.

When first created we need to know whether this subject file will be used for questionnaires. Questionnaire subject files have a few more components to them. These can also be added later if you wish.

Up to 25 subject variables can be created per subject. This is where you define what those variables are. The variable names will serve to identify the data on reports.

This menu shows you what can be done with subject files. A bit overwhelming at first but necessary as your study evolves and you need to manage more and more of your data. For now we will just concentrate on entering subject recall data.

Candat allows up to 10 characters for a subject code. We have tried to make this a bit meaningful, as you can probably see from the characters. This can also be another place for you to classify the subjects. You must, however, make sure that the code you create is unique.

This is where you would populate the variable data that you defined before. Note that the definitions are specific to the subject file though the variable data changes from subject to subject (of course).

…and now we are able to enter actual food recall information, up to 7 days per subject. Specify the date using yyyymmdd coding. The date is important as Candat uses it to figure out the day of the week in “week-weighted” reports.

This is the main data entry screen for food data. You can also change the date here. A convenient way to navigate this screen is through the tab key and the arrow cursor controls. The “F6” key is especially convenient:
- in the Meal Code field it will give you a selection of valid meal codes (note that you can define your own meal codes, up to 999 or them!)
- in the Food Code field it will allow you to search foods. It will insert the code of the food you choose
- in the Unit Code field it will give you a list of valid units for that food and insert the code of the one you choose
  All you have left to enter is the Quantity. The Food Frequency field is not used in recalls, it is meant for questionnaire data.
The next few screen pictures will show you data at various stages as well as detail information of your input you can see by pressing the “F1” key.

This screen shows a few foods entered for a breakfast. The highlighted area shows the foods that were entered. It was generated by pressing the “F1” key on the date field. It could also have been generated by pressing the “F1” key on the Unit Code field. Pressing the “F1” key on the other fields will translate the code for the information it generates. For example, if pressed on the “1” under Meal Code, it would show “Breakfast”, if pressed on the food code “28730” it would show “Coffee Brewed”.

How do you know which codes to enter? You just use the “F6” key. The “F6” key pressed in the Meal Code field would give you a list of valid meals (the list you defined if you did that). By selecting the meal you wish the code is automatically inserted. Under the Food Code field “F6” generates the same search it did in the Food Profile area above. The selected food has its code inserted. “F6” in the Unit Code field lists all the units valid for that code. Again, selecting the desired unit inserts its code in the right place and generates the description.

Experiment a bit, add a few more code, fill in the day. When you are done simply press Esc to be prompted for another day of entry.

You can continue entering intake dates until you have the maximum of 7 per subject or you can stop at this point by entering a “0” for the date.

A “0” date will bring up the subject editing menu where you can perform other actions on this subject.

When you are done simply press the Esc key repeatedly until you reach the menu to choose another task:

At this point the prompts become intuitive. In a big study we would do a lot of data entry, perhaps even by multiple data entry clerks. Their subject files could be combined, all the data validated using tasks 310 and 330, the nutrient data compiled and reports produced using task 340.

The validation from task 310 is shown here… if we had made entry errors they would appear in the last column. This validation is also a good way of checking against your entry data to make sure the right foods were coded and none were missed.

and then we simply skip to task 340 to show you some of the possible ways of reporting.

Toggle the Options to Yes for those you would like to see in reports. There are many ways of setting your options.
- “Ctrl Y” selects them all to “Yes”,
- “Ctrl N” selects them all to “No”,
- “Y” make a “Yes”
- “N” makes “No”
- Just pressing “Enter” changes an entry back and forth.
For this example we will choose them all, “Ctrl Y”

…and press Esc to go to the next screen.

This allows us to select the nutrient we wish to see in the report.

We can add as many nutrients as we wish, pressing “F6” (standard now) on a code field allows us to choose nutrients we want to add. This process is shown below.

Here we have added LUTEIN, nutrient 837, you should see it in your reports, along with the others.

You can also choose to view other data in your reports, you can see the expressions and ratios we have chosen here…

The next few prompts allow you to select the subjects you wish to analyze and other reporting options such as printing, files to save, etc… they are explained below;

As you use Candat the above 4 screen shots will be very familiar to you. They are the standard selection sequence of database records, whether they be subjects (as they are here) or foods, recipes or questionnaires, the main databases used in Candat.

You can choose to sort by one of your selected nutrients, in this case we will leave the first nutrient, protein, as our sorting nutrient. Note that if you sort by nutrient and you view your reports at the food detail level, the foods will not be in the order entered but in the order of the selected nutrient value.

A nutrient report title which will appear on your printed reports…

In Candat you can define your own food groups. Food groups you define are stored in “category” files, each category file containing a list of food groups defined for that category and each food group containing a list of foods. Pressing “F6” here gives you a choice of available category files. Candat comes with the Canadian and USA category files pre-defined.

In this case we will bypass the category file selection by pressing Enter. This will have the effect of cancelling the reporting by food groups we previously selected.

The next few screens ask us for the name of the files to store the results. You can use the same name for each, very convenient as the same name can represent a particular part of your study or some particular purpose for the report, as here, “demo”

At this point Candat produces all the reports, ends the task and provides you with this friendly reminder.

You can then proceed to Start the task again (if you want to produce reports with other options) or choose another task.

View Reports

Of course, once you reach this stage you will probably press the “F5” key to look at your results. We go through this with you in the screens beginning on the next page.

Pressing Enter here lists all the text files (.TXT) and, in this case, gives the only file there.

The menu below is presented when you press the slash (“/”). You can then choose one of the presented options to manage the selected file.

This is the printed results file that was generated by Candat. For the columns to line up properly in external programs the font must be a fixed font, for example Courier New.

This file can be opened with a word processor or a text editor and printed from there if needed. Most of the time these files are not printed. We recently examined a file of 82 subjects and it had over 3,600 pages. That file included each food and showed detail by subject, by day and by meal with a meals summary at the end of each day and a days summary at the end of each subject.

In specifying default printers for CANDAT you can use a printer driver that creates .pdf files directly. This is very convenient as CANDAT allows you to specify very large page sizes which can be viewed in landscape (sideways) mode. If you do not have a .pdf printer driver you can still create this very large .txt file and then convert it using freely available .txt to .pdf converters. One that we have found very useful can be found at

http://www.verypdf.com/app/text-to-pdf-converter/

You can then use your favorite pdf reader application to easily browse through all the data.

You scan the .txt or the .pdf files to verify that the data you needed has indeed been generated. Once you are ready to use the data you can access it directly through the files in the SAVE folder. Those files can be imported directly either into a spreadsheet program or in a statistical program. Please see the next pages for examples of those files.

Once you have viewed the text files, you can proceed to the “save” files, the files that you can use to import your data into other programs.

Pressing Enter here give you a list of the save files generated by Candat.

Notice that the name of these files are the names we specified when we ran the reports. Some of these files document the layout of the data so that it can be provided to external programs that require that layout.

Most programs today will accept delimited files. Candat uses the “tab” character as a delimiter. The first row of these “tab-delimited” files contains a variable name which corresponds to the data in the following rows. You can change these variable names in three ways, if desired:
- within Candat by changing the short description of the nutrient names;
- by using an editor and changing the first row of the data file;
- within the program you are using to view the data, using the features of that program (in spreadsheets you would change the first row, in statistical packages you would change the variable names).
The following pages are screen captures from these files and show what the information looks like. Remember, these files are formatted for computers to read, they may look a bit strange to you.

Some of the following files are wider than a screen. In Candat you view them by using the Ctrl-{arrow-right} key. In this case we have just done a screen capture of each screen that came up as we scrolled to the right. Remember, these files are meant to be used by computer programs.

1 of 5

2 of 5

3 of 5

4 of 5

5 of 5

1 of 4

2 of 4

3 of 4

4 of 4

This file has 25 fields, the first 10 only are shown here. No data was input after the weight field.

1 of 3

2 of 3

3 of 3

1 of 4

2 of 4

3 of 4

4 of 4

The DEMO.TXW and DEMO.WTD files look like the above DEMO.TXS files and are not reproduced here for simplicity. They would be different if there were multiple days in the week and some of the days in the week included weekend days as well.

I am sure that when you enter your own data and start looking at these reports you will get a good feeling for how thorough and accurate they really are.

A small aside but important note: You will notice that some of the values in the reports are negative. These are missing values generated by foods that did not have values for particular nutrients. In totals they are counted as zero. Statistical software usually treats missing values differently than regular values and take them into consideration in some calculations.

This concludes the “getting started” tutorial. By now you should have acquired the keyboarding skills required to get your information into and out of Candat and be sufficiently confident to explore the other areas of Candat.

Remember we are here to answer each and every one of your questions. Candat is the calculation engine that generates your data. You can now use whatever software you wish to complete your analyses and present or publish your data.

If you have any difficulties with the software please contact us and let us know. It is the only way we can help.
University of PEI

CANDAT is superior to other nutrient analysis programs because it is designed to accommodate the diverse needs of researchers. Being able to handle food recalls and records and food frequency questionnaires in the same program, to create user files of special food items not included in the CNF and to easily import files into statistical analysis packages makes it unique in Canada. Most of all, having a support person who has extensive experience with nutrition researchers makes CANDAT an easy choice for me.

I have been working with CANDAT since 1984, and have also tried a number of other nutrient analysis programs. I keep coming back to CANDAT for two reasons: the support with CANDAT is superb. I have emailed Gaetan and received a phone call within minutes. He “knows his stuff” and can pinpoint and resolve any problems or challenges quickly. The program he uses to take over the computer is amazing and very efficient.

Since he has worked with a number of academics in nutrition, he understands research and the special needs researchers have. My research group had a challenging task a few years ago which involved adapting an American Food Frequency questionnaire for Canadian use and adding data on dietary nitrates. This was a very complex and time consuming task, and Gaetan was an active participant in the research process. When we were under time pressures to complete the analysis, Gaetan committed time and was able to get us the data when we needed it. He goes beyond a technical role, and sometimes poses questions which I don’t think of, preventing problems before they happen.

Dr. Jennifer Taylor, Associate Professor & Chair
Department of Family & Nutritional Sciences
Dalton Hall 204
University of Prince Edward Island
550 University Ave, Charlottetown PEI C1A 4P3 (902)
566-0475 Fax (902) 628-4367
Food history questionnaires
A lot of nutrition research is done through the use of the food history questionnaire. This is different from food recalls in that it is shorter, easier to administer and does not require special interviewing skills.

Sometimes studies need to identify patterns of food consumption in a large population. This is done, for instance, as part of a general population health survey. These are done on a fairly regular basis, usually funded by governments, to be proactive in the formulation of heath related polices. This saves our tax money in the long run.

Food history questionnaires are related to food recalls in that they collect information on commonly eaten foods. These questionnaires tend to be simpler in that they do not seek to know each individual food that was eaten. They ask questions like “When you eat pasta how much do you eat and do you eat it once a week, or 3-5 times a month”. You get the idea. Typically a food history questionnaire will have fewer than 100 such questions.

Before a questionnaire like this can be formulated it is wise to know what kinds of foods the population usually eats. A smaller number of individuals are asked what they ate recently, using the recall format. Using the example above, from the results of these recalls, a pattern of “pasta” consumption can be determined. A composite “pasta” food can then be created and used as the nutrient profile for the “pasta” question in the food history questionnaire.

The downfall is that it is a fairly “blunt” instruments. With a bit of care in its construction some of the bluntness can be removed.

The following techniques can be used to make them better research instruments:
- Make up your own;
- Make them specific to your target population;
- Make them as detailed as possible while keeping them still realistically long;
- Base the questions on foods your population eats. Collect as many food recalls as you can and base the questions on the foods in those recalls;
- Create composite foods from similar foods in the recalls and use those to calculate nutrient contributions for corresponding questions in the food history questionnaire;
- Validate your questionnaire… does it over/under-estimate specific nutrients in comparison to your food recall data?
There is enough variability in food intake and nutrient concentrations of individual foods to try to minimize the effect of further variability introduced by blunt food history questionnaires.

Given the amount of variability in this data it is a wonder that any conclusion can be reached about the predicted effect of a nutrient intervention or of specific nutrient consumption.

By all means find and use good software that will make your task bearable. Variability can only be reduced by eliminating mistakes, using the best sources of data possible and collecting large amounts of data. A significant task indeed.
Questionnaire concepts
A subject’s nutrient profile, whether obtained from food recall information or from food history (questionnaire) information, is obtained by a simple arithmetic calculation. Each nutrient is the product of the food quantity consumed and the nutrient’s concentration in that food. The subject’s consumption is the sum of these products over the foods consumed in one day. Simple.

Problems:
In a recall… using the right food code to represent the food consumed and estimating the quantity consumed as accurately as possible. For a recall there are thousands of food codes to choose from, one of those is likely to represent fairly accurately the actual food consumed. The quantity of that food consumed can also be fairly accurately recorded as reported.
An example of a recall record would be “I ate a banana for breakfast”. The corresponding coding would have the food code for bananas and quantity being typically “one medium banana”. Hence, nutrient profile and quantity fairly accurately recorded and yield fairly good nutrient information.

In a questionnaire very few questions (usually less than 200, sometimes as few as 100) are used to represent historical intake. Every question is matched to a food with a nutrient profile representing the consumption for that question. A single food taken from a database used in recalls is not likely to be indicative of the group of foods represented by any one question in the questionnaire.

An example question could be “Do you consume soup?”. There are many soups with different nutrient profiles. Which one to use for the question as a nutrient profile? The soup code used should be a “composite” of all the possible soups. Which composite to use? One formed of the relative use all the soups consumed in the population in question. This information can be obtained from a food recall study of the population. This “composite” food could then be taken as the food representing the question’s nutrient profile… a good estimate on a population basis, probably not so good on an individual basis.

Composite food calculations from food recalls:
- Determine the unique food codes from all the recalls. Typically should be 400-600 food codes
- Divide those codes into food groups corresponding to questions you would want in the questionnaire
- Run the nutrient calculations on the recalls looking at food details by food group, sorted in descending order of food quantity in grams
- Create a recipe from each food group using the main foods in that group and their corresponding quantities as recipe ingredients
- Run the nutrient analysis on the recipe file and export each recipe and its nutrients per 100G to a food file
- Use this food file in your nutrient calculations of the relevant questions
Of course, this assumes you have the software to do all these calculations and conversions automatically. Doing the calculations manually or using a spreadsheet would be very onerous indeed.

The question of quantity to record is a bit more difficult. Usually such a question asks “How often do you consume this soup? Per day? Per week? Per month?”. No problem here, just a mathematical calculation.
The problem is in the next part of the estimate, the portion size. If the portion size is indicated precisely as in .5 cup, 1 cup, 2 cups… again, no problem. The composite soup can have a weighted density based on the density of the soups making up the composite. Cup weights can then be precisely calculated. 250 ml x 1.06 G/ml would give us a cup weight of 265 G.

Technique A:
How does one estimate portion sizes when the portion is not so precisely indicated? As in, .5 of a cup or less, .5 cup to 2 cups, 2 cups or more? One logical estimate would be to take the mid-point of the ranges.
For minimal consumption to .5 cups, use .25 cup;
for .5 to 2 cups, use 1.25 cups; for 2 cups or more use 4 cups (maximal consumption assumed to be 6 cups).

Technique B:
Much more intensely computational… not using the composite weighted densities…
An alternative to the above would be to use population based estimates. For each of the range of consumption, .5 cup or less, .5 cup to 2 cups and 2 cups and more, establish the distribution of consumption and calculate the median or average value. The median value would probably be best as it would negate the effects of outlier consumption.

In the population there is no consumption of the composite soup. The distributions have to be calculated for each and every soup making up the composite. One median per soup! For example for the lower range, less than .5 cup, how does one obtain a composite median from the individual soup medians? A weighted average of medians? Based on what weighting factor? The relative weight of the total weight of the soup consumed in the population (used to get the weighted density of the composite) or the relative weight of the total weight consumed in the range less than .5 cup? I would guess the latter to be the better estimate.

How does one establish the cut-off weight for .5 cup. The best value would be obtained by using the density for the soup whose median is being calculated. If the soup has a density of 1.06, one would look at all consumption of that soup of .5 cup or less or of 265G/2 = 132 G or less. The range .5 and above would start at 133G…

Should the basis of the distribution of consumption be each consumption of soup or the total soup consumption for the day? This question may not seem relevant here (each consumption would be the best information for the typical portion size) but what about other foods, such as milk in all its possible consumption portion sizes (see below)?

Assumptions:
Technique B assumes that all consumptions recorded in the population recalls are based on portions that are cups. In soups this is probably reasonable. What about questions that ask questions about foods such as milk. “Do you consume milk?” If yes, how many times per day/week/month and how many glasses? Recalls will record all kinds of consumptions of milk. In cereal, in coffee or tea, in glasses or cups. Each one of these will be converted to Grams. The total of those consumptions, on a daily basis, or on a per consumption basis, may not reflect typical population median gram values for typical glass or cup portion sizes.
Estimates of portion sizes for questionnaire data should be based on recall data collected using those same portion sizes.
Food frequency questionnaires

Sometimes studies need to identify patterns of food consumption in a large population. This is done, for instance, as part of a general population health survey. These are done on a fairly regular basis, usually funded by governments, to be proactive in the formulation of heath related polices. This saves our tax money in the long run.

Food history questionnaires are related to food recalls in that they collect information on commonly eaten foods. These questionnaires tend to be simpler in that they do not seek to know each individual food that was eaten. They ask questions like “When you eat pasta how much do you eat and do you eat it once a week, or 3-5 times a month”. You get the idea. Typically a food history questionnaire will have fewer than 100 such questions.

Before a questionnaire like this can be formulated it is wise to know what kinds of foods the population usually eats. A smaller number of individuals are asked what they ate recently, using the recall format. Using the example above, from the results of these recalls, a pattern of “pasta” consumption can be determined. A composite “pasta” food can then be created and used as the nutrient profile for the “pasta” question in the food history questionnaire.
SPSS or SAS or PSPP or … and CANDAT
SPSS, SAS (or other good statistical packages) is used to process Candat calculations into results for your scientific report.

At its most detailed Candat will produce data files consisting of:
- Subject code
- Date
- Day of Week code (0-6, where 0 is Sunday)
- Food Group code
- Meal code
- Food code
- Food description
- Nutrient variables (Weight of foods and all nutrients you selected at reporting)
The printed (text or PDF) file (hopefully you did not print to paper) can have all of the above information as well as basic statistics (for a quick perusal, not meant to be used instead of a statistical package).

Candat also produces computer readable files that can be directly read into statistical packages or spreadsheet software (such as Excel or Open or Libre Office Calc or ….) . . Your study will probably want to make use of daily average intake data, as representative as possible of your subject’s usual intake.

Where you have days of the week and weekend days you will probably want to make use week-weighted average daily intakes, where weekend days carry less weight than week days. These week-weighted averages are calculated in Candat but should be re-calculated in the statistical package so that you can make use of the proper variance calculations for weighted data.

Systematically then here are the procedures to follow for managing your data:
- Generate the data in Candat you need in a compact way. If you are not interested in data by meals or by food group or at the food detail level, leave those out of the Candat calculation.
- Read the Candat generated file into a statistical package
- Identify missing data as -1 (the Candat value at the food level. In Candat summaries (means) missing values are considered a zero. This makes sense because food databases do not spend much time analyzing nutrients that are not likely to exist in the food but they do not report them as having a value of zero (usually).
- Convert the Day of Week code to a weight to be used in week weighted calculations. In Candat we use 5 for codes 1 to 5 and 2 for codes 0 and 6.
- Weight the cases using the converted Day of Week variable
- Aggregate the cases. In most cases you will want to aggregate the data using Subject code and Date code as independent variables, an average weight code for the weighting variable (average will maintain the weight code for the day) and sum (which adds up all the contributions for that day) for the nutrient variables. At this point your data is ready for statistical processing.
- Apply exploratory statistics to all your variables and make sure the data seems reasonable
- Merge the data variables that identify your subject variables. This may be from a file produced externally from Candat or from the Candat Description file. In either case you must make sure to merge on the Subject codes.
- Compare your subjects to your control groups (subject variables) using statistical procedures and save the results.
Report these results, write the other sections of the paper and you are done.
Course & Documentation

Please follow the outline in the column on the right to navigate through food research techniques and documentation on using Candat with those techniques.

Please enjoy the site and all its information.
Recipes & CANDAT
Recipes are another source of food information. Not all foods can be found in food databases, There is a limitless number of recipes and thus a limitless number of foods. You only need to look at the number of recipe books that are available to have an idea of the number of foods that are possible.

A good study needs to be able to identify commonly used recipes in the target populations and find suitable foods to code for those recipes. Where there are no suitable codes, the ingredients of the recipe need to be identified and a suitable food defined from these ingredients. CANDAT has tools to do this and to manage the consequent data

The following are the two menu selections needed manage the tasks in the recipe module.

This from the main CANDAT menu and

this from the choices then presented:

The standard startup choices below are presented after the selection of a task and

are explained in Appendix A (click on the screen to see the explanation). The prompts that follow activating “START the task” control the options for the task in question and are explained in the appropriate task chapter.

The list of tasks in the task menu area are listed in the order one would use them. More detailed information about each task can be found in that task’s chapter.
1. 400 Recipe file maintenance – this is where recipes are defined, copied, modified, listed, etc.
2. 410 Recipe file validation – once recipes have been created this task validates that the ingredients exist and that proper units have been specified for quantities
3. 420 Recipe nutrient calculations – nutrients are calculated for each recipe and listed on a total and per ingredient basis. Ratings of recipes based on system Recommended Daily Intakes (RDI) are listed here as well
4. 430 Recipe to food file conversion – to use recipe data as part of subject recalls or even as ingredients in other recipes, they must be made into foods using food files. The foods in these food files can then be used as input to subject data or recipe data and managed like regular food files
5. 100 Food file maintenance – this task is part of the food files module. It is included here as a convenient link to food files once recipes have been converted to foods
6. 440 Recipe reports by types – part of the definition of foods has optional fields which define attributes of the recipe. The reports in this task allow listing of recipes that satisfy those attributes.
Task 400 – Recipe Maintenance
This task begins by prompting for the file name. Files that exist already will be listed and the one desired selected. If the new… choice is selected a new file name needs to be entered. New file names must start with a letter, have no spaces within the name and have 8 characters (letters or numbers) or less.

Recipe files can contain the definition of many recipes. Each recipe is identified by a code of up to 7 digits. As recipe files may eventually be converted to foods it is a good idea to use the same convention for their codes as are used for food codes.

A food code is entered here. If the code exists already, the next prompt will be the options prompt where the recipe information can be modified. Otherwise a message will appear prompting for the creation of a recipe. Again, the options prompt will appear allowing the entry of recipe information.

The various parts of a recipe can be defined using the recipe options listed here. The ingredient editing options change the same ingredient data, there are two ways of editing these for convenience.

Attributes define a recipes profile. There can be two preparations for each recipe to be used as desired. Some will use preparion one for imperial units and preparation two for metric units. Some will use preparation one for a recipe with a small number of servings and preparation two for a much larger number of servings. Each preparation has its own profile and its own quantities for each ingredients. Other uses of preparations are up to the imagination and needs of the user. Of course, a single preparation can be used as well.

The fields defining a recipe are as follows:
1. Recipe name: A description for the recipe. Remember this will also be used as a description for the food if the recipe data is ever converted to a food.
2. Remarks: Any remark specific to this recipe. It serves as additional documentation to the recipe and is not used in conversion to a food and is an optional field.
3. Hints line 1::Any hint specific to this recipe. It serves as additional documentation to the recipe and is not used in conversion to a food. Also an optional field.
4. Target % H2O: This is used to compensate for cooking method. The recipe nutrient data will be adjusted to force this % moisture in the final recipe. It consists of a single number like 8.5 or 10 or 33.8, etc. It this field is not used for target moisture it can be used for additional hint data and is an optional field.
5. Where used:,Product Group:,Main Product: These are arbitraty and optional descriptions used to further define the profile of recipes. To use these fields as selectors in future report groupings a consistent set of descriptions must be developed and used.
6. Number of servings: A number, required to calculate nutrients per serving.
7. Serving size: Specified in grams.
8. Unit of measure: This is always 0, the unit code for grams.
9. Density: If known, is entered here as a number of grams per 100 ml. Is used in conversion to food. A food with a density can be used with volumetric units.
10. Dates: The date created is automatically entered here. The other dates are optional and can serve as record keeping to monitor the progress of the recipe through its phases. Dates are entered using the format yyyymmdd.
11. Recipe Type Code: These codes are arbitraty and optional and can be used as selectors in future report groupings. This requires a consistent set of descriptions to be developed and used
12. Preparation Time (mins): A number of minutes, again, used as selectors in future report groupings
Ingredients are inserted and modified using either of the two following panels. Fields describing each ingredient are:
1. Ingredient code: Ingredient codes come from food files and must exist there before they are used. Food codes for ingredients are found using the F6 information key with a keyword search term. Multiple keywords can be used. If seperated by spaces each keyword must be present, separated by commas, one or the other keyword can be present. The result of the search is a list of foods satisfying the search parameters. Choosing the desired food inserts its code in the ingredient code field.
2. Unit code: This code is specific to the ingredient and a list of possible codes are accessed using the F6 information key. Choosing the desired unit code inserts its code int he unit field.
3. Quantity: This is the quantity of the ingredient required for the recipe in “unit code” units.
4. The E/F and the % Yield fields are not presently used and are reserved. The concept behind the E/F field was as an “each” parameter as in each of the servings would have this quantity of the ingredient. The total quantity of the ingredient would thus depend on the recipe’s profile information.
The above panel consists of multiple lines of entry per ingredient. As you tab from field to field you will see the space highlighted for each.

The above panel consists of a single line of entry per ingredient. Again, As you tab from field to field you will see the space highlighted for each.

The preparation panel is used to describe the preparation of the recipe. It is mostly meant to be used in managing cookbooks and is not used in the conversion of recipes to food codes. It consists of arbitrary text and it too is an optional field.

Once all the data has been entered pressing the Esc key will save all the recipe data automatically and navigate back to the previous step or menu.
Recipe – Validation

One of the purposes of creating recipes is to be able to calculate their nutrient profile. If you were doing this manually you would look for the nutrient profile of each ingredient and sum each ingredient’s nutrient contribution. From this you would get an estimate of the recipe’s nutrient profile.

Validating a recipe is a step that ensures, in an automated system, that all ingredients are present in the food database. The nutrient calculation of the recipe can then proceed automatically. Validation will report either that the ingredient does not exist or that the units used to quantify the ingredient are not valid. In either case one must correct the mistake before instructing the program to calculate the recipe values.
Recipe – management
A database of recipes needs to be managed. Management means the following:
- Being able to list the recipes (simple code and description only)
- Being able to list the recipes with contents. This allows verification of information entered to ensure accuracy
- Being able to copy recipes to other recipe databases to save re-entry
- Being able to delete recipes no longer relevant
- Being able to copy a recipe into another recipe. This allows creating variations on a recipe without having to re-enter all the duplicated information
This management is best done by computer programs. Recipe database files can then be easily stored, backed-up and shared with others.
Recipe – Input
There is more to recipe input than just entering the ingredients and their quantities. Below is a list of requirements to define recipes. (R) indicates a required item, (O) indicates an optional item but useful if you are going to be creating a large collection of recipes for many purposes.
- (R) A recipe database. If a database does not exist, it must be easy to create one.
- (R) Recipe code. The unique identification for the recipe. One can build in some information in this code, for instance, all 100 recipes could be salads, all 500 recipes could be deserts, etc.
- (R) Recipe description. This is the name of the recipe.
- (R) Ingredients. A list of ingredients with quantities. Each ingredient is a food code from the active food database. Quantities of each ingredient are specified in grams.
- (R) Number of servings. How many servings does this recipe produce.
- (R) Serving size. This can usually be derived from the sum of the ingredients weights and the number of servings. If the recipe loses moisture in cooking the serving size will not be correct and an actual serving size will need to be inserted.
- (O) Preparation. A description of how to prepare the recipe. This is only needed if one is creating recipes for others to use. It is not required for the calculation of nutrients.
- (O)Many other attributes could be collected for recipe documentation, such as remarks; a couple of hints lines; where the recipe is used; its main product, its product group, its type, its density, various dates such as date created, tested, analyzed, converted and preparation time.
This defines a single recipe which then becomes a single entry in the database.
First Nations Food, Nutrition and Environment Study

We are a multi-organization research team (University of Northern British Columbia, University of Ottawa, Université de Montréal, Assembly of First Nations and Health Canada) that has used CANDAT since 2008. So far, we have used CANDAT to enter over 5000 single and repeated recalls. The software is easy to learn and use. One of our students doing data entry mentioned that CANDAT was easier and more friendly to use than another program he had been using. We are able to incorporate traditional foods and recipes from our study easily into the database. The software developer is always open to suggestions from CANDAT users to facilitate data entry, such as viewing the food description when reviewing data entry and automatic repeat of past key word searches. Above all, the technical support provided by Godin London Incorporated (Gaetan) is always top-notch. He is prompt to answer questions and to solve problems. Recently, we requested a new feature which would enable users to view the food description when reviewing data entry and they were able to do this within a few days. Also, when validation of a dietary file with a more recent version of the Canadian Nutrient File was needed, they were able to write a program that allowed the file to be imported back into CANDAT (from SAS).

This was all done under the no-charge support provided with all CANDAT licences. I would highly recommend CANDAT to any nutrition researcher. Gaetan always strives to adapt CANDAT’s capabilities to suit its users.

Amy Ing, M.Sc.
Data Analyst
First Nations Food, Nutrition and Environment Study
www.fnfnes.ca
University of Guelph

I have used Candat for many years for the analysis of 24 hour recall data, and for the development and subsequent analysis of food frequency questionnaires for two different studies. The range of analytic options provided by Candat, and the fact that it is based on the latest version of the Canadian Nutrient File, are key features.
Assistance with the development of FFQs and translation of responses into the format that can be analyzed with Candat is available on an as needed and timely basis. Response is usually within 24 hours. And, there is no problem asking for help.
I have used Candat for years because of GLI’s knowledge and continuous efforts to make the software ever more powerful.

Susan Evers PhD
Professor
Department of Family Relations and Applied Nutrition
University of Guelph
Guelph, Ontario, N1G 2W1
McGill University

We use CANDAT because it is the best software giving access to the Canadian Nutrient File (CNF) that we have been able to find. It allows us to structure our data to provide the results that we need. For example, we have made consistent use of the food grouping capability that provides us with nutrients from these food groups so we can write manuscripts pertaining not only to the nutrients but also the food sources in the Canadian population. Support from the software provider is exceptional and very responsive to user needs. For example, when Health Canada removed some units from the Canadian nutrient file that were essential to the data entry, Godin London Incorporated extracted these from an older version of the CNF and added these back to CANDAT. Godin London Incorporated has also responded to our needs by adding functions that save us time when doing corrections and data cleaning.

Louise Johnson-Down
Survey Coordinator Food habits of Canadians
McGill University 21,111 Lakeshore
Ste Anne de Bellevue QC
H9X 3V9
Tel:514-398-7808
Fax: 514-398-7739
View food and meal descriptions by pressing “F1” over the code

When reviewing data input it is now possible to see the descriptions of the food and the meal codes that have been entered. Pressing the “F1” key at the top left of the keyboard will show the description for about 2 seconds. If that is too long for you simply press another key. A quick way to go through all your input for foods is to simply go to the first food code, press F1, glance at the result and then press the arrow down key to go to the next food. You will need to press the arrow down key twice (if you are fast), once to remove the description of the active food and once to go to the next food. Pressing “F1” again shows you that food’s description. It can become a quick two finger action, very efficient.

The time the description is shown can vary. You can set a longer time using the system message utility. Again, pressing any key will remove the description and allow you to proceed.

The F1 key has also been implemented for the quantity field. Pressing F1 while in that field will show the food quantity in grams and its energy value based on the quantity and unit codes entered.