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Guide to Developing Accurate Nutrient Values

4. Are Canadian and US Nutrition Facts the same?

Although Canadian and US Nutrition Facts tables appear to be similar, it is not appropriate simply to portray the US information in the Canadian format. Canadian values may differ from US values for several reasons:

• optional elements (non-core nutrients) may differ
• rounding rules for nutrient values are not the same
• technical definitions of some nutrients are different
• the reference standards for % DV calculations are not the same for some nutrients

Additional information on the differences between the Canadian and US Nutrition Facts tables can be found in Section 5.17 of the CFIA 2003 Guide to Food Labelling and Advertising.²⁹ If you have the original unrounded values underlying a set of US Nutrition Facts, you may be able to use them to calculate Canadian values.

5. Who is responsible for the accuracy of nutrient values on labels?

Regardless of how nutrient values are determined, food manufacturers, importers and distributors are responsible for the accuracy of label values and for maintaining appropriate documentation related to those values. Ingredient suppliers are also accountable for the nutrition information they provide to their customers.

There are particular requirements for a nutrition labelling value. These standards can make treatment of data for this use quite different from other uses:

• There are compliance standards (tests) that describe expected agreement between a nutrient value on a label and what is found in a sample of packages (page 68).
• Presentation of the results on the label must adhere to certain rounding rules.

If you start with sound, valid data for calculating nutrient values, it is more likely that the values you put on the label will meet these standards. No amount of statistical treatment at this stage can rectify weaknesses at the data gathering stage.

• In the case of laboratory analysis (see Part 2, Chapter III, page 39), you need to have sufficient samples, samples that are representative of your product or the entirety of the product line, and appropriate laboratory methods. These factors all affect the quality and the confidence you have in the final results.
• When nutrient values are established indirectly (see Part 2, Chapter IV, page 54), their quality will depend on the reliability and specificity of the ingredient information, the extent of natural variation and magnitude of processing effects.

Further information on the specific issues related to using these different approaches to generate nutrient labels can be found in Section F (page 73).

B. Compliance Expectations for Nutrition Labelling

CFIA conducts compliance tests to assess the accuracy of nutrient values used for nutrition labelling. The CFIA Nutrition Labelling Compliance Test³⁰ provides detailed definitions and guiding principles for compliance expectations. A brief summary of a few key points are provided here, but it is important that the full document be consulted prior to developing values for nutrition labelling. In the compliance test, two different categories of nutrients are described:

Class I: a vitamin or mineral nutrient that is added
Class II: a nutrient, other than an added vitamin or mineral nutrient, that is in the Nutrition Facts table or that is subject to regulations for nutrient content claims or diet-related health claims

Note that these classes pertain to a nutrient. Thus a single food can contain nutrients in either or both classes. For instance, enriched pasta has added vitamins and minerals (Class I) and naturally occurring nutrients such as carbohydrates and protein (Class II).

Briefly, when CFIA performs a compliance test to verify the accuracy of declared values or the truthfulness of claims, it takes at least 12 individual consumer units randomly from a single lot in the future, and combines them to make 3 composites with at least 4 individual consumer units each. The three composites are analyzed separately and the average of the three is used to estimate the nutrient value of the lot. CFIA uses this compliance sample to assess three specific criteria for the expectations between the label values and the marketed product. As well as these three criteria, CFIA also considers whether the look and contents of the Nutrition Facts table and rounding rules are in compliance with regulations. Table 5 on the next page summarizes the three criteria.

Looking specifically at Criterion 2, CFIA outlines different expectations for a label value for nutrients in the different classes:

• Class I nutrients (added vitamins and minerals):
• The average of a future test of 12 individual units from the same lot must be not less than the label value.
• Class II nutrients (min) nutrients (e.g. protein, carbohydrate, fibre):
• The average of a future test of 12 individual units from the same lot must be not less than 80% of the label value.
• Class II nutrients (max) nutrients (e.g. Calories, fat, saturated fat, trans fat, cholesterol, sugars, naturally occurring vitamins and minerals, sodium):
• The average of a future test of 12 individual units from the same lot must be no more than 120% of the label value.

These expectations must be considered when you determine the values to use for nutrition labelling.

Table 5: Sampling Plan and Tolerances
Sample is three composite sub-samples of four consumer units randomly selected from a lot.

Class of Nutrient	Description	Nutrients	Acceptance Criterion 1: Sub-Sample	Acceptance Criterion 2: Tolerances1,2	Acceptance Criterion 3: 99% Confidence Interval
Class I (min)3	added nutrients (e.g. added Vitamin C)	added vitamins, mineral nutrients, amino acids	each sub-sample ≥ 50% declared nutrient value	≥ declared nutrient value	4
Class II (min)3	a naturally occurring nutrient that is declared in the Nutrition Facts table and/or for which a health or nutrient content claim is made	protein, polyunsaturated fatty acids, omega-3 fatty acids, omega-6 fatty acids, monounsaturated fatty acids, carbohydrates, starch, fibre, soluble fibre, insoluble fibre, potassium, vitamins, minerals	each sub-sample ≥ 50% declared nutrient value	≥ 80% declared nutrient value	does not apply
Class II (max)3	a naturally occurring nutrient that is declared in the Nutrition Facts table and/or for which a health or nutrient content claim is made	calories, fat, saturated fat, trans fat, cholesterol, sodium, sugars, sugar alcohols	≤150% declared nutrient value	≤ 120% declared nutrient value	does not apply

Notes:

1 Tolerances are one-sided. Nutrient content may vary within good manufacturing practices, either above declared value, where a minimum is required or below declared value, where a maximum is required and provided there is no risk to health and the label is not misleading.
2 Tolerances are based on declared nutrient value and applied to pre-round value.
3 (min) - where minimum level required; (max) - where maximum level required
4 s = standard deviation; x = mean nutrient value

Source: CFIA: Nutrition Labelling Compliance Test, Part I, CFIA, 2003

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C. Using Means as a Label Value

It is often tempting, after taking care to implement a good sample design and then calculating the appropriate representative average, to use this average for the label value.

• In many cases this will be a suitable value and an acceptable product would have a high probability of passing the compliance test.
• However, in many other instances you may need a more conservative value than the simple average so that an acceptable product is not found erroneously to be out of compliance.

For the Class I nutrients, the compliance test average of 12 units must not be less than the label value. If the distribution of nutrient values is symmetric, then about half of all averages of 12 items chosen in the future from the product line will be below your observed production average, and half will be above (see Figure B). If you chose to label with the observed production average, there is a significant risk that an acceptable lot will fail the compliance test. In this case, it would be better to use a value that is lower than the observed production average as a label value to reduce the chance of failing to comply.

Figure B: Class I Nutrients (Symmetric Distribution)

For the Class II (min) nutrients, the compliance test average of 12 units will be compared with a threshold of 80% of the value declared on the label. If the product has a large amount of variability, then there is a significant chance that an average of 12 units chosen in the future from the product line will be below 80% of your observed production average.

Compare Figures C and D below. These graphs show the distribution of production averages for two products that have the same average value but quite different amounts of variability.

If both products use the average value for the label, then the probability is much higher that the average of a future sample of 12 units of the product line with the greater variability (Figure D) will fall below the threshold. If a slightly lower value were chosen to label this particular product (such as the solid line below the average in Figure E), then 80% of this lower value would be the compliance threshold. As a result, the likelihood of compliance samples falling below the threshold would be reduced.

Therefore, for products with a large amount of variation, you may want to use a conservative value for the label that is lower than the average to reduce the chance of your product failing to comply with the regulations.

Figure E: Class II (Min) Nutrients - Choosing a Conservative Label Value

Conversely, for Class II (max) nutrients, you should consider using a label value that is somewhat higher than the average. Unlike Class I nutrients, assessing the likelihood of compliance failure for Class II nutrients is not always straightforward. The process must take into account a complex combination of factors:

• the underlying variability in the product line (the key factor)
• the sample size used to estimate the lot average and variability
• the degree of certainty the producer wants to have for the prediction of future lot averages

There is no regulated approach to arriving at an alternative label value. It is a decision that is ultimately guided by the risk management approach that the manufacturer or industry chooses with respect to nutrition labelling-the degree of certainty around compliance testing that is desired.

There is, however, an approach that might be used that takes the three factors above into consideration and yields what is often referred to as a predictive value as a possible alternative to an average for labels for all three classes of nutrients.

The statistical formulas for determining predictive values can be found in the FDA Nutrition Labeling Manual.³¹ These formulas may seem complicated, but the underlying concept for calculating predictive values is quite straightforward. The calculation itself can be set up in most spreadsheets. You use what is known about the product, such as the observed production lot average and variability, to estimate the likely behaviour of the average for 12 individual unit samples selected in the future from the product. This specific information is gained most directly from product sampling and laboratory testing.

From this information, and using the formula noted above, a conservative value (derived from the predictive value) for a label can be found. The next step is to compare the calculated conservative value to the mean. There are specific rules, also described in the FDA Nutrition Labeling Manual, to help you decide when to choose the mean and when to choose the more conservative value for your product. The choice is made so that when the compliance test is applied, future averages of acceptable product would likely be within the compliance tolerances and would have a high likelihood of passing the compliance test.

To obtain a sound calculated result, you need representative estimates of the average and the variability in the product. This highlights again the importance of an appropriate sample design and sample size at the outset.

D. Calculating Nutrients per Serving Size

Once you have established whether the average or a more conservative value is to be used for your label, the value needs to be converted to the appropriate portion size for the product. The regulations pertaining to serving sizes and reference amounts can be found in the tables following Section B.01.401 of the Food and Drug Regulations.³²

This calculation usually consists of converting your results to the number of grams in the appropriate serving. Note that the serving size itself has no impact on whether an average or a more conservative value should be used.

E. Rounding

The final step is to apply the rounding rules to determine how the nutrient value per serving size is to be represented on the label. Rounding is a process whereby a range of numbers is represented by a single number. For example, "nutrient values greater than 4.5 grams and less than 5.5 grams" are to be represented by "5 grams". These ranges of pre-rounded values are taken into account when the compliance test is applied. Specific rounding rules exist for different nutrients and for different levels of nutrients. These rules can be found in the table of core information in the revised Food and Drug Regulations following Sections B.01.401 and B.01.402.³³

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F. Use of Different Approaches to Generate Label Values

The various issues related to using different approaches for establishing nutrient levels have been described in general in Part 2, Chapters III and IV. Here we consider some specific issues related to generating nutrient labels.

1. Direct approach

Using a product sampling and laboratory analysis approach, the industry or manufacturer can have more direct involvement in determining the nature of the sampling, the accuracy and precision required and the calculation of the results. The answers to the key questions about data quality that allow assessment of the data will be directly at hand or readily available from the laboratory hired to conduct sampling and analysis. You will know that the results are specific to your finished product and take into account your ingredients, your processing, and your current product. This will provide greater confidence that the results are representative of the actual nutrient values in your product.

The raw laboratory results should be available and the steps for the calculations documented. This will give you the flexibility to assess variability and to determine whether you need to use a predictive value for the label (see page 72). With the raw data as a starting point, you will have control over when and how rounding is applied in the data treatment process. This complete transparency will provide greater confidence in the nature of the data treatment. All of the pieces will be available to allow for informed decisions related directly to your specific risk management approach.

Sampling and laboratory analysis also provide a firm baseline for potential future labels when minor product modifications have been made; you may not need to conduct a whole new cycle of sampling and testing.

2. Indirect approach

When databases and other existing sources of information on nutrient values are used, transparency and control over the collection of data and the derivation of results involved is reduced compared with the direct sampling and laboratory analysis approach. It can be more difficult to get comprehensive answers to the questions about data quality. Combining the results from a database may require further adjustment to take into account the impacts of processing; some verification of this adjustment would provide greater confidence that the data reflect your finished product appropriately. It may not be clear where and how rounding of values in the ingredient database has taken place unless this information has been specified. It can be difficult to assess the impact of any rounding on the label value to choose.

Assessing the variability present in the finished product is difficult from the information in ingredient databases. Calculating predictive values (as described on page 72) is not possible technically from ingredient databases. This makes it difficult to make an informed decision about the label value to use that would be compatible with your risk management approach. Again, validation by laboratory analysis would help provide insight into how well the database calculation represents the nutrient values for your product.

If you use ingredient composition databases, you will need procedures to ensure that the nutrient values are used only for specific applications. For example, you should have a procedure to ensure that nutrient data specific for one product formulation or process are not used to prepare nutrient declarations for similar product formulations or processes, without assurance that the data are applicable to those products or processes. You should also have procedures to ensure that the nutrient values receive reviews, audits, and validation through nutrient analysis as often as necessary.

Appendices

Appendix A: Glossary of Terms and Acronyms

Appendix B: Technical Definitions of Certain Nutrients

Appendix C: Choosing a Consultant

Appendix D: Choosing a Laboratory

Appendix E: Accounting for Effects of Processing

Appendix F: Nutrient Data Gaps in Reference Databases

Appendix G: Reviewing Results of Laboratory Analysis

Appendix H: Critical Features of Databases and Software

Appendix A: Glossary of Terms and Acronyms

The terminology related to product sampling used in this Guide is similar to that found in the CFIA Nutrition Labelling Compliance Test.¹ There can be significant confusion in discussions about product sampling due to the language and vocabulary used. This confusion arises in part from the different definitions used by different organizations and individuals (for example, there are definitions provided by standards organizations, international organizations, and quality assurance groups). For the most part, you need to consider the important distinctions intended by these definitions rather than the specifics of the definitions themselves. When examining any document describing sampling procedures, it is worthwhile to confirm the definitions intended.

Glossary of Terms

accuracy

• The closeness of agreement between a test result and the accepted reference value.

Atwaterfactors

• The factors developed by W.O. Atwater to calculate the energy contributed by protein, fat, and carbohydrates to foods.

average
or mean

• A measure of a typical value for a large number of product units, often calculated using the familiar total of the values divided by the number of values.

database

• A collection of data brought together and stored in some manner for future retrieval. It could be as simple as a file folder containing information on each ingredient, or as complex as a set of relational electronic files. A database can contain ingredient-specific data, generic data, or a combination of both. There are several types of databases, including databases that are company-specific, government reference databases, and commercial databases.

calculated or treated values

• Nutrient values computed or estimated by mathematical adjustment. Normalizing nutrients to an average moisture or fat value, use of retention factors, and substitution of similar ingredients in a formulation or recipe are examples of calculated values.

commingling

• Units capturing different factors (such as from different breeds, over different factories or over different lots) are mixed; the resulting laboratory analysis reflects an average over the different factors. The information on the nature and magnitude of nutrient differences due to these factors is lost.

compliance test

• A test conducted by CFIA for verifying the accuracy of nutrient values on labels and in advertising via laboratory analysis as part of assessing compliance with the Food and Drug Regulations.

compositing

• Units that were produced under similar conditions (such as within an orchard, from the same herding region or from the same production lot) are mixed. The mixing of "like" units preserves the information about the factors that make the nutrient values vary.

design effect

• The impact of the type of sample design chosen on the number of samples needed to estimate a mean with a given precision and certainty.

formulation

• The estimated proportion by weight of ingredients in a multi-ingredient commercial food item when other characteristics of the food item are known or can be set. Characteristics which may be known or can be set include: order of predominance of ingredients, retention codes, target moisture level of individual ingredients and final product, and lower and upper bounds on the proportion of any individual ingredient. As a minimum, to derive a formulation, some nutrient values must be known and flagged for matching.

imputed

• Nutrient values developed when analytical values are unavailable. Nutrient values from another form of the same food, or another species of the same genus are examples of imputed values.

item
unit (or individual unit)

• An identifiable element of the finished product or food, usually in the form or package potentially provided to a consumer.
• It may be a raw, single-ingredient food, such as carrots, or a manufactured product.
• The unit does not necessarily match the usual portion or serving size. The units defined must not overlap, and taken together must account for every element in that product line.
• Sometimes the unit may be obvious (e.g. single eggs in an egg production, or boxes of ready-to-eat cereal for a cereal manufacturer); while at other times there may be a choice of how to determine a unit (e.g. a quantity of sugar from a bulk shipment).
• At times the finished units from one producer (e.g. sugar) may be an ingredient for another producer to form quite a different end product unit (e.g. chocolate bar).

lot (or batch)

• A collection of identically labelled units produced under conditions as nearly uniform as possible.
• Note that for nutrition labelling, there are some additional descriptors for "lots".²

manufacturer

• In this document the term manufacturer in most instances refers collectively to producers, manufacturers, processors, importers, distributors and suppliers of food products and ingredients.

median

• A measure of a typical value; the mid-point in the set of values that are being considered, once the values have been arranged in order of size.

outliers

• Unusually large or small values.

pilot

• A small representative study. Useful in helping to determine the factors that need to be considered in sampling, such as the amount of variability in the product line overall. This may involve collecting a limited number of units in a structured way, followed by laboratory analysis.

pooling

• Individual product units are grouped into a number of pooled units, and laboratory tests are conducted on the pooled groups. When units are pooled for laboratory analysis, each resulting nutrient value reflects the average of the units that went into the pooling.
• Compositing and commingling are two terms often used to describe different ways to pool units.

precision

• The ability of a measurement to be consistently reproduced.

predictive values

• Nutrient values determined using statistical formulas that estimate the likely behaviour of future averages; can be used as an alternative to average values for labelling purposes.

probability sampling

• Every unit has a chance to be selected, and this chance can be calculated. Probability sampling, properly implemented, will allow appropriate treatment of the resulting data to provide representative estimates, and will allow estimation of the degree of certainty for that estimate.

proximate components

• Fat, protein, carbohydrates, ash and moisture content, determined by prescribed methods.

raw data

• Original data that have not been treated (on which no calculations have been performed).

raw, single-ingredient food

• Examples include fresh vegetables and fruit, cuts of meat, fish, eggs.

recipe

• The known weight or measure of ingredients in a multi-ingredient food item. Amounts of ingredients may be expressed in household volume measure units such as cups and tablespoons or may be expressed as gram weights. The term recipe is generally applied to a food item prepared from component ingredients in a household or institutional setting. The term may also apply to a commercial multi-ingredient food item for which the amounts of ingredients are set, rather than estimated.

sample

• This term can be used in two different ways depending on the context, which can lead to confusion.
• On the one hand, a sample is a collection of individual units or items. There should be an accompanying plan called a sampling plan or sample design that explains how this number of units was collected from different lots or parts of the entire product line.
• However, it is also common to see the term sample used to refer to a part of the individual unit that undergoes laboratory tests, sometimes also called a test sample.
• In this document, sample is used to describe a collection of a number of individual units or items.

sample frame

• The complete population of product units to which the nutrient values are to pertain and from which the sample will be chosen.

standard deviation

• A measure of variability (spread); the square root of the variance.

standard error of the mean

• An estimate of variability (spread); the standard deviation expected in the set of means from repeated random samples of a specific sample size.

variability or
variance

• The spread, range, or dispersion of the values.

yield

• The weight of the prepared item divided by the weight of the unprepared item. Yield is affected by such factors as moisture loss.

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Acronyms Used in this Guide

AOAC INTERNATIONAL

AOAC is no longer used as an acronym; AOAC INTERNATIONAL is the legal name
An independent association of analytical communities which published a reference of methods used in analyzing the composition of foods.

CAN-P-4D

Canadian Procedural Document 4D
CAN-P documents provide the Canadian public with the policies, procedures and criteria of the Standards Council of Canada for activities such as accreditation and international standardization.

CFIA

Canadian Food Inspection Agency
CFIA is the federal agency that delivers all inspection services related to food; animal health; and plant protection. CFIA is responsible for enforcing the food requirements of the Food and Drugs Act and the Food and Drug Regulations.

CNF

Canadian Nutrient File
A computerized, bilingual generic food composition database containing average values for nutrients in foods available in Canada.

DFE

Dietary Folate Equivalents

DRI

Dietary Reference Intake

DV

Daily Value
A reference value based on recommendations for a healthy diet; appears in the Nutrition Facts table.

% DV

Percent Daily Value
A simple benchmark for evaluating the nutrient content of foods quickly and easily; appears in the Nutrition Facts table.

FDA

Food and Drug Administration, US Department of Health and Human Services

ISO

International Organization for Standardization

IU

International Units

PALCAN

Program for the Accreditation of Laboratories/Canada
PALCAN is the Standards Council of Canada's internationally recognized laboratory accreditation program.

RAE

Retinol Activity Equivalents

SCC

Standards Council of Canada
SCC accredits the organizations that develop standards and that verify the conformity of products or services to standards.

RE

Retinol Equivalents

TDF

Total Dietary Fibre

UPC

Universal Product Code

USDA

United States Department of Agriculture

USDA-SR

USDA National Nutrient Database for Standard Reference
A generic reference database on food composition maintained by the USDA Agricultural Research Service.

Food and Nutrition Features

Canada's Food Guide

Bottled Water

Nutrition Labelling

Prenatal nutrition

Food Allergies and Intolerances