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There are many publications on the effects of processing and preparation on the nutrient content of foods in specific processing/food/nutrient situations [9, 42, 74, 88]. While quite detailed predictive equations have been derived for a few of these situations [91], in general it is not possible to predict the nutrient changes that will occur in unstudied situations [76]. However, because of the need for food composition data, tables of approximate factors for US foods have been compiled for three important aspects of food preparation [49, 97]:
Weight Adjustments for Inedible Parts
Foods are commonly expressed in terms either of the "as purchased" weight of the food or of the "as consumed" weight of the food, or, in some cases, in both ways (e.g., the
Chinese tables [16]). Moreover, many food items have multiple stages of preparation, and therefore multiple stages at which they may be consumed (e.g., few persons would consume the bones in meat, but the fat of meat may not be trimmed by all A data base developer may have to interconvert nutrient data between these various forms; the mechanism for this is the "refuse factor", the ratio, expressed as a percentage, of the weight of the "inedible" portion of a food to the total weight of the food as purchased. It is important to realize that this is only a weight adjustment since the nutrient content of the inedible portion of a food often differs significantly from the content of the edible portion.
EXAMPLE: Table 1 of the 1963 edition of Agriculture Handbook No. 8 [95] gives nutrient values of foods in 100 g edible portions as consumed. Table 2 in this same publication gives nutrient values in the edible portion of one pound of food as purchased, with an additional column labelled "Refuse". Using this factor, the nutrient data of the two tables can be easily converted from one to the other. For example, item number 1411, green olives, is listed as containing 20% of its weight as pits; thus an energy value of 338 calories per 100 g edible portion converts first to 1,536 calories per pound edible portion (338/0.22, converting from grams to pounds) and then to 1,228 calories per edible portion of one purchased pound (80% of 1,536) since only 80% of a pound of green olives is edible (the 20% refuse subtracted).
Given a food/process-specific refuse factor, a data base user or compiler can convert between "as purchased" and "edible portions". Many tables contain such a factor for their entries (e.g., the USDA tables [96] give both sets of values and the factors for many entries; the FAO African table [46] uses "g inedible per 100 g purchased", while the Indian [25] and Chinese [16] tables use "g edible per 100 g purchased"). Additionally, within the United States there are a number of other sources of such information: Agriculture Handbook No. 102 (Food Yields Summarized by Different Stages of Preparation) [49], Agriculture Handbook No. 8 [96, 95], and the American Home Economics Association Handbook of Food Preparation [4].
There are two important assumptions upon which refuse factors are based:
Given full recognition of the oversimplification of these assumptions, refuse factors will permit food composition data users to convert nutrient calculations from "as purchased" to "edible portion".
Adjustments for Losses and Gains During Preparation
Often, in preparation of foods for consumption, there is a loss or gain of water, a loss of fat, or both. (When water is gained, it may be included as an ingredient in a recipe, and similar conventions can be used for fat gain.) Volatile components other than moisture may be lost during preparation, but these are seldom measured and thus are included in the moisture loss.
If cooking time is short, or the container is tightly covered, the water loss due to evaporation may be small; however, if cooking time is long and the container is not covered, the loss can be large. Evaporative losses can also occur without cooking, if a food item is left standing uncovered for some time. If the assumption is made that only water is lost (or added), nutrient ratios will not change, but their density will. These weight losses are traditionally summarized by a "yield factor", the weight of the prepared item divided by the weight of the unprepared item. A major compilation of yield percentages of losses and gains appears in Agriculture Handbook No. 102 [49]. These factors can be used to predict the nutrient contents of prepared foods from the values in the same foods raw.
EXAMPLE: Agriculture Handbook No. 102149] lists the "yield" from cooking carrots as 92%. Selected nutrient values for 100 g raw carrots are: energy 43 kcal, protein 1.03 6 calcium 27 mg, and ascorbic acid 93 mg Use of the yield factor predicts:
43 kcal / 0.92 = 47 kcal energy, 1.03 g / 0.92 = 1.12 g protein, 27 mg / 0.92 = 31 mg calcium, and
93 mg / 0.92 = 10.1 mg ascorbic acid, all per 100 g of cooked carrots.
These numbers compare favorably with the entries for cooked carrots for energy (measured value of 45 kcal), protein (measured 1.09 g), and calcium (29 mg), but not at all with the measured ascorbic acid value of 23.
As this example shows, yield percentages provide only rough estimates of the cooking losses that occur, and further, one must be aware that the cooking process may affect certain nutrients directly (e.g., ascorbic acid is sensitive to heat and, further, may be leached into the cooking water).
To estimate the nutrient content of an item cooked in water, with the water drained off and discarded, it is necessary to estimate the nutrient value of the discarded liquid. It is likely that some water-soluble vitamins and minerals will be lost into the discarded liquid. These losses may be estimated from measurements made on similar foods. Drained and undrained nutrient values are given in many food composition tables. Once these losses are applied, the values of all nutrients must be adjusted to reflect the new yield of the food item.
If fats (drippings) are drained, the nutrient value of the fat can be roughly estimated from yield factors in the same fashion as above. The situation is more complex since both water and fat losses must be considered, and due to the extremely high caloric density of fat, small errors can have a large impact on nutrient values. Thus, since these estimates are often crude, they must be used with great care.
EXAMPLE: The USDA tables [96] give 57.54 g fat and 8.66 g protein in 100 g raw bacon. For broiling, Handbook No. 102 [49] gives a yield factor of 29%; this 71% loss is separated into losses from drippings (49%) and from volatiles (22%). Assuming that the drippings are all fat, this gives, after cooking 100 g of bacon, 8.5 g (57.54 - 49.0) of fat in 29 g of cooked bacon. This converts into 29.5 g (8.54 / 0.29) per 100 g. By comparison, the protein conversion is a single step since there is no loss of protein during cooking: 8.66 g protein per 100 g in raw bacon / 0.29 (the overall yield factor) = 29.0 g protein per 100 g in broiled bacon. Comparison of these values with measured USDA values shows that the fat calculation is not accurate (49.24 g fat reported in cooked bacon) while the protein value is quite acceptable (30.45 g reported). The difficulty arises in assuming that the drippings are all fat; clearly, some is water loss, but one cannot estimate how much from Handbook No. 102.
Loss of Nutrients During Preparation
Because of the special interest in vitamin and mineral losses during food preparation, the USDA has published a table of "Nutrient Retention Values" [97]. This lists percentages of retention of nine vitamins and nine minerals for a number of foods and cooking methods, based on standard cooking times and temperatures. These factors are averages of a wide range of possible values and reflect food preparation practices in the United States. They may be used when more specific data are unavailable. As with the factors above for refuse and yield, these values are approximations and may not be appropriate in all situations. Such values are available in many countries. The most appropriate values can be obtained by comparing the cooking methods used to develop these factors with the habitual cooking methods in a region.
In many cases, the retention factors will include losses due to heating and losses due to draining. If there is also evaporative loss, a yield factor must be applied in addition to the retention factors.
EXAMPLE: The USDA tables [96] give the folacin level in raw onions as 19.9 µg. The retention factor of folacin is 70% for general "preparing and draining" of root vegetables, which predicts 13.93 µg per 100 g (19.9 x 0.70) folacin in cooked onions. Since the cooking of 100 g of onions yields 95 g cooked and drained onions, the final predicted folacin level is 13.93 / 0.95 or 14.7 µg per 100 g cooked onions. This compares with the measured value of 12.7 µg.
This example, like the previous ones, is presented both to illustrate the calculations necessary to apply these factors, and to show the potential inaccuracies of these methods of estimating nutrient contents of prepared foods. In most cases, data from similar foods modified by factors of refuse, yield, and nutrient retention must be regarded as a temporary measure to obtain entries in a food composition data base until more specific foods are analysed, more extensive and accurate theoretical procedures are developed, or both.
Most data base developers estimate some nutrient values by assuming they are zero (i.e., not present in any detectable amount in the food item). Often these are decisions based on logic alone. For example, if there is no fat in a food item, there is obviously no saturated fat; if there is no carbohydrate, there is no sucrose. Or there may be a biological basis for this assumption (e.g., certain nutrients such as vitamin B-12 do not appear in plant products, while others such as dietary fibre do not appear in animal products). If these estimated zeros appear in a data base, an explanatory annotation should accompany them.
Alternatively, if a nutrient is present in trace amounts, the data base compiler must decide how to represent that information, and must avoid misleading the user into believing that "trace" is equivalent to "zero". If data are "not available", the compiler must ensure that the user will not assume a zero by default. See page 10 for more on this topic.
Types
of recipes
Recipe calculation guidelines
Limitations
Mixed dishes or multi-ingredient foods represent the majority of items in diets worldwide. These include not only foods prepared in the home but also foods prepared in restaurants, by food vendors, in institutions such as hospitals, schools, and the military, and by the food industry. To enable dietitians, nutritionists, and epidemiologists to evaluate the role of these foods in the health of individuals, there is a need for composition data on these foods. Obtaining and using data on the content of multi-ingredient foods presents a number of inherent difficulties, primarily because of the abundance and diversity of these kinds of foods. Many mixed dishes, as prepared for consumption, are variable and poorly defined, differing from kitchen to kitchen, day to day, around the world. Analytic data do not exist for most of these foods, and accurate estimation of their nutrient content is perhaps impossible. However, such data are needed, and are routinely being estimated and used. For example, in the food industry, predicted nutrient content of proposed new product formulations is critical to decisions regarding further work on products.
This chapter provides guidelines for estimating the nutrient levels of multi-ingredient foods based on the nutrient levels of the ingredients. For simplicity, a multi-ingredient food is defined as a food with two or more ingredients. In addition to including standard mixed dishes such as curries, stews, casseroles, salads, and many dessert items, the term "multi-ingredient food" is used for simple mixtures such as foods prepared with the addition of water (e.g., reconstituted condensed milk or gelatin dessert made from dry powder), foods prepared with the addition of fat (e.g., sauteed or fried foods), and foods which have added sauces, gravies, or toppings (e.g., asparagus with hollandaise sauce or ice cream with chocolate syrup, whipped cream, nuts, and cherries).
The procedure for calculating the nutrient content of a multi-ingredient food starts from a recipe-a list of ingredients and a description of how they are combined-and the nutrient content of the ingredients. It is possible that some of the nutrient values for some ingredients may be missing and must be obtained by methods described above in this document (i.e., from another data base or by analogy with a similar ingredient). It is also possible that an ingredient item may itself be a multi-ingredient food (e.g., bread or a sauce) and may require calculation from a recipe with its own individual components. Types of recipes are discussed below.
Given a recipe and data on the ingredients, the problem is then how to combine these data. Since many multi-ingredient foods involve the processing of foods in ways which change their nutrient content (at least on a per weight or volume basis), calculations often employ the factors described in the previous section:
Guidelines for recipe calculations are given below. It must be stressed that calculation of the nutrient content of multi-ingredient foods from the nutrient data of the ingredients are estimates and not meant to replace nutrient values obtained by laboratory analysis. Calculating the values is an intermediate solution until adequate analytic data become available. A major deficiency of recipe calculation is the lack of information on how foods and components interact. It is obvious that they do, and such interactions are especially important in mixed dishes; however, such information does not currently exist. For many foods, recipe calculation may be the only cost-effective way to obtain nutrient data. Analytic data for multi-ingredient foods are not generally as available as they are for single foods, with data especially lacking for ethnic and regional dishes, many variations of homemade and restaurant-made foods, and the numerous varieties of industrially prepared canned, frozen, and packaged entrees. Because of the difficulties and costs of analyses of mixtures of different food types, and limitations on resources to adequately sample these mixtures, calculated values that rely on a broad base of representative samples may actually be more accurate than values derived from laboratory analysis of one or two samples of the prepared food.
It is conceptually useful to distinguish between recipes for simple combinations which require only mixing of ingredients and then correction for weight or volume, and recipes which require more extensive modifications of the data through estimation of losses or gains of water, fat, vitamins, and minerals. A third class of recipes includes those which require estimation of the amounts of the ingredients, and occasionally the nature of the ingredients themselves.
Simple Combinations
Some recipes require only the addition of the nutrients of the specified quantities of ingredients, followed by adjustment of the weight or volume of the final food, in order to express the nutrient levels per standard amount of the food (such as 100 g or household measures). In these cases the nutrient values should be for the ingredients in an "as consumed" form (i.e., cooked if the ingredients are cooked and containing no refuse). Examples include:
The nutrient levels of multi-ingredient foods that are prepared with the addition of water (such as milk reconstituted from powder) are also of this type, and can be estimated quite well if water (e.g., tap water, well water, bottled spring water, distilled water, mineral water) and its associated nutrient levels are included in the data base. If a data base does not include water as a food item, the nutrient contribution from water can be considered zero when calculating the nutrient content of a multi-ingredient food; however, this must be noted, since in some areas and for some nutrients the contribution from water is important. The weight contribution of water must, of course, be considered when expressing the nutrient levels per unit of multi-ingredient food.
A special aspect of these recipes is that, while the weight of ingredients add directly, volume often does not, and special care must be taken to ensure that results expressed on a per volume basis are correct. For example, one cup of powdered milk plus three cups of water yields significantly less than four cups of fluid milk; likewise, 1/2 cup mayonnaise added to one cup chopped apples, Y. cup raisins, and Y. cup chopped walnuts yields about 1.3 cups salad.
Occasionally, simple combinations may be used to calculate the nutrient content of a food from its component parts. In this case, the components are treated as "ingredients". For example, the nutrient composition of a chicken leg may be calculated from the nutrient composition and proportions of meat, skin, and separable fat.
Recipes Which Involve Nutrient Changes
Many recipes specify the combination of "as consumed" ingredients, but then require additional cooking which modifies the levels or densities of the nutrients of the foods involved. Here it is necessary to apply the various factors to correct for these changes due to water or fat loss or gain. Examples of these multi-ingredient items include:
Recipes become more complicated when starting with the raw ingredients. These recipes require adjustment for refuse and yield during preparation and cooking as well as nutrient losses and gains. Consider, for example, the above shepherd's pie when data are available only for raw meat and vegetables.
Recipes for Which Ingredient Amounts Must Be Estimated
Occasionally, the amounts of ingredients are unknown and must be estimated. If partial nutritional data are available for the recipe, these may be used to estimate ingredient proportions, which in turn may be used to estimate the missing nutrient data. This procedure may be necessary in the case of proprietary food mixtures for which only some data on content (e.g., from the package label) are available, in order to obtain estimates for the other nutrients. These estimates are usually less certain than the usual recipe estimates.
In the following example the zinc content of a product estimated on the basis of information available about proximate nutrients. A more complex example (for a chocolatecoated ice cream bar) is given by Posati [68].
EXAMPLE: A compiler wishes to estimate the zinc content of canned corned beef hash and has only the manufacturer's information for proximate nutrients. The two main ingredients are potatoes and corned beef, but the ratio is not known. However, it is known that the product contains 11 g carbohydrate and 8 g protein per 100 g edible portion. Assuming all the carbohydrate is from the potatoes, and knowing that cooked potatoes have approximately 15 g carbohydrate per 100 g, a proportion of 11/15 or 73% potatoes (27% corned beef) can be assumed. This assumption can be checked by calculating the protein content. The corned beef component contains 25 g protein/100 g, which would contribute about 7 g/100 g to the hash (0.27 x 25). The protein in potatoes (about 2 g/100 g) would contribute about 1 g, giving the correct total of 8 g/100 g for the hash. Given this information, the zinc content of the hash can be estimated if the zinc content of corned beef and of cooked potatoes is known.
Another illustration of the procedure is the use of known ratios to estimate amounts of individual fatty acids and amino acids. Well-defined patterns of fatty acids for different classes of foods can be used to calculate specific fatty acid levels based on known total lipid content, and, similarly, patterns of amino acids can be used to calculate specific amino acid levels based on known total nitrogen content.
EXAMPLE: The amino acid pattern of milk can be used to estimate the individual amino acids of cream given the nitrogen content of cream and milk, and the assumption that cream has the same amino acid pattern as does milk. From Paul and Southgate [59], whole milk has 510 mg of methionine per gram of nitrogen and single cream (21% fat) has 0.376 g of nitrogen per 100 g. From this it is estimated that the methionine content of cream is 0.376 x 510 = 192 mg per 100 g.