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Chapter: Introduction to Human Nutrition: Food Composition

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Nutrient losses and gains during food processing and preparation

In the absence of analytical data for all forms of foods nutrient values can be estimated by calculation using standard algorithms that have been experimentally derived.

Nutrient losses and gains during food processing and preparation

 

In the absence of analytical data for all forms of foods nutrient values can be estimated by calculation using standard algorithms that have been experimentally derived. Since the content of nutrients per unit mass of food changes when foods are prepared, such losses and gains can be classified in two ways. The first can be described by a food yield factor, when the weight of the primary ingredients at the precooking stage is compared with the weight of the prepared food at the cooking stage and also with the final weight of the food as consumed at the post-cooking stage. The weight of the food can be increased due to the hydra-tion of the dry form of a food (e.g., rice, macaroni) with cooking liquid, (e.g., water or broth) or increased due to the absorption of fat during frying of the food (e.g., potato). Alternatively, the weight of the food can decrease due to dehydration during cooking as a result of evaporative and drip losses.

The second, the nutrient retention factor, is related to changes in the amount of specific nutrients when foods are prepared. Changes in the nutrient levels can occur due to partial destruction of the nutrient as a result of the application of heat, alkalization, etc. Also, for some dietary components (e.g. β-carotene) the amount of available component may increase due to the breakdown of cell walls in the plant-based sample. Although original analytical data would be the most desirable type of data for foods at all stages of prepa-ration, they are seldom available. Efforts are in prog-ress in several regions to revise the nutrient losses and gain factors, including nutrient retention and yield factors, in order to compare and harmonize them and thereby improve the quality of food composition data calculated.

As food composition data are frequently lacking for cooked foods, estimates based on the use of these factors for calculating the nutrient content of prepared foods from raw ingredients are made. Thus, the nutrient composition of a prepared or cooked food is calculated from the analytical data of uncooked food by applying suitable nutrient retention and yield factors. To obtain the nutrient content per 100 g of cooked food, the nutrient content per 100 g of raw food is multiplied by the percentage retained after cooking, and this is divided by the percentage retained after cooking, divided by the percentage yield* of the cooked product:

Nutrient content of cooked food per 100 g = [(nutrient content of raw food × retention factor)/yield of cooked food] × 100

The retention factor accounts for the loss of solids from foods that occurs during preparation and cooking. The resulting values quantify the nutrient content retained in a food after nutrient losses due to heating or other food preparations. This is called the true retention method and is calculated as follows:


 %True retention = [(nutrient content per g of cooked food × g cooked food)/(nutrient content per g of raw food/g of food before cooking)] × 100

 

The following example uses only the yield factor to predict the nutrient content of the cooked food. The yield factors for different foods are reported in the USDA Agriculture Handbook 102 and for cooked carrots it is 92%. Selected nutrient values in SR 21 for 100 g of raw carrots are 0.93 g of protein, 33 mg of calcium and 5.9 mg of ascorbic acid. Using the yield factor the composition of 100 g of cooked carrots is calculated as 0.93 g/0.92 = 1.01 g protein, 33 mg/0.92= 36 mg calcium and 5.9 mg/0.92 = 6.4 mg of ascorbic acid. This compares favorably to the deter-mined values for carrots of 0.76 g of protein and 30 mg of calcium, but less so for ascorbic acid at a value of 3.6 mg, probably because it is heat sensitive; therefore, applying the nutrient retention factor for ascorbic acid (70%) would have resulted in a more accurate prediction (5.9 × 0.7/0.92 = 4.9) of 4.9 mg/100 g) (http://www.ars.usda.gov/nutrientdata).


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