Front. Nutr. Frontiers in Nutrition Front. Nutr. 2296-861X Frontiers Media S.A. 10.3389/fnut.2024.1388645 Nutrition Original Research Establishing a nutrition calculation model for catering food according to the influencing factors of energy and nutrient content in food processing Li Nan 1 2 † Cong Liangzi 3 † Wang Heng 4 † Chen Yamin 1 Liu Zhaowei 5 Li Mingliang 5 Yang Dong 6 Li Huzhong 1 * Fang Haiqin 1 * 1NHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, China 2School of Biological Science and Technology, University of Jinan, Jinan, China 3Huaiyin District Center for Disease Control and Prevention, Jinan, China 4Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province, Zhoushan Municipal Center for Disease Control and Prevention, Zhoushan, China 5School of Computer and Control Engineering, YanTai University, Yantai, China 6Department of Computer Science, Georgia State University, Atlanta, GA, United States

Edited by: Reza Rastmanesh, American Physical Society, United States

Reviewed by: Fatima Ezzahra Housni, Instituto de Investigaciones en Comportamiento Alimentario y Nutrición (IICAN), Mexico

Smith Nkhata, Lilongwe University of Agriculture and Natural Resources, Malawi

 *Correspondence: Haiqin Fang, fanghaiqin@cfsa.net.cn Huzhong Li, lihuzhong@cfsa.net.cn;

†These authors have contributed equally to this work

 18 04 2024 2024 11 1388645 20 02 2024 05 04 2024 Copyright © 2024 Li, Cong, Wang, Chen, Liu, Li, Yang, Li and Fang. 2024 Li, Cong, Wang, Chen, Liu, Li, Yang, Li and Fang

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

 Objective

This study aimed to establish an accurate and efficient scientific calculation model for the nutritional composition of catering food to estimate energy and nutrient content of catering food.

 Methods

We constructed a scientific raw material classification database based on the Chinese food composition table by calculating the representative values of each food raw material type. Using China’s common cooking methods, we cooked 150 dishes including grains, meat, poultry, fish, eggs, and vegetables and established a database showing the raw and cooked ratios of various food materials by calculating the ratio of raw to cooked and the China Total Diet Research database. The effects of various cooking methods on the nutritional composition of catering food were analyzed to determine correction factors for such methods on the nutritional components. Finally, we linked the raw material classification, raw and cooked ratio, and nutritional component correction factor databases to establish a model for calculating the nutritional components of catering food. The model was verified with nine representative Chinese dishes.

 Results

We have completed the construction of an accurate and efficient scientific calculation model for the nutritional composition of catering food, which improves the accuracy of nutrition composition calculation.

 Conclusion

The model constructed in this study was scientific, accurate, and efficient, thereby promising in facilitating the accurate calculation and correct labeling of nutritional components in catering food.

 catering food nutritional components calculation model raw and cooked ratio correction factor section-at-acceptance Nutrition and Sustainable Diets Introduction

With the development of society and the acceleration of the pace of life, an increasing number of people chose to dine out in China (1). The nutritional quality of food supplied provided by the catering industry is of particular concerning, as these foods may account for a significant proportion of the daily food intake, and even of the total intake of the population. However, many consumers are less aware of the energy, fat, and sodium (Na) content of the food provided by restaurants, and excessive consumption of these foods increases the risk of chronic diseases, such as coronary heart disease (2, 3).

The nutrition label of catering food can help consumers understand the nutrient type and content in various dishes and guide them to make reasonable choices according to their own needs to improve their quality of life (4, 5). In December 2020, the National Health Commission of the People’s Republic of China issued “Guidelines for Nutritional Labeling of Catering Food.” This guideline clearly defines the basic labeling content (energy, fat, and Na) and optional labeling content (protein, carbohydrates, sugar, minerals, and vitamins) (6). Since the release of this guideline, catering enterprises have provided nutrient content labels. However, the accuracy of the calculated nutritional components is uncertain. Therefore, it is necessary to establish a scientific and accurate model for calculating and labeling the nutrient content of catering food.

Currently, data from the nutritional composition database of catering food are primarily based on the Chinese food composition tables (7, 8). It is difficult to scientifically classify and calculate some food subcategories with different names in different regions (9), and the nutrient composition of the same food can vary with the maturity of the food materials. Therefore, a scientific nutrient classification database and a raw and cooked ratio database of food materials are necessary for a nutritional composition calculation model. In addition, considering the diverse cooking methods in traditional Chinese food, it is necessary to explore the impact of different cooking methods on nutritional components, in order to determine the differences between the calculated value and the measured value of nutritional components in catering food, and to identify the correction factors for the effects of these cooking methods.

Therefore, we established three databases regarding the raw material classification database, raw-to-ripe ratio of materials, and correction factors. Using computer technology, we linked these databases to our calculation model. Furthermore, we validated the model using nine test dishes and found that the model is scientific and reasonable, indicating its potential in providing technical support for the accurate calculation and correct identification of nutritional components in catering food.

 Materials and methods Materials

After interviewing with 24 nutrition experts and 18 workers in the catering food industry thrice, we formulated a food sampling plan for different cooking methods (Table 1). We then included 150 samples encompassing the following five categories: grains, meat (chicken, pig, beef, and mutton), fish, eggs, and vegetables (green leafy vegetables and potatoes). The sampling principle followed the requirements of national food safety risk monitoring (10). From different supermarkets, farmers’ markets, and e-commerce platforms (Carrefour supermarkets, local farmers’ markets, and Jing dong online malls) in Beijing, three raw material samples were collected at 2 kg each for each ingredient type (rice, noodles, chicken breast, chicken leg, pork belly, pork legs, brisket, beef tendon, leg of lamb, grass carp, scallops, shrimp, eggs, leafy vegetables, potato). We strictly follow the basic principles of timely sampling, rapid transportation, complete information, and safe storage to collect, transport, preserve, and cook these samples.

Number of catering food processing plans.

Catering food categories (classified by main ingredients) Subclass Number of samples Processing method Corresponding dishes
Grain Rice 9 Steaming, boiling, stir-frying Steamed rice, boiled rice, stir-fried rice
Noodles 9 Boiling, stir-frying Noodles in soup, lao mein, chow mein
Meat Chicken Chicken breast 9 Stewing (with soy sauce), deep-frying, stir-frying Braised chicken breast, fried chicken breast (covered with flour), fried chicken
Chicken leg 12 Stewing, deep-frying, steaming, roasting Braised chicken thighs, steamed chicken thighs (cut into pieces and steamed), fried chicken thighs (covered with flour), baked chicken thighs
Pork pork belly 12 Steaming, Stewing (with soy sauce), stir-frying, roasting Braised Pork, Button Pork, Shredded Pork with Green Pepper, Roasted Pork
Pork legs 12 Steaming, Stewing (with soy sauce), stir-frying, deep-frying Steamed pork with vermicelli, braised pork, roasted pork, crispy pork
Beef and mutton Brisket 9 Stewing (with soy sauce), stir-frying, roasting Braised beef brisket, roast beef brisket, stir-fried beef brisket
Beef tendon 9 Stewing (with soy sauce), stir-frying, roasting Stir-fried beef, roast beef tendon with sauce, braised beef tendon
Leg of lamb 9 Stewing (with soy sauce), stir-frying, roasting (Cumin) lamb, roast lamb, braised lamb leg
Fish and shrimp Grass carp 12 Steaming, Stewing (with soy sauce), stir-frying, roasting Braised grass carp, fried grass carp, steamed grass carp, grilled grass carp
Scallops 9 Steaming, Stewing (with soy sauce), deep-frying Steamed scallops, braised scallops, fried scallop pieces
Shrimp 9 Steaming, Stewing (with soy sauce), deep-frying Prawns in oil, steamed prawns, boiled prawns
Egg Eggs 9 Stewing (with soy sauce), stir-frying Scrambled eggs with green peppers, boiled eggs, fried eggs
Vegetables Leafy vegetables 6 Quick-boiling, stir-frying Vegetable stir-fry with stir-fried greens and boiled greens
Rootstock (potato) 9 Stewing (with soy sauce), stir-frying, roasting, deep-frying Hot and sour shredded potatoes, roasted potatoes, french fries (slices), boiled potatoes
Total 150 copies
Weighing and cooking

After collecting the raw material samples, we processed the food according to the plan with different cooking methods. A total of 150 dishes were cooked. Before cooking, we weighed and recorded the raw materials and seasonings (oil, salt, chicken essence, soy sauce, curd, vinegar, and sugar). The ingredients accurate to 1 g, and the seasonings accurate to 0.1 g. Supplementary Figure S1 shows the weighing procedure. After weighing, we cooked the food, conforming to the traditional Chinese cooking methods, including steaming, boiling (usually with soy sauce), and stir-frying. Supplementary Figure S2 illustrates the cooking process of 150 dishes and some finished dishes. Dishes with different cooking methods were weighed after cooking.

 Nutrient composition detection method and quality control

On the basis of the Guidelines for Nutritional Labeling of Catering Foods, we detected fat, protein, carbohydrate and Na in the 150 sample dishes after cooking. Before detection, the inedible parts such as bones/shells were removed and weighed. Fat, protein, ash, and Na were determined according to the relevant national food safety standards (11–14). Carbohydrate content was calculated using the following formula: carbohydrate = total mass – water – ash – protein − fat.

The standard substance, which was used for quality control, was protein/fat in milk powder (FD080-QC) purchased from LGC Technology. Na [GBW10045 (GSB-23)] and ash (RM-18023) in milk powder were both purchased from the Center Testing International Group Co., Ltd. Based on the experimental scheme, the detection results were compared to verify the reliability of the detection method.

 Establishment of a nutritional composition calculation model for catering food

In this study, we classified the raw food materials according to the nutritional composition data in the Chinese food composition tables and calculated the representative value of nutrients. Based on this classification, we established a scientific nutritional composition classification database with a wider coverage. Afterward, we selected rice, noodles, beef, pork, mutton, chicken, fish, eggs, potatoes, and green vegetables, which contributed major of nutrients intake in Chinese cooking. Using the most common cooking methods (steaming, boiling, stir-frying, and deep-frying), we cooked 150 Chinese dishes. Thereafter, the nutrients were detected in the laboratory and calculated according to a nutrition calculation formula. Subsequently, we compared the differences between the detected and calculated values of macronutrients and Na in 150 dishes by the various cooking methods. The main effects of the cooking methods on the macronutrients and Na for each ingredient were analyzed, and the correction factors were determined according to the influence of the different cooking methods. In addition, we calculated the actual raw and cooked ratio of different ingredients in 150 dishes and established a raw and cooked ratio database based on the various cooking methods. Finally, we linked the nutrient composition classification, raw and cooked ratio, and correction factor databases to the calculation model. We verified the accuracy of the model‘s calculation results by comparing the calculated and the detected values of nutritional components in nine dishes.

Establishment of a nutritional component classification database for catering food raw materials

We first referred the nutritional composition data of food raw materials to the China Food Composition Tables to establish the general category of each food raw material; then, we divided the general category into subcategories (Figure 1). After that, we assigned representative values of nutritional components to each subcategory to ensure the reference of nutritional components for the same or similar categories.

Diagram of the food raw material subcategories.

 Establishment of a raw and cooked ratio database

To represent the raw and cooked ratio of the food, we calculated the mean value of the raw and cooked ratio of food samples in different provinces included in the Fifth China Total Diet Study (15). First, the grains (rice, flour), poultry meat, livestock meat, fish, shrimp, eggs, and vegetables (green leafy vegetables and potatoes) that contribute greatly to energy and macronutrients (carbohydrates, protein, and fat) were selected and then cooked by steaming, boiling, stir-frying, deep-frying, and roasting. Next, we weighed the raw materials and finished products of the catering food and calculated the actual raw and cooked ratio, thereby establishing a raw and cooked ratio database based on cooking methods.

 Establishment of a calculation model program

The calculation of nutritional components of catering food should be to multiply the content of nutritional components in the raw materials by the quality and obtain the amount of nutritional components in each portion. At the same time, it should be considered that the difference of the quality of raw materials and finished products. Therefore, the quality of cooked food should be obtained by combining the ratio of raw and cooked food, and then the value of nutritional components of catering food in the quality of 100 g cooked food should be calculated. At present, the nutritional content of raw materials has a corresponding database, that is, the Chinese food composition table, so it is possible to calculate the nutritional content of catering food through the model.

The calculation model program of the present study was based on Browser/Server architecture. For the calculation of the nutritional value of the catering food, in the back-end code, the list of ingredients to be calculated is used as input, and the list contains the nutrient content information of each ingredient (content per 100 g) and the quality of the ingredients. In the calculation process, first, the list of ingredients is traversed to calculate the content of each nutrient in each ingredient. After that, the calculated nutrient content of each food material is summed according to the type, and obtained the nutrient content of the catering food.

Nutrient calculation formula is:

S u m Nutrients = ∑ i n Nutrient s i × quantit y i 100

Where “n” represents the number of ingredients, “i” represents ingredient i, “Nutrientsi” represents the nutrient content of the ingredient i, and “quantityi” represents the quality of the ingredient i.

The calculation formula of nutrient reference value is:

NutrientsNR V act = s u m Nutrients NutrientsNR V r e c

In the formula, NutrientsNR V act represents the actual nutrient reference value of the catering food, and NutrientsNR V r e c represents the recommended nutrient intake.

The above calculation content pseudo-code can be expressed as:

Nutrient and reference value calculation
Input: foodlistOutput: NutrientsSum and NrvAct

N = foodlist.size()

NutrientsSum = ()

for i = 0; i ≤ N; i ← i + 1do

Quantaity = foodlist[i]

NutrientsSum = NutrientsSum + (Nutrients ´ quantity/100)

end

NrvAct = NutrientsSum/NrvRec

return NutrientsSum; NrvAct

Note:

In the specific calculation process, Nutrientsi can be replaced by any nutrient, which is an abstract expression here.

Pseudo-code is only the general calculation process of the program, which is a method to describe the calculation logic.

Based on the above nutrient calculation, the calculation model was designed using two formulas, as shown in Equation 1 and Equation 2. Finally, a calculation model program with user-friendly interface is established. Users could freely enter food information, such as food name, and description, quantity into the system. Food material information was directly linked to the food database in this system. After entering the name of the ingredient, the system would automatically search and caculate for relevant information.

Equation 1 Calculation formula for nutrient composition

C nutrients,excluding sodium and fat / 100 g = Food quality of edible part raw g × nutrient content of each food g / 100 g × ratio of raw-to-cooked weight g × 100 g

Equation 2 Calculation formula for Sodium and fat calculation formula

C fat or sodium / 100 g = Food quality of edible part raw g × nutrient content g / 100 g × ratio of raw-to-cooked + Cooking oil or salt g weight g × 100 g Calculation model program correction

It has been reported that nutrients in food are affected by cooking methods (16–19). By practical operation in the present study, we find the difference between the detection values and the calculated values of catering foods under different cooking methods, such as the fat content of roast beef is higher than that of stewed beef of the same piece of beef. After computing the ratio of the calculated and detection values, it is obvious that many ratios were ≤ 0.5 or ≥ 2, indicating a large difference between these two values. This study proposes correction factors under different cooking methods used to calculate the nutritional components.

By identifying the ratio of the calculated and detection values of raw materials for different cooking methods, we established a correction factor database between these values and linked it to the model to correct the model. Equation 3 and 4 show the corrected model.

Equation 3 Corrected calculation model.

Calculation formula for nutrient composition

C nutrients,excluding sodium and fat / 100 g = Food quality of edible part raw g × nutrient content of each food g / 100 g × ratio of raw-to-cooked × correction factor weight g × 100 g

Equation 4 Corrected calculation model.

Sodium and fat calculation formula.

C fat or sodium / 100 g = Food quality of edible part raw g × nutrient content g / 100 g × ratio of raw-to-cooked × correction factor + Cooking oil or salt g weight g × 100 g

Note: The “Food quality of edible part (raw)” in Equations 1—4 refers to the weight of the edible part of the raw material before cooking food. “Nutrient content” refers to the content value of each nutrient in the raw material in the Chinese food composition table, and “weight” is the weight of the food after cooking. In this study, the amount of fat not fully absorbed by food in food or edible oil was also included.

 Validation of the corrected model

Based on the common catering food of Chinese residents, we selected and weighed various food raw materials (such as rice, eggs, chicken, pork, zucchini, lettuce, broccoli, etc) and seasonings, and then processed nine dishes. Subsequently, we detected the actual nutritional components (total fat, carbohydrate, protein, and Na) and energy values of these dishes. We used the model before and after correction to calculate these nutritional components and energy values. Thereafter, we compared the difference between the calculated values of the model and the detected values before and after correction.

 Data analysis

The detection values of nutrient content in various catering foods were analyzed by IBM SPSS Statistics version 26 (IBM SPSS Inc., Chicago, United States), and the data were statistically analyzed in the form of mean ± standard deviation.

 Results Raw and cooked ratio calculation results

Generally speaking, as food material matures, their quality and nutrient content will change. In this study, we calculated the raw and cooked ratio of 150 samples and established the raw and cooked ratio database. Table 2 shows the results.

Calculation results of the raw and cooked ratio of the processed food samples.

Category Dishes name Cooking way Ratio
Rice Steamed Rice Steaming 0.49
Fried Rice Stir-frying 0.48
Noodles Noodles in Soup Boiling 0.84
Fried Noodles Stir-frying 0.78
Chicken breast Braised Chicken Breast Stewing (with soy sauce) 1.05
Deep-fried chicken breast (covered with flour)a Deep-frying 1.25
Fried Chicken Stir-frying 0.97
Chicken Legs Braised Chicken Thighs Stewing (with soy sauce) 1.22
Steamed chicken thighs (cut into pieces and steamed)a Steaming 0.95
Fried Chicken Legs (Flour Coated)a Deep-frying 1.08
Grilled chicken thighs Roasting 1.43
Pork Five-flower Braised Pork Stewing (with soy sauce) 1.28
Stir-fried Shredded Pork with Green Pepper Stir-frying 1.05
Pork shank Steamed Pork in Vermicelli Steaming 0.65
Small crispy pork (breaded and fried) Deep-frying 1.25
Back-pot meat Stir-frying 1.12
Braised Pork Stewing (with soy sauce) 1.26
Beef Brisket Braised Beef Brisket Stewing (with soy sauce) 1.35
Roast Beef Brisket Roasting 1.07
Stir Fried Beef Brisket Stir-frying 1.16
Beef Tendon Stir Fried Beef Stir-frying 1.06
Roast Beef Tendon with Sauce Roasting 1.10
Braised Beef Tendon Stewing (with soy sauce) 1.34
Leg of lamb (Cumin) Lamb Stir-frying 1.21
Roast Lamb Roasting 1.24
Braised Lamb Shank Stewing (with soy sauce) 1.47
Grass carp Braised Grass Carp Stewing (with soy sauce) 0.96
Deep Fried Grass Carp Deep-frying 1.11
Steamed Grass Carp Steaming 1.01
Grilled Grass Carp Roasting 1.11
Hairtail Steamed hairtail Steaming 1.09
Braised hairtail Stewing (with soy sauce) 0.91
Deep Fried hairtail Pieces Deep-frying 1.17
Shrimp Stewed Prawns Deep-frying 0.82
Steamed Prawns Steaming 1.27
Boiled Prawns Stewing (with soy sauce) 1.13
Egg Scrambled Eggs with Green Peppers Stir-frying 0.92
Boiled Eggs Stewing (with soy sauce) 1.01
Fried Eggs Stir-frying 1.14
Pakchoi cabbage Stir Fried pakchoi cabbage Stir-frying 1.05
Quick-boiledpPakchoi cabbage Quick-boiling 1.00
Potatoes Spicy and sour shredded potatoes Stir-frying 1.12
Fried French fries (slices) Deep-frying 1.53
Boiled potatoes Stewing (with soy sauce) 1.05
Detection results of the sample dishes

Using the detection method, we identified the nutritional components of the 150 samples. Table 3 shows the results.

Detection value of macronutrients in catering food.

Category Dishes name Cooking way Total fat g/100 g Protein g/100 g Sodium g/100 g Carbohydrate g/100 g
Rice Rice Boiling 1.12 ± 0.59 3.16 ± 0.15 0.0014 ± 0.0004 59.73 ± 14.33
Steamed rice Steaming 1.22 ± 0.11 3.77 ± 0.28 0.0051 ± 0.0007 53.39 ± 2.45
Fried rice Stir-frying 5.97 ± 2.85 3.21 ± 0.44 0.487 ± 0.0954 47.8 ± 6.89
Noodles Soup noodles Boiling 1.53 ± 1.31 4.61 ± 0.46 0.0029 ± 0.0002 44.74 ± 1.89
Fried noodles Stir-frying 4.35 ± 1.38 6.25 ± 0.26 0.4837 ± 0.0205 53.69 ± 1.54
Chicken breast Braised chicken breast Stewing (with soy sauce) 5.14 ± 1.58 24.91 ± 0.21 0.273 ± 0.053 13.9 ± 2.07
Deep-fried chicken breast (breaded with flour) Deep-frying 9.4 ± 2.14 29.66 ± 3.23 0.2277 ± 0.0612 13.84 ± 2.47
Fried Chicken Stir-frying 7.05 ± 2.16 21.43 ± 3.76 0.1893 ± 0.025 16.4 ± 4.22
Chicken Legs Braised Chicken Legs Stewing (with soy sauce) 8.87 ± 2.7 21.21 ± 2.14 0.425 ± 0.0965 14.83 ± 1.64
Steamed chicken thighs (cut into pieces and steamed) Steaming 8.13 ± 1.6 21.47 ± 1.34 0.2617 ± 0.01 12.45 ± 0.81
Fried chicken thighs (coated with flour) Deep-frying 12.74 ± 0.6 22.27 ± 0.85 0.3687 ± 0.0438 14.84 ± 2.65
Grilled Chicken Legs Roasting 9.7 ± 1.42 24.61 ± 0.44 0.8093 ± 0.1361 13.86 ± 1.36
Pork belly Braised Pork Stewing (with soy sauce) 40.27 ± 9.79 19.03 ± 1.11 0.3167 ± 0.0898 10.36 ± 5.32
Steamed pork practice Steaming 24.6 ± 14.32 18.4 ± 3.63 0.251 ± 0.1191 14.47 ± 3.41
Stir-fried Shredded Pork with Green Pepper Stir-frying 31.24 ± 9.89 18.62 ± 3.32 0.4323 ± 0.0498 11.1 ± 1.68
Grilled Pork Roasting 35.98 ± 12.02 18.55 ± 4.12 0.24 ± 0.0148 11.47 ± 1.13
Pork Legs Steamed pork with vermicelli Steaming 13.42 ± 6.66 14.23 ± 2.79 0.7963 ± 0.042 27.72 ± 2.44
Small crispy pork (coated with flour and fried) Deep-frying 15.83 ± 1.68 25.92 ± 4.35 0.6317 ± 0.2183 17.56 ± 3.46
Pasteurized pork Stir-frying 34.1 ± 21.23 16.83 ± 4.67 0.5587 ± 0.1042 7.85 ± 8.73
Braised Pork Stewing (with soy sauce) 29.88 ± 12.04 21.77 ± 4.91 0.4813 ± 0.099 11.84 ± 0.93
Brisket Braised Beef Brisket Stewing (with soy sauce) 24 ± 2.49 22.78 ± 1.5 0.686 ± 0.2842 12.54 ± 2.7
Grilled Beef Brisket Roasting 16.11 ± 11.87 18.28 ± 4.82 0.349 ± 0.1615 19.78 ± 13.52
Stir Fried Beef Brisket Stir-frying 25.2 ± 11.52 17.5 ± 3.71 0.794 ± 0.1455 11.07 ± 2.85
Beef Tendon Stir Fried Beef Stir-frying 11.87 ± 4.65 22.39 ± 1.86 0.9497 ± 0.2829 9.88 ± 4.4
Grilled Beef Tendon with Sauce Roasting 6.9 ± 1.38 23.07 ± 1.45 0.4377 ± 0.0941 16.93 ± 0.28
Braised Beef Tendon Stewing (with soy sauce) 17.02 ± 7.6 25.4 ± 1.6 0.6823 ± 0.1627 13.63 ± 3.95
Leg of lamb (Cumin) Lamb Stir-frying 24.47 ± 11.5 22.17 ± 3.71 0.6925 ± 0.1138 8.79 ± 2.09
Roast Lamb Roasting 18.38 ± 1.42 25.27 ± 3.08 0.524 ± 0.0226 9.14 ± 1.74
Braised Lamb Shank Stewing (with soy sauce) 19.57 ± 0.21 28.48 ± 1.91 0.577 ± 0.1117 4.63 ± 0.73
Grass carp Braised Grass Carp Stewing (with soy sauce) 15 ± 3.73 15.59 ± 0.79 0.7773 ± 0.0917 16.38 ± 2.24
Fried Grass Carp Deep-frying 8.39 ± 4.33 21.53 ± 0.54 0.41 ± 0.0806 19.27 ± 1.73
Steamed Grass Carp Steaming 3.85 ± 0.94 20.33 ± 2.28 0.6653 ± 0.1049 16.56 ± 2.57
Grilled Grass Carp Roasting 8.5 ± 3.45 24.87 ± 1.97 0.571 ± 0.0676 11.68 ± 0.6
Scallops Steamed Scallop Steaming 4.1 ± 0.25 21.87 ± 1.56 0.6107 ± 0.0408 14.76 ± 2.11
Braised Scallops Stewing (with soy sauce) 8.55 ± 1.55 19.49 ± 1.07 0.7253 ± 0.0804 15.89 ± 2.58
Deep Fried Scallop Pieces Deep-frying 14.54 ± 4.87 23.74 ± 0.9 0.6553 ± 0.1562 12.69 ± 2.4
Shrimp Prawns in oil Deep-frying 9.24 ± 1.88 21.03 ± 1.45 0.483 ± 0.0759 23.56 ± 2.19
Steamed shrimp Steaming 3.02 ± 1.05 23.29 ± 3.08 0.7947 ± 0.1456 12.91 ± 1.49
Boiled Shrimp Stewing (with soy sauce) 2.35 ± 0.31 21.03 ± 3.43 0.178 ± 0.0823 13.89 ± 1.16
Eggs Scrambled eggs with green peppers Stir-frying 25.4 ± 2.78 11.12 ± 1.38 0.8295 ± 0.1519 8.77 ± 1.38
Hard-boiled eggs Stewing (with soy sauce) 11.24 ± 2.37 14.34 ± 1.56 0.1613 ± 0.0164 13.97 ± 1.42
Fried Eggs Stir-frying 24.56 ± 2.18 13.9 ± 0.58 0.1788 ± 0.0142 11.65 ± 2.51
Pakchoi cabbage Stir Fried Pakchoi cabbage Stir-frying 7.13 ± 2.8 1.98 ± 0.09 0.8767 ± 0.1747 17.89 ± 4
Quick-boiled Pakchoi cabbage Quick-boiling 3.72 ± 0.76 1.51 ± 0.31 0.0805 ± 0.0193 22.39 ± 0.63
Potatoes Hot and Sour Shredded Potatoes Stir-frying 10 ± 2.32 1.23 ± 0.15 0.6153 ± 0.1569 25.53 ± 1.57
Baked Potatoes Roasting 24.57 ± 2.7 2.65 ± 0.79 0.0076 ± 0.0055 29.26 ± 5.79
French fries (chips) Deep-frying 7.38 ± 0.44 2.73 ± 0.81 0.0141 ± 0.0071 33.56 ± 1.72
boiled potatoes Boiling 3.77 ± 1.04 2.13 ± 0.39 0.5337 ± 0.2191 28.37 ± 1.47
Nutrient composition model calculation results of the sample dishes

Using the established model, we calculated the nutritional components of the 150 sample dishes (Table 4).

Calculated values of macronutrients of the sample dishes with different cooking methods.

Category Dish name Cooking method Model calculates nutritional value
Total fat g/100 g Protein g/100 g Sodium g/100 g Carbohydrate g/100 g
Rice Rice Boiling 0.62 3.41 0.0005 37.47
Steamed rice Steaming 0.76 3.23 0.0046 35.48
Fried rice Stir-frying 10.05 6.98 0.2295 68.91
Noodles Soup noodles Boiling 0.06 4.39 0.0210 34.33
Fried noodles Stir-frying 15.24 20.27 0.2261 32.26
Chicken breast Braised chicken breast Stewing (with soy sauce) 4.77 22.94 0.3808 1.01
Deep-fried chicken breast (breaded with flour) Deep-frying 46.52 24.99 0.5104 1.30
Fried Chicken Stir-frying 28.63 20.73 0.4245 1.08
Chicken Legs Braised Chicken Legs Stewing (with soy sauce) 5.98 12.28 0.2069 0.02
Steamed chicken thighs (cut into pieces and steamed) Steaming 6.46 18.52 0.1040 0.03
Fried chicken thighs (coated with flour) Deep-frying 18.83 18.59 0.4129 1.30
Grilled Chicken Legs Roasting 11.17 26.23 0.9632 4.58
Pork belly Braised Pork Stewing (with soy sauce) 30.76 14.40 0.3509 1.14
Steamed pork practice Steaming 28.88 15.12 0.2579 0.47
Stir-fried Shredded Pork with Green Pepper Stir-frying 27.02 12.08 0.1010 3.47
Grilled Pork Roasting 30.68 13.88 0.0930 2.85
Pork Legs Steamed pork with vermicelli Steaming 16.23 12.05 0.1672 4.31
Small crispy pork (coated with flour and fried) Deep-frying 45.09 28.47 0.6683 10.05
Pasteurized pork Stir-frying 23.20 34.17 0.3828 8.28
Braised Pork Stewing (with soy sauce) 26.24 15.01 0.1059 6.87
Brisket Braised Beef Brisket Stewing (with soy sauce) 23.85 11.16 0.1261 1.09
Grilled Beef Brisket Roasting 31.22 17.27 0.1196 2.01
Stir Fried Beef Brisket Stir-frying 36.19 17.64 0.2580 0.67
Beef Tendon Stir Fried Beef Stir-frying 10.19 21.05 0.1859 0.39
Grilled Beef Tendon with Sauce Roasting 17.82 60.10 0.3190 7.70
Braised Beef Tendon Stewing (with soy sauce) 8.10 15.74 0.4907 2.04
Leg of lamb (Cumin) Lamb Stir-frying 11.11 16.91 0.2043 0.65
Roast Lamb Roasting 10.75 16.94 0.1787 0.56
Braised Lamb Shank Stewing (with soy sauce) 12.46 24.37 0.1977 0.27
Grass carp Braised Grass Carp Stewing (with soy sauce) 9.58 4.72 0.1969 2.06
Fried Grass Carp Deep-frying 9.60 7.49 0.1985 6.08
Steamed Grass Carp Steaming 4.35 14.33 0.6949 0.42
Grilled Grass Carp Roasting 34.22 45.66 1.0224 0.15
Hairtail Steamed hairtail Steaming 8.94 38.96 1.8415 7.68
Braised hairtail Stewing (with soy sauce) 8.04 5.35 0.1396 1.51
Deep Fried hairtail Pieces Deep-frying 8.53 9.92 0.2946 6.46
Shrimp Prawns in oil Deep-frying 14.65 15.84 0.4560 14.03
Steamed shrimp Steaming 2.71 39.10 0.8164 2.66
Boiled Shrimp Quick-boiling 2.21 42.11 0.7947 2.63
Eggs Scrambled eggs with green peppers Stir-frying 11.07 6.71 0.4120 5.84
Hard-boiled eggs Stewing (with soy sauce) 6.52 15.22 0.1891 5.43
Fried Eggs Stir-frying 32.66 9.70 0.0767 0.81
Pakchoi cabbage Stir Fried Pakchoi cabbage Stir-frying 8.76 1.10 0.8042 2.21
Quick-boiled Pakchoi cabbage Quick-boiling 0.00 1.23 0.1210 2.47
Potatoes Hot and Sour Shredded Potatoes Stir-frying 29.70 5.63 1.2167 47.70
Baked Potatoes Roasting 3.21 1.74 0.0043 14.78
French fries (chips) Deep-frying 16.50 1.45 0.0041 12.32
Boiled potatoes Stewing (with soy sauce) 4.20 0.76 0.0361 5.97
Model calibration

According to the screening principle of the ratio of the calculation value to the detection value of ≤0.5 or ≥ 2, we corrected the calculation models of the nutritional components. Table 5 enumerates the examples of specific corrections.

Ratio of the calculation value to the detection valuea.

Category Dish name Cooking method Ratio
Total fat Protein Sodium Carbohydrate
Rice Rice Boiling 0.55 1.08 0.36* 0.63
Steamed rice Steaming 0.62 0.86 0.90 0.66
Fried rice Stir-frying 1.68 2.17* 0.47* 1.44
Noodles Soup noodles Boiling 0.04* 0.95 7.24* 0.77
Fried noodles Stir-frying 3.50* 3.24* 0.47* 0.60
Chicken breast Braised chicken breast Stewing (with soy sauce) 0.93 0.92 1.39 0.07*
Deep-fried chicken breast (breaded with flour) Deep frying 4.95* 0.84 2.24* 0.09*
Fried Chicken Stir-frying 4.06* 0.97 2.24* 0.07*
Chicken Legs Braised Chicken Legs Stewing (with soy sauce) 0.67 0.58 0.49* 0.00*
Steamed chicken thighs (cut into pieces and steamed) Steaming 0.79 0.86 0.40* 0.00*
Fried chicken thighs (coated with flour) Deep frying 1.48 0.83 1.12 0.09*
Grilled Chicken Legs Roasting 1.15 1.07 1.19 0.33*
Pork belly Braised Pork Stewing (with soy sauce) 0.76 0.76 1.11 0.11*
Steamed pork practice Steaming 1.17 0.82 1.03 0.03*
Stir-fried Shredded Pork with Green Pepper Stir-frying 0.86 0.65 0.23* 0.31*
Grilled Pork Roasting 0.85 0.75 0.39* 0.25*
Pork Legs Steamed pork with vermicelli Steaming 1.21 0.85 0.21* 0.16*
Small crispy pork (coated with flour and fried) Deep frying 2.85* 1.10 1.06 0.57
Pasteurized pork Stir-frying 0.68 2.03* 0.69 1.06
Braised Pork Stewing (with soy sauce) 0.88 0.69 0.22* 0.58
Brisket Braised Beef Brisket Stewing (with soy sauce) 0.99 0.49* 0.18* 0.09*
Grilled Beef Brisket Roasting 1.94 0.94 0.34* 0.10*
Stir Fried Beef Brisket Stir-frying 1.44 1.01 0.32* 0.06*
Beef Tendon Stir Fried Beef Stir-frying 0.86 0.94 0.20* 0.04*
Grilled Beef Tendon with Sauce Roasting 2.58* 2.61* 0.73 0.45*
Braised Beef Tendon Stewing (with soy sauce) 0.48* 0.62 0.72 0.15*
Leg of lamb (Cumin) Lamb Stir-frying 0.45* 0.76 0.30* 0.07*
Roast Lamb Roasting 0.58 0.67 0.34* 0.06*
Braised Lamb Shank Stewing (with soy sauce) 0.64 0.86 0.34* 0.06*
Grass carp Braised Grass Carp Stewing (with soy sauce) 0.64 0.30* 0.25* 0.13*
Fried Grass Carp Deep frying 1.14 0.35* 0.48* 0.32*
Steamed Grass Carp Steaming 1.13 0.70 1.04 0.03*
Grilled Grass Carp Roasting 4.03* 1.84 1.79 0.01*
Hairtail Steamed hairtail Steaming 2.18* 1.78 3.02* 0.52
Braised hairtail Stewing (with soy sauce) 0.94 0.27* 0.19* 0.10*
Deep Fried hairtail Pieces Deep frying 0.59 0.42* 0.45* 0.51
Shrimp Prawns in oil deep frying 1.59 0.75 0.94 0.60
Steamed shrimp Steaming 0.90 1.68 1.03 0.21*
Boiled Shrimp Quick-boiling 0.94 2.00* 4.46* 0.19*
Eggs Scrambled eggs with green peppers Stir-frying 0.44* 0.60 0.50* 0.67
Hard-boiled eggs Stewing (with soy sauce) 0.58 1.06 1.17 0.39*
Fried Eggs Stir-frying 1.33 0.70 0.43* 0.07*
Pakchoi cabbage Stir Fried Pakchoi cabbage Stir-frying 1.23 0.56 0.92 0.12*
Quick-boiled Pakchoi cabbage Quick-boiling 0.00* 0.81 1.50 0.11*
Potatoes Hot and Sour Shredded Potatoes Stir-frying 2.97* 4.58* 1.98 1.87
Baked Potatoes Roasting 0.13* 0.66 0.57 0.51
French fries (chips) Deep frying 2.24* 0.53 0.29* 0.37*
Boiled potatoes Stewing (with soy sauce) 1.11 0.36* 0.07* 0.21*

Ratio = Model values of nutritional components / Detection values of nutritional components.

*The ratio between the caculation value and the detection value is ≤ 0.5 or ≥.

 Validation results of the corrected model

Using the model before and after correction, we calculated the nutritional components and energy values of nine selected dishes and compared their calculated and detected values. Figure 2 illustrates the results.

Comparison of the calculated and detected values of the nutrients found in nine dishes.

 Discussion Construction of a raw material classification database

The nutritional components of catering food raw materials have a fundamental and direct impact on the calculation of the nutritional components of dishes (9). Therefore, a professional nutritional component database is needed for the correct labeling of catering food. Currently, the nutritional composition database of catering food primarily refers to the China Food Composition Tables 2002 and 2004 (7, 8). However, considering the significant differences in China’s geographical environment, the types and nutritional characteristics of raw food materials are diverse. For example, the same type of raw food materials may have different varieties or names. Considering the different environments in different provinces, the same raw materials may have different nutritional components, making it difficult to include the entire catering ingredient in the composition table. This study established a scientific classification database of catering food raw materials. The average values of each nutrient in each type of food ingredients were calculated as the representative values of each nutrient in this type of food raw materials. These representative values were then used to replace the new varieties of this food type, thereby greatly improving the accuracy of the calculation of the catering food’s nutritional components.

 Effect of cooking on nutritional components

Based on different food matrices, different cooking methods have different effects on various nutritional components in catering food. For carbohydrate-rich cereal and potato foods, boiling and stir-frying have a greater impact on their carbohydrate content, deep-frying had the greatest impact on their fat, and stewing and stir-frying had a greater impact on protein. Relevant results have also been reported in previous studies. For example, one study found that three cooking methods, namely, steaming, boiling, and deep-frying, can lose starch and reduce sugar in sweet potato, and the degree of loss from high to low was observed in deep-frying, boiling, and steaming (20). Furthermore, when food is deep-fried or stir-fried, the protein is severely damaged (21, 22).

For fish, meat, and eggs with high fat content, deep-frying affects fat the most, steaming greatly affects protein, and stewing and stir-frying mostly affect carbohydrates. Fat reportedly becomes seriously damaged when the oil temperature is excessively high in the process of deep-frying or stir-frying (23, 24). Compared with steaming and stir-frying, boiling alone can maintain a higher content of amino acids and fatty acids, but it can also induce the production of saturated fatty acids (25). However, cooking studies on foal meat from the Galicia Mountains revealed that steaming and stewing did not affect the total essential amino acids and nonessential amino acids (26). For vegetables, stir-frying affects the nutrients the most. Therefore, for traditional Chinese food, boiling, stir-frying, and deep-frying clearly affect the nutritional components of catering food.

 Establishment of a calculation model

It is very difficult to identify the nutritional information of catering food, because the nutritional components are influenced by the raw materials and processing methods, they are difficult to be accurately calculated, and the cost of detection is too high. Through the classification database, the practical problem of some raw materials do not have nutrient composition data in the Food Composition Table is solved, and all most dishes can be included in the model calculation. After the influencing factors in the processing of catering food are corrected, the data calculated by the model are closer to the true value of the dishes. In this study, we successfully constructed a scientific, accurate, and efficient calculation model for the nutritional components, indicating its potential in providing technical support for the accurate calculation and correct identification of nutritional components in catering food. Accurate nutrition information identification will help consumers make scientific choices and maintain physical health.

 Limitations of the study

This model established in this study improved the accuracy of calculation by taking into account as many key influential factors to reduce the errors. However, this study has some limitations. Chinese food is extremely rich in raw materials and cooking methods. This model only investigates and corrects for the most common cooking methods through the most commonly consumed meat (pig, beef, sheep, chicken, fish) and cereals (rice), which are only a small part of Chinese food. So we should further improve this model by multiple verification and relevant data supplement, analyze each influencing factor on the nutrition content of foods for catering.

 Conclusion

Catering food nutrition labeling can help consumers identify the type and content of nutrients in dishes. A scientific, accurate, and efficient calculation model is the key to accurately calculate and label catering food nutrients. In this study, we established three complete databases of food classification, raw and cooked ratio, and correction factors according to different cooking methods. Using computer technology, we linked these databases to the calculation model. Ultimately, we were able to develop a scientific, accurate, and efficient calculation model of the nutritional components of catering food to provide support for the accurate calculation and correct identification of nutritional components.

 Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.

 Author contributions

NL: Data curation, Methodology, Writing – original draft. LC: Formal analysis, Writing – original draft. HW: Validation, Writing – original draft. ZL: Formal analysis, Writing – review & editing. ML: Software, Writing – original draft. DY: Software, Writing – original draft. HL: Conceptualization, Writing – review & editing. HF: Conceptualization, Supervision, Writing – review & editing. YC: Formal analysis, Writing – original draft.

 Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by World Health Organization Representative Office in China (20220303NUT) and China National Center for Food Safety Risk Assessment high-level talent team construction 523 project.

The authors thank the editor and reviewers for their constructive comments and suggestions to improve the quality of this paper.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 Author disclaimer

The views expressed in this report are those of the authors alone and do not necessarily reflect the policies or views of the World Health Organization.

 Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2024.1388645/full#supplementary-material

 References Du WW Li Y Zhang JG Zhang B Zhang PH Wang HJ. Action on salt in China: an overview of restaurant intervention study. Acta Nutrimenta Sin. (2022) 44:112–20. doi: 10.13325/j.cnki.acta.nutr.sin.2022.02.004 World Health Organization. Diet, nutrition and the prevention of chronic diseases: Report of a joint WHO/FAO expert consultation. Geneva: WHO (2003). Burton S Howlett E Tangari AH. Food for thought: how will the nutrition Labeling of quick service restaurant menu items influence consumers’ product evaluations, purchase intentions, and choices? J Retail. (2009) 85:258–73. doi: 10.1016/j.jretai.2009.04.007 World Health Organization. Salt reduction and iodine fortification strategies in public health. Australia: WHO (2013). World Health Organization. Global action plan for the prevention and control of NCDs 2013–2020. Geneva: WHO (2013). National Health Commission of the People's Republic of China. Notice on the issuance of guidelines on nutrition Labeling of catering food and other 3 guidelines Available at: http://www.nhc.gov.cn/sps/s7885u/202012/95a58c9edaa645e1adab956e278c2794.shtml Yang YX Wang GY Pan XC. Chinese food composition 2002. Beijing: Beijing Medical University Press (2002). National Institute for Nutrition and Health Chinese Center for Disease Control and Prevention In: Chinese food composition 2004. Beijing: Beijing Medical University Press (2005) Li L Zhou SS. Investigation on the issues of implementing nutrition labeling for Chinese dishes. Chin J Food Hygiene. (2011) 5:455–8. doi: 10.13590/j.cjfh.2011.05.013 China National Center for Food Safety Risk Assessment. National Food Contaminants and hazardous factors risk monitoring and work manual for 2020. (2019). Available at: https://www.doc 88.com/p-80329237912352.html National Health Commission of the People's Republic of China. National standards for food safety determination of fat in food (GB 5009.6-2016). (n.d.). Available at: http://down.foodmate.net/standard/sort/3/50382.html National Health Commission of the People's Republic of China. National standards for food safety determination of protein in food (GB 5009.5-2016). (n.d.). Available at: http://down.foodmate.net/standard/sort/3/50381.html National Health Commission of the People's Republic of China. National standards for food safety determination of ash in food (GB 5009.4-2016). (n.d.). Available at: http://down.foodmate.net/standard/sort/3/49326.html National Health Commission of the People's Republic of China. National standards for food safety determination of multielement in food (GB 5009.268-2016). (n.d.). Available at: http://down.foodmate.net/standard/sort/3/50423.html Wu YN Zhao YF Li JG. The fifth China Total diet study. Beijing: China Science Publishing & Media Ltd. (Cspm) (2018). Liu LL Gao CX Jiang TH . Effects of cooking methods and conditions on nutritional content of potato tubers. J Chin Cereals Oils Assoc. (2023) 38:61–70. doi: 10.3969/j.issn.1003-0174.2023.07.011 Huang QH Xiong J Wu L. Influence of different cooking methods on the nutrients in food. Food Eng. (2013) 126:62–4. doi: 10.3969/j.issn.1673-6044.2013.01.021 Jiang YT. Discussion on the influence of cooking processing on different nutrients and countermeasures. Nutr Health. (2023) 29:108–10. doi: 10.16736/j.cnki.cn41-1434/ts.2023.15.027 Liu Q Tarn R Lynch D Skjodt NM. Physicochemical properties of dry matter and starch from potatoes grown in Canada. Food Chem. (2007) 105:897–907. doi: 10.1016/j.foodchem.2007.04.034 Chen WH Huang LL. Effects on sweet potato nutrient of different cooking methods. Food Sci Technol. (2013) 38:88–91. doi: 10.13684/j.cnki.spkj.2013.01.045 Zhou GX. Influence of different cooking methods on food nutrients. China Food. (2021) 8:89–91. Gál R Kameník J Salek RN Polášek Z Macharáčková B Valenta T . Research note: impact of applied thermal treatment on textural, and sensory properties and cooking loss of selected chicken and Turkey cuts as affected by cooking technique. Poult Sci. (2022) 101:101923. doi: 10.1016/j.psj.2022.101923, PMID: 35679669 Zhou Z. Explore the influence of different cooking methods on food nutrients. China Food Safety Magazine. (2020) 39. doi: 10.16043/j.cnki.cfs.2020.30.031 Purchas RW Wilkinson BH Carruthers F Jackson F. A comparison of the nutrient content of uncooked and cooked lean from New Zealand beef and lamb. J Food Compos Anal. (2014) 35:75–82. doi: 10.1016/j.jfca.2014.04.008 Li Quanquan L Jie CY . Effect of different cooking methods on nutritional intake and different storage treatments on nutritional losses of abalone. Food Chem. (2022) 377:132047. doi: 10.1016/j.foodchem.2022.132047 Domínguez R Borrajo P Lorenzo JM. The effect of cooking methods on nutritional value of foal meat. J Food Compos Anal. (2015) 43:61–7. doi: 10.1016/j.jfca.2015.04.007