# MGHT: Statistical Methodology for Missing-Data Imputation in Food Composition Databases
## 1 Introduction  
In food chemistry, chemical properties and interactions of food components are studied. Many chemical components, such as nutrients, occur in foods naturally and most of them contribute to balanced diet and eating pleasure. Components, called functional chemicals, play an important role in food production and preservation. Moreover, they can be effectively applied in the treatment and prevention of diseases. In foods, however, there are also chemicals with toxicological properties, some of which can cause harmful effects in humans and animals. Although it is very important to know which chemicals are built in foods, most countries still lack complete sets of food composition data (FCD). FCD is presented as a detailed set of information about the chemical components of foods, providing values for energy, nutrients and other bioactive components of foods (basic data elements), as well as food classifiers and descriptors (metadata). This type of data is available in Food Composition Databases (FCDBs) [1]. One of the most well-known applications of FCD is the nutrient-intake assessment at individual, regional, national or international level. FCDBs represent fundamental information resources for Food Science, however, they are also used in other public-health domains since the food industry, legislation and consumers all need and/or use FCD [2]. Two of the main limitations of FCDBs are: (i) variability in data between countries [3]; and (ii) incomplete coverage of foods or nutrients and bioactive components leading to missing data [4; 5]. The variability in FCD between countries is a problem that occurs because different FCDBs may use different metadata to describe the same data. This problem can be solved by data harmonization, which is the process of bringing together data of varying file formats, naming conventions, and columns, and transforming them into one cohesive dataset, or in this case matching the same food items or nutrient descriptions from different FCDBs considering their matadata (i.e., classifiers and descriptors). Recently, normalization of short text segments (e.g., names or descriptions of nutrients) was proposed using two approaches: (i) standard text similarity measures; and (ii) a modified version of Part of Speech (POS) tagging probability weighted method [6; 7; 8; 9; 10]. The second limitation are missing data, which distort the integrity of the database. Missing FCD must never be assigned a zero value [5], which is a rule for FCDBs because a nutrient value can be zero [1]. A good quality FCDB should aim towards minimizing the amount of missing data. When there are no analytical values for missing FCD, the most used approach for resolving missing data is borrowing data from tables and databases from other countries, where a citation back to the original source may or may not be possible [11]. The FCDBs currently available in Europe and worldwide contain compositional values of differing quality, reflecting the various ways in which they were obtained [12; 13; 14]. They differ in basic data elements that are available, the metadata used to describe them, and in the quantity of the FCD that is held in the database. However, if FCD is intended to be used internationally, it must be of consistent and compatible quality, so that it can be used collaboratively between individuals and countries. In FCDBs, data types and sources are identified by codes as done in many countries and by references [12; 13; 14]. These include, in order of preference [15]:  
* Original analytical values: The data are taken from published literature or unpublished laboratory reports, whether or not prepared explicitly for the purpose of compiling the database. * Estimated values: Estimates are derived from analytical values obtained for a similar food or for another form of the same food. * Calculated values: Data are derived from recipes, calculated from the nutrient contents of the ingredients and corrected for preparation factors [16]. * Borrowed values: The data are taken from other tables and databases.