# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 2 Analysis of Modern-Day Smart Services
### Food Analysis  
The aim of food chemistry is to investigate and research all biological and non-biological components of foods. This research discipline is fully committed to consumer protection and is intended to ensure the safety of all food products. To this end, chemical analyses are used to determine, e.g., whether sufficient quantities of a valuable ingredient are included in a food product or whether food producers comply with the maximum permitted quantities of unhealthy substances [80]. Here, the use of biosensors can contribute significantly to food safety, as contaminations or toxins in food can be reliably detected [81]. For this purpose, the vast number of proteins in a food sample must be split into peptides in order to detect even the smallest traces of substances that can be unsafe to consume. However, this results in a significantly larger number of analytes. Special tools and techniques are therefore needed to handle this huge amount of data [82]. In simple terms, it is necessary to search for specific patterns in the source material [83]. To cope with the vast amount of analysis data, machine learning is becoming increasingly popular. This enables a much more thorough analysis than a manual analysis. Models are trained from examples. These models generalize the data from the given samples. For new samples, it must be determined to which of the training samples the new sample has the best match. This allows one to analyze even very large datasets, such as complete DNA sequences, for contained patterns [84]. An analysis process in the food chemistry domain is shown in Figure 5. A food sample is analyzed to determine whether it contains certain allergens. Such allergens must be explicitly declared by food producers to protect consumers who have a certain food allergy. In this example, a food chemist determines whether the sample contains glutamate. Yet, the same procedure applies to other allergens as well, such as gluten, crustaceans, or nut seeds. For this purpose, the sample is analyzed with a mass spectrometer to determine the mass-to-charge ratio of all peptides present in a sample. For the separation of contained peptides, liquid chromatography is used. From a proteomics and genomics database for foods, e.g., _UniProt_ (see [[https://www.uniprot.org/](https://www.uniprot.org/)]([https://www.uniprot.org/](https://www.uniprot.org/)), accessed on 30 August 2022), comparison samples of the allergens of interest can be obtained. Then, it can be cross-checked whether sufficiently similar patterns can be recognized in the peptides obtained from the food sample [85]. For this comparison and all subsequent analyses, however, a thorough refinement of the sample data is necessary, e.g., to filter out inaccurate or irrelevant data to be able to focus on the relevant aspects in the data, only [86]. This way, it is possible to detect whether allergens are present in a food sample in any decomposing stage [87]. Such a pattern recognition in a data stream, as it is generated by the measurement devices during the analysis, is also known from the domain of _complex event processing_ (_CEP_). A pattern consists of individual facts--in the context of food analysis for instance peptides--which are in a given sequential context [88]. The patterns can be defined by means of pattern templates. These templates provide a level of abstraction to the actual data, as they specify the target sequences based on pairs of measurement values and constraints on those values. A CEP engine then searches for these patterns in the data stream [89]. In the context of food analysis, however, it is not sufficient to find only exact matches but also reasonably good matches. For instance, post-translational protein changes can cause deviations from the entries in the protein sequence database. To this end, data mining techniques such as \(k\)-nearest neighbor analysis can be used to find similarity matches [90].