Investigating the correlation of analytical data on pesticide residues in fruits and vegetables with local climatic condition.

The use of pesticides is increasing steadily, and even though pesticides are essential for food security, they are known for having adverse effects on human health, and the environment. Further, as pesticides are often a reaction to pests, which are influenced by environmental conditions, the environment might influence the use of pesticides-when assuming, that the use is optimized, and adjusted to those conditions. Therefore, it would be helpful to know how environmental conditions influence the pesticide residue levels of fruits and vegetables. In this work, we investigated the correlation between residue levels of ten different pesticides and the weather parameters air temperature, maximum and minimum temperature, wind speed, precipitation, and sun hours using the Pearson correlation coefficient, linear, and polynomial regression. Also, the pesticide residue levels were analyzed regarding outliers. No correlation between the measured residue levels and the weather parameters could be found for most pesticides. However, for Acetamiprid and Fluopyram, a slight correlation between the pesticide residue levels, the air, minimum-, and maximum temperature could be found. The polynomial regression model was better suited to describe the relationship between pesticide residue levels and weather parameters than the linear regression model, but R2 was not higher than 0.069 for any model.

Anticipating food structure of meat products from mastication physics applying machine learning.

Alternatives to animal-based products are becoming more relevant. Most of those products rely at some stage on a structuring process; hence researchers are developing techniques to measure the goodness of the structured material. Conventionally, a typical sensory study or texture analysis by measuring deformation forces would be applied to test the produced material for its texture. However, meat alternatives and meat differ in more points than just the texture, making it hard to extract the isolated texture impression. To objectively obtain qualitative and quantitative differences between different food structures, evaluation of oral processing features is an upcoming technology which qualifies as promising addon to existing technologies. The kinematic data of the jaw and exerted forces regarding muscle activities are recorded during mastication. Resulting datasets are high in dimensionality, covering thousands of individual chews described by often more than ten features. Evaluating such a dataset could benefit from applying computational evaluation strategies designed for large datasets, such as machine learning and neural networks. The aim of this work was to assess the performance of machine learning algorithms such as Support Vector Machines and Artificial Neural Networks or ensemble learning algorithms like Extra Trees Classifier or Extreme Gradient Boosting. We evaluated different pre-processing techniques and various machine algorithms for learning models with regard to their performance measured with established benchmark values (Accuracy, Area under Receiver-Operating Curve score, F1 score, precision-recall Curve, Matthews Correlation Coefficient (MCC)). Results show remarkable performance of classification of each single chew between isotropic and anisotropic material (MCC up to 0.966). According to the feature importance, the lateral jaw movement was the most important feature for classification; however, all features were necessary for an optimal learning process.

Unleashing the Potential of Digitalization in the Agri-Food Chain for Integrated Food Systems.

Digitalization transforms many industries, especially manufacturing, with new concepts such as Industry 4.0 and the Industrial Internet of Things. However, information technology also has the potential to integrate and connect the various steps in the supply chain. For the food industry, the situation is ambivalent: It has a high level of automatization, but the potential of digitalization is so far not used today. In this review, we discuss current trends in information technology that have the potential to transform the food industry into an integrated food system. We show how this digital transformation can integrate various activities within the agri-food chain and support the idea of integrated food systems. Based on a future-use case, we derive the potential of digitalization to tackle future challenges in the food industry and present a research agenda. Expected final online publication date for the Annual Review of Food Science and Technology, Volume 15 is April 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Online Monitoring of Sourdough Fermentation Using a Gas Sensor Array with Multivariate Data Analysis.

Sourdough can improve bakery products' shelf life, sensory properties, and nutrient composition. To ensure high-quality sourdough, the fermentation has to be monitored. The characteristic process variables for sourdough fermentation are pH and the degree of acidity measured as total titratable acidity (TTA). The time- and cost-intensive offline measurement of process variables can be improved by utilizing online gas measurements in prediction models. Therefore, a gas sensor array (GSA) system was used to monitor the fermentation process of sourdough online by correlation of exhaust gas data with offline measurement values of the process variables. Three methods were tested to utilize the extracted features from GSA to create the models. The most robust prediction models were achieved using a PCA (Principal Component Analysis) on all features and combined two fermentations. The calibrations with the extracted features had a percentage root mean square error (RMSE) from 1.4% to 12% for the pH and from 2.7% to 9.3% for the TTA. The coefficient of determination (R2) for these calibrations was 0.94 to 0.998 for the pH and 0.947 to 0.994 for the TTA. The obtained results indicate that the online measurement of exhaust gas from sourdough fermentations with gas sensor arrays can be a cheap and efficient application to predict pH and TTA.

CortexVR: Immersive analysis and training of cognitive executive functions of soccer players using virtual reality and machine learning.

Goal: This paper presents an immersive Virtual Reality (VR) system to analyze and train Executive Functions (EFs) of soccer players. EFs are important cognitive functions for athletes. They are a relevant quality that distinguishes amateurs from professionals. Method: The system is based on immersive technology, hence, the user interacts naturally and experiences a training session in a virtual world. The proposed system has a modular design supporting the extension of various so-called game modes. Game modes combine selected game mechanics with specific simulation content to target particular training aspects. The system architecture decouples selection/parameterization and analysis of training sessions via a coaching app from an Unity3D-based VR simulation core. Monitoring of user performance and progress is recorded by a database that sends the necessary feedback to the coaching app for analysis. Results: The system is tested for VR-critical performance criteria to reveal the usefulness of a new interaction paradigm in the cognitive training and analysis of EFs. Subjective ratings for overall usability show that the design as VR application enhances the user experience compared to a traditional desktop app; whereas the new, unfamiliar interaction paradigm does not negatively impact the effort for using the application. Conclusion: The system can provide immersive training of EF in a fully virtual environment, eliminating potential distraction. It further provides an easy-to-use analyzes tool to compare user but also an automatic, adaptive training mode.

A machine learning-based workflow for automatic detection of anomalies in machine tools.

Despite the increased sensor-based data collection in Industry 4.0, the practical use of this data is still in its infancy. In contrast, academic literature provides several approaches to detect machine failures but, in most cases, relies on simulations and vast amounts of training data. Since it is often not practical to collect such amounts of data in an industrial context, we propose an approach to detect the current production mode and machine degradation states on a comparably small data set. Our approach integrates domain knowledge about manufacturing systems into a highly generalizable end-to-end workflow ranging from raw data processing, phase segmentation, data resampling, and feature extraction to machine tool anomaly detection. The workflow applies unsupervised clustering techniques to identify the current production mode and supervised classification models for detecting the present degradation. A resampling strategy and classical machine learning models enable the workflow to handle small data sets and distinguish between normal and abnormal machine tool behavior. To the best of our knowledge, there exists no such end-to-end workflow in the literature that uses the entire machine signal as input to identify anomalies for individual tools. Our evaluation with data from a real multi-purpose machine shows that the proposed workflow detects anomalies with an average F1-score of almost 93%.

Food Informatics-Review of the Current State-of-the-Art, Revised Definition, and Classification into the Research Landscape.

Background: The increasing population of humans, changing food consumption behavior, as well as the recent developments in the awareness for food sustainability, lead to new challenges for the production of food. Advances in the Internet of Things (IoT) and Artificial Intelligence (AI) technology, including Machine Learning and data analytics, might help to account for these challenges. Scope and Approach: Several research perspectives, among them Precision Agriculture, Industrial IoT, Internet of Food, or Smart Health, already provide new opportunities through digitalization. In this paper, we review the current state-of-the-art of the mentioned concepts. An additional concept is Food Informatics, which so far is mostly recognized as a mainly data-driven approach to support the production of food. In this review paper, we propose and discuss a new perspective for the concept of Food Informatics as a supportive discipline that subsumes the incorporation of information technology, mainly IoT and AI, in order to support the variety of aspects tangent to the food production process and delineate it from other, existing research streams in the domain. Key Findings and Conclusions: Many different concepts related to the digitalization in food science overlap. Further, Food Informatics is vaguely defined. In this paper, we provide a clear definition of Food Informatics and delineate it from related concepts. We corroborate our new perspective on Food Informatics by presenting several case studies about how it can support the food production as well as the intermediate steps until its consumption, and further describe its integration with related concepts.

Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry.

The food industry faces many challenges, including the need to feed a growing population, food loss and waste, and inefficient production systems. To cope with those challenges, digital twins that create a digital representation of physical entities by integrating real-time and real-world data seem to be a promising approach. This paper aims to provide an overview of digital twin applications in the food industry and analyze their challenges and potentials. Therefore, a literature review is executed to examine digital twin applications in the food supply chain. The applications found are classified according to a taxonomy and key elements to implement digital twins are identified. Further, the challenges and potentials of digital twin applications in the food industry are discussed. The survey revealed that the application of digital twins mainly targets the production (agriculture) or the food processing stage. Nearly all applications are used for monitoring and many for prediction. However, only a small amount focuses on the integration in systems for autonomous control or providing recommendations to humans. The main challenges of implementing digital twins are combining multidisciplinary knowledge and providing enough data. Nevertheless, digital twins provide huge potentials, e.g., in determining food quality, traceability, or designing personalized foods.