Various analytical methods have been utilized in food quality and safety inspection. Depending on the analytical instruments used, various types of signals are obtained for measuring the external and internal quality attributes of food. Dealing with these signals is a crucial step for the deep analysis of food attributes. Machine learning methods have been proven to be effective in data analysis in various fields including for food, as machine learning methods can help analyze the descriptive data obtained by different analytical methods. At present, various machine learning methods have been proposed and have been applied in food-related aspects. With the development of artificial intelligence and computing power, further application of machine learning methods in food can be expected.
With the development of modern analytical techniques, various techniques (High-performance liquid chromatography, gas chromatography-mass spectrometry, electronic tongue, electronic nose, spectroscopic techniques (visible/infrared, Raman, laser-induced breakdown spectroscopy, etc.) have been used in the food industry. As for different analytical techniques, optimal machine learning methods should be explored. This Research Topic attempts to explore the application of machine learning methods in food quality and safety inspection using different analytical methods, including both pre-existed and newly -proposed machine learning methods. Recently, artificial intelligence methods, taking deep learning as an example, have also shown the great potential of data evaluation for food analysis. As an artificial intelligence method, deep learning has a strong ability to learn deep features from various data, and various studies have proved the advance of deep learning over traditional machine methods.
The scope of the Research Topic includes the exploration and extension of the application of machine learning methods via various analytical techniques for food quality and safety inspection. Machine learning is a broad topic, and various machine learning methods have been proposed by researchers in different fields. This Research Topic covers the proposal and the application of machine learning methods in various food applications.
The manuscripts focusing on the following aspects are welcome:
• Proposed new machine learning methods;
• Application of existing machine learning methods;
• Proposal and application of deep learning approaches;
• Food compositions;
• Food texture analysis;
• Food sensory analysis;
• Food processing analysis;
• Food classification;
• Food analytical methods.
Various analytical methods have been utilized in food quality and safety inspection. Depending on the analytical instruments used, various types of signals are obtained for measuring the external and internal quality attributes of food. Dealing with these signals is a crucial step for the deep analysis of food attributes. Machine learning methods have been proven to be effective in data analysis in various fields including for food, as machine learning methods can help analyze the descriptive data obtained by different analytical methods. At present, various machine learning methods have been proposed and have been applied in food-related aspects. With the development of artificial intelligence and computing power, further application of machine learning methods in food can be expected.
With the development of modern analytical techniques, various techniques (High-performance liquid chromatography, gas chromatography-mass spectrometry, electronic tongue, electronic nose, spectroscopic techniques (visible/infrared, Raman, laser-induced breakdown spectroscopy, etc.) have been used in the food industry. As for different analytical techniques, optimal machine learning methods should be explored. This Research Topic attempts to explore the application of machine learning methods in food quality and safety inspection using different analytical methods, including both pre-existed and newly -proposed machine learning methods. Recently, artificial intelligence methods, taking deep learning as an example, have also shown the great potential of data evaluation for food analysis. As an artificial intelligence method, deep learning has a strong ability to learn deep features from various data, and various studies have proved the advance of deep learning over traditional machine methods.
The scope of the Research Topic includes the exploration and extension of the application of machine learning methods via various analytical techniques for food quality and safety inspection. Machine learning is a broad topic, and various machine learning methods have been proposed by researchers in different fields. This Research Topic covers the proposal and the application of machine learning methods in various food applications.
The manuscripts focusing on the following aspects are welcome:
• Proposed new machine learning methods;
• Application of existing machine learning methods;
• Proposal and application of deep learning approaches;
• Food compositions;
• Food texture analysis;
• Food sensory analysis;
• Food processing analysis;
• Food classification;
• Food analytical methods.