ArticleScientific reports2020
A machine learning workflow for raw food spectroscopic classification in a future industry.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- A Comprehensive Review on Seaweed Preservation Techniques: Impacts on Nutritional Quality, Safety and Shelf Life.Food science & nutrition · 2026Review
- Evaluating the Effect of Thermal Treatment on Phenolic Compounds in Functional Flours Using Vis-NIR-SWIR Spectroscopy: A Machine Learning Approach.Foods (Basel, Switzerland) · 2025Article
- Applications of Big Data and Blockchain Technology in Food Testing and Their Exploration on Educational Reform.Foods (Basel, Switzerland) · 2024Review
- Predicting multiple taste sensations with a multiobjective machine learning method.NPJ science of food · 2024Article
- Improved organic and pesticide-free rice (Heliyon · 2024Article
- Real-Time AI-Assisted Push-Broom Hyperspectral System for Precision Agriculture.Sensors (Basel, Switzerland) · 2024Article
- Deep learning framework for sensor array precision and accuracy enhancement.Scientific reports · 2023Article
- A robust and resilience machine learning for forecasting agri-food production.Scientific reports · 2022Article
- Assessment of the Microbial Spoilage and Quality of Marinated Chicken Souvlaki through Spectroscopic and Biomimetic Sensors and Data Fusion.Microorganisms · 2022Article
- Rapid Assessment of Microbial Quality in Edible Seaweeds Using Sensor Techniques Based on Spectroscopy, Imaging Analysis and Sensors Mimicking Human Senses.Sensors (Basel, Switzerland) · 2022Article
- Spectroscopic Data for the Rapid Assessment of Microbiological Quality of Chicken Burgers.Foods (Basel, Switzerland) · 2022Article
- Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-supervised Learning.Frontiers in artificial intelligence · 2022Article
- New Non-Invasive Method for the Authentication of Apple Cultivars.Foods (Basel, Switzerland) · 2021Article
- Food Microbial Diversity.Microorganisms · 2021Article
- Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis.Foods (Basel, Switzerland) · 2021Article
- Detection of Meat Adulteration Using Spectroscopy-Based Sensors.Foods (Basel, Switzerland) · 2021Article
- Assessing the Biofilm Formation Capacity of the Wine Spoilage YeastMicroorganisms · 2021Article
- Quest of Intelligent Research Tools for Rapid Evaluation of Fish Quality: FTIR Spectroscopy and Multispectral Imaging Versus Microbiological Analysis.Foods (Basel, Switzerland) · 2021Article
- Spoilage assessment of chicken breast fillets by means of fourier transform infrared spectroscopy and multispectral image analysis.Current research in food science · 2021Article
- Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms.Molecules (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the years, technology has changed the way we produce and have access to our food through the development of applications, robotics, data analysis, and processing techniques. The implementation of these approaches by the food industry ensure quality and affordability, reducing at the same time the costs of keeping the food fresh and increase productivity. A system, as the one presented herein, for raw food categorization is needed in future food industries to automate food classification according to type, the process of algorithm approaches that will be applied to every different food origin and also for serving disabled people. The purpose of this work was to develop a machine learning workflow based on supervised PLS regression and SVM classification, towards automated raw food categorization from FTIR. The system exhibited high efficiency in multi-class classification of 7 different types of raw food. The selected food samples, were diverse in terms of storage conditions (temperature, storage time and packaging), while the variability within each food was also taken into account by several different batches; leading in a classifier able to embed this variation towards increased robustness and efficiency, ready for real life applications targeting to the digital transformation of the food industry.
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Registered trials
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