| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Liu, Jun
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (25/25 displayed)
- 2024Role of Solvent in the Oriented Growth of Conductive Ni‐CAT‐1 Metal‐Organic Framework at Solid–Liquid Interfaces
- 2023Plasma-induced energy band evolution for two-dimensional heterogeneous anti-ambipolar transistorscitations
- 2023Damage and energy absorption behaviour of composite laminates under impact loading using different impactor geometriescitations
- 2023Modelling the effects of patch-plug configuration on the impact performance of patch-repaired composite laminatescitations
- 2023Non-destructive evaluation of magnetic anisotropy associated with crystallographic texture of interstitial free steelscitations
- 2023Non-destructive evaluation of magnetic anisotropy associated with crystallographic texture of interstitial free steelscitations
- 2023Machine learning on spectral data from miniature devices for food quality analysis - a case study
- 2022Non-destructive evaluation of magnetic anisotropy associated with crystallographic texture of interstitial free steels using an electromagnetic sensor
- 2020Wurtzite materials in alloys of rock salt compoundscitations
- 2019Magnetic characterisation of grain size and precipitate distribution by major and minor BH loop measurementscitations
- 2017Optimized setup and protocol for magnetic domain imaging with in Situ hysteresis measurementcitations
- 2017Mild oxalic-acid-catalyzed hydrolysis as a novel approach to prepare cellulose nanocrystalscitations
- 2017Spatial Control of Functional Response in 4D-Printed Active Metallic Structurescitations
- 2016Brush-painting and photonical sintering of copper and silver inks on cotton fabric to form antennas for wearable ultra-high-frequency radio-frequency identification tagscitations
- 2016Development of nanocellulose scaffolds with tunable structures to support 3D cell culturecitations
- 2015Tailor-made hemicellulose-based hydrogels reinforced with nanofibrillated cellulosecitations
- 2015Electromagnetic evaluation of the microstructure of grade 91 tubes/pipescitations
- 2015Binding kinetics of lock and key colloidscitations
- 2015Conductivity of PEDOT:PSS on spin-coated and drop cast nanofibrillar cellulose thin filmscitations
- 2014Differential permeability behaviour of P9 and T22 power station Steelscitations
- 2014Assessment of microstructural changes in Grade 91 power station tubes through incremental permeability and magnetic Barkhausen noise measurements
- 2014Biocomposites of Nanofibrillated Cellulose, Polypyrrole, and Silver Nanoparticles with Electroconductive and Antimicrobial Propertiescitations
- 2014Incremental permeability and magnetic Barkhausen noise for the assessment of microstructural changes in Grade 91 power station tubes
- 2013Magnetic evaluation of microstructure changes in 9Cr-1Mo and 2.25Cr-1Mo steels using electromagnetic sensorscitations
- 2008Interaction of the cytochrome P4501A2, SULT1A1 and NAT gene polymorphisms with smoking and dietary mutagen intake in modification of the risk of pancreatic cancercitations
Places of action
| Organizations | Location | People |
|---|
document
Machine learning on spectral data from miniature devices for food quality analysis - a case study
Abstract
Food quality analysis can be carried out by spectral data acquired from spectrometers with its advantage of non-destructive way of testing. Portable and miniature spectroscopy can be a suitable solution when it meets the specifications such as portability, cost, and short processing time requirements, to enable ordinary citizens to use such a device in the fight against food fraud. Compared to more expensive, bulky, and non-portable devices, the data collected using miniature and portable spectrometers is of a lower quality and thus adversely affect the quality of the analysis. Research have been carried out to use machine learning (ML) classifiers on spectral data analysis for food quality assessment. The present work focuses on two aspects: firstly, preliminary exploratory statistical analysis is conducted on the real spectral data on different food products including oils, fruits and spices acquired from such miniature devices, which aims to evaluate and illustrate the distinctive characteristics of such spectral data, data distribution and difference in the spectra across multiple data acquisitions etc. along with a summary of the key challenges to face and explore. Secondly, a case study for the differentiation ofextra virgin olive from adulterated with vegetable oil is provided to analyze and evaluate how some commonly used ML classifiers can be used for classification, while the impact of different preprocessing methods to improve the accuracy and efficiency is also provided. The case study demonstrates the good potential of using data analytics for spectral data from miniature device, although the overall performance of those ML classifiers is not exceptional (the classification rates of up to 83.32%) which is partially due to the quality of data, and partially due to limiting to only some classifiers. More elaborate data pre-processing and cleaning methods can be used to address the key challenges of the spectral data from miniature device, and other types of classifiers can be also explored further in future work.