| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Wang, Hui
Queen's University Belfast
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (23/23 displayed)
- 2024CNC-Machined and 3D-Printed Metal G-band Diplexers for Earth Observation Applicationscitations
- 2023Evaluation of 3D printed monolithic G-band waveguide componentscitations
- 2023Machine learning on spectral data from miniature devices for food quality analysis - a case study
- 2023Halide-guided active site exposure in bismuth electrocatalysts for selective CO2 conversion into formic acidcitations
- 2023Halide-guided active site exposure in bismuth electrocatalysts for selective CO2 conversion into formic acid
- 2022A 3D printed 300 GHz waveguide cavity filter by micro laser sinteringcitations
- 2022D-band waveguide diplexer fabricated using micro laser sinteringcitations
- 2022Waste‐Derived Copper‐Lead Electrocatalysts for CO<sub>2</sub> Reductioncitations
- 2022Waste-Derived Copper-Lead Electrocatalysts for CO 2 Reductioncitations
- 2022Waste-Derived Copper-Lead Electrocatalysts for CO2 Reduction
- 2021125 GHz frequency doubler using a waveguide cavity produced by stereolithographycitations
- 2021Trileucine as a dispersibility enhancer of spray-dried inhalable microparticlescitations
- 2016Thermoelectric Properties of Polymeric Mixed Conductorscitations
- 2014Self-assembled nano- to micron-size fibers from molten R11Ni4In9 intermetallicscitations
- 2014Semi-metallic polymerscitations
- 2014Semi-metallic polymerscitations
- 2013Spatially resolved investigation of strain and composition variations in (In,Ga)N/GaN epilayerscitations
- 2013Exploring suitable oligoamines for phantom ring-closing condensation polymerization with guanidine hydrochloridecitations
- 2013A novel method to measure diffusion coefficients in porous metal-organic frameworks (vol 12, pg 8093, 2010)
- 2010A metadata-based approach for multimedia service mashup in IMS
- 2010A novel method to measure diffusion coefficients in porous metal-organic frameworkscitations
- 2010Phenotypic characterization of shewanella oneidensis MR-1 under aerobic and anaerobic growth conditions by using fourier transform infrared spectroscopy and high-performance liquid chromatography analysescitations
- 2010Impact of silver(I) on the metabolism of Shewanella oneidensiscitations
Places of action
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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.