A new sub-class linear discriminant for miniature spectrometer based food analysis

Autor: Nibouche, Omar, Asharindavida, Fayas, Wang, Hui, Vincent, Jordan, Liu, Jun, van Ruth, Saskia, Maguire, Paul, Rahman, Enayet

Publikováno v: In Chemometrics and Intelligent Laboratory Systems 15 July 2024 250

Machine learning on spectral data from miniature devices for food quality analysis - a case study

Autor: Asharindavida, Fayas, Nibouche, Omar, Uhomoibhi, James, Liu, Jun, Wang, Hui

Publikováno v: Asharindavida, F, Nibouche, O, Uhomoibhi, J, Liu, J & Wang, H 2023, Machine learning on spectral data from miniature devices for food quality analysis-a case study . in ICMLSC '23: Proceedings of the 2023 7th International Conference on Machine Learning and Soft Computing . The International Conference on Machine Learning and Soft Computing (ICMLSC): Proceedings, ACM, pp. 89–95, ICMLSC 2023: The 7th International Conference on Machine Learning and Soft Computing, Chongqing, China, 05/01/2023 . https://doi.org/10.1145/3583788.3583801

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 portab

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______2607::b94d8245822e9d14a1495fa91ea5f1f1
https://pure.qub.ac.uk/en/publications/0c57256d-ac6b-4ce7-977f-c9b1e378152b

Evaluation of olive oil quality using a miniature spectrometer: a machine learning approach.

Autor: Kim, Moon S., Cho, Byoung-Kwan, Asharindavida, Fayas, Nibouche, Omar, Uhomoibhi, James, Wang, Hui, Vincent, Jordan

Publikováno v: Proceedings of SPIE; 3/27/2021, Vol. 11754, p1175405-1175405, 1p