KFU. Card publication. Employing artificial neural networks and fluorescence spectrum for food vegetable oils identification

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

EMPLOYING ARTIFICIAL NEURAL NETWORKS AND FLUORESCENCE SPECTRUM FOR FOOD VEGETABLE OILS IDENTIFICATION

Form of presentation

Articles in international journals and collections

Year of publication

2022

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Authors, employees of KFU

Yushhenko Natalya Anatolevna, author

Bibliographic description in the original language

Employing artificial neural networks and fluorescence spectrum for food vegetable oils identification / W. J. Pranoto, S. G. Al-Shawi, P. Chetthamrongchai [et al.] // Ciencia e Tecnologia de Alimentos. – 2022. – Vol. 42. – P. e80921. – DOI 10.1590/fst.80921. – EDN CSBQCJ.

Annotation

CIENCIA E TECNOLOGIA DE ALIMENTOS

Keywords

vegetable oils quality; spectrometry; food; mathematical treatment

The name of the journal

CIENCIA E TECNOLOGIA DE ALIMENTOS

URL

https://www.scielo.br/j/cta/a/Xxp4GJWmkQ6wNFmHyksyxtd/?lang=en

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=283271&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Yushhenko Natalya Anatolevna	ru_RU
dc.date.accessioned	2022-01-01T00:00:00Z	ru_RU
dc.date.available	2022-01-01T00:00:00Z	ru_RU
dc.date.issued	2022	ru_RU
dc.identifier.citation	Employing artificial neural networks and fluorescence spectrum for food vegetable oils identification / W. J. Pranoto, S. G. Al-Shawi, P. Chetthamrongchai [et al.] // Ciencia e Tecnologia de Alimentos. – 2022. – Vol. 42. – P. e80921. – DOI 10.1590/fst.80921. – EDN CSBQCJ.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=283271&p_lang=2	ru_RU
dc.description.abstract	CIENCIA E TECNOLOGIA DE ALIMENTOS	ru_RU
dc.description.abstract	Vegetable oils (VOs) come in a wide range of flavors and trademarks. VOs are very similar in appearance, flavor, and taste, and it's frequently difficult to tell them from just by looking at them. Approaches for classifying these oils are sometimes expensive and time-intensive, and they frequently include analytical chemical techniques as well as mathematical algorithms like as Artificial Neural Networks (ANNs), Properties of Partial Least Squares (PLS), Principal Components Regression (PCR), and Principal Component Analysis (PCA) to enhance their effectiveness. Because of the large range of goods available, more productive techniques for qualifying, characterizing, and classifying these substances are required, as the ultimate cost should indicate the quality of the commodity that reaches the user. This study provides a technique for classifying VOs such as different manufacturers' soybean, corn, sunflower, and canola. This method utilized a Charge-Coupled Device (CCD) array sensor, a light emission diode, and a straightforward mathematical approach to capture the generated fluorescence spectrum (FS) in diluted oil. The spectrum classifications are performed using an ANN with three layers, each having four neurons. The approach can categorize VO and enables rapid network training with a 72% success rate utilizing only a few mathematical changes in the spectra data.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject		ru_RU
dc.title	Employing artificial neural networks and fluorescence spectrum for food vegetable oils identification	ru_RU
dc.type	Articles in international journals and collections	ru_RU