Inner Properties Estimation of Gala Apple Using Spectral Data and Two Statistical and Artificial Intelligence Based Methods
Fruits provide various vitamins to the human body. The chemical properties of fruits provide useful information to researchers, including determining the ripening time of fruits and the lack of nutrients in them. Conventional methods for determining the chemical properties of fruits are destructive...
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MDPI AG
2021-12-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/10/12/2967 |
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| author | Vali Rasooli Sharabiani Sajad Sabzi Razieh Pourdarbani Mariusz Szymanek Sławomir Michałek |
| author_facet | Vali Rasooli Sharabiani Sajad Sabzi Razieh Pourdarbani Mariusz Szymanek Sławomir Michałek |
| author_sort | Vali Rasooli Sharabiani |
| collection | DOAJ |
| description | Fruits provide various vitamins to the human body. The chemical properties of fruits provide useful information to researchers, including determining the ripening time of fruits and the lack of nutrients in them. Conventional methods for determining the chemical properties of fruits are destructive and time-consuming methods that have no application for online operations. For that, various researchers have conducted various studies on non-destructive methods, which are currently in the research and development stage. Thus, the present paper focusses on a non-destructive method based on spectral data in the 200–1100-nm region for estimation of total soluble solids and BrimA in Gala apples. The work steps included: (1) collecting different samples of Gala apples at different stages of maturity; (2) extracting spectral data of samples and pre-preprocessing them; (3) measuring the chemical properties of TSS and BrimA; (4) selecting optimal (effective) wavelengths using artificial neural network-simulated annealing algorithm (ANN-SA); and (5) estimating chemical properties based on partial least squares regression (PLSR) and hybrid artificial neural network known as the imperialist competitive algorithm (ANN-ICA). It should be noted that, in order to investigate the validity of the methods, the estimation algorithm was repeated 500 times. In the end, the results displayed that, in the best training, the ANN-ICA predicted the TSS and BrimA with correlation coefficients of 0.963 and 0.965 and root mean squared error of 0.167% and 0.596%, respectively. |
| first_indexed | 2024-03-10T04:07:57Z |
| format | Article |
| id | doaj.art-1eff90f44d1a4f07b4435e2ecdcf9932 |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-10T04:07:57Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-1eff90f44d1a4f07b4435e2ecdcf99322023-11-23T08:16:56ZengMDPI AGFoods2304-81582021-12-011012296710.3390/foods10122967Inner Properties Estimation of Gala Apple Using Spectral Data and Two Statistical and Artificial Intelligence Based MethodsVali Rasooli Sharabiani0Sajad Sabzi1Razieh Pourdarbani2Mariusz Szymanek3Sławomir Michałek4Department of Biosystems Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, IranDepartment of Biosystems Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, IranDepartment of Biosystems Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, IranDepartment of Machine Science, University of Life Sciences in Lublin, 20-950 Lublin, PolandDepartment of Botany and Plant Physiology, University of Life Sciences in Lublin, 20-950 Lublin, PolandFruits provide various vitamins to the human body. The chemical properties of fruits provide useful information to researchers, including determining the ripening time of fruits and the lack of nutrients in them. Conventional methods for determining the chemical properties of fruits are destructive and time-consuming methods that have no application for online operations. For that, various researchers have conducted various studies on non-destructive methods, which are currently in the research and development stage. Thus, the present paper focusses on a non-destructive method based on spectral data in the 200–1100-nm region for estimation of total soluble solids and BrimA in Gala apples. The work steps included: (1) collecting different samples of Gala apples at different stages of maturity; (2) extracting spectral data of samples and pre-preprocessing them; (3) measuring the chemical properties of TSS and BrimA; (4) selecting optimal (effective) wavelengths using artificial neural network-simulated annealing algorithm (ANN-SA); and (5) estimating chemical properties based on partial least squares regression (PLSR) and hybrid artificial neural network known as the imperialist competitive algorithm (ANN-ICA). It should be noted that, in order to investigate the validity of the methods, the estimation algorithm was repeated 500 times. In the end, the results displayed that, in the best training, the ANN-ICA predicted the TSS and BrimA with correlation coefficients of 0.963 and 0.965 and root mean squared error of 0.167% and 0.596%, respectively.https://www.mdpi.com/2304-8158/10/12/2967spectroscopyartificial neural networkripeningapplenon-destructive predictionoptimization algorithm |
| spellingShingle | Vali Rasooli Sharabiani Sajad Sabzi Razieh Pourdarbani Mariusz Szymanek Sławomir Michałek Inner Properties Estimation of Gala Apple Using Spectral Data and Two Statistical and Artificial Intelligence Based Methods Foods spectroscopy artificial neural network ripening apple non-destructive prediction optimization algorithm |
| title | Inner Properties Estimation of Gala Apple Using Spectral Data and Two Statistical and Artificial Intelligence Based Methods |
| title_full | Inner Properties Estimation of Gala Apple Using Spectral Data and Two Statistical and Artificial Intelligence Based Methods |
| title_fullStr | Inner Properties Estimation of Gala Apple Using Spectral Data and Two Statistical and Artificial Intelligence Based Methods |
| title_full_unstemmed | Inner Properties Estimation of Gala Apple Using Spectral Data and Two Statistical and Artificial Intelligence Based Methods |
| title_short | Inner Properties Estimation of Gala Apple Using Spectral Data and Two Statistical and Artificial Intelligence Based Methods |
| title_sort | inner properties estimation of gala apple using spectral data and two statistical and artificial intelligence based methods |
| topic | spectroscopy artificial neural network ripening apple non-destructive prediction optimization algorithm |
| url | https://www.mdpi.com/2304-8158/10/12/2967 |
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