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Application of MOS gas sensors coupled with chemometrics methods to predict the amount of sugar and carbohydrates in potatoes
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Khorramifar, Ali, Rasekh, Mansour, Karami, Hamed, Covington, James A., Derakhshani, Sayed M., Ramos, Jose and Gancarz, Marek (2022) Application of MOS gas sensors coupled with chemometrics methods to predict the amount of sugar and carbohydrates in potatoes. Molecules, 27 (11). e3508. doi:10.3390/molecules27113508 ISSN 1420-3049.
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WRAP-Application-MOS-gas-sensors-chemometrics-methods-predict-carbohydrates-potatoes-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (3311Kb) | Preview |
Official URL: https://doi.org/10.3390/molecules27113508
Abstract
Five potato varieties were studied using an electronic nose with nine MOS sensors. Parameters measured included carbohydrate content, sugar level, and the toughness of the potatoes. Routine tests were carried out while the signals for each potato were measured, simultaneously, using an electronic nose. The signals obtained indicated the concentration of various chemical components. In addition to support vector machines (SVMs that were used for the classification of the samples, chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models for sugar and carbohydrates. The predictive power of the regression models was characterized by a coefficient of determination (R2), a root-mean-square error of prediction (RMSEP), and offsets. PLSR was able to accurately model the relationship between the smells of different types of potatoes, sugar, and carbohydrates. The highest and lowest accuracy of models for predicting sugar and carbohydrates was related to Marfona potatoes and Sprite cultivar potatoes. In general, in all cultivars, the accuracy in predicting the amount of carbohydrates was somewhat better than the accuracy in predicting the amount of sugar. Moreover, the linear function had 100% accuracy for training and validation in the C-SVM method for classification of five potato groups. The electronic nose could be used as a fast and non-destructive method for detecting different potato varieties. Researchers in the food industry will find this method extremely useful in selecting the desired product and samples.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > QD Chemistry Q Science > QK Botany S Agriculture > SB Plant culture T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TP Chemical technology T Technology > TX Home economics |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
| SWORD Depositor: | Library Publications Router | ||||||
| Library of Congress Subject Headings (LCSH): | Detectors, Gas detectors, Chemometrics, Potatoes -- Composition, Food -- Composition, Carbohydrates, Sugar | ||||||
| Journal or Publication Title: | Molecules | ||||||
| Publisher: | MDPI | ||||||
| ISSN: | 1420-3049 | ||||||
| Official Date: | 30 May 2022 | ||||||
| Dates: |
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| Volume: | 27 | ||||||
| Number: | 11 | ||||||
| Article Number: | e3508 | ||||||
| DOI: | 10.3390/molecules27113508 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 13 June 2022 | ||||||
| Date of first compliant Open Access: | 13 June 2022 | ||||||
| RIOXX Funder/Project Grant: |
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