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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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Official URL: https://doi.org/10.3390/molecules27113508

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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
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
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:
DateEvent
30 May 2022Published
27 May 2022Accepted
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:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDUniversity of Mohaghegh Ardabilihttp://dx.doi.org/10.13039/501100007073
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  • https://creativecommons.org/licenses/by/...

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