Quantification of Total Phenol in Grape by Near Infrared Spectroscopy and Artificial Neural Network
Grape is one of the most important fruits in the world. Phenolic compounds are antioxidants are important compositions of grape. Phenolic compounds phrase includes all the aromatic molecules consisting amino acids to complex molecules like tannins and lignin’s. Near infrared spectroscopy is one of t...
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| Format: | Article |
| Language: | fas |
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Research Institute of Food Science and Technology
2017-11-01
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| Series: | Pizhūhish va Nuāvarī dar ̒Ulūm va Sanāyi̒-i Ghaz̠āyī |
| Subjects: | |
| Online Access: | http://journals.rifst.ac.ir/article_68435_59c6bdf6db6ee6ce27bd231326d83c54.pdf |
| _version_ | 1818495621688983552 |
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| author | Reza Mohammadigol Farzad Azadshahraki Valiollah Lotfi |
| author_facet | Reza Mohammadigol Farzad Azadshahraki Valiollah Lotfi |
| author_sort | Reza Mohammadigol |
| collection | DOAJ |
| description | Grape is one of the most important fruits in the world. Phenolic compounds are antioxidants are important compositions of grape. Phenolic compounds phrase includes all the aromatic molecules consisting amino acids to complex molecules like tannins and lignin’s. Near infrared spectroscopy is one of the most common nondestructive methods for fruits and vegetables qualification analysis. This research is conducted to evaluate the possibility of the quantification of total phenol in grape by near infrared spectroscopy and artificial neural network (perceptron). The number of 444 samples (107 Asgari, 106 Bidane, 111 shahroodi and 120 khoshnav varieties) were selected to model calibrating and test as well. Developed ANNs were compared on phenol prediction by residual prediction deviation (RPD) index in the test sample dataset (101 samples).The maximum RPD was 1.66 by 8-5-1 topology with correlation coefficient and root mean square (RMSE) equal to 0.79 and 48.66 respectively. It was concluded that NIR spectroscopy and back propagation perceptron ANN could be used to discriminate low and high amounts of grape total phenol as a nondestructive method. |
| first_indexed | 2024-12-10T18:23:05Z |
| format | Article |
| id | doaj.art-db36af0723334c8dbc84dc9afb20ef00 |
| institution | Directory Open Access Journal |
| issn | 2252-0937 2538-2357 |
| language | fas |
| last_indexed | 2024-12-10T18:23:05Z |
| publishDate | 2017-11-01 |
| publisher | Research Institute of Food Science and Technology |
| record_format | Article |
| series | Pizhūhish va Nuāvarī dar ̒Ulūm va Sanāyi̒-i Ghaz̠āyī |
| spelling | doaj.art-db36af0723334c8dbc84dc9afb20ef002022-12-22T01:38:09ZfasResearch Institute of Food Science and TechnologyPizhūhish va Nuāvarī dar ̒Ulūm va Sanāyi̒-i Ghaz̠āyī2252-09372538-23572017-11-016331332010.22101/jrifst2017.11.18.63868435Quantification of Total Phenol in Grape by Near Infrared Spectroscopy and Artificial Neural NetworkReza Mohammadigol0Farzad Azadshahraki1Valiollah Lotfi2Assistant Professor, Department of Biosystem Engineering, Faculty of Agriculture and Natural Resources, Arak University, Arak, IranAssistant Professor, Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, IranMSc, Department of Biosystem Engineering, Faculty of Agriculture and Natural Resources, Arak University, Arak, IranGrape is one of the most important fruits in the world. Phenolic compounds are antioxidants are important compositions of grape. Phenolic compounds phrase includes all the aromatic molecules consisting amino acids to complex molecules like tannins and lignin’s. Near infrared spectroscopy is one of the most common nondestructive methods for fruits and vegetables qualification analysis. This research is conducted to evaluate the possibility of the quantification of total phenol in grape by near infrared spectroscopy and artificial neural network (perceptron). The number of 444 samples (107 Asgari, 106 Bidane, 111 shahroodi and 120 khoshnav varieties) were selected to model calibrating and test as well. Developed ANNs were compared on phenol prediction by residual prediction deviation (RPD) index in the test sample dataset (101 samples).The maximum RPD was 1.66 by 8-5-1 topology with correlation coefficient and root mean square (RMSE) equal to 0.79 and 48.66 respectively. It was concluded that NIR spectroscopy and back propagation perceptron ANN could be used to discriminate low and high amounts of grape total phenol as a nondestructive method.http://journals.rifst.ac.ir/article_68435_59c6bdf6db6ee6ce27bd231326d83c54.pdfgrapeneural networksnondestructivespectroscopytotal phenol |
| spellingShingle | Reza Mohammadigol Farzad Azadshahraki Valiollah Lotfi Quantification of Total Phenol in Grape by Near Infrared Spectroscopy and Artificial Neural Network Pizhūhish va Nuāvarī dar ̒Ulūm va Sanāyi̒-i Ghaz̠āyī grape neural networks nondestructive spectroscopy total phenol |
| title | Quantification of Total Phenol in Grape by Near Infrared Spectroscopy and Artificial Neural Network |
| title_full | Quantification of Total Phenol in Grape by Near Infrared Spectroscopy and Artificial Neural Network |
| title_fullStr | Quantification of Total Phenol in Grape by Near Infrared Spectroscopy and Artificial Neural Network |
| title_full_unstemmed | Quantification of Total Phenol in Grape by Near Infrared Spectroscopy and Artificial Neural Network |
| title_short | Quantification of Total Phenol in Grape by Near Infrared Spectroscopy and Artificial Neural Network |
| title_sort | quantification of total phenol in grape by near infrared spectroscopy and artificial neural network |
| topic | grape neural networks nondestructive spectroscopy total phenol |
| url | http://journals.rifst.ac.ir/article_68435_59c6bdf6db6ee6ce27bd231326d83c54.pdf |
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