Machine Learning Algorithms Applied to Semi-Quantitative Data of the Volatilome of Citrus and Other Nectar Honeys with the Use of HS-SPME/GC–MS Analysis, Lead to a New Index of Geographical Origin Authentication
The scope of the current study was to monitor if semi-quantitative data of volatile compounds (volatilome) of citrus honey (ch) produced in different countries could potentially lead to a new index of citrus honey authentication using specific ratios of the identified volatile compounds in combinati...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/12/3/509 |
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| author | Ioannis Konstantinos Karabagias Gulzar Ahmad Nayik |
| author_facet | Ioannis Konstantinos Karabagias Gulzar Ahmad Nayik |
| author_sort | Ioannis Konstantinos Karabagias |
| collection | DOAJ |
| description | The scope of the current study was to monitor if semi-quantitative data of volatile compounds (volatilome) of citrus honey (ch) produced in different countries could potentially lead to a new index of citrus honey authentication using specific ratios of the identified volatile compounds in combination with machine learning algorithms. In this context, the semi-quantitative data of the volatilome of 38 citrus honey samples from Egypt, Morocco, Greece, and Spain (determined by headspace solid phase microextraction coupled to gas chromatography mass spectrometry (HS-SPME/GC–MS)) was subjected to supervised and unsupervised chemometrics. Results showed that honey samples could be classified according to the geographical origin based on specific volatile compounds. Data were further evaluated with additional nectar honey samples introduced in the multivariate statistical analysis model and the classification results were not affected. Specific volatile compounds contributed to the discrimination of citrus honey in different amounts according to geographical origin. These were lilac aldehyde D, dill ether, 2-methylbutanal, heptane, benzaldehyde, α,4-dimethyl-3-cyclohexene-1-acetaldehyde, and herboxide (isomer II). The numerical data of these volatile compounds was summed up and divided by the total semi-quantitative volatile content (R<sub>ch</sub>, Karabagias–Nayik index) of citrus honey, according to geographical origin. Egyptian citrus honey had a value of R<sub>ch</sub> = 0.35, Moroccan citrus honey had a value of R<sub>ch</sub> = 0.29, Greek citrus honey had a value of R<sub>ch</sub> = 0.04, and Spanish citrus honey had a value of R<sub>ch</sub> = 0.27, leading to a new hypothesis and a complementary index for the control of citrus honey authentication. |
| first_indexed | 2024-03-11T09:44:58Z |
| format | Article |
| id | doaj.art-71e4560f8c0a4b6393201ca5d20b3e09 |
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| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-11T09:44:58Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-71e4560f8c0a4b6393201ca5d20b3e092023-11-16T16:40:22ZengMDPI AGFoods2304-81582023-01-0112350910.3390/foods12030509Machine Learning Algorithms Applied to Semi-Quantitative Data of the Volatilome of Citrus and Other Nectar Honeys with the Use of HS-SPME/GC–MS Analysis, Lead to a New Index of Geographical Origin AuthenticationIoannis Konstantinos Karabagias0Gulzar Ahmad Nayik1Department of Food Science & Technology, School of Agricultural Sciences, University of Patras, G. Seferi 2, 30100 Agrinio, GreeceDepartment of Food Science & Technology, Government Degree College Shopian, Jammu & Kashmir 192303, IndiaThe scope of the current study was to monitor if semi-quantitative data of volatile compounds (volatilome) of citrus honey (ch) produced in different countries could potentially lead to a new index of citrus honey authentication using specific ratios of the identified volatile compounds in combination with machine learning algorithms. In this context, the semi-quantitative data of the volatilome of 38 citrus honey samples from Egypt, Morocco, Greece, and Spain (determined by headspace solid phase microextraction coupled to gas chromatography mass spectrometry (HS-SPME/GC–MS)) was subjected to supervised and unsupervised chemometrics. Results showed that honey samples could be classified according to the geographical origin based on specific volatile compounds. Data were further evaluated with additional nectar honey samples introduced in the multivariate statistical analysis model and the classification results were not affected. Specific volatile compounds contributed to the discrimination of citrus honey in different amounts according to geographical origin. These were lilac aldehyde D, dill ether, 2-methylbutanal, heptane, benzaldehyde, α,4-dimethyl-3-cyclohexene-1-acetaldehyde, and herboxide (isomer II). The numerical data of these volatile compounds was summed up and divided by the total semi-quantitative volatile content (R<sub>ch</sub>, Karabagias–Nayik index) of citrus honey, according to geographical origin. Egyptian citrus honey had a value of R<sub>ch</sub> = 0.35, Moroccan citrus honey had a value of R<sub>ch</sub> = 0.29, Greek citrus honey had a value of R<sub>ch</sub> = 0.04, and Spanish citrus honey had a value of R<sub>ch</sub> = 0.27, leading to a new hypothesis and a complementary index for the control of citrus honey authentication.https://www.mdpi.com/2304-8158/12/3/509citrus honeyvolatilescharacterizationmachine learningdiscriminationnew index |
| spellingShingle | Ioannis Konstantinos Karabagias Gulzar Ahmad Nayik Machine Learning Algorithms Applied to Semi-Quantitative Data of the Volatilome of Citrus and Other Nectar Honeys with the Use of HS-SPME/GC–MS Analysis, Lead to a New Index of Geographical Origin Authentication Foods citrus honey volatiles characterization machine learning discrimination new index |
| title | Machine Learning Algorithms Applied to Semi-Quantitative Data of the Volatilome of Citrus and Other Nectar Honeys with the Use of HS-SPME/GC–MS Analysis, Lead to a New Index of Geographical Origin Authentication |
| title_full | Machine Learning Algorithms Applied to Semi-Quantitative Data of the Volatilome of Citrus and Other Nectar Honeys with the Use of HS-SPME/GC–MS Analysis, Lead to a New Index of Geographical Origin Authentication |
| title_fullStr | Machine Learning Algorithms Applied to Semi-Quantitative Data of the Volatilome of Citrus and Other Nectar Honeys with the Use of HS-SPME/GC–MS Analysis, Lead to a New Index of Geographical Origin Authentication |
| title_full_unstemmed | Machine Learning Algorithms Applied to Semi-Quantitative Data of the Volatilome of Citrus and Other Nectar Honeys with the Use of HS-SPME/GC–MS Analysis, Lead to a New Index of Geographical Origin Authentication |
| title_short | Machine Learning Algorithms Applied to Semi-Quantitative Data of the Volatilome of Citrus and Other Nectar Honeys with the Use of HS-SPME/GC–MS Analysis, Lead to a New Index of Geographical Origin Authentication |
| title_sort | machine learning algorithms applied to semi quantitative data of the volatilome of citrus and other nectar honeys with the use of hs spme gc ms analysis lead to a new index of geographical origin authentication |
| topic | citrus honey volatiles characterization machine learning discrimination new index |
| url | https://www.mdpi.com/2304-8158/12/3/509 |
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