The Development of Honey Recognition Models Based on the Association between ATR-IR Spectroscopy and Advanced Statistical Tools
The newly developed prediction models, having the aim to classify Romanian honey samples by associating ATR-FTIR spectral data and the statistical method, PLS-DA, led to reliable differentiations among the samples, in terms of botanical and geographical origin and harvesting year. Based on this appr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/23/17/9977 |
| _version_ | 1797495094254764032 |
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| author | Maria David Ariana Raluca Hategan Camelia Berghian-Grosan Dana Alina Magdas |
| author_facet | Maria David Ariana Raluca Hategan Camelia Berghian-Grosan Dana Alina Magdas |
| author_sort | Maria David |
| collection | DOAJ |
| description | The newly developed prediction models, having the aim to classify Romanian honey samples by associating ATR-FTIR spectral data and the statistical method, PLS-DA, led to reliable differentiations among the samples, in terms of botanical and geographical origin and harvesting year. Based on this approach, 105 out of 109 honey samples were correctly attributed, leading to true positive rates of 95% and 97% accuracy for the harvesting differentiation model. For the botanical origin classification, 83% of the investigated samples were correctly predicted, when four honey varieties were simultaneously discriminated. The geographical assessment was achieved in a percentage of 91% for the Transylvanian samples and 85% of those produced in other regions, with overall accuracy of 88% in the cross-validation procedure. The signals, based on which the best classification models were achieved, allowed the identification of the most significant compounds for each performed discrimination. |
| first_indexed | 2024-03-10T01:43:32Z |
| format | Article |
| id | doaj.art-a9a54fb5c2fe41af9ebc0919cf883f43 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-10T01:43:32Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-a9a54fb5c2fe41af9ebc0919cf883f432023-11-23T13:20:52ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672022-09-012317997710.3390/ijms23179977The Development of Honey Recognition Models Based on the Association between ATR-IR Spectroscopy and Advanced Statistical ToolsMaria David0Ariana Raluca Hategan1Camelia Berghian-Grosan2Dana Alina Magdas3National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaNational Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaNational Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaNational Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaThe newly developed prediction models, having the aim to classify Romanian honey samples by associating ATR-FTIR spectral data and the statistical method, PLS-DA, led to reliable differentiations among the samples, in terms of botanical and geographical origin and harvesting year. Based on this approach, 105 out of 109 honey samples were correctly attributed, leading to true positive rates of 95% and 97% accuracy for the harvesting differentiation model. For the botanical origin classification, 83% of the investigated samples were correctly predicted, when four honey varieties were simultaneously discriminated. The geographical assessment was achieved in a percentage of 91% for the Transylvanian samples and 85% of those produced in other regions, with overall accuracy of 88% in the cross-validation procedure. The signals, based on which the best classification models were achieved, allowed the identification of the most significant compounds for each performed discrimination.https://www.mdpi.com/1422-0067/23/17/9977honey authenticationATR-IR spectroscopyPLS-DApreprocessing |
| spellingShingle | Maria David Ariana Raluca Hategan Camelia Berghian-Grosan Dana Alina Magdas The Development of Honey Recognition Models Based on the Association between ATR-IR Spectroscopy and Advanced Statistical Tools International Journal of Molecular Sciences honey authentication ATR-IR spectroscopy PLS-DA preprocessing |
| title | The Development of Honey Recognition Models Based on the Association between ATR-IR Spectroscopy and Advanced Statistical Tools |
| title_full | The Development of Honey Recognition Models Based on the Association between ATR-IR Spectroscopy and Advanced Statistical Tools |
| title_fullStr | The Development of Honey Recognition Models Based on the Association between ATR-IR Spectroscopy and Advanced Statistical Tools |
| title_full_unstemmed | The Development of Honey Recognition Models Based on the Association between ATR-IR Spectroscopy and Advanced Statistical Tools |
| title_short | The Development of Honey Recognition Models Based on the Association between ATR-IR Spectroscopy and Advanced Statistical Tools |
| title_sort | development of honey recognition models based on the association between atr ir spectroscopy and advanced statistical tools |
| topic | honey authentication ATR-IR spectroscopy PLS-DA preprocessing |
| url | https://www.mdpi.com/1422-0067/23/17/9977 |
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