Vibrational Spectroscopy Coupled to a Multivariate Analysis Tiered Approach for Argentinean Honey Provenance Confirmation
In the present work, the provenance discrimination of Argentinian honeys was used as case study to compare the capabilities of three spectroscopic techniques as fast screening platforms for honey authentication purposes. Multifloral honeys were collected among three main honey-producing regions of A...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-10-01
|
| Series: | Foods |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-8158/9/10/1450 |
| _version_ | 1827704418306359296 |
|---|---|
| author | Tito Damiani Rosa M. Alonso-Salces Inés Aubone Vincent Baeten Quentin Arnould Chiara Dall’Asta Sandra R. Fuselli Juan Antonio Fernández Pierna |
| author_facet | Tito Damiani Rosa M. Alonso-Salces Inés Aubone Vincent Baeten Quentin Arnould Chiara Dall’Asta Sandra R. Fuselli Juan Antonio Fernández Pierna |
| author_sort | Tito Damiani |
| collection | DOAJ |
| description | In the present work, the provenance discrimination of Argentinian honeys was used as case study to compare the capabilities of three spectroscopic techniques as fast screening platforms for honey authentication purposes. Multifloral honeys were collected among three main honey-producing regions of Argentina over four harvesting seasons. Each sample was fingerprinted by FT-MIR, NIR and FT-Raman spectroscopy. The spectroscopic platforms were compared on the basis of the classification performance achieved under a supervised chemometric approach. Furthermore, low- mid- and high-level data fusion were attempted in order to enhance the classification results. Finally, the best-performing solution underwent to SIMCA modelling with the purpose of reproducing a food authentication scenario. All the developed classification models underwent to a “year-by-year” validation strategy, enabling a sound assessment of their long-term robustness and excluding any issue of model overfitting. Excellent classification scores were achieved by all the technologies and nearly perfect classification was provided by FT-MIR. All the data fusion strategies provided satisfying outcomes, with the mid- and high-level approaches outperforming the low-level data fusion. However, no significant advantage over the FT-MIR alone was obtained. SIMCA modelling of FT-MIR data produced highly sensitive and specific models and an overall prediction ability improvement was achieved when more harvesting seasons were used for the model calibration (86.7% sensitivity and 91.1% specificity). The results obtained in the present work suggested the major potential of FT-MIR for fingerprinting-based honey authentication and demonstrated that accuracy levels that may be commercially useful can be reached. On the other hand, the combination of multiple vibrational spectroscopic fingerprints represents a choice that should be carefully evaluated from a cost/benefit standpoint within the industrial context. |
| first_indexed | 2024-03-10T15:40:20Z |
| format | Article |
| id | doaj.art-4d65bae73f0f4e91aa45d5be72e0060f |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-10T15:40:20Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-4d65bae73f0f4e91aa45d5be72e0060f2023-11-20T16:51:06ZengMDPI AGFoods2304-81582020-10-01910145010.3390/foods9101450Vibrational Spectroscopy Coupled to a Multivariate Analysis Tiered Approach for Argentinean Honey Provenance ConfirmationTito Damiani0Rosa M. Alonso-Salces1Inés Aubone2Vincent Baeten3Quentin Arnould4Chiara Dall’Asta5Sandra R. Fuselli6Juan Antonio Fernández Pierna7Department of Food and Drugs, University of Parma, Viale delle Scienze 17/A, 43124 Parma, ItalyGrupo de Investigación Microbiología Aplicada, Centro de Investigación en Abejas Sociales, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, Dean Funes B7602AYL, Mar del Plata 3350, ArgentinaGrupo de Investigación Microbiología Aplicada, Centro de Investigación en Abejas Sociales, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, Dean Funes B7602AYL, Mar del Plata 3350, ArgentinaQuality and Authentication of Products Unit, Knowledge and Valorization of Agricultural Products Department, Walloon Agricultural Research Centre (CRA-W), Chée de Namur, 24, 5030 Gembloux, BelgiumQuality and Authentication of Products Unit, Knowledge and Valorization of Agricultural Products Department, Walloon Agricultural Research Centre (CRA-W), Chée de Namur, 24, 5030 Gembloux, BelgiumDepartment of Food and Drugs, University of Parma, Viale delle Scienze 17/A, 43124 Parma, ItalyGrupo de Investigación Microbiología Aplicada, Centro de Investigación en Abejas Sociales, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, Dean Funes B7602AYL, Mar del Plata 3350, ArgentinaQuality and Authentication of Products Unit, Knowledge and Valorization of Agricultural Products Department, Walloon Agricultural Research Centre (CRA-W), Chée de Namur, 24, 5030 Gembloux, BelgiumIn the present work, the provenance discrimination of Argentinian honeys was used as case study to compare the capabilities of three spectroscopic techniques as fast screening platforms for honey authentication purposes. Multifloral honeys were collected among three main honey-producing regions of Argentina over four harvesting seasons. Each sample was fingerprinted by FT-MIR, NIR and FT-Raman spectroscopy. The spectroscopic platforms were compared on the basis of the classification performance achieved under a supervised chemometric approach. Furthermore, low- mid- and high-level data fusion were attempted in order to enhance the classification results. Finally, the best-performing solution underwent to SIMCA modelling with the purpose of reproducing a food authentication scenario. All the developed classification models underwent to a “year-by-year” validation strategy, enabling a sound assessment of their long-term robustness and excluding any issue of model overfitting. Excellent classification scores were achieved by all the technologies and nearly perfect classification was provided by FT-MIR. All the data fusion strategies provided satisfying outcomes, with the mid- and high-level approaches outperforming the low-level data fusion. However, no significant advantage over the FT-MIR alone was obtained. SIMCA modelling of FT-MIR data produced highly sensitive and specific models and an overall prediction ability improvement was achieved when more harvesting seasons were used for the model calibration (86.7% sensitivity and 91.1% specificity). The results obtained in the present work suggested the major potential of FT-MIR for fingerprinting-based honey authentication and demonstrated that accuracy levels that may be commercially useful can be reached. On the other hand, the combination of multiple vibrational spectroscopic fingerprints represents a choice that should be carefully evaluated from a cost/benefit standpoint within the industrial context.https://www.mdpi.com/2304-8158/9/10/1450honeyvibrational spectroscopygeographical originchemometricsdata fusion |
| spellingShingle | Tito Damiani Rosa M. Alonso-Salces Inés Aubone Vincent Baeten Quentin Arnould Chiara Dall’Asta Sandra R. Fuselli Juan Antonio Fernández Pierna Vibrational Spectroscopy Coupled to a Multivariate Analysis Tiered Approach for Argentinean Honey Provenance Confirmation Foods honey vibrational spectroscopy geographical origin chemometrics data fusion |
| title | Vibrational Spectroscopy Coupled to a Multivariate Analysis Tiered Approach for Argentinean Honey Provenance Confirmation |
| title_full | Vibrational Spectroscopy Coupled to a Multivariate Analysis Tiered Approach for Argentinean Honey Provenance Confirmation |
| title_fullStr | Vibrational Spectroscopy Coupled to a Multivariate Analysis Tiered Approach for Argentinean Honey Provenance Confirmation |
| title_full_unstemmed | Vibrational Spectroscopy Coupled to a Multivariate Analysis Tiered Approach for Argentinean Honey Provenance Confirmation |
| title_short | Vibrational Spectroscopy Coupled to a Multivariate Analysis Tiered Approach for Argentinean Honey Provenance Confirmation |
| title_sort | vibrational spectroscopy coupled to a multivariate analysis tiered approach for argentinean honey provenance confirmation |
| topic | honey vibrational spectroscopy geographical origin chemometrics data fusion |
| url | https://www.mdpi.com/2304-8158/9/10/1450 |
| work_keys_str_mv | AT titodamiani vibrationalspectroscopycoupledtoamultivariateanalysistieredapproachforargentineanhoneyprovenanceconfirmation AT rosamalonsosalces vibrationalspectroscopycoupledtoamultivariateanalysistieredapproachforargentineanhoneyprovenanceconfirmation AT inesaubone vibrationalspectroscopycoupledtoamultivariateanalysistieredapproachforargentineanhoneyprovenanceconfirmation AT vincentbaeten vibrationalspectroscopycoupledtoamultivariateanalysistieredapproachforargentineanhoneyprovenanceconfirmation AT quentinarnould vibrationalspectroscopycoupledtoamultivariateanalysistieredapproachforargentineanhoneyprovenanceconfirmation AT chiaradallasta vibrationalspectroscopycoupledtoamultivariateanalysistieredapproachforargentineanhoneyprovenanceconfirmation AT sandrarfuselli vibrationalspectroscopycoupledtoamultivariateanalysistieredapproachforargentineanhoneyprovenanceconfirmation AT juanantoniofernandezpierna vibrationalspectroscopycoupledtoamultivariateanalysistieredapproachforargentineanhoneyprovenanceconfirmation |