Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning
Honey is one of the most adulterated foods, usually through the addition of sweeteners or low-cost honeys. This study presents a method based on visible near infrared spectroscopy (Vis-NIRs), in combination with machine learning (ML) algorithms, for the correct identification and quantification of a...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | Foods |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-8158/12/13/2491 |
| _version_ | 1797591674997702656 |
|---|---|
| author | José Luis P. Calle Irene Punta-Sánchez Ana Velasco González-de-Peredo Ana Ruiz-Rodríguez Marta Ferreiro-González Miguel Palma |
| author_facet | José Luis P. Calle Irene Punta-Sánchez Ana Velasco González-de-Peredo Ana Ruiz-Rodríguez Marta Ferreiro-González Miguel Palma |
| author_sort | José Luis P. Calle |
| collection | DOAJ |
| description | Honey is one of the most adulterated foods, usually through the addition of sweeteners or low-cost honeys. This study presents a method based on visible near infrared spectroscopy (Vis-NIRs), in combination with machine learning (ML) algorithms, for the correct identification and quantification of adulterants in honey. Honey samples from two botanical origins (orange blossom and sunflower) were evaluated and adulterated with low-cost honey in different percentages (5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50%). The results of the exploratory analysis showed a tendency to group the samples according to botanical origin, as well as the presence of adulteration. A supervised analysis was performed to detect the presence of adulterations. The best performance with 100% accuracy was achieved by support vector machines (SVM) and random forests (RF). A regression study was also carried out to quantify the percentage of adulteration. The best result was obtained by support vector regression (SVR) with a coefficient of determination (R<sup>2</sup>) of 0.991 and a root mean squared error (RMSE) of 1.894. These results demonstrate the potential of combining ML with spectroscopic data as a method for the automated quality control of honey. |
| first_indexed | 2024-03-11T01:41:40Z |
| format | Article |
| id | doaj.art-0e1f42728b1f4ac49761a699fb59b43b |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-11T01:41:40Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-0e1f42728b1f4ac49761a699fb59b43b2023-11-18T16:33:17ZengMDPI AGFoods2304-81582023-06-011213249110.3390/foods12132491Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine LearningJosé Luis P. Calle0Irene Punta-Sánchez1Ana Velasco González-de-Peredo2Ana Ruiz-Rodríguez3Marta Ferreiro-González4Miguel Palma5Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, SpainDepartment of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, SpainDepartment of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, SpainDepartment of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, SpainDepartment of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, SpainDepartment of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, SpainHoney is one of the most adulterated foods, usually through the addition of sweeteners or low-cost honeys. This study presents a method based on visible near infrared spectroscopy (Vis-NIRs), in combination with machine learning (ML) algorithms, for the correct identification and quantification of adulterants in honey. Honey samples from two botanical origins (orange blossom and sunflower) were evaluated and adulterated with low-cost honey in different percentages (5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50%). The results of the exploratory analysis showed a tendency to group the samples according to botanical origin, as well as the presence of adulteration. A supervised analysis was performed to detect the presence of adulterations. The best performance with 100% accuracy was achieved by support vector machines (SVM) and random forests (RF). A regression study was also carried out to quantify the percentage of adulteration. The best result was obtained by support vector regression (SVR) with a coefficient of determination (R<sup>2</sup>) of 0.991 and a root mean squared error (RMSE) of 1.894. These results demonstrate the potential of combining ML with spectroscopic data as a method for the automated quality control of honey.https://www.mdpi.com/2304-8158/12/13/2491honeyadulterationmachine learningvisible near infrared spectroscopysupport vector machinerandom forest |
| spellingShingle | José Luis P. Calle Irene Punta-Sánchez Ana Velasco González-de-Peredo Ana Ruiz-Rodríguez Marta Ferreiro-González Miguel Palma Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning Foods honey adulteration machine learning visible near infrared spectroscopy support vector machine random forest |
| title | Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning |
| title_full | Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning |
| title_fullStr | Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning |
| title_full_unstemmed | Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning |
| title_short | Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning |
| title_sort | rapid and automated method for detecting and quantifying adulterations in high quality honey using vis nirs in combination with machine learning |
| topic | honey adulteration machine learning visible near infrared spectroscopy support vector machine random forest |
| url | https://www.mdpi.com/2304-8158/12/13/2491 |
| work_keys_str_mv | AT joseluispcalle rapidandautomatedmethodfordetectingandquantifyingadulterationsinhighqualityhoneyusingvisnirsincombinationwithmachinelearning AT irenepuntasanchez rapidandautomatedmethodfordetectingandquantifyingadulterationsinhighqualityhoneyusingvisnirsincombinationwithmachinelearning AT anavelascogonzalezdeperedo rapidandautomatedmethodfordetectingandquantifyingadulterationsinhighqualityhoneyusingvisnirsincombinationwithmachinelearning AT anaruizrodriguez rapidandautomatedmethodfordetectingandquantifyingadulterationsinhighqualityhoneyusingvisnirsincombinationwithmachinelearning AT martaferreirogonzalez rapidandautomatedmethodfordetectingandquantifyingadulterationsinhighqualityhoneyusingvisnirsincombinationwithmachinelearning AT miguelpalma rapidandautomatedmethodfordetectingandquantifyingadulterationsinhighqualityhoneyusingvisnirsincombinationwithmachinelearning |