Classification of Plant-Based Drinks Based on Volatile Compounds
The increasing popularity of plant-based drinks has led to an expanded consumer market. However, available quality control technologies for plant-based drinks are time-consuming and expensive. Two alternative quality control methods, gas chromatography with ion mobility spectrometry (GC-IMS) and an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/13/24/4086 |
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| author | Zsigmond Papp Laura Gabriela Nemeth Sandrine Nzetchouang Siyapndjeu Anita Bufa Tamás Marosvölgyi Zoltán Gyöngyi |
| author_facet | Zsigmond Papp Laura Gabriela Nemeth Sandrine Nzetchouang Siyapndjeu Anita Bufa Tamás Marosvölgyi Zoltán Gyöngyi |
| author_sort | Zsigmond Papp |
| collection | DOAJ |
| description | The increasing popularity of plant-based drinks has led to an expanded consumer market. However, available quality control technologies for plant-based drinks are time-consuming and expensive. Two alternative quality control methods, gas chromatography with ion mobility spectrometry (GC-IMS) and an electronic nose, were used to assess 111 plant-based drink samples. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to compare 58 volatile organic compound areas of GC-IMS gallery plots and 63 peptide sensors of the electronic nose. PCA results showed that GC-IMS was only able to completely separate one sample, whereas the electronic nose was able to completely separate seven samples. LDA application to GC-IMS analyses resulted in classification accuracies ranging from 15.4% to 100%, whereas application to electronic nose analyses resulted in accuracies ranging from 96.2% to 100%. Both methods were useful for classification, but each had drawbacks, and the electronic nose performed slightly better than GC-IMS. This study represents one of the first studies comparing GC-IMS and an electronic nose for the analysis of plant-based drinks. Further research is necessary to improve these methods and establish a rapid, cost-effective food quality control system based on volatile organic compounds. |
| format | Article |
| id | doaj-art-f0dc3669af66421d98aca2d214aad51f |
| institution | Kabale University |
| issn | 2304-8158 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-f0dc3669af66421d98aca2d214aad51f2024-12-27T14:26:27ZengMDPI AGFoods2304-81582024-12-011324408610.3390/foods13244086Classification of Plant-Based Drinks Based on Volatile CompoundsZsigmond Papp0Laura Gabriela Nemeth1Sandrine Nzetchouang Siyapndjeu2Anita Bufa3Tamás Marosvölgyi4Zoltán Gyöngyi5Department of Public Health Medicine, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, HungaryDepartment of Public Health Medicine, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, HungaryDepartment of Public Health Medicine, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, HungaryInstitute of Bioanalysis, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, HungaryInstitute of Bioanalysis, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, HungaryDepartment of Public Health Medicine, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, HungaryThe increasing popularity of plant-based drinks has led to an expanded consumer market. However, available quality control technologies for plant-based drinks are time-consuming and expensive. Two alternative quality control methods, gas chromatography with ion mobility spectrometry (GC-IMS) and an electronic nose, were used to assess 111 plant-based drink samples. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to compare 58 volatile organic compound areas of GC-IMS gallery plots and 63 peptide sensors of the electronic nose. PCA results showed that GC-IMS was only able to completely separate one sample, whereas the electronic nose was able to completely separate seven samples. LDA application to GC-IMS analyses resulted in classification accuracies ranging from 15.4% to 100%, whereas application to electronic nose analyses resulted in accuracies ranging from 96.2% to 100%. Both methods were useful for classification, but each had drawbacks, and the electronic nose performed slightly better than GC-IMS. This study represents one of the first studies comparing GC-IMS and an electronic nose for the analysis of plant-based drinks. Further research is necessary to improve these methods and establish a rapid, cost-effective food quality control system based on volatile organic compounds.https://www.mdpi.com/2304-8158/13/24/4086plant-based drinksclassificationgas chromatography ion mobility spectrometryGC-IMSelectronic nosee-nose |
| spellingShingle | Zsigmond Papp Laura Gabriela Nemeth Sandrine Nzetchouang Siyapndjeu Anita Bufa Tamás Marosvölgyi Zoltán Gyöngyi Classification of Plant-Based Drinks Based on Volatile Compounds Foods plant-based drinks classification gas chromatography ion mobility spectrometry GC-IMS electronic nose e-nose |
| title | Classification of Plant-Based Drinks Based on Volatile Compounds |
| title_full | Classification of Plant-Based Drinks Based on Volatile Compounds |
| title_fullStr | Classification of Plant-Based Drinks Based on Volatile Compounds |
| title_full_unstemmed | Classification of Plant-Based Drinks Based on Volatile Compounds |
| title_short | Classification of Plant-Based Drinks Based on Volatile Compounds |
| title_sort | classification of plant based drinks based on volatile compounds |
| topic | plant-based drinks classification gas chromatography ion mobility spectrometry GC-IMS electronic nose e-nose |
| url | https://www.mdpi.com/2304-8158/13/24/4086 |
| work_keys_str_mv | AT zsigmondpapp classificationofplantbaseddrinksbasedonvolatilecompounds AT lauragabrielanemeth classificationofplantbaseddrinksbasedonvolatilecompounds AT sandrinenzetchouangsiyapndjeu classificationofplantbaseddrinksbasedonvolatilecompounds AT anitabufa classificationofplantbaseddrinksbasedonvolatilecompounds AT tamasmarosvolgyi classificationofplantbaseddrinksbasedonvolatilecompounds AT zoltangyongyi classificationofplantbaseddrinksbasedonvolatilecompounds |