Hyperspectral Imaging to Characterize Table Grapes
Table grape quality is of importance for consumers and thus for producers. Its objective quality is usually determined by destructive methods mainly based on sugar content. This study proposed to evaluate the possibility of hyperspectral imaging to characterize table grapes quality through its sugar...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Chemosensors |
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| Online Access: | https://www.mdpi.com/2227-9040/9/4/71 |
| _version_ | 1797539259725381632 |
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| author | Mario Gabrielli Vanessa Lançon-Verdier Pierre Picouet Chantal Maury |
| author_facet | Mario Gabrielli Vanessa Lançon-Verdier Pierre Picouet Chantal Maury |
| author_sort | Mario Gabrielli |
| collection | DOAJ |
| description | Table grape quality is of importance for consumers and thus for producers. Its objective quality is usually determined by destructive methods mainly based on sugar content. This study proposed to evaluate the possibility of hyperspectral imaging to characterize table grapes quality through its sugar (TSS), total flavonoid (TF), and total anthocyanin (TA) contents. Different data pre-treatments (WD, SNV, and 1st and 2nd derivative) and different methods were tested to get the best prediction models: PLS with full spectra and then Multiple Linear Regression (MLR) were realized after selecting the optimal wavelengths thanks to the regression coefficients (β-coefficients) and the Variable Importance in Projection (VIP) scores. All models were good at showing that hyperspectral imaging is a relevant method to predict sugar, total flavonoid, and total anthocyanin contents. The best predictions were obtained from optimal wavelength selection based on β-coefficients for TSS and from VIPs optimal wavelength windows using SNV pre-treatment for total flavonoid and total anthocyanin content. Thus, good prediction models were proposed in order to characterize grapes while reducing the data sets and limit the data storage to enable an industrial use. |
| first_indexed | 2024-03-10T12:42:30Z |
| format | Article |
| id | doaj.art-5ca42be844ff423480df24b65abc7a03 |
| institution | Directory Open Access Journal |
| issn | 2227-9040 |
| language | English |
| last_indexed | 2024-03-10T12:42:30Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Chemosensors |
| spelling | doaj.art-5ca42be844ff423480df24b65abc7a032023-11-21T13:44:26ZengMDPI AGChemosensors2227-90402021-04-01947110.3390/chemosensors9040071Hyperspectral Imaging to Characterize Table GrapesMario Gabrielli0Vanessa Lançon-Verdier1Pierre Picouet2Chantal Maury3USC 1422 GRAPPE, INRAE, Ecole Supérieure d’Agricultures, SFR 4207 QUASAV, 55 Rue Rabelais, BP 30748, 49007 Angers CEDEX 01, FranceUSC 1422 GRAPPE, INRAE, Ecole Supérieure d’Agricultures, SFR 4207 QUASAV, 55 Rue Rabelais, BP 30748, 49007 Angers CEDEX 01, FranceUSC 1422 GRAPPE, INRAE, Ecole Supérieure d’Agricultures, SFR 4207 QUASAV, 55 Rue Rabelais, BP 30748, 49007 Angers CEDEX 01, FranceUSC 1422 GRAPPE, INRAE, Ecole Supérieure d’Agricultures, SFR 4207 QUASAV, 55 Rue Rabelais, BP 30748, 49007 Angers CEDEX 01, FranceTable grape quality is of importance for consumers and thus for producers. Its objective quality is usually determined by destructive methods mainly based on sugar content. This study proposed to evaluate the possibility of hyperspectral imaging to characterize table grapes quality through its sugar (TSS), total flavonoid (TF), and total anthocyanin (TA) contents. Different data pre-treatments (WD, SNV, and 1st and 2nd derivative) and different methods were tested to get the best prediction models: PLS with full spectra and then Multiple Linear Regression (MLR) were realized after selecting the optimal wavelengths thanks to the regression coefficients (β-coefficients) and the Variable Importance in Projection (VIP) scores. All models were good at showing that hyperspectral imaging is a relevant method to predict sugar, total flavonoid, and total anthocyanin contents. The best predictions were obtained from optimal wavelength selection based on β-coefficients for TSS and from VIPs optimal wavelength windows using SNV pre-treatment for total flavonoid and total anthocyanin content. Thus, good prediction models were proposed in order to characterize grapes while reducing the data sets and limit the data storage to enable an industrial use.https://www.mdpi.com/2227-9040/9/4/71hyperspectral imagingphenolicsanthocyanintable grapestotal soluble solidsPLS |
| spellingShingle | Mario Gabrielli Vanessa Lançon-Verdier Pierre Picouet Chantal Maury Hyperspectral Imaging to Characterize Table Grapes Chemosensors hyperspectral imaging phenolics anthocyanin table grapes total soluble solids PLS |
| title | Hyperspectral Imaging to Characterize Table Grapes |
| title_full | Hyperspectral Imaging to Characterize Table Grapes |
| title_fullStr | Hyperspectral Imaging to Characterize Table Grapes |
| title_full_unstemmed | Hyperspectral Imaging to Characterize Table Grapes |
| title_short | Hyperspectral Imaging to Characterize Table Grapes |
| title_sort | hyperspectral imaging to characterize table grapes |
| topic | hyperspectral imaging phenolics anthocyanin table grapes total soluble solids PLS |
| url | https://www.mdpi.com/2227-9040/9/4/71 |
| work_keys_str_mv | AT mariogabrielli hyperspectralimagingtocharacterizetablegrapes AT vanessalanconverdier hyperspectralimagingtocharacterizetablegrapes AT pierrepicouet hyperspectralimagingtocharacterizetablegrapes AT chantalmaury hyperspectralimagingtocharacterizetablegrapes |