Enhanced near infrared spectral data to improve prediction accuracy in determining quality parameters of intact mango
Presented manuscript aimed to describes enhanced near infrared spectral dataset used to improve prediction performances of near infrared models in determining quality parameters of intact mango fruits. The two mentioned quality parameters are total acidity (TA) and vitamin C which corresponds to mai...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-06-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340920304650 |
| _version_ | 1818293166406631424 |
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| author | Rita Hayati Agus Arip Munawar F. Fachruddin |
| author_facet | Rita Hayati Agus Arip Munawar F. Fachruddin |
| author_sort | Rita Hayati |
| collection | DOAJ |
| description | Presented manuscript aimed to describes enhanced near infrared spectral dataset used to improve prediction performances of near infrared models in determining quality parameters of intact mango fruits. The two mentioned quality parameters are total acidity (TA) and vitamin C which corresponds to main inner attributes of fruits. Near infrared (NIR) spectra data were acquired and recorded as absorbance spectral data in wavelength range from 1000 to 2500 nm. These data were then enhanced by means of several algorithms like multiplicative scatter correction (MSC), baseline linear correction (BLC) and combination of them (MSC+BLC). Prediction models, used to determine TA and vitamin C were established using most common approach: partial least square regression (PLS) based on raw and enhanced spectral data respectively. Prediction performances can be evaluated based on prediction accuracy and robustness, by looking statistical indicators presented as coefficient of determination (R2) and correlation (r), root mean square error (RMSE) and residual predictive deviation (RPD). Enhanced NIR spectral dataset can be employed as a rapid, effective and non-destructive method to determine inner quality parameters of intact fruits. |
| first_indexed | 2024-12-13T03:11:32Z |
| format | Article |
| id | doaj.art-1e107aaae25f4257b2b7a4905ba8c65b |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-12-13T03:11:32Z |
| publishDate | 2020-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-1e107aaae25f4257b2b7a4905ba8c65b2022-12-22T00:01:36ZengElsevierData in Brief2352-34092020-06-0130105571Enhanced near infrared spectral data to improve prediction accuracy in determining quality parameters of intact mangoRita Hayati0Agus Arip Munawar1F. Fachruddin2Department of Agro-technology, Syiah Kuala University, Banda Aceh, Indonesia; Corresponding authors.Department of Agricultural Engineering, Syiah Kuala University, Banda Aceh, Indonesia; Agricultural Mechanization Research Centre, Syiah Kuala University, Banda Aceh, Indonesia; Corresponding authors.Department of Agricultural Engineering, Syiah Kuala University, Banda Aceh, IndonesiaPresented manuscript aimed to describes enhanced near infrared spectral dataset used to improve prediction performances of near infrared models in determining quality parameters of intact mango fruits. The two mentioned quality parameters are total acidity (TA) and vitamin C which corresponds to main inner attributes of fruits. Near infrared (NIR) spectra data were acquired and recorded as absorbance spectral data in wavelength range from 1000 to 2500 nm. These data were then enhanced by means of several algorithms like multiplicative scatter correction (MSC), baseline linear correction (BLC) and combination of them (MSC+BLC). Prediction models, used to determine TA and vitamin C were established using most common approach: partial least square regression (PLS) based on raw and enhanced spectral data respectively. Prediction performances can be evaluated based on prediction accuracy and robustness, by looking statistical indicators presented as coefficient of determination (R2) and correlation (r), root mean square error (RMSE) and residual predictive deviation (RPD). Enhanced NIR spectral dataset can be employed as a rapid, effective and non-destructive method to determine inner quality parameters of intact fruits.http://www.sciencedirect.com/science/article/pii/S2352340920304650DatasetsEnhancementSpectraNIRSMango |
| spellingShingle | Rita Hayati Agus Arip Munawar F. Fachruddin Enhanced near infrared spectral data to improve prediction accuracy in determining quality parameters of intact mango Data in Brief Datasets Enhancement Spectra NIRS Mango |
| title | Enhanced near infrared spectral data to improve prediction accuracy in determining quality parameters of intact mango |
| title_full | Enhanced near infrared spectral data to improve prediction accuracy in determining quality parameters of intact mango |
| title_fullStr | Enhanced near infrared spectral data to improve prediction accuracy in determining quality parameters of intact mango |
| title_full_unstemmed | Enhanced near infrared spectral data to improve prediction accuracy in determining quality parameters of intact mango |
| title_short | Enhanced near infrared spectral data to improve prediction accuracy in determining quality parameters of intact mango |
| title_sort | enhanced near infrared spectral data to improve prediction accuracy in determining quality parameters of intact mango |
| topic | Datasets Enhancement Spectra NIRS Mango |
| url | http://www.sciencedirect.com/science/article/pii/S2352340920304650 |
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