Non-Destructive Determination of Starch Gelatinization, Head Rice Yield, and Aroma Components in Parboiled Rice by Raman and NIR Spectroscopy
Vibrational spectroscopy, including Raman and near-infrared techniques, enables the non-destructive evaluation of starch gelatinization, head rice yield, and aroma-active volatile compounds in parboiled rice subjected to varying soaking and drying conditions. Raman and NIR spectra were collected for...
| Autores principales: | , , , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/20.500.11939/9083 https://www.mdpi.com/1420-3049/30/14/2938 |
| _version_ | 1855492620889882624 |
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| author | Taghinezhad, Ebrahim Szumny, Antoni Figiel, Adam Ehsan, Sheidaee Sylwester, Mazurek Latifi-Amoghin, Meysam Bagherpour, Hossein Blasco, Jose |
| author_browse | Bagherpour, Hossein Blasco, Jose Ehsan, Sheidaee Figiel, Adam Latifi-Amoghin, Meysam Sylwester, Mazurek Szumny, Antoni Taghinezhad, Ebrahim |
| author_facet | Taghinezhad, Ebrahim Szumny, Antoni Figiel, Adam Ehsan, Sheidaee Sylwester, Mazurek Latifi-Amoghin, Meysam Bagherpour, Hossein Blasco, Jose |
| author_sort | Taghinezhad, Ebrahim |
| collection | ReDivia |
| description | Vibrational spectroscopy, including Raman and near-infrared techniques, enables the non-destructive evaluation of starch gelatinization, head rice yield, and aroma-active volatile compounds in parboiled rice subjected to varying soaking and drying conditions. Raman and NIR spectra were collected for rice samples processed under different conditions and
integrated with reference analyses to develop and validate partial least squares regression and artificial neural network models. The optimized PLSR model demonstrated strong predictive performance, with R2 values of 0.9406 and 0.9365 for SG and HRY, respectively, and residual predictive deviations of 3.98 and 3.75 using Raman effective wavelengths. ANN models reached R2 values of 0.97 for both SG and HRY, with RPDs exceeding 4.2 using NIR effective wavelengths. In the aroma compound analysis, p-Cymene exhibited the highest predictive accuracy, with R2 values of 0.9916 for calibration, and 0.9814 for cross-validation. Other volatiles, such as 1-Octen-3-ol, nonanal, benzaldehyde, and limonene, demonstrated high predictive reliability (R2 ≥ 0.93; RPD > 3.0). Conversely, farnesene, menthol, and menthone showed poor predictability (R2 < 0.15; RPD < 0.4). Principal component analysis revealed that the first principal component explained 90% of the total variance in the Raman dataset and 71% in the NIR dataset. Hotelling’s T2 analysis identifies influential outliers and enhances model robustness. Optimal processing conditions for achieving maximum HRY and SG values were determined at 65 ºC soaking for 180 min, followed by drying at 70 ºC. This study underscores the potential of integrating vibrational spectroscopy with machine learning techniques and targeted wavelength selection for the high-throughput, accurate, and scalable quality evaluation of parboiled rice. |
| format | Artículo |
| id | ReDivia9083 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | ReDivia90832025-07-28T08:04:09Z Non-Destructive Determination of Starch Gelatinization, Head Rice Yield, and Aroma Components in Parboiled Rice by Raman and NIR Spectroscopy Taghinezhad, Ebrahim Szumny, Antoni Figiel, Adam Ehsan, Sheidaee Sylwester, Mazurek Latifi-Amoghin, Meysam Bagherpour, Hossein Blasco, Jose Parboiled rice Raman spectroscopy Starch gelatinization Head rice yield N01 Agricultural engineering Aroma compounds Vibrational spectroscopy, including Raman and near-infrared techniques, enables the non-destructive evaluation of starch gelatinization, head rice yield, and aroma-active volatile compounds in parboiled rice subjected to varying soaking and drying conditions. Raman and NIR spectra were collected for rice samples processed under different conditions and integrated with reference analyses to develop and validate partial least squares regression and artificial neural network models. The optimized PLSR model demonstrated strong predictive performance, with R2 values of 0.9406 and 0.9365 for SG and HRY, respectively, and residual predictive deviations of 3.98 and 3.75 using Raman effective wavelengths. ANN models reached R2 values of 0.97 for both SG and HRY, with RPDs exceeding 4.2 using NIR effective wavelengths. In the aroma compound analysis, p-Cymene exhibited the highest predictive accuracy, with R2 values of 0.9916 for calibration, and 0.9814 for cross-validation. Other volatiles, such as 1-Octen-3-ol, nonanal, benzaldehyde, and limonene, demonstrated high predictive reliability (R2 ≥ 0.93; RPD > 3.0). Conversely, farnesene, menthol, and menthone showed poor predictability (R2 < 0.15; RPD < 0.4). Principal component analysis revealed that the first principal component explained 90% of the total variance in the Raman dataset and 71% in the NIR dataset. Hotelling’s T2 analysis identifies influential outliers and enhances model robustness. Optimal processing conditions for achieving maximum HRY and SG values were determined at 65 ºC soaking for 180 min, followed by drying at 70 ºC. This study underscores the potential of integrating vibrational spectroscopy with machine learning techniques and targeted wavelength selection for the high-throughput, accurate, and scalable quality evaluation of parboiled rice. 2025-07-28T08:01:58Z 2025-07-28T08:01:58Z 2025 publishedVersion Taghinezhad E, Szumny A, Figiel A, Sheidaee E, Mazurek S, Latifi-Amoghin M, Bagherpour H, Pachura N, Blasco J (2025) Non-destructive determination of starch gelatinization, head rice yield, and aroma components in parboiled rice by Raman and NIR spectroscopy. Molecules, 30, 2938. 1420-3049 https://hdl.handle.net/20.500.11939/9083 10.3390/molecules30142938 https://www.mdpi.com/1420-3049/30/14/2938 en This project was financed by the NAWA—Polish National Agency for Academic Exchange under the Ulam NAWA Programme (project no. BPN/ULM/2021/1/00231) and the National Centre for Research and Development (NCBR) (project no. POIR.01.01.01-00-0682/21). Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ openAccess MDPI electronico |
| spellingShingle | Parboiled rice Raman spectroscopy Starch gelatinization Head rice yield N01 Agricultural engineering Aroma compounds Taghinezhad, Ebrahim Szumny, Antoni Figiel, Adam Ehsan, Sheidaee Sylwester, Mazurek Latifi-Amoghin, Meysam Bagherpour, Hossein Blasco, Jose Non-Destructive Determination of Starch Gelatinization, Head Rice Yield, and Aroma Components in Parboiled Rice by Raman and NIR Spectroscopy |
| title | Non-Destructive Determination of Starch Gelatinization, Head Rice Yield, and Aroma Components in Parboiled Rice by Raman and NIR Spectroscopy |
| title_full | Non-Destructive Determination of Starch Gelatinization, Head Rice Yield, and Aroma Components in Parboiled Rice by Raman and NIR Spectroscopy |
| title_fullStr | Non-Destructive Determination of Starch Gelatinization, Head Rice Yield, and Aroma Components in Parboiled Rice by Raman and NIR Spectroscopy |
| title_full_unstemmed | Non-Destructive Determination of Starch Gelatinization, Head Rice Yield, and Aroma Components in Parboiled Rice by Raman and NIR Spectroscopy |
| title_short | Non-Destructive Determination of Starch Gelatinization, Head Rice Yield, and Aroma Components in Parboiled Rice by Raman and NIR Spectroscopy |
| title_sort | non destructive determination of starch gelatinization head rice yield and aroma components in parboiled rice by raman and nir spectroscopy |
| topic | Parboiled rice Raman spectroscopy Starch gelatinization Head rice yield N01 Agricultural engineering Aroma compounds |
| url | https://hdl.handle.net/20.500.11939/9083 https://www.mdpi.com/1420-3049/30/14/2938 |
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