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...
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2025-07-01
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| author | Ebrahim Taghinezhad Antoni Szumny Adam Figiel Ehsan Sheidaee Sylwester Mazurek Meysam Latifi-Amoghin Hossein Bagherpour Natalia Pachura Jose Blasco |
| author_facet | Ebrahim Taghinezhad Antoni Szumny Adam Figiel Ehsan Sheidaee Sylwester Mazurek Meysam Latifi-Amoghin Hossein Bagherpour Natalia Pachura Jose Blasco |
| author_sort | Ebrahim Taghinezhad |
| collection | DOAJ |
| 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 R<sup>2</sup> 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 R<sup>2</sup> values of 0.97 for both SG and HRY, with RPDs exceeding 4.2 using NIR effective wavelengths. In the aroma compound analysis, <i>p</i>-Cymene exhibited the highest predictive accuracy, with R<sup>2</sup> 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 (R<sup>2</sup> ≥ 0.93; RPD > 3.0). Conversely, farnesene, menthol, and menthone showed poor predictability (R<sup>2</sup> < 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 T<sup>2</sup> analysis identifies influential outliers and enhances model robustness. Optimal processing conditions for achieving maximum HRY and SG values were determined at 65 °C soaking for 180 min, followed by drying at 70 °C. 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 | Article |
| id | doaj-art-e85b7e12d77b47cc8cf7101307ec69eb |
| institution | DOAJ |
| issn | 1420-3049 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Molecules |
| spelling | doaj-art-e85b7e12d77b47cc8cf7101307ec69eb2025-08-20T02:47:07ZengMDPI AGMolecules1420-30492025-07-013014293810.3390/molecules30142938Non-Destructive Determination of Starch Gelatinization, Head Rice Yield, and Aroma Components in Parboiled Rice by Raman and NIR SpectroscopyEbrahim Taghinezhad0Antoni Szumny1Adam Figiel2Ehsan Sheidaee3Sylwester Mazurek4Meysam Latifi-Amoghin5Hossein Bagherpour6Natalia Pachura7Jose Blasco8Biosystems Engineering Department, Faculty of Agriculture, Tarbiat Modares University, Tehran 14117-13116, IranDepartment of Food Chemistry and Biocatalysis, Wroclaw University of Environmental and Life Sciences, 50-375 Wrocław, PolandInstitute of Agricultural Engineering, Wroclaw University of Environmental and Life Sciences, 51-630 Wrocław, PolandBiosystems Engineering Department, Faculty of Agriculture, Tarbiat Modares University, Tehran 14117-13116, IranDepartment of Chemistry, University of Wrocław, 50-383 Wrocław, PolandDepartment of Biosystems Engineering, College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, IranDepartment of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan 65178-33131, IranDepartment of Environmental Hygiene and Animal Welfare, Wroclaw University of Environmental and Life Sciences, 51-630 Wrocław, PolandCentro de Agroingeniería, Instituto Valenciano de Investigaciones Agrarias, 46113 Valencia, SpainVibrational 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 R<sup>2</sup> 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 R<sup>2</sup> values of 0.97 for both SG and HRY, with RPDs exceeding 4.2 using NIR effective wavelengths. In the aroma compound analysis, <i>p</i>-Cymene exhibited the highest predictive accuracy, with R<sup>2</sup> 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 (R<sup>2</sup> ≥ 0.93; RPD > 3.0). Conversely, farnesene, menthol, and menthone showed poor predictability (R<sup>2</sup> < 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 T<sup>2</sup> analysis identifies influential outliers and enhances model robustness. Optimal processing conditions for achieving maximum HRY and SG values were determined at 65 °C soaking for 180 min, followed by drying at 70 °C. 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.https://www.mdpi.com/1420-3049/30/14/2938parboiled riceRaman spectroscopystarch gelatinizationhead rice yieldaroma components |
| spellingShingle | Ebrahim Taghinezhad Antoni Szumny Adam Figiel Ehsan Sheidaee Sylwester Mazurek Meysam Latifi-Amoghin Hossein Bagherpour Natalia Pachura Jose Blasco Non-Destructive Determination of Starch Gelatinization, Head Rice Yield, and Aroma Components in Parboiled Rice by Raman and NIR Spectroscopy Molecules parboiled rice Raman spectroscopy starch gelatinization head rice yield aroma components |
| 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 aroma components |
| url | https://www.mdpi.com/1420-3049/30/14/2938 |
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