Rapid Determination of Pachymic Acid Content by Near-Infrared Spectroscopy
In this paper, a rapid model for the determination of pachymic acid content in Poria was established by partial least squares (PLS) regression and near-infrared spectroscopy (NIR). During the research, a total of 108 batches of Poria samples from different producing regions were used, while their co...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2022/9746414 |
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| _version_ | 1832550642659360768 |
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| author | Jie Lu Changqin Li Lijun Liu Wangjing Chai Yabin Hou Changyang Ma |
| author_facet | Jie Lu Changqin Li Lijun Liu Wangjing Chai Yabin Hou Changyang Ma |
| author_sort | Jie Lu |
| collection | DOAJ |
| description | In this paper, a rapid model for the determination of pachymic acid content in Poria was established by partial least squares (PLS) regression and near-infrared spectroscopy (NIR). During the research, a total of 108 batches of Poria samples from different producing regions were used, while their corresponding pachymic acid contents by high-performance liquid chromatography (HPLC) were adopted as reference. These samples were divided randomly into calibration sets for model establishment and validation sets for model validation. The test results from the calibration set showed that the best preprocessing method of the NIR spectra model was the standard normal variate (SNV) + second derivatives (SD), and the most suitable number of principal factors was 9. In this model, the coefficient of determination of the calibration set (rc2) and validation set (rv2) was 0.915 and 0.917, respectively. Meanwhile, the root mean square error of calibration (RMSEC) and the root mean square error of validation (RMSEP) with the calibration set were 0.051 mg/g and 0.054 mg/g, respectively. These results indicated this model could rapidly and reliably predict the pachymic acid content in Poria and increase the determination efficiency of pachymic acid in Poria. This is conducive to promote the development of industry. |
| format | Article |
| id | doaj-art-91d37939414d404bb7c02627b79c309a |
| institution | Kabale University |
| issn | 1745-4557 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-91d37939414d404bb7c02627b79c309a2025-02-03T06:06:22ZengWileyJournal of Food Quality1745-45572022-01-01202210.1155/2022/9746414Rapid Determination of Pachymic Acid Content by Near-Infrared SpectroscopyJie Lu0Changqin Li1Lijun Liu2Wangjing Chai3Yabin Hou4Changyang Ma5National R & D Center for Edible Fungus Processing TechnologyNational R & D Center for Edible Fungus Processing TechnologyNational R & D Center for Edible Fungus Processing TechnologyNational R & D Center for Edible Fungus Processing TechnologyNational R & D Center for Edible Fungus Processing TechnologyNational R & D Center for Edible Fungus Processing TechnologyIn this paper, a rapid model for the determination of pachymic acid content in Poria was established by partial least squares (PLS) regression and near-infrared spectroscopy (NIR). During the research, a total of 108 batches of Poria samples from different producing regions were used, while their corresponding pachymic acid contents by high-performance liquid chromatography (HPLC) were adopted as reference. These samples were divided randomly into calibration sets for model establishment and validation sets for model validation. The test results from the calibration set showed that the best preprocessing method of the NIR spectra model was the standard normal variate (SNV) + second derivatives (SD), and the most suitable number of principal factors was 9. In this model, the coefficient of determination of the calibration set (rc2) and validation set (rv2) was 0.915 and 0.917, respectively. Meanwhile, the root mean square error of calibration (RMSEC) and the root mean square error of validation (RMSEP) with the calibration set were 0.051 mg/g and 0.054 mg/g, respectively. These results indicated this model could rapidly and reliably predict the pachymic acid content in Poria and increase the determination efficiency of pachymic acid in Poria. This is conducive to promote the development of industry.http://dx.doi.org/10.1155/2022/9746414 |
| spellingShingle | Jie Lu Changqin Li Lijun Liu Wangjing Chai Yabin Hou Changyang Ma Rapid Determination of Pachymic Acid Content by Near-Infrared Spectroscopy Journal of Food Quality |
| title | Rapid Determination of Pachymic Acid Content by Near-Infrared Spectroscopy |
| title_full | Rapid Determination of Pachymic Acid Content by Near-Infrared Spectroscopy |
| title_fullStr | Rapid Determination of Pachymic Acid Content by Near-Infrared Spectroscopy |
| title_full_unstemmed | Rapid Determination of Pachymic Acid Content by Near-Infrared Spectroscopy |
| title_short | Rapid Determination of Pachymic Acid Content by Near-Infrared Spectroscopy |
| title_sort | rapid determination of pachymic acid content by near infrared spectroscopy |
| url | http://dx.doi.org/10.1155/2022/9746414 |
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