The Rapid Determination of Three Toxic Ginkgolic Acids in the Decolorized Process of Ginkgo Ketone Ester Based on Raman Spectroscopy and ResNeXt50 Deep Neural Network
The decolorization process plays a pivotal role in refining Ginkgo ketone ester by primarily eliminating ginkgolic acids, a toxic component. Presently, the conventional testing method involves sending samples for analysis, causing delays that impact formulation production. Hence, the development of...
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MDPI AG
2023-12-01
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| Series: | Chemosensors |
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| Online Access: | https://www.mdpi.com/2227-9040/12/1/6 |
| _version_ | 1797344399873540096 |
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| author | Qing Liu Meifang Jiang Jun Wang Dandan Wang Yi Tao |
| author_facet | Qing Liu Meifang Jiang Jun Wang Dandan Wang Yi Tao |
| author_sort | Qing Liu |
| collection | DOAJ |
| description | The decolorization process plays a pivotal role in refining Ginkgo ketone ester by primarily eliminating ginkgolic acids, a toxic component. Presently, the conventional testing method involves sending samples for analysis, causing delays that impact formulation production. Hence, the development of a rapid process control method becomes imperative. This study introduces a swift detection approach for three ginkgolic acids during Ginkgo ketone ester’s decolorization. Initially, an ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method assessed ginkgolic acid C13:0, ginkgolic acid C15:1, and ginkgolic acid C17:1 concentrations in 91 decolorized solution samples, establishing reference values. Subsequently, using a portable Raman spectrometer, Raman spectra of the decolorized liquid within the 3200–200 cm<sup>−1</sup> wavelength range were collected. Ultimately, employing partial least squares regression (PLSR) and ResNeXt50 deep learning algorithms, two quantitative calibration models correlated the ginkgolic acid content to Raman spectral data. Both models exhibited high predictive accuracy, with the ResNeXt50 model demonstrating superior performance. The prediction set correlation coefficients (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>p</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula>) for ginkgolic acid C13:0, ginkgolic acid C15:1, and ginkgolic acid C17:1 were 0.9962, 0.9971, and 0.9974, respectively, with root mean square error of prediction (RMSEP) values of 0.0144, 0.0130, and 0.0122 μg/mL. In contrast, the PLSR model yielded <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>p</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> values of 0.9862, 0.9839, and 0.9480, with RMSEP values of 0.0273, 0.0305, and 0.0545 μg/mL for the three ginkgolic acids. The ResNeXt50 model not only showcased higher precision but also enhanced interpretability, as analyzed through gradient-weighted class activation mapping (Grad-CAM). The integration of Raman spectroscopy and the ResNeXt50 quantitative calibration model furnishes a real-time and precise approach to monitor ginkgolic acid content in the decolorized solution during Ginkgo ketone ester preparation. This significant advancement establishes a robust framework for implementing quality control measures in the decolorization process. |
| first_indexed | 2024-03-08T11:01:50Z |
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| id | doaj.art-1d6a871542d44f74b219c910fbc25a36 |
| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-03-08T11:01:50Z |
| publishDate | 2023-12-01 |
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| spelling | doaj.art-1d6a871542d44f74b219c910fbc25a362024-01-26T15:45:58ZengMDPI AGChemosensors2227-90402023-12-01121610.3390/chemosensors12010006The Rapid Determination of Three Toxic Ginkgolic Acids in the Decolorized Process of Ginkgo Ketone Ester Based on Raman Spectroscopy and ResNeXt50 Deep Neural NetworkQing Liu0Meifang Jiang1Jun Wang2Dandan Wang3Yi Tao4College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310032, ChinaShanghai Shangyao Xingling Technology Pharmaceutical Co., Ltd., Shanghai 200120, ChinaShanghai Shangyao Xingling Technology Pharmaceutical Co., Ltd., Shanghai 200120, ChinaShanghai Shangyao Xingling Technology Pharmaceutical Co., Ltd., Shanghai 200120, ChinaCollege of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310032, ChinaThe decolorization process plays a pivotal role in refining Ginkgo ketone ester by primarily eliminating ginkgolic acids, a toxic component. Presently, the conventional testing method involves sending samples for analysis, causing delays that impact formulation production. Hence, the development of a rapid process control method becomes imperative. This study introduces a swift detection approach for three ginkgolic acids during Ginkgo ketone ester’s decolorization. Initially, an ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method assessed ginkgolic acid C13:0, ginkgolic acid C15:1, and ginkgolic acid C17:1 concentrations in 91 decolorized solution samples, establishing reference values. Subsequently, using a portable Raman spectrometer, Raman spectra of the decolorized liquid within the 3200–200 cm<sup>−1</sup> wavelength range were collected. Ultimately, employing partial least squares regression (PLSR) and ResNeXt50 deep learning algorithms, two quantitative calibration models correlated the ginkgolic acid content to Raman spectral data. Both models exhibited high predictive accuracy, with the ResNeXt50 model demonstrating superior performance. The prediction set correlation coefficients (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>p</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula>) for ginkgolic acid C13:0, ginkgolic acid C15:1, and ginkgolic acid C17:1 were 0.9962, 0.9971, and 0.9974, respectively, with root mean square error of prediction (RMSEP) values of 0.0144, 0.0130, and 0.0122 μg/mL. In contrast, the PLSR model yielded <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>p</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> values of 0.9862, 0.9839, and 0.9480, with RMSEP values of 0.0273, 0.0305, and 0.0545 μg/mL for the three ginkgolic acids. The ResNeXt50 model not only showcased higher precision but also enhanced interpretability, as analyzed through gradient-weighted class activation mapping (Grad-CAM). The integration of Raman spectroscopy and the ResNeXt50 quantitative calibration model furnishes a real-time and precise approach to monitor ginkgolic acid content in the decolorized solution during Ginkgo ketone ester preparation. This significant advancement establishes a robust framework for implementing quality control measures in the decolorization process.https://www.mdpi.com/2227-9040/12/1/6Raman spectroscopy<i>Ginkgo ketone ester</i>decolorizationResNeXt50 networkquantitative calibration models |
| spellingShingle | Qing Liu Meifang Jiang Jun Wang Dandan Wang Yi Tao The Rapid Determination of Three Toxic Ginkgolic Acids in the Decolorized Process of Ginkgo Ketone Ester Based on Raman Spectroscopy and ResNeXt50 Deep Neural Network Chemosensors Raman spectroscopy <i>Ginkgo ketone ester</i> decolorization ResNeXt50 network quantitative calibration models |
| title | The Rapid Determination of Three Toxic Ginkgolic Acids in the Decolorized Process of Ginkgo Ketone Ester Based on Raman Spectroscopy and ResNeXt50 Deep Neural Network |
| title_full | The Rapid Determination of Three Toxic Ginkgolic Acids in the Decolorized Process of Ginkgo Ketone Ester Based on Raman Spectroscopy and ResNeXt50 Deep Neural Network |
| title_fullStr | The Rapid Determination of Three Toxic Ginkgolic Acids in the Decolorized Process of Ginkgo Ketone Ester Based on Raman Spectroscopy and ResNeXt50 Deep Neural Network |
| title_full_unstemmed | The Rapid Determination of Three Toxic Ginkgolic Acids in the Decolorized Process of Ginkgo Ketone Ester Based on Raman Spectroscopy and ResNeXt50 Deep Neural Network |
| title_short | The Rapid Determination of Three Toxic Ginkgolic Acids in the Decolorized Process of Ginkgo Ketone Ester Based on Raman Spectroscopy and ResNeXt50 Deep Neural Network |
| title_sort | rapid determination of three toxic ginkgolic acids in the decolorized process of ginkgo ketone ester based on raman spectroscopy and resnext50 deep neural network |
| topic | Raman spectroscopy <i>Ginkgo ketone ester</i> decolorization ResNeXt50 network quantitative calibration models |
| url | https://www.mdpi.com/2227-9040/12/1/6 |
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