The Feasibility of Using Near Infrared Spectroscopy for Rapid Discrimination of Aged Shiitake Mushroom (Lentinula edodes) after Long-Term Storage
Long-term storage can largely degrade the taste and quality of dried shiitake mushroom (Lentinula edodes). This paper aimed at developing a rapid method for discrimination of the regular and aged shiitake by near infrared (NIR) spectroscopic analysis and chemometrics. Regular (n=197) and aged (n=133...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Journal of Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2015/692983 |
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| author | Lu Xu Xian-Shu Fu Chen-Bo Cai Yuan-Bin She |
| author_facet | Lu Xu Xian-Shu Fu Chen-Bo Cai Yuan-Bin She |
| author_sort | Lu Xu |
| collection | DOAJ |
| description | Long-term storage can largely degrade the taste and quality of dried shiitake mushroom (Lentinula edodes). This paper aimed at developing a rapid method for discrimination of the regular and aged shiitake by near infrared (NIR) spectroscopic analysis and chemometrics. Regular (n=197) and aged (n=133) samples of shiitake were collected from six main producing areas in two successive years (2013 and 2014). NIR reflectance spectra (4000–12000 cm−1) were measured with finely ground powders. Different data preprocessing method including smoothing, taking second-order derivatives (D2), and standard normal variate (SNV) were investigated to reduce the unwanted spectral variations. Partial least squares discriminant analysis (PLSDA) and least squares support vector machine (LS-SVM) were used to develop classification models. The results indicate that SNV and D2 can largely enhance the classification accuracy. The best sensitivity, specificity, and accuracy of classification were 0.967, 0.953, and 0.961 obtained by SNV-LS-SVM and 0.933, 0.930, and 0.932 obtained by SNV-PLSDA, respectively. Moreover, the low model complexity and the high accuracy in predicting objects produced in different years demonstrate that the classification models had a good generalization performance. |
| format | Article |
| id | doaj-art-8862b8078abc4f8d9b0815292c4a3ce0 |
| institution | Kabale University |
| issn | 2090-9063 2090-9071 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Chemistry |
| spelling | doaj-art-8862b8078abc4f8d9b0815292c4a3ce02025-02-03T01:26:14ZengWileyJournal of Chemistry2090-90632090-90712015-01-01201510.1155/2015/692983692983The Feasibility of Using Near Infrared Spectroscopy for Rapid Discrimination of Aged Shiitake Mushroom (Lentinula edodes) after Long-Term StorageLu Xu0Xian-Shu Fu1Chen-Bo Cai2Yuan-Bin She3College of Material and Chemical Engineering, Tongren University, Tongren, Guizhou 554300, ChinaCollege of Life Sciences, China Jiliang University, Hangzhou 310018, ChinaCollege of Chemistry and Life Science, Chuxiong Normal University, Chuxiong 675000, ChinaCollege of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, ChinaLong-term storage can largely degrade the taste and quality of dried shiitake mushroom (Lentinula edodes). This paper aimed at developing a rapid method for discrimination of the regular and aged shiitake by near infrared (NIR) spectroscopic analysis and chemometrics. Regular (n=197) and aged (n=133) samples of shiitake were collected from six main producing areas in two successive years (2013 and 2014). NIR reflectance spectra (4000–12000 cm−1) were measured with finely ground powders. Different data preprocessing method including smoothing, taking second-order derivatives (D2), and standard normal variate (SNV) were investigated to reduce the unwanted spectral variations. Partial least squares discriminant analysis (PLSDA) and least squares support vector machine (LS-SVM) were used to develop classification models. The results indicate that SNV and D2 can largely enhance the classification accuracy. The best sensitivity, specificity, and accuracy of classification were 0.967, 0.953, and 0.961 obtained by SNV-LS-SVM and 0.933, 0.930, and 0.932 obtained by SNV-PLSDA, respectively. Moreover, the low model complexity and the high accuracy in predicting objects produced in different years demonstrate that the classification models had a good generalization performance.http://dx.doi.org/10.1155/2015/692983 |
| spellingShingle | Lu Xu Xian-Shu Fu Chen-Bo Cai Yuan-Bin She The Feasibility of Using Near Infrared Spectroscopy for Rapid Discrimination of Aged Shiitake Mushroom (Lentinula edodes) after Long-Term Storage Journal of Chemistry |
| title | The Feasibility of Using Near Infrared Spectroscopy for Rapid Discrimination of Aged Shiitake Mushroom (Lentinula edodes) after Long-Term Storage |
| title_full | The Feasibility of Using Near Infrared Spectroscopy for Rapid Discrimination of Aged Shiitake Mushroom (Lentinula edodes) after Long-Term Storage |
| title_fullStr | The Feasibility of Using Near Infrared Spectroscopy for Rapid Discrimination of Aged Shiitake Mushroom (Lentinula edodes) after Long-Term Storage |
| title_full_unstemmed | The Feasibility of Using Near Infrared Spectroscopy for Rapid Discrimination of Aged Shiitake Mushroom (Lentinula edodes) after Long-Term Storage |
| title_short | The Feasibility of Using Near Infrared Spectroscopy for Rapid Discrimination of Aged Shiitake Mushroom (Lentinula edodes) after Long-Term Storage |
| title_sort | feasibility of using near infrared spectroscopy for rapid discrimination of aged shiitake mushroom lentinula edodes after long term storage |
| url | http://dx.doi.org/10.1155/2015/692983 |
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