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Karamichailidou D, Koletsios S, Alexandridis A. An RBF online learning scheme for non-stationary environments based on fuzzy means and Givens rotations. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Yang RQ, Li JH, Feng HS, Yao YB, Guo XY, Yu SL, Cui Y, Zou HQ, Yan YH. Identification of Nutmeg With Different Mildew Degree Based on HPLC Fingerprint, GC-MS, and E-Nose. Front Nutr 2022; 9:914758. [PMID: 35836589 PMCID: PMC9274197 DOI: 10.3389/fnut.2022.914758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 06/03/2022] [Indexed: 11/13/2022] Open
Abstract
Nutmeg (Myristicae Semen), the so-called Rou-Dou-Kou in Chinese, is one kind of Chinese herbal medicines (CHMs) as well as a globally popular spice. Hence, its stable quality and safe application attract more attention. However, it is highly prone to mildew during storage due to its rich volatile components and fatty oil. Therefore, in this study, an electronic nose (E-nose) was introduced to attempt to reliably and rapidly identify nutmeg samples with different degrees of mildew. Meanwhile, the chemical composition and volatile oil were analyzed using HPLC fingerprint and GC-MS, respectively, which could support and validate the result of E-nose. The results showed that the cluster results of HPLC fingerprint and GC-MS were generally consistent with E-nose, and they all clustered into two categories. Additionally, a discriminant model was established, which divided the samples into three categories: mildew-free, mildew-slight, and mildew, and a high DPR was obtained, which indicates that the E-nose could be a novel and promising approach for the establishment of a quality evaluation system to identify CHMs with different degrees of mildew rapidly, especially to identify early mildew.
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Affiliation(s)
- Rui-Qi Yang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Jia-Hui Li
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Hui-Shang Feng
- Department of Dermatology, Dongzhimen Hospital Beijing University of Chinese Medicine, Beijing, China
| | - Yue-Bao Yao
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Xing-Yu Guo
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Shu-Lin Yu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Yang Cui
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Hui-Qin Zou
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
- *Correspondence: Hui-Qin Zou
| | - Yong-Hong Yan
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
- Yong-Hong Yan
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