Rapid identification of the authenticity of iron rod yam by in-situ mass spectrometry based on random forest algorithm
ObjectiveTo establish a fast and nondestructive analysis method for identifying iron rod yam.MethodsAtmospheric pressure chemical ionization mass spectrometry (APCI-MS) was employed to detect the chemical constituents of iron rod yam(TG) and non-iron rod yam (FTG) from different origins under ambien...
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The Editorial Office of Food and Machinery
2024-11-01
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| Series: | Shipin yu jixie |
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| Online Access: | http://www.ifoodmm.com/spyjx/article/abstract/20241107?st=article_issue |
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| author | ZHONG Hengyan CHEN Chun OUYANG Yongzhong ZHOU Lin GUO Weiqing |
| author_facet | ZHONG Hengyan CHEN Chun OUYANG Yongzhong ZHOU Lin GUO Weiqing |
| author_sort | ZHONG Hengyan |
| collection | DOAJ |
| description | ObjectiveTo establish a fast and nondestructive analysis method for identifying iron rod yam.MethodsAtmospheric pressure chemical ionization mass spectrometry (APCI-MS) was employed to detect the chemical constituents of iron rod yam(TG) and non-iron rod yam (FTG) from different origins under ambient temperature and pressure, With 200 sets of data collected from each type of TG and FTG, and a total of 3 600 mass spectrometry data points were obtained. Subsequently, the initial level of the mass spectrometry data obtained was analyzed using Principal Component Analysis (PCA) and the random forest (RF) algorithm. Pattern recognition analysis established a model to differentiate between TG and FTG based on their chemical compositions.ResultsThe difference between the first-level mass spectra obtained by HS-APCI-MS was obvious between TG samples and FTG samples. The cumulative variance contribution plot of the principal components showed that the first seven principal components accounted for 85.63% (≥85%) of the variance. The accuracy of the training set and detection set reached 100% when the number of decision trees was 25. HS-APCI-MS combined with RF algorithm had a significant identification effect on TG, and the classification effect of RF was superior to that of PCA.ConclusionAtmospheric pressure chemical ionization mass spectrometry, combined with the RF algorithm, can rapidly and non-destructively identify TG and FTG, providing a new technical method for authenticating TG. |
| format | Article |
| id | doaj-art-02eab35de1c84f04848ca2b2640e9c74 |
| institution | DOAJ |
| issn | 1003-5788 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | The Editorial Office of Food and Machinery |
| record_format | Article |
| series | Shipin yu jixie |
| spelling | doaj-art-02eab35de1c84f04848ca2b2640e9c742025-08-20T02:52:52ZengThe Editorial Office of Food and MachineryShipin yu jixie1003-57882024-11-014011475310.13652/j.spjx.1003.5788.2024.801301003-5788（2024）11-0047-07Rapid identification of the authenticity of iron rod yam by in-situ mass spectrometry based on random forest algorithmZHONG Hengyan0CHEN Chun1OUYANG Yongzhong2ZHOU Lin3GUO Weiqing4School of Foshan University, Foshan, Guangdong528000, ChinaSchool of Foshan University, Foshan, Guangdong528000, ChinaSchool of Foshan University, Foshan, Guangdong528000, ChinaPharmacy Department, Guangdong Yifang Pharmaceutical Co., Ltd., Foshan, Guangdong528244, ChinaSchool of Foshan University, Foshan, Guangdong528000, ChinaObjectiveTo establish a fast and nondestructive analysis method for identifying iron rod yam.MethodsAtmospheric pressure chemical ionization mass spectrometry (APCI-MS) was employed to detect the chemical constituents of iron rod yam(TG) and non-iron rod yam (FTG) from different origins under ambient temperature and pressure, With 200 sets of data collected from each type of TG and FTG, and a total of 3 600 mass spectrometry data points were obtained. Subsequently, the initial level of the mass spectrometry data obtained was analyzed using Principal Component Analysis (PCA) and the random forest (RF) algorithm. Pattern recognition analysis established a model to differentiate between TG and FTG based on their chemical compositions.ResultsThe difference between the first-level mass spectra obtained by HS-APCI-MS was obvious between TG samples and FTG samples. The cumulative variance contribution plot of the principal components showed that the first seven principal components accounted for 85.63% (≥85%) of the variance. The accuracy of the training set and detection set reached 100% when the number of decision trees was 25. HS-APCI-MS combined with RF algorithm had a significant identification effect on TG, and the classification effect of RF was superior to that of PCA.ConclusionAtmospheric pressure chemical ionization mass spectrometry, combined with the RF algorithm, can rapidly and non-destructively identify TG and FTG, providing a new technical method for authenticating TG.http://www.ifoodmm.com/spyjx/article/abstract/20241107?st=article_issueiron rod yamatmospheric chemical ionization sourceprincipal component analysisrandom forest algorithmidentification |
| spellingShingle | ZHONG Hengyan CHEN Chun OUYANG Yongzhong ZHOU Lin GUO Weiqing Rapid identification of the authenticity of iron rod yam by in-situ mass spectrometry based on random forest algorithm Shipin yu jixie iron rod yam atmospheric chemical ionization source principal component analysis random forest algorithm identification |
| title | Rapid identification of the authenticity of iron rod yam by in-situ mass spectrometry based on random forest algorithm |
| title_full | Rapid identification of the authenticity of iron rod yam by in-situ mass spectrometry based on random forest algorithm |
| title_fullStr | Rapid identification of the authenticity of iron rod yam by in-situ mass spectrometry based on random forest algorithm |
| title_full_unstemmed | Rapid identification of the authenticity of iron rod yam by in-situ mass spectrometry based on random forest algorithm |
| title_short | Rapid identification of the authenticity of iron rod yam by in-situ mass spectrometry based on random forest algorithm |
| title_sort | rapid identification of the authenticity of iron rod yam by in situ mass spectrometry based on random forest algorithm |
| topic | iron rod yam atmospheric chemical ionization source principal component analysis random forest algorithm identification |
| url | http://www.ifoodmm.com/spyjx/article/abstract/20241107?st=article_issue |
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