Discrimination of Boletaceae mushrooms based on data fusion of FT-IR and ICP-AES combined with SVM
文献类型： 外文期刊
作者： Yao, Sen 1 ; Li, JieQing 1 ; Li, Tao 3 ; Liu, HongGao 1 ; Wang, YuanZhong 1 ;
作者机构： 1.Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming, Yunnan, Peoples R China
2.Yunnan Acad Agr Sci, Inst Med Plants, Kunming, Yunnan, Peoples R China
3.Yuxi Normal Univ, Coll Resources & Environm, Yuxi, Peoples R China
关键词： Boletaceae mushrooms; Data fusion; Discrimination; Quality control; Support vector machine (SVM)
期刊名称：INTERNATIONAL JOURNAL OF FOOD PROPERTIES （ 影响因子：2.727； 五年影响因子：2.938 ）
ISSN： 1094-2912
年卷期： 2018 年 21 卷 1 期
页码：
收录情况： SCI
摘要： In this study, the individual and data fusion of Fourier transform infrared (FT-IR) spectroscopy and inductively coupled plasma atomic emission spectrometry (ICP-AES) were used for the discrimination of five species of Boletaceae mushrooms with the aid of support vector machine (SVM). First, the original FT-IR spectra of 230 samples with different species were preprocessed and optimized by second derivative (2D), Savitzky-Golay filter (15:1) and standardized normal variate. Second, the datasets of FT-IR spectra and ICP-AES were integrated, and the low-level data fusion strategy was used to classify different species mushrooms. Third, the latent variables of elements concentration and FT-IR spectra were extracted by partial least square discriminant analysis and two datasets were fused into a new matrix. Finally, the classification models were established by SVM. Compared with single spectroscopic technique, the mid-level data fusion strategy can provide better result. Especially, the accuracy of correct classification of samples in calibration and test sets were 100.00% and 98.68%, respectively. The results demonstrated that the mid-level data fusion of FT-IR and ICP-AES can provide higher synergic effect for the discrimination of different species Boletaceae mushrooms, which could be benefited for the further authentication and quality control of edible mushrooms.
- 相关文献
作者其他论文 更多>>
-
Rapid determination of geographical authenticity of Gastrodia elata f. glauca using Fourier transform infrared spectroscopy and deep learning
作者：Deng, Guangmei;Li, Jieqing;Deng, Guangmei;Wang, Yuanzhong;Liu, Honggao
关键词：Gastrodia elata f. glauca; Fourier transform infrared spectroscopy; Deep learning; Data driven version of soft independent; modeling of class analogy
-
ResD-Net: A model for rapid prediction of antioxidant activity in gentian root using FT-IR spectroscopy
作者：Li, Xiaokun;Zeng, Pan;Wu, Xunxun;Yang, Xintong;Liu, Peizhong;Diao, Yong;Lin, Jingcang;Liu, Peizhong;Wang, Yuanzhong
关键词：Antioxidant activity; FT-IR; Gentian; Deep Learning; Chemometrics
-
Development of machine learning models using multi-source data for geographical traceability and content prediction of Eucommia ulmoides leaves
作者：Zhang, Yanying;Zhang, Yanying;Zhu, Xinyan;Wang, Yuanzhong
关键词：Machine learning; Eucommia ulmoides leaves; Geographical traceability; Content prediction; Quality evaluation
-
ATR-FTIR Spectroscopy Preprocessing Technique Selection for Identification of Geographical Origins of Gastrodia elata Blume
作者：Liu, Hong;Li, Jieqing;Liu, Hong;Wang, Yuanzhong;Liu, Honggao
关键词：ATR-FTIR spectroscopy; data preprocessing; DD-SIMCA; Gastrodia elata Blume; GBM; PLS-DA; SVM
-
The method based on ATR-FTIR spectroscopy combined with feature variable selection for the boletus species and origins identification
作者：Ji, Zhiyi;Li, Jieqing;Ji, Zhiyi;Wang, Yuanzhong;Liu, Honggao
关键词：feature variable selection; food safety; mid-infrared spectroscopy; species identification; traceability; wild boletus
-
Effect of drying temperature on composition of edible mushrooms: Characterization and assessment via HS-GC-MS and IR spectral based volatile profiling and chemometrics
作者：Zheng, Chuanmao;Li, Jieqing;Zheng, Chuanmao;Wang, Yuanzhong;Liu, Honggao
关键词：Boletus bainiugan; HS-SPME-GC-MS; VOCs; 2DCOS; Chemometrics; Quality estimation
-
A fast method for predicting adenosine content in porcini mushrooms using Fourier transform near-infrared spectroscopy combined with regression model
作者：Deng, Guangmei;Li, Jieqing;Deng, Guangmei;Wang, Yuanzhong;Liu, Honggao
关键词：Fourier transform near-infrared spectroscopy; Porcini mushrooms; Adenosine; Partial least squares regression
