Synergistic strategy for the geographical traceability of wild Boletus tomentipes by means of data fusion analysis
文献类型： 外文期刊
作者： Li, Yun 1 ; Wang, Yuanzhong 1 ;
作者机构： 1.Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China
关键词： Boletus tomentipes; Geographical traceability; FT-MIR; ICP-AES; Data fusion; Quality control
期刊名称：MICROCHEMICAL JOURNAL （ 影响因子：4.821； 五年影响因子：4.364 ）
ISSN： 0026-265X
年卷期： 2018 年 140 卷
页码：
收录情况： SCI
摘要： Due to the worldwide importance and public interest concerning to the food quality and safety, there is a growing trend in the geographical trace of food products with various techniques. The purpose of the present study is to evaluate integrated information as an effective strategy for the geographical traceability of wild Boletus tomentipes. To this goal, 76 fruiting bodies were collected, and two mushroom parts (pileus and stipe) were analyzed by Fourier transform-mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES). Five related data matrices (FT-MIR_P, FT-MIR_S, ELE_P, ELE_S and ELE_Q(C/S)) were fused at low- and mid-level, support vector machine (SVM) and random forest (RF) classification algorithms combined with data matrices were used for authenticating the geographical origin. The results of multivariate statistical analysis indicate that data fusion, taking advantage of the information synergy, shows the better classification performance than individual decision making. Also, the comparison of categorized result between two fusion levels suggests that, mid-level data fusion is more stable than low-level among different type models, which could be used as a reliable method for the geographical authentication purposes of wild porcini mushrooms. (C) 2018 Elsevier B.V. All rights reserved.
- 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
