scholarly journals Application of an innovative metabolomics approach to discriminate geographical origin and processing of black pepper by untargeted UHPLC-Q-Orbitrap-HRMS analysis and mid-level data fusion

2021 ◽  
pp. 110722
Author(s):  
Araceli Rivera-Pérez ◽  
Roberto Romero-González ◽  
Antonia Garrido Frenich Conceptualisation
Keyword(s):  
Data Fusion ◽  
Geographical Origin ◽  
Black Pepper ◽  
Level Data

scholarly journals Assessing Geographical Origin of Gentiana Rigescens Using Untargeted Chromatographic Fingerprint, Data Fusion and Chemometrics

Molecules ◽  
2019 ◽  
Vol 24 (14) ◽  
pp. 2562 ◽  
Author(s):  
Shen ◽  
Yu ◽  
Wang
Keyword(s):  
Data Fusion ◽  
Geographical Origin ◽  
Data Sets ◽  
Classification Models ◽  
Hplc Fingerprint ◽  
Low Level ◽  
Level Data ◽  
Stems And Leaves ◽  
Gentiana Rigescens ◽  
Fingerprint Data

Gentiana rigescens Franchet, which is famous for its bitter properties, is a traditional drug of chronic hepatitis and important raw materials for the pharmaceutical industry in China. In the study, high-performance liquid chromatography (HPLC), coupled with diode array detector (DAD) and chemometrics, were used to investigate the chemical geographical variation of G. rigescens and to classify medicinal materials, according to their grown latitudes. The chromatographic fingerprints of 280 individuals and 840 samples from rhizomes, stems, and leaves of four different latitude areas were recorded and analyzed for tracing the geographical origin of medicinal materials. At first, HPLC fingerprints of underground and aerial parts were generated while using reversed-phase liquid chromatography. After the preliminary data exploration, two supervised pattern recognition techniques, random forest (RF) and orthogonal partial least-squares discriminant analysis (OPLS-DA), were applied to the three HPLC fingerprint data sets of rhizomes, stems, and leaves, respectively. Furthermore, fingerprint data sets of aerial and underground parts were separately processed and joined while using two data fusion strategies (“low-level” and “mid-level”). The results showed that classification models that are based OPLS-DA were more efficient than RF models. The classification models using low-level data fusion method built showed considerably good recognition and prediction abilities (the accuracy is higher than 99% and sensibility, specificity, Matthews correlation coefficient, and efficiency range from 0.95 to 1.00). Low-level data fusion strategy combined with OPLS-DA could provide the best discrimination result. In summary, this study explored the latitude variation of phytochemical of G. rigescens and developed a reliable and accurate identification method for G. rigescens that were grown at different latitudes based on untargeted HPLC fingerprint, data fusion, and chemometrics. The study results are meaningful for authentication and the quality control of Chinese medicinal materials.

scholarly journals Data Fusion of Fourier Transform Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopies to Identify Geographical Origin of Wild Paris polyphylla var. yunnanensis

Molecules ◽  
2019 ◽  
Vol 24 (14) ◽  
pp. 2559 ◽  
Author(s):  
Pei ◽  
Zuo ◽  
Zhang ◽  
Wang
Keyword(s):  
Fourier Transform ◽  
Data Fusion ◽  
Near Infrared ◽  
Geographical Origin ◽  
Spectroscopic Techniques ◽  
Mid Infrared ◽  
Level Data ◽  
Paris Polyphylla ◽  
High Level ◽  
Geographical Origin Traceability

Origin traceability is important for controlling the effect of Chinese medicinal materials and Chinese patent medicines. Paris polyphylla var. yunnanensis is widely distributed and well-known all over the world. In our study, two spectroscopic techniques (Fourier transform mid-infrared (FT-MIR) and near-infrared (NIR)) were applied for the geographical origin traceability of 196 wild P. yunnanensis samples combined with low-, mid-, and high-level data fusion strategies. Partial least squares discriminant analysis (PLS-DA) and random forest (RF) were used to establish classification models. Feature variables extraction (principal component analysis—PCA) and important variables selection models (recursive feature elimination and Boruta) were applied for geographical origin traceability, while the classification ability of models with the former model is better than with the latter. FT-MIR spectra are considered to contribute more than NIR spectra. Besides, the result of high-level data fusion based on principal components (PCs) feature variables extraction is satisfactory with an accuracy of 100%. Hence, data fusion of FT-MIR and NIR signals can effectively identify the geographical origin of wild P. yunnanensis.

A Decision-Level Data Fusion Approach to Surface Roughness Prediction

2019 ◽  
Author(s):  
Yupeng Wei ◽  
Dazhong Wu ◽  
Janis Terpenny
Keyword(s):  
Surface Roughness ◽  
Data Fusion ◽  
Process Monitoring ◽  
Predictive Models ◽  
Sensor Data ◽  
Fused Deposition ◽  
Decision Level ◽  
Level Data ◽  
Fusion Approach

Abstract To improve the quality of additively manufactured parts, it is crucial to develop real-time process monitoring systems and data-driven predictive models. While various sensor- and image-based process monitoring methods have been developed to improve the quality of additively manufactured parts, very limited research has been conducted to predict surface roughness. To fill this gap, this paper presents a decision-level data fusion approach to predicting surface roughness in the fused deposition modeling (FDM) process. The predictive models are trained by the random forests method using multiple sensor signals. A decision-level data fusion method is introduced to integrate sensor data sources. Experimental results have shown that the decision-level data fusion approach can predict surface roughness in FDM with high accuracy.

A Threat Assessment Method Based on Hierarchies and Modules

2014 ◽  
Vol 18 (1) ◽  
pp. 93-99
Author(s):  
Fang Deng ◽  
◽  
Xinan Liu ◽  
Zhihong Peng ◽  
Jie Chen
Keyword(s):  
Data Fusion ◽  
Threat Assessment ◽  
Assessment Method ◽  
Fusion Technology ◽  
Low Level ◽  
Information Classification ◽  
Level Data ◽  
High Level

With the development of low-level data fusion technology, threat assessment, which is a part of high-level data fusion, is recognized by an increasing numbers of people. However, the method to solve the problem of threat assessment for various kinds of targets and attacks is unknown. Hence, a threat assessment method is proposed in this paper to solve this problem. This method includes tertiary assessments: information classification, reorganization, and summary. In the tertiary assessments model, various threats with multi-class targets and attacks can be comprehensively assessed. A case study with specific algorithms and scenarios is shown to prove the validity and rationality of this method.

Error Level Data Fusion Algorithm in WSN

2011 ◽  
Vol 403-408 ◽  
pp. 5303-5307
Author(s):  
Yan Kong Yan ◽  
Fei Fei Qian ◽  
Wen Yi Shen ◽  
Ning Ning Qin
Keyword(s):  
Data Collection ◽  
Data Fusion ◽  
Experimental Results ◽  
Classification Error ◽  
Virtual Node ◽  
Polynomial Fitting ◽  
Fusion Algorithm ◽  
Error Level ◽  
Level Data ◽  
Target Set

After analysis of the performance of data fusion based on virtual node algorithm (VNB-DF), design of a classification error (EG) the virtual node data fusion algorithm. The algorithm's accuracy depending on the target, set the error level, expressed through the polynomial fitting coefficients of a range of monitoring the distribution of data in memory to generate a virtual node. Experimental results show that compares with the data obtained with the cluster mean and VNB-DF algorithm, the algorithm greatly improves the accuracy of data collection, applications in types of environmental can show a good performance.

High Level Data Fusion on a Multimodal Interactive Applications Platform

2008 ◽  
Author(s):  
Olga Vybornova ◽  
Hildeberto Mendona ◽  
Jean-Yves Lionel Lawson ◽  
Benoit Macq
Keyword(s):  
Data Fusion ◽  
Interactive Applications ◽  
Level Data ◽  
High Level
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