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 Determination of the Geographical Origin of Hazelnuts (Corylus avellana L.) by Near-Infrared Spectroscopy (NIR) and a Low-Level Fusion with Nuclear Magnetic Resonance (NMR)

2021 ◽  
Author(s):  
Navid Shakiba ◽  
Annika Gerdes ◽  
Nathalie Holz ◽  
Sören Wenck ◽  
René Bachmann ◽  
...  
Keyword(s):  
Data Fusion ◽  
Near Infrared ◽  
Geographical Origin ◽  
Nir Spectroscopy ◽  
Test Accuracy ◽  
Classification Models ◽  
Food Fraud ◽  
Low Level ◽  
The Individual

Fourier-transform near-infrared (FT-NIR) spectroscopy was used to determine the geographical origin of 233 hazelnut samples of various varieties from five different countries (Germany, France, Georgia, Italy, Turkey). The experimental determination of the geographical origin of hazelnuts is important, because there are usually large price differences between the producer countries and thus a risk of food fraud that should not be underestimated. The present work is a feasibility study using a low-cost method, as high-field NMR and UPLC-QTOF-MS have already been used for this question. Sample sets were split with repeated nested cross validation and an ensemble of discriminant classifiers with random subspaces was used to build the classification models. By using a preprocessing strategy consisting of multiplicative scatter correction, bucketing and the mean averaging of five measured spectra per sample, a test accuracy of 90.6 ± 3.9% was achieved, which rivals results obtained with much more expensive infrastructure. The application of the feature selection approach surrogate minimal depth showed that the successful classification is mainly caused by protein signals. In addition, a low-level data fusion of the NIR and NMR data was performed to assess how well the two methods complement each other. The data fusion was compared to a complementary approach, where the classification results based on the individual NIR and NMR models were jointly examined. The data fusion performed better than the individual methods with a test accuracy of 96.6 ± 2.8%. A comparison of the outliers in all classification models shows conspicuities in always the same samples, indicating that robust classification models are obtained.

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.

Calibration and low-level data fusion algorithms for a parallel 2D/3D-camera

2011 ◽  
Vol 12 (1) ◽  
pp. 37-47 ◽  
Author(s):  
T. Hanning ◽  
A. Lasaruk ◽  
T. Tatschke
Keyword(s):  
Data Fusion ◽  
Low Level ◽  
Level Data ◽  
3D Camera

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.

scholarly journals Data Fusion for the Prediction of Elemental Concentrations in Polymetallic Sulphide Ore Using Mid-Wave Infrared and Long-Wave Infrared Reflectance Data

Minerals ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 235 ◽  
Author(s):  
Feven Desta ◽  
Mike Buxton ◽  
Jeroen Jansen
Keyword(s):  
Data Fusion ◽  
Multivariate Data ◽  
Infrared Reflectance ◽  
Low Level ◽  
Elemental Concentrations ◽  
Long Wave ◽  
Level Data ◽  
Long Wave Infrared ◽  
Plsr Model ◽  
Reflectance Data

The increasing availability of complex multivariate data yielded by sensor technologies permits qualitative and quantitative data analysis for material characterization. Multivariate data are hard to understand by visual inspection and intuition. Thus, data-driven models are required to derive study-specific insights from large datasets. In the present study, a partial least squares regression (PLSR) model was used for the prediction of elemental concentrations using the mineralogical techniques mid-wave infrared (MWIR) and long-wave infrared (LWIR) combined with data fusion approaches. In achieving the study objectives, the usability of the individual MWIR and LWIR datasets for the prediction of the concentration of elements in a polymetallic sulphide deposit was assessed, and the results were compared with the outputs of low- and mid-level data fusion methods. Prior to low-level data fusion implementation, data filtering techniques were applied to the MWIR and LWIR datasets. The pre-processed data were concatenated and a PLSR model was developed using the fused data. The mid-level data fusion was implemented by extracting features using principal component analysis (PCA) scores. As the models were applied to the MWIR, LWIR, and fused datasets, an improved prediction was achieved using the low-level data fusion approach. Overall, the acquired results indicate that the MWIR data can be used to reliably predict a combined Pb–Zn concentration, whereas LWIR data has a good correlation with the Fe concentration. The proposed approach could be extended for generating indicative element concentrations in polymetallic sulphide deposits in real-time using infrared reflectance data. Thus, it is beneficial in providing elemental concentration insights in mining operations.

scholarly journals Geographical Authentication of Macrohyporia cocos by a Data Fusion Method Combining Ultra-Fast Liquid Chromatography and Fourier Transform Infrared Spectroscopy

Molecules ◽  
2019 ◽  
Vol 24 (7) ◽  
pp. 1320 ◽  
Author(s):  
Qin-Qin Wang ◽  
Heng-Yu Huang ◽  
Yuan-Zhong Wang
Keyword(s):  
Fourier Transform ◽  
Data Fusion ◽  
Geographical Origin ◽  
Fourier Transform Infrared ◽  
Fusion Model ◽  
Edible Fungi ◽  
Fusion Strategy ◽  
Low Level ◽  
Geographical Authentication ◽  
Level Fusion

Macrohyporia cocos is a medicinal and edible fungi, which is consumed widely. The epidermis and inner part of its sclerotium are used separately. M. cocos quality is influenced by geographical origins, so an effective and accurate geographical authentication method is required. Liquid chromatograms at 242 nm and 210 nm (LC242 and LC210) and Fourier transform infrared (FTIR) spectra of two parts were applied to authenticate the geographical origin of cultivated M. cocos combined with low and mid-level data fusion strategies, and partial least squares discriminant analysis. Data pretreatment involved correlation optimized warping and second derivative. The results showed that the potential of the chromatographic fingerprint was greater than that of five triterpene acids contents. LC242-FTIR low-level fusion took full advantage of information synergy and showed good performance. Further, the predictive ability of the FTIR low-level fusion model of two parts was satisfactory. The performance of the low-level fusion strategy preceded those of the single technique and mid-level fusion strategy. The inner parts were more suitable for origin identification than the epidermis. This study proved the feasibility of the data fusion of chromatograms and spectra, and the data fusion of different parts for the accurate authentication of geographical origin. This method is meaningful for the quality control of food and the protection of geographical indication products.

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