Data handling in data fusion: Methodologies and applications

Azcarate, Silvana M.; Ríos-Reina, Rocío; Amigo, José Manuel; Goicoechea, Héctor C.

doi:10.1016/j.trac.2021.116355

Cited by 90 publications

(34 citation statements)

References 62 publications

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“…In this respect, we can differentiate between zero-, first- and second-order structures. Zeroth-order data contains one response per sample, first-order data describes sample properties using multiple variables (a vector), whereas second-order data includes a matrix for each sample [ 12 ].…”

Section: Data Types In the Pharmaceutical Industrymentioning

confidence: 99%

“…Several review papers are available on DF, focusing on the chemometric/data processing or the application side of data integration. Azcarate et al published a review on DF, focusing on the structure of data originating from different sources along with DF strategies [ 12 ]. Mishra et al reviewed the application of multi-block analysis methods for multi-source data integration, highlighting the advantages, disadvantages, and particularities of different techniques [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

et al. 2022

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The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

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Section: Data Types In the Pharmaceutical Industrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

et al. 2022

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“…A recent advance is given by the ability of combining fingerprints obtained from different chromatographic systems to compose a supra-fingerprint. For this purpose, data fusion methods are applied, within which three levels are defined depending upon whether the original fingerprints are used for the fusion model (low level) or the previously extracted information (medium and high levels) . Detailed information on the peculiarities of each of the methods mentioned in this section is again beyond the scope of this perspective and should be consulted in the specialized literature that has been referenced.…”

Section: Food Quality and Food Authentication Focusmentioning

confidence: 99%

Chromatographic Fingerprinting and Food Identity/Quality: Potentials and Challenges

Cuadros-Rodrı́guez

Ortega-Gavilán

Martín‐Torres

et al. 2021

J. Agric. Food Chem.

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Chromatograms are a valuable source of information about the chemical composition of the food being analyzed. Sometimes, this information is not explicit and appears in a hidden or not obvious way. Thus, the use of chemometric tools and data-mining methods to extract it is required. The fingerprint provided by a chromatogram offers the possibility to perform both identity and quality testing of foodstuffs. This perspective is aimed at providing an updated opinion of chromatographic fingerprinting methodology in the field of food authentication. Furthermore, the limitations, its absence in official analytical methods, and the future directions of this methodology are discussed.

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“…In general, there are three different levels of information fusion, including low-level fusion (fusion of original data), middle-level fusion (fusion of features extracted by a feature extractor), and high-level fusion (decision fusion) [44][45][46]. However, most existing information fusion models are built based on a two-stage training procedure, requiring individual feature extractors and classifiers.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses

et al. 2022

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Herbicides and heavy metals are hazardous substances of environmental pollution, resulting in plant stress and harming humans and animals. Identification of stress types can help trace stress sources, manage plant growth, and improve stress-resistant breeding. In this research, hyperspectral imaging (HSI) and chlorophyll fluorescence imaging (Chl-FI) were adopted to identify the rice plants under two types of herbicide stresses (butachlor (DCA) and quinclorac (ELK)) and two types of heavy metal stresses (cadmium (Cd) and copper (Cu)). Visible/near-infrared spectra of leaves (L-VIS/NIR) and stems (S-VIS/NIR) extracted from HSI and chlorophyll fluorescence kinetic curves of leaves (L-Chl-FKC) and stems (S-Chl-FKC) extracted from Chl-FI were fused to establish the models to detect the stress of the hazardous substances. Novel end-to-end deep fusion models were proposed for low-level, middle-level, and high-level information fusion to improve identification accuracy. Results showed that the high-level fusion-based convolutional neural network (CNN) models reached the highest detection accuracy (97.7%), outperforming the models using a single data source (<94.7%). Furthermore, the proposed end-to-end deep fusion models required a much simpler training procedure than the conventional two-stage deep learning fusion. This research provided an efficient alternative for plant stress phenotyping, including identifying plant stresses caused by hazardous substances of environmental pollution.

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Data handling in data fusion: Methodologies and applications

Cited by 90 publications

References 62 publications

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

Chromatographic Fingerprinting and Food Identity/Quality: Potentials and Challenges

End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses

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