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Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...

Extraction: Partition and Distribution Coefficients

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...

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A shell dataset, for shell features extraction and recognition.

Qi Zhang¹, Jianhang Zhou¹, Jing He²

¹Department of Computer and Information Science, University of Macau, Taipa, Macau, China.

Scientific Data

|October 24, 2019

Summary

This summary is machine-generated.

This study introduces a novel machine learning approach for shell identification, utilizing a large dataset of shell images. The developed method accurately extracts shell features for automatic recognition, advancing conchology research.

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Area of Science:

Malacology
Computer Science
Data Science

Background:

Shells are diverse and challenging to identify manually due to numerous species.
Existing methods lack automated shell recognition capabilities.
Machine learning offers potential for efficient shell identification.

Purpose of the Study:

To develop and present a comprehensive dataset for shell recognition.
To extract and validate key shell features (color, shape, texture) using machine learning.
To establish a baseline for automatic shell identification and encourage further research.

Main Methods:

Creation of a large shell dataset (29,622 samples, 59,244 images across 7,894 species).
Extraction of shell features: color, shape, and texture.
Validation of features using k-nearest neighbors (k-NN) and random forest classifiers.

Main Results:

Successfully generated and validated shell features using two distinct machine learning classifiers.
Demonstrated the feasibility of automated shell recognition through feature extraction.
The dataset provides a robust resource for advancing shell identification techniques.

Conclusions:

The presented shell dataset is a valuable resource for automatic shell recognition and conchology.
Extracted shell features can aid in developing and optimizing machine learning techniques.
Further research is encouraged to enhance shell recognition performance using this dataset.