Multivariate Image Analysis in Mineral Processing

Some special topics in multivariate image analysis

Multi-way methods in image analysis—relationships and applications

Considering the influence of reagent adjustment in different flotation bank on the final production index and the difficulty of establishing an effective mathematical model, a coordinated optimization method for dosage reagent based on key characteristics variation tendency and case-based reasoning is proposed. On the basis of the expert reagent regulation method in antimony flotation process, the reagent dosage pre-setting model of the roughing–scavenging bank is constructed based on case-based reasoning. Then, the sensitivity index is used to calculate the key features of reagent dosage. The reagent dosage compensation model is constructed based on the variation tendency of the key features in the roughing and scavenging process. At last, the prediction model is used to finish the classification and discriminant analysis. The simulation results and industrial experiment in antimony flotation process show that the proposed method reduces fluctuation of the tailings indicators and the cost of reagent dosage. It can lay a foundation for optimizing the whole process of flotation.

We recently established a high-speed, label-free, spectral imaging method based on stimulated Raman scattering (SRS). This method enables examination of cellular features within relatively short periods, thus enabling new imaging applications in pathology. Previously, we reported on label-free visualization of mouse tissue using SRS spectral microscopy combined with multivariate image analysis, but the feasibility of applying this approach to diseased tissues with diverse morphology and irregular chemical species has not been examined. We, therefore, assessed acetaminophen-induced liver injury to evaluate the potential use of Raman spectral microscopy for visualizing histopathologic specimens. Acetaminophen-overdosed mouse liver was prepared and the pathologic changes including centrilobular necrosis were confirmed. Multi-colored images were reconstructed through principal component analysis (PCA) of a multi-band SRS dataset, which provided rich information compared with a monochrome single-band SRS dataset. A wide view of the multi-colored principal component (PC) images showed the distribution of cellular constituents, which was similar to that observed by fat staining. In addition, different types of cells in liver parenchyma were also demonstrated. In conclusion, the combination of SRS spectral microscopy and PCA has the potential to reveal both the morphological and chemical features of specimens and therefore has potential utility in diagnostic pathology.

Multivariate image analysis (MIA) aims to extract useful information from multivariate image structures such as hypercubes. Multivariate image regression is particularly useful for the development of calibration models for quantitative analysis of hypercubes. The key step in multivariate calibration is model selection; this includes the selection of method (e.g. partial least squares or ridge regression) and setting of parameters within the selected method (e.g. number of latent variables or ridge parameter). Sub-optimal selection of method and or parameters generally leads to poor predictive performance on future samples. While conventional methods such as cross-validation are indispensible in this task, the inherent nature of hyperspectral images may also be exploited. Application of a given calibration model to each pixel in a hypercube results in a prediction map whose features may be used to evaluate the model itself. This paper explores the application of various image statistics extracted from prediction images as model performance metrics for MIA.

Reagent dosage inference based on graph convolutional memory perception network for zinc roughing flotation

Application of partial least squares and radial basis function neural networks in multivariate imaging analysis-quantitative structure activity relationship: Study of cyclin dependent kinase 4 inhibitors

MIA and NIR Chemical Imaging for pharmaceutical product characterization

Comparative study of different approaches for multivariate image analysis in HPTLC fingerprinting of natural products such as plant resin

An Improved Multivariate Image Analysis Method for Quality Control of Nanofiber Membranes

In situ particle size estimation for crystallization processes by multivariate image analysis

Results of the analysis of scientific, technical and patent literature show that while having a great variety of constructive implementation of the proposed catchers there are no efficient and sufficiently reliable devices for catching conveyor belts at its reverse motion which are relatively simple in design. This is proved by practical activity of enterprises involved in extraction and processing of commercial minerals and other industries. A new design of a conveyor belt catcher that meets most requirements for the given equipment has been developed at the Institute of Mining, Ural of Branch of the Russian Academy of Sciences and then it has been tested under industrial conditions. The design makes provision for retention of the conveyor belt at its reverse motion beyond free edges from transported material. Catching devices are installed on both sides of the belt. Braking action of the belt at the reverse motion occurs due to its friction interaction with a catcher friction shoe located above the belt, and an eccentric mounted on the frame under the belt. A friction shoe is made with a concave curved surface facing the belt. The paper presents a calculation methodology of main parameters for the proposed design of catcher conveyor belts that permits to determine a force catching a conveyor belt down and for every braking period distance which has been passed by the belt, value of its compression, value of braking force, braking time and acceleration of belt motion, time and length of the distance passed by the belt to its full stop, parameters of a catcher and supporting structures, and other parameters. The paper also provides results of calculations for main design parameters of catching devices with an inclined conveyor having a belt width of 1400 mm for two catcher design versions: with location of an eccentric under the belt, and a brake shoe over the belt (option I) and with location of the eccentric over the conveyor belt, and the brake friction shoe under the belt (option II).

Solid-liquid interfaces in mineral processing

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d25845e56"> <b>Introduction</b> <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d25845e61">Mineral separation processes are highly dependent on the physicochemical properties of the mineral surfaces. In this presentation, I will show some applications of using ToF-SIMS analysis to examine the mineral’s surface chemistry and how this information correlates with other properties that relate to process behaviour. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d25845e63"> <b>Overview of minerals processing</b> <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d25845e68">Mineral processing consists of two main stages: (1) crushing and grinding the rocks to a small size to liberate mineral particles and (2) froth flotation, where the valuable minerals are physically separated from the waste material based on their differences in surface properties. In flotation the valuable minerals are selectively separated from waste by attachment to air bubbles ( <a class="xref-link" href="#fg001">Fig. 1</a>). For this attachment to occur, the mineral surfaces need to be sufficiently hydrophobic (i.e. water repellent). Chemical reagents are used in practice to target the mineral surfaces and change their surface properties to achieve mineral separation. Thus, the <i>surface chemistry of mineral particles is key in the selective attachment of the valuable minerals to air bubbles </i> in the flotation process. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d25845e76"> <b>The importance of surface science in minerals processing</b> <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d25845e81">The surface chemistry of minerals determine their hydrophobicity. However, the surface of minerals is heterogenous due to their association with other minerals in the ores. In addition to mineral composition, mineral surfaces are reactive and oxidise in the presence of oxygen and water, thus, changing the surface speciation. Thus, it is very important to be able to examine the minerals surfaces and understand their surface chemistry ( <a class="xref-link" href="#fg002">Fig. 2</a>). The hydrophobicity of mineral surfaces (measured through the contact angle) is directly related to surface chemistry. <div class="fig panel" id="fg001"> <a class="named-anchor" id="fg001"> <!-- named anchor --> </a> <div class="figure-container so-text-align-c"> <img alt="" class="figure" src="/document_file/e92b3f25-b482-4c07-86b9-0a419921e7ea/ScienceOpen/image/237_6724b6cbf2038-Susana+Brito+e+Abreu+AMPC+13_fig1.jpg"/> </div> <div class="panel-content"> <div class="label">Fig. 1</div> <div class="caption" id="d25845e89"> <p class="first" dir="auto" id="d25845e90">Illustration of the flotation process: (a) Flotation cell (b) Magnified view of an air bubble carrying mineral particles. </div> </div> </div> <div class="fig panel" id="fg002"> <a class="named-anchor" id="fg002"> <!-- named anchor --> </a> <div class="figure-container so-text-align-c"> <img alt="" class="figure" src="/document_file/e92b3f25-b482-4c07-86b9-0a419921e7ea/ScienceOpen/image/237_6724b6cbf2038-Susana+Brito+e+Abreu+AMPC+13_fig2.jpg"/> </div> <div class="panel-content"> <div class="label">Fig. 2</div> <div class="caption" id="d25845e97"> <p class="first" dir="auto" id="d25845e98">ToF-SIMS image of chalcopyrite particles showing the distribution of Cu (red), Mg (blue) and Si (green) on the surface. Scale bar = 100 µm. </div> </div> </div>

Integrated use of ultrasound imaging and multivariate image analysis for detecting bone fragments in poultry meat.