Monitoring roughness and edge shape on semiconductors through multiresolution and multivariate image analysis

Inclusion of shape parameters increases the accuracy of 3D models for microplastics mass quantification

As microplastics may bring about adverse effects on living organisms, it is important to establish more precise quantification approaches to better understand their dynamics. One method to determine the concentration of microplastics is to estimate their mass using three-dimensional (3D) models, but its accuracy is not well known. In this study, we evaluated the shape of the particles and verified the accuracy of a 3D model-based mass estimation using samples from a tidal flat facing Tokyo Bay. The particle shape evaluation suggested that the microplastics were flat and irregular in shape; based on these data, we created two types of models to estimate their mass. As a result, an accuracy of mass estimation by our model was higher than other models that consider the slenderness and flatness of particles. The optimization of mass estimation methods based on 3D models may improve the reliability of microplastic evaluation in monitoring studies.

Simultaneous determination of some common food dyes in commercial products by digital image analysis

A simple and relatively fast image-analysis method using digital images, obtained with a flatbed scanner, has been described. The method was used for the simultaneous determination of four common food dyes, namely, carmoisine, brilliant blue, sunset yellow, and quinoline yellow, in binary mixtures in commercial products without a need for any prior separation steps. The results obtained were validated against a standard high-performance liquid chromatography method and a good agreement was obtained. The parameters affecting the experimental results were optimized. Under the optimal conditions, the method provided acceptable linear ranges (20–250 mg/L) with correlation coefficients higher than 0.998, suitable precision (relative standard deviation ≤ 4.5%), and limits of detection between 4.82 and 8.05 mg/L.

Multivariate wavelet texture analysis for pharmaceutical solid product characterization

The application of multivariate wavelet texture analysis (MWTA) is presented and discussed as it is applied to three different types of pharmaceutical materials: (a) tablet cores, (b) wet granules and (c) controlled release tablets. The application of MWTA is initially proposed as a quantitative replacement to the human visual judgment of the textural appearance of the different materials. In all cases, the metrics obtained with MWTA agree with visual assessment on the progression of textural features such as erosion and surface roughness. This work further demonstrates that MWTA also represents a useful tool to increase the understanding of the manufacturing process, as it provides diagnostics to relate process parameters with textural features of the material that are difficult or costly to measure otherwise (such as granule size for wet material or surface appearance for a controlled release product). MWTA is also presented as a potential tool for real-time release for those cases where the textural features can be proven to provide accurate enough predictions of the final product performance; as shown here with the obtained prediction of dissolution from the controlled release tablet using the texture of the product as an input.