Computer-vision based second-order (kinetic-color) data generation: arsenic quantitation in natural waters

NIR-Reflective and Hydrophobic Bio-Inspired Nano-Holed Configurations on Titanium Alloy

“…The a-Ti6Al4V sample image, Figure 1a, was processed, generating a second-order data, and followed by a multivariate curve resolution alternating least squares methodology. 22 The frequency histograms of standard RGB color levels of three selected ROIs presented almost the same configuration, demonstrating that no macroscopic observable disturbances are generated, please refer to Figure S10. After inspection of HR-SEM microphotographs, Figure 1b,c, no disruptions were observed on the nanoscale surface either.…”

“…A pre-inspection of a-Ti6Al4V nano-holed surface uniformity was performed on the basis of digital microscope images, Figure a, and then was confirmed by the analysis of HR-SEM images, Figure b–d. The a-Ti6Al4V sample image, Figure a, was processed, generating a second-order data, and followed by a multivariate curve resolution alternating least squares methodology . The frequency histograms of standard RGB color levels of three selected ROIs presented almost the same configuration, demonstrating that no macroscopic observable disturbances are generated, please refer to Figure S10.…”

Open Source Automated Flow Analysis Instrument for Detecting Arsenic in Water

“…Beaker, reaction and measurement chambers were built in the lab, like the PCB and the assembly of the electronics together with different pieces specially design and built with a 3D printer for the peristaltic pump and several other elements. Other methods proposed in the literature to determine arsenic can be found in [3] , [4] , [5] .…”

Region of interest selection in heterogeneous digital image: Wine age prediction by comprehensive two-dimensional gas chromatography

“…This makes it possible to acquire a vector ( x 1 × J ) of information by samples resulting in a matrix ( X I × J ) for a set of I samples. Subsequently, the matrices are used as input for multivariate classification ( Fernandes et al, 2023 ) and/or multivariate calibration models ( Belén et al, 2020 ; Vallese et al, 2021 ). Although less common, there are also DI applications into multiway data acquisition for both calibration and classification ( Belén et al, 2020 ; Vallese et al, 2021 ).…”