PhotoMetrix UVC: A New Smartphone-Based Device for Digital Image Colorimetric Analysis Using PLS Regression

Costa, Adilson Ben da; Helfer, Gilson Augusto; Barbosa, Jorge Luís Victória; Teixeira, Iberê Damé; Santos, Roberta Oliveira; Voss, Mônica; Schlessner, Sandra K.; Barin, Juliano Smanioto

doi:10.21577/0103-5053.20200199

Cited by 9 publications

(9 citation statements)

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“…Table 7 summarizes machine learning applications in material science categorized into the material field, application, description, and the AI and ML mechanism used. Painting materials Colorimetric analysis materials [80] Photometric UVC based on PLS regression for colorimetric analysis materials PLS regression…”

Section: First Shell Particle-clustermentioning

confidence: 99%

A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery

et al. 2022

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Research has become increasingly more interdisciplinary over the past few years. Artificial intelligence and its sub-fields have proven valuable for interdisciplinary research applications, especially physical sciences. Recently, machine learning-based mechanisms have been adapted for material science applications, meeting traditional experiments’ challenges in a time and cost-efficient manner. The scientific community focuses on harnessing varying mechanisms to process big data sets extracted from material databases to derive hidden knowledge that can successfully be employed in technical frameworks of material screening, selection, and recommendation. However, a plethora of underlying aspects of the existing material discovery methods needs to be critically assessed to have a precise and collective analysis that can serve as a baseline for various forthcoming material discovery problems. This study presents a comprehensive survey of state-of-the-art benchmark data sets, detailed pre-processing and analysis, appropriate learning model mechanisms, and simulation techniques for material discovery. We believe that such an in-depth analysis of the mentioned aspects provides promising directions to the young interdisciplinary researchers from computing and material science fields. This study will help devise useful modeling in the materials discovery to positively contribute to the material industry, reducing the manual effort involved in the traditional material discovery. Moreover, we also present a detailed analysis of experimental and computation-based artificial intelligence mechanisms suggested by the existing literature.

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Section: First Shell Particle-clustermentioning

confidence: 99%

A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery

et al. 2022

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“…The image data is taken by the smartphone camera and transformed into RGB histograms (red, green, and blue). 34 The RBG colour model is based on a colour perception hypothesis in which the human eye has various sensitivity peaks located around red, green, and blue. Multivariate analysis (e.g., partial least squares, PLS) could be used in this software to improve Colorimetry's RGB colour system applicability.…”

Section: Introduction Of Sofosbuvir (Phosphorus Atom Containing Drug)mentioning

confidence: 99%

New Smartphone Based Colorimetric Method Development and Validation of Drugs Containing Nitrogen, Phosphorus and Sulphur

Goswami¹,

Kadam²,

Mashru³

2022

J. Drug Delivery Ther.

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Colorimetry is a method for determining the concentration of coloured substances in a solution. The intensity of colour is directly proportional to the concentration of compound being measured. Smartphone based colorimetry has grown in popularity as analytical instruments due to their low cost and ability to collect, store and process data all in one device. In smartphone colorimetry, the camera on the phone serves as a detector. The colorimetric method based on a smartphone and the UV method both are based on the detection of colour intensity. The ammonium metavanadate is having different oxidation states and it produce different colours in its oxidation state. The +5-oxidation state shows yellow colour, +4 oxidation state shows blue colour, +3 oxidation state shows green colour and +2 oxidation state shows purple colour. The ammonium metavanadate reagent is orange red in colour; however, it transforms into green colour complex when it reacts with drugs containing nitrogen, phosphorus and sulphur in its structure. In this article the developed method for all the drugs exhibits good linearity having Correlation coefficient about 0.998(Amoxicillin trihydrate), 0.998 (Silodosin) and for 0.999(Sofosbuvir). With increasing the concentrations of API, the colour intensity increases. All photographs were taken with smartphone and analysed using the photometrix PRO software. This application converts an image to an RGB histogram and regression models are included into the photometrix PRO application. By using Photometrix PRO and UV technique, the percent RSD of for all the three drugs was Using statistical tool i.e two paired test on both procedures for all the three different drugs, the results show that both are equally significant. Keywords: UV spectrophotometry, Smartphone based colorimetry, Photometrix PRO, RGB Histogram

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“…In addition, machine learning tools as Partial Least Regression (PLSR) have been applied for multivariate calibration in soil spectroscopy [20], images [21] and sensor data [22]. These algorithms eliminate variables that do not correlate with the property of interest, such as those that add noise, non-linearities or irrelevant information [23].…”

Section: Introductionmentioning

confidence: 99%

Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology

Helfer

Barbosa

Alves

et al. 2021

JSAN

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The present work proposed a low-cost portable device as an enabling technology for agriculture using multispectral imaging and machine learning in soil texture. Clay is an important factor for the verification and monitoring of soil use due to its fast reaction to chemical and surface changes. The system developed uses the analysis of reflectance in wavebands for clay prediction. The selection of each wavelength is performed through an LED lamp panel. A NoIR microcamera controlled by a Raspberry Pi device is employed to acquire the image and unfold it in RGB histograms. Results showed a good prediction performance with R2 of 0.96, RMSEC of 3.66% and RMSECV of 16.87%. The high portability allows the equipment to be used in a field providing strategic information related to soil sciences.

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PhotoMetrix UVC: A New Smartphone-Based Device for Digital Image Colorimetric Analysis Using PLS Regression

Cited by 9 publications

References 0 publications

A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery

A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery

New Smartphone Based Colorimetric Method Development and Validation of Drugs Containing Nitrogen, Phosphorus and Sulphur

Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology

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