Machine-Learning-Assisted Analysis of Colorimetric Assays on Paper Analytical Devices

Abstract

Paper-based analytical devices (PADs) employing colorimetric detection and smartphone images have gained wider acceptance in a variety of measurement applications. PADs are primarily meant to be used in field settings where assay and imaging conditions greatly vary, resulting in less accurate results. Recently, machine-learning (ML)-assisted models have been used in image analysis. We evaluated a combination of four ML models—logistic regression, support vector machine (SVM), random forest, and artificial neural network (ANN)—as well as three image color spaces, RGB, HSV, and LAB, for their ability to accurately predict analyte concentrations. We used images of PADs taken at varying lighting conditions, with different cameras and users for food color and enzyme inhibition assays to create training and test datasets. The prediction accuracy was higher for food color than enzyme inhibition assays in most of the ML models and color space combinations. All models better predicted coarse-level classifications than fine-grained concentration classes. ML models using the sample color along with a reference color increased the models’ ability to predict the result in which the reference color may have partially factored out the variation in ambient assay and imaging conditions. The best concentration class prediction accuracy obtained for food color was 0.966 when using the ANN model and LAB color space. The accuracy for enzyme inhibition assay was 0.908 when using the SVM model and LAB color space. Appropriate models and color space combinations can be useful to analyze large numbers of samples on PADs as a powerful low-cost quick field-testing tool.

Polysaccharide Biomacromolecule Film Integrated with Lycium ruthenicum Anthocyanins for Food Freshness Monitoring

“…When pH was 3–7, the composite films’ color varied from purple to grayish blue; when pH increased to 8–11, the corresponding color changed from light green to tan. Usually, the CIELab color model was used to characterize color quantitatively, it consisted of three elements, one element was luminance ( L ), and the other two elements were chroma a and chroma b [ 26 ] ; chroma a mainly included a dark green section, gray section, and bright pink section, and chroma b mainly contained light blue section, gray section, and yellow section, these sections mainly corresponded to low, medium, and high lightness values, respectively. Figure 5B,C showed the hunter a ‐value and hunter b ‐value of HPS/CMC/Gly/LRA films with different pH.…”

Machine Learning-Based Analytical Systems: Food Forensics

“…A smartphone image-based colorimetric detection is a feasible and convincing field-based approach in contrast to established approaches such as optical techniques. In order to determine which machine learning model could most effectively estimate the quantification of analytes on paper devices, Khanal et al examined four machine learning modelsLogistic regression, SVM, random forest, and ANN with three color models (RGB, HSV, and LAB) . They used pictures of paper-based devices taken at different light conditions, with various camera and enzyme inhibition assays, to make training and test data sets.…”

“…They used pictures of paper-based devices taken at different light conditions, with various camera and enzyme inhibition assays, to make training and test data sets. The expectation of precision was higher for food color than enzyme inhibition assays in the majority of machine learning models and color space combinations . Pounds et al designed a rapid, on-site food spoilage identification technique utilizing a smartphone application that can check and examine the color of a novel designed sensor film installed inside a quick response (QR) sticker to recognize features related with food deterioration.…”

“…In order to determine which machine learning model could most effectively estimate the quantification of analytes on paper devices, Khanal et al examined four machine learning models—Logistic regression, SVM, random forest, and ANN with three color models (RGB, HSV, and LAB). 86 They used pictures of paper-based devices taken at different light conditions, with various camera and enzyme inhibition assays, to make training and test data sets. The expectation of precision was higher for food color than enzyme inhibition assays in the majority of machine learning models and color space combinations.…”

“…The expectation of precision was higher for food color than enzyme inhibition assays in the majority of machine learning models and color space combinations. 86 Pounds et al designed a rapid, on-site food spoilage identification technique utilizing a smartphone application that can check and examine the color of a novel designed sensor film installed inside a quick response (QR) sticker to recognize features related with food deterioration. This strategy incorporates an inherent food categorization machine learning technique from the developed QR sticker with fabricated pH responsive sensor film in Figure 17 A–C.…”