Machine Learning-Mediated Ultrasensitive Detection of Citrinin and Associated Mycotoxins in Real Food Samples Discerned from a Photoluminescent Carbon Dot Barcode Array

Abstract

Economically viable remote sensing of foodborne contaminants using minimalistic chemical reagents and simultaneous automation calls for a concrete integration of a chemical detection strategy with artificial intelligence. In a first of its kind, we report the ultrasensitive detection of citrinin and associated mycotoxins like aflatoxin B1 and ochratoxin A using an Alizarin Red S (ARS) and cystamine-derived carbon dot (CD) that aptly amalgamate with machine learning algorithms for automation. The photoluminescence response of the CD as a function of various solvents and pH is used to generate array channels that are further modulated in the presence of the mycotoxins whose digital images were acquired to determine pixelation, essentially creating a barcode. The barcode was fed to machine learning algorithms that actualize and intertwine convoluted databases, demonstrating Extreme Gradient Boosting (XGBoost) as the optimized model out of eight algorithms tested. Spiked samples of wheat, rice, gram, maize, coffee, and milk were used to evaluate the testing model where an exemplary accuracy of 100% even at 10 pmol of mycotoxin concentration was achieved. Most importantly, the coexistence of mycotoxins could also be detected through the CD array and XGBoost synergy hinting toward a broader scope of the developed methodology for smart detection of foodborne contaminants.

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

“…Carbon dots were also used to detect the mycotoxins citrinin, alfatoxin B1 and ochratoxin A by Aggarwal et al [181]. In short, the generated dots were dispersed in different solvents or water at different pHs as a barcode.…”

“…The use of barcodes or multiple sensing systems selected to partially interact with more than one analyte, generating many detections [181,203]. The technology described involves the formation of an array of nanosensors containing receptors which only bind the target analytes partially.…”

Fluorescent graphene quantum dots-enhanced machine learning for the accurate detection and quantification of Hg2+ and Fe3+ in real water samples

“…24 In the area of sensors, ML has been also applied with nanomaterials to build predictive correlation models for the determination of tetracyclines 27 and various metal ions in aqueous solution. 10,29,30 Related to detection, this tool has been combined with a dual-channel CDs sensor array for classifying among four different tetracycline analytes. 27 In the area of ion detection, ML has assisted CDs to demonstrate a 100% predictive accuracy toward the detection of proteins 29 and mycotoxins 30 using the gradient boosted trees (GBT) and XGBoost algorithms, respectively.…”

“…10,29,30 Related to detection, this tool has been combined with a dual-channel CDs sensor array for classifying among four different tetracycline analytes. 27 In the area of ion detection, ML has assisted CDs to demonstrate a 100% predictive accuracy toward the detection of proteins 29 and mycotoxins 30 using the gradient boosted trees (GBT) and XGBoost algorithms, respectively. It has been also incorporated with an array of carbon nanoparticles in order to discriminate between different heavy metal ions.…”