Portable Deep Learning-Driven Ion-Sensitive Field-Effect Transistor Scheme for Measurement of Carbaryl Pesticide

Abstract

This research proposes a multiple-input deep learning-driven ion-sensitive field-effect transistor (ISFET) scheme to predict the concentrations of carbaryl pesticide. In the study, the carbaryl concentrations are varied between 1 × 10−7–1 × 10−3 M, and the temperatures of solutions between 20–35 °C. To validate the multiple-input deep learning regression model, the proposed ISFET scheme is deployed onsite (a field test) to measure pesticide concentrations in the carbaryl-spiked vegetable extract. The advantage of this research lies in the use of a deep learning algorithm with an ISFET sensor to effectively predict the pesticide concentrations, in addition to improving the prediction accuracy. The results demonstrate the very high predictive ability of the proposed ISFET scheme, given an MSE, MAE, and R2 of 0.007%, 0.016%, and 0.992, respectively. The proposed multiple-input deep learning regression model with signal compensation is applicable to a wide range of solution temperatures which is convenient for onsite measurement. Essentially, the proposed multiple-input deep learning regression model could be adopted as an effective alternative to the conventional statistics-based regression to predict pesticide concentrations.

Biosensors Based on Acetylcholinesterase Immobilized on Clay–Gold Nanocomposites for the Discrimination of Chlorpyrifos and Carbaryl

“…To analyze pesticides in real samples (using carrots as a representative), a previously reported method 31 was used for the preparation of carrot juice. Briefly, 5 g of carrots obtained from a local market were chopped into cubes of 0.5 cm and were put in the test tube containing 10 mL of 5% ethanol.…”

Wearable and printable devices for electrolytes sensing

“…ISFETs provide the advantages of fast response, high sensitivity, low output impedance, low sample consumption, easy mass production and low cost. Therefore, ISFET has been widely used in clinical medicine [40,41], food [41,42], environmental science [43][44][45], military science, and even robot technology [46,47]. In recent years, it has displayed great potential in many fields, especially in biochemical sensors.…”

OFET Informatics: Observing the impact of organic transistor's design parameters on the device output performance using a machine learning algorithm

“…29,30 Following the literature, few attempts have demonstrated the utilization of ML models in optimizing OFET performance. [31][32][33][34][35][36] Most of the reported literature was on optimizing sensors based on OFET structure. 2,36 The molecular and the atomistic highest occupied molecular orbital-lowest unoccupied molecular orbital material arrangement is discussed in Lee.…”

“…Following the literature, few attempts have demonstrated the utilization of ML models in optimizing OFET performance 31–36 . Most of the reported literature was on optimizing sensors based on OFET structure 2,36 .…”