Multi-channel optical sensing system with a BP-ANN for heavy metal detection

Abstract

A multi-channel optical sensing system for heavy metal concentration detection is presented in this paper. The system utilizes a multi-channel optical path combined with a unique circuit design and BP neural network (BP-ANN) to perform the online analysis of multi-wavelength signals, achieving accurate concentration detection of a heavy metal solution. An array photodiode is used to detect the transmission light intensities at multiple wavelengths through the optical path of the system, which enables the collection of useful spectral information of the solution. The system uses a unique signal acquisition method to effectively improve the efficiency of both signal acquisition and operation. BP-ANN is applied to the online analysis of multi-channel information, which overcomes the influential issue of nonlinear effect on data detection, optimizes the anti-interference ability, and lowers the detection limit of the system. This system eliminates the necessary employment of the expensive and large spectrometers and therefore greatly reduces the instrument cost and occupying space. Additionally, the detection limit of the system is extended lower than that of the conventional spectrophotometer. Compared with the detection limits of heavy metal solution obtained by using a single characteristic light wavelength, the detection limits of Cd2+, Cu2+ and Cr6+ achieved through using multi-channel detection system can be reduced by 42.64%, 38.12%, and 20.62%, respectively, and these detection limits are found as 0.0041mg/L, 0.0091mg/L, and 0.0112mg/L, respectively.

Assessing the impact of occlusal plane rotation on facial aesthetics in orthodontic treatment: a machine learning approach

“…Artificial intelligence and machine learning, standing as landmark invention of twenty-first century, has gained reputation in both clinical application [ 21 ] and scientific research of modern dentistry [ 22 ]. Among diversified strategy of machine learning, ANN-BP [ 23 ] could be an optimal choice for statistical tasks characterized by perplex interactions among variables and involving mass data where traditional mathematics analysis could fail. It could make the utmost of data without pre-filtering by human, which avoid subjective bias as mankind clinical practitioner could usually have.…”

Intelligent analysis of carbendazim in agricultural products based on a ZSHPC/MWCNT/SPE portable nanosensor combined with machine learning methods

“…BP-ANN is a well-known ML modeling algorithm tool, a neural network with strong nonlinear mapping capability based on error back propagation. 32 Its structure comprises three parts: input, implicit and output layers. The BP-ANN learning rule used the gradient descent method to determine the mapping between the peak current and concentration of CBZ by continuously adjusting the weights and thresholds of the network through the error back propagation class.…”