Innovative rapid liquid concentration measurement based on thermal lens effect and machine learning.

This study addresses the critical need for rapid and online measurement of liquid concentrations in industrial applications. Although the thermal lens effect (TLE) is extensively explored in laser systems for determining thermal lens focal lengths, its application in quantifying solution concentrations remains underexplored. This research explores the relationship between various liquid concentrations and the interference fringes induced by the TLE. A novel approach is introduced, utilizing TLE to measure solution concentrations, with integration of image processing and discrete Fourier transform (DFT) techniques for feature extraction from interference rings. Further, machine learning, specifically backpropagation artificial neural network (BP-ANN), is employed to model concentration measurement. The model demonstrates high accuracy, evidenced by low root mean square error (RMSE) values of 3.055 and 5.396 for the training and test sets, respectively. This enables precise, real-time determination of soy sauce concentration, offering significant implications for industrial testing, environmental monitoring, and other related fields.

Polluted soil-plant interaction analysis and soil classification based on laser-induced breakdown spectroscopy and machine learning.

A new method is introduced for the swift and precise detection of soil pollution and its effects on crops. Soil quality is essential for human well-being, with heavy metal pollution presenting considerable risks to both the ecological environment and human health. In crops, heavy metal contamination primarily occurs through mediums such as soil and water sources. This study introduces a system combining Laser-Induced Breakdown Spectroscopy (LIBS) with machine learning (ML) to analyze garlic contaminated by soil and the soil used for its cultivation. The simulation conducted in this study focuses on the impact of heavy metal-contaminated soil on garlic. Detection results indicate a significant influence of soil on garlic, resulting in heavy metal accumulation. Further analysis shows that metals from contaminated soil accumulate differently in various garlic plant parts, as per spectral data, underscoring the need for targeted detection methods to assess crop contamination. Conducting LIBS analysis on various soil samples enables the classification of different soil types. This indicates that tracing the origin of contaminated garlic through its residual soil is feasible. These findings imply the feasibility of tracing contaminated garlic's origin through its residual soil.