Simultaneous determination of Ba, Co, Fe, and Ni in nuts by high-resolution continuum source atomic absorption spectrometry after extraction induced by solid-oil-water emulsion breaking

Artificial Intelligence-Based Microfluidic Platform for Detecting Contaminants in Water: A Review

Water pollution greatly impacts humans and ecosystems, so a series of policies have been enacted to control it. The first step in performing pollution control is to detect contaminants in the water. Various methods have been proposed for water quality testing, such as spectroscopy, chromatography, and electrochemical techniques. However, traditional testing methods require the utilization of laboratory equipment, which is large and not suitable for real-time testing in the field. Microfluidic devices can overcome the limitations of traditional testing instruments and have become an efficient and convenient tool for water quality analysis. At the same time, artificial intelligence is an ideal means of recognizing, classifying, and predicting data obtained from microfluidic systems. Microfluidic devices based on artificial intelligence and machine learning are being developed with great significance for the next generation of water quality monitoring systems. This review begins with a brief introduction to the algorithms involved in artificial intelligence and the materials used in the fabrication and detection techniques of microfluidic platforms. Then, the latest research development of combining the two for pollutant detection in water bodies, including heavy metals, pesticides, micro- and nanoplastics, and microalgae, is mainly introduced. Finally, the challenges encountered and the future directions of detection methods based on industrial intelligence and microfluidic chips are discussed.

DNA damage in inhabitants exposed to heavy metals near Hudiara drain, Lahore, Pakistan

The current study was conducted on the inhabitants living in the area adjacent to the Hudiara drain using bore water and vegetables adjacent to the Hudiara drain. Toxic heavy metals badly affect human health because of industrial environmental contamination. Particularly hundreds of millions of individuals globally have faced the consequences of consuming water and food tainted with pollutants. Concentrations of heavy metals in human blood were elevated in Hudiara drainings in Lahore city, Pakistan, due to highly polluted industrial effluents. The study determined the health effects of high levels of heavy metals (Cd, Cu, Zn, Fe, Pb, Ni, Hg, Cr) on residents of the Hudiara draining area, including serum MDA, 8-Isoprostane, 8-hydroxyguanosine, and creatinine levels. An absorption spectrophotometer was used to determine heavy metals in wate water, drinking water, soil, plants and human beings blood sampleas and ELISA kits were used to assess the level of 8-hydroxyguanosine, MDA, 8-Isoprostane in plasma serum creatinine level. Waste water samples, irrigation water samples, drinking water samples, Soil samples, Plants samples and blood specimens of adult of different weights and ages were collected from the polluted area of the Hudiara drain (Laloo and Mohanwal), and control samples were obtained from the unpolluted site Sheiikhpura, 60 km away from the site. Toxic heavy metals in blood damage the cell membrane and DNA structures, increasing the 8-hydroxyguanosine, MDA, creatinine, and 8-Isoprostane. Toxic metals contaminated bore water and vegetables, resulting in increased levels of creatinine, MDA, Isoprostane, and 8-hydroxy-2-guanosine in the blood of inhabitants from the adjacent area Hudiara drain compared to the control group. In addition,. This study also investigated heavy metal concentrations in meat and milk samples from buffaloes, cows, and goats. In meat, cow samples showed the highest Cd, Cu, Fe and Mn concentrations. In milk also, cows exhibited elevated Cu and Fe levels compared to goats. The results highlight species-specific variations in heavy metal accumulation, emphasizing the need for targeted monitoring to address potential health risks. The significant difference between the two groups i.e., the control group and the affected group, in all traits of the respondents (weight, age, heavy metal values MDA, 8-Isoprostane, 8-hydroxyguaniosine, and serum creatinine level). Pearson's correlation coefficient was calculated. The study has shown that the level of serum MDA, 8-Isoprostane, 8-hydroxyguaniosine, or creatinine has not significantly correlated with age, so it is independent of age. This study has proved that in Pakistan, the selected area of Lahore in the villages of Laloo and Mohanwal, excess of heavy metals in the human body damages the DNA and increases the level of 8-Isoprostane, MDA, creatinine, and 8-hydroxyguaniosine. As a result, National and international cooperation must take major steps to control exposure to heavy metals.

Extraction Induced by Emulsion and Microemulsion Breaking for Metal Determination by Spectrometric Methods - A Review

This review focuses on extraction induced by the destabilization of emulsified systems combined with spectrometric techniques for metal analysis in oily samples. This approach is based on the formation and breaking of an emulsion (extraction induced by emulsion breaking - EIEB) or microemulsion (extraction induced by microemulsion breaking - EIMB) to transfer the analytes from the oil sample to the aqueous phase, which is separated in the process. Its simplicity, speed, and low cost have contributed to its growing popularity among researchers. However, the potential of EIEB and EIMB is far from being fully exploited. Therefore, this paper aims to provide relevant information to expand the applicability of these methods. The principle of the methods is discussed, and a brief description of emulsified systems is presented. The parameters affecting the extraction efficiency and calibration strategy are also critically discussed. Furthermore, the analytical applications of the methods are reviewed. Trends and opportunities in this field are also considered.

Ag-decorated polymer chip for the determination of the respective concentrations of TTD and Hg2+ by surface-enhanced raman scattering

A “hotspot”-rich Ag-nanoparticle-decorated three-dimensional polymer substrate was fabricated, exhibiting an excellent surface-enhanced Raman scattering (SERS) activity.

Remotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network

BACKGROUND

The estimation of total iron content at the regional scale is of much significance as iron deficiency has become a routine problem for many crops.

METHODS

In this study, a novel method for estimating total iron content in soil (TICS) was proposed using harmonic analysis (HA) and back propagation (BP) neural network model. Several data preprocessing methods of first derivative (FD), wavelet packet transform (WPT), and HA were conducted to improve the correlation between the soil spectra and TICS. The principal component analysis (PCA) was exploited to obtained three kinds of characteristic variables (FD, WPT-FD, and WPT-FD-HA) for TICS estimation. Furthermore, the estimated accuracy of three BP models based on these variables was compared.

RESULTS

The results showed that the BP models of different soil types based on WPT-FD-HA had better estimation accuracy, with the highest R² value of 0.95, and the RMSE of 0.68 for the loessial soil. It was proved that the characteristic variable obtained by harmonic decomposition improved the validity of the input variables and the estimation accuracy of the TICS models. Meanwhile, it was identified that the WPT-FD-HA-BP model can not only estimate the total iron content of a single soil type with high accuracy but also demonstrate a good effect on the estimation of TICS of mixed soil.

CONCLUSION

The HA method and BP neural network combined with WPT and FD have great potential in TICS estimation under the conditions of single soil and mixed soil. This method can be expected to be applied to the prediction of crop biochemical parameters.