A novel method for multi-pollutant monitoring in water supply systems using chemical machine vision.

Drinking water is vital for human health and life, but detecting multiple contaminants in it is challenging. Traditional testing methods are both time-consuming and labor-intensive, lacking the ability to capture abrupt changes in water quality over brief intervals. This paper proposes a direct analysis and rapid detection method of three indicators of arsenic, cadmium, and selenium in complex drinking water systems by combining a novel long-path spectral imager with machine learning models. Our technique can obtain multiple parameters in about 1 s. The experiment involved setting up samples from various drinking water backgrounds and mixed groups, totaling 9360 injections. A raw visible light source ranging from 380 to 780 nm was utilized, uniformly dispersing light into the sample cell through a filter. The residual beam was captured by a high-definition camera, forming a distinctive spectrum. Three deep learning models-ResNet-50, SqueezeNet V1.1, and GoogLeNet Inception V1-were employed. Datasets were divided into training, validation, and test sets in a 6:2:2 ratio, and prediction performance across different datasets was assessed using the coefficient of determination and root mean square error. The experimental results show that a well-trained machine learning model can extract a lot of feature image information and quickly predict multi-dimensional drinking water indicators with almost no preprocessing. The model's prediction performance is stable under different background drinking water systems. The method is accurate, efficient, and real-time and can be widely used in actual water supply systems. This study can improve the efficiency of water quality monitoring and treatment in water supply systems, and the method's potential for environmental monitoring, food safety, industrial testing, and other fields can be further explored in the future.

A spectral learning path for simultaneous multi-parameter detection of water quality.

Water quality parameters (WQP) are the most intuitive indicators of the environmental quality of water body. Due to the complexity and variability of the chemical environment of water body, simple and rapid detection of multiple parameters of water quality becomes a difficult task. In this paper, spectral images (named SPIs) and deep learning (DL) techniques were combined to construct an intelligent method for WQP detection. A novel spectroscopic instrument was used to obtain SPIs, which were converted into feature images of water chemistry and then combined with deep convolutional neural networks (CNNs) to train models and predict WQP. The results showed that the method of combining SPIs and DL has high accuracy and stability, and good prediction results with average relative error of each parameter (anions and cations, TOC, TP, TN, NO3--N, NH3-N) at 1.3%, coefficient of determination (R2) of 0.996, root mean square error (RMSE) of 0.1, residual prediction deviation (RPD) of 16.2, and mean absolute error (MAE) of 0.067. The method can achieve rapid and accurate detection of high-dimensional water quality multi-parameters, and has the advantages of simple pre-processing and low cost. It can be applied not only to the intelligent detection of environmental waters, but also has the potential to be applied in chemical, biological and medical fields.

Image learning to accurately identify complex mixture components.

The study of complex mixtures is very important for exploring the evolution of natural phenomena, but the complexity of the mixtures greatly increases the difficulty of material information extraction. Image perception-based machine-learning techniques have the ability to cope with this problem in a data-driven way. Herein, we report a 2D-spectral imaging method to collect matter information from mixture components, and the obtained feature images can be easily provided to deep convolutional neural networks (CNNs) for establishing a spectral network. The results demonstrated that a single CNN trained end-to-end from the proposed images can directly accomplish synchronous measurement of multi-component samples using only raw pixels as inputs. Our strategy has some innate advantages, such as fast data acquisition, low cost, and simple chemical treatment, suggesting that it can be extensively applied in many fields, including environmental science, biology, medicine, and chemistry.

Machine learning based on holographic scattering spectrum for mixed pollutants analysis.

Determination of complex pollutants often involves many high-cost and laborious operations. Today's pop machine-learning (ML) technology has exhibited their amazing successes in image recognition, drug designing, disease detection, natural language understanding, etc. ML-driven samples testing will inevitably promote the development of related subjects and fields, but the biggest challenge ahead for this process is how to provide some intelligible and sufficient data for various algorithms. In this work, we present a full strategy for rapid detecting mixed pollutants through the synergistic application of holographic spectrum and convolutional neural network (CNN). The results have shown that a well-trained CNN model could realize quantitative analysis of the mixed pollutants by extracting spectral information of matters, suggesting the strategy's value in facilitating the study of complex chemical systems.

Machine learning for total organic carbon analysis of environmental water samples using high-throughput colorimetric sensors.

Due to the complexity of nonlinear reactions, the analysis of environmental samples often relies on expensive equipment as well as tedious and time-consuming experimental procedures. Currently, the efficient machine learning (ML) strategy based on big data offers some new insights for the analysis of complex components in the environmental field. In this study, ML was applied for the analysis of total organic carbon (TOC). We prepared a special colorimetric sensor (c-sensor) by inkjet printing. The sensor reacted with water samples in a high-throughput process, producing characteristic patterns to map TOC information in water samples. To quickly acquire TOC information on c-sensors, a ML model was proposed to describe the relationship between the c-sensor and TOC value. According to this study, the c-sensor and ML can be effectively applied to TOC information analysis of environmental water samples, which provides convenience for environmental research. It is foreseeable that ML has a broad prospect of application in environmental research.

Machine learning for mixture toxicity analysis based on high-throughput printing technology.

Analysis on mixture toxicity (Mix-tox) of the multi-chemical space is constantly followed with interest for many researchers. Conventional toxicity tests with time-consuming and costly operations make researchers can only establish some toxicity prediction models aiming to a limited sampling dimension. The rapid development of machine learning (ML) algorithm will accelerate the exploration of many fields involving toxicity analysis. Rather than the model calculation capacity, the challenge of this process mainly comes from the lack of toxicology big-data to perform toxicity perception through the ML model. In this paper, a full strategy based a standardized high-throughput experiment was developed for Mix-tox analysis throughout the whole routine, from big-sample dataset design, model building, and training, to the toxicity prediction. Using the concentration variates as input and bio-luminescent inhibition rate as output, it turned out that a well-trained random forest algorithm was successfully applied to assess the mixtures' toxicity effect, suggesting its value in facilitating adoption of Mix-tox analysis.

A color-spectral machine learning path for analysis of five mixed amino acids.

A machine learning (ML) strategy based on color-spectral images for mixed amino acid (AA) analysis is presented. The results showed that a well-trained ML model could accurately predict multiple AAs at the same time, suggesting its value for facilitating quantitative analysis of mixed AA systems.

Preparation and catalytic performance of copper-containing magnetic catalysts for degradation of azo dye (direct violet).

A novel magnetically separable magnetic activated carbon supporting-copper (MCAC) catalyst for catalytic wet peroxide oxidation (CWPO) was prepared by chemical impregnation. The prepared samples were characterized by X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET) method, and scanning electron microscopy (SEM) equipped with energy dispersive spectrometry (EDS). The catalytic performance of the catalysts was evaluated by direct violet (D-BL) degradation in CWPO experiments. The influence of preparative and operational parameters (dipping conditions, calcination temperature, catalyst loading H2O2 dosage, pH, reaction temperature, additive salt ions and initial D-BL concentration) on degradation performance of CWPO process was investigated. The resulting MCAC catalyst showed higher reusability in direct violet oxidation than the magnetic activated carbon (MAC). Besides, dynamic tests also showed the maximal degradation rate reached 90.16% and its general decoloring ability of MCAC was 34 mg g-1 for aqueous D-BL.

An Ink-jet Printing Strategy for Extensive Exploration of One Chemical Action with Three Interactive Variations.

A simple method was created and implemented through the technology of ink-jet printing to study the effects of three chemical factors (chemical reagents) to the ninhydrin reaction. The effects of each single reagent and their interactions on the reaction were studied in one experiment. The three reagents all have effects on ninhydrin reaction, and the effects under the different combinations of reagents were presented on a chip. This work was completed efficiently with a smaller experimental workload compared with the traditional method.

Distribution of Heavy Metal Pollution in Surface Soil Samples in China: A Graphical Review.

Soil pollution in China is one of most wide and severe in the world. Although environmental researchers are well aware of the acuteness of soil pollution in China, a precise and comprehensive mapping system of soil pollution has never been released. By compiling, integrating and processing nearly a decade of soil pollution data, we have created cornerstone maps that illustrate the distribution and concentration of cadmium, lead, zinc, arsenic, copper and chromium in surficial soil across the nation. These summarized maps and the integrated data provide precise geographic coordinates and heavy metal concentrations; they are also the first ones to provide such thorough and comprehensive details about heavy metal soil pollution in China. In this study, we focus on some of the most polluted areas to illustrate the severity of this pressing environmental problem and demonstrate that most developed and populous areas have been subjected to heavy metal pollution.

Fluorescence imaging technology (FI) for high-throughput screening of selenide-modified nano-TiO2 catalysts.

A high-throughput screening (HTS) method based on fluorescence imaging (FI) was implemented to evaluate the catalytic performance of selenide-modified nano-TiO2. Chemical ink-jet printing (IJP) technology was reformed to fabricate a catalyst library comprising 1405 (Ni(a)Cu(b)Cd(c)Ce(d)In(e)Y(f))Se(x)/TiO2 (M6Se/Ti) composite photocatalysts. Nineteen M6Se/Tis were screened out from the 1405 candidates efficiently.