Chemical Detection Methods Research Articles

Comparative analysis of spectroradiometric and chemical methods for nutrient detection in black gram leaves

This study evaluated the nutrient content in Vigna mungo leaves using a visible near-infrared spectroradiometer (VNIR 650–900 nm) and compared it with traditional chemical analysis. Nutrient deficiencies were induced in a controlled environment, focusing on Nitrogen (N), Phosphorus (P), and Potassium (K). The VNIR spectroradiometer measured the spectral reflectance of the leaves at different intervals. Peak reflectance for N, P, and K deficiencies occurred at 716 nm, 737 nm, and 720 nm, respectively. A 30 % deficiency in N, P, and K resulted in reflectance values of 45 %, 42 %, and 41 %, respectively, while a 40 % deficiency increased these values to 48 %, 46 %, and 43 %. Results showed a strong correlation between nutrient deficiency and spectral reflectance, providing a quicker, more economical method than traditional chemical analysis. The study concludes that VNIR spectroradiometry is an effective tool for non-destructive, real-time assessment of nutrient status in Vigna mungo, which could lead to optimized fertilizer application and improved crop management.

Detection of Chemical Contaminants in Water for Irrigation Systems: A Systematic Review

Water pollution is a detrimental issue that occurs when there are bad changes in water quality parameters. It directly disrupts water usage and poses a danger to society, environment, economy, and agriculture. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. Improper water management pollutes rivers and lakes in Malaysia such as Klang River, Semenyih River, Kim Kim River, Slim River, and others. Various techniques are introduced to detect contaminants in water such as electronic sensors, biosensor approaches, laboratory analysis, and optical techniques. The functionality tool varies depending on the specific contaminants or parameters that are targeted, and the resources allocated. Several common contaminant types are found in polluted water including heavy metals, organic and inorganic chemicals, industrial pollutants, suspended solids, and others. The objective of the review is to study various non-optical and optical contaminant detection methods in water to identify the strengths and weaknesses of the methods. In this review, water pollution problems mainly due to the agricultural sector in several countries are discussed. Besides, conventional, and modern methods are compared in terms of parameters, complexity, and reliability. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Recent contaminant detection methods in water are also reviewed to study any loopholes in the latest methods. We found that the spectroscopy method based on light propagation theory is suitable and one of the promising methods for chemical contaminants detection for water quality analysis. This method offers fast analysis time, high sensitivity detection, non-invasive analysis, provides reliable data, and others. Undoubtedly, some contaminants in water can be challenging to be identified rapidly and confidently even though there are numerous tools and techniques available for water quality analysis. Thus, the review is important to compare previous methods and to improve current chemical contaminants detection analysis in terms of reliability with a minimum operating system and cost effectiveness.

Ultraviolet visible spectral water chemical oxygen demand detection method based on two deep neural network model

Water resources are precious resources around the world, but pollution is becoming increasingly severe. While the degree of damage to water caused by organic pollutants is reflected by chemical oxygen demand, which is also an important indicator for hydrological monitoring. Therefore, accurate measurement of chemical oxygen demand is necessary. The rapid development of water quality detection by ultraviolet visible spectrum analysis as an efficient, convenient, and pollution-free new water quality detection method has been achieved. When we were processing ultraviolet visible spectral absorbance data, various environmental factors can affect the chemical oxygen demand light absorption value, thus affecting the accuracy of detection. So there are some goals, such as more comprehensively extracting the feature wavelength and reducing the interference to achieve better detection results. It is expected that the model can be applied to a portable water quality detection device. It is necessary to consider detection time, reduce model training parameters, and improve detection speed. Therefore, this article proposes Competitive Adaptive Weighted Sampling-Convolutional Neural Network-Long Short-Term Memory and Competitive Adaptive Weighted Sampling-Convolutional Long Short-Term Memory models for detecting chemical oxygen demand in water. To verify the effectiveness of the proposed model, the model was trained on the experimental dataset and the detection results of all models involved in the article were compared. It can be confirmed that the application of these two proposed algorithms results in less detection time and higher accuracy in concentration detection.

Detection of food additives based on an integrated self-injected metasurface microfluidic sensor

Advanced sensing equipment exhibits high sensitivity and reliability in detecting food additives, enabling the practical assessment of the safety of processed foods. Currently, chemical detection methods are commonly utilized for identifying food additives. However, these approaches tend to be intricate and time-consuming. In this study, we designed and fabricated an integrated terahertz microfluidic sensor, which achieves high sensitivity by incorporating a metasurface within the microfluidic chip. The metasurface comprises metal wires and split-ring resonators, with three optional sensing sites within the frequency domain of 0.1–1.2 THz, thereby enhancing the reliability of the sensor. Additionally, the use of a self-injection micropump improves the stability of the liquid flow rate, preventing experimental errors caused by manual injection. Utilizing this sensor, we conducted concentration sensing experiments on potassium sorbate and sodium benzoate solutions, successfully identifying sugar-containing and sugar-substituted beverages with high sensitivity and rapid sensing speed. The average sensitivity of the sensor is 152.8 GHz·RIU−1. The results of this study provide a feasible method for the development of microfluidic metasurface sensors.

Double tunable Fano resonance based on a metal–insulator–metal waveguide with double coupled cavities and its application

A metal–insulator–metal (MIM) waveguide with a ring cavity (RC) and a half ring cavity (HRC) is proposed to realize the detection of two mediums simultaneously based on independently tunable double Fano resonances. Utilizing numerical simulation of the finite element method, the transmission characteristics and magnetic field distribution are investigated. The simulation findings indicate that the structure is capable of generating double Fano resonances, and the two Fano resonances are tuning independently. The maximum sensitivity and figure of merit (FOM) are 2385 nm/RIU (refractive index unit) and 31886RIU−1, respectively, and these values are achieved by changing the structural parameters and the refractive index of the insulator. Moreover, the sugar content in flavor and the concentration of ethanol solution can be detected at the same time, which indicates the high efficiency of the sensor. Therefore, these performances demonstrate that the tunable double Fano resonance based on a MIM waveguide is a hopeful method for chemical detection.

Highly Sensitive, Easy-to-Use, One-Step Detection of Peroxide-, Nitrate- and Chlorate-Based Explosives with Electron-Rich Ni Porphyrins.

Homemade explosives, such as peroxides, nitrates, and chlorates, are increasingly abused by terrorists, criminals, and amateur chemists. The starting materials are easily accessible and instructions on how to make the explosives are described on the Internet. Safety considerations raise the need to detect these substances quickly and in low concentrations using simple methods. Conventional methods for the detection of these substances require sophisticated, electrically operated, analytical equipment. The simpler chemical detection methods are multistep and require several chemicals. We have developed a simple, one-step method that works similarly to a pH test strip in terms of handling. The analytical reaction is based on an acid-catalyzed oxidation of an electron-rich porphyrin to an unusually stable radical cation and dication. The detection limit for the peroxide-based explosive triacetone triperoxide (TATP), which is very frequently used by terrorists, is 40 ng and thus low enough to detect the substance without direct contact via the gas phase. It is sufficient to bring the stick close to the substance to observe a color change from red to green. Nitrates and chlorates, such as ammonium nitrate, urea nitrate, or potassium chlorate, are detected by direct contact with a sensitivity of 85-350 ng. A color change from red to dark brown is observed. The test thus detects all homemade explosives and distinguishes between the extremely impact-, shock-, and friction-sensitive peroxides and the less sensitive nitrates and chlorates by color change of a simple test strip.

A genetically encoded fluorescent protein sensor for mitochondrial membrane damage detection

Mitochondria are essential cellular organelles; detecting mitochondrial damage is crucial in cellular biology and toxicology. Compared with existing chemical probe detection methods, genetically encoded fluorescent protein sensors can directly indicate cellular and molecular events without involving exogenous reagents. In this study, we introduced a molecular sensor system, MMD-Sensor, for monitoring mitochondrial membrane damage. The sensor consists of two molecular modules. Module I is a fusion structure of the mitochondrial localization sequence (MLS), AIF cleavage site sequence (CSS), nuclear localization sequence (NLS), N-terminus of mNeonGreen and mCherry. Module II is a fusion structure of the C-terminus of mNeonGreen, NLS sequence, and mtagBFP2. Under normal condition, Module I is constrained in the inner mitochondrial membrane anchored by MLS, while Module II is restricted to the nucleus by its NLS fusion component. If the mitochondrial membrane is damaged, CSS is cut from the inner membrane, causing Module I to shift into the nucleus guided by the NLS fusion component. After Module I enters the nucleus, the N- and C-terminus of mNeonGreen meet each other and rebuild its intact 3D structure through fragment complementation and thus generates green fluorescence in the nucleus. Dynamic migration of red fluorescence from mitochondria to the nucleus and generation of green fluorescence in the nucleus indicate mitochondrial membrane damage. Using the MMD-Sensor, mitochondrial membrane damage induced by various reagents, such as uncoupling agents, ATP synthase inhibitors, monovalent cationic carriers, and ROS, in HeLa and 293T cells are directly observed and evaluated.

Development of a Triphenylmethyl Alkylation Pre-Column Derivatization Method for HPLC Quantitative Analysis of Chemically Unstable Halogenated Compounds.

The primary limitations of the quantitative analysis of thermally labile halogenated compounds by traditional gas chromatography (GC) are the inadequacy of identifying the insufficiently volatile impurity (often with a high boiling point) and the difficulty in obtaining a standard substance with a reliable standardized assay. Taking the 4-(Chloromethyl)-5-methyl-1,3-dioxol-2-one (DMDO-Cl, 1) as an example, we reported a triphenylmethanamino-derivatization method to overcome the challenges of the assay determination of such species. During the quantification of 1, the presence of GC-undetectable polymeric impurity 10 poses a critical challenge in assessing the material quality. Moreover, the standard substance of 1 is not available on the market due to its inherent instability during storage and handling, further complicating the quantitative analysis. In this work, a precolumn HPLC-UV derivatization method based on triphenylmethanamino-alkylation was developed to quantitatively analyze 1. The resulting derivative 2 exhibits excellent crystallinity and superior physical and chemical stability and possesses effective chromophores for UV detection. The conversion from analyte 1 to derivative 2 demonstrates desirable reactivity and purity, facilitating quantitative analysis using the external standard method. The chemical derivatization-chromatographic detection method was optimized and validated, demonstrating its high specificity, good linearity, precision, accuracy, and stability. This method offers a valuable alternative to the general quantitative NMR (qNMR) detection technique, which exhibits reduced specificity in the presence of increased levels of impurities in compound 1.

E-nose and colorimetric sensor array combining homologous data fusion strategy discriminating the roasting degree of large-leaf yellow tea

Different degrees of roasting result in differences in the quality and flavor of large-leaf yellow tea. The current sensory evaluation and chemical detection methods cannot meet the requirement of online differentiation of LYT roasting degree, so an accurate and comprehensive assessment method needs to be developed urgently. First, the two aroma sensing technologies were compared. Two variable screening methods and three recognition algorithms were employed to build discriminant models. The results showed that the discrimination rate of the colorimetric sensor array (CSA) in the prediction set reached 91.89 %, outperforming that of the E-nose. Subsequently, three fusion strategies were applied to improve the discrimination accuracy. The discrimination rate of the middle fusion strategy resulted in an optimal resolution of 94.59 %. The results obtained from the homologous fusion were able to evaluate the roasting degree comprehensively and accurately, which provides a new method and idea for tea aroma quality.

Improved boosting and self-attention RBF networks for COD prediction based on UV-vis.

Chemical Oxygen Demand (COD) is crucial for assessing water quality. Compared to traditional chemical detection methods, UV-vis spectroscopy for measuring COD offers advantages such as speed, reduced consumption of materials, and no secondary pollution. Considering the impact of suspended particles in water, this paper proposes an optimized boosting model based on a combination strategy for turbidity compensation, using absorption spectra obtained from reservoir water samples via UV-vis. A self-attention mechanism is introduced into the radial basis function (RBF) network, resulting in a COD detection model based on the saRBF framework. This model facilitates comprehensive optimization of the entire process, from turbidity compensation of the original absorption spectrum to the subsequent COD prediction. Experimental results show that the proposed COD measurement model achieves a coefficient of determination of 0.9267, a root mean square error of 1.2669, and a mean absolute error of 1.0097, outperforming other COD measurement models. This work provides a new approach for turbidity compensation and COD detection research.

Development of a fumonisin-sensitive Saccharomyces cerevisiae indicator strain and utilization for activity testing of candidate detoxification genes.

Fumonisins can cause diseases in animals and humans consuming Fusarium-contaminated food or feed. The search for microbes capable of fumonisin degradation, or for enzymes that can detoxify fumonisins, currently relies primarily on chemical detection methods. Our constructed fumonisin B1-sensitive yeast strain can be used to phenotypically detect detoxification activity and should be useful in screening for novel fumonisin resistance genes and to elucidate fumonisin metabolism and resistance mechanisms in fungi and plants, and thereby, in the long term, help to mitigate the threat of fumonisins in feed and food.

Picomolar-Level Sensing of Cannabidiol by Metal Nanoparticles Functionalized with Chemically Induced Dimerization Binders.

Simple and fast detection of small molecules is critical for health and environmental monitoring. Methods for chemical detection often use mass spectrometers or enzymes; the former relies on expensive equipment, and the latter is limited to those that can act as enzyme substrates. Affinity reagents like antibodies can target a variety of small-molecule analytes, but the detection requires the successful design of chemically conjugated targets or analogs for competitive binding assays. Here, we developed a generalizable method for the highly sensitive and specific in-solution detection of small molecules, using cannabidiol (CBD) as an example. Our sensing platform uses gold nanoparticles (AuNPs) functionalized with a pair of chemically induced dimerization (CID) nanobody binders (nanobinders), where CID triggers AuNP aggregation and sedimentation in the presence of CBD. Despite moderate binding affinities of the two nanobinders to CBD (equilibrium dissociation constants KD of ∼6 and ∼56 μM), a scheme consisting of CBD-AuNP preanalytical incubation, centrifugation, and electronic detection (ICED) was devised to demonstrate a high sensitivity (limit of detection of ∼100 picomolar) in urine and saliva, a relatively short sensing time (∼2 h), a large dynamic range (5 logs), and a sufficiently high specificity to differentiate CBD from its analog, tetrahydrocannabinol. The high sensing performance was achieved with the multivalency of AuNP sensing, the ICED scheme that increases analyte concentrations in a small assay volume, and a portable electronic detector. This sensing system is readily applicable for wide molecular diagnostic applications.

Time series prediction of the chemical components of PM2.5 based on a deep learning model

Modeling-based prediction methods enable rapid, reagent-free air pollution detection based on inexpensive multi-source data than traditional chemical reaction-based detection methods in order to quickly understand the air pollution situation. In this study, a convolutional neural network (CNN) and long and short-term memory (LSTM) neural networks are integrated to create a CNN-LSTM time series prediction model to predict the concentration of PM2.5 and its chemical components (i.e., heavy metals, carbon component, and water-soluble ions) using meteorological data and air pollutants (PM2.5, SO2, NO2, CO, and O3). In the integrated CNN-LSTM model, the CNN uses convolutional and pooling layers to extract features from the data, whereas the powerful nonlinear mapping and learning capabilities of LSTM enable the time series prediction of air pollution. The experimental results showed that the CNN-LSTM exhibited good generalization ability in the prediction of As, Cd, Cr, Cu, Ni, and Zn, with a mean R2 above 0.9. Mean R2 predicted for PM2.5, Pb, Ti, EC, OC, SO42−, and NO3− ranged from 0.85 to 0.9. Shapley value showed that PM2.5, NO2, SO2, and CO had a greater influence on the predicted heavy metal results of the model. Regarding water-soluble ions, the predicted results were dominantly influenced by PM2.5, CO, and humidity. The prediction of the carbon fraction was affected mainly by the PM2.5 concentration. Additionally, several input variables for various components were eliminated without affecting the prediction accuracy of the model, with R2 between 0.70 and 0.84, thereby maximizing modeling efficiency and lowering operational costs. The fully trained model prediction results showed that most predicted components of PM2.5 were lower during January to March 2020 than those in 2018 and 2019. This study provides insight into improving the accuracy of modeling-based detection methods and promotes the development of integrated air pollution monitoring toward a more sustainable direction.

The endogenous antioxidant ability of royal jelly in Drosophila is independent of Keap1/Nrf2 by activating oxidoreductase activity.

Royal jelly (RJ) is a biologically active substance secreted by the hypopharyngeal and mandibular glands of worker honeybees. It is widely claimed that RJ reduces oxidative stress. However, the antioxidant activity of RJ has mostly been determined by in vitro chemical detection methods or by external administration drugs that cause oxidative stress. Whether RJ can clear the endogenous production of reactive oxygen species (ROS) in cells remains largely unknown. Here, we systematically investigated the antioxidant properties of RJ using several endogenous oxidative stress models of Drosophila. We found that RJ enhanced sleep quality of aging Drosophila, which is decreased due to an increase of oxidative damage with age. RJ supplementation improved survival and suppressed ROS levels in gut cells of flies upon exposure to hydrogen peroxide or to the neurotoxic agent paraquat. Moreover, RJ supplementation moderated levels of ROS in endogenous gut cells and extended lifespan after exposure of flies to heat stress. Sleep deprivation leads to accumulation of ROS in the gut cells, and RJ attenuated the consequences of oxidative stress caused by sleep loss and prolonged lifespan. Mechanistically, RJ prevented cell oxidative damage caused by heat stress or sleep deprivation, with the antioxidant activity in vivo independent of Keap1/Nrf2 signaling. RJ supplementation activated oxidoreductase activity in the guts of flies, suggesting its ability to inhibit endogenous oxidative stress and maintain health, possibly in humans.

Rapid identification of hazardous heavy metal-containing waste by combining EDXRF with machine learning: Taking zinc smelting waste as an example

To address the time-consuming identification and poor timeliness of environmental pollution tracing problems of traditional chemical detection methods for hazardous solid waste, a rapid identification and characterization methodology for hazardous waste containing heavy metals is established by using the EDXRF spectroscopy principle combined with an optical intelligent optimization algorithm. Two years of long-time serial optical spectrum information are collected from nine typical types of hazardous waste containing heavy metals produced in the zinc smelting process using EDXRFs, online analytical instruments installed on different process lines. Then, different machine learning models are applied to identify the key spectral factors that are first screened after noise reduction and standardization to pretreat all spectrum information data to distinguish different characteristics of each type of hazardous waste. Finally, the random forest model is selected and optimized to improve its identification accuracy. The results show that the spectral line signals corresponding to heavy metal elements such as Fe, Zn, Cu, Cd, and Ag in zinc smelting materials can be used as important factors to distinguish different types of zinc smelting solid waste. Because different smelting processes lead to different heavy metal contents in different types of zinc smelting hazardous waste, the optimized random forest model can be implemented to accurately identify and characterize nine typical types of hazardous waste containing heavy metals, and an accuracy rate of 100% is achieved. This method provides an efficient tool for the environmental management of hazardous waste and the rapid tracing of environmental risks in the future. Especially in tracking soil or surface water pollution caused by the illegal dumping of solid waste, the rapid identification and characterization of solid waste are of great significance for the quality supervision of regional ecological environments.

Comparative Evaluation of Chemical and Photolytic Denitrosation Methods for Chemiluminescence Detection of Total N-Nitrosamines in Wastewater Samples.

N-Nitrosamines form as byproducts during oxidative water treatment and occur as impurities in consumer and industrial products. To date, two methods based on chemiluminescence (CL) detection of nitric oxide liberated from N-nitrosamines via denitrosation with acidic triiodide (HI3) treatment or ultraviolet (UV) photolysis have been developed to enable the quantification of total N-nitrosamines (TONO) in environmental water samples. In this work, we configured an integrated experimental setup to compare the performance of HI3-CL and UV-CL methods with a focus on their applicability for TONO measurements in wastewater samples. With the use of a large-volume purge vessel for chemical denitrosation, the HI3-CL method achieved signal stability and detection limits comparable to those achieved by the UV-CL method which utilized a microphotochemical reactor for photolytic denitrosation. Sixty-six structurally diverse N-nitroso compounds (NOCs) yielded a range of conversion efficiencies relative to N-nitrosodimethylamine (NDMA) regardless of the conditions applied for denitrosation. On average, TONO measured in preconcentrated raw and chloraminated wastewater samples by the HI3-CL method were 2.1 ± 1.1 times those measured by the UV-CL method, pointing to potential matrix interferences as further confirmed by spike recovery tests. Overall, our comparative assessment of the HI3-CL and UV-CL methods serves as a basis for addressing methodological gaps in TONO analysis.

Application of Luminescent Bacteria Bioassay in the Detection of Pollutants in Soil

The luminescent bacteria bioassay has been commonly used in the detection of environmental pollutants. Compared with traditional chemical and other biological detection methods, the luminescent bacteria bioassay has many demonstrated advantages such as a sensitive response, low cost, high efficiency, and environmental friendliness. The traditional luminescent bacteria bioassay has poor reproducibility and cannot achieve undisturbed soil testing, and the use of leach liquor also affects the results. This paper reviews the research progress and existing issues for the traditional luminescent bacteria bioassay used in the detection of soil pollutants. The luminescence mechanisms and detection principles of three commonly used luminescent bacteria, i.e., Vibrio fischeri, Photobacterium phosphoreum, and Vibrio qinghaiensis, are discussed and compared. In addition, two new luminescent bacteria bioassays are introduced to detect soil pollutants. One method is based on recombinant luminescent bacteria obtained with a gene-modification technique. This method can realize specific detection and enhance sensitivity, but it still cannot achieve undisturbed soil detection. The other method involves using magnetic nanoparticle (MNP)-based biosensors made from luminescent bacteria and MNPs. It can realize the accurate detection of the biological toxicity of the combined pollutants in soil without disturbing the soil’s integrity. This study shows that MNP-based biosensors have good application prospects in soil pollution detection, but the mechanism behind their utility still needs to be investigated to realize their popularization and application.

Non-Destructive Detection of Different Pesticide Residues on the Surface of Hami Melon Classification Based on tHBA-ELM Algorithm and SWIR Hyperspectral Imaging.

In the field of safety detection of fruits and vegetables, how to conduct non-destructive detection of pesticide residues is still a pressing problem to be solved. In response to the high cost and destructive nature of existing chemical detection methods, this study explored the potential of identifying different pesticide residues on Hami melon by short-wave infrared (SWIR) (spectral range of 1000-2500 nm) hyperspectral imaging (HSI) technology combined with machine learning. Firstly, the classification effects of classical classification models, namely extreme learning machine (ELM), support vector machine (SVM), and partial least squares discriminant analysis (PLS-DA) on pesticide residues on Hami melon were compared, ELM was selected as the benchmark model for subsequent optimization. Then, the effects of different preprocessing treatments on ELM were compared and analyzed to determine the most suitable spectral preprocessing treatment. The ELM model optimized by Honey Badger Algorithm (HBA) with adaptive t-distribution mutation strategy (tHBA-ELM) was proposed to improve the detection accuracy for the detection of pesticide residues on Hami melon. The primitive HBA algorithm was optimized by using adaptive t-distribution, which improved the structure of the population and increased the convergence speed. Compared the classification results of tHBA-ELM with HBA-ELM and ELM model optimized by genetic algorithm (GA-ELM), the tHBA-ELM model can accurately identify whether there were pesticide residues and different types of pesticides. The accuracy, precision, sensitivity, and F1-score of the test set was 93.50%, 93.73%, 93.50%, and 0.9355, respectively. Metaheuristic optimization algorithms can improve the classification performance of classical machine learning classification models. Among all the models, the performance of tHBA-ELM was satisfactory. The results indicated that SWIR-HSI coupled with tHBA-ELM can be used for the non-destructive detection of pesticide residues on Hami melon, which provided the theoretical basis and technical reference for the detection of pesticide residues in other fruits and vegetables.

Quantitative Analysis of Mixed Minerals with Finite Phase Using Thermal Infrared Hyperspectral Technology

It is crucial but challenging to detect intermediate or end products promptly. Traditional chemical detection methods are time-consuming and cannot detect mineral phase content. Thermal infrared hyperspectral (TIH) technology is an effective means of real-time imaging and can precisely capture the emissivity characteristics of objects. This study introduces TIH to estimate the content of potassium salts, with a model based on Competitive Adaptive Reweighted Sampling (CARS) and Partial Least Squares Regression (PLSR). The model takes the emissivity spectrum of potassium salt into account and accurately predicts the content of Mixing Potassium (MP), a mineral mixture produced in Lop Nur, Xinjiang. The main mineral content in MP was measured by Mineral Liberation Analyzer (MLA), mainly including picromerite, potassium chloride, magnesium sulfate, and less sodium chloride. 129 configured MP samples were divided into calibration (97 samples) and prediction (32 samples) sets. The CARS-PLSR method achieved good prediction results for MP mineral content (picromerite: correlation coefficient of correction set (Rp2) = 0.943, predicted root mean square error (RMSEP) = 2.72%, relative predictive deviation (RPD) = 4.24; potassium chloride: Rp2 = 0.948, RMSEP = 2.86%, RPD = 4.42). Experimental results convey that TIH technology can effectively identify the emissivity characteristics of MP minerals, facilitating quantitative detection of MP mineral content.

Application of Planarian Brain Regeneration: Detection of Water Pollution

Due to over industrialisation, the environmental pollution problem is becoming increasingly serious, especially in aquatic ecosystems. Compared with traditional physical and chemical detection methods, the use of biological indicators has become popular. The freshwater planarian Dugesia japonica is distributed extensively in aquatic ecosystems and has been applied to the area of environmental toxicology for its high chemical sensitivity. Moreover, D. japonica also has a powerful regenerative capability in which the injured planarian can regenerate a new brain in 5 days and complete an adult individual remodelling in 14 days. Therefore, it has been used as a new model organism in the field of neuro-regeneration toxicology. In our past study, D. japonica can be used as a biological indicator to detect water pollution. This can provide basic data for the detection of water pollution and provide a warning system in regard to aquatic ecosystems.