Advances on Water Quality Detection by UV-Vis Spectroscopy

Spectral fusion-based machine learning classifiers for discriminating membrane breakage in multiple scenarios

Membrane breakage can lead to filtration failure, which allows harmful substances to enter the effluent, posing potential hazards to human health and the environment. This study is an innovative combination of fluorescence and ultraviolet-visible (UV-Vis) spectroscopy to identify membrane breakage. It aims to unravel more comprehensive information, improve detection sensitivity and selectivity, and enable real-time monitoring capabilities. Fluorescence and UV-Vis data are extracted through variance partitioning analysis (VPA) and integrated through a decision tree algorithm to form a superior system with enhanced discrimination capabilities. VPA improves discrimination efficiency by extracting key information from spectral data and eliminating redundancy. The decision tree algorithm, on the other hand, can process large amounts of data simultaneously. In addition, the method has a wide range of applications and can be used in various scenarios accurately. The scenarios include domestic sewage, micropollutant water, aquaculture wastewater, and secondary treated sewage. The experimental results validate the application of machine learning classifiers in membrane breakage detection with an accuracy rate of 96.8 % to 97.4 %.

Prediction of cyanidin 3-rutinoside content in Michelia crassipes based on near-infrared spectroscopic techniques

Currently the determination of cyanidin 3-rutinoside content in plant petals usually requires chemical assays or high performance liquid chromatography (HPLC), which are time-consuming and laborious. In this study, we aimed to develop a low-cost, high-throughput method to predict cyanidin 3-rutinoside content, and developed a cyanidin 3-rutinoside prediction model using near-infrared (NIR) spectroscopy combined with partial least squares regression (PLSR). We collected spectral data from Michelia crassipes (Magnoliaceae) tepals and used five different preprocessing methods and four variable selection algorithms to calibrate the PLSR model to determine the best prediction model. The results showed that (1) the PLSR model built by combining the blockScale (BS) preprocessing method and the Significance multivariate correlation (sMC) algorithm performed the best; (2) The model has a reliable prediction ability, with a coefficient of determination (R2) of 0.72, a root mean square error (RMSE) of 1.04%, and a residual prediction deviation (RPD) of 2.06. The model can be effectively used to predict the cyanidin 3-rutinoside content of the perianth slices of M. crassipes, providing an efficient method for the rapid determination of cyanidin 3-rutinoside content.

Determination of seawater COD spectra using double-loop contraction and sorted frog optimization

This study develops a novel double-loop contraction and C value sorting selection-based shrinkage frog-leaping algorithm (double-contractive cognitive random field [DC-CRF]) to mitigate the interference of complex salts and ions in seawater on the ultraviolet-visible (UV-Vis) absorbance spectra for chemical oxygen demand (COD) quantification. The key innovations of DC-CRF are introducing variable importance evaluation via C value to guide wavelength selection and accelerate convergence; a double-loop structure integrating random frog (RF) leaping and contraction attenuation to dynamically balance convergence speed and efficiency. Utilizing seawater samples from Jiaozhou Bay, DC-CRF-partial least squares regression (PLSR) reduced the input variables by 97.5% after 1,600 iterations relative to full-spectrum PLSR, RF-PLSR, and CRF-PLSR. It achieved a test R2 of 0.943 and root mean square error of 1.603, markedly improving prediction accuracy and efficiency. This work demonstrates the efficacy of DC-CRF-PLSR in enhancing UV-Vis spectroscopy for rapid COD analysis in intricate seawater matrices, providing an efficient solution for optimizing seawater spectra.

Spectrophotometric determination of COD based on synergistic photocatalysis redox reaction using titanium dioxide nanoparticles and phosphomolybdic heteropoly acid

Chemical oxygen demand (COD) is one of the important indicators to measure the degree of organic pollution in water. In this work, a rapid spectrophotometric method for detection of COD was achieved based on the oxidation of organics in water by photogenerated holes or free radicals and the reduction of phosphomolybdic heteropolyacid by photogenerated electrons by using TiO2 nanoparticles as a photocatalyst. Taking potassium hydrogen phthalate as the COD standard, under the optimal conditions, the absorbance of reduced phosphomolybdic heteropoly acid was linear with COD in the range of 0.50-100 mg L -1. The detection limit for was COD detection was 0.171 mg L -1. The proposed methods was used for the determination of COD in real water samples, and the results were in general agreement with the national standard method. Compared with the direct photo initiated reduction of phosphomolybdic heteropoly acid without TiO2 nanoparticles, the photocatalytic reaction has better stability and higher efficiency.

In Situ Water Quality Monitoring Using an Optical Multiparameter Sensor Probe

Optical methods such as ultraviolet/visible (UV/Vis) and fluorescence spectroscopy are well-established analytical techniques for in situ water quality monitoring. A broad range of bio-logical and chemical contaminants in different concentration ranges can be detected using these methods. The availability of results in real time allows a quick response to water quality changes. The measuring devices are configured as portable multi-parameter probes. However, their specification and data processing typically cannot be changed by users, or only with difficulties. Therefore, we developed a submersible sensor probe, which combines UV/Vis and fluorescence spectroscopy together with a flexible data processing platform. Due to its modular design in the hardware and software, the sensing system can be modified to the specific application. The dimension of the waterproof enclosure with a diameter of 100 mm permits also its application in groundwater monitoring wells. As a light source for fluorescence spectroscopy, we constructed an LED array that can be equipped with four different LEDs. A miniaturized deuterium-tungsten light source (200-1100 nm) was used for UV/Vis spectroscopy. A miniaturized spectrometer with a spectral range between 225 and 1000 nm permits the detection of complete spectra for both methods.

BR-Net: Band reweighted network for quantitative analysis of rapeseed protein spectroscopy

Compared with the complexity of chemical methods, near-infrared spectroscopy (NIRS) is widely used in the detection of protein content because of its advantages of being fast and non-destructive. Aiming to tackle the problem that the raw near-infrared spectroscopy contains many redundant wavelengths, which affects the accuracy of quantitative prediction and requires expertise to process, we propose an end-to-end network: Band Reweighted Network (BR-Net) that automates wavelength reweighted and quantitative prediction of protein content in rapeseed. Unlike extracting part of wavelengths by the traditional wavelength selection methods, BR-Net retains all spectral wavelengths and assigns different weights to the wavelengths to express the correlation with the corresponding concentration, which enables wavelength selection without ignoring the information contained in the less relevant wavelengths. We compare BR-Net with traditional selection methods such as SPA, LARS, CARS, and UVE to verify its efficiency and robustness, finding that the R² of the training set and test set are 0.9797 and 0.9215, the RMSEC and RMSEP are 0.4053 and 0.8501, respectively, and the RPD is 3.5686, which prove BR-Net outperforms all the traditional methods. The network described here is universally applicable to a variety of NIR quantitative analyses.

Advances in Catchment Science, Hydrochemistry, and Aquatic Ecology Enabled by High-Frequency Water Quality Measurements

High-frequency water quality measurements in streams and rivers have expanded in scope and sophistication during the last two decades. Existing technology allows in situ automated measurements of water quality constituents, including both solutes and particulates, at unprecedented frequencies from seconds to subdaily sampling intervals. This detailed chemical information can be combined with measurements of hydrological and biogeochemical processes, bringing new insights into the sources, transport pathways, and transformation processes of solutes and particulates in complex catchments and along the aquatic continuum. Here, we summarize established and emerging high-frequency water quality technologies, outline key high-frequency hydrochemical data sets, and review scientific advances in key focus areas enabled by the rapid development of high-frequency water quality measurements in streams and rivers. Finally, we discuss future directions and challenges for using high-frequency water quality measurements to bridge scientific and management gaps by promoting a holistic understanding of freshwater systems and catchment status, health, and function.

Development of Innovative Online Modularized Device for Turbidity Monitoring

Given progress in water-quality analytical technology and the emergence of the Internet of Things (IoT) in recent years, compact and durable automated water-quality monitoring devices have substantial market potential. Due to susceptibility to the influence of interfering substances, which lowers measurement accuracy, existing automated online monitoring devices for turbidity, a key indicator of a natural water body, feature a single light source and are thus insufficient for more complicated water-quality measurement. The newly developed modularized water-quality monitoring device boasts dual light sources (VIS/NIR), capable of measuring the intensity of scattering, transmission, and reference light at the same time. Coupled with a water-quality prediction model, it can attain a good estimate for continuing monitoring of tap water (<2 NTU, error < 0.16 NTU, relative error < 19.6%) and environmental water samples (<400 NTU, error < 3.86 NTU, relative error < 2.3%). This indicates the optical module can both monitor water quality in low turbidity and provide water-treatment information alerts in high turbidity, thereby materializing automated water-quality monitoring.

Continuous, Nondestructive Detection of Microorganism Growth at Buried Interfaces with Vascularized Polymers

Evaluating surface bacterial growth at buried interfaces can be problematic due to the difficulties associated with obtaining samples. In this work, we present a new method to detect signals from microorganisms at buried interfaces that is nondestructive and can be conducted continuously. Inspired by vascular systems in nature that permit chemical communication between the surface and underlying tissues of an organism, we created a system in which an inert carrier fluid could be introduced into an empty vascular network embedded in a polymer matrix. When a microorganism layer was grown on top, small molecules produced by the growth process would diffuse down into the carrier fluid, which could then be collected and analyzed. We used this system to nondestructively detect signals from a surface layer of Escherichia coli using conductivity, ultraviolet-visible (UV-vis) absorbance spectroscopy, and high-performance liquid chromatography (HPLC) for organic acids, methods that ranged in sensitivity, time-to-result, and cost. Carrier fluid from sample vascularized polymers with surface bacterial growth recorded significantly higher values in both conductivity and absorbance at 350 nm compared to controls with no bacteria after 24 h. HPLC analysis showed three clear peaks that varied between the samples with bacteria and the controls without. Tests tracking the change in signals over 48 h showed clear trends that matched the bacterial growth curves, demonstrating the system's ability to monitor changes over time. A 2D finite element model of the system closely matched the experimental results, confirming the predictability of the system. Finally, tests using clinically relevant Staphylococcus aureus and Pseudomonas aeruginosa yielded differences in conductivity, absorbance, and HPLC peak areas unique to each species. This work lays the foundation for the use of vascularized polymers as an adaptive system for the continuous, nondestructive detection of surface microorganisms at buried interfaces in both industry and medicine.

A rapid and simplified method for evaluating the performance of fungi-algae pellets: A hierarchical analysis model

Microbial pellets technology has undergone extensive research recently and has increasingly matured, showing significant promise. However, the performance of microbial pellets cannot be predicted quickly by the current evaluating methods because they are complicated to operate, take a long time, and pose a risk to the environment. In this study, a representative microbial pellet, fungi-algae pellet, was selected as the research object. Eight evaluation parameters and four evaluation indices were chosen to construct the performance evaluation system of the fungal-algal pellets using the analytic hierarchy process (AHP) and weighting method. Combining the correlation analysis and expert opinion, we found that among the eight parameters selected, the adsorption saturation rate of mycelial pellets on algae had the most significant influence weight on the performance of fungi-algae pellet, followed by algal culture time and fungal incubation time. This research proposes and validates the Performance Evaluation Value (PEV) of fungi-algae pellet and its calculation method. We also discuss the effectiveness of this new evaluation system in saving time, cost, and emission reductions. The results of this paper enable the rapid evaluation of fungi-algae pellets and promote the better development of fungi-algae pellets technology and even other multi-microbial symbiotic pellet technologies.

A combined UV-visible with fluorescence detection method based on an unlabeled aptamer and AuNPs for the sensitive detection of acetamiprid

A simple spectral method with a wider detection range is proposed for the detection of acetamiprid.

A method based on a one-dimensional convolutional neural network for UV-vis spectrometric quantification of nitrate and COD in water under random turbidity disturbance scenario

This paper proposed a novel spectrometric quantification method for nitrate and COD concentration in water using a double-channel 1-D convolution neural network for relatively long UV-vis absorption spectra data (2600 points). To improve the model's ability to resist turbidity disturbance, a new dataset augmentation method was applied and the absorption spectra of nitrate and COD under different turbidity disturbances were successfully simulated. Compared to the PLSR model, the value of RRMSEP for the CNN model was reduced from 6.1% to 1.4% in nitrate solution and 4.5% to 1.3% in COD solution. Compared to the PLSR model, the regression accuracy of the CNN model was increased from 56% to 93% in nitrate solution and 68% to 91% in COD solution. The test on the actual solution under different turbidity disturbances shows that the 1D-CNN model had a bias rate of less than 2% in both nitrate and COD solutions, while the worst bias rate in the PLSR method was 15%.

Detection Limits of Antibiotics in Wastewater by Real-Time UV–VIS Spectrometry at Different Optical Path Length

Real-time monitoring of antibiotics in hospital and pharmaceutical wastewater using ultraviolet–visible (UV–Vis) spectroscopy is considered a promising method. Although gas chromatography–mass spectrometry (GC–MS) and other methods can detect antibiotics with quite low limits of detection (LOD), they possess various limitations. UV–Vis spectroscopy combined with chemometric methods is a promising choice for monitoring antibiotics. In this study, two immersed in situ UV–Vis sensors were used to explore the relationship between absorption spectra and antibiotics and study the influence of the optical path length on the LOD. The LODs of sensor 2 using a 10 cm optical path is up to 300 times lower than that of sensor 1 using a 0.5 mm optical path. Moreover, multiple antibiotics in the wastewater were investigated in real-time manner. The absorption spectra of 70 groups of wastewater samples containing different concentrations of tetracycline, ofloxacin, and chloramphenicol were measured. The results indicate that the nine wavelengths selected by interval partial least squares (iPLS) after the second derivative pretreatment have better predictability for ofloxacin and the six wavelengths selected by competitive adaptive reweighted sampling (CARS) after the first derivative. The multi-fold cross-validation results indicate that the model has a good predictive ability.

Fast Detection of Different Water Contaminants by Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy

Fast monitoring of water quality is a fundamental part of environmental management and protection, in particular, the possibility of qualitatively and quantitatively determining its contamination at levels that are dangerous for human health, fauna and flora. Among the techniques currently available, Raman spectroscopy and its variant, Surface-Enhanced Raman Spectroscopy (SERS), have several advantages, including no need for sample preparation, quick and easy operation and the ability to operate on the field. This article describes the application of the Raman and SERS technique to liquid samples contaminated with different classes of substances, including nitrates, phosphates, pesticides and their metabolites. The technique was also used for the detection of the air pollutant polycyclic aromatic hydrocarbons and, in particular, benzo(a)pyrene, considered as a reference for the carcinogenicity of the whole class of these compounds. To pre-concentrate the analytes, we applied a methodology based on the well-known coffee-ring effect, which ensures preconcentration of the analytes without any pretreatment of the sample, providing a versatile approach for fast and in-situ detection of water pollutants. The obtained results allowed us to reveal these analytes at low concentrations, close to or lower than their regulatory limits.

Contemporary Developments and Emerging Trends in the Application of Spectroscopy Techniques: A Particular Reference to Coconut (Cocos nucifera L.)

The number of food frauds in coconut-based products is increasing due to higher consumer demands for these products. Rising health consciousness, public awareness and increased concerns about food safety and quality have made authorities and various other certifying agencies focus more on the authentication of coconut products. As the conventional techniques for determining the quality attributes of coconut are destructive and time-consuming, non-destructive testing methods which are accurate, rapid, and easy to perform with no detrimental sampling methods are currently gaining importance. Spectroscopic methods such as nuclear magnetic resonance (NMR), infrared (IR)spectroscopy, mid-infrared (MIR)spectroscopy, near-infrared (NIR) spectroscopy, ultraviolet-visible (UV-VIS) spectroscopy, fluorescence spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and Raman spectroscopy (RS) are gaining in importance for determining the oxidative stability of coconut oil, the adulteration of oils, and the detection of harmful additives, pathogens, and toxins in coconut products and are also employed in deducing the interactions in food constituents, and microbial contaminations. The objective of this review is to provide a comprehensive analysis on the various spectroscopic techniques along with different chemometric approaches for the successful authentication and quality determination of coconut products. The manuscript was prepared by analyzing and compiling the articles that were collected from various databases such as PubMed, Google Scholar, Scopus and ScienceDirect. The spectroscopic techniques in combination with chemometrics were shown to be successful in the authentication of coconut products. RS and NMR spectroscopy techniques proved their utility and accuracy in assessing the changes in coconut oil’s chemical and viscosity profile. FTIR spectroscopy was successfully utilized to analyze the oxidation levels and determine the authenticity of coconut oils. An FT-NIR-based analysis of various coconut samples confirmed the acceptable levels of accuracy in prediction. These non-destructive methods of spectroscopy offer a broad spectrum of applications in food processing industries to detect adulterants. Moreover, the combined chemometrics and spectroscopy detection method is a versatile and accurate measurement for adulterant identification.

Non-destructive assessment of vitamin C in foods: a review of the main findings and limitations of vibrational spectroscopic techniques

The constant increase in the demand for safe and high-quality food has generated the need to develop efficient methods to evaluate food composition, vitamin C being one of the main quality indicators. However, its heterogeneity and susceptibility to degradation makes the analysis of vitamin C difficult by conventional techniques, but as a result of technological advances, vibrational spectroscopy techniques have been developed that are more efficient, economical, fast, and non-destructive. This review focuses on main findings on the evaluation of vitamin C in foods by using vibrational spectroscopic techniques. First, the fundamentals of ultraviolet–visible, infrared and Raman spectroscopy are detailed. Also, chemometric methods, whose use is essential for a correct processing and evaluation of the spectral information, are described. The use and importance of vibrational spectroscopy in the evaluation of vitamin C through qualitative characterization and quantitative analysis is reported. Finally, some limitations of the techniques and potential solutions are described, as well as future trends related to the utilization of vibrational spectroscopic techniques.

Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review

Water quality monitoring is an essential component of water quality management for water utilities for managing the drinking water supply. Online UV-Vis spectrophotometers are becoming popular choices for online water quality monitoring and process control, as they are reagent free, do not require sample pre-treatments and can provide continuous measurements. The advantages of the online UV-Vis sensors are that they can capture events and allow quicker responses to water quality changes compared to conventional water quality monitoring. This review summarizes the applications of online UV-Vis spectrophotometers for drinking water quality management in the last two decades. Water quality measurements can be performed directly using the built-in generic algorithms of the online UV-Vis instruments, including absorbance at 254 nm (UV₂₅₄), colour, dissolved organic carbon (DOC), total organic carbon (TOC), turbidity and nitrate. To enhance the usability of this technique by providing a higher level of operations intelligence, the UV-Vis spectra combined with chemometrics approach offers simplicity, flexibility and applicability. The use of anomaly detection and an early warning was also discussed for drinking water quality monitoring at the source or in the distribution system. As most of the online UV-Vis instruments studies in the drinking water field were conducted at the laboratory- and pilot-scale, future work is needed for industrial-scale evaluation with ab appropriate validation methodology. Issues and potential solutions associated with online instruments for water quality monitoring have been provided. Current technique development outcomes indicate that future research and development work is needed for the integration of early warnings and real-time water treatment process control systems using the online UV-Vis spectrophotometers as part of the water quality management system.

Review on Detection Methods of Nitrogen Species in Air, Soil and Water

Nitrogen species present in the atmosphere, soil, and water play a vital role in ecosystem stability. Reactive nitrogen gases are key air quality indicators and are responsible for atmospheric ozone layer depletion. Soil nitrogen species are one of the primary macronutrients for plant growth. Species of nitrogen in water are essential indicators of water quality, and they play an important role in aquatic environment monitoring. Anthropogenic activities have highly impacted the natural balance of the nitrogen species. Therefore, it is critical to monitor nitrogen concentrations in different environments continuously. Various methods have been explored to measure the concentration of nitrogen species in the air, soil, and water. Here, we review the recent advancements in optical and electrochemical sensing methods for measuring nitrogen concentration in the air, soil, and water. We have discussed the advantages and disadvantages of the existing methods and the future prospects. This will serve as a reference for researchers working with environment pollution and precision agriculture.

Heme Protein Binding of Sulfonamide Compounds: A Correlation Study by Spectroscopic, Calorimetric, and Computational Methods

Protein-ligand interaction studies are useful to determine the molecular mechanism of the binding phenomenon, leading to the establishment of the structure-function relationship. Here, we report the binding of well-known antibiotic sulfonamide drugs (sulfamethazine, SMZ; and sulfadiazine, SDZ) with heme protein myoglobin (Mb) using spectroscopic, calorimetric, ζ potential, and computational methods. Formation of a 1:1 complex between the ligand and Mb through well-defined equilibrium was observed. The binding constants obtained between Mb and SMZ/SDZ drugs were on the order of 10⁴ M^-1. SMZ with two additional methyl (-CH₃) substitutions has higher affinity than SDZ. Upon drug binding, a notable loss in the helicity (via circular dichroism) and perturbation of the three-dimensional (3D) protein structure (via infrared and synchronous fluorescence experiments) were observed. The binding also indicated the dominance of non-polyelectrolytic forces between the amino acid residues of the protein and the drugs. The ligand-protein binding distance signified high probability of energy transfer between them. Destabilization of the protein structure upon binding was evident from differential scanning calorimetry results and ζ potential analyses. Molecular docking presented the best probable binding sites of the drugs inside protein pockets. Thus, the present study explores the potential binding characteristics of two sulfonamide drugs (with different substitutions) with myoglobin, correlating the structural and energetic aspects.

Fluorescence detection of malachite green and cations (Cr3+, Fe3+ and Cu2+) by a europium-based coordination polymer

A europium-based CP fluorescent sensor was synthesized and exhibited excellent recognition ability for malachite green (MG) and metal cations (Cr3+, Fe3+ and Cu2+).

Lead Assays with Smartphone Detection Using a Monolithic Rod with 4-(2-Pyridylazo) Resorcinol

A monolithic rod of polyurethane foam-[4-(2-pyridylazo) resorcinol] (PUF-PAR) as a simple chemical sensor for lead assays with smartphone detection and image processing was developed. With readily available simple apparatus such as a plastic cup and a stirrer rod, the monolithic PUF rod was synthesized in a glass tube. The monolithic PUF-PAR rod could be directly loaded by standard/sample solution without sample preparation. A one-shot image in G/B value from a profile plot in ImageJ for a sample with triplicate results via a single standard calibration approach was obtained. A linear single standard calibration was: [G/B value] = -0.038[µg Pb2+] + 2.827, R2 = 0.95 for 10-30 µg Pb2+ with a limit of quantitation (LOQ) of 33 µg L-1. The precision was lower than 15% RSD. The proposed method was tested by an assay for Pb2+ contents in drinking water samples from Bangkok. The results obtained by the proposed method agree with those of ICP-OES and with 100-120% recovery, demonstrating that the method is useful for screening on-site water monitoring.

Refractive index-assisted UV/Vis spectrophotometry to overcome spectral interference by impurities

A major challenge hindering the application of techniques like UV/Vis spectrophotometry in determining concentration is spectral interference from contaminants. Since molar absorptivities vary significantly, even minuscule amounts of specific contaminants may cause relatively large errors in UV/Vis spectrophotometry based quantification. Current methods to deal with this are slow, cost-intensive, or ineffective for unknown interferents. We propose constrained refractometry as an expedient technique to aid UV/Vis spectrophotometry, avoiding large errors due to spectral interference. Based on a modified Lorentz Lorenz equation, the technique helps not only in detecting and reducing error from unknown contaminants but also in identifying the significant impurity. Experimental results show a significant reduction of error in concentration determination even for multiple unknown interfering contaminants.

Prediction and Utilization of Malondialdehyde in Exotic Pine Under Drought Stress Using Near-Infrared Spectroscopy

Drought is a major abiotic stress that adversely affects the growth and productivity of plants. Malondialdehyde (MDA), a substance produced by membrane lipids in response to reactive oxygen species (ROS), can be used as a drought indicator to evaluate the degree of plasma membrane damage and the ability of plants to drought stress tolerance. Still measuring MDA is usually a labor- and time-consuming task. In this study, near-infrared (NIR) spectroscopy combined with partial least squares (PLS) was used to obtain rapid and high-throughput measurements of MDA, and the application of this technique to plant drought stress experiments was also investigated. Two exotic conifer tree species, namely, slash pine (Pinus elliottii) and loblolly pine (Pinus taeda), were used as plant material exposed to drought stress; different types of spectral preprocessing methods and important feature-selection algorithms were applied to the PLS model to calibrate it and obtain the best MDA-predicting model. The results show that the best PLS model is established via the combined treatment of detrended variable-significant multivariate correlation algorithm (DET-sMC), where latent variables (LVs) were 6. This model has a respectable predictive capability, with a correlation coefficient (R ²) of 0.66, a root mean square error (RMSE) of 2.28%, and a residual prediction deviation (RPD) of 1.51, and it was successfully implemented in drought stress experiments as a reliable and non-destructive method to detect the MDA content in real time.