Spectroscopy-Based Methods for Water Quality Assessment: A Comprehensive Review and Potential Applications in Livestock Farming

Abstract

Water quality monitoring and evaluation are essential across multiple sectors, including public health, environmental protection, agriculture and livestock management, industrial processes, and broader sustainability efforts. Conventional water analysis techniques, although accurate, are often constrained by their labor-intensive nature, extended processing times, and limited applicability for in situ, real-time monitoring. In recent years, spectroscopy-based methods have gained prominence as alternatives for water quality assessment, particularly when combined with chemometric analyses and advanced technological systems. This review provides an overview of the current advancements of spectroscopy-based water monitoring, with a focus on spectroscopy techniques operating within ultraviolet–visible (UV–Vis) and infrared (IR) spectral regions, which are currently applied for the assessment of a broad range of physicochemical and biological parameters relevant to livestock water management, including chemical oxygen demand (COD), dissolved organic carbon (DOC), nitrates, microbial contamination, and heavy metal ions. The findings highlight the growing utility of spectroscopy as a reliable tool in water quality assessment (e.g., COD detection with R² = 0.86 and nitrate detection with R² = 0.95 compared to traditional methods) and underpin the need for continued research into scalable, sensor-integrated solutions tailored for use in livestock farming environments.

1. Introduction

The most vital resource on Earth is water, and it is indispensable for sustaining life. It occupies approximately 71% of the planet’s surface [1], playing a crucial role in climate regulation and ecological balance. In the human body, it constitutes about 60–65% and serves biochemical reactions, thermoregulation, and metabolic function [2]. Roughly 97% of the Earth’s water is saline, leaving only 3% as freshwater available for consumption and other anthropogenic applications. However, merely 0.06% of this freshwater is contained in accessible reservoirs such as lakes and rivers, while the remaining 99.4% is found in subsurface aquifers, glaciers, and wetland ecosystems, showcasing the dependence on groundwater resources for potable and agricultural water supply [3].

The universal dependency of all life forms on Earth underscores the critical role of water in maintaining biological homeostasis and ecosystem stability. Nowadays, the One Health approach emphasizes the interdependence of human, animal, and environmental health and includes water safety as a basic component. Globally, institutions such as the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), and the World Organisation for Animal Health (WOAH) actively support integrative approaches to public health and environmental sustainability [4,5,6]. In this context, addressing the increasing challenge of improperly managed water resources and sanitation services remains a priority for billions of people worldwide. The Sustainable Development Goal (SDG) 6 emphasizes the necessity of sustainable management of water resources, wastewater treatment, and ecosystems preservation, and by 2030, spectroscopy-based methods can help achieve this goal by enabling rapid, on-site detection of water contaminants, improving monitoring of water quality, and supporting data-driven decision making toward safer and more equitable water access for everyone [7].

Currently, industrial manufacturing, agriculture, and livestock farming have resulted in various pollutants entering critical water sources, either directly via seas, rivers, and lakes or indirectly through atmospheric deposition and soil infiltration impacting water quality and safety.

Various factors, including natural disasters, seasonal variations, and fluctuations in water availability, can lead to rapid or gradual changes in water quality with respect to its potability and in some cases its suitability for other applications [8].

Therefore, the need for sustainable water resource exploitation and advanced, real-time, in situ, monitoring systems of water quality has emerged, with applications aiming to support evidence-based and sustainable water management strategies of surface and groundwater sources [9]. Spectroscopy has emerged as a versatile and efficient tool for water quality assessment across a wide range of applications. Spectroscopic techniques are extensively utilized in almost all technological and scientific domains [10], enabling the determination of the composition, structural properties, and physicochemical characteristics of materials and substances [11] and the applications of key importance for agriculture and livestock farming, where reliable and precise monitoring of water quality is essential for optimizing productivity, reinforcing animal health and welfare, and maintaining sustainable resource management.

The objective of this review was to summarize and compile the available literature on spectroscopy for water quality assessment applications, with particular focus on contaminants that are commonly present in livestock farming environment. In particular, ultraviolet–visible (UV–Vis) and infrared (IR) spectroscopy are mainly examined, with applications ranging from monitoring key water quality parameters, such as chemical oxygen demand (COD), dissolved organic carbon (DOC), and phosphorus, nitrate, and microbial contamination, across various water sources such as surface water, groundwater, wastewater, and drinking water supplies. Additionally, the performance, applicability, and adaptability of each spectroscopic method and applications thereof are discussed. Finally, the review explores chemometric techniques, discussing their contribution to improving data interpretation, accuracy, and predictive modeling in spectroscopic analyses of water quality.

2. Spectroscopy Principles

2.1. Spectroscopy

Spectroscopy is the study of the light absorbance and emission in relation to the wavelength of the radiation that a medium is exposed to. It measures and interprets electromagnetic spectra [10,12] based on the principle that the energy of light is inversely related to its wavelength ($\mathrm{\lambda }$) as described by the Planck’s equation:

$$\mathrm{E}= {\displaystyle \frac{\mathrm{h}\times \mathrm{c}}{\mathrm{\lambda }}}$$

(1)

where $\mathrm{E}$ is the energy of the light in $\mathrm{J}$, $\mathrm{h}$ is the Planck’s constant $\left(6.62\times {10}^{-34 }\mathrm{J}\times {\mathrm{Hz}}^{-1}\right)$, $\mathrm{\lambda }$ is the wavelength of the light in $\mathrm{m}$, and $\mathrm{c}$ is the speed of light $\left(\approx 3\times {10}^8 \mathrm{m}/\mathrm{s}\right)$.

2.2. Electromagnetic Spectrum

The distribution of electromagnetic radiation by wavelength or frequency is known as the electromagnetic spectrum [13]. The term spectrum was first used by Isaac Newton in 1666 to refer to the multicolored band of light that appeared when he placed a glass prism in the path of a sunbeam [14]. Later, in 1800, William Herschel was researching the temperature of various colors when he discovered infrared radiation while passing a thermometer through prism-split light [15]. One year later, Johann Ritter noticed invisible rays beyond the violet end of the visible spectrum that cause specific chemical reactions; he called them “chemical rays”. This spectral region was given the name ultraviolet [16]. The electromagnetic spectrum consists of the whole range of electromagnetic radiation and includes many subranges, such as visible (Vis) light in the region from 380 nm to 750 nm, ultraviolet (UV) radiation in the region from 10 nm to 400 nm, and infrared (IR) radiation in the region from 750 nm to 1 mm as illustrated Figure 1. Currently, the majority of frequencies and wavelengths of electromagnetic radiation are exploited in spectroscopic applications.

Figure 1. The electromagnetic spectrum and wavelength ranges of electromagnetic radiation applied in water quality analyses [17] (modified).

Figure 1. The electromagnetic spectrum and wavelength ranges of electromagnetic radiation applied in water quality analyses [17] (modified).

2.3. Lambert–Beer Law

The quantitative analysis of a range of substances’ quality parameters using absorption spectra is based on the Lambert–Beer law [18,19]. An illuminated medium partially absorbs the light energy that irradiates its surface, allowing a weakened intensity to pass through it. This phenomenon is governed by Lambert–Beer law [20] and the following mathematical expression:

$$A={ \log }_{10}\left({\displaystyle \frac{{\mathrm{I}}_0}{\mathrm{I}}}\right)={ \log }_{10}\left({\displaystyle \frac{1}{\mathrm{T}}}\right)=\mathrm{\epsilon }\times \mathrm{d}\times \mathrm{c}$$

(2)

In Equation (2), the absorbance (A) is proportional to the product of the optical path length (d) in $\left(\mathrm{cm}\right)$ and the analyte’s concentration (c) in $\left(\mathrm{mol}/\mathrm{L}\right)$, and (ε) is the molar absorptivity in $(\mathrm{L}/(\mathrm{mol}\ast \mathrm{cm}$)) and inversely proportional to the transmittance (T). In Figure 2, a schematic representation is shown.

It is important to note that Lambert–Beer law assumes the use of homogeneous liquids, low analyte concentrations, and monochromatic light. High concentrations might cause deviations from linearity because of scattering effects, stray light, or chemical interactions.

3. Water Quality and Its Importance in Livestock Farming

3.1. Water and Livestock Farming

Chemical contamination of water is a challenging issue of major concern with the potential detrimental immediate and long-term effects on ecosystems and public health [21]. Water resources are broadly classified into surface water and groundwater distinguished by their origin [22]. Water pollution occurs in both categories due to a variety of anthropogenic factors associated with industrial discharge, agricultural runoff, and waste from livestock operations.

Livestock farming and the animal-derived product industry account for nearly one-third of the global agricultural water consumed, highlighting their substantial demand for freshwater resources [23]. The livestock sector alone consumes approximately 10% of the estimated annual global water flows [24], while the quality standards of potable water are similar for both animals and humans [25]. Nevertheless, in many cases, the source of water in livestock farms may vary significantly. Table 1 summarizes the acceptable thresholds of different analytes in the water used for animal watering and irrigation in the USA, with the variation between the two thresholds being evidenced; irrigation thresholds are shown as livestock often consume water from agricultural sources.

Table 1. Water quality thresholds for animal watering and crop irrigation in the USA [25].

Elements	Threshold for Animal Watering	Threshold for Crop Irrigation
Chlorine (ppm)	300
Chromium (ppm)	1.0	1.0
Copper (ppm)	0.5	5.0
Lead (ppm)	0.1	10.0
Mercury (ppm)	0.01
Nickel (ppm)	1.0	2.0
Nitrate nitrogen (ppm)	100
pH	8.5
Phosphorus (ppm)	0.7
Sulfates (ppm)	300
Total bacteria (n/100 mL)	1000
Total dissolved solids (ppm)	5000

Note: ppm: parts per million, n: numbers.

Table 1. Water quality thresholds for animal watering and crop irrigation in the USA [25].

Elements	Threshold for Animal Watering	Threshold for Crop Irrigation
Chlorine (ppm)	300
Chromium (ppm)	1.0	1.0
Copper (ppm)	0.5	5.0
Lead (ppm)	0.1	10.0
Mercury (ppm)	0.01
Nickel (ppm)	1.0	2.0
Nitrate nitrogen (ppm)	100
pH	8.5
Phosphorus (ppm)	0.7
Sulfates (ppm)	300
Total bacteria (n/100 mL)	1000
Total dissolved solids (ppm)	5000

Note: ppm: parts per million, n: numbers.

Some water sources other than tap water utilized to cover farm animal water demands include (i) wells; (ii) rivers, ponds, lakes and other surface water spots; and (iii) boreholes constructed for water extraction, providing a reliable alternative in areas with limited surface water availability.

The livestock sector needs a substantial quantity of water to support the increasing production demands and has a considerable impact on water resource quality and hygiene [26]. The excessive manure and overall livestock-related waste production that affects water quality has resulted by the intensification of livestock farming, also known as the “livestock revolution” [27]. Particularly in intensively reared animals, mineral and protein supplementation frequently surpass the animals’ nutritional requirements, leading to nutrient-rich manure. While this can be beneficial for agriculture, it also poses environmental risks (Figure 3). Excess nitrogen and phosphorus from manure can seep into groundwater and flow into surface waters, contributing to water contamination [28,29,30].

Additionally, manure application introduces heavy metals like copper and zinc which accumulate in soils and enter the aquifer and the food chain [31]. Moreover, fertilizers, pesticides, and other agrochemicals in agricultural production introduce relevant residues and pollutants into water bodies [32]. Finally, antibiotics used in livestock prophylaxis and metaphylaxis further contribute to water pollution, as 30–90% of them are excreted unmetabolized into the environment, facilitating antimicrobial resistance within the ecosystems [33]. The persistent nature of antibiotic residues in agricultural environments around the world, their substantial contribution to environmental contamination, and the pressing demand for efficient monitoring and mitigation measures to protect ecosystems and public health are all highlighted in a recent systematic review on the worldwide traceability of antibiotic residues from livestock in wastewater and soil [34].

Figure 3. Diffuse and source point paths of different pollutants in catchment water in livestock farming areas, adapted from [29], with permission of Elsevier, 2025.

Figure 3. Diffuse and source point paths of different pollutants in catchment water in livestock farming areas, adapted from [29], with permission of Elsevier, 2025.

3.2. Water Quality Parameters and Standards

Water quality is defined by a variety of characteristics that determine its suitability for different purposes. Water is classified in four categories according to its quality, namely, potable, palatable, polluted, and infected water (Figure 4) [22].

Various parameters are essential for the comprehensive assessment of water quality [35]. These parameters are typically classified into three main groups, namely, physical, chemical, and biological, as outlined in Table 2 [36]. The presence of extraneous substances in water, which do not inherently pose a health risk, such as sediments, color, or taste-altering compounds, is described as water contamination [37]. In contrast, water pollution denotes contamination that adversely affects the suitability of water for specific uses and resulted from anthropogenic activities [38].

Table 2. Physical, chemical, biological, and radiological parameters used to define water quality [22,39].

Physical	Chemical	Biological	Radiological
Turbidity	pH	Bacteria	Radioactive substances
Temperature	Acidity	Algae
Color	Alkalinity	Viruses
Taste and odor	Chloride	Protozoa
Solids	Chloride residual
Electrical conductivity (EC)	Sulfate
	Fluoride
	Iron and manganese
	Copper and zinc
	Hardness
	dissolved oxygen
	Biochemical oxygen demand (BOD)
	Chemical oxygen demand (COD)
	Toxic inorganic substances
	Toxic organic substances

Table 2. Physical, chemical, biological, and radiological parameters used to define water quality [22,39].

Physical	Chemical	Biological	Radiological
Turbidity	pH	Bacteria	Radioactive substances
Temperature	Acidity	Algae
Color	Alkalinity	Viruses
Taste and odor	Chloride	Protozoa
Solids	Chloride residual
Electrical conductivity (EC)	Sulfate
	Fluoride
	Iron and manganese
	Copper and zinc
	Hardness
	dissolved oxygen
	Biochemical oxygen demand (BOD)
	Chemical oxygen demand (COD)
	Toxic inorganic substances
	Toxic organic substances

Physical parameters refer to the observable properties of the water such as temperature, turbidity, color, electrical conductivity, and total suspended solids. Physical parameters of the water influence its appearance and its capacity to carry or dissolve other substances, while excessive turbidity or temperature fluctuations can reduce water intake in farm animals impairing their welfare, productivity, and thermoregulation efficiency.

Chemical parameters refer to inorganic and organic compounds, including pH, dissolved oxygen, nitrates, phosphates, heavy metals (e.g., lead, arsenic), and salinity. These elements affect the metabolic functions of animals, with contaminants like nitrates or abnormal pH levels potentially causing toxicity or reduced productivity. Some of the chemical parameters are explained below:

• Chemical oxygen demand (COD) is defined as the quantity of oxidants consumed by the reducing substances in an one-liter oxidized water sample under specific conditions, expressed in milligrams per liter. It demonstrates the level of contamination brought on by introducing reducing agents into water [19].
• Dissolved organic carbon (DOC) represents a universal element of the freshwater carbon cycle. It is a water disinfection byproduct that affects human health and can impair the effectiveness of aquatic ecosystems and weaken ultraviolet radiation [40].
• Nitrogen- and phosphorus-containing compounds (e.g., detergents, fertilizers) are being released into the aquifer in mass quantities, disrupting the nitrogen and phosphorus equilibrium in aquatic ecosystems and causing eutrophication, the rapid and excessive growth of algae and other microorganisms, and the aquatic ecosystem degradation due to hypoxia. Consequently, water quality will eventually deteriorate, and aquatic life will be challenged, which will have a remarkable impact on marine ecosystems and human production activities. [19].

The permissible limits for the aforementioned water quality parameters are variable and contingent upon factors, including the intended application of the water (e.g., drinking, agricultural irrigation, industrial use), as well as the regulatory framework established by individual countries or international organizations [36]. Drinking quality indicators and the thresholds of various relevant analytes in European Union and as proposed by the WHO are presented in Table 3.

Table 3. Indicative parameters for drinking water quality and associated limits, adapted from [36], with permission from IOP Publishing, 2025.

Parameter	European Union [41]	WHO [39]
pH	6.5–9.5	-
Electrical conductivity (μS cm−1 at 20 °C)	2500	-
Ammonia/ammonium (mg/L)	0.5	0.2
Chloride (mg/L)	250	250
Chromium (μg/L)	25	50
Copper (mg/L)	2.0	2.0
Fluoride (mg/L)	1.5	1.5
Lead (μg/l)	5.0	10
Nitrate (mg/L)	50	50
Nitrite (mg/L)	0.5	3.0
Sulfate (mg/L)	250	-
Pesticides (total) (μg/L)	0.5	-

Table 3. Indicative parameters for drinking water quality and associated limits, adapted from [36], with permission from IOP Publishing, 2025.

Parameter	European Union [41]	WHO [39]
pH	6.5–9.5	-
Electrical conductivity (μS cm−1 at 20 °C)	2500	-
Ammonia/ammonium (mg/L)	0.5	0.2
Chloride (mg/L)	250	250
Chromium (μg/L)	25	50
Copper (mg/L)	2.0	2.0
Fluoride (mg/L)	1.5	1.5
Lead (μg/l)	5.0	10
Nitrate (mg/L)	50	50
Nitrite (mg/L)	0.5	3.0
Sulfate (mg/L)	250	-
Pesticides (total) (μg/L)	0.5	-

Biological parameters refer to microorganisms including bacteria, viruses, protozoa, and algae; some of these are important in livestock systems because of their connection to waterborne infections and associated illnesses.

Waterborne diseases arise when pathogens are transmitted through the consumption of contaminated water. Water contamination primarily occurs via fecal pollution (Figure 5), with enteric bacteria such as Escherichia coli serving as key indicators of fecal presence; however, their detection alone does not suffice for assessing water safety. Moreover, environmental factors such as rising water temperature, flooding, and extreme weather events exacerbate the risk of pathogens’ survival and transmission, increasing the likelihood of disease outbreaks in both animals and humans [21]. Indeed, diarrheal diseases—often caused by waterborne pathogens—are responsible for 1.8 million deaths annually, particularly among children in developing countries. Some of the most common waterborne pathogens include Vibrio cholerae (Cholera), Shigella spp. (Dysentery), and Salmonella spp. (Typhoid) [42].

The integration of spectroscopy-based spectrum acquisition with machine learning-based analytical techniques has emerged as an increasingly popular approach for the detection of pathogens in the water. A representative example is the study by Feng et al. [43], in which the UV–Vis spectra were captured for samples contaminated with bacteria species such as E. coli and Salmonella typhi, while artificial neural networks were used to build classification models enabling the automated pathogen identification based on distinct spectral features. However, there are still several challenges. Extensive, high-quality training datasets are scarce and limited, and non-representative data pose the risk of models’ overfitting. In addition, decreased reliability and inaccurate predictions of the model may also result from the lack of established validation procedures [44].

Figure 5. Paths of catchment water contamination with microbial and protozoan micro-organisms in livestock farming areas, adapted from [29], with permission of Elsevier, 2025.

Figure 5. Paths of catchment water contamination with microbial and protozoan micro-organisms in livestock farming areas, adapted from [29], with permission of Elsevier, 2025.

4. Machine Learning and Chemometrics

Spectroscopic techniques and chemometrics are harmoniously and effectively combined in water quality analysis. Chemometrics refer to a broad range of statistical methods utilized for data mining and chemical data analysis and can be considered a subcategory of machine learning (ML) algorithms. Based on the occurrence of data labeling, chemometric methods are divided into supervised and unsupervised methods. Supervised methods are applied either on classification problems of items assigned to discrete categories or on regression problems where the input data are associated with a continuous variable. Contrarily, unsupervised learning builds on uncovering typical patterns or structures in data sets without external labeling input. Unsupervised learning is commonly employed during the initial stages of analysis to reduce the dimensionality of datasets, ensuring that only the most meaningful and informative data are kept for further processing.

One of the most widely used unsupervised learning algorithms is principal component analysis (PCA) [45], which reduces a large dataset of intercorrelated variables to a smaller dataset, while explaining the largest variance of the dataset through newly established independent variables known as principal components [46].

The most popular supervised algorithms used in water analysis applications include the following:

• Partial Least Squares (PLS): It is a regression model that predicts a group of dependent variables from a group of independent variables (predictors). By projecting both input and output variables into a new space that maximizes the covariance allowing to model the interactions between them, it is considered efficient when there are more predictors than observations or in cases of multicollinearity [47].
• Support Vector Machines (SVM): They are supervised learning models, considered as binary linear classifiers that classify observations by finding the optimal boundary which maximizes the distance (margin) between different classes [48]. Using various kernel functions, they are effective in high-dimensional issues and perform well on both linear and non-linear classification tasks.
• Artificial Neural Networks (ANNs): ANNs are computational models based on a network that consists of several connected nodes inspired by the human brain and are known as artificial neurons, with the simplest one being perceptron (used for binary classification of linearly separable data) [49]. There are many types of NNs specialized in different scientific areas and applications, including spatial data and image-like pattern analyses (convolutional NNs) [50].
• Decision Trees (DT): It is a tree-like non-parametric method that can be used to solve classification and regression problems. The leaves of the trees represent different labels or outcomes (classes). They are easy to implement; however, in the case of complex datasets, they are sensitive to overfitting [51].
• Random Forest (RF): RF is considered an ensemble learning method for which multiple DTs are trained. For classification problems, the model outputs the class chosen by the majority of trees, while for regression problems, it returns the average of the trees’ outputs [52].

Recent research emphasizes the significance of enhancing data quality and iteratively upgrading models to improve performance, in addition to the choice of machine learning methods. Wang et al. [53] recently pointed out that applying drift correction, baseline adjustment, and selecting relevant wavelengths are key steps to clean up the data and remove unnecessary variables. Using techniques like sparse representation and dimensionality filtering can help reduce the number of features, making models simpler without losing predictive strength. These methods are particularly helpful when dealing with complex spectral data from real-world conditions, like in water quality assessment [54]. Combined with feedback loops and cross-validation, they support the development of more reliable and transferable models.

There are numerous studies assessing the performance of ML algorithms in water quality analysis (

Table 4. Comparison of machine learning algorithms.

Algorithm	Accuracy	Computational Cost	Real-Time Suitability	Overfitting Risk	Interpretability
PLS	++	+	+++	+	+++
SVM	+++	++	++	++	++
ANNs	++++	+++	++	+++	+
DT	++	+	+++	+++	+++
RF	+++	+	++	++	++

Note: PLS: partial least squares, SVM: support vector machines, ANNs: artificial neural networks, DT: decision trees, RF: random forest, +: low, ++: moderate, +++: high, ++++: very high.

and Table 5). In one of them, Zhu et al. [55] reviewed various ML methods across different aquatic environments (groundwater, drinking water, wastewater, and marine environments), with support vector machines (SVM) and artificial neural networks showing the best performance. In other studies, various ML algorithms were evaluated based on their efficiency when used either in water quality classification or in prediction applications. Specifically, Nasir et al. [56] studied numerous different classifiers, such as RF, DT, and SVM, on a dataset derived from the analysis of 1679 water samples and found that the CATBoost algorithm achieved the highest accuracy for the prediction of the studied water quality parameters. Similarly, in the study by Kaddoura [57], the aim was to evaluate different ML algorithms for the prediction of water quality, with eleven algorithms being tested. Based on the F1 scores and the ROC curve values, SVM and k-nearest neighbor (k-nn) algorithms were found to perform better. Finally, Najah Ahmed et al. [58] also investigated the use of ML methods with adaptive neuro-fuzzy inference system (ANFIS), radial basis function neural networks (RBF-ANNs), and multi-layer perceptron neural networks (MLP-ANNs) being exploited to improve the prediction capacity of water quality.

In Table 4, the abovementioned machine learning techniques are listed and evaluated in terms of accuracy (classification or prediction performance), computational cost (training and running time/resources needed), real-time suitability (feasibility of the model for real-time prediction), overfitting risk (tendency to learn noise instead of underlying patterns), and interpretability (understanding of how the model makes decisions).

5. Applications of Spectroscopy in Water Quality Assessment

Spectroscopy has a wide range of applications in water quality analysis across various aquatic systems including surface water, groundwater, and wastewater [32,59,60]. Herein, we present different applications in these aquatic systems; however, it is important to note that spectroscopic methods also play a significant role in water quality assessment for fish farming and in the analysis of water content in soil [61,62]. Spectroscopic techniques, such as NIR spectroscopy and UV–Vis spectroscopy, are primarily employed in evaluating livestock water resources, facilitating the detection of analytes and specific contaminants and assessing the quality of valuable water resources. A key advantage of these methods is their ability to enable real-time, on-site analysis, in contrast to conventional water quality assessment techniques, which are time-consuming and typically conducted in specialized laboratories.

A typical spectroscopical water analysis set-up is demonstrated in Figure 6. Using the appropriate spectroscopic method, spectral data are collected, analyzed, and combined with a variety of chemometric techniques to acquire the desirable results for water quality.

5.1. Ultraviolet–Visible (UV–Vis) Spectroscopy

Ultraviolet–visible spectroscopy is a low-cost spectroscopic method that can efficiently and accurately detect and identify a variety of physical, chemical, and microbiological water quality parameters [18]. Its absorption depends on electronic structure, and it detects electronic transitions in molecules, resulting in high molar absorptivities and enabling sensitive quantification of bulk water parameters such as COD and nitrate. It is currently utilized for a variety of water analyses applications extending from bacterial or contamination detection to dissolved organic carbon (DOC) quantification.

Feng et al. [63] used a UV–Vis spectrophotometer to acquire the spectra of water samples contaminated by bacteria at 200–900 nm; for the characterization of samples, principal component analysis-Monte Carlo (PCA-MC) was used to separate the spectrum of mixed-bacteria contaminated water samples and measure the microbial content. The method succeeded to efficiently monitor water microbial content, achieving a 0.9954 coefficient of determination (R²) in the testing set, which though could be attributed to overfitting considering the small sample (11 samples). Similarly, several studies have applied UV–Vis spectroscopy combined with chemometric tools for the monitoring of chemical oxygen demand (COD). Chen et al. [64] monitored COD in wastewater using UV–Vis spectroscopy combined with partial least squares (PLS). Measuring the spectra of 82 samples with wavelengths ranging from 200 to 650 nm for different wavelength paths, they generated a slope-derived spectrum achieving a coefficient of determination (R²) equal to 0.936 with a relatively high RMSE of 122 mg/L. The combination of UV–Vis spectroscopy with PLS to measure COD was also used by Li et al. [65]; specifically, the best prediction performance was acquired using synergy interval PLS (siPLS) with correlation coefficient of prediction (R_pred) of 0.8334 and root mean square error of prediction (RMSEP) of 2.63. Diffuse reflectance UV–Vis spectroscopy and PLS were also used by Agustsson et al. [66] to assess COD and turbidity in water, achieving R² values equal to 0.85 and 0.96, respectively, between the reference and the measured concentrations. Further advancing COD analysis, Hu et al. [67] measured COD by compensating the impact of turbidity in the spectral measurements acquired using UV–Vis spectroscopy. The method reached 0.99 R² and 2.42 mg/L root mean square error (RMSE).

The applications of UV–Vis spectroscopy have also been extended to natural water bodies, demonstrating the potential for real-time, field monitoring. Zhu et al. [68], used a portable UV–Vis spectrophotometer (spectro:lyser, S:CAN Messtechnik GmbH, Graz, Austria) for the quantification of DOC and iron (Fe) in river water samples. This spectrophotometer offers measurement stability and is designed for long-term use with automatic cleaning and automation functions. They acquired in situ and ex situ spectral data and exploited three different analysis models, namely, PLS, principal component regression (PCR) and multiple stepwise regression (MSR). For both DOC and Fe, the MSR model proved to be the most efficient one, reaching R² values of 0.971 and 0.989 and low RMSE values of 2.599 mg/L and 108.905 μg/L, respectively, suggesting that the predictions were accurate. Similarly, Cook et al. [69] studied DOC in tropical peatlands, achieving validation R² values that ranged from 0.86 to 0.93.

The classification of drainage water into four different categories (domestic sewage, mixed rainwater, rainwater, and industrial sewage) using the UV–Vis spectral data and neural networks (NN) algorithms was studied by Zhu et al. [70], achieving an overall sensitivity of 97.9% and specificity of 99.3% with the application of a convolutional NN (CNN). In the study by Wang et al. [71], the goal was to detect real-time contamination events in the water distribution system with UV–Vis spectrometry and Bayesian analysis for event classification. The system demonstrated reliable operation for real-time monitoring with sufficient detection sensitivity for contamination levels with concentrations above 30 μg/L, though further work was deemed necessary to improve its stability under long-term measurements and maintenance demands to broaden the applicability of the proposed method.

Finally, UV–Vis spectroscopy has also proven effective for quantifying a variety of analytes in different aquatic environments. In the study by Etheridge et al. [72], the concentration of nitrogen, carbon, phosphorus, and suspended solids in tidal marsh was investigated, among other parameters. Combining UV–Vis absorption spectrum with different calibration models, depending on the parameter, they achieved R² values ranging from 0.750 to 0.995. Furthermore, UV–Vis spectroscopy was also employed by Mason et al. [73] in their work to detect and quantify residues of the antibiotics lincomycin and tylosin, achieving limit of detection (LOD) 0.25 μg/L and 0.20 μg/L, respectively. Li et al. [74] used in situ UV–Vis sensors to assess various antibiotics in wastewater in real-time, including tetracycline, ofloxacin, and chloramphenicol. They demonstrated that, in comparison to a 0.5 mm path, a longer optical path length (10 cm) might reduce the LOD by up to 300 times and obtain detection limits as low as ~3.1 µg/L for tetracycline. These in-situ instruments have stability over a number of measurement cycles and exhibit long-term accuracy, as long as the lens is periodically cleaned and recalibrated. Their chemometric modeling resulted in precise antibiotic concentration predictions, with R² between 0.933 and 0.998, using iPLS and competitive adaptive reweighted sampling (CARS) wavelength selection on spectra from 70 wastewater samples. Table 5 summarizes UV/Vis spectroscopy applications on water assessment.

Table 5. Ultraviolet–visible spectroscopy (UV–Vis) applications and performance.

Wavelength $\left(\mathbf{n}\mathbf{m}\right)$	No of Samples	Origin of Sample	Chemometrics	Application	${\mathit{R}}^2$	RMSE	Reference
200–900	11	Cultivated bacteria	PCA-MC	Bacteria detection	0.9954	NA	[63]
220–750	66	Fabricated	PLS	COD, turbidity	0.99	2.42 mg/L	[67]
270, 350	252	Tropical peatlands	NLR, LR	DOC quantification	0.86–0.98	1.51–6.89 mg/L	[69]
200–800	183, 142	Catchment water	MSR	DOC, Fe	0.973, 0.989	2.599 mg/L, 108.905 μg/L	[68]
193.91–1121.69	144	Lake water	siPLS	COD	0.8334	2.63 1	[65]
200–650	98	Wastewater	FiPLS	COD	0.936	122 mg/L 2	[64]
200–1100	48	Fabricated	PLS	COD, turbidity	0.69, 0.95	35%, 21% 3	[66]
220–700 *	192	Different sewer networks	FNN, CNN	Drainage type recognition	NA	NA	[70]
225–260 260–320 320–700	144	Fabricated	PLS	Nitrate, COD, turbidity	0.993, 0.982, 0.998	1.29 mg/L, 2.337 mg/L, 0.696 mg/L	[75]
250–600	ND	Fabricated	EKF-DM	Copper, cobalt, nickel	0.9958, 0.9976, 0.9915	NA	[76]
520/610 **	ND	Fabricated	ND	metal ions	NA	NA	[77]

Note: RMSE: root mean square error, PCA: principal component analysis, MC: Monte Carlo, PLS: partial least squares, COD: chemical oxygen demand, NLR: non-linear regression, LR: linear regression, DOC: dissolved organic carbon, MSR: multiple stepwise regression, siPLS: synergy interval partial least squares, ^1,2 RMSEP: root mean square error of prediction, FiPLS: forward interval partial least squares, ³ SEP: standard error of prediction, FNN: fully connected neural network, CNN: convolutional neural network, EKF-DM: extended Kalman filter and derivative method. * sensitivity (%): 95.8, 97.8, specificity (%): 98.6, 99.3, ** Limit of Detection: 30 parts per billion for lead ions and 89 parts per billion for aluminum ions, ND: not defined, NA: not available.

Table 5. Ultraviolet–visible spectroscopy (UV–Vis) applications and performance.

Wavelength $\left(\mathbf{n}\mathbf{m}\right)$	No of Samples	Origin of Sample	Chemometrics	Application	${\mathit{R}}^2$	RMSE	Reference
200–900	11	Cultivated bacteria	PCA-MC	Bacteria detection	0.9954	NA	[63]
220–750	66	Fabricated	PLS	COD, turbidity	0.99	2.42 mg/L	[67]
270, 350	252	Tropical peatlands	NLR, LR	DOC quantification	0.86–0.98	1.51–6.89 mg/L	[69]
200–800	183, 142	Catchment water	MSR	DOC, Fe	0.973, 0.989	2.599 mg/L, 108.905 μg/L	[68]
193.91–1121.69	144	Lake water	siPLS	COD	0.8334	2.63 1	[65]
200–650	98	Wastewater	FiPLS	COD	0.936	122 mg/L 2	[64]
200–1100	48	Fabricated	PLS	COD, turbidity	0.69, 0.95	35%, 21% 3	[66]
220–700 *	192	Different sewer networks	FNN, CNN	Drainage type recognition	NA	NA	[70]
225–260 260–320 320–700	144	Fabricated	PLS	Nitrate, COD, turbidity	0.993, 0.982, 0.998	1.29 mg/L, 2.337 mg/L, 0.696 mg/L	[75]
250–600	ND	Fabricated	EKF-DM	Copper, cobalt, nickel	0.9958, 0.9976, 0.9915	NA	[76]
520/610 **	ND	Fabricated	ND	metal ions	NA	NA	[77]

Note: RMSE: root mean square error, PCA: principal component analysis, MC: Monte Carlo, PLS: partial least squares, COD: chemical oxygen demand, NLR: non-linear regression, LR: linear regression, DOC: dissolved organic carbon, MSR: multiple stepwise regression, siPLS: synergy interval partial least squares, ^1,2 RMSEP: root mean square error of prediction, FiPLS: forward interval partial least squares, ³ SEP: standard error of prediction, FNN: fully connected neural network, CNN: convolutional neural network, EKF-DM: extended Kalman filter and derivative method. * sensitivity (%): 95.8, 97.8, specificity (%): 98.6, 99.3, ** Limit of Detection: 30 parts per billion for lead ions and 89 parts per billion for aluminum ions, ND: not defined, NA: not available.

5.2. Infrared Spectroscopy

Infrared (IR) spectroscopy, with its high precision and variety of applications, is considered a rapid spectroscopic analytical method [18]. Vibrational transitions of molecular bonds and dipole changes are probed by IR spectroscopy, which provides molecular “fingerprints” that allow the identification of particular functional groups or individual contaminants. In water analysis, its range of application extends from measuring key water quality parameters, such as COD and alkalinity [78], to detecting various analytes including heavy metals [79] (Table 6). A number of methods are included in infrared spectroscopy, such as Fourier transform infrared spectroscopy (FTIR), mid-infrared spectroscopy (MIRS), and near-infrared spectroscopy (NIRS). The primary difference among NIRS, MIRS, and FTIR is in their operational wavelength ranges, with NIRS operating in the range of 750 to 2500 nm and MIRS from 2.5 to 10 μm. However, FTIR typically operates within the MIR region, but, in some cases, extends into the NIR and the far-infrared (FIR) regions.

The absorbance spectrum of 83 polluted samples was collected using a NIR spectrophotometer (NIRSystems 5000, produced by Foss, Hillerød, Denmark) in the study by Chen et al. [80]. The aim was the rapid evaluation of water pollution in agricultural applications. To this end, a CNN was designed and trained to enhance the prediction accuracy of NIR spectroscopy, attaining a calibration correlation coefficient Rc of 0.938 and reducing the RMSE of calibration to 19.86 mg/L, which is still relatively high. In another study by Chen et al. [60], focusing on water pollution assessment, the prediction of the samples’ COD values was performed using a least square support vector machine (LSSVM) algorithm. The optimal results were obtained using a five-fold cross-validation LSSVM model, with five hidden layers and 12 neurons per layer, enhanced with logistic-based kernel function. The RMSECV of the model was 20.19 mg/L, indicating potential generalization problems. Similarly, in a study by Skou et al. [81], the combination of NIRS and PLS was applied to monitor water quality in dairy processing units. In particular, they focused on urea content, a compound with potential biological risk and achieved a RMSEP of 12.1 ppm concerning the process model validation. Bacterial identification was the focus of studies by Alexandrakis et al. [82] and Cámara-Martos et al. [83], both employing NIRS alongside multivariate analysis. Alexandrakis et al. suspended bacteria in water and collected NIR spectra from aqueous solutions, achieving classification accuracies ranging from 77.4% to 100.0% using different chemometrics methods. Cámara-Martos et al. utilized FT-NIR, demonstrating successful quantification at high bacterial concentrations (3–9 log cfu/mL), with R values between 0.98 and 0.99. Quintelas et al. [84] used FT-NIR spectroscopy for the quantification of pharmaceutical substances such as ibuprofen (IBU), sulfamethoxazole (SMX), and β-estradiol (E2) in wastewater. The results were promising, with R² values ranging from 0.858 to 0.963 across the compounds under investigation. The application of FTIR-ATR (attenuated total reflectance) spectroscopy to urban water bodies was investigated by Wu et al. [85] and Zheng et al. [86], focusing on nitrate and phosphorus concentrations, respectively. Fourier transform infrared is used as a biochemical fingerprint technique [87], while ATR serves as a sampling method used alongside with FTIR, allowing solid and liquid samples to be placed directly onto a crystal surface, where infrared light is directed onto them [88,89]. Cleaning the crystal surface and routine performance checks are crucial for maintaining low detection limits and avoiding measurement drift, even though the ATR-FTIR setup offers accurate and consistent results with minimal preparation. In another study, Wu et al. [85] reported R² values ranging from 0.889 to 0.972, depending on the sampling month, and R² values ranging from 0.784 to 0.994 for different nitrate concentrations. Regarding phosphorus monitoring, Zheng et al. [86] found that integrating FTIR-ATR with machine learning techniques was highly effective; indeed, self-adaptive PLS model yielded the most promising results with R² = 0.973 and RMSEV = 0.015 mg/L, indicating statistical and practical accuracy of the proposed method.

Table 6. Infrared spectroscopy applications and performance in water analysis.

Wavelength	No of Samples	Origin of Sample	Chemometrics	Application	${\mathit{R}}^2$	RMSE	Reference
200–14000 cm−1	276	Sludge wastewater treatment	PCA, PLS	IBU, SMX, E2, EE2, CRB	0.943, 0.948, 0.951, 0.858, 0.963	5.47%, 4.91%, 6.16%, 10.12%, 5.10%	[84]
4000–650 cm−1	94	River and lake water	PCA, PLSR	Nitrate monitoring	0.8868–0.9720, 0.7836–0.9938	NA	[85]
4000–800 cm−1	100	River and lake water	PCA, SA-PLS	Phosphorus monitoring	0.973	0.015 mg/L	[86]
390–1000 nm	ND	Lake water	NA	Polluted/ non-polluted	NA	NA	[32]
780–2500 nm	83	Industrial wastewater	CNN	Pollution level	0.914	25.47 1	[80]
780–2500 nm	83	Wastewater	LSSVM	COD	0.912	20.19 mg/L *	[60]
700–900 nm *	418	Cultivated bacteria	PCA, PLS2-DA, SIMCA	Bacterial identification	NA	NA	[82]
1100–2500 nm	140	Cultivated bacteria	PCA, PLS	Bacterial identification	0.983–0.99	0.09–0.28 log cfu/mL	[83]
700–2500 nm	32	Dairy process	PLS	Urea, lactose	NA	12.1 ppm	[81]

Note: RMSE: root mean square error, PCA: principal component analysis, PLS: partial least squares, IBU: ibuprofen, SMX: sulfamethoxazole, E₂: β-estradiol, EE₂: ethinylestradiol, CRB: carbamazepine, PLSR: partial least squares regression, SA-PLS: self-adaptive partial least squares, CNN: convolutional neural network, ¹: RMSEC: root mean square error of calibration, COD: chemical oxygen demand, ND: not defined, LSSVM: least squares support vector machine, * accuracy (%): 77.4–100, PLS2-DA: partial least squares discriminant analysis, SIMCA: soft independent modeling of class analogy, NA: not available.

Table 6. Infrared spectroscopy applications and performance in water analysis.

Wavelength	No of Samples	Origin of Sample	Chemometrics	Application	${\mathit{R}}^2$	RMSE	Reference
200–14000 cm−1	276	Sludge wastewater treatment	PCA, PLS	IBU, SMX, E2, EE2, CRB	0.943, 0.948, 0.951, 0.858, 0.963	5.47%, 4.91%, 6.16%, 10.12%, 5.10%	[84]
4000–650 cm−1	94	River and lake water	PCA, PLSR	Nitrate monitoring	0.8868–0.9720, 0.7836–0.9938	NA	[85]
4000–800 cm−1	100	River and lake water	PCA, SA-PLS	Phosphorus monitoring	0.973	0.015 mg/L	[86]
390–1000 nm	ND	Lake water	NA	Polluted/ non-polluted	NA	NA	[32]
780–2500 nm	83	Industrial wastewater	CNN	Pollution level	0.914	25.47 1	[80]
780–2500 nm	83	Wastewater	LSSVM	COD	0.912	20.19 mg/L *	[60]
700–900 nm *	418	Cultivated bacteria	PCA, PLS2-DA, SIMCA	Bacterial identification	NA	NA	[82]
1100–2500 nm	140	Cultivated bacteria	PCA, PLS	Bacterial identification	0.983–0.99	0.09–0.28 log cfu/mL	[83]
700–2500 nm	32	Dairy process	PLS	Urea, lactose	NA	12.1 ppm	[81]

Note: RMSE: root mean square error, PCA: principal component analysis, PLS: partial least squares, IBU: ibuprofen, SMX: sulfamethoxazole, E₂: β-estradiol, EE₂: ethinylestradiol, CRB: carbamazepine, PLSR: partial least squares regression, SA-PLS: self-adaptive partial least squares, CNN: convolutional neural network, ¹: RMSEC: root mean square error of calibration, COD: chemical oxygen demand, ND: not defined, LSSVM: least squares support vector machine, * accuracy (%): 77.4–100, PLS2-DA: partial least squares discriminant analysis, SIMCA: soft independent modeling of class analogy, NA: not available.

5.3. Limitations and Challenges in UV–Vis and IR Spectroscopy for Water Quality Assessment

Despite their ability to enable real-time, on-site monitoring, UV–Vis and IR spectroscopy are both affected by environmental factors and spectrum interferences. In UV–Vis spectroscopy, turbidity-induced scattering and overlapping absorbance bands between nitrate and COD might reduce the detection accuracy and bias data. In their study, Chen et al. [75] proposed interval analysis and difference spectra compensation to mitigate such effects. Using partial least squares (PLS) and first-derivative spectra, Dong et al. [90] created an equivalent concentration offset model to remove DOC interference in UV–Vis for nitrate detection. Furthermore, a variety of environmental factors, such as electrical conductivity, pH, and temperature, might influence UV–Vis absorption. To eliminate these effects, Li et al. [91] used data fusion techniques in COD prediction models. Lastly, Maguire et al. [92] showed how to enhance nitrate predictions in intricate water matrices utilizing multivariate regression techniques such as Lasso and PLSR. In IR spectroscopy, high water absorbance in specific spectral regions, such as 3000–3800 cm⁻¹ and 1500–1800 cm⁻¹, can disturb nitrate absorbance. especially when nitrate is in low concentrations [93]. The distinction of nitrate, phosphate, and sulfate is a complicated procedure due to their spectral proximity, especially when attempted without chemometric tools. Zheng et al. [94] investigated the interference of sulfate with total phosphorus estimation and concluded that the application of selective adaptive PLS is required in order to avoid it.

5.4. Other Spectroscopy Methods

Over the years, various methods have been studied for water analysis. Some of the studies have focused on water quality monitoring, addressing either instrumentation or application aspects [95,96,97], while others have investigated numerous techniques from a biosensor perspective, emphasizing the analytes detected [98]. Collectively, these studies illustrate the advancing landscape of analytical technologies in environmental monitoring and, more specifically, water quality assessment.

Raman spectroscopy, fluorescence spectroscopy, and laser-induced breakdown spectroscopy (LIBS) are among the methods applied in water quality analysis. These techniques have different applications and capabilities. Raman is a vibrational method that uses scattered light to quantify the sample’s vibrational energy modes. A medium particle scatters light in various directions when it is illuminated by a light source, which causes the light to diverge from its initial path. Essentially, matter and photons, or light particles, interact resulting in light scattering. It practically uses a substance’s unique “fingerprint” to identify and provide structural and chemical information for different kinds of materials. On the other hand, fluorescence occurs when a substance absorbs energy from a light source and emits light quickly. Light is released when photons are absorbed by electrons, which causes them to rapidly return to their ground state after jumping to a higher energy level. This phenomenon, which produces light without heat, is a rapid form of luminescence. Finally, LIBS is used to determine a material’s elemental composition. Using a high-energy pulsed laser, plasma is created on the samples’ surface by vaporizing a small amount of the sample. The plasma emits light across the UV, Vis, and IR regions while it cools. The atoms and ions in a sample can be identified by spectroscopic analysis of the light that is released.

By combining Raman scattering and LIBS, Liao et al. [99] were able to detect bacteria with linear detection ranges of 5 × 10³ to 5 × 10⁷ CFU/mL and recovery values ranging from 81.0% to 101.7%. In the study by Meng et al. [100], LIBS was applied in on-line/on-site water analysis for heavy metal detection. Although aluminum enrichment resulted in a good limit of detection, the method was time consuming and unsuitable for real-time monitoring. In contrast, graphite enrichment proved more efficient in practical applications. Zhang et al. [59] employed fluorescence spectroscopy to identify water pollution originating from livestock farming. By integrating fluorescence measurements with multivariate statistical methods, the characteristics of dissolved organic matter (DOM) in groundwater were examined. Their findings demonstrated that fluorescence-based techniques are effective for water pollution assessment, as hierarchical cluster analysis successfully classified samples into three distinct clusters based on varying degrees of contamination. Similarly, Angelotti De Ponte Rodrigues et al. [101] showed that fluorescence spectroscopy can successfully identify microbial contamination in urban waterbodies, especially when combined with parallel factor analysis (PARAFAC) modeling. The study confirmed the diagnostic capability of this method for detecting microbiological pollution by identifying unique protein-like fluorescent components that have a strong correlation with fecal indicator bacteria.

5.5. Comparative Assessment of Spectral Techniques

The methods reviewed in this paper are listed and comparatively presented in

Table 7. Comparison of spectral methods with respect to sample preparation demands, sensitivity, cost, portability, and convenience.

Spectral Technique	Sample Preparation	Sensitivity	Cost	Portability	Convenience
UV–Vis	+	++	+	+++	+++
NIRS	+	++	++	+++	+++
MIRS	++	+++	+++	+	+
FTIR	++	+++	+++	++	++
LIBS	+	+++	++	++	++
Raman	+	++	+++	++	++
Fluorescence	+	+++	++	+++	+++

Note: UV–Vis: ultraviolet–visible, NIRS: near-infrared spectroscopy, MIRS: mid-infrared spectroscopy, FT-IR: Fourier transform infrared, LIBS: laser-induced breakdown spectroscopy, +: low, ++: moderate, +++: high.

in terms of sample preparation requirements, sensitivity, cost (purchase of equipment and operational costs), portability (on-site applications), and convenience (user-friendly and easy to integrate).

6. Future Research

Further development, specialization, and validation of spectroscopic methods for water analysis applications are still needed to overcome operational challenges, while improving their reliability under real-world conditions. Although numerous studies demonstrate strong predictive performance in controlled laboratory settings, their applicability in livestock settings remains uncertain. Confounding factors, such as variable temperature and humidity, suspended particles, and interfering chromophoric substances, can affect spectral fidelity; therefore, in situ validation and assessment of their analytical capacity under real world conditions should be prioritized in future studies.

One of the main challenges is the use of portable or handheld instruments to obtain real-time water assessment results, either with respect to pollutant concentration measurement or to microbial/bacterial detection. Several studies have focused on this direction, employing different methods, such as LIBS or fluorescence spectroscopy for on-site analysis [9,100]; however, further research is still needed to conclude their performance. Other spectroscopic methods with a proven analytical potential should be further explored for the water analysis, such as quantum cascade lasers (QCLs), which have demonstrated great promise in detecting ammonia in water [102]. Quantum cascade lasers are advantageous as a light source due to their adjustable energy bandgap engineering, which enables the design and production of lasers with highly specific emission spectra in the MIR and Terahertz (THz) regions. Additionally, the integration of QCLs into photonic integrated circuits (PIC) presents an attractive solution offering resilience, a compact device footprint, and the potential for low-cost production [103]. However, barriers such as the comparatively high cost of manufacturing and the limited commercial availability of water monitoring applications remain unresolved, with further technological development and economies of scale being significant for wider use in practice. Considering this potential, QCLs should be a key focus of future investigations in the field of water analysis. Moreover, a very promising field in water analysis refers to the ultrafast laser-based molecular fingerprinting methods, such as attosecond field-resolved spectroscopy [104,105]. These methods take advantage of the time-resolved interaction between femtosecond laser pulses and molecular structures and provide the possibility of rapid, label-free, and very precise chemical identification. They can provide detailed molecular fingerprints that could improve contaminant detection and microbiological characterization. Although these methods for water analysis are still in their infancy, promising research is being undertaken, like femtosecond laser-induced breakdown spectroscopy (LIBS) that has been successfully applied in the study by Chen et al. [106], where chromium (Cr), lead (Pb), and copper (Cu) were detected utilizing them. However, factors like cost and portability need to be taken into account as ultrafast laser systems are costly and require specialized operators.

An important direction that should be prioritized in future research is the implementation of ensemble methods wherein multiple algorithms will be combined to enhance prediction accuracy, rather than relying to a single algorithm. A similar approach could also be applied to spectroscopic techniques, where the fusion of multiple methods could yield more robust analytical capabilities.

Finally, another highly promising direction in water analysis is the combination of spectral analyses with other emerging digital technologies such as Internet of Things (IoT), cloud computing, remote sensing, blockchain, satellite imaging, etc. These technologies have the potential to significantly enhance spectroscopic methods using real-time monitoring, data fusion, and secure data management [107]. For instance, IoT sensors and cloud platforms can facilitate continuous analysis and instantaneous processing, while blockchain technology can provide data integrity and traceability [108,109,110]. IoT-based spectroscopic systems have already started to be developed in pilot applications. For example, “SpectroGLY” is a low-cost portable device (Vis-NIR spectrophotometer) that can be used to detect glyphosate contamination in water, delivering results to a mobile app or an online platform within 10 min [111]. Integrating these technologies with spectroscopic water analysis methods is leading to smarter, faster, and more reliable water quality assessment systems.

7. Conclusions

Based on the reviewed studies, spectroscopy-based methods appear as some of the most advanced tools for water quality assessment. In livestock farming, although water quality is linked to animal health, welfare, and productivity, the water provided to animals is often of poor quality, and its monitoring is usually limited or absent. The fast, non-invasive, and sensitive nature of spectroscopy-based tools makes them well-suited for environmental monitoring, especially under real-time conditions. Techniques like IR and UV–Vis spectroscopy have demonstrated robust performance across diverse aquatic environments (such as surface, drinking, or industrial water sources) and could also be used in livestock water sources like storage tanks or wells. When combined with chemometric modeling, these methods exhibit enhanced precision and strong predictive potential for the classification, forecasting, and continuous monitoring of water quality parameters, including key parameters for animal health such as nitrates, heavy metals, and microbial contamination.

The exploration of ensemble learning techniques and hybrid AI models should be further explored to more effectively leverage the strengths of spectroscopic data and chemometric tools. Moreover, the fusion of spectroscopy with emerging digital technologies, such as the IoT, cloud computing, and blockchain, could facilitate the development of automated, tamper-proof systems that enable real-time diagnostics, remote monitoring, and secure data traceability. Such systems have the potential to increase the knowledge and transparency of the water quality assessment systems that could be integrated in livestock farms. Despite the promising results, real-world implementation of spectroscopic methods for water analysis remains limited in the livestock sector, showcasing a pressing need for field-scale validation and deployment of relevant technologies, as well as for a relevant and updated regulatory framework. In any case, targeted spectral-based water assessment applications should be prioritized in livestock farming, emphasizing the detection of contaminants and pathogens of high relevance to animal health and food safety and utilizing sustained collaboration across disciplines—including analytical chemistry, data science, engineering, and agricultural sciences—to ensure that these applications are both scalable and sustainable.