Enhancing bacterial detection via laser-induced fluorescence: a comparison of methods and detection limits
Dina S. Arabi, Zienab Abdel-Salam, Mohamed Abdel-Harith
TL;DR
This paper compares different laser techniques to detect bacteria more quickly and accurately, finding that a new combined method is the most effective.
Contribution
The study introduces and evaluates a novel combined LIF method (WERELIF) that achieves the lowest bacterial detection limit.
Findings
WERELIF achieved the highest fluorescence intensity and lowest detection limit among tested LIF methods.
RELIF showed significant signal enhancement through reflective optimization.
Conventional LIF was outperformed by enhanced techniques in sensitivity and detection capability.
Abstract
Rapid bacterial detection is essential in clinical diagnostics, environmental monitoring, and food industry quality control, where sensitivity and speed are critical. This study evaluates four Laser-Induced Fluorescence (LIF) techniques, Conventional LIF, Reflection-Enhanced LIF (RELIF), Wavefront-Enhanced LIF (WELIF), and a combined approach (WERELIF), to improve sensitivity and lower detection limits for bacterial quantification. Using Pseudomonas aeruginosa as a model organism, fluorescence was excited at 405 nm, with a peak at approximately 500 nm. Calibration curves were constructed to determine the limit of detection (LOD) of each method and assess its performance in trace bacterial analysis. WERELIF demonstrated the highest fluorescence intensity and the lowest limit of detection (LOD) among the tested techniques, making it the most effective method for detecting low bacterial…
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Taxonomy
TopicsBacterial biofilms and quorum sensing · Biosensors and Analytical Detection · Listeria monocytogenes in Food Safety
Introduction
Detection and monitoring of bacteria and bacterial pathogens have been a topic of significant discussion. The early detection of bacterial contaminants is critical in various fields, including medicine (Rajapaksha et al. 2019), environmental applications (Price and Wildeboer), pharmaceuticals (Hashim and Celiksoy 2025), food processing (Chatterjee and Abraham 2018; Hameed et al. 2018), and biological warfare (Green et al. 2019). Although adequate knowledge of the genus, species, and strain of a specific organism involved in a contamination incident is essential, traditional diagnostic methods for microorganism identification are laborious, costly, and time-consuming. Therefore, a robust, reliable, and rapid pathogen identification system is urgent in an epidemic, food contamination crisis, or biological war. One of the recent advances in rapid, sensitive pathogen diagnostics is fluorescence-based methods (Girotti et al. 2008; Tokonami and Iida 2017). Numerous studies have demonstrated that Laser-induced fluorescence (LIF) is a reliable method for detecting bacteria in both liquid suspensions (Fellner et al. 2021; Rehse et al. 2010) and on solid surfaces (Alimova et al. 2003; Bhartia et al. 2010; He et al. 2019), including pure and mixed culture settings (Fellner et al. 2021; Rehse et al. 2010).
Label-free techniques that target native fluorescence are conventional for monitoring or detecting bacteria, as several amino acids and other biochemicals provide native fluorescence (Alimova et al. 2003, 2005; Bhartia et al. 2010; He et al. 2019). Autofluorescence in biological cells originates from photoaccepting or photoreceptor molecules, such as chlorophyll. In bacterial cells, autofluorescence primarily results from photoacceptors, including several amino acids such as tryptophan, tyrosine, and phenylalanine, as well as reduced coenzymes like nicotinamide adenine dinucleotide (NADH), oxidized flavins (FAD and FMN), and porphyrins (Qazi et al. 2024).
In previous reports, fluorescence in the blue region is due to amino acids; fluorescence in the blue-green region (400 nm to 490 nm) is mainly due to NADH and NADPH. While flavins emit in the green part of the spectrum (~500 nm), yellow-red fluorescence emission is attributed to porphyrin compounds, including Uroporphyrin, Coproporphyrin, and Protoporphyrin (Amat et al. 2005; Ammor 2007; Arabi et al. 2018; Mao et al. 2021; Pan 2015). This provides characteristic fingerprint-like fluorescence spectra identifying pathogenic microorganisms based on their molecular structures (Joshi et al. 2013; Pan et al. 2009).
Fluorescence spectroscopy has long been used for bacterial identification because it relies on intrinsic autofluorescence. As a sensitive and non-invasive technique, it continues to play a crucial role in numerous analytical applications. Recent work has explored signal modeling strategies, including modified Beer–Lambert (MBL) approaches, to interpret compound-specific laser-induced fluorescence behavior in complex matrices (Ahmadinouri et al. 2024; Shamsi et al. 2023). Building on this broader analytical context, our research focuses on the native fluorescence emissions of Pseudomonas aeruginosa, without isolating or labeling individual chromophores. In a previously reported study (Arabi et al. 2018), we successfully discriminated between two bacterial species using dual-wavelength excitation, underscoring the feasibility of autofluorescence-based differentiation.
Laser-induced fluorescence (LIF) spectroscopy offers distinct advantages, providing superior detection sensitivity by minimizing background interference. The emitted fluorescence can also be collected at multiple angles relative to the collimated laser beam, enabling high-resolution 2D and 3D imaging (Taylor and Lai 2021).
This study compares four Laser-Induced Fluorescence (LIF) techniques: a conventional approach and three enhanced methods developed in our laboratory. These novel enhancements, designed to improve sensitivity, lower the limit of detection (LOD), and strengthen quantification accuracy, were systematically assessed to optimize bacterial detection. The findings advance LIF-based methodologies and hold promise for broader scientific and practical applications in bacterial detection, particularly for analyzing fluorescence signal amplification and spectral precision.
In this study, four Laser-Induced Fluorescence (LIF) configurations are considered. Conventional LIF serves as the baseline approach, relying on direct excitation and emission collection. Reflection-enhanced LIF (RELIF) incorporates one reflective surface on the cuvette containing the sample to increase photon return, while wavefront-enhanced LIF (WELIF) employs laser beam profile modulation to a flattop instead of a quasi-Gaussian to improve excitation uniformity. The combined wavefront- and reflection-enhanced (WERELIF) approach integrates both strategies, achieving maximal sensitivity and signal stability.
A standard strain of Pseudomonas aeruginosa (ATCC 9027) was selected as the model organism. This gram-negative bacterium is known for its rapid growth and is crucial in clinical, environmental, and industrial settings. Its inherent fluorescence properties, metabolic flexibility, and ability to thrive across diverse environments make it an ideal choice for evaluating fluorescence-based detection techniques. It excretes an extracellular siderophore compound (pyoverdine) with fluorescence characteristics to scavenge iron from its environment (Cornelis 2010). As a common opportunistic pathogen associated with infections and biofilm formation, P. aeruginosa is particularly significant for research focused on detecting and diagnosing trace bacteria.
Materials and methods
Growth and preparation of bacterial samples
Cells were grown for 48 h. in Trypticase soya broth (TSB; Laboratories Conda SA, Madrid, Spain). Cells were harvested by centrifugation (3000 rpm) for 10 min. Resuspended in normal saline solution (0.9%) and washed twice to remove all residual media. Finally, the bacterial pellet was resuspended in normal saline to an OD = 0.72, which is equivalent to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7.9\times {10}^{10}$$\end{document} CFU/mL. Seven dilutions were prepared at concentrations of 1:10, 1:20, 1:40, 1:80, 1:160, 1:320, and 1:640. 10 µL × 3 (Triplicates) of each dilution was plated on Trypticase soy agar (TSA; Laboratories Conda SA, Madrid, Spain) plates for CFU/mL evaluation (Miles et al. 1938). 200 µL of each suspension was used for OD evaluation using a spectrophotometer measurement at 600 nm (BioTek FLx 800).
Experimental setups and design
The excitation light source employed was a continuous-wave (CW) 405 nm DPSS laser [Changchun New Industries Optoelectronics Tech Co., Ltd. (CN)] with a peak power of 100 mW. The laser beam is delivered through an optical fiber (FO) to one side of the quartz cuvette containing the bacterial sample solution. The fluorescence light is collected perpendicularly through another fiber and fed to the compact spectrometer (USB2000 FLG, Ocean Optics, USA). Figure 1 illustrates the LIF/RELIF setup. In the RELIF configuration, a thick aluminum coating was applied externally to the cuvette wall, facing the incoming laser beam. This setup created a mirror-like surface that enhanced signal reflectivity without direct contact with the bacterial sample, maintaining chemical isolation. Aluminum was selected for its strong reflective properties at 405 nm, affordability, cost-effectiveness, and oxidation resistance. (Abdel-Harith and Abdel-Salam 2023).Fig. 1LIF/RELIF experimental setup (a). The special cuvette prepared for the RELIF measurements (b)
The hybrid RELIF/WELIF setup combines Wavefront-Enhanced and Reflection-Enhanced Laser-Induced Fluorescence (WERELIF), as illustrated in Fig. 2. In this system, the incident laser beam passes twice through a single crystalline quartz plate, reshaping its wavefront from a quasi-Gaussian to a flattop profile, a key feature of Wavefront-Enhanced LIF (WELIF) (Abdel-Harith et al. 2023, 2024; Elhassan et al. 2024). A flattop beam redistributes this energy evenly across a wider area, ensuring uniform illumination of fluorophores throughout the cuvette. This uniform excitation minimizes spatial fluorescence saturation and enhances signal consistency, especially under conditions of sample inhomogeneity. It also reduces scattering artifacts and improves the reliability of integrated fluorescence intensity measurements (Abdel-Harith et al. 2024). The flattop beam passes through the cuvette, which has one aluminum-coated side and contains the sample, further amplifying the signal via reflection enhancement, as employed in the RELIF technique.Fig. 2WELIF/WERELIF experimental configuration, QS is a single-crystalline quartz slide, and FL is a focusing lens
Data acquisition and spectral data processing
The experimental setup included a 405 nm laser excitation source, which efficiently excites the intrinsic fluorophores in Pseudomonas aeruginosa, such as NADH and NADPH, flavin compounds, and porphyrins (Arabi et al. 2018). Emission spectra were recorded over a wavelength range encompassing the bacterial sample’s fluorescence band, centered at approximately 500 nm.
The LIF system was connected to a computer to display and analyze the collected spectra. The commercial SpectraSuite software (Ocean Optics, US) was used to acquire and analyze the spectra from the spectroscopic system. Further analysis and preprocessing of the collected fluorescence data were performed using OriginPro (OriginLab Corporation, Northampton, MA, USA; Version 2021). The area under the peak (AUP) of the fluorescence band at 500 nm was measured for each spectrum, yielding the integrated fluorescence signal for each sample at a specific bacterial dilution.
Calculation of limit of detection (LOD)
Blank measurements
Blank samples (bacteria-free) were analyzed to determine the background fluorescence signal for each method. These blank measurements’ standard deviation (σ) was calculated to assess baseline noise.
Calibration curve
A calibration curve was generated by plotting log10 CFU/mL against the area under the peak (AUP) at 500 nm. Linear regression was applied to obtain the Slope (S) of the best-fit line.
Limit of detection (LOD) determination
The LOD for each fluorescence-based method was determined using the following equation:
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text{LOD }} = \frac{3\sigma }{S} $$\end{document}where:
σ = Standard deviation of the blank signal (background noise),
S = Slope of the calibration curve, log_10_ CFU/mL vs. AUP.
Data statistical analysis
To evaluate the consistency and precision of the fluorescence measurements, the Coefficient of Variability (CV) was calculated. CV expresses the standard deviation (σ) of a dataset relative to its mean (μ), providing a normalized measure of variability:
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ CV = \left( {\frac{\sigma }{\mu }} \right) \times 100 $$\end{document}This metric was applied across all four laser-induced fluorescence configurations, LIF, RELIF, WELIF, and WERELIF. Low CV values (<5%) indicated high measurement precision and repeatability, while higher CV values (>20%) were interpreted as signs of greater dispersion and reduced reliability.
Building on this foundation, partial least squares regression (PLSR) was employed to model and predict fluorescence intensity from spectral data. PLSR is a multivariate technique that generates a linear regression model by projecting both actual (X) and predicted (Y) variables onto a new latent space(Rehse et al. 2010). It was used here to assess the predictive robustness of the four LIF-based techniques. The model was fitted using the “PLS” function in OriginPro 2021 with three latent PLS components. Each input point represented an average spectrum derived from five replicates of a representative Pseudomonas aeruginosa sample.
Results
Figure 3 compares the fluorescence spectra obtained from the four LIF methods at the highest (Fig. 3a) and lowest (Fig. 3b) concentrations studied. Deconvoluted spectral peaks shown in Fig. 3c correspond to the microbial molecules responsible for the fluorescence, as reported in the literature (Alimova et al. 2003, 2005; Bhartia et al. 2010; He et al. 2019; Qazi et al. 2024). The sequence of fluorescence intensities is consistent across all four LIF-based methods, from undiluted to the most diluted samples.Fig. 3. Comparison of fluorescence intensity trends across all methods for a the original working concentration (log_10_CFU/mL = 10.9) and b the most diluted sample (log_10_CFU/mL = 6.2) c, d deconvolution of the resulting spectra revealing the peaks attributed to the corresponding responsible chromophores according to the literature
Figure 4 presents the spectral profiles of bacterial dilutions obtained using four laser-induced fluorescence (LIF) techniques (LIF, WELIF, RELIF, WERELIF).Fig. 4. Fluorescence intensity versus wavelength for the four LIF methods. Emission profiles are shown for increasing sample dilution factors, depicting the response behavior of each technique
Figure 5 illustrates the corresponding exponential decay in fluorescence intensity with increasing dilution. The inset provides a magnified view of the early dilution region, allowing more precise visualization of the initial decay behavior among the methods.Fig. 5. Area under the peak (AUP) intensity as a function of dilution factor, demonstrating exponential decay trends across all LIF methods. The inset highlights early signal decay to improve visualization of the fitting behavior
To confirm internal consistency across the dataset, the comparative Coefficient of Variability CV was estimated (Fig. 6).Fig. 6. Coefficient of variability (%) ranges for different laser-induced fluorescence (LIF) techniques, including LIF, RELIF, WELIF, and WERELIF. The figure highlights the methods’ comparative consistency, with lower coefficients indicating greater reliability
Also, the PLSR model was constructed (Fig. 7) using the “PLS” function in OriginPro 2021 with three latent components. Each data point represents the average spectrum from five replicate P. aeruginosa samples.Fig. 7PLSR plots showing predicted versus actual fluorescence intensity for LIF, RELIF, WELIF, and WERELIF indicate that WERELIF has the highest coefficient of determination (R^2^ = 1.00), confirming its superior performance
To further evaluate performance, linear regression analysis was conducted between bacterial concentration (CFU/mL) and fluorescence intensity. The Slope and intercept of each regression equation were used to calculate the limit of detection (LOD) for each technique (Table 1). These LOD and slope values serve as quantitative indicators of sensitivity and detection efficiency, providing a robust basis for comparative assessment (Chiang et al. 2024). Figure 8 shows the linear relationship between AUP intensity and bacterial count.Table 1. Calibration slopes, limits of detection (LOD), and fluorescence sensitivities for various LIF methodsMethodCalibration slope (S)LOD (CFU/mL)Fluorescence response (AUP Range)LIF13,027.940.002119,002.28 to 15,918.1WELIF7915.560.005156,065.81 to 26,604.27RELIF25,912.710.001233,189.77 to 57,350.08WERELIF46,539.030.001351,946.93 to 117,280.1Fig. 8Linear regression analysis of fluorescence intensity (AUP) versus bacterial concentration (log_10_CFU/mL) for the LIF, RELIF, WELIF, and WERELIF methods, with corresponding R^2^ values indicating correlation strength
Discussion
Four laser-induced fluorescence approaches (LIF, RELIF, WELIF, and their hybrid WERELIF) were employed to detect bacterial concentrations ranging from 6.2 to 10.9 log_10_ CFU/mL. When exposed to 405 nm laser light, these bacteria produce fluorescence peaking at 500 nm. As mentioned earlier, the fluorescence band around 500 nm is mainly associated with the autofluorescence of reduced pyridine coenzymes and flavins, which emit light in the blue-green spectral region. The secondary peak observed at 473 nm in bacterial and blank samples is likely due to Raman scattering from water in the standard saline solution used for dilution and bacterial suspensions (Mobley March 13, 2021; Tian et al. 2022).
As shown in Figure 3, WERELIF exhibits the highest intensity, demonstrating robust fluorescence and high sensitivity for both diluted and undiluted bacterial samples. RELIF follows closely and also produces a strong fluorescence signal. WELIF ranks third with moderate intensity. In contrast, LIF shows the lowest fluorescence intensity, highlighting its limitations for measuring highly diluted samples compared to the other methods.
The fluorescence spectra obtained from each method were analyzed to assess signal behavior across varying bacterial concentrations. Fluorescence intensity was quantified by integrating the area under the emission band centered around 500 nm. This area under the peak (AUP) changes with bacterial concentration (Chiang et al. 2024), making it a useful quantitative metric for correlating bacterial dilution factors, colony-forming unit (CFU/mL) values, and fluorescence intensity. As such, the AUP provides a standardized framework for comparing the effectiveness of spectroscopic methods.
In conventional LIF (Fig. 4a), fluorescence intensity is highest at elevated bacterial concentrations and decreases progressively with dilution, demonstrating a clear concentration-dependent response. This reduction aligns with the expected decline in bacterial autofluorescence. However, background fluorescence interference at extremely diluted concentrations leads to signal saturation, reducing measurement consistency (deconvoluted spectral peaks shown in Figure 3d). In contrast, the RELIF spectral profile (Fig. 4 b) exhibits significantly stronger fluorescence signals at lower bacterial concentrations than conventional LIF. The sharper spectral peaks indicate an improved signal-to-noise ratio (SNR), enhancing detection accuracy. RELIF’s refined calibration approach ensures reliable bacterial quantification across a broader dilution range, which is particularly useful for samples with low concentrations.
WELIF (Fig. 4c) demonstrates moderately enhanced fluorescence signals compared to conventional LIF but exhibits greater variability in signal decay across dilutions. This suggests that while WELIF improves sensitivity, sample heterogeneity may influence its performance, potentially due to distortions in the incident laser’s flattop profile. The combined wavefront- and reflection-enhanced LIF, WERELIF (Fig. 4d), achieves optimal fluorescence intensity even at high dilutions, confirming its superior sensitivity. By combining wavefront modulation and reflection enhancement, WERELIF effectively mitigates nonlinear optical effects, resulting in more stable and accurate bacterial detection.
Among the four methods, WERELIF consistently outperforms conventional LIF and RELIF. It offers the highest fluorescence retention and quantification sensitivity, making it ideal for detecting traces of bacteria in highly diluted samples.
Figure 5 illustrates the exponential decay of fluorescence intensity with increasing dilution. Conventional LIF (black line) follows a single-phase decay, indicating rapid signal loss. In contrast, RELIF (red line), WELIF (blue line), and WERELIF (green line) exhibit multi-phase decay, with an initial faster decline followed by slower attenuation. The inset magnifies the early dilution region, making the onset of multi-phase behavior, compared to LIF, more apparent. RELIF shows enhanced stability at lower concentrations; WELIF displays fluctuations likely due to beam distortions; and WERELIF exhibits the most gradual and stable decay, confirming its superior performance for trace-level detection.
The comparative Coefficient of Variability (CV) results, presented in Figure 6, confirm strong internal consistency across the dataset, where the estimated CV was (< 5%) for the four methods.
Partial least squares regression (PLSR) analysis of the experimental data demonstrated robust predictive performance for all fluorescence techniques, LIF, RELIF, WELIF, and WERELIF. The coefficients of determination (R^2^) for the measured versus predicted spectral data were 0.83, 0.88, 0.98 and 1.00, respectively, reflecting high model fidelity and consistency (Fig. 7).
In Figure 8, the WERELIF (represented by the green line) has the steepest Slope for the linear fit, with fluorescence intensity decreasing from 351946.9 in the undiluted sample to 117280.1 in the most diluted sample (as detailed in Table 1). WERELIF consistently maintains strong fluorescence signals across the entire dilution range, demonstrating the greatest linearity and highest sensitivity.
RELIF (red line) also exhibits high sensitivity, with fluorescence intensities ranging from 233,189.8 in the undiluted sample to 57,350.08 in the most diluted sample. A slightly lower slope value (Table 1) maintains good linearity; however, slight deviations from linearity are observed as the concentration approaches the lower detection limit. Its sensitivity is slightly less than WERELIF’s, yet it still shows a reliable performance over a wide concentration range.
WELIF, on the other hand, demonstrates moderate sensitivity, with fluorescence intensities decreasing from 156065.8 in the undiluted sample to 26604.27 in the most diluted sample. While less sensitive than RELIF and WERELIF, it remains somewhat effective for mid-range bacterial concentrations. It demonstrates acceptable linearity at higher concentrations. However, it struggles with nonlinearity and significant deviations at lower concentrations, likely due to weaker fluorescence signal enhancement.
LIF shows the lowest fluorescence intensities, decreasing values from 119002.3 in the undiluted sample to 15918.1 in the most diluted sample. While LIF shows improved linearity at higher concentrations compared to WELIF, it is most affected by noise and background interference at low concentrations. Its flatter Slope reflects reduced sensitivity and narrower dynamic range compared to the enhanced methods.
The observed R-squared values reflect the variability in fluorescence (Gupta et al. 2024), which can be interpreted as a shift in the linear relationship between bacterial suspensions (log_10_CFU/mL) and fluorescence intensity. Bacterial suspensions can exhibit nonlinear relationships with fluorescence due to physical and optical phenomena (Lakowicz 2013). At lower concentrations, the fluorescence signal often weakens and becomes more susceptible to noise or background interference, leading to deviations from a linear trend. Factors such as self-quenching, saturation at higher concentrations, or scattering due to the inhomogeneity of the bacterial suspension (Arabi et al. 2018)can also disrupt the linear behavior. RELIF and WERELIF demonstrate superior reliability, as evidenced by their high R^2^ values (Fig. 8), robust linear response, and low variability in fluorescence intensity measurements.
The PLSR model reduces the dimensionality of high-dimensional independent variables by extracting latent variables, thereby mitigating multicollinearity and noise interference. However, overfitting can still occur; thus, cross-validation is essential for assessing the model’s generalization capability. The coefficient of determination (R^2^ CV) measures the ability to explain variance, while Root Mean Squared Error- cross-validation (RMSECV) directly quantifies the prediction error. These two metrics complement each other. To evaluate the system’s sensitivity and precision, RMSECV values were calculated in OriginPro 2019. The lowest RMSECV values, 0.28, 0.36, 0.53 and 1.11, and the highest coefficient of determination R^2^ values, 0.83, 0.88, 0.98 and 1, were obtained for LIF, RELIF, WELIF, and WERELIF, respectively.
The comparative evaluation of the Limit of Detection (LOD) values for LIF and enhanced LIF methods confirmed the previously observed performance characteristics. The LOD values for RELIF and WERELIF were the highest sensitivity, at 0.001 CFU/mL. This highlights their superior ability to detect low bacterial concentrations. The steep Slope in the relationship between fluorescence intensity and CFU/mL further ensures accurate signal detection, even at minimal bacterial levels. In contrast, LIF and WELIF exhibited LODs of 0.002 and 0.005 CFU/mL, respectively, indicating lower sensitivity for detecting minimal bacterial concentrations.
Conclusion
This study presents three innovative fluorescence enhancement techniques for bacterial detection: Reflection-Enhanced LIF (RELIF), Wavefront-Enhanced LIF (WELIF), and, most significantly, their synergistic integration, WERELIF. Among these, WERELIF represents the most significant advancement, demonstrating superior performance in addressing the limitations of conventional LIF-based methods, particularly in sensitivity, spectral stability, and detection accuracy. By significantly improving fluorescence signal retention, spectral stability, and detection sensitivity, these approaches address critical challenges in microbiological analysis, offering a more precise and adaptable framework for bacterial quantification. The most significant advancement arises from the synergistic combination of WELIF and RELIF into WERELIF, which successfully merges wavefront and reflection enhancement to achieve unparalleled performance. WERELIF consistently outperforms both standalone methods, demonstrating superior fluorescence retention with minimal signal attenuation, the highest sensitivity for detecting traces of bacteria, even in highly diluted samples, and enhanced spectral consistency by mitigating distortions inherent to WELIF. Spectroscopic data statistical analysis validated the reliability of the techniques, with low variability across replicates (CV) and strong predictive performance (PLSR), particularly for WERELIF, which achieved the highest R^2^ values.
While RELIF leverages reflection-based signal amplification to extend its quantitative range, WELIF’s performance alone exhibits spectral distortions arising from the nonlinear relationship between bacterial concentration inhomogeneity and fluorescence intensity. These distortions are further amplified by beam-profile modifications introduced by the wavefront enhancement mechanism, leading to spectral inconsistencies. Integrating RELIF’s reflection enhancement into WERELIF resolves these issues, improving excitation, uniformity, and detection accuracy.
Future work could further integrate machine learning-driven calibration and multi-excitation fluorescence to enhance sensitivity and accuracy. Refining these methodologies provides the scientific and technological community with adaptable, high-precision tools for diverse applications, ranging from biotechnology to infectious disease diagnostics.
The reference list from the paper itself. Each links out to its DOI / PubMed record.
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