Mini-Tutorial on NIR Aquaphotomics for Rapid, Non-Destructive Biofluid and Food Analysis

Introduction and Relevance

Water is ubiquitous in biological and agricultural systems, yet its molecular structure and interactions have traditionally been treated as a spectral background in near-infrared (NIR) analysis. Recent advances in aquaphotomics have reframed water as a molecular sensor capable of reporting complex biochemical changes through its NIR absorption patterns (1,3). Clinical studies have demonstrated its utility in cancer detection (2,4), food safety applications (5), and monitoring animal health (6,7).

NIR aquaphotomics is used for biofluid and food analysis © By Sona-chronicles-stock.adobe.com

Aquaphotomics integrates chemometric analysis with NIR spectroscopy to quantify subtle variations in water’s hydrogen bonding network (1–6). These variations serve as multidimensional biomarkers, detectable in plasma, milk, or even maize kernels. Such non-destructive, rapid methods are increasingly relevant for early disease detection, food safety monitoring, and quality control. For example, recent studies demonstrate high-accuracy applications: screening esophageal squamous cell carcinoma (ESCC) in human plasma (4), detecting aflatoxin contamination in maize (5), and diagnosing Johne’s disease in dairy cattle milk (6).

For laboratory scientists and spectroscopists, understanding how to implement aquaphotomics provides a practical route to leverage NIR data beyond traditional absorbance or chemometric analysis. This tutorial will explore the principles, practical implementation, applications, and potential pitfalls of NIR aquaphotomics for both clinical and agricultural contexts.

Core Tutorial Content

Principles and Definitions

Near-Infrared Spectroscopy (NIRS)
NIR spectroscopy measures molecular overtones and combination vibrations, primarily of O–H, N–H, and C–H bonds, in the 780–2500 nm region. Water dominates NIR spectra of aqueous samples, exhibiting broad absorption bands associated with different hydrogen bonding environments (1–3).

Aquaphotomics Explained
Aquaphotomics interprets the NIR spectrum of water in complex systems as a multidimensional biomarker (1,3). Key concepts include:

• Water Matrix Coordinates (WAMACs): Specific wavelengths where water absorption is sensitive to biochemical perturbations (1390 nm for free water, 1460 nm for strongly hydrogen-bonded water) (4–6).
• Water Absorption Spectrum Pattern (WASP): Composite spectral profile reflecting the distribution of hydrogen-bonded water types in a sample (1–3).
• Aquagram: A star-plot visualization of WASP that highlights relative changes in WAMACs, enabling intuitive comparison across sample groups (1,3,7).

Chemometric Integration
Statistical methods such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), quadratic discriminant analysis (QDA), support vector machines (SVM), and partial least squares regression (PLSR) are essential for extracting diagnostic information from NIR data (1–3,4–6). PCA identifies variance in spectral data, PLS-DA and QDA classify samples, SVM enables robust supervised classification, and PLSR quantifies analytes.

How It Works in Practice

Sample Preparation

• Biological fluids (plasma, milk) require minimal preparation—generally centrifugation or dilution to standardize optical density (4,6). Be sure to have temperature control of the sample.
• Solid agricultural samples, such as maize, are scanned intact or as finely ground powders to optimize NIR light penetration (5).

Spectral Acquisition

• NIR instruments collect spectra in wavelength ranges optimized for water sensitivity (typically 1300–1600 nm for first overtone O–H, extended to 1900–2300 nm for combination bands) (1–3,5,6,8).
• Averaging multiple scans per sample reduces instrumental noise.
• Preprocessing may include Savitzky-Golay smoothing, first or second derivatives, or multiplicative scatter correction to improve signal-to-noise and account for baseline shifts (3,5).

Aquaphotomic Analysis Workflow

1. Define WAMACs: Identify wavelengths responsive to water structural changes induced by the analyte of interest. For ESCC plasma, WAMACs included 1390, 1434, 1460, 1480, and 1494 nm (4,8).
2. Compute WASP: Quantify absorption intensities at WAMACs to create a spectral fingerprint (1,4,5,8).
3. Visualize with Aquagram: Plot relative changes across WAMACs to distinguish healthy versus diseased or contaminated samples (1–3,7,8).
4. Apply Chemometrics: Use PCA for exploratory data analysis, PLS-DA/QDA for classification, and PLSR for quantitative predictions (1–3,4–6,8).

Validation and Model Performance

• Internal validation (cross-validation) ensures robustness, while external validation with independent datasets confirms generalizability (4–6).
• Typical performance metrics: accuracy, precision, sensitivity, specificity, coefficient of determination for cross-validation (R²CV), root mean square error of cross-validation (RMSECV), limit of detection (LOD), and limit of quantification (LOQ). For example, NIR aquaphotomics reported>95% accuracy in ESCC plasma screening (4), 100% classification for maize aflatoxin datasets (5), and 100% sensitivity in milk-based Johne’s disease detection (6).

Application Examples

1. Clinical Plasma Screening for ESCC
Lu and colleagues (4) demonstrated a rapid, non-invasive ESCC screening model:

• NIR spectra of plasma samples (1300–1600 nm) captured subtle water structural differences.
• PLS-DA model achieved 95.12% accuracy and 97.10% sensitivity.
• Aquagrams revealed increased free and weakly hydrogen-bonded water in ESCC plasma, serving as a biomarker for early detection.

2. Food Safety: Detecting Aflatoxin in Maize
Appaw and co-authors (5) applied Vis/NIRS with aquaphotomics to maize:

• Spectral data from 450–1050 nm and 1150–2400 nm captured aflatoxin-correlated absorption bands.
• PCA-LDA achieved 92–100% classification accuracy, and PLSR predicted concentrations with R²CV of 0.99.
• Aquagrams highlighted water matrix changes corresponding to toxin contamination, illustrating WAMAC shifts as biomarkers.

3. Dairy Health: Johne’s Disease Diagnosis via Milk
Behdad and team (6) used milk NIR spectroscopy for Paratuberculosis detection:

• Milk samples collected pre- and post-calving were analyzed at 1300–1600 nm.
• SVM and QDA models based on 12 water absorbance bands achieved 100% classification accuracy.
• Water spectral changes reflected antibody-induced perturbations, providing a sensitive, non-invasive biomarker (6,7).

Tips and Common Pitfalls

1. Water Dominance: NIR spectra are dominated by water; careful preprocessing is essential to avoid masking subtle spectral features (1,3).
2. Sample Variability: Temperature, pH, and storage conditions can influence water structure. Standardize conditions to reduce noise (4–6).
3. Overfitting Risk: High-dimensional WAMAC datasets may overfit small sample sizes. Use cross-validation and external validation (4–6).
4. Instrument Calibration: Maintain wavelength accuracy and detector linearity; small shifts can affect WAMAC identification (1–3).
5. Interpretation Caution: Aquagrams provide relative changes, not absolute molecular concentrations. Integrate with chemometrics for reliable classification (1,3).
6. Integration with Conventional Methods: NIR aquaphotomics complements, but does not replace, confirmatory techniques such as enzyme linked immunosorbent assay (ELISA) or high performance liquid chromatography (HPLC) for regulatory purposes (4–6).

Figures and Diagrams (Suggested Presentation)

To support the tutorial, several illustrative figures and diagrams are recommended. A first figure could present an aquagram diagram, shown as a star-chart plotting the relative absorption intensities at selected WAMACs. By overlaying profiles for healthy versus diseased samples, this diagram would clearly highlight the shifts in water absorbance patterns that characterize pathological or contaminated states.

A second figure would show an NIR spectral overlay. Here, both raw and preprocessed spectra are displayed to emphasize how signal processing enhances interpretation. The overlay would highlight the key water absorption bands in the 1300–1550 nm and 1800–2100 nm regions, where structural perturbations in water most strongly reflect underlying biochemical differences.

A third figure could illustrate the workflow schematic. This diagram would outline the stepwise process of aquaphotomic analysis: beginning with sample preparation, proceeding through spectral acquisition, followed by WAMAC identification, aquagram visualization, and concluding with chemometric classification. Such a workflow figure would provide a concise roadmap for practitioners learning how to apply aquaphotomics in clinical or food science settings.

Finally, a fourth figure might display a validation plot, showing predicted versus actual concentrations derived from PLS regression. This could be illustrated using either the maize aflatoxin dataset or the milk antibody classification dataset. By plotting calibration and validation results, the figure would demonstrate model accuracy and reliability, reinforcing the quantitative power of aquaphotomic approaches.

Conclusion and Practical Takeaways

NIR aquaphotomics transforms the water signal from background interference into a sensitive biomarker of biochemical and physiological states (1–3,4–6). By integrating WAMACs, WASPs, and aquagrams with chemometric modeling, researchers can rapidly, non-destructively, and accurately detect diseases in humans and animals or contaminants in foods (8).

For laboratory spectroscopists, practical implementation requires attention to sample preparation, spectral acquisition, preprocessing, and robust multivariate analysis. While NIR aquaphotomics is still an emerging field, its applications in clinical diagnostics, food safety, and dairy herd health demonstrate transformative potential. By embracing water as a molecular sensor, scientists gain a multidimensional, reproducible, and interpretable tool for real-time monitoring and decision-making.

In practice, the method offers:

• Early detection of subtle biochemical changes.
• Non-destructive, rapid, and cost-effective analysis.
• Integration with standard chemometric workflows.
• High classification accuracy across biological and agricultural matrices.

Future research will likely expand its application to other biofluids, environmental monitoring, and industrial process control, further establishing aquaphotomics as a standard analytical technique in NIR spectroscopy.

References

(1) Tsenkova, R.; Munćan, J.; Pollner, B.; Kovacs, Z. Essentials of Aquaphotomics and Its Chemometrics Approaches. Front. Chem. 2018, 6, 363. DOI: 10.3389/fchem.2018.00363.

(2) Surkova, A.; Boichenko, E.; Bibikova, O.; Artyushenko, V.; Muncan, J.; Tsenkova, R. Near-Infrared Spectroscopy and Aquaphotomics in Cancer Research: A Pilot Study. J. Chemom. 2024, 38 (12), e3600. DOI: 10.1002/cem.3600.

(3) Roger, J. M.; Mallet, A.; Marini, F. Preprocessing NIR Spectra for Aquaphotomics. Molecules 2022, 27 (20), 6795. DOI: 10.3390/molecules27206795.

(4) Lu, Q.; Li, L.; Liang, W.; Xu, G.; Zhu, J.; Ma, X.; Tian, W.; Gao, L.; Tian, M.; Chen, Z.; Zang, H. Rapid Screening of Esophageal Squamous Cell Carcinoma by Near-Infrared Spectroscopy Combined with Aquaphotomics. Talanta 2025, 285, 127399. DOI: 10.1016/j.talanta.2024.127399.

(5) Appaw, W.; Zaukuu, J. L. Z.; Aouadi, B.; Mensah, E. T.; Oduro, I. N.; Kovacs, Z. Predicting Aflatoxin Contamination in White and Yellow Maize Using Vis/NIR Spectroscopy Combined with PCA-LDA and PLSR Models through Aquaphotomics Approaches. Appl. Food Res. 2025, 5 (1), 100841. DOI: 10.1016/j.afres.2025.100841.

(6) Behdad, S.; Massudi, R.; Pakdel, A.; et al. Milk NIR Spectroscopy and Aquaphotomics Novel Diagnostic Approach to Paratuberculosis in Dairy Cattle. Sci. Rep. 2025, 15, 19239. DOI: 10.1038/s41598-025-99421-x.

(7) Giovinazzo, S.; Brambilla, M.; Cattaneo, T. M. P.; Lazzari, A.; Bisaglia, C.; Muncan, J.; Tsenkova, R. Aquaphotomics and NIR Water Spectral Patterns in Dairy Production: A Review Exploring Potentials and Challenges. J. Dairy Res. 2024, 91 (3), 249–261. DOI: 10.1017/S0022029924000608.

(8) Aquaphotomics Research Field Page. Available at: https://www.lab.kobe-u.ac.jp/ans-nirslab/about.html (accessed 2025-08-28).