Through-Container Adulterant Detection

• Through-Container Adulterant Detection is a suite of non-invasive analytical methods that utilize high-energy particles, spectroscopy, and capacitive sensing to detect illicit substances in sealed containers.
• It combines principles from physics, signal processing, and machine learning to classify and quantify adulterants with high sensitivity and accuracy.
• Applications span border security and consumer safety, ensuring quality control in industrial cargo, food, and drug products.

Through-Container Adulterant Detection refers to the ensemble of non-invasive analytical methodologies developed for identifying, classifying, and quantifying illicit or unreported substances concealed within sealed containers—encompassing industrial cargo, consumer packaging, and food/drug products. These approaches leverage high-energy particle interactions, advanced spectroscopic and capacitive signatures, and data-driven or statistical discrimination schemes to detect adulteration without breaching the physical integrity of the container or its contents. The field intersects material-specific physics (e.g., scattering, attenuation, Raman shifts), algorithmic inversion, and machine learning, supplying actionable intelligence in areas from border security to supply chain assurance and consumer safety.

1. Underlying Physical and Analytical Principles

Through-container adulterant detection exploits the differential interactions of externally accessible probes—charged particles, photons, or electromagnetic fields—with matter. Three prominent physical channels are established:

1. High-energy particle interactions:
   • Muon tomography (MT): Utilizes the multiple Coulomb scattering and absorption (stopping) of cosmic-ray muons in the bulk of sealed cargo. Changes in scattering density ($\lambda$) and absorbed muon counts ($N_{\rm abs}$) encode information on the $Z$, density, and stopping power of concealed materials (Georgadze, 1 Jul 2024).
   • X-ray and gamma radiography: Relies on dual-energy photon transmission and semi-empirical models for reconstructing atomic number $Z$ and area density $\lambda$ distributions within the container. Attenuation coefficients $\mu(E, Z)$ and transparency models are fundamental (Lalor et al., 2023, Lalor et al., 2023).
2. Photon-matter interaction (spectroscopy):
   • Raman spectroscopy: Exploits inelastic photon scattering to reveal molecular vibrational modes specific to both base contents and typical adulterants. Optical innovations—annular/Bessel beam formation via axicon, wavefront shaping, and wavelength modulation—enable signal extraction even through colored/scattering glass by suppressing container-origin backgrounds (Kritzinger et al., 8 Oct 2025, Fleming et al., 2020).
3. Dielectric/conductive signature detection:
   • Capacitive touchscreen sensing: Utilizes the alteration of mutual capacitance between screen electrodes introduced by overlying liquids’ relative permittivity ($\epsilon_r$) and conductivity ($\sigma$), modulated through a physics-informed priming protocol to circumvent adaptive filtering. Statistically significant capacitive changes are mapped to adulteration signatures using learning-based classification (Zhang et al., 4 Nov 2025).

2. Methodological Frameworks and Signal Processing

Technological platforms are distinguished by targeted combination of hardware design, signal extraction algorithms, and statistical or learning-based discrimination:

• Muon Tomography (MT):
  • Scattering/Absorption Fusion: Events are processed to extract both $\lambda$ (via Point of Closest Approach, PoCA) and $N_{\rm abs}$ for each reconstructed muon. A bivariate Gaussian mixture model (GMM) is applied to cluster observations for material-class assignment (e.g., discriminating tobacco from paper towels with up to 5.5$\sigma$ confidence at prototype tracker resolution) (Georgadze, 1 Jul 2024).
  • Automatic Anomaly Detection: Spatial voxel grids accumulate per-event metrics, then undergo baseline subtraction, median/nonlinear filtering, and adaptive nearest-neighbor thresholding to isolate high-$Z$ or anomalous clusters for real-time flagging (Georgadze, 2023).
• Cargo Radiography:
  • Dual-Energy Semiempirical Model: The total attenuation is decomposed as $$\mu(E, Z) = a\,\mu_{\rm PE}(E,Z) + b\,\mu_{\rm CS}(E,Z) + c\,\mu_{\rm PP}(E,Z)$$. Parameters $(a, b, c)$ are calibrated using a small set of phantoms; measurements are then inverted via precomputed transparency tables or $\chi^2$-minimization to yield $Z$ and $\lambda$ maps robust to spectral and detector uncertainties (Lalor et al., 2023, Lalor et al., 2023).
  • Deep Learning Approaches: Dense convolutional neural networks with domain-specific augmentation (multiplicative Beer–Lambert projections, log-transformation) are trained to identify and localize synthetic or real threat signatures in high-resolution x-ray images, achieving $<6\%$ FPR at $90\%$ TPR for metallic threats (Jaccard et al., 2016). Adaptation to non-metallic adulterants is proposed via multi-energy/spectral data.
• Optical Spectroscopy:
  • Beam Engineering for Signal Isolation: Axicon-generated Bessel beams and wavelength-modulated RS (WMRS) greatly suppress glass fluorescence and baseline, yielding pronounced increases in signal-to-noise ratio (SNR up to 10$\times$) for target analyte peaks (e.g., methanol at 1039 cm$^{-1}$ through colored glass) (Kritzinger et al., 8 Oct 2025).
  • Multivariate Classification: Principal Component Analysis (PCA), PLS-Discriminant Analysis, and calibration curves are used to achieve linear quantification and class separation (e.g., single-digit percent LODs for adulterants) (Fleming et al., 2020).
• Capacitive Liquid Sensing:
  • Nonlinear Calibration: Mappings from $(\sigma, \epsilon_r)$ to device units are established via second-order polynomial or fringing-corrected linear models, achieving $R^2 \sim 0.97$ in empirical fits. Region-based statistics (e.g., rim $\Delta C$ values) are used as discriminative features in Random Forest classifiers (Zhang et al., 4 Nov 2025).

3. Quantitative Performance and Detection Limits

The detection sensitivity and discrimination power of through-container adulterant identification technologies are quantifiably established:

Technique	Test Case / Metric	Detection Accuracy / Separation
Muon Tomography (scattering+abs.)	Tobacco vs. Paper	5.5$\sigma$ (0.235 mm FWHM), 10 s scan (Georgadze, 1 Jul 2024)
MST, object detection	SNM cubes in pasta	100% recovery in 1000 trials, $<1\%$ FP (Georgadze, 2023)
Dual-energy cargo radiography	$Z$ recovery (Pb)	$\pm$1–2 Z units for $Z=82$ after calibration (Lalor et al., 2023)
Deep-learned x-ray detection	SMTs in dense cargo	6% FPR at 90% detection (window-based, CNN) (Jaccard et al., 2016)
Raman (axicon/wavefront+WMRS)	Methanol in spirits	LOD 0.2% (v/v), calibration $R^2=0.9994$ (Kritzinger et al., 8 Oct 2025)
Raman (axicon-geometry)	Commercial whisky class	100% separation of 11 products (PCA) (Fleming et al., 2020)
Tablet capacitive (DropleX)	Milk/detergent, soda	98.1% and 96.4% for direct adulterant, 90.9% for rim-based, through-container (Zhang et al., 4 Nov 2025)

The metrics are explicitly reported or directly cited; any inference beyond provided data is noted as such.

4. Simulation, Calibration, and Uncertainty

All high-precision non-invasive detection relies on detailed calibration and simulation:

• GEANT4-based Monte Carlo models are central to cosmic ray muon and x-ray dual-energy platforms for realistic particle transport, interaction, and detector geometry simulation (Georgadze, 1 Jul 2024, Georgadze, 2023, Lalor et al., 2023, Lalor et al., 2023).
• Phantom-based calibration is minimal—three materials spanning the anticipated $Z$ range (e.g., graphite, iron, lead) suffice for setting semiempirical attenuation parameters (Lalor et al., 2023).
• Statistical uncertainty scales as $1/\sqrt{N}$ where $N$ is, for example, the number of muons or signal accumulations. Systematic uncertainty often derives from detector misalignment, spectral errors, and compositional inhomogeneity, typically addressed by regular recalibration and expanding template libraries (Georgadze, 1 Jul 2024).
• Algorithmic/segmentation uncertainty affects machine learning and x-ray CNN approaches, where performance relies on realism of data augmentation, correct segmentation, and, for bulk/chemical adulterants, the availability of labeled data for non-metallic signatures (Jaccard et al., 2016).

5. Application Domains and Case Studies

Applications span security, quality control, and consumer safety:

• Container/Cargo Verification: Muon tomography and dual-energy radiography provide high-probability discrimination of dense organic (e.g., tobacco vs. paper) or metallic adulterants in uniform fill or palletized cargo in $<10$ s to several minutes, suitable for high-throughput port or border crossings (Georgadze, 1 Jul 2024, Georgadze, 2023).
• Food and Beverage Authenticity: Raman spectroscopy, especially with axicon-based or waveform-modulated setups, robustly quantifies methanol, ethanol, glycerol, and other common spirit and juice adulterants, providing LODs below regulatory limits and 100% class separation among commercial samples (Kritzinger et al., 8 Oct 2025, Fleming et al., 2020).
• Consumer Devices: Capacitive tablet-based sensing (DropleX) demonstrates through-container detection of adulteration in open and sealed beverages, with $>90\%$ accuracy and no hardware modification, indicating significant potential for consumer-level screening and at-point checks (Zhang et al., 4 Nov 2025).
• Automated Threat Detection: Deep learning algorithms on x-ray imaging can achieve high true-positive and low false-positive rates on metallic threats, with extensions foreseen for organic, liquid, and powder adulterant detection via spectral and structural data (Jaccard et al., 2016).

6. Limitations, Trade-offs, and Improvement Strategies

Limitations and trade-offs are substantial and context-dependent:

• Statistical Limitations: Detection of small inclusions/hidden adulterants is fundamentally limited by the number of probe particles or photons and sensor resolution (voxel size)—sensitivity scales as $1/\sqrt{N}$, and contrast must exceed minimal density or compositional differences (Georgadze, 1 Jul 2024).
• Systematic Effects and Model Dependency: All indirect methods (x-ray, MT) depend on the accuracy of physical models, real-time detector calibration, and beam characterization; the semiempirical model provides significant robustness but cannot fully circumvent degeneracies inherent in dual-energy physics (Lalor et al., 2023).
• Class Generalization: Deep-learning approaches require risk-mitigated augmentation for rare, novel, or non-metallic threats; true field validation with real smuggling attempts or contamination is still lagging (Jaccard et al., 2016).
• Operational Constraints: Rapid screening (sub-10s scans for muon tomography, sub-minute Raman quantification) requires specific investments in module area/detector speed and is constrained by photon/particle arrival rates, sample preparation (e.g., glass orientation for Raman), or device capabilities (Georgadze, 1 Jul 2024, Kritzinger et al., 8 Oct 2025, Zhang et al., 4 Nov 2025).
• Extension Across Container Types: Optical and capacitive methods are generally effective for glass and plastic, with adjustments for colored or scattering materials via advanced wavefront shaping or calibration. Muon and x-ray methods generalize to opaque/metal containers (Kritzinger et al., 8 Oct 2025, Zhang et al., 4 Nov 2025, Georgadze, 1 Jul 2024).

Potential improvements include increased probe fluence, finer spatial resolution, incorporation of momentum or spectral analysis, advanced inversion/tomographic algorithms (MLEM, deep learning-based), and the accumulation of richer calibration libraries for material reference standards.

7. Ongoing Challenges and Emerging Directions

Remaining technical and practical challenges include:

• Degeneracy in Material Discrimination: Dual-energy techniques are fundamentally limited in resolving all material pairs due to overlapping attenuation signatures unless a third independent measure or prior information is available (Lalor et al., 2023).
• Adaptation to Heterogeneous/Complex Loads: Multi-component or spatially heterogeneous cargo requires sophisticated segmentation and possibly mixture-modelling at the voxel/region level (Lalor et al., 2023).
• Consumer-Grade Device Integration: Extending capacitive and optical methods onto unmodified consumer platforms (tablets, handhelds) is promising but requires continued refinement of signal interpretation, calibration protocols, and real-world validation (Zhang et al., 4 Nov 2025).
• Automated, Real-time Processing: End-to-end automated flagging with low false positives, especially for large-volume cargo or high-throughput settings, demands significant computational and algorithmic investment—optimized C++, GPU-accelerated pipelines, or on-device neural inference (Georgadze, 2023, Jaccard et al., 2016).
• Regulatory and Validation Needs: While detection limits now reside below relevant health and safety thresholds (e.g., LOD of 0.2% v/v MeOH vs. 2% legal threshhold), deployment requires systematic blind trials and regulatory harmonization.

These domains continue to evolve, with increased cross-fertilization between physics-based inversion, modern machine learning for unstructured data, and robust, user-friendly instrumentation, enabling broader adoption of through-container adulterant detection across industry and security sectors.