Mr. Gabriel H. Weedon Profile

Gabriel H. Weedon

at Physical Sciences Inc.

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Proceedings Article | 14 June 2023 Presentation + Paper

One-dimensional convolutional neural networks for Raman spectral analysis

Michael Primrose, Gabriel Weedon, Jay Giblin

Proceedings Volume 12541, 125410L (2023) https://doi.org/10.1117/12.2664042

KEYWORDS: Convolutional neural networks, Machine learning, Target detection, Signal to noise ratio, Raman spectroscopy

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A machine learning based approach has been developed to classify Raman spectroscopic data. The algorithm is based on a one dimensional neural network (1D-CNN) architecture which is trained with synthetic data that can incorporate sensor specific characteristics such as spectral range, spectral resolution and noise. The synthetic spectra are based on high SNR measurements which are then augmented by mixing target and background signatures. The CNN is trained to consider target representations in the presence of certain background materials including glass and HDPE. These additional target representations allow the CNN to make detections for materials taken through a container. Within this paper the performance of CNNs trained for Raman sensor systems has been evaluated using real data collected using the ThermoFisher FirstDefender. The evaluation data consists of various target chemicals (including explosives) and interferents (including household materials) collected through glass and plastic vials. The data was acquired with a controlled range of collection settings, including integration time and laser power, available on the unit. The performance of the 1D-CNN approach has demonstrated high classification accuracies, high probability of detection and low false alarm rates. Specifically, these metrics have been calculated as a function of signal to noise ratio. Additionally, a sensitivity analysis was conducted using an acetonitrile standard diluted in water which demonstrates the CNN’s capability of detecting all dilutions of acetonitrile down to weight concentrations of <1%. This sensitivity analysis was mirrored using a mixture of potassium chlorate and Vaseline. The CNN demonstrated detections down to 10% by weight of potassium chlorate.

Proceedings Article | 31 May 2022 Presentation + Paper

One dimensional convolutional neural networks for spectral analysis

Michael Primrose, Jay Giblin, Christian Smith, Martin Anguita, Gabriel Weedon

Proceedings Volume 12094, 120940C (2022) https://doi.org/10.1117/12.2618487

KEYWORDS: Signal to noise ratio, Raman spectroscopy, Sensors, Reflectivity, Detection and tracking algorithms, Data modeling

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A platform for building sensor specific machine learning detection algorithms has been developed to classify spectroscopic data. The algorithms are focused on long wave infrared reflectance (LWIR) and Raman spectroscopies. The classification algorithm is based on a one dimensional (1D) convolutional neural network (CNN) architecture. Training data is generated using an appropriate signal model that is combined with sensor specific characteristics such as spectral range, spectral resolution, and noise. Within this paper, the performance of trained CNNs for both LWIR and Raman sensor systems has been evaluated. The evaluation uses both real and synthetic data to benchmark the performance in terms of the discriminant signal. The evaluation data consists of various chemical representations and varied noise levels. The performance of the 1D CNN approach has demonstrated high classification accuracies on data with low discriminant signals. Specifically, the CNNs have demonstrated a classification accuracy <90% for infrared reflectance data down to a wavelength averaged discriminant SNR<1. For Raman systems, we have demonstrated classification accuracies <90% for data with a peak discriminant SNR of approximately 6.

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