Raman spectroscopy is an inelastic scattering technique that measures the molecular vibrational states of a sample with little to no sample preparation. These vibrational states are molecule-specific, therefore different compounds can be identified through rapid analysis. Raman spectroscopy has been implemented in a variety of different research areas, for example, forensic analysis, pharmaceutical product design, material identification, disease diagnostics, etc. Although Raman spectroscopy has been demonstrated in various applications, it still has limitations with data processing due to its innate weak signals. Historically, chemometrics techniques have been widely used for Raman spectroscopy for preprocessing data such as feature extraction (or feature selection), and data modeling. These models are often generated by using analytical data from different sources, enhancing model discrimination and prediction abilities, but this is limited by how much data is provided. Our group has designed a portable A.I. Raman spectrometer using machine learning through training and deep learning. This spectrometer uses a miniature Raman spectrometer paired with a well plate reader for multiple and rapid sample measurement. As sample measurements are taken the system will implement machine learning software to preprocess and postprocess Raman spectral data. This will minimize the workload of complicated analysis on the condition that there exists sufficient training data. Implementing a well plate reader aids in data collection for the AI training by mimicking experiments for preprocess and adding Raman standards. Through machine learning as more data is provided the system will learn how to implement past data on new data sets, therefore minimizing the amount of time and analysis needed by human interaction.
The development of techniques to rapidly identify samples ranging from, molecule and particle imaging to detection of high explosive materials, has surged in recent years. Due to this growing want, Raman spectroscopy gives a molecular fingerprint, with no sample preparation, and can be done remotely. These systems can be small, compact, lightweight, and with a user interface that allows for easy use and sample identification. Ocean Optics Inc. has developed several systems that would meet all these end user requirements. This talk will describe the development of different Ocean Optics Inc miniature Raman spectrometers. The spectrometer on a phone (SOAP) system was designed using commercial off the shelf (COTS) components, in a rapid product development cycle. The footprint of the system measures 40x40x14 mm (LxWxH) and was coupled directly to the cell phone detector camera optics. However, it gets roughly only ~40 cm-1 resolution. The Accuman system is the largest (290x220X100 mm) of the three, but uses our QEPro spectrometer and get ~7-11 cm-1 resolution. Finally, the HRS-30 measuring 165x85x40 mm is a combination of the other two systems. This system uses a modified EMBED spectrometer and gets ~7-12 cm-1 resolution. Each of these units uses a peak matching algorithm that then correlates the results to the pre-loaded and customizable spectral libraries.
A miniature Raman spectrometer was designed in a rapid development cycle (< 4 months) to investigate the performance capabilities achievable with two dimensional (2D) CMOS detectors found in cell phone camera modules and commercial off the shelf optics (COTS). This paper examines the design considerations and tradeoffs made during the development cycle. The final system developed measures 40 mm in length, 40 mm in width, 15 mm tall and couples directly with the cell phone camera optics. Two variants were made: one with an excitation wavelength of 638 nm and the other with a 785 nm excitation wavelength. Raman spectra of the following samples were gathered at both excitations: Toluene, Cyclohexane, Bis(MSB), Aspirin, Urea, and Ammonium Nitrate. The system obtained a resolution of 40 cm-1. The spectra produced at 785 nm excitation required integration times of up to 10 times longer than the 1.5 seconds at 638 nm, however, contained reduced stray light and less fluorescence which led to an overall cleaner signal.
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