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.
Color is an important metric for determining the quality of petroleum products, as it is a characteristic readily observed by operators and end users and can also be indicative of the degree of refinement of a petroleum product. There are two primary color standards covering a wide range of petroleum color in industry; ASTM D 156 (Saybolt Color Scale) and ASTM D 1500 (ASTM Color Scale). For highly refined petroleum products the industry uses the Saybolt color scale, ranging from 30 at the clearest to -16 at the darkest. Fuels that are darker in color than -16 on the Saybolt scale are tested using the ASTM Color scale, which ranges from 0.5 at the clearest to 8 at the darkest. As fuels age (increased time from the point of refinement), their color darkens because of oxidizing olefins, such as ethylene and propylene. Traditionally, this color scale is measured using a series of photodiodes and optical filters with a blackbody light source. The spectroscopic method described in this paper incorporates a white LED designed for maximizing color measurements. The spectra are processed using CIE 1931 color space, which is then converted into CIELab color space. Results using this method are accurate and repeatable.
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.
We are making a comprehensive study of the ablation of elemental materials by femtosecond lasers. Specifically, we are examining the ablation of a wide range of metals, under vacuum and in ambient air, using 850-nm wavelength, 100-fs laser pulses in an intensity range approaching and extending beyond the air ionization threshold. We compare ablation rates and examine in detail the morphology and structural integrity of the ablation region, towards gaining greater knowledge of the interaction science as well as constructing empirical models for fabrication guidelines across the periodic table.
Acousto-optic tunable filters have ben well documented throughout the last 30 years. Unfortunately, although the filtering characteristics of these system have been explored until recently little has been done with regards to the data analysis or exploring the advantages of processing multi- spectral information. Here we use the wavelet transform to process multiple banded images like ones acquired in an acousto optical system. We demonstrate the uses of spectrally sensitive wavelets to detect a small object presents by suing an arbitrary low-resolution image in a high clutter background.
We used a 3D wavelet denoising method to reduce noise from multispectral imagery so that small objects may be more readily detected. Our approach exploits the correlation between bands typically present in multispectral imagery. Using our approach, the resulting image generally consists of a weighted sum of both spectral bands and spatial frequencies. We found that we could generally increase the SNR of a multispectral image more than if the spectral bands wee processed independently.
Analog optical vector matrix processors (AOVMP) have been implemented over the past three decades utilizing a variety of methodologies. Most of these methodologies were dependent on external modulation of the laser source. Photonic Systems Incorporated has furthered the development of the AOVMP by using a 64 channel analog modulated vertical cavity surface emitting laser diode (VCSEL). The novel analog modulation of the VCSEL is performed by linearizing the output of the VCSEL to 8 bits using real-time 12 bit look-up tables. VCSEL analog modulation characteristics and linearization techniques are discussed along with AOVMP performance.
We previously reviewed the architecture and basic analytic results for a hybrid optical-digital processor capable of generating target range Doppler profiles in real time. Here we describe the optical design and present preliminary results for the optical correlator portion of the hybrid system. Phase control, fringe stability, and preliminary correlation data for the optical system are reported.
We review the basic operating principles involved in development of a real time hybrid opto-electronic radar processor to form a 2-D range-Doppler image for ISAR applications. An overview description of the physical implementation of the processor developed at PSI is included, along with a discussion of the high level processor control structure required for performing the radar algorithm and system test functions. Finally, we present the results of preliminary testing of the pulse compression processor (PCP) which forms the cornerstone of the optical processing operation. The key capability of the optical correlator to map precise phase shifts via direct control of the radar transmitter carrier IF is demonstrated.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.