No abstract

We describe the development of a novel method for real-time in situ characterization of polycyclic aromatic hydrocarbons (PAHs) in submerged freshwater sediments. Laser-induced fluorescence (LIF) spectroscopy, a mature technique for PAH characterization in terrestrial sediments, was adapted for shipboard use. A cone penetrometer-type apparatus was designed for probe penetration at a constant rate (1 cm/s) to a depth of 3 m. A field-portable LIF system was used for in situ measurements in which the output of a pulsed excimer laser was transmitted by optical fiber to a sapphire window (6.4-mm o.d.) in the probe wall; fluorescent emission was collected by a separate optical fiber for transmission to the spectrometer on deck. Four wavelengths (340, 390, 440, 490 nm) were selected via optical delay lines, and multiple-wavelength waveforms were created. These multiple-wavelength waveforms contain information on the fluorescence frequency, intensity, and emission decay rate. Field testing was conducted at 10 sites in Milwaukee Harbor (total PAH concentrations ranged from approximately 10 to 650 microg/g); conventional sediment core samples were collected concurrently. The core samples were analyzed by EPA methods 3545 (pressurized fluid extraction, PFE) and 8270C (gas chromatography-mass spectrometry, GC-MS) for PAHs. A partial least-squares regression (PLSR) model wasthen created based on laboratory LIF measurements and PFE-GC-MS of the core samples. The PLSR model was applied to the in situ field test data, and 13 of the 16 EPA-regulated PAHs were quantified with a relative error of <30% overall (the remaining three PAHs were found at levels insufficient to quantify). We additionally describe preliminary source apportionment relationships that were revealed by the PLSR model for the in situ LIF measurements.

In a recent publication we explored the development of quantitative structure property relationships for the calculation of dielectric constants, which resulted in a general model for a wide range of compounds. Our current work explores the division of the set of compounds into eight more homogeneous subsets for which local models are developed. The full data set consists of 454 compounds with dielectric constants ranging from 1 to 40. A pool of up to 16 molecular descriptors is calculated for each of the eight data sets. The descriptors include dipole moment, polarizability, counts of elemental types or functional groups, charged partial surface area, and molecular connectivity. All possible 4-16 descriptor models are calculated for each of the eight data sets, and the best models are selected and compared to the results obtained from the best general model for all 454 compounds. Neural networks using the Broyden-Fletcher-Goldfarb-Shanno training algorithm are employed to build the models. The resulting combined mean test set error for the eight local models of 1.31 is significantly better than the mean test set error of 1.85 for the general model.

. Significance : Peripheral pitting edema is a clinician-administered measure for grading edema. Peripheral edema is graded 0, , , , or , but subjectivity is a major limitation of this technique. A pilot clinical study for short-wave infrared (SWIR) molecular chemical imaging (MCI) effectiveness as an objective, non-contact quantitative peripheral edema measure is underway. Aim : We explore if SWIR MCI can differentiate populations with and without peripheral edema. Further, we evaluate the technology for correctly stratifying subjects with peripheral edema. Approach : SWIR MCI of shins from healthy subjects and heart failure (HF) patients was performed. Partial least squares discriminant analysis (PLS-DA) was used to discriminate the two populations. PLS regression (PLSR) was applied to assess the ability of MCI to grade edema. Results : Average spectra from edema exhibited higher water absorption than non-edema spectra. SWIR MCI differentiated healthy volunteers from a population representing all pitting edema grades with 97.1% accuracy ( shins). Additionally, SWIR MCI correctly classified shin pitting edema levels in patients with 81.6% accuracy. Conclusions : Our study successfully achieved the two primary endpoints. Application of SWIR MCI to monitor patients while actively receiving HF treatment is necessary to validate SWIR MCI as an HF monitoring technology.

The current opioid epidemic represents a significant health and security threat. This epidemic has affected correctional facilities with an increased smuggling of illicit drugs concealed in envelopes, letters, greeting cards, and business cards to inmates. Short‐wave infrared chemical imaging sensors are being successfully applied to the automated, high confidence detection of drugs concealed in prison mail. Once detected, end users have a need to confirm the detection and to identify the specific drug being detected. The challenge for identification is that the spectral signature of the concealed drug is often convolved with the spectral signature of the piece of mail (substrate) in which the drug is concealed. This paper presents a method to remove the substrate signal from the substrate/drug mixture signal followed by a set of 3 spectral identification methods. The substrate signature is estimated by a region of interest that is spatially local to the detection, and linear unmixing uses this substrate signature to calculate the residual spectra in the detection pixels. These residual spectra represent the isolated drug spectra, and they are compared with a spectral drug library via Euclidean distance, target factor analysis, and adaptive cosine estimator methods. This methodology was applied to a set of 116 positive‐ and negative‐control samples spanning a range of drugs and concealment methods with the result that 90 of the 104 positive‐control samples were identified correctly (86.5%) and 0 of the 12 negative‐control samples were incorrectly identified as a drug in the library (0%).