Toward the Development of Raman Spectroscopy as a Nonperturbative Online Monitoring Tool for Gasoline Adulteration

Development of Crime Scene Intelligence Using a Hand-Held Raman Spectrometer and Transfer Learning

The classification of ignitable liquids, such as gasoline, is critical crime scene intelligence to assist arson investigations. Rapid field gasoline classification is challenging because the current forensic testing standard requires gas chromatography-mass spectrometry analysis of evidence in an accredited laboratory. In this work, we reported a new intelligent analytical platform for field identification and classification of gasoline evidence. A hand-held Raman spectrometer was utilized to collect Raman spectra of reference gasoline samples with various octane numbers. The Raman spectrum pattern was converted into image presentations by continuous wavelet transformation (CWT) to facilitate artificial intelligence development using the transfer learning technique. GoogLeNet, a pretrained convolutional neural network (CNN), was adapted to train the classification model. Six different classification models were also developed from the same data set using conventional machine learning algorithms to evaluate the performance of our new approach. The experimental results indicated that the pretrained CNN model developed by our new data workflow outperformed other models in several performance benchmarks, such as accuracy, precision, recall, F1, Cohen's Kappa, and Matthews correlation coefficient. When the transfer learning model was challenged with the data collected from weathered gasoline samples, the classifier could still offer 73 and 53% accuracy for 50 and 25% weathered gasoline samples, respectively. In conclusion, wavelet transforms combined with transfer learning successfully processed and classified complex Raman spectral data without feature engineering. We envision that this nondestructive, automated, and accurate platform will accelerate crime scene intelligence development based on evidence's chemical signatures detected by hand-held Raman spectrometers.

Background Subtraction of Raman Spectra Based on Iterative Polynomial Smoothing

In this paper, a novel background subtraction algorithm is presented that can automatically recover Raman signal. This algorithm is based on an iterative polynomial smoothing method that highly reduces the need for experience and a priori knowledge. First, a polynomial filter is applied to smooth the input spectrum (the input spectrum is just an original spectrum at the first iteration). The output curve of the filter divides the original spectrum into two parts, top and bottom. Second, a proportion is calculated between the lowest point of the signal in the bottom part and the highest point of the signal in the top part. The proportion is a key index that decides whether to go into a new iteration. If a new iteration is needed, the minimum value between the output curve and the original spectrum forms a new curve that goes into the same filter in the first step and continues as another iteration until no more iteration is needed to finally get the background of the original spectrum. Results from the simulation experiments not only show that the iterative polynomial smoothing algorithm achieves good performance, processing time, cost, and accuracy of recovery, but also prove that the algorithm adapts to different background types and a large signal-to-noise ratio range. Furthermore, real measured Raman spectra of organic mixtures and non-organic samples are used to demonstrate the application of the algorithm.

Identification of accelerants, fuels and post-combustion residues using a colorimetric sensor array

A linear (1 × 36) colorimetric sensor array has been integrated with a pre-oxidation technique for detection and identification of a variety of fuels and post-combustion residues. The pre-oxidation method permits the conversion of fuel vapor into more detectable species and therefore greatly enhances the sensitivity of the sensor array. The pre-oxidation technique used a packed tube of chromic acid on an oxide support and was optimized in terms of the support and concentration. Excellent batch to batch reproducibility was observed for preparation and use of the disposable pre-oxidation tubes. Twenty automotive fuels including gasolines and diesel from five gasoline retailers were individually identifiable with no confusions or misclassifications in quintuplicate trials. Limits of detection were at sub-ppm concentrations for gasoline and diesel fuels. In addition, burning tests were performed on commonly used fire accelerants, and clear differentiation was achieved among both the fuels themselves and their volatile residues after burning.

Antitags: SERS-encoded nanoparticle assemblies that enable single-spot multiplex protein detection

Simultaneous detection of multiple proteins on a single spot can be efficiently achieved by using multiplexed surface-enhanced Raman spectroscopy (SERS)-encoded nanoparticle 'antitags' consisting of poly(ethylene glycol) (PEG)-protected silver dimers (and higher aggregates) and antibody-tagging entities. The effective SERS-based multivariate deconvolution approach guarantees an accurate and successful distinguishable identification of single and multiple proteins in complex samples. Their potential application in multiplexed SERS bioimaging technology can be easily envisaged.