Quantitative analysis of cocaine in solid mixtures using Raman spectroscopy and chemometric methods

Ryder, Alan G.; O’Connor, Gerard M.; Glynn, Thomas J.

doi:10.1002/(sici)1097-4555(200003)31:3<221::aid-jrs518>3.0.co;2-5

Cited by 99 publications

(73 citation statements)

References 26 publications

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“…There are only about 10 -8 of the incident photons that are inelastically scattered from tissue to generate unique Raman spectroscopic patterns. In addition, Raman spectral differences are usually subtle with apparently spectral overlapping and variations in intensity between different tissue types (5,7,8,(13)(14)(15)(16)(17)(18). Therefore, the powerful and robust spectral data processing and sophisticated diagnostic algorithms are much needed to best extract the most diagnostically significant Raman spectral features in order to accurately correlate them with the tissue histopathology.…”

Section: Introductionmentioning

confidence: 99%

“…Multivariate statistical techniques [e.g. principal components analysis (PCA), linear discriminant analysis (LDA), artificial neural network (ANN) and fuzzy C-means] (6,7,12,14,(16)(17)(18)(19) have been successfully utilized in developing effective diagnostic algorithms for spectroscopic diagnosis of cancers. For example, employing PCA-LDA techniques, a high diagnostic accuracy (>90%) can be achieved for identifying Raman spectra of cancer from normal tissue in the colon (17).…”

Section: Introductionmentioning

confidence: 99%

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Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines

Widjaja

Zheng²,

Huang³

2008

Int J Oncol

117

137

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Abstract. The ability of combining near-infrared (NIR) Raman spectroscopy with support vector machines (SVM) for improving multi-class classification between different histopathological groups in tissues was evaluated in this study. A total of 105 colonic tissue specimens from 59 patients including 41 normal, 18 hyperplastic polyps and 46 adenocarcinomas were used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was utilized for tissue Raman spectroscopic measurements at 785-nm laser excitation. A total of 817 tissue Raman spectra were acquired and subjected to principal components analysis (PCA) for SVM-based multi-class classification, in which 324 Raman spectra were from normal, 184 from polyps and 309 from adenocarcinomatous colonic tissue. Two types of SVM (i.e., C-SVM and ν-SVM) with three different kernel functions (linear, polynomial and Gaussian radial basis function (RBF) in combination with PCA were used to develop effective diagnostic algorithms for classification of Raman spectra of different colonic tissues. The performance of various SVMbased algorithms was evaluated and compared using a leave-one-out, cross-validation method. The results showed that in the C-SVM classification, the maximum overall diagnostic accuracy of 99.3, 99.4 and 99.9% can be achieved using the linear, polynomial and RBF kernels, respectively; while in the ν-SVM classification, the maximum overall diagnostic accuracy of 98.4, 98.5 and 99.6% can be obtained using the linear, polynomial and RBF kernels, respectively. All the polyps can be identified from normal and adenocarcinomatous tissue using the C-SVM algorithms. The RBF C-SVM algorithm was proven to be the best classifier for providing the highest diagnostic accuracy (99.9%) for multi-class classification. This study demonstrates that NIR Raman spectroscopy in combination with a powerful SVM technique has great potential for providing an effective and accurate diagnostic schema for cancer diagnosis in the colon.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines

Widjaja

Zheng²,

Huang³

2008

Int J Oncol

117

137

View full text Add to dashboard Cite

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“…[23] PCA is a statistical technique that is well used in Raman spectroscopy and vibrational spectroscopy in general. [10,21,24] It is used to identify the underlying structure of large datasets with the aim of identifying groups within the data while at the same time removing any contribution from noise. Pre-processing of the data was performed to reduce inherent noise within the spectra from instrumental or sample variability.…”

Section: Chemometric Data Analysismentioning

confidence: 99%

Comparison of near infrared laser excitation wavelengths and its influence on the interrogation of seized drugs‐of‐abuse by Raman spectroscopy

Hargreaves

Burnett

Munshi

et al. 2009

J Raman Spectroscopy

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aThe laser excitation wavelength is an important parameter in obtaining Raman spectra from drugs-of-abuse. This article compares the effect of near infrared wavelengths, 785 nm, using both benchtop and portable instrumentation and benchtop 1064 nm on the Raman spectra of seized drugs-of-abuse, including cocaine hydrochloride, cocaine freebase (crack), methylenedioxymethamphetamine ('ecstasy'), amphetamine, diamorphine (heroin) and cannabis. The significant benefit of using 1064 nm for the interrogation of this type of sample is highlighted.

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“…96 A fiber optic Raman probe and portable Raman spectrograph have been used for cocaine identification 97 and near-IR Raman has been used for cocaine identification in mixtures. 98 Recently, femtosecond adaptive spectroscopic technique via coherent anti-Stokes Raman spectroscopy (FAST-CARS) has been used for bacterial system identification. 99 The remote sensing of medicinal plants, in addition to drug plants, will be of vital significance in the future for gauging the illicit collection of plants and the optimum collection time for medicinal plant harvesting once active (or at least marker) compounds have been identified.…”

Section: Location and Detection Techniquesmentioning

confidence: 99%

Sustainable drugs and global health care

Cordell¹

2009

Quím. Nova

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Each day, Earth's finite resources are being depleted for energy, for material goods, for transportation, for housing, and for drugs. As we evolve scientifically and technologically, and as the population of the world rapidly approaches 7 billion and beyond, among the many issues with which we are faced is the continued availability of drugs for future global health care. Medicinal agents are primarily derived from two sources, synthetic and natural, or in some cases, as semi-synthetic compounds, a mixture of the two. For the developed world, efforts have been initiated to make drug production "greener", with milder reagents, shorter reaction times, and more efficient processing, thereby using less energy, and reactions which are more atom efficient, and generate fewer by-products. However, most of the world's population uses plants, in either crude or extract form, for their primary health care. There is relatively little discussion as yet, about the long term effects of the current, non-sustainable harvesting methods for medicinal plants from the wild, which are depleting these critical resources without concurrent initiatives to commercialize their cultivation. To meet future public health care needs, a paradigm shift is required in order to adopt new approaches using contemporary technology which will result in drugs being regarded as a sustainable commodity, irrespective of their source. In this presentation, several approaches to enhancing and sustaining the availability of drugs, both synthetic and natural, will be discussed, including the use of vegetables as chemical reagents, and the deployment of integrated strategies involving information systems, biotechnology, nanotechnology, and detection techniques for the development of medicinal plants with enhanced levels of bioactive agents.

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Quantitative analysis of cocaine in solid mixtures using Raman spectroscopy and chemometric methods

Cited by 99 publications

References 26 publications

Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines

Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines

Comparison of near infrared laser excitation wavelengths and its influence on the interrogation of seized drugs‐of‐abuse by Raman spectroscopy

Sustainable drugs and global health care

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