Fiber-optic Raman System Research Articles

Identification of Lesional Tissues and Nonlesional Tissues in Early Gastric Cancer Endoscopic Submucosal Dissection Specimens Using a Fiber Optic Raman System

Aim To identify lesional and nonlesional tissues from early gastric cancer (EGC) patients by Raman spectroscopy to build a diagnostic model and effectively diagnose EGC. Method Specimens were collected by endoscopic submucosal dissection from 13 patients with EGC, and 55 sets of standard Raman spectral data (each integrated 10 times) were obtained using the fiber optic Raman system; there were 33 sets of lesional tissue data, including 18 sets of high-grade intraepithelial neoplasia (HGIN) data and 15 sets of adenocarcinoma data, and 22 sets of nonlesional tissue data. After the preprocessing steps, the average Raman spectrum was obtained. Results The nonlesional tissues showed peaks at 891 cm−1, 1103 cm−1, 1417 cm−1, 1206 cm−1, 1234 cm−1, 1479 cm−1, 1560 cm−1, and 1678 cm−1. Compared with the peaks corresponding to nonlesional tissues, the peaks of the lesional tissues shifted by different magnitudes, and a new characteristic peak at 1324 cm−1 was observed. Comparing the peak intensity ratio and the integral energy ratio of the lesional tissues with those of the nonlesional tissues revealed a significant difference between the two groups (independent-samplest-test, P < 0.05). Considering the peak intensity ratio of I1560 cm−1/I1103 cm−1 as a diagnostic indicator, the accuracy, sensitivity, and specificity of diagnosing EGC were 98.8%, 93.9%, and 91.9%, respectively. Considering the integral energy ratio (noncontinuous frequency band and continuous frequency band) as a diagnostic indicator, the accuracy, sensitivity, and specificity of diagnosing EGC were 99.2-99.6%, 93.9-97.0%, and 95.5%, respectively. Conclusions The integral energy ratio of the Raman spectrum could be considered an effective indicator for the diagnosis of EGC.

Diverse Region-Based CNN for Tongue Squamous Cell Carcinoma Classification With Raman Spectroscopy

Border discrimination is very important in the treatment of tongue squamous cell carcinoma (TSCC). This study proposes an ensemble convolutional neural network (CNN) framework based on fiber optic Raman spectroscopy and deep learning techniques to distinguish between TSCC and non-tumor tissue frameworks. First, the data used in the experiments was collected by a fiber optic Raman system. A total of 44 tissues of 22 patients were collected for Raman spectroscopy, with TSCC and adjacent normal tissues each accounting for half. The spectral data range used in the model from a full spectrum of 600-4000 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . Then, the ensemble CNN model was used in the experiment. By using two convolution kernels, the model is able to extract nonlinear feature representations from different spectral regions. It has two advantages, on the one hand, it reduces the generation of noise, on the other hand, it obtains a stronger distinguishing ability. Finally, a feature vector is formed by the fusion layer, and is sent to the fully connected layer for the TSCC classification task. The results showed that the sensitivity and specificity of the model were 99.2% and 99.2%, respectively. In addition, comparison with existing methods shows that our method achieves the highest accuracy of TSCC classification. By comparing the different channels, the results show that the spectral range of 1380-2250cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> data has the greatest impact on the results. Therefore, Raman spectroscopy combined with the ensemble CNN model has great potential and can provide a useful technique for intraoperative evaluation of the margins of oral tongue squamous cell carcinoma.

In vitro discrimination and classification of Microbial Flora of Poultry using two dispersive Raman spectrometers (microscope and Portable Fiber-Optic systems) in tandem with chemometric analysis

Discrimination and classification of eight strains related to meat spoilage and pathogenic microorganisms commonly found in poultry meat were successfully carried out using two dispersive Raman spectrometers (Microscope and Portable Fiber-Optic systems) in combination with chemometric methods. Principal components analysis (PCA) and multi-class support vector machines (MC-SVM) were applied to develop discrimination and classification models. These models were certified using validation data sets which were successfully assigned to the correct bacterial species and even to the right strain. The discrimination of bacteria down to the strain level was performed for the pre-processed spectral data using a 3-stage model based on PCA. The spectral features and differences among the species on which the discrimination was based were clarified through PCA loadings. In MC-SVM the pre-processed spectral data was subjected to PCA and utilized to build a classification model. When using the first two components, the accuracy of the MC-SVM model was 97.64% and 93.23% for the validation data collected by the Raman Microscope and the Portable Fiber-Optic Raman system, respectively. The accuracy reached 100% for the validation data by using the first eight and ten PC's from the data collected by Raman Microscope and by Portable Fiber-Optic Raman system, respectively. The results reflect the strong discriminative power and the high performance of the developed models, the suitability of the pre-processing method used in this study and that the low accuracy of the Portable Fiber-Optic Raman system does not adversely affect the discriminative power of the developed models.

Identification and characterization of bladder cancer by low-resolution fiber-optic Raman spectroscopy.

Raman spectroscopy has been proved to be a promising diagnostic technique for various cancers detection. A major drawback for its clinical translation is the intrinsic weakness of Raman effects. Highly sensitive equipment and optimal measurement conditions are generally applied to overcome this drawback. However, these equipment are usually bulky, expensive and may also be easily influenced by surrounding environment. In this preliminary work, a low-resolution fiber-optic Raman sensing system is applied to evaluate the diagnostic potential of Raman spectroscopy to identify different bladder pathologies ex vivo. A total number of 262 spectra taken from 32 bladder specimens are included in this study. These spectra are categorized into 3 groups by histopathological analysis, namely normal bladder tissues, low-grade bladder tumors and high-grade bladder tumors. Principal component analysis fed artificial neural network are used to train a classification model for the spectral data with 10-fold cross-validation and an overall prediction accuracy of 93.1% is obtained. The sensitivities and specificities for normal bladder tissues, low-grade bladder tumors and high-grade bladder tumors are 88.5% and 95.1%, 90.3% and 98%, and 97.5% and 96.4%, respectively. These results demonstrate the potential of using a low-resolution fiber-optic Raman system for in vivo bladder cancer diagnosis.

Assessment of a custom-built Raman spectroscopic probe for diagnosis of early oesophageal neoplasia

We evaluate the potential of a custom-built fiber-optic Raman probe, suitable for in vivo use, to differentiate between benign, metaplastic (Barrett's oesophagus), and neoplastic (dysplastic and malignant) oesophageal tissue ex vivo on short timescales. We measured 337 Raman spectra (λ(ex)=830 nm; P(ex)=60 mW; t=1 s) using a confocal probe from fresh (298) and snap-frozen (39) oesophageal tissue collected during surgery or endoscopy from 28 patients. Spectra were correlated with histopathology and used to construct a multivariate classification model which was tested using leave one tissue site out cross-validation in order to evaluate the diagnostic accuracy of the probe system. The Raman probe system was able to differentiate, when tested with leave one site out cross-validation, between normal squamous oesophagus, Barrett's oesophagus and neoplasia with sensitivities of (838% to 6%) and specificities of (89% to 99%). Analysis of a two group model to differentiate Barrett's oesophagus and neoplasia demonstrated a sensitivity of 88% and a specificity of 87% for classification of neoplastic disease. This fiber-optic Raman system can provide rapid, objective, and accurate diagnosis of oesophageal pathology ex vivo. The confocal design of this probe enables superficial mucosal abnormalities (metaplasia and dysplasia) to be classified in clinically applicable timescales paving the way for an in vivo trial.

Assessment of fiberoptic near-infrared raman spectroscopy for diagnosis of bladder and prostate cancer

Objectives To determine whether a fiberoptic Raman system, suitable for in vivo use, is able to differentiate between benign and malignant bladder and prostate pathologic findings in vitro. Raman spectroscopy is an optical technique that provides a measure of the molecular composition of tissue by analyzing the way that tissue scatters laser light. Laboratory studies have shown that the technique can be used to identify and characterize transitional cell carcinoma and prostate adenocarcinoma in vitro. Methods A total of 220 Raman spectra were recorded from 29 snap-frozen bladder samples collected at cystoscopic procedures, and 197 Raman spectra were recorded from 38 snap-frozen prostate samples collected at transurethral resection of the prostate. The spectra were correlated with the histologic features and used to construct separate diagnostic algorithms for the bladder and prostate. These algorithms were tested as to their ability to determine the pathologic finding of a sample from its Raman spectrum. Results The bladder algorithm was able to differentiate benign samples (normal and cystitis) from malignant samples (transitional cell carcinoma), with an overall accuracy of 84%. The prostate algorithm was able to differentiate benign samples (benign prostatic hyperplasia and prostatitis) from malignant samples (prostate cancer), with an overall accuracy of 86%. Conclusions The results of this study have demonstrated that the clinical Raman system can provide an accurate and objective method to diagnose prostate and bladder cancer in vitro. Because the Raman probe is suitable for use during endoscopic, laparoscopic, or open procedures, this work paves the way for in vivo studies.

Development of an in situ fiber optic Raman system to monitor hydrothermal vents.

The development of a field portable fiber optic Raman system modified from commercially available components that can operate remotely on battery power and withstand the corrosive environment of the hydrothermal vents is discussed. The Raman system is designed for continuous monitoring in the deep-sea environment. A 785 nm diode laser was used in conjunction with a sapphire ball fiber optic Raman probe, single board computer, and a CCD detector. Using the system at ambient conditions the detection limits of SO(4)(2-), CO(3)(2-) and NO(3)(-) were determined to be approximately 0.11, 0.36 and 0.12 g l(-1) respectively. Mimicking the cold conditions of the sea floor by placing the equipment in a refrigerator yielded slightly worse detection limits of approximately 0.16 g l(-1) for SO(4)(-2) and 0.20 g l(-1) for NO(3)(-). Addition of minerals commonly found in vent fluid plumes also decreased the detection limits to approximately 0.33 and 0.34 g l(-1) respectively for SO(4)(-2) and NO(3)(-).

Nonintrusive Analysis of Chemical Agent Identification Sets Using a Portable Fiber-Optic Raman Spectrometer

A portable fiber-optic Raman system comprising a diode laser, echelle spectrograph, charge-coupled device (CCD) detector, and filtered fiber-optic probe has been used to analyze chemical agents and other toxic chemicals in sealed glass containers. These containers include ampoules and bottles that are contents of chemical agent identification sets (CAIS) developed for use in training military personnel in chemical agent identification, safe handling, and decontamination. Real-time nonintrusive analysis of these sets is required so that the items containing chemical agents can be identified for proper disposal. This paper details the laboratory measurement of Raman spectra of CAIS chemicals, the determination of detection limits for mustard gas and lewisite in chloroform, and the analysis of CAIS items in the field.

Stability and Surface Uniformity of Selected Thiol-Coated SERS Surfaces

The use of surface-enhanced Raman scattering (SERS) as an analytical technique continues to grow, but questions about its viability remain. This paper addresses the concern about the long-term stability of the surfaces used with the SERS technique. We have studied the long-term stability of Ag surfaces coated with three different thiol compounds. For this study, we have used 1-propanethiol, 1-do-decanethiol, and p-cresolthiol to coat the Ag surfaces. These surfaces showed long-term stabilities of over a month with minimal surface degradation. In order to mimic a realistic application of SERS, we stored the coated surfaces in a solution of water. Sodium dodecylsulfate (SDS) was added to maintain consistent surface wetting conditions so that reproducible results were obtainable on a day-to-day basis. Spatial heterogeneity of the surfaces and coating was analyzed with a fiber-optic Raman system. The spatial heterogeneity of the surfaces explains the day-to-day variations observed in the detection when the same portion of the surface used for analysis varies from day to day.