scholarly journals Detection of Gastric Cancer with Fourier Transform Infrared Spectroscopy and Support Vector Machine Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-4 ◽  
Author(s):  
Qingbo Li ◽  
Wei Wang ◽  
Xiaofeng Ling ◽  
Jin Guang Wu
Keyword(s):  
Support Vector Machine ◽  
Fourier Transform ◽  
Fourier Transform Infrared ◽  
Support Vector ◽  
Tissue Samples ◽  
Normal Tissues ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Ft Ir ◽  
Leave One Out

Early diagnosis and early medical treatments are the keys to save the patients' lives and improve the living quality. Fourier transform infrared (FT-IR) spectroscopy can distinguish malignant from normal tissues at the molecular level. In this paper, programs were made with pattern recognition method to classify unknown samples. Spectral data were pretreated by using smoothing and standard normal variate (SNV) methods. Leave-one-out cross validation was used to evaluate the discrimination result of support vector machine (SVM) method. A total of 54 gastric tissue samples were employed in this study, including 24 cases of normal tissue samples and 30 cases of cancerous tissue samples. The discrimination results of SVM method showed the sensitivity with 100%, specificity with 83.3%, and total discrimination accuracy with 92.2%.

Multivariate Analysis of Attenuated Total Reflection—Fourier Transform Infrared Spectroscopic Data to Confirm the Origin of Honeys

2008 ◽  
Vol 62 (10) ◽  
pp. 1115-1123 ◽  
Author(s):  
Siobhán Hennessy ◽  
Gerard Downey ◽  
Colm O'Donnell
Keyword(s):  
Fourier Transform ◽  
Discriminant Analysis ◽  
Fourier Transform Infrared ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Correct Classification ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Ft Ir ◽  
Solids Content

Fourier transform infrared (FT-IR) spectroscopy and chemometrics were used to verify the origin of honey samples ( n = 150) from Europe and South America. Authentic honey samples were collected from five sources, namely unfiltered samples from Mexico in 2004, commercially filtered samples from Ireland and Argentina in 2004, commercially filtered samples from the Czech Republic in 2005 and 2006, and commercially filtered samples from Hungary in 2006. Samples were diluted with distilled water to a standard solids content (70° Brix) and their spectra (2500–12 500 nm) recorded at room temperature using an FT-IR spectrometer equipped with a germanium attenuated total reflection (ATR) accessory. First- and second-derivative and standard normal variate (SNV) data pretreatments were applied to the recorded spectra, which were analyzed using partial least squares (PLS) regression analysis, factorial discriminant analysis (FDA), and soft independent modeling of class analogy (SIMCA). In general, when an attenuated wavelength range (6800–11 500 nm) rather than the whole spectrum (2500–12 500 nm) was studied, higher correct classification rates were achieved. An overall correct classification of 93.3% was obtained for honeys by PLS discriminant analysis, while FDA techniques correctly classified 94.7% of honey samples. Correct classifications of up to 100% were achieved using SIMCA, but models describing some classes had very high false positive rates.

scholarly journals Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Yushuai Yuan ◽  
Li Yang ◽  
Rui Gao ◽  
Cheng Chen ◽  
Min Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Renal Failure ◽  
Fourier Transform ◽  
Chronic Renal Failure ◽  
Ir Spectroscopy ◽  
Fourier Transform Infrared ◽  
Diagnostic Methods ◽  
Support Vector ◽  
Ft Ir ◽  
Ft Ir Spectroscopy

Chronic renal failure (CRF) is a clinically serious kidney disease. If the patient is not treated in a timely manner, CRF will develop into uremia. However, current diagnostic methods, such as routine blood examinations and medical imaging, have low sensitivity. Therefore, it is important to explore new and effective diagnostic methods for CRF, such as serum spectroscopy. This study proposes a cost-effective and reliable method for detecting CRF based on Fourier transform infrared (FT-IR) spectroscopy and a support vector machine (SVM) algorithm. We measured and analyzed the FT-IR spectra of serum from 44 patients with CRF and 54 individuals with normal renal function. The partial least squares (PLS) algorithm was applied to reduce the dimensionality of the high-dimensional spectral data. The samples were input into the SVM after division by the Kennard–Stone (KS) algorithm. Compared with other models, the SVM optimized by a grid search (GS) algorithm performed the best. The sensitivity of our diagnostic model was 93.75%, the specificity was 100%, and the accuracy was 96.97%. The results demonstrate that FT-IR spectroscopy combined with a pattern recognition algorithm has great potential in screening patients with CRF.

scholarly journals Penerapan metode non linier dan linier : support vector machine regression dan multiple linier regression untuk prediksi kualitas internal buah mangga (Mangifera indica LINN)

2020 ◽  
Vol 4 (4) ◽  
pp. 452-461
Author(s):  
Mohd Nazar Isza Putra ◽  
Dewi Sri Jayanti ◽  
Agus Arip Munawar
Keyword(s):  
Support Vector Machine ◽  
Linear Regression ◽  
Vitamin C ◽  
Multiple Linear Regression ◽  
Mangifera Indica ◽  
Support Vector ◽  
Support Vector Machine Regression ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Ft Ir

Abstrak, Penelitian ini bertujuan untuk menguji dan mengevaluasi teknologi NIRS sebagai metode non destruktif untuk memprediksi kadar gula dan vitamin C pada mangga menggunakan Support Vector Machine Regression (SVMR) dan Multiple Linear Regression (MLR) sebagai metode kalibrasi serta menentukan pretreatment terbaik menggunakan Standar Normal Variate (SNV) dan Peak Normalalization (PN). Penelitian ini menggunakan 30 sampel mangga Arumanis dan FT-IR Science and Technology T-1516. Pengolahan data menggunakan Unscramble software® X versi 10.5. Prediksi TPT dengan metode SVMR menghasilkan nilai RPD 1,8 dengan interpretasi yang cukup baik, sedangkan metode MLR non-pretreatment menghasilkan nilai RPD sebesar 1,04 dengan interpretasinya adalah prediksi masih kasar. Pretreatment terbaik untuk memperkirakan TPT dengan metode MLR adalah Peak Normalization dengan nilai RPD adalah 0,98, r sebesar 0,421, R2sebesar 0,053 dan RMSEC sebesar 4,537. Hasil prediksi Vitamin C pada mangga dengan metode SVMR menghasilkan model kinerja yang baik dengan nilai RPD 2,4. Pretreatment terbaik untuk memperkirakan Vitamin C dengan metode Multiple Linear Regression (MLR) adalah Standard Normal Variate (SNV) dengan nilai RPD sebesar 0,92, r sebesar 0,339, R2sebesar 0,114 dan RMSEC sebesar 11,268. Berdasarkan penelitian ini dapat dinyatakan bahwa NIRS salah satu teknologi yang dapat digunakan untuk memprediksi kadar gula dan Vitamin C mangga dengan baik.Application of Non-Linier and Linier Method : Support Vector Machine Regression and Multiple Linier Regression to Prediction Internal Quality of Mango (Mangifera indica LINN)Abstract, This research aims to test and evaluate NIRS technology as a non-destructive method to predict SSC and Vitamin C in mangoes using Support Vector Machine Regression (SVMR) and Multiple Linear Regression (MLR) as a calibration method and determine the best pretreatment using Standard Normal Variate (SNV) and Peak Normalalization (PN). This study used 30 samples of Arumanis mangoes and FT-IR Science and Technology T-1516. Processing data using the Unscramble software® X version 10.5. The prediction results of SSC in mangoes by the SVMR method produces an RPD value of 1.8 with a fairly good interpretation, while the MLR method non pretreatment produces an RPD value of 1.04 with its interpretation is still a rough prediction. The best pretreatment for estimating SSC using the MLR method is Peak Normalization with the value of RPD is 0.98, r is 0.421, R2 is 0.053 and RMSEC is 4.537. The prediction results of Vitamin C in mangoes by the SVMR method produced a good performance model with a value of RPD is 2.4. The best pretreatment for estimating Vitamin C by the MLR method is Standard Normal Variate with an RPD value of 0.92, r is 0.339, R2 is 0.114 and RMSEC of 11.268. Based on this research, it can be stated that NIRS is one of the technologies that can be used to predict SSC and Vitamin C well enough.

Phenotyping Austrian Pine for Resistance Using Fourier-Transform Infrared Spectroscopy

2020 ◽  
Vol 46 (4) ◽  
pp. 276-286
Author(s):  
Anna Conrad ◽  
Caterina Villari ◽  
Patrick Sherwood ◽  
Pierluigi (Enrico) Bonello
Keyword(s):  
Fourier Transform ◽  
Urban Landscape ◽  
Economic Value ◽  
Fourier Transform Infrared ◽  
Pinus Nigra ◽  
Support Vector ◽  
Least Squares Regression ◽  
Midwestern Usa ◽  
Ft Ir ◽  
Plant Pathosystems

Austrian pine (Pinus nigra) is a valuable component of the urban landscape in the Midwestern USA. In this area, it is impacted by the fungal pathogen Diplodia sapinea, which causes a tip blight and canker on infected trees. While the disease can be managed through the application of fungicides and/or by preventing environmental conditions that are favorable for the pathogen, these practices only temporarily alleviate the problem. A more sustainable solution is to use resistant trees. The objective of this study was to evaluate whether Fourier-transform infrared (FT-IR) spectroscopy combined with chemometric analysis can distinguish between trees that vary in susceptibility to D. sapinea. Trees were phenotyped for resistance to D. sapinea by artificially inoculating shoots and measuring ensuing lesions seven days following inoculation. Then, three different chemometric approaches, including a type of machine learning called support vector machine (SVM), were used to evaluate whether or not trees that varied in susceptibility could be distinguished. Trees that varied in susceptibility could be discriminated based on FT-IR spectra collected prior to pathogen infection using the three chemometric approaches: soft independent modeling of class analogy, partial least squares regression, and SVM. While further validation of the predictive models is needed, the results suggest that the approach may be useful as a tool for screening and breeding Austrian pine for resistance to D. sapinea. Furthermore, this approach may have wide applicability in other tree/plant pathosystems of concern and economic value to the nursery and ornamental industries.

scholarly journals Digital Staining of High-Definition Fourier Transform Infrared (FT-IR) Images Using Deep Learning

2019 ◽  
Vol 73 (5) ◽  
pp. 556-564 ◽  
Author(s):  
Mahsa Lotfollahi ◽  
Sebastian Berisha ◽  
Davar Daeinejad ◽  
David Mayerich
Keyword(s):  
Fourier Transform ◽  
Fourier Transform Infrared ◽  
Cellular Level ◽  
Label Free ◽  
High Definition ◽  
Tissue Samples ◽  
Ft Ir ◽  
Downstream Analysis ◽  
Ir Spectroscopic ◽  
Multiple Samples

Histological stains, such as hematoxylin and eosin (H&E), are routinely used in clinical diagnosis and research. While these labels offer a high degree of specificity, throughput is limited by the need for multiple samples. Traditional histology stains, such as immunohistochemical labels, also rely only on protein expression and cannot quantify small molecules and metabolites that may aid in diagnosis. Finally, chemical stains and dyes permanently alter the tissue, making downstream analysis impossible. Fourier transform infrared (FT-IR) spectroscopic imaging has shown promise for label-free characterization of important tissue phenotypes and can bypass the need for many chemical labels. Fourier transform infrared classification commonly leverages supervised learning, requiring human annotation that is tedious and prone to errors. One alternative is digital staining, which leverages machine learning to map IR spectra to a corresponding chemical stain. This replaces human annotation with computer-aided alignment. Previous work relies on alignment of adjacent serial tissue sections. Since the tissue samples are not identical at the cellular level, this technique cannot be applied to high-definition FT-IR images. In this paper, we demonstrate that cellular-level mapping can be accomplished using identical samples for both FT-IR and chemical labels. In addition, higher-resolution results can be achieved using a deep convolutional neural network that integrates spatial and spectral features.

Determination of ß-Carotene and Lutein in Green Tea Using Fourier Transform Infrared Spectroscopy

2019 ◽  
Vol 62 (1) ◽  
pp. 75-81 ◽  
Author(s):  
Yong He ◽  
Yong He ◽  
Yiying Zhao ◽  
Chu Zhang ◽  
Chanjun Sun ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Ir Spectroscopy ◽  
Least Squares ◽  
Fourier Transform Infrared Spectroscopy ◽  
Green Tea ◽  
Fourier Transform Infrared ◽  
Support Vector ◽  
Ft Ir ◽  
Ft Ir Spectroscopy

Abstract. The feasibility of using Fourier transform infrared (FT-IR) spectroscopy combined with chemometrics to determine the ß-carotene and lutein contents in green tea was investigated in this study. The relationship between pigment contents and spectral responses was explored by partial least squares (PLS), least squares support vector machine (LS-SVM), and extreme learning machine (ELM) methods. Next, 30 and 29 effective wavenumbers (EWs) for ß-carotene and lutein, respectively, were selected according to the weighted regression coefficients of the PLS regression models, and simplified determinant models were built on the extracted EWs. The ELM models based on the EWs obtained the best results, with correlation coefficients of calibration (rc) and prediction (rp), and residual prediction deviation (RPD) of 0.977, 0.946, and 2.84, respectively, for ß-carotene and 0.975, 0.937, and 2.88, respectively, for lutein. The overall results indicate that FT-IR spectroscopy combined with chemometrics could be a rapid and accurate alternative method for determining carotenoid pigments in green tea. Keywords: ß-carotene, Chemometrics, Fourier transform infrared spectroscopy, Green tea, Lutein.

scholarly journals WiDMove - um sensor de movimento direcional baseado em perturbações do sinal eletromagnético de interfaces 802.11

2018 ◽  
Author(s):  
Bruno Soares da Silva ◽  
Gustavo Teodoro Laureano ◽  
Kleber Vieira Cardoso
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Fourier Transform ◽  
Principal Component ◽  
Support Vector ◽  
Short Time Fourier Transform ◽  
Channel State ◽  
State Information ◽  
Leave One Out ◽  
Short Time

A detecção acurada de indivíduos em ambientes fechados demanda dispositivos de alto custo, enquanto dispositivos de baixo custo, além da baixa acurácia, oferecem poucas informações sobre os eventos monitorados. As perturbações que podem afetar o sinal eletromagnético utilizado por interfaces de rede 802.11 tornam esse tipo de dispositivo um sensor de baixo custo, amplamente disponível e com acurácia satisfatória para várias aplicações. Neste trabalho, apresentamos o WiDMove, uma proposta para detecção da entrada e saída de pessoas em ambientes fechados utilizando medidas de qualidade do canal oferecidas pelo padrão IEEE 802.11n, conhecidas como Channel State Information (CSI). Nossa proposta é baseada em técnicas de processamento de sinal e de aprendizado de máquina, as quais nos permitem extrair e classificar assinaturas de eventos usando as medidas CSI. Em testes de laboratório com interfaces 802.11 convencionais, coletamos medidas CSI influenciadas por 8 pessoas distintas e extraímos as assinaturas de entrada e saída utilizando, dentre outras técnicas, Principal Component Analysis (PCA) e Short-Time Fourier Transform (STFT). Treinamos um classificador do tipo Support Vector Machine (SVM) e o validamos com validação cruzada, utilizando as técnicas K-Fold e Leave-One-Out. Os testes demonstraram que o WiDMove pode atingir a uma acurácia média superior a 85%. 

FR-IR Molecular Microanalysis System

1990 ◽  
Vol 48 (2) ◽  
pp. 270-271
Author(s):  
John A. Reffner ◽  
William T. Wihlborg
Keyword(s):  
Fourier Transform ◽  
Fourier Transform Infrared ◽  
Integrated System ◽  
Morphological Examination ◽  
Fully Integrated ◽  
Ir Microscopy ◽  
Normal Functions ◽  
Infrared Microspectroscopy ◽  
Infrared Spectral ◽  
Ft Ir

The IRμs™ is the first fully integrated system for Fourier transform infrared (FT-IR) microscopy. FT-IR microscopy combines light microscopy for morphological examination with infrared spectroscopy for chemical identification of microscopic samples or domains. Because the IRμs system is a new tool for molecular microanalysis, its optical, mechanical and system design are described to illustrate the state of development of molecular microanalysis. Applications of infrared microspectroscopy are reviewed by Messerschmidt and Harthcock.Infrared spectral analysis of microscopic samples is not a new idea, it dates back to 1949, with the first commercial instrument being offered by Perkin-Elmer Co. Inc. in 1953. These early efforts showed promise but failed the test of practically. It was not until the advances in computer science were applied did infrared microspectroscopy emerge as a useful technique. Microscopes designed as accessories for Fourier transform infrared spectrometers have been commercially available since 1983. These accessory microscopes provide the best means for analytical spectroscopists to analyze microscopic samples, while not interfering with the FT-IR spectrometer’s normal functions.

Sign in / Sign up

Export Citation Format

Share Document