Interpretation of FTIR Spectra by Principal Components–Artificial Neural Networks

Liu, Bing-ping; Li, Yan; Zhang, Lin; Wang, Junde

doi:10.1080/00387010600803664

Cited by 9 publications

(2 citation statements)

References 26 publications

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“…PCA highlights important data information and works to reduce dimensions by transforming data into variables in a new basis [14]. This variable is a linear combination of the original data and is calculated and sorted by order of importance [15].…”

Section: Pre-processing and Feature Extractionmentioning

confidence: 99%

Classification of Chili Plant Origin by Using Multilayer Perceptron Neural Network

Agustika

Ariyanti

Wardana³

et al. 2021

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

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Section: Pre-processing and Feature Extractionmentioning

confidence: 99%

Classification of Chili Plant Origin by Using Multilayer Perceptron Neural Network

Agustika

Ariyanti

Wardana³

et al. 2021

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

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“…Over the past few years, the artificial neural network modeling technique has attracted an increasing interest as a very promising method in many aspects, such as nonlinear calibration, quantitative structure-activity relationship, optimization of experimental conditions, and modeling of kinetic data. 7,8 In this paper, FTIR spectroscopy, using a single bounce HATR accessory, was used to detect normal and different stage colonic cancer tissues of rates. We focused primarily on how to identify normal, dysplasia, early cancerous, and advanced cancerous tissues more efficiently.…”

Section: Introductionmentioning

confidence: 99%

Classification of Rat FTIR Colon Cancer Data Using Wavelets and BPNN

2009

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A feature extracting method based on wavelets for horizontal attenuated total reflectance Fourier transform infrared spectroscopy (HATR-FTIR) and the cancer classification using artificial neural network trained with back-propagation algorithm is presented. The FTIR data collected from 36 normal Sprague-dawley (SD) rats, 60 1,2-DMH-induced SD rats, and 44 second generation rats of those induced rats were first preprocessed. Then, 12 feature variants were extracted using continuous wavelet analysis. Based on BPNN classification, all spectra were classified into two categories: normal and abnormal ones. The accuracy values of identifying normal, dysplastic, early carcinoma, and advanced carcinoma were 100%, 94%, 97.5%, and 100%, respectively. This result indicated that FTIR with continuous wavelet transform (CWT) and the back-propagation neural network (BPNN) could effectively and easily diagnose colon cancer in its early stages.

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Matrix Methods in Analytical Spectroscopy

Roach

Neal

Vincent-Finley³

2010

Encyclopedia of Analytical Chemistry

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The goal of this article is to survey the matrix methods that spectroscopists and data analysts are using to recover chemical information from electronic and vibrational spectral measurements. After an introduction, we review the principles of the spectral measurements at the focus of this article as it defines notation and describes several types of spectral measurements that are either acquired or analyzed in matrix format. These include steady‐state, dynamic, and correlation data. Next, we provide a rigorous mathematical description of the most important numerical methods for matrix analysis. Finally, five classes of matrix analysis methods that have been applied to molecular spectra are surveyed: exploratory data analysis, pattern recognition, spectral calibration, curve resolution, and multiway data methods. Short discussions about software alternatives and future developments are also included. A description of the notational conventions followed and algorithm listings are also provided.

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Interpretation of FTIR Spectra by Principal Components–Artificial Neural Networks

Cited by 9 publications

References 26 publications

Classification of Chili Plant Origin by Using Multilayer Perceptron Neural Network

Classification of Chili Plant Origin by Using Multilayer Perceptron Neural Network

Classification of Rat FTIR Colon Cancer Data Using Wavelets and BPNN

Matrix Methods in Analytical Spectroscopy

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