Near Infrared Spectroscopy

1998 ◽  
Vol 6 (1) ◽  
pp. 3-17 ◽  
Author(s):  
L. Bokobza
Keyword(s):  
Infrared Spectroscopy ◽  
Infrared Spectrum ◽  
Near Infrared Spectroscopy ◽  
Spectral Properties ◽  
Near Infrared ◽  
High Energy ◽  
Local Mode ◽  
Mode Effects ◽  
Vibrational Anharmonicity ◽  
Frequency Intensity

Some of the concepts that make a near infrared spectrum understandable are reviewed. The origin of vibrational anharmonicity which determines the occurrence and the spectral properties (frequency, intensity) is discussed. The importance of the effects of the resonances which increase with increasing excitation are mentioned. Some of the characteristics of high energy overtone/combination spectra are considered in relation to local mode effects. The location of some particular group frequencies is provided.

Qualitative identification of waste textiles based on near-infrared spectroscopy and the back propagation artificial neural network

2021 ◽  
pp. 004051752110075
Author(s):  
Wenxia Li ◽  
Zihan Wei ◽  
Zhengdong Liu ◽  
Yujun Du ◽  
Jiahui Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Infrared Spectroscopy ◽  
Infrared Spectrum ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Back Propagation ◽  
Bp Network ◽  
Artificial Neural ◽  
Near Infrared Spectrum

Hand sorting for different types of waste textiles is time-consuming, laborious and inaccurate. The non-destructive and efficient identification of fibers in waste fabrics is of great significance to the reuse of textile materials. In this paper, 593 samples were selected as the research objects, including polyester, cotton, wool, viscose, nylon, silk, acrylic, polyester/nylon, polyester/cotton, polyester/wool and silk/cotton waste textiles. The near-infrared spectrum of each sample was obtained by a portable near-infrared spectrometer, and the influence of environmental humidity and fabric thickness on the near-infrared spectrum of the sample was discussed to obtain the best test conditions. On this basis, the back propagation artificial neural network (BP-ANN) was applied to the qualitative classification of waste textiles to complete the automatic identification of fabric components in the sorting process. Firstly, a standard sample set was established by waveform clipping and normalization, and a BP-ANN deep web suitable for near-infrared spectroscopy was established. Then the BP network was trained according to the input near-infrared spectrum data of known sample categories and the classification results of the preset 11 types of labels, and the weights and thresholds of each layer were adjusted in the repeated training process. Finally, a 1500 × 100 × 11 network structure was established when the network error was the smallest, and the number of corresponding hidden layer nodes was 100. When the number of training steps was 500, the sum of squared errors reached 0.001, and the model recognition effect was the best. Meanwhile, the validity of the model was verified by inspecting additional 299 samples outside the model, and the recognition accuracy rate of the established model also exceeded 99%, which verified the effectiveness of the model. These results show that this near-infrared qualitative analysis model can more accurately classify and identify waste textiles, especially polyester waste textiles. In addition, it provides a new idea for the recycling and reuse of waste textiles for enterprises.

Near-infrared spectroscopy (NIRS) – a promising brain imaging method in Psychiatry?

2008 ◽  
Vol 39 (01) ◽  
Author(s):  
AJ Fallgatter ◽  
AC Ehlis ◽  
MM Richter ◽  
M Schecklmann ◽  
MM Plichta
Keyword(s):  
Infrared Spectroscopy ◽  
Brain Imaging ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Imaging Method

A Study of Frontal Signals in Brain Computer Interfaces: Interpretation of EEG & FNIRS

2020 ◽  
pp. 136-142
Author(s):  
S. Srilekha ◽  
B. Vanathi
Keyword(s):  
Infrared Spectroscopy ◽  
Healthy Subject ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Conditions ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
Computer Interfaces ◽  
Rehabilitation Devices

This paper focuses on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) comparison to help the rehabilitation patients. Both methods have unique techniques and placement of electrodes. Usage of signals are different in application based on the economic conditions. This study helps in choosing the signal for the betterment of analysis. Ten healthy subject datasets of EEG & FNIRS are taken and applied to plot topography separately. Accuracy, Sensitivity, peaks, integral areas, etc are compared and plotted. The main advantages of this study are to prompt their necessities in the analysis of rehabilitation devices to manage their life as a typical individual.

Sign in / Sign up

Export Citation Format

Share Document