A novel technique based on diffuse reflectance near-infrared spectrometry and back-propagation artificial neural network for estimation of particle size in TiO2 nano particle samples

2010 ◽  
Vol 95 (2) ◽  
pp. 337-340 ◽  
Author(s):  
Mohammadreza Khanmohammadi ◽  
Amir Bagheri Garmarudi ◽  
Nafiseh Khoddami ◽  
Keyvan Shabani ◽  
Mohammadreza Khanlari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Size ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Back Propagation ◽  
Infrared Spectrometry ◽  
Nano Particle ◽  
Novel Technique ◽  
Near Infrared Spectrometry

scholarly journals Simultaneous Determination of Some Components in Detergent Washing Powder by Mid-Infrared Spectrometry and Artificial Neural Network

2011 ◽  
Vol 94 (1) ◽  
pp. 322-326
Author(s):  
Mohammadreza Khanmohammadi ◽  
Amir Bagheri Garmarudi ◽  
Mohammad Babaei Rouchi ◽  
Nafiseh Khoddami
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Simultaneous Determination ◽  
Sodium Tripolyphosphate ◽  
Back Propagation ◽  
Infrared Spectrometry ◽  
Feed Forward Back Propagation ◽  
Powder Samples ◽  
Artificial Neural

Abstract A method has been established for simultaneous determination of sodium sulfate, sodium carbonate, and sodium tripolyphosphate in detergent washing powder samples based on attenuated total reflectance Fourier transform IR spectrometry in the mid-IR spectral region (800–1550 cm−1). Genetic algorithm (GA) wavelength selection followed by feed forward back-propagation artificial neural network (BP-ANN) was the chemometric approach. Root mean square error of prediction for BP-ANN and GA-BP-ANN was 0.0051 and 0.0048, respectively. The proposed method is simple, with no tedious pretreatment step, for simultaneous determination of the above-mentioned components in commercial washing powder samples.

Spectral Amplification in near Infrared Spectrometry

1998 ◽  
Vol 6 (A) ◽  
pp. A207-A210
Author(s):  
Marc Meurens
Keyword(s):  
Prediction Error ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Infrared Spectrometry ◽  
Chemical Analyses ◽  
Wavelength Selection ◽  
Near Infrared Spectrometry

“SPECTRAL AMPLIFICATION” is the significant name of a new algorithm of wavelength selection developed to improve the precision of the partial least squares (PLS) calibration of near infrared a (NIR) spectrometer for quantitative chemical analyses. This algorithm amplifies selectively some spectral data by mutiplicative coefficients so that they are predominant in the spectra and lower the prediction error of the PLS calibration. The poster presents a demonstration of “spectral amplification” in the determination of moisture on milk powders by NIR diffuse reflectance spectroscopy.

scholarly journals Application of diffuse reflectance near-infrared spectrometry and chemometrics in characterization of micro and mesoporous ZSM-5 zeolites

RSC Advances ◽  
2018 ◽  
Vol 8 (61) ◽  
pp. 34830-34837 ◽  
Author(s):  
Shahin Amani ◽  
Amir Bagheri Garmarudi ◽  
Mohammadreza Khanmohammadi ◽  
Fereydoon Yaripour
Keyword(s):  
Diffuse Reflectance ◽  
Near Infrared ◽  
Infrared Spectrometry ◽  
Near Infrared Spectrometry

Evaluation of porosity type of zeolites is one of the critical topics in catalysis science.

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.

scholarly journals Classification of RRIM clone series using artificial neural network

2020 ◽  
Vol 9 (2) ◽  
pp. 297
Author(s):  
Faridatul Ama Ismail ◽  
Nina Korlina Madzhi ◽  
Noor Ezan Abdullah ◽  
Hadzli Hashim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Near Infrared ◽  
Input Parameter ◽  
Back Propagation ◽  
Comparative Investigation ◽  
Sensing Element ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper presents comparative investigation on the classification of rubber latex clone series using Artificial Neural Network (ANN) based on optical sensing technique. Rubber Research Institute of Malaysia (RRIM) introduced the rubber breeding program known as RRIM clone series in order to increase the yield of latex production and the rubber wood to meet the requirement for export and import in upstream sector. Due to the large numbers of clones launched with different characteristics and properties, this bring difficulty such as lack of information regarding to the identification on cloning. Therefore, this work developed an optical based sensing system for classification of the selected RRIM 2000 and 3000 clone series based. Near Infrared Sensors was used as sensing element in order to measure the latex from the top surface and photodiode which received the reflected light from the sensor via reflectance index in term of voltage. The raw obtained data was then used as input parameter for ANN tool which supervised by scaled gradient back propagation and the performance was optimized at 25 neurons with 74.4% accuracy. By using ANN the sensitivity, specificity and accuracy for each clones are measured.  RRIM 3001 shows the highest sensitivity, 94.1% while RRIM 2002 shows the highest specificity of 99.1% accuracy, 93.1%. As a result, the system could differentiate RRIM 2002 more compare to other clones.

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