Determination of Fiber Contents in Blended Textiles by NIR Combined with BP Neural Network

Li Liu,Li Yan,Jie Xu,Yaocheng Xie

doi:10.1155/2013/546481

Abstract

Fiber contents in cotton/terylene and cotton/wool blended textiles were tested by near infrared (NIR) spectroscopy combined with back propagation (BP) neural network. Near infrared spectra of samples were obtained in the range of 4000 cm−1~10000 cm−1. Wavelet Transform (WT) was used for noise reduction and compression of spectra data. The correction models of cotton/terylene and cotton/wool contents based on BP neural network and reconstructed spectral signals were established. The number of hidden neurons, learning rate, momentum factor, and learning times was optimized, and decomposition scale of WT was discussed. Experimental results have shown that this approach by Fourier transformation NIR based on the BP neural network to predict the fiber content of textile can satisfy the requirement of quantitative analysis and is also suitable for other fiber content measurements of blended textiles.

Highlights

By using Wavelet Transform (WT)-ca3-back propagation (BP) model, the AE was less than 4%, the mean absolute error (MAE) was 1.26% for cotton and 1.29% for terylene, Table 2: Comparison of prediction results of BP models with partial least square (PLS) model for cotton and terylene blended textile
Comparing the BP models with PLS model, it can be found that the prediction precisions at the wavelet analyzing scales of [3, 4], and 5 both were better than PLS model
A method for quantitative measurement of contents of blended textile by near infrared (NIR) based on BP neural network was investigated. 51 samples were prepared, in which 10 samples were selected randomly as validation set and the other 41 samples were used as calibration set

Summary

Introduction

Traditional chemical solution is a quantitative detecting method, which has long testing time, and a series of solvents should be prepared to dissolve the fiber, and lots of harmful gases would be produced and pollute the working environment [1]. Combination of NIR with stechiometry is more suitable to quantitive analysis and has been already applied to the fields of medicine, food, agriculture, chemical industry, and environment monitoring [2–8]. Applications of NIR to testing the fiber contents in textiles have been reported [9–12]. Most of these reports used the pretreatment method to preprocess spectrum data and sieve method to screening variables and built up the model of partial least square (PLS) method.

Methods

Results

Conclusion