Back to Search
Start Over
Comparative study of multivariate methods to identify paper finishes ssing infrared spectroscopy
- Source :
- Repositorio Abierto de la UdL, Universitad de Lleida, Recercat. Dipósit de la Recerca de Catalunya, instname, Recercat: Dipósit de la Recerca de Catalunya, Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya), UPCommons. Portal del coneixement obert de la UPC, Universitat Politècnica de Catalunya (UPC)
- Publication Year :
- 2012
- Publisher :
- IEEE, 2012.
-
Abstract
- Recycled paper is extensively used worldwide. In the last decades its market has expanded considerably. The increasing use of recycled paper in papermaking has led to the production of paper containing several types of impurities. Consequently, wastepaper mills are forced to implement quality control schemes for evaluating the incoming wastepaper stock, thus guarantying the specifications of the final product. The main objective of this work is to present a fast and reliable system for identifying different paper types. Therefore, undesirable paper types can be refused, improving the performance of the paper machine and the final quality of the paper manufactured. For this purpose two fast techniques, i.e., Fourier transform mid-infrared (FTIR) and reflectance near-infrared (*IR) were applied to acquire the infrared spectra of the paper samples. *ext, four processing multivariate methods, i.e., principal component analysis (PCA), canonical variate analysis (CVA), extended canonical variate analysis (ECVA) and support vector machines (SVM) were employed in the feature extraction –or dimension reduction– stage. Afterwards, the k nearest neighbors algorithm (k**) was used in the classification phase. Experimental results show the usefulness of the proposed methodology and the potential of both FTIR and *IR spectroscopic methods. Using the FTIR spectrum in association with SVM and k** the system achieved maximum classification accuracy of 100%, whereas using the *IR spectrum in association with ECVA or SVM and k** the system achieved maximum classification accuracy of 96.4%
- Subjects :
- Multivariate statistics
Engineering
Feature extraction
k-nearest neighbors algorithm
Paper -- Acabat
symbols.namesake
Enginyeria química [Àrees temàtiques de la UPC]
Process control
Electrical and Electronic Engineering
Quality improvement
Instrumentation
Infrared spectroscopy
Enginyeria paperera [Àrees temàtiques de la UPC]
business.industry
Near-infrared spectroscopy
Paper finish
Espectroscòpia infraroja
Pattern recognition
Paper finishing
Support vector machine
Fourier transform
Principal component analysis
symbols
Multivariate methods
Artificial intelligence
business
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Repositorio Abierto de la UdL, Universitad de Lleida, Recercat. Dipósit de la Recerca de Catalunya, instname, Recercat: Dipósit de la Recerca de Catalunya, Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya), UPCommons. Portal del coneixement obert de la UPC, Universitat Politècnica de Catalunya (UPC)
- Accession number :
- edsair.doi.dedup.....b9fb4e454cd211af5cc6ba319b7984ed