Comparative Study of Multivariate Methods to Identify Paper Finishes Using Infrared Spectroscopy

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%Postprint (published version

Multivariate identification of extruded PLA samples from the infrared spectrum

This is a post-peer-review, pre-copyedit version of an article published in Journal of materials science. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10853-019-04091-6Polylactic acid (PLA) is a biodegradable thermoplastic polymer that is presented as a good alternative to petroleum-derived plastics. Some of the major drawbacks of this material are its lack of thermal stability and rapid degradation in large-scale production; thus, special care must be taken during processing. To improve their properties, a reactive extrusion with a multi-epoxy chain extender (SAmfE) has been performed at pilot plant scale. The induced topological modifications produce a mixture of several types of non-uniform structures. Conventional chromatographic (SEC—static light scattering) or spectroscopic (nuclear magnetic resonance) techniques usually fail in characterizing non-uniform structures. A method for the classification of modified PLA samples based on a multivariate treatment of the spectral data obtained by Fourier-transform infrared spectroscopy, jointly with the application of feature extraction and classification algorithms, was applied in this study. The results of this work show the potential of the methodology proposed to improve quality control during manufacturing.Peer ReviewedPostprint (author's final draft

Portable instruments based on NIR sensors and multivariate statistical methods for a semiautomatic quality control of textiles

Near-infrared (NIR) spectroscopy is a widely used technique for determining the composition of textile fibers. This paper analyzes the possibility of using low-cost portable NIR sensors based on InGaAs PIN photodiode array detectors to acquire the NIR spectra of textile samples. The NIR spectra are then processed by applying a sequential application of multivariate statistical methods (principal component analysis, canonical variate analysis, and the k-nearest neighbor classifier) to classify the textile samples based on their composition. This paper tries to solve a real problem faced by a knitwear manufacturer, which arose because different pieces of the same garment were made with “identical” acrylic yarns from two suppliers. The sweaters had a composition of 50% acrylic, 45% wool, and 5% viscose. The problem occurred after the garments were dyed, where different shades were observed due to the different origins of the acrylic yarns. This is a challenging real-world problem for two reasons. First, there is the need to differentiate between acrylic yarns of different origins, which experts say cannot be visually distinguished before garments are dyed. Second, measurements are made in the field using portable NIR sensors rather than in a controlled laboratory using sophisticated and expensive benchtop NIR spectrometers. The experimental results obtained with the portable sensors achieved a classification accuracy of 95%, slightly lower than the 100% obtained with the high-performance laboratory benchtop NIR spectrometer. The results presented in this paper show that portable NIR sensors combined with appropriate multivariate statistical classification methods can be effectively used for on-site textile quality control.This research was partially funded by Generalitat de Catalunya under grant numbers ACE033/21/000028, 2021 SGR 00392, and 2021 SGR 01501.Peer ReviewedPostprint (published version

Research on the organic binders in archaeological wall paintings

Wall painting realized using organic binders is the oldest form of parietal painting and precedes the birth of the affresco by about 20,000 years. This paper reports the results obtained from the main studies in the field of archaeological wall paintings. The attention was paid to the study of organic binders used for the application of the color, as well as on the instrumental techniques chosen to obtain such information. Different techniques can be used for the study of organic material in archeological paintings: non-destructive techniques, which can be applied directly in situ without sampling, and laboratory micro-invasive techniques for a more in-depth characterization. Among these, the chromatographic techniques represent a potential tool to acquire as much information as possible about chemical composition of binders

Classification of textile samples using data fusion combining near- and mid-infrared spectral information

There is an urgent need to reuse and recycle textile fibers, since today, low recycling rates are achieved. Accurate classification methods for post-consumer textile waste are needed in the short term for a higher circularity in the textile and fashion industries. This paper compares different spectroscopic data from textile samples in order to correctly classify the textile samples. The accurate classification of textile waste results in higher recycling rates and a better quality of the recycled materials. The data fusion of near- and mid-infrared spectra is compared with single-spectrum information. The classification results show that data fusion is a better option, providing more accurate classification results, especially for difficult classification problems where the classes are wide and close to one another. The experimental results presented in this paper prove that the data fusion of near- and mid-infrared spectra is a good option for accurate textile-waste classification, since this approach allows the classification results to be significantly improved.This research study was partially funded by Ministerio de Industria, Comercio y Turismo de España under grant number AEI-010400-2020-206 and by the Generalitat de Catalunya under grant numbers 2017 SGR 967 and 2017 SGR 828.Peer ReviewedPostprint (author's final draft

Chemical characterisation and classification of forensic trace evidence

Automotive paint, in the form of paint chips and/or smears, is one of the most commonly encountered forms of forensic trace evidence located at automotive related incidents. There is an increasing demand for more scientifically rigorous approaches to the interpretation of such evidence. This dissertation presents studies examining the use of a suite of spectroscopic techniques in conjunction with multivariate statistics, in order to develop analytical and interpretational protocols for automotive paint evidence

Forensic Analysis Of Automobile Paints By Atomic And Molecular Spectroscopic Methods And Statistical Data Analyses

The analysis of 110 automotive paint samples was conducted for the research presented here. Laser-induced breakdown spectroscopy (LIBS) was the central instrument utilized for analysis although scanning electron microscopy / energy dispersive x-ray spectroscopy (SEM/EDS) and Fourier transform infrared spectroscopy - attenuated total reflection (FTIR-ATR) analyses were also performed. Two separate methods of LIBS analysis of samples were used: a cross sectional analysis and a drill down analysis. SEM/EDS analysis focused on the cross section while FTIR-ATR analysis concentrated on the clearcoat layer. Several different data/statistical analyses were evaluated including principal components analysis (PCA), two tailed t-tests based on several different metrics (Hit Quality Index (HQI), Pearson\u27s correlation and Sorenson index), multivariate analysis of variance and receiver operating characteristic (ROC) curves. Full spectrum data analysis from LIBS spectra resulted in 99.7% discrimination between different sample comparisons and 12% between same sample comparisons based on HQI and t-tests. Peak analysis of LIBS spectra resulted in 87.5% discrimination between different sample comparisons and 5% between same sample comparisons based on MANOVA. When combining the results of the FTIR-ATR and SEM/EDS analyses, 88% of the samples could be discriminated

Identificación de muestras de papel mediante espectrometria IR y métodos multivariables

Actualmente en todo el mundo se consume una gran cantidad de papel reciclado. Ello ha provocado la fabricación de papel que contiene muchos tipos de impurezas, por lo que las empresas papeleras se están viendo con la necesidad de desarrollar métodos para controlar la calidad del papel entrante para así garantizar su calidad. Para ello en este trabajo se investiga un método muy rápido y no destructivo para identificar diferentes tipos de papel que permita desechar el papel entrante que no cumpla con unos requisitos mínimos de calidad. De este modo se puede mejorar el comportamiento de la máquina de papel y al mismo tiempo asegurar la calidad del producto final. Ello se hace en base al estudio de los espectros FTIR (espectroscopia del infrarrojo medio por transformada de Fourier) y NIR (espectroscopia del infrarrojo cercano) tratados mediante ICA (análisis de componentes independientes) como técnica multivariable de extracción de características para reducir la cantidad de variables utilizadas y k-NN (k vecinos más cercanos) como técnica de clasificación. Los resultados experimentales muestran que es posible identificar con éxito más de un 90% de las muestras estudiadas, de una forma rápida, muy automatizada y de forma no destructiva.Postprint (published version