Brief Proposal for UTHM Master Project (Scholarship)

Indentification and Classification of Waste Material Using Near-Infrared (NIR) Spectroscopic Methods

Muhammad Aiyub,S.T (B.Eng)aiyubmuhammad@gmail.com

Supervisor: Dr. Mohamad Hairol Bin JabbarUniversity Tun Hussein Onn MalaysiaJabatan Kejuruteraan Komputer - Fakulti Kejuruteraan Elektrik dan Elektronik

ABSTRACT

Waste, especially household and commercial waste, is a heterogeneous mixture of different kinds of materials. Waste is currently classified in terms of the Minimum Requirements for the Handling, Classification and Disposal of Hazardous Waste. In this proposal we focused on classification of waste materials such as aluminum can, paper, glass, etc. Image processing will be applied for waste level verification based on several researchers working in various applications in image processing. In this work, we investigate the potential offered by using information outside of the visible spectrum, specifically near-infrared (NIR) images. Several studies have shown that near infrared (NIR) spectroscopy is capable to meet the aforementioned sorting requirements for material classifiaction. Infrared spectroscopy is one of the most important and widely used analytical techniques available to scientists working in a whole range of fields. There are a number of texts on the subject available, ranging from instrumentation to specific applications.

Keys: Waste Material, Images Processing, Near-InfraRed,spectroscopy.

IntroductionImage processing is a part of modern digital technology has made it possible to

manipulate multidimensional signals with systems that range from simple digital circuits to

advanced parallel computer[1]. Digital image processing technology has been widely used in

many scopes such as biology, food engineering, environment and medical care and so on.

Image processing of very close range video images or digital photographs are currently used

for verification of level volume of waste inside the bin [1][2][3].

Waste, especially household and commercial waste, is a heterogeneous mixture of

different kinds of materials. On the one hand it contains reusable or recyclable pieces of

plastic, ferrous- or nonferrous metals, wood or textiles, which could be a substitute for natural

resources. On the other hand disturbing materials like pieces containing PVC, papers, heavy

metals or toxic materials are included, alumunium can, glass, too.

Solid waste management is a complex process because it involves many technologies

and disciplines. These include technologies associated with the control of generation,

handling, storage, collection, transfer, transportation, processing, and disposal of solid

wastes. Waste management system aggressively moving towards in computerization

production over the past century. However, due to the working environment, waste

characterization, or costs, there are still tasks, which have remained largely untouched by

computerization. Waste is currently classified in terms of the Minimum Requirements for the

Handling, Classification and Disposal of Hazardous Waste[1][4][5][6].

Common steps of waste treatment are comminution, sizing, sorting and conditioning,

whereas sorting is the most important step. For a successful sorting process, every particle in

the mass flow has to be considered. The sorting criteria are specific attributes like

conductibility, susceptibility, density, size, shape, texture, color, mass or a combination of

these.

Image processing will be applied for waste level verification based on several

researchers working in various applications in image processing (Shylaja et al. 2011; Hannan

et al. 2011; Arebey et al. 2010;Hamarneh et al. 1999; Jiazhi et al. 2007)[1][2]. Image

analysis is a part in image processing stage, by using the specific algorithm to process digital

image presented a method to analyze the effect of drying on shrinkage, color and image

texture which were classified into classes depending on external image feature (Jiazhi et al.

2007)[1][7][8][9].

In this work, we investigate the potential offered by using information outside of the

visible spectrum, specifically near-infrared (NIR) images. Several studies have shown that

near infrared (NIR) spectroscopy is capable to meet the aforementioned sorting requirements

for material classifiaction [7][10][11][12]. In this paper, we classify several different types of

waste materials such as alumunium can, paper, glass, and etc.

Image classification using NIR information has been widely used in remote sensing.

As opposed to our method where, the image is single channel in the NIR range, in remote

sensing it is multispectral and contains the spectral information of the samples being

observed in the visible and NIR part of the spectrum. Other scientific applications of near-

infrared for material classification focus on near- infrared spectroscopy (NIRS), where in a

small part of the sample is placed inside the spectroscope and the near-infrared light is

employed to measure spectral characteristics of test objects [9][10][11].

Near infrared is the portion of the radiation spectrum that ranges from 700 to 1100

nm. Digital camera sensors are sensitive to this part of thelight spectrum[11]. In order to

capture near infrared images, the hot-mirror has to be removed from the camera.

All the images were taken under controlled viewpoint and illumination conditions and

their analysis was conducted in both the frequency and spatial domain. Image features

include the relation between materials’ intensity in the NIR and luma in the color images,

texture (in the frequency domain), and color[12].

Methods

Near-infrared spectroscopy (NIRS) is a fast and non-destructive analytical method.

Infrared spectroscopy is a technique based on the vibrations of the atoms of a molecule. An

infrared spectrum is commonly obtained by passing infrared radiation through a sample and

determining what fraction of the incident radiation is absorbed at a particular energy. The

energy at which any peak in an absorption spectrum appears corresponds to the frequency of

a vibration of a part of a sample molecule.

Qualitative and quantitative near-infrared (NIR) spectroscopic methods require the

application of multivariate calibration algorithms commonly referred to as chemometric

methods to model spectral response to chemical or physical properties of a calibration,

teaching, or learning sample set.

The identification of unique wavelength regions where changes in the response of the

near-infrared spectrometer are proportional to changes in the concentration of chemical

components, or changes in the physical characteristics of samples under analysis, is required

for a scientific understanding of cause and effect, even for routine method development.

Fig.1 Basic Instruments Configuration

The first step to developing an analytical method using NIR is to measure a spectrum

of the sample using an NIR spectrophotometer. It is helpful to note that the near-infrared

spectrum obtained by using a spectrophotometer is the result of the convolution of the

measuring instrument function with the unique optical and chemical characteristics of the

sample measured.

The sample participates as an optical element in the spectrometer. The resultant

spectrum contains information specific to the molecular vibrational aspects of the sample, its

physical properties, and its unique interaction with the measuring instrument. Relating the

spectra to the chemical structure of the measured samples is referred to as spectra–structure

correlation.

Fig.2 NIRS Library Development Activities

This correlation or interpretation of spectra converts the abstract absorption data

(spectrum) into structural information representing the molecular details about a measured

sample. Interpretive spectroscopy of this sort provides a basis for the establishment of known

cause-and-effect relationships between the spectrometer response (spectrum) and the

molecular properties of the sample.

Fig.3 NIRS Calibaration Flow Chart

When performing multivariate calibrations, analytically valid calibration models

require a relationship between X (the instrument response data or spectral data) and Y (the

reference data); probability tells us only if X and Y “appear” to be related. If no cause–effect

relationship exists between X and Y, the analytical method will have no true predictive

significance. Interpretation of NIR spectra provide the knowledge basis for understanding the

cause-and-effect of molecular structure as it relates to specific types of absorptions in the

NIR. Interpretive spectroscopy is a key intellectual process in approaching NIR

measurements if one is to achieve an analytical understanding of these measurements.

Main task of the computer in NIR spectroscopy, aside from driving the instrument or

collecting data, is to interpret the spectra using a variety of multivariate mathematical

techniques. These techniques are used to produce a mathematical calibration model.

The NIR spectroscopy algorithms used to “interpret” optical data for absorbing

samples may be explained as different approaches to relating sample absorbance ( A ) at

specific wavelengths to analyte concentrations via Beer’s law. To continue:

A = M c ~ (1)

where

A = absorbancc (optical density)

M = molar absorptivity

c = molar concentration of absorber

d = sample pathlength

and thus

A=C/Md

So the multiregression equation commonly used for calibration is:

Y = Bo + Bi(- log Rj) + E

where

Y = percent concentration of absorber

Bo = intercept from regression

Bi = regression coefficient

i = index of the wavelength used and its corresponding reflectance (Ri)

N = total number of wavelengths used in regression

E = random error

This is actually a form of Beer’s law with each B term containing both pathlength and molar

absorptivity (extinction coefficient) terms.

Most simply, the concentration is related to the optical data as

Conc. = change in concentration/change in absorbance * absorbance + some error

or

Conc. = K * absorbance + some error

Thus,

K the regression coefficient is equal to the change in concentration divided by the

change in absorbance.

Fig 4. Illustration of overview System NIRS for classification of

Glass,Paper and Alumunium Can.

Strengths

1. Infrared spectroscopy is certainly one of the most important analytical techniques

available to today’s scientists. One of the great advantages of infrared spectroscopy is

that virtually any sample in virtually any state may be studied.

2. Higher energy levels because radiation levels from black body emitters peak at shorter

wavelengths.

3. High sensitivity photo conductive detectors function in the NIR but not in the mid-IR.

4. Perhaps most important, low cost materials such as glass and quartz transmit NIR

radiation and can be used as cell windows, focusing lenses and optical fibers.

5. Liquids, solutions, pastes, powders, films, fibres, gases and surfaces can all be

examined with a judicious choice of sampling technique. As a consequence of the

improved instrumentation, a variety of new sensitive techniques have now been

developed in order to examine formerly intractable samples.

6. NIRS is generally chosen for its speed, its low cost and its non-destructive

characteristic towards the analyzed sample. On one hand, the interest in NIR has

increased thanks to the instrument improvements and the development of fibre optics

that allow the delocalization of the measurements.

7. On the other hand it has increased because of the computer progresses and the

development of new mathematical methods allowing data treatment.

Weakness1. The major weakness of the NIR region is that the absorption bands occurring there are

the overtones of the fundamental bands residing in the mid-IR region. As a result,

they are relatively weak and not clearly delineated. This makes quantitative

calculations complex and calibration procedures quite laborious and not transferable

from one instrument to another.

2. If samples were not dried prior to NIR analysis, the changes in hydrogen bonding due

to the effects of sample temperature, ionic strength, and analyte concentration would

complicate the interpretation of over-lappinng IR spectral bands.

3. Changes in hydrogen bonding bring about real and apparent band shifts as well as

flattening or broadening of band shapes. The overtone and combination molecular

absorptions found within then IR region inherently have significantly lower intensity

as compared to the fundamental IR absorptions

References

[1]. W Zailah, M A Hannan, Abdulla Al Mamun, 2012. Image Acquisition for Solid Waste Bin level Classification and GradingJournal of Applied Sciences Research, 8(6): 3092-3096, 2012 ISSN 1819-544XDepartment of Electrical, Electronic and Systems Engineering,UniversitiKebangsaan Malaysia, Malaysia

[2]. L. Yaroslavsky, Course 0510.7211, Semester B.DIGITAL IMAGE PROCESSING: APPLICATIONS, Lecture notes.Tel Aviv University Faculty of Engineering, Department of Interdisciplinary Studies

[3].Arebey, M., M.A. Hannan, B. Hassan, R.A. Begum, A. Huda, 2010. Intergrated technologies for solid waste bin monitoring system, Environ Monit Assess DOI 10.1007/s10661-010-1642-x

[4].United Nations Environment Programme, 2009Developing Integrated Solid Waste Management Plan Training Manual, Volume 2. Assessment of Current Waste Management System and Gaps therein United Nations Environmental Programme Division of Technology, Industry and Economics International Environmental Technology Centre Osaka/Shiga, Japan

[5]. Hand, Caroline, MSc., 2006Waste Management: The New Legislative ClimateA Specially Commissioned Report. ISBN 1 85418 367 2Thorogood Publishing Ltd 10-12 Rivington Street, London EC2A 3DU

[6].Tchobanoglous, George.,Prof., Kreith, Frank.,Prof., 2002.Hand Book Of Solid Waste Management,Second Edition.The McGraw-Hill Companies, Inc. All rights reserved. Manufactured in the United States of America.

[7]. Nixon , Mark S. ,and S. Aguado,Alberto., 2002.Feature Extraction and Image Processing.Newnes, An imprint of Butterworth-Heinemann Linacre House, Jordan Hill, Oxford OX2 8DP

[8].Acharya, Tinku., K. ,Ray, Ajoy., 2005Image Processing Principles and ApplicationsA JOHN WILEY & SONS, INC., PUBLICATION

[9].W.H.A.M. van den Broeka, D. Wienkeb, W.J. Melssenb, L.M.C. Buydensb,* Plastic material identication with spectroscopic near infrared imaging and artifcial neural networks Optical Measurement Systems, P.O. Box 17, 6700 AA Wageningen, The Netherlands

[10]. Ozaki,Yukihiro,PhD., McClure,W Fred,PhD.,A Christy, Alfred,PhD.,2007Near InfraRed Spectroscopy In Food Science and Technology, A John Wiley & Sons,Inc., Publication.

[11]. N.Salamati,C.FredembachandS.Süsstrunk. ,2009.Material Classification Using Colorand NIR Images.InProc.ofIS&T17thColorImagingConference.NewMexico

[12]. P.J. de Groot, G.J. Postma, W.J. Melssen, L.M.C. Buydens,2001. Validation of remote, on-line, near-infrared measurements for the classification of demolition waste.

Laboratory of Analytical Chemistry, University of Nijmegen, Toernooiveld 1, 6525 ED Nijmegen, The Netherlands.

[13]. P. Tatzer, M. Wolf, and T. Panner, Apr. 2005.“Industrial Application for Inline Material Sorting Using Hyperspectral Imaging in the NIR Range,”Real-Time Imaging, vol. 11, no. 2, pp. 99–107,

[14]. A Burn,Donald., W. Ciurczak, Emil.,2001HandBook of Near-Infrared Analysis, Second EditionMarcel Dekker, Inc. New York.

[15]. T.Randenand, J.H.Husoy. ,1999.Filtering for Texture Classification: Acomparative Study. IEEE Trans. On Pattern Analysis and Machine Intelligence.vol.21(4),pp.291-309