1.1 Background of Study
Rice (Oryza Sativa) is a type of cereal food in which most people consumes. As acereal grain, it is the most widely consumed staple food for a large part of the world’s human population, especially in Asia . Rice is perhaps the most remarkable of cultivated crops, for although possessing the roots of a dry land plant, it flourishes in swamps or under irrigation, and in Asia has produced one or more crop annually for centuries (Grist, 1965). Commercially available rice is distributed in different varieties in the market.
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Classification of product brands and type of products is a very active area for the application of chemometric classification procedures . The use of specific sensors for characterizing foodstuffs or in this case rice is being replaced by a trend to draw on the wealth of information available from the data provided by current analytical instrumentation. The extraction of useful information from an amount of data and the optimum use of this analytical information are important objectives of chemometrics .
Since the infrared spectra contain significant information about all the components of a complex mixture, FTIR is a very powerful and general technique for investigating the structure of rice components. In association with chemometric treatments such as principal component analysis (PCA), vibrational spectroscopy allows classification of foods (rice) to be undertaken without any chemical analysis . The main advantage is that no prior information on the sample is needed since the significant information is extracted during statistical treatment. The spectral information will constitute the experimental data which are analysed by PCA and HCA.
1.2 Problem Statement
Research on rice till this date mostly focused on its genome in order to increase the nutritional values. An example of product that has made it through this kind of research is Golden Rice. There is very little research on focusing in determination of types of rice using combination of spectroscopy and chemometrics technique let alone combination of Infrared Spectroscopy and chemometrics. Due to this, little is known about which or what variables is responsible in the types of rice grouping when pattern recognition is applied. Hence, this research is important in identifying what variable is responsible for the grouping of samples.
1.3 Research Objective
The aim of this study is to apply chemometric technique to determine the types of rice that will be analyze through Infrared Spectroscopy in order to assess the potential relationship between the element content and types of rice.
1.4 Significance of Study
This study is important to determine the variables that responsible in differentiation and variety types of rice. With the combination of Infrared Spectroscopy spectra of the samples prior to grouping of samples using pattern recognition, this is a quick method to classify rice compared to the use of nuclear magnetic resonance (NMR) spectroscopy with pattern recognition or other instruments.
1.5 Scope of the Research
In this research, type of rice to be samples is based on definition of rice’s type in Malaysia by Padi Beras Nasional Berhad . There are 7 samples to be test which all of them are to acquire at local stores. Analysis of the sample will be done through Fourier Transform Infrared Spectroscopy (FTIR) and pattern recognition which include Principal Component Analysis (PCA) and Cluster analysis.
2.1 Rice in History
Humankind progression in term of social and cultural was partly due to the discovery of agriculture. This development gave a massive impact on the way human been living their life by choosing to settle down in one place instead of constantly moving around looking for places with new food sources. Archeological evidence founds all over Asia proposed that rice must be at least eight thousand years old, thus eliminating theory that rice was among the first cereal to be cultivated due to general believed that agriculture first started around the Mesopotamian region in the valley between Euphrates and Tigris .
One of these is related to the extraordinary concentration of rice production in a small part of the world. Approximately 90% or more of the world’s rice is produced in the relatively tiny area in south, southeast and northeast Asia which often be refer to as ‘rice country’ . Rice is produced in a wide range of locations and under a variety of climatic conditions, from the wettest areas in the world to the driest deserts. It is produced along Myanmar’s Arakan Coast, where the growing season records an average of more than 5100mm of rainfall, and at Al Hasa Oasis in Saudi Arabia .
Rice plays a major role economically, especially in countries where rice is considered as the main food. This is more accurate in most countries in Asia, because not only they consume rice daily, but also Asia is the main producer of rice . Country like China, India and Thailand have long played major role in the development of rice, economically. The success of the crops not only important to the grower, but also affect the community either directly or indirectly .
2.2 Types of Rice
There are dozens of different ways to classify the scores of types of rice from all over the world, but rice is generally described as being long-, medium- or short-grained . These are some of the most common types you’ll find in supermarkets andgourmet stores, as well as a few specialty rices that we’re seeing more and more often. In the world market as well as in Malaysia, much emphasis is placed on grain length and whiteness as a criterion of grade and quality. Other factors such as palatability characteristics (appearance, cohesiveness, tenderness and flavor) also constitute as important considerations in quality grading .
In Malaysia, the main varieties of rice found in retail outlets are ordinary local and imported white rice, brown unpolished rice and specialty rice such as fragrant rice, Basmati, parboiled and glutinous rice. The main criteria in the classification are length of grain, content of head rice, content of broken rice and milling degree .
2.3 Fourier Transform Infrared Spectroscopy (FTIR)
Fourier Transform Infrared Spectroscopy (FTIR) provides specific information about chemical bonding and molecular structure, making it useful for analyzing organic materials and certain inorganic material. It can be utilized to quantitative some components of an unknown mixture. It can be applied to the analysis of solids, liquid and gasses. The term FTIR spectroscopy refers to a fairly recent development in the manner in which the data is collected and converted from an interference pattern to a spectrum .When the material under investigation is put into an FTIR spectrometer, it will absorb the radiation emitted and the successful absorption will display the uniqueness or fingerprint of the material under investigation .
Samples for FTIR can be prepared in multiple ways depending on its physical state. For solid samples, it will be ground into a fine powder with an agate mortar and pestle with an amount of the suspending salt, which usually be KBr due to it being transparent to infrared radiation. This powder is then compressed through a bench top hydraulic press into becoming a thin pellet which can be analyzed . Another method to prepare solid samples is by dissolving it in a suitable solvent such as methylene chloride and the solution is dropped onto a salt plate. After the solvent evaporates, a thin-solid film of the compound remains on the plate . Meanwhile, liquid samples can be examined directly as a thin film between two sodium chloride plates.
The term chemometrics was coined in the 1970s and is defined as the chemical discipline that uses statistical and mathematical methods for selecting and optimizing analytical and preparative methods, as well as procedures for the analysis and interpretation of data .
2.4.1 Pattern Recognition
The overall goal of pattern recognition is classification. Developing a classifier from spectral, chromatographic, or compositional data may be desirable for any number of purposes including source identification, presence or absence of disease in a patient or animal from which the sample has been taken, and food quality testing to name just a few . The classification step is often accomplished using one of several techniques that are now fairly well established including PCA, HCA, KNN, statistical and regularized discriminant analysis.
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Techniques of pattern recognition are applicable to data drawn from virtually any physical process. The data may be qualitative, quantitative, or both which is they may be numerical, pictorial, textural, linguistic, or any combination thereof. Meanwhile, one of the most important and oft-used data analysis methods is the eyeball technique, Subjective assessment of data patterns has long been a method accepted by many traditional data analyzers. Statistical analysis proceeds slowly by hand, more rapidly with hand calculators and can be quite fast with modern computers .
A total of 7 different samples of rice will be use for this study. All of the samples will be obtain from various supermarkets and grocery stores in Johor Bahru and Seremban area. Various brands and types of rice are selected in order to get as much variation as possible.
3.2 Analysis of Sample
Spectra of the rice samples will be acquired using (model number) instrument with KBr disc. The wavelength is set up to range from 4000cm-1 to 400cm-1.
Chemometrics analysis is the main part in this research as the data obtained from spectroscopic analysis will be analyses so that the important data can be identified and useable information can be deduced from the data. The key to chemometrics is to understand how to perform meaningful calculations on data. In most cases these calculations are too complex to do by hand or using a calculator, so it is necessary to use some software. Three softwares will be use for the data analysis as listed in table below.
MATLAB version 7.6
The Mathworks Inc.
PLS_Toolbox version 7.9
Eigenvectors Research Inc.
4.1 Expected Result
It is expected that Principal Component Analysis (PCA) will reveal multiple grouping due to different types of rice being used as samples. Furthermore, by comparing the score plot with the loading plot, the unknown variable that causing the samples to be group as it is will be identify.
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