[IEEE 2013 IEEE Conference on Open Systems (ICOS) - Kuching, Malaysia (2013.12.2-2013.12.4)] 2013...

2013 IEEE Conference on Open Systems (ICOS), December 2 - 4, 2013, Sarawak, Malaysia

The Assessment of Amylose Content using Near Infrared Spectroscopy Analysis of

Rice Grain Samples

Syahira Ibrahim

Dept. Of Control & Instrumentation Faculty of Electrical Engineering, Universiti Teknologi

Malaysia 10hor Bahru, Malaysia i_ [email protected]

Abstract-Nowadays there are several methods in assessing the

traits of rice including destructive and non-destructive method.

The objective of this study is to evaluate the amylose content

(AC) on seven types of rice grain in Malaysian market by using

the Near-Infrared (NIR) Spectroscopy. AC is one of the main

criteria to determine the quality of cooked rice. The total

sample is 210, which includes two types of brown rice and

basmati rice respectively, and three types of milled white rice

where each of them were divided into 30 samples per group.

Spectrums from 210 samples were collected between the range

680nm and 1280 nm using a non-destructive, rapid, and

economical method. Reflectance spectra was converted into

absorbance spectra and the baseline shift effect in the

absorbance spectra was removed using first and second order

derivative coupled with a first and second order Savitzky

Golay smoothing filter. Next, the spectral data was processed

and examined independently, by applying partial least square

(PLS) analysis. Results show that NIR Spectroscopy helped to

determine the amylose content based on the spectra at 878 nm,

979 nm, 989 nm, 1100 nm and 1210 nm. The prediction on

amylose content in Malaysian rice by using NIR Spectroscopy

needs to be further investigated on a large number of samples

with different varieties, growing, cultivation methods and processing after harvesting.

Keywords-near-infrared spectroscopy; iodine colorimetric; amylose content; Malaysia rice grain; basmati rice; milled rice; brown rice

I. INTRODUCTION

Food quality and safety are crucial issues throughout the world since it is closely related to consumers' growth and health. Staple food quality is even more an important issue because staple food gives massive contribution in gaining energy. Rice is a staple food in Asian countries because several countries like Thailand, Cambodia, Indonesia, Vietnam, Myanmar and China produce rice for world distribution [1]. Furthermore, rice has high carbohydrates that will turn into energy. However, compared to other Asian countries, the productivity of rice cultivation in Malaysia is far lower so that Malaysia has to import rice from Thailand and other foreign countries.

BERNAS (Malaysian National Paddy Rice Corporation) reported that rice production in Malaysia in 2009 was 1.91 million tons, while the annual requirement of rice was 2.19

978-1-4799-0285-9/13/$31.00 ©2013 IEEE 32

Herlina Abdul Rahim

Dept. Of Control & Instrumentation Faculty of Electrical Engineering, Universiti Teknologi

Malaysia 10hor Bahru, Malaysia [email protected]

million tonnes, this shows that the level of rice sufficiency in Malaysia is only 70% [2]. In 2010, due to flood and diseases, annual rice productions substantially decreased to l.8 million [2]. Therefore, BERNAS has put some effort to increase paddy production at least enough for Malaysia's population. In order to achieve their annual target, BERNAS Rice Factory has hired more workers in the grading process section to ensure that all rice is graded fairly and correctly [2]. However this effort is not efficient because it will increase the production cost since the workers need to get paid for every harvesting season. Therefore, the usage of simple, fast and low cost grading process technology such as Near-Infrared Spectroscopy is necessary.

Near Infrared spectroscopy is a form of overtones and vibrational spectroscopy utilizing absorption or reflection measurements in the near-infrared region of the electromagnetic spectrum [3]. The overtones causes NIR spectra to become complex and overlapping and it happens when the molecules reaches a vibrational state above the fundamental. Overtone was identified because the existing of chemical groups, C-H, S-H, N-H and O-H stretching modes [3, 4]. Besides, it offers the non-destructive analysis even for intact solid or liquid samples and gives both chemical and physical information in development or production. Additional advantage is that it is a chemical free technique, requires no sample preparation and the quality of the sample can be assessed in just one scan [5]. It is usually used in agriculture area to determine the amino acid [6,7], protein [8], moisture [9] and it also will produce an accurate result for amylose content [10,11,12] in rice.

Amylose is made up of a long carbohydrate molecule, it is insoluble in water, linear and is a long polymer of a - (1 � 4) linked D-glucose (IUPAC) units [13] and occasionally carries a few « 0.5 %) moderately long chain links of a - (1 � 6) unit of starch [7,14,15] making up approximately 20 -30 % of the structure.


CH,OH -Q/"1.6J�"."(ti'b.nd

� '""-'�' �o�o�o "I �" .<-'�'

�o�o�o OH OH OH OH OH OH

(a) amylose (b) amylopectin

Figure I. Absorption band for thailand brown rice.

Amylose content (AC) has a major influence on the characteristics or texture of cooked rice and it is used for determining the processing quality [16,11]. this is because, AC correlates negatively with taste panel scores for roughness, protein [11], cohesiveness, tenderness, color, and gloss of the boiled rice, but positively correlates to the degree of milling, whiteness, adhesiveness and stickiness to lips [11]. Therefore, AC also gives an important trait in food industries, start from cultivation phase until the production stage [17]. On the paper of William R. Windham et al [11] they concluded that NIR Spectroscopy is the best method to predict the texture characteristic of cooked rice by using uncooked rice as a sample. Then, they also stated the relationship between AC and the characteristics of cooked rice.

Besides that, rice with high AC will also give extra advantages to human health, since high AC will reduce the digestion rate and as a results the postprandial glucose and insulin response become lower [16,18]. Based on the formulation of diet for diabetes, the slower digestion and absorption rate of carbohydrate in human body will help maintain the regular levels of glucose in blood and reduce the insulin response [19]. So, rice with high AC is good for diabetic patients to sustain their regular blood glucose level.

II. MA TERlALS AND METHODS

A. Samples Preparation A total of 210 samples of rice grain used in this

experiment were obtained from the BERNAS factory in Kempas Johor, Malaysia. Rice in the factory were harvested and imported from local and foreign countries. Seven types of rice were chosen randomly; they include two types of brown rice and basmati rice respectively and three types of milled white rice. Each rice type was divided into thirty groups and each group has 200 single rice grains. Then, all of the 210 samples were packed in plastic seals and labeled individually before the experiment began. After that, rice grain samples were stored in the laboratory (Robotic Laboratory, Faculty of Electrical Engineering, Universiti Teknologi Malaysia) at room temperature (�27°C) before spectra acquisition. After the spectrum acquisition using NIR Spectroscopy was completed, they were grinded using a dry blender (Philips, Model HR200 1 170/AC, made in China) followed by crushing them with white porcelain mortar and pestle to produce softened rice powder. Lastly, before the conventional method of the amylose content measurement

33

via iodine colorimetric was carried out, the rice powder were sieved using Laboratory test sieve, sizedl06/lm.

B. Spectra Measurements A small ring cup was constructed using OHP film

(internal diameter 30mm, depth 10mm) on a transparent glass to place the sample of rice grains. The reflectance spectrum of each sample of rice was acquired using low cost portable NIR Spectroscopy (Ocean Optics USB4000 Miniature Fibre Optic Spectrometer (650-1300 nm), ORNET Sdn. Bhd., Selangor, Malaysia) from the surface of rice grains. The NIR energy source and the optical standard reference used in this work was a tungsten halogen light (360-2000 nm) (LS- l, Ocean Optics, USA) and a diffuse reflectance standard (WS-l, Ocean Optics, USA), respectively. A reflection probe (R400-7-VISINIR, Ocean Optics, USA) was positioned at a 45° angle at the top of rice samples and the distance to the surface of rice grain is 7 mm for the diffused reflection measurement.

Both spectrometer and the energy source were switched ON for at least 25 minutes to warm up the instruments before spectra acquIsition began. The spectrometer was interfaced to a laptop computer by using software (SpectraSuite® , Ocean Optics, USA) for spectral data acquisition. One reflectance spectrum from the middle position of small ring cup of each rice sample was acquired. In order to increase the signal-to-noise ratio of the acquired spectrum, the acquired spectrum was smoothed with the boxcar value of 60. All the acquired spectral data were stored in the laptop computer and processed via MATLAB simulation software (MATLAB (R2009b».

c. Chemical Analysis, Reference Measurement The iodine colorimetric method is a destructive and

conventional method to determine the value of AC in rice. Firstly, in the extraction stage, 13 mg of rice flour in centrifugal tube was weighed on a microbalance. The samples were centrifuged with 5ml methanol for 30minutes at 60° and then the supernatant liquors were discarded. The extraction step was repeated twice to get the trace of lipids. After that, 2 ml of NaOH and 4ml distil water were added to the lipid free samples, and it was heated for 30 minutes in a water bath (or boiled water of 60°C) - solution L O.lml of solution I and 5ml of trichloroacetic acid (TCA) was added in a separate test tube. Next, 0.05ml of I2-KI solution was added and it was mixed immediately. After 30 minutes, the blue color of the solution was read at 620 nm, using the UVlVis Spectrophotometer, Lambda-25 by Perkin Eimers. The details of the experiment method can be referred at [20].

D. Data pre-processing Since there were big scattering effect at the beginning

and the end of the spectrum, this study utilized the spectrum from 680 nm to 1280 nm and the wavelengths beyond the range were removed. Next, NIR Spectroscopy data was transformed from reflectance value (R) to absorbance (-log


(R». After that, first and second order derivative coupled with a first and second order Savitzky-Golay smoothing filter was implemented to remove baseline shift effect and "smooth out" high frequency noises in the spectral data. And then, data was examined using partial least square analysis. Number of component was identified depending on the model parsimony.

III. RESUL TS AND DISCUSSION

A. Assignment and Absorbance ofNIR Spectroscopy This is the results for non-destructive (NIRS) method and

the results for colorimetric method are summarized in table II. The time taken for NIR Spectroscopy measured the quality (diffuse reflection light) for 210 rice samples was three days. Referring to the figure below, three days worth of data was separated into seven graphs based on their types. Each of them represented the absorption band from the NIR spectroscopy and the wavelength range is 680 nm until 1280 nm.

Figure 2. Absorption band for thailand brown rice.

Figure 3. Absorption band for Malaysia brown rice.

�

!'"I�� l,,��

,---�-, \/,-�--

Figure 4. Absorption band for Pakistan maharani basmati rice.

Figure 5. Absorption band for Kainaat basmati rice.

Figure 6. Absorption band for local Milled rice from Pahang.

Figure 7. Absorption band for local Milled rice from Selangor.

Figure 8. Absorption band for local milled rice from Johor.

TABLE I. THE NEAR INFRARED BAND ASSIGNMENT TABLE FROM 700 NM TO 2000 NM (FOR A BETTER PICTURE, REFER TO [21])

34


1'0_ ••• _ .1'_�· __ 11 ... �o;tn .... h 0-11 0.:'--:._ ""�'G_ 0'::.<_ C_Ci!".. U.'.�tDrut u_ ••• _

2- 0::.-._ •• _

10000 1700 1800 2000

rkSO S882

Wavy points of the spectrum can be evaluated by referring to Figure 8, the near infrared band assignment table and other researched references for more understanding on the overtone of the stretching vibration for rice spectra. By searching all of the graphs with the assignment table of NIR spectra, there are lots of information on overtone because of the existing of the chemical compound such as C-H, O-H and N-H bond on the wavelength range at 680 nm until 1280 nm. Some articles [4] state that absorption band from this range is suitable for food analysis because it is suitable for measuring the moisture and sugar content since it is associated with hydroxyl bonds. Moreover, article in the website [22] also stated that the absorption band from the third overtones of the fundamental stretch bond is most suited for food analysis which have thick sample such as seed. Besides, it also stated that this region is suitable for measuring the high moisture and fat content.

Referring to the entire graph, there are some information and differentiation that can be generally recognised. First, the profile curves were similar except for Thailand brown rice and there were three important wavelengths around 960 nm, 1090 nm and 1220 nm where the curves occur. Thailand brown rice had a different spectrum shape because the rice color was dark brown, so it affected the reflection band on NIR Spectroscopy [23]. Next, at 1090 nm and 1220 nm showed that there were dip depression for both types of basmati rice, but it become smaller for milled rice and Malaysia's brown rice. Lastly, the amplitude illustrated that both basmati rice has higher range of absorbance compared to milled rice and Malaysia brown rice.

Starting from the first peak of the graph at around 958 nm to 960 nm, there were a few chemical bonding that might be occur at that point (fig. 8). However, most researchers proved that it corresponded to the O-H second overtone of absorbance due to the moisture content [3, 11, 21, 24]. Hence, the studies in [24] and [11] show that at around wavelengths 878 nm, 979 nm and 985 nm, there was indication of C-H stretch due to starch absorption. The next crucial points on the graphs was close to 1100 nm where it was indication of ArC-H second overtone band [21] where Ar is described as an aromatic compound which have strong characteristic odour [25]. Last but not least, the wavelength around 1210 nm to 1222 nm also specified second harmonics of C-H stretching vibrations of CH2 [26]. Since the chemical

35

bonding of starch is made up of a combination of the C-H and O-H bonding, so both of them is important for evaluating the amylose content in rice.

B. Amylose Content Distribution Table 1 summarized the distribution of AC for seven

types of rice. All types of rice were labelled as BPT, BPM, BBK, BBPM, BJS, BTR and BSBS, which represented Thailand brown rice, Malaysia brown rice (local rice), Kainaat basmati rice, Pakistan Maharani basmati rice, Super tempatan rice from Johor (local rice), Super tempatan rice from Pahang (local rice) and Super Tempatan rice from Selangor (local rice) respectively. The mean values for three types of local white rice, two types of basmati rice and two types of brown rice is in the range of 6.1845 to 10.2381, 5.4621 to 6.5514 and 11.3541 to 1l.8640 respectively. As mentioned in [10,16,11] there were several categories for AC and it was shown that both white rice and basmati rice had very low AC. Meanwhile, brown rice was categorized as low AC.

TABLE II. TABLE TYPE STYLE AMYLOSE CONTENT (AC) DISTRIBUTION OF SEVEN TYPES OF RICE CUL TlVAR

Rice No.of Variety samples

BPT 30

BPM 30

BBK 30

BBPM 30

BJS 30

BTR 30

BSBS 30

Amylose Content (mg of amylose per L in Cuvet)

Min * Max ** Mean S.D ***

8.2990 16.1353 11.3541 2.2232

6.8105 25.1534 11.8640 4.3486

2.2396 9.6959 5.4621 1.8638

2.3037 12.0546 6.5514 2.2425

5.5922 17.1887 10.2381 3.1364

4.0945 10.9233 6.1845 1.4978

3.3113 23.0374 7.2321 3.8432

* ** *** Minimum value, Maximum value, Standard deviation

C. PLS analysis The results illustrated that only BPT rice gave good

results, because the accuracy was above 50 %, specifically 85.04% after it went through the second order smoothing and derivative. Some researchers also verified that NIR spectroscopy gave good results in analyzing the brown rice grain model [5,27]. By and large, the overall result for coefficient of determination (r2) for seven types of rice gave unfavourable results. However, after the second order derivative and second order Savitzky-Golay smoothing filter, the results for (r2) improved, although the value of r2

is still very small.

TABLE 111. SUMMARY OF THE COEFFICIENT OF DETERMINATION (R2) AND THE ROOT MEAN SQUARE ERROR (RMSE) OF SEVEN TYPES OF RICE

USING PARTIAL LEAST SQUARE (PLS) MODEL

Rice No.ofPLS Spectral R' RMSE Variety Components Treatment

BPT 6 SG+ 1" 0.7171 1.1626

5 SG + 2nd 0.8504 0.8453

BPM 3 SG+ 1" 0.3132 3.5432

4 SG + 2nd 0.4452 3.1845

BBK 3 SG+ 1" 0.2024 1.6366

3 SG + 2nd 0.2820 1.5527


BBPM 3 SG+ I" 0.3524 1.7743

3 SG + 2nd 0.4041 1.7020

BJS 3 SG+ I" 0.3908 2.4069

3 SG + 2nd 0.4726 2.2394

BTR 4 SG+ I" 0.2290 1.2931

3 SG + 2nd 0.2411 1.2829

BSBS 3 SG+ I" 0.1738 3.4346

3 SG + 2nd 0.2646 3.2404

Since the spectra are superimpose, so second order derivative is needed to treat it to find some peaks and valleys and also to improve the model. From these results, PLS analysis perhaps cannot be simply applied for this experiment. So, PLS analyses need to be combined with chemometric such as back-propagation artificial neural network (ANN) for qualitative and quantitative analysis. Since ANN is anti-jamming, anti-noise and has robust nonlinear transfer ability [28], it will become a more superior predictive model, apart from providing a better predictive performance than linear model [29]. Besides that, PLS analysis requires Unscramble software like (v.8.0, CAMO, Trondheim, Norway) [10,11,12] to carry out the preprocessing of the spectral data and chemometric analyses in an easy, appropriate and reliable way. If we want to proceed with PLS method using manual technique, it probably needs a complex technique because PLS uses both the variation of X and Y to construct new factors that will play the role of explanatory variable [30]. Since, this experiment was not involved with this software, therefore principle component regression (PCR) is the best alternative to analyze these results easily since PCR uses only the variation of X to construct new factors [30].

IV. CONCLUSION

As a conclusion, NIR spectroscopy is an easy, reliable and efficient instrument to measure the quality of rice and also other quality in food industries. The information within third overtone region, range 700 nm until 1100 nm is suitable for foods analysis since it is associated with hydroxyl bonds (O-H) and aliphatic chain (C-H). NIR Spectroscopy required further investigated on a large number of samples with different varieties, growing, cultivation methods, processing after harvesting and also the application on variety of chemometrics methods such as principle component regression and artificial neural network in analysing data. Hopefully, this study is one step to enlarge the reference on Malaysia rice and in the same time, to ensure Malaysia be a on

e of leading country in science and technology fields especially in food analysis based on Malaysia rice.

ACKNOWLEDGMENT

This work is partially supported by Ministry of Higher Education Malaysia and Universiti Teknologi Malaysia under research student grant, with project number Q.J13000.2523.04H88.

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