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Sensors & Transducers

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Classification of Fatty Fat Acid in Palm Oil Using Near Infrared Spectroscopy

1 Herlina ABDUL RAHIM, 2 Siti Nurhidayah Naqiah ABDULL RANI

1, 2 Process Tomography and Instrumentation Research Group (PROTOM-I), INFOCOMM Research Alliance, Universiti Teknologi Malaysia, 81310 Skudai, Johor

1 Tel.: +609-7714208, fax: +607-5566272 1 E-mail: herlina@fke.utm.my

Received: 21 June 2013 /Accepted: 25 August 2013 /Published: 30 September 2013 Abstract: This paper propose an experiment to determine compatibility of near infrared (NIR) application in crude palm oil (CPO) quality measurement, considering only the Free Fatty Acid (FFA). 100 samples were taken from Felda Johor Bulkers, Terminal 1 in 7 consecutive days of experiment. FFA reading from conventional wet chemical analysis is used as the reference and validation for NIR method. Partial Least Square Regression (PLSR) method is used for the linear analysis, generated from MATLAB software. However, the finding from this experiment not really support the application at which the accuracy percentage acquire is only 48.12 %. Copyright © 2013 IFSA. Keywords: Palm oil, Fatty fat acid, Near infrared spectroscopy, Partial least square regression. 1. Introduction

Near-infared spectroscopic is a well-known technique in agriculture and food engineering. Mid 1960, USDA had developed NIR method of analysis to detect internal qualities of apple crops. Besides, this technology too is used to predict fruit maturity level and sugar content. Nowadays, NIR is a popular method to perform rapid, non-destructive analyses especially across the agriculture and food industries [1].

In Malaysia palm oil (Elaeis Guineensis) is one of the important sources of revenue [2]. Two main oil yields from palm oil fruit are extracted from the mesocarp; Crude Palm Oil (CPO) and from the kernel; Palm Kernel Oil (PKO) [3, 4]. These two oils have different characteristic and properties. The properties of CPO make it suitable for food product (e.g. butter, cooking oil) while PKO is used for non-food product (e.g cosmetic) [5]. However the usage

of CPO is more likely interested for this project. Thus, CPO has been chosen for the time being.

The main parameter for CPO quality can be classified in five term; Free Fatty Acid content (FFA), moisture level, Deterioration of Bleachability Index (DOBI), iodine value (IV) and carotene[6]. Among these internal quality attributes, FFA gave the most influence for consumer decision on trading purpose. The usage of analytical instrumentation in fats and oil is outdated and need adjustment to current technology development. Palm oil quality assessment need less time consuming method in order to preserve its quality [7].

2. Problem Statement In Malaysia palm oil is one of the important

sources of revenue. Yet, Malaysia too is the largest palm oil producer in the world [7]. Hence,

Article number P_1353

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researchers have always finding ways of maintaining this sector to be at best performance and quality. Continuous studies have being developed in order to find ways to maintain and improved oil palm production in Malaysia.

A common problem occurred during harvesting is failure to determine the right ripe fruit. Fruit that is too ripe contain high fatty acid (FFA) which is lowering the quality of palm oil produced. While young fruit does not contain much oil. This will be a waste as for every 100 kilograms of right ripe fruit bunches, typically 22 kilograms of palm oil and 1.6 kilograms of palm kernel oil can be extracted.

Therefore, this project is proposing a method of determining the right ripe fruit to be harvested by using near-infrared spectroscopy. Thus, problems like harvesting too ripe or too young fruit can be avoided in future.

Mid 1960, USDA had developed NIR method of analysis to detect internal qualities of apple crops. Besides, this technology too is used to predict fruit maturity level and sugar content as shown in Fig. 1. Nowadays, NIR is a popular method to perform rapid, non-destructive analyses especially across the agriculture and food industries [1-5].

Fig. 1. Near infrared spectroscopy.

In 1998, Y. B Che Man and M. H Moh have developed a near-infrared spectroscopy calibration for determination of free fatty acid (FFA) in crude palm oil [4]. However, their objective is just to replace the common complex lab process to a simpler but reliable NIR method. A similar project was been done a year later by Y. B. Che Man et. al., this time by using FTIR spectroscopy. Both project shows positive improvement of conventional method and consistent results [7].

Conventionally, ripe palm oil fruit is determine by number of mature fruit fall from its bunches. In Malaysia, the fruit will turn from black to orange when ripen. The outer layer of the ripe fruit is oily and fleshy. Beside, when ripe, the intensity of fruit is increasing thus make it weighted around 40-50 kg per bunch.

Based on this characteristic, we are developing an analysis to recognize the ripe fruit by significant number of wavelength by applying NIR. This should be a promising yet reliable outcome from this project for an improvement in palm oil industry. 3. Materials and Method

The experiment took place at Felda Johore Bulkers-Terminal 1, Pasir Gudang, Johor from 1st December 2012 to 7th December 2012. 100 random selection of oil are collected from time to time in between these seven consecutive days. Wet chemical analysis is been done by qualified laboratory attendance immediately after the NIR scanning to preserve the same quality attributes of the chosen sample. 3.1. Wet Chemical Analysis

Manual procedure for determining the acidity of CPO is being conducted based on the Malaysian Standard for Laboratory, MS 817:1989. (Standard of Test for Palm Oil & Palm Oil Products). From this standard, the acid value is defined as the amount (in milligram) of natrium hydroxide (NaOH) necessary to neutralize the free acids in 1 gram of sample.

Expression of result:

25.6 N VFFA%

W

,

where

- 25.6 is the weight for palmitic acid (palm oil and fractions);

- N is the normality of NaOH; - V is the volume, in milliliters, of NaOH used; - W is the weight, in gram, of the test portion. For FFA below 0.15 %, FFA is expressed to three

decimal places while for FFA above 0.15 %, two decimal places.

3.2. Sample Preparation

100 oil samples received from random palm oil mills were used in this study. Each sample was divided into two parts to be used for chemical analysis and NIR scanning. The samples are collected in ambient temperature of 27 °C before inserted into water bath of 80 °C just before the analysis. 3.3. Measurement

The NIR instrument used for this work is FOSS NIRSystem run by Vision Software. The wavelength measured range from 400 nm to 2500 nm but only 1600 nm to 1900 nm taken into account for the

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analysis [6-9]. A single sample contained a total of 601 data points. Data were collected by absorbance spectroscopic and directly being used for the analysis.

4. Results and Discussion A data set comprising spectral intensities of

100 samples of CPO at region 1600 to 1900 nm wavelengths and their respective FFA value was loaded as matrix format into Matlab workspace [MATLAB 7.9.0 <R2009b>] [10]. Fig. 2 shows the raw absorbance spectrum of the CPO.

Fig. 2. Raw absorbance spectrum of the CPO.

The pattern shows in the graph is convincing that

data were reliable for the analysis. Savitzky-Golay filter is applied to smooth the data before proceeds with the analysis.

Derivation is one of most widely used mathematical treatment for scatter in NIR spectra data [11, 12]. Fig. 3 and Fig. 4 show the first and second derivative respectively. After eliminating the scattering data by derivation, PLSR with 4 components was applied to it.

1600 1650 1700 1750 1800 1850 1900-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05First Derivative

Wavelength(nm)

Abs

orba

nce

Fig. 3. First derivative for palm oil spectra data.

1600 1650 1700 1750 1800 1850 1900-4

-3

-2

-1

0

1

2

3x 10

-3 Second Derivative

Wavelength(nm)

Abs

orba

nce

Fig. 4. Second derivative for palm oil spectra data.

Due to this result, it can be said that PLSR is not suitable to predict the FFA content of palm oil. The accuracy is only 48.12 % which is low as shown in Fig. 5. Hence, the data must undergo an advance analysis such as Artificial Neural Network for betterment in future [10].

3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5 5.2 5.43.4

3.6

3.8

4

4.2

4.4

4.6

4.8

5

5.2

5.4

Observed Response

Fitt

ed R

espo

nse

PLSR with 4 Components

Fig. 5. Prediction of FFA.

6. Conclusions

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. The finding from this experiment not really support the application at which the accuracy percentage acquire is only 48.12 %. Next stage for further classification we will be used Artificial Neural Network for improving the accuracy.

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Acknowledgements This work is partially supported by Ministry of Higher Education Malaysia and Universiti Teknologi Malaysia under research grant, with project number Q.J13000.2523.04H88. References [1]. Brodersen, K., Exloration of the Use of NIR

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[2]. Atinmo, T., Palm Fruit in Traditional African Food Culture, Asia Pasific J. Clin Nutr, 12, 3, 2003, pp. 350-354.

[3]. Shaarani, S. M., A. Cárdenas-Blanco, M. H. G. Amin, N. G. Soon, L. D. Hall, Monitoring development and ripeness of oil palm fruit (Elaeis guneensis) by MRI and bulk NMR, J. Agric. Biol, 12, 2010, pp. 101-105.

[4]. Mohd. Hudzairi Razali, A. S., M. A. Halim and Syazli Roslan, A Review On Crop Plant Production And Ripeness Forecasting, International Journal Of Agriculture and Crop Sciences, 4-2, 2012, pp. 54-63.

[5]. Kalyana Sundram, R. S., Yew-Ai Tan, Palm fruit chemistry and nutrition, Asia Pacific J Clin Nutr, 12, 3, 2003, pp. 355-356.

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[7]. Man, Y. B. C., Determination of Free Fatty Acids in Palm Oil by Near-Infrared Reflectance Spectroscopy, Journal of the America Oil Chemists’ Society (JAOCS), Vol. 75, 5, 1998.

[8]. El-Rahman, A. A., Ultraviolet, Visible and Infrared Wavelengths for Determination of Palm Oil Quality, New Trends in Agricultural Engineering, Nov. 2006, pp. 777-793.

[9]. Tetsuo Sat, S. K., Mutsuo Iwamot, Near Infrared Spectral Patterns of Fatty Acid Analysis from Fats and Oils, Journal of the America Oil Chemists’ Society (JAOCS), November 1991, Vol 68.

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[11]. Blanco, M., Critical Review: Near-Infrared Spectroscopy in the Pharmaceutical Industry, Analyst, August 1998, Vol. 123, pp. 135R-150R.

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