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1 LWT - Food Science and Technology 64 (2015) 483e489 Contents lists available at ScienceDirect LWT - Food Science and Technology journal homepage: Fractal dimension in palm oil crystal networks during storage by image analysis and rheological measurements Zaliha Omar a, *, Norizzah Abd. Rashid b, Siti Hazirah Mohamad Fauzi a, Zaizuhana Shahrim a, Alejandro G. Marangoni c a Product Development and Advisory Services Division, Malaysian Palm Oil Board, No. 6 Persiaran Institusi, Bandar Baru Bangi, Kajang, Selangor, Malaysia b Faculty of Applied Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia c Food, Health and Aging, Department of Food Science, University of Guelph, Ontario, ON NIG2WI, Canada article info abstract Article history: Received 30 October 2014 Received in revised form 22 April 2015 Accepted 28 April 2015 Available online 8 May 2015 Keywords: Crystallisation properties Polymorphic form Fractal dimension Recrystallisation Crystal network The aims of this study are to determine the physicochemical properties and to quantify the fractal dimension of refined, bleached and deodourised palm oil (RBDPO) crystal network using image analysis and rheological methods during four weeks of storage at 15, 20 and 25 C. There was no progressive increase in solid fat content (SFC) after one week of storage. X-ray diffraction indicated RBDPO stabilised in the b 0 polymorphic form without transformation to b polymorph upon storage. However, photomicrographs of the crystal networks showed new small crystals formation in between the crystal clusters and on the surface of the existing crystals. The fractal dimensions of RBDPO crystal networks by image analysis (Db) and rheological measurements (Df) increased from day 1 to week 4 of storage. The results also showed good agreement between (Db) and (Df) in quantifying the hardness, storage modulus (G 0 ) and mass distribution of RBDPO crystal networks during storage The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( 1. Introduction Oils and fats play a crucial part in the human diet and have important role in improving the palatability of foods. Because of that, palm oil (PO) is becoming important raw material and choice for food manufacturers in producing plastic fats products such as margarine and shortening (De Clercq, Danthine, Nguyen, Gibon, & Dewettinck, 2012; Fauzi, Norizzah, & Zaliha, 2013). The increase in demand of PO has encouraged researchers and food manufactures to carry out many studies on nutritional, food formulation and its crystallisation behaviour. Palm oil crystallisation is known to be a complex process due to the slow crystallisation behaviour that leads to post hardening phenomenon. The product tends to harden slowly with time and gives an unacceptable grainy or sandy mouthfeel texture a few weeks after manufacturing (Duns, 1985; Ward, 1998; Zaliha, Chong, Cheow, & Norizzah, 2005; Zaliha, Chong, Cheow, Norizzah, & Kellens, 2004). According to Timms (1985) the slow crystallisation behaviour of PO is due to the high levels * Corresponding author. Tel.: þ ; fax: þ address: zaliha@mpob.gov.my (Z. Omar). of diacylglycerols present in PO which affect the crystallisation rates and polymorphic transformation. Later, Timms (1990) reported that post-crystallisation is caused by an increased interlocking of the crystals in addition to an increase in the solid phase. He also reported that post-crystallisation process was due to the smallness of the crystals. Smaller crystals lead to firmer fats with smooth texture or mouthfeel, whereas larger crystals aggregates produce softer fats and may impart grainy texture or mouthfeel to the final products (Pande & Akoh, 2013). When PO fractions are used in products, a slow crystallisation can take place after manufactured. Recrystallisation can occur to form a new solid phase as thermodynamic equilibrium is approached (Smith, Finley, & Leveille, 1994). Crystal growth could also lead to the formation of solid bridges (sintering) between the narrow gaps of the crystal networks upon storage (Johansson, Bergenstahl, & Lundgren, 1995) which could also contribute to the recrystallisation phenomenon. However, the post-hardening in palm oil/sunflower oil soft margarine formulation could be minimised by optimising the processing conditions (Mat Sahri & Omar, 2014). To date, not much published information is available on the post-hardening mechanism of fat crystal network in PO products / 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (

2 484 Z. Omar et al. / LWT - Food Science and Technology 64 (2015) 483e489 Fig. 1. Determination of the fractal dimension by Polarised Light Microscopy using the particle box counting method. A grayscale image (A) of the fat crystal network is thresholded and inverted (B). The number of the crystal particles within the boxes of increasing sizes size placed over the thresholded image are counted (C). The fractal dimension is the slope of the logelog plot (D) of the number of particles (Np) as a function of the box size (R), cited from (Marangoni, 2005). Note: D f ¼ 1.69 (0.01), R 2 ¼ because no suitable technique is available in the past to investigate this phenomenon except for the use of penetrometry. The first model to describe fat crystal network was proposed by van den Tempel (1961; 1979) and then extended by other researchers (Litwinenko, Rojas, Gerschenson, & Marangoni, 2002; Litwinenko, Singh, & Marangoni, 2004; Marangoni, 2005; Narine & Marangoni, 1999a; Vreeker, Hoekstra, den Boer, & Agterof, 1992). Fat crystal network is arranged in a hierarchical fashion, with combinations of different levels being implicated in varying degrees depending on the nature of the rheological measurement (Heertje, van Eendenburg, Cornelissen, & Juriaanse, 1988). An interpretation of rheological data for aggregate fat networks in the framework of fractal theories was presented. The elastic modulus of the network (G 0 ) varied with particle concentration of solid fat (F), or solid fat content, according to a power law, similar to models for the elasticity of colloidal gels. It was reported that the value of fractal dimension of tristearin in olive oil is about 1.7e1.8. This value is increased upon aging of the fat to D ¼ 2. Low fractal dimensions are indicative of low-density, open structures (Vreeker et al., 1992). Fractal geometry was first proposed to quantify natural objects with a complex geometrical structure that defined quantification by regular Euclidean geometry. Fractal systems are self-similar at all levels of magnification and have fractional dimensions (D) (Mandelbrot, 1982). The fractal theory was adapted and used by researchers in characterising the structure of fat crystal networks of cocoa butter, milk fat, milk fat-canola oil blends, and shortening on the basis of an original study (Heertje, 1993; Heertje et al., 1988). The scaling theory was applied by researchers to calculate the fractal dimensions of fat crystal network at the microstructural level from rheological and macroscopic fractal analysis. Polarised light microscopy was used as a method of imaging samples of fat crystal networks at the microstructural level, and the images were analysed to yield fractal dimensions. The fractal dimensions were compared to the values determined rheologically for similar fat systems. A good agreement between the fractal dimensions determined rheologically and by image analysis of the networks was reported by Marangoni and Rousseau (1996), Narine and Marangoni (1999a, b). In this study, fractal analyses via image analysis and rheological properties were applied to the analysis of fat crystal structure in PO products to shed light on the post-crystallisation phenomenon upon storage. The theory was used to quantify the fractality of the fat crystal networks of PO and its products during storage at various temperatures. This will help to give a better understanding on the complex crystallisation behaviour of PO and palm-based products. Fig. 2. Effects of crystallisation temperatures on Solid Fat Content (g/100 g) using Pulsed Nuclear Magnetic Resonance (pnmr) during storage. Note: abc comparisons of RBDPO at 15, 20 and 25 C. Note: ¼ RBDPO 15 C, ¼ RBDPO 20 C, ¼ RBDPO 25 C.

3 Z. Omar et al. / LWT - Food Science and Technology 64 (2015) 483e Fig. 4. Fractal dimension (Db) of two dimensional of RBDPO crystal networks during storage at 15, 20 and 25 C. Note: ¼ 15 C, ¼ 20 C, ¼ 25 C. Fig. 5. Fractal dimension (Df) of three dimensional of RBDPO crystal networks during storage at 15, 20 and 25 C. Note: ¼ 15 C, ¼ 20 C, ¼ 25 C. Fig. 3. a: XRD pattern of b 0 polymorphic form of palm oil (RBDPO) after 1 day of storage at 15 C. Note: The short spacings of the b 0 form are at 4.2 and 3.8 Å and that of the b form is at 4.6 Å (Souza et al., 1990). b: XRD pattern of b 0 polymorphic form of palm oil (RBDPO) after 28 days of storage at 15 C. 2. Materials and methods 2.1. Materials Refined, bleached and deodourised palm oil (RBDPO) was obtained from Jomalina Sdn. Bhd. Klang, Selangor, Malaysia Solid fat content Sample was melted at 70 C for 30 min to erase all crystal memory. Approximately 3.0 g of melted samples were pipetted into pulsed nuclear magnetic (pnmr) tubes (10.0 mm diameter, 1.0 mm thickness and 180 mm height). The samples were melted for 30 min in the drying oven (Memmert, Germany) before being stored in controlled temperature incubators (Binder, Germany) at 15, 20 and 25 C for 4 weeks. The measurements were carried out on a weekly basis using pnmr spectrometer (Karlsuhe, Germany). After each measurement, the samples were returned to the same storage temperatures for the next measurement (the same tubes were used for the storage test). The measurement was carried out in duplicates Polymorphic forms The polymorphic forms of the samples was determined using an Enraf Nonius model FR 592 (Enraf Nonius, Delft, The Netherlands) and X-ray diffractometer (Rigaku, Japan). The instrument was fitted with a fine focus copper X-ray tube. The samples were heated to 70 C for 10 min to destroy all crystal memory before being stored in controlled temperature incubators prior to measurements for 4 weeks (Zaliha et al., 2005). On the measurement day, the X-ray sample holder was pre-set at the desired crystallisation temperatures prior to measurements. The short spacings of the b 0 form are at 4.2 and 3.8 Å and that of the b form is at 4.6 Å (Souza, deman, & deman, 1990). Levels of b 0 and b crystals in the mixtures were

4 486 Z. Omar et al. / LWT - Food Science and Technology 64 (2015) 483e489 Plate 1a. Microstructures of RBDPO during storage at 20 C [A] Day 1 [B] Day 3 [C] Week 1 [D] Week 2 [E] Week 3 [F] Week 4 SFC (g/100 g): [A] 24.8 [B] 31.5 [C] 32.0 [D] 31.8 [E] 31.9 [F] estimated by the relative intensities of the short spacings at 4.2 and 4.6 Å Microstructure observation Samples were heated at 70 C for 30 min, one droplet was placed on a pre-heated glass microscope slide, and a preheated cover slip was gently dropped on top. Samples were then stored at 15 C, 20 C and 25 C for 4 weeks. The images were captured on a weekly basis using an LTS 350 cold stage operated by a TPP 93 temperature programmer (Linkam Scientific Instruments Ltd. Surrey, England) linked to an Olympus microscope. A black and white Sony XCe75 CCD Video Camera (Sonny Corporation, Tokyo, Japan) recorded images and Scion Image Software (Sion Corporation, Fredrick, MD, USA) was used to digitise the images. Using the Sion Software and an LGe3 PCI frame grabber, image quality was enhanced by taking an average of 16 frames Mass fractal dimension by image analysis (particle counting) The fractal dimension was determined from in situ polarised light microscopy (PLM) images. The fractal dimension by box counting method was developed by Narine and Marangoni (1999b). The particleecounting procedure is based on counting the number of crystal reflection within boxes of increasing sizes placed over a properly threshold and inverted images. Threshold converts white crystals to black with black background. Various boxes of different length perimeter (R) are laid over the threshold image. Then the number of microstructural elements, N in each box is counted. Graph of log N vs log R is plotted, the slope of the regression line corresponds to the fractal dimension (Db) of the crystal network and c is a proportionality constant as in Fig. 1. The developed method is based on a polarised light microscopy (PLM) technique. In this case, crystals crystallised are restricted to a 2-dimensional growth (crystals are between lines and plane) and the fractal dimension is between 1 and 2 (1 D 2). N ¼ cr Db (1) 2.6. Viscoelasticity properties The viscoelasticity properties of RBDPO crystal network was determined using a Rheostress RS 600 Rheometer (Haake, Germany) equipped with a water circulator at the base of a 35 cm

5 Z. Omar et al. / LWT - Food Science and Technology 64 (2015) 483e Plate 1b. Microstructures of RBDPO during storage at 15 C [A] Day 1 [B] Day 3 [C] Week 1 [D] Week 2 [E] Week 3 [F] Week 4 SFC (g/100 g): [A] 39.2 [B] 42.0 [C] 41.1 [D] 41.4 [E] 41.8 [F] serrated parallel measuring plate. Sample was melted at 70 Cinan oven for 30 min before being pipetted into round molds. The moulds were immediately transferred into controlled temperature incubators (Memmert, Germany) and kept for 4 weeks during measurements. An oscillatory stress sweep of 50e3000 Pa was performed to determine the storage modulus G 0 within a linear viscoelastic region (LVR) at a frequency of 1 Hz. The fractal dimension (Df) of the PO crystal network was evaluated by applying the fractal model of: G 0 ¼ F 1=d Df (2) where F is the solid fat fraction, d is the Euclidean dimension (d ¼ 3 for a 3 e dimensional crystal network) and Df is the fractal dimension of the crystal network Statistical analysis One-way ANOVA was performed using GraphPad Prism version 4 for Windows (Prism GraphPad, 2003). Differences between means were considered significant at p < All data reported are means of at least two replicates. 3. Results and discussion 3.1. Solid fat content The palm oil crystal networks showed rapid increase in solid levels from day 1 to week 1 of storage at all storage temperatures. The crystal network of RBDPO at 15 and 20 C showed higher increase in solid levels from 33 to 4 g/100 g and 27e32 g/100 g from day 1 to week 1, respectively as shown in Fig. 2. This could be due to the slow crystallisation behaviour of PO to achieve maximum solid levels. At 25 C a slight increase in the solid levels, from 18.0 to 18.7 g/100 g was observed. At this temperature (25 C), some of the low melting triacylglycerols are still dissolved in the olein matrix of RBDPO. This will reduce the compactness of the crystals present as in the microscopy observations shown in Plate 1c. From week 1 to week 4 of storage, there was no significant changes in their solid levels (p > 0.05). However, there was a significant difference

6 488 Z. Omar et al. / LWT - Food Science and Technology 64 (2015) 483e489 Plate 1c. Microstructures of RBDPO during storage at 25 C [A] Day 1 [B] Day 3 [C] Week 1 [D] Week 2 [E] Week 3 [F] Week 4 SFC (g/100 g): [A] 12.4 [B] 13.0 [C] 13.3 [D] 13.0 [E] 13.2 [F] (p < 0.05) in solid levels at the different storage temperatures of 15, 20 and 25 C. No progressive increment of the solid fat content was observed up to 4 weeks of storage. This implies that the crystal network of RBDPO has achieved its maximum solid levels within 1 week Polymorphic forms Table 1 shows the polymorphic form by X-ray diffractometry carried on the samples stored at 15, 20 and 25 C. All the RBDPO crystal networks stabillised in the b 0 polymorphic form from week 1 to week 4, except for a very weak b polymorph observed in the RBDPO crystal network at 15 C at the earlier stage of the storage time. As more b 0 crystals are formed during the subsequent storage period, this trace amount of b crystals could be swamped by the amounts of b 0 crystals as was also indicated in Fig. 3a and b. This observation is consistent with the previous work carried out by Zaliha et al. (2005), Zaliha, Siti Hazirah, Zaizuhana, and Norizzah (2014) Microscopy observation and mass fractal dimension by image analysis (particle counting method) Plate 1a shows the photomicrograph of microstructures of RBDPO crystal networks stored at 20 C. During the crystallisation process, the RBDPO crystallised and aggregated to form a combination of larger clusters and small crystals. It was observed that those clusters formed at 20 C were larger than the clusters formed Table 1 Polymorphic forms using X-ray diffractometry at 15, 20 and 25 C. Storage time (days) Polymorphic form 15 ( C) 20 ( C) 25 ( C) 1 b þ b 0 (b 0 >>> b) b 0 b 0 3 b 0 b 0 b 0 7 b 0 b 0 b 0 14 b 0 b 0 b 0 21 b 0 b 0 b 0 28 b 0 b 0 b 0

7 Z. Omar et al. / LWT - Food Science and Technology 64 (2015) 483e at 15 C as shown in Plate 1b. After 1 week of storage it was noted that more small crystals filled up the spaces in between the clusters until week 4 of storage. The SFC of the microstructure also increased from 24.8 to 31.5 g/100 g from day 1 to day 3 and then remained constant (±32.1 g/100 g) until day 28. At 25 C, the photomicrograph shows larger crystals aggregated to form larger clusters as compared to the microstructures at 15 and 20 C as shown in Plate 1c. These larger clusters lead to less clusterecluster interactions and the crystal networks can be easily broken down by an external force. Hence, the crystal network of RBDPO at 25 C is a weak crystal network and is less structured. After 1 week of storage, the clusters became more spherulitic and increase in size (the white spot) as shown in Plate 1c. However, the SFC (±13.0 g/100 g) remained the same throughout the storage. Fig. 4 shows the fractal dimensions (Db) of the crystal networks of RBDPO measured using the box counting method. It was observed that the Db of the crystal networks increased from 1.20 to 1.57, 1.14 to 1.48 and 1.05 to 1.19 at 15, 20 and 25 C, from day 1 to week 4 of storage, respectively. Thus, the number of clusterecluster interactions of the crystal networks continuously increased upon storage with the formation of more new crystals in between and on the surfaces of existing crystals causing the whole system became compact and dense Fractal dimension by rheological measurements The result showed that the fractal dimension (Df) of RBDPO crystal network increased from day 1 to week 4 of storage as shown in Fig. 4. The fractal dimension of the three dimensional crystal networks (Df) by rheological measurements increased from 2.19 to 2.59, 2.09 to 2.50 and 2.04 to 2.21 at 15, 20 and 25 C, respectively. This observation was in agreement with the fractal dimensions of oils and fats crystal networks as reported by Vreeker et al. (1992). It was reported that upon aging, further crystallisation would continue while aggregation and network formation would give rise to a denser, more compact and tightly packed structure with a higher fractal dimension (Fig. 5). 4. Conclusion Based on this study, it was shown that post-crystallisation had occurred upon storage without transformation of polymorphic form from b 0 to b. However, more work on polymorphism need to be carried out using a sensitive instrument to study the subtle changes in polymorphic form within the same type of polymorph (e.g. b 0 1, b 0 2) to evaluate whether they will contribute to postcrystallisation too. The post-crystallisation could also be due to the secondary nucleation that formed slowly on the surface of the primary crystals upon storage which will change the viscoelasticity of the end-products. Fractal dimension by image analysis (Db) can be a good indicator for viscoelasticity and mass distribution of the fat crystal networks for palm-based products. The fractal dimension is able to detect small changes in hardness of the fat microstructures with no significant changes in their SFCs. Hence, fractal dimension by image analysis (Db) can be a good indicator for the post-hardening or post-crystallisation of oils and fats. Acknowledgement The authors would like to thank the Director General of MPOB and Faculty of Applied Sciences of Universiti Teknologi MARA Shah Alam for permission to publish this paper and to all the Physics and Chemistry Laboratory staff of MPOB for their assistance. Greatest appreciation and thanks to Prof. Alejandro Marangoni, Department of Food Science, University of Guelph for the valuable guidance and arrangement for the experiments in his laboratory. 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