Modeling ovoproduct spoilage with red led light

Heat-treatment of ovoproducts is often required to ensure microbial safety. However, it has been shown that in most microbial species slow heating, or heat shocks may induce a higher heat resistance, that means that it is not possible to remove the microbial flora completely. These microorganisms produce on ovoproducts spoilage especially when the cold chain is broken along the transportation and/or storage. As a result, the life span for the product is shortened. The microbial activity inside the product causes changes in several physical properties, which can be supervised using optical methods. The aim of this work is to monitor the sigmoid behaviour underlying the ovoproduct evolution and spoilage by means of red LED light. For two commercial types of liquid and pasteurized egg white, storage at 9oC, an average correlation of r=0.94 has been found between microorganism growth and mean grey level of LED light passing though the sample. The results show that it is possible to develop very simple predictive models taking into account only one optical parameter corresponding to a single LED. INTRODUCTION Ovoproducts show very high sensitivity to microbiological spoilage which makes necessary to apply a thermal treatment to ensure its innocuity. Industrial pasteurisation of liquid egg (white, yolk and whole egg) typically consists of heating at temperatures between 57 and 72 oC for times ranging from a few seconds to about 10 min, depending on the temperature, the product and the pasteurisation system. This treatment is designed to inactivate pathogenic microorganisms such as Salmonella without damaging the physico-chemical and functional properties of ovoproducts. However, such thermal treatment does not ensure complete eradication of the microbial flora (Guilmineau and Kulozik 2007). Slow heating, or heating for short periods of time at temperatures above the optimum for growth (heat shocks) may induce for most microbial species a higher heat resistance (Mañas et al. 2003), which leads to limited shelf life of the ovoproduct and the need for storing at 4 oC maximum (Guilmineau and Kulozik 2007). Spanish producers recommend 2oC as optimal storage temperature for every step in the cold chain. One of the main consumers of ovoproduct, the catering trade industry, is not able to ensure such conditions due to frequent doors opening which leads to storage temperatures of 8 or 9 oC in cold rooms. The reference method for addressing the microbiological load of one sample is the determination of the concentration of mesophilic microorganisms counting the amount of colonies forming units (CFU). A sigmoid function can be used as primary model to describe the growth curve of microorganisms (Cairé et al 2007). On the other hand, several studies show the effect of heat treatment and or microbiological spoilage on some properties of ovoproducts as turbidity, colour and viscosity (Hou et al. 1996) and gel properties (Coimbra et al. 2006). These physical properties can be supervised by means of optical techniques. At present time there are some instruments that make use of light emitter diodes (LEDs) to the automatic supervision of coagulation processes as i.e. the cheese clotting process (Castillo et al 2006). The aim of this work is to address the sigmoid behaviour underlying the ovoproduct evolution and spoilage by means of red LED light. MATERIALS AND METHODS The study was carried out at summer time, critic season for preservation of perishable goods. Liquid egg white pasteurized ovoproduct was transported directly from the producing industries in refrigerate trucks and later it was stored in industrial refrigeration cameras at the School of Agricultural Technical Engineering of the UPM for its conservation at of 2oC (control) and 9oC temperatures. Two kinds of commercial products were used, the first one corresponds to Traditional Pasteurization to 57-58oC during 3 minutes (TP), while the second one refers to a High Temperature 60-62oC, and Short Time 150s product (HTST) with aseptic packaging, which implies that the life span of HTST product is longer than TP product. The optical test was carried out on three samples for ovoproduct with three repetitions for sample, at three or four times along the life span of ovoproduct depending on the storage temperature and product. For storage at 2oC, TP was tested at 0, 14 and 40 days and HTST at 0, 21 and 63 days from reception. For storage at 9oC, TP was tested at 0, 6 and 9 days and HTST at 0, 5, 6 and 8 days. The samples were characterized by determination of their microbiological load by counting the CFU/ml along the life span of each type of ovoproduct. The optical system consisted of a black chamber where a transparent vat, filled with 1cm deep of ovoproduct, is placed directly on top of the LEDs ́ array. A methacrylate ́s plate inlaid with eight LEDs mounted linearly was used to build that array, using red LED at 650 nm of wavelength. Fig. 1 shows how the detector used, a 3CCD IRRB camera (MS3100 Duncantech ®) with a sensibility window at 650 nm of 20 nm, takes the image of red light passing through out the sample. The images obtained were analysed using denoted Matlab programs. A treatment of raw image was implemented by image segmentation to convert an intensity image to a binary image. Each image was segmented twice: once by automatically generated segmentation threshold (Fig. 2, right) based on Otsu's method (Grey Level 1) which chooses the threshold to minimize the intraclass variance of the pixels classified as black or white and to maximize the variance inter black and white classes. The second one (Fig. 3, right) is based again on Otsu ́s method but applied on a modified histogram performed by the adjustment in the raw histogram of pixels with a grey level low (between 0 and 50) to the total range of grey intensity from 0 to 255. The Matlab program performs morphological operations on binary image in order to remove isolated pixels, this allows using Grey level 1 to individualize each LED (Fig. 3, left) while Grey Level 2 includes the red scattered light area. The variables extracted for each segmented image are: Illuminated area (number of pixels above level), Mean grey level (MGL) for the illuminated area (256 grey levels were considered) and Standard deviation (SD) of grey levels in the illuminated area ( grey level). Significant differences between treatments of ovoproducts were determined using variance analysis (one-way ANOVA, Statistica 6.1 StatSoft). Fisher’s least significant differences test was used to determine the significant differences between group means (p<0.05). RESULTS AND DISCUSSION Table 1 shows ANOVA results for the three optical parameters extracted from raw image categorized by each individual LED. For following ovoproducts evolution at 9oC was considered as especially relevant the information corresponding to HTST product with four dates of sample analysis along storage at cold room. Based on Table 1 information it was decided to use the MGL as more important parameter for following 9oC evolution. While illuminated area seems to be most explicative parameter for address the ovoproducts behaviour at 2oC. Experiment at storage temperature of 9oC Fig. 4 shows the increase of the number of microorganism regarding storage time, with a fit to the sigmoid model of r 2 =0.99 for both types of ovoproducts. Also some optical parameter can be fitted (r 2 HTST = 0.96) to such sigmoid behavior using the equation 1, where MGL is de mean grey level at t time, MGLmin and MGLmax are the minimum and maximum values respectively of full grey level range registered in that test, and a and b are the adjusted parameters of model. The analysis of variance carried out shows that the MGL increases significantly with higher storage times (for MGL emitted by LED1 FTP=13.18 and FHTST= 26.69). ) ( min max min 1 ) ( bt a e MGL MGL MGL t MGL (1) Egg white liquefaction along storage time is one of the main product alterations described. The microorganisms’ growth causes the proteins denaturalization, which are the 10% of egg white composition. Taking into account that the other 85% of egg white is water, that proteins ́ denaturalization would explain a decrease of product viscosity (Thapon 1994), lower optical density of the ovoproduct with not only significant higher values of red light intensity registered by the IRRB camera but also significant bigger illuminated areas by each LEDs (for parameter illuminated area by LED1 FTP=98.76 and FHTST= 10.95). The correlation coefficients between the MGL optical parameter and microorganism growth, ranges from 0.9 to 0.98 for HTST and TP ovoproducts respectively. Fig. 5 shows an estimation of biomass concentration based on optical properties of samples. Taking into account only one optical parameter corresponding to a single LED (LED1) it is possible to develop very simple predictive models defined by equation 2 for HTST product and equation 3 for TP product, where x corresponds to MGL and y refers to Log (UFC/ml).