A comparison of CIE L*a*b* and spectral methods for the analysis of sliced ham fading
C Sheridan1, M O’Farrell1, E Lewis1, C Flanagan1, J Kerry2 and N Jackman2 1Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland
2Echo Food Solutions International Ltd., Coolbanagher, Portlaoise, Co. Laois, Ireland
Abstract. In the modern retail environment, the appearance of a product is frequently the only quality indicator available to consumers. This is especially true of products such as sliced ham that have been sealed into packages to maintain product freshness. It has been shown that sliced ham products undergo discolouration from their original pink colour to a dull grey colour when exposed to a combination of oxygen and light. This is unappealing to consumers who expect a pink colour for sliced ham. Two methods for the measurement of the discolouration were investigated – CIE L*a*b* measurements and analysis of the spectral reflectance of the colour of the ham. Several sliced ham products with differing amounts of fading were examined using both methods. It was observed that the products used covered a wide range of variation in colour. Reproducibility of CIE L*a*b* values proved to be quite difficult and significant overlapping of the L* (lightness) and a* (redness) values measured for pink and grey coloured ham was observed. The variations in these values can be attributed to differences in the intensity of reflected light for different products. L*a*b* measurements are sensitive to light intensity and pigment concentration. Analysis of the spectral reflectance readings did not encounter these problems as the spectral response was normalised (to reduce intensity errors) before data analysis was carried out using principal component analysis (PCA) and artificial neural networks (ANN). A classifier based on PCA and ANN was successfully implemented that can discriminate different stages of fading for the ham slices.
1. Introduction The appearance of the food is of paramount importance to manufacturers and retailers. It has been shown that the colour of sweetened water, for example, influences consumers’ expectations and attitudes about that beverage in terms of how sweet it tastes and its thirst quenching abilities [1]. In the modern retail environment, colour and “best before” dates are frequently the only factors by which a consumer can judge the quality of a product. This is especially the case for products that are sealed to preserve them for longer. Cured sliced ham is an example of one such product. In this case, the ham is sealed into mostly clear plastic packaging, which have the result of rendering ineffectual the senses of touch and smell. However consumers already have expectations about cured ham; it should have the rich pink colour that is induced in pork meat as part of the curing process. Ensuring the colour stability of food products during storage and retail display has become an important issue for producers and retailers in the food industry [2]. In addition, retailers often assign a large proportion of their floor space to meat display cabinets and butcher counters. It is important, therefore, for retailers to maximise the sale of goods stored in these areas. Barbut [3] investigated the effects of three illumination sources on the appearance of three fresh meat cuts – beef, pork and chicken meat. A panel of fifteen people who regularly buy meat was convened and presented with meat cuts illuminated by fluorescent (FL), incandescent (INC) and metal halide (MH) light sources. The results indicate that panellists preferred meat cuts under INC light. This is attributed to the fact that the INC light source has a uniform output across the visible light spectrum whereas the other two sources have very little luminance within the red region. It can be concluded that the visual appearance of food products are very important to consumers and that meat display cabinets need to be optimally lit to maximise sales. Nitrosylmyoglobin and denatured nitrosohemochrome are the pigments responsible for the pink colour of cured and heat-treated ham products, respectively. It has been shown that when cured ham is exposed to light and oxygen, these pigments oxidize to metmyoglobin [4]. Metmyoglobin causes exposed ham surfaces to fade to a dull grey colour that is unappealing to consumers. An additional effect of exposure to the combination of light and oxygen is lipid oxidation – radicals created during the pigment oxidation process attack lipids [5]. Lipid oxidation causes deterioration in the quality of the ham by affecting its colour, flavour and texture. Investigations have been carried out examining the stability of cured ham colour [2,4] that have shown a decrease in the redness of the surface colour of the ham. It is recommended that low illuminance levels and packaging that has been modified to allow low Oxygen Transmission Rates (OTR) be used but these measures will only prolong the amount of time before fading occurs. Furthermore, it can be concluded from the study in [3] that low illuminance levels could potentially result in diminished sales. Thus, by predicting the rate of colour fading, cured ham manufacturers could place priority on ham that shows a poor initial colour. This prediction could lead to a “sell by” date: a date that is independent of the “best before” date that would give retailers an indication of when the ham will fade to an unacceptable colour for certain known conditions, e.g. the packaging used, retail light intensity, the OTR and so on. Such a prediction requires the measurement of the colour of the ham just prior to packaging.
1.1. Colour measurement techniques The most commonly used technique for evaluating the colour of cured ham is to measure its CIE tristimulus XYZ values, which are then transformed mathematically to a co-ordinate system that describes colour in a manner that is easier understood. Among the co-ordinate systems commonly used are Hunter Lab [6] and CIE L* a* b* [2, 4-6]. For both of the co-ordinate systems, lightness is measured by the first value, (L, L*), redness is measured using the second value, (a, a*), and yellowness is measured using the third value, (b, b*). Lightness and redness are important for ham colour fading measurement, as they should show a change in value if the ham changes from its original pink colour to a dull grey colour (i.e., the ham will be duller, affecting the (L, L*) values, and less red, affecting the (a, a*) values). It has also been reported that the CIE L*a*b* co-ordinate system gives good reproducibility for measuring these two values [6]. However instrumental metamerism is a common and serious defect of colorimeters, even for the same make and model. This means that it is difficult to make the spectral response of two colorimeters exactly alike [7]. An alternative approach is to examine the spectral reflections from the surface of the cured ham. Optical fibre methods have been used for the prediction of texture and colour of cured ham [8]. Visible and near infrared spectroscopy was used to examine cross-sections of the ham for colour and pastiness defects – pastiness is defined as a textural defect that involves a lack of elasticity and an oily touch in regions of the cured ham. Spectra of normal and defective hams were recorded in the range 400nm to 2200nm. The partial least squares regression classification technique was then used to classify the samples into the two categories. For texture discrimination, the classifier correctly predicted 94.2% of samples to the correct group. For colour, the classifier correctly assigned 75.7% of samples accurately. The present study investigated the use of an optical fibre probe based sensor system for the purpose of establishing a scale to determine the palatability of pre-packaged, cured sliced ham, as displayed in cabinets in retail outlets, using “best before” date and colour of the ham. Two methods of measuring the colour were compared: CIE L*a*b* and visible spectrum analysis. The spectral wavelength range was limited to the visible region (440-700nm) – this has the advantage of limiting the overall cost of the system, as more expensive detectors are required for sensing in the infrared region. Artificial Neural Network techniques were then applied to the data sets to categorise the samples. The goal of this study was to establish (i) if an optical fibre based sensor could detect a change in the colour of the ham and (ii) to investigate use data analysis techniques to classify the results. The overall aim of this research is to create a sensor system that would inform producers of ham products approximately how long the ham can be displayed in retail outlets before it becomes aesthetically unacceptable for consumers.
2. Experimental set up Seven cured ham products (A-G) were purchased from two different retailers on two non-consecutive dates. The products include two “own brand” store products and five widely available brands. For each product type, packages were selected according to two criteria: (i) relative light exposure as indicated by position in the display cabinet and amount of fading observed prior to purchase and (ii) “best before” dates. Table 1 shows the “best before” dates for each of the packages purchased. Products D and E had the same best before date for each package. These packages were chosen from different sections of the display cabinet with varying amounts of pinkness and greyness on the ham surface – the result of different amounts of exposure to light. Table 1: Ham products and their "best before" dates Product Packet 1 Packet 2 Packet 3 Packet 4 A 20th Feb 17th Feb 12th Feb 8th Feb B 22nd Feb 17th Feb C 20th Feb 10th Feb D 11th Feb 11th Feb 11th Feb E 20th Feb 20th Feb 20th Feb F 27th Feb 12th Feb G 16th Feb 13th Feb
All the products contained the same basic ingredients and were stored in the same display cabinets in the retail outlets. They should, therefore, have been exposed to the same ambient conditions from the time they were initially displayed in the shops. Differences were noted in the packaging of each product. All except C and D were vacuum packed. The packaging for products A and E allowed the most amount of light to enter. Slices in these packages showed the largest variation in colour, probably due to the amount of extra light they were exposed to in comparison with a product whose packaging allowed less light in. The packages were opened and immediately sampled so as to minimise their exposure to oxygen and limit any further discolouration of the slices. Spectra and CIE L*a*b* values of the ham slices were recorded using an optical fibre probe connected to an Ocean Optics S2000 spectrometer. The probe was placed on various parts of the ham slices and samples were taken. As the readings were recorded, they were assigned to three categories, in accordance with AMSA guidelines on ham colour fading [7]: “no fade”, “moderate fade” and “complete fade”. This was done by visual inspection by the authors. For each slice, over 250 spectra in the wavelength range 440-700nm were recorded along with 20 or more different CIE L* a* b* readings. An in-house application written with LabVIEW 6.1 from National Instruments was used to control the spectrometer and record the spectra. Ocean Optics supplied software (OOIIRAD-C) was used to calculate the CIE L* a* b* values. A reference reading of L = 100, a = 0.4 and b = 0.2 were used to standardise the equipment before the readings were taken. The spectral analysis method, on the other hand, did not require standardisation prior to measurement. The S2000 spectrometer is capable of taking readings in the range between 200nm and 1100nm. Because the visible region was of interest in this investigation only data points between 440nm and 700nm were considered. The spectrometer output contains 945 data points in this region. Principal Component Analysis (PCA) [9] was carried out on the spectra to remove redundant information and reduce the dimensionality of the dataset from 945 to 3 dimensions. The Principal Components (P.C.s) generated using PCA were then used as inputs to a Multilayer Preceptron (MLP) [9] that was trained using the backpropagation technique. MLPs have previously been used successfully in the grading of eggshell quality [10]. The authors concluded that the use of the neural network allowed accuracy levels to be achieved that would exceed USDA requirements. Taurino et al [10] used an electronic nose to recognise common volatile organic compounds usually present in the headspace of foods. PCA was used to cluster the results from the e-nose and the performance of two different classifiers – MLP and Radial Basis Function (RBF) networks – were compared. For the MLP, the trained classifier had an error of 10% when tested using the leave-one-out procedure. The RBF network gave an error of 15% when tested using the same method. This shows the viability of using PCA as a pre-processing stage prior to classification by an MLP.
3. Results
3.1. CIE L*a*b* readings The CIE L*a*b* values for the slices were recorded and the L* and a* values are shown in Figure 1. Here the no fade and complete fade samples overlap significantly and both sets of samples group in roughly the same area of the plot, with some outliers elsewhere.
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l No Fade 4 a v
Complete Fade *
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0 0 20 40 60 80 100 120
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-6 L* values Figure 1 L* and a* values for cured ham slices While there is a slight trend in that the a* decreases with fading, the samples presented in Figure 1 are very difficult to categorise, since there are no distinct clusters present. The variations in the L* (lightness) and a* (redness) values can be attributed to differences in intensity of reflected light for different products. CIE L*a*b* measurements are very sensitive to light intensity and the pigment concentration in each slice will affect the light intensity of the measurement. These results are in agreement with those presented by Moller et al [2] which showed a non-linear trend of a general decrease in the a* value over time and Juncher et al [5] which showed a slight increase in L* values over time. Figure 1 illustrates both of these trends. An attempt was made to train a neural network using the L* and a* values recorded. The network would not train to give an acceptable error value because of the overlapping nature of the training data.
3.2. Spectral reflectance readings The first set of readings taken was on products A-D and were categorised into two broad groups – pink and grey. The spectra are shown in Figure 2(a). From this plot, it can be seen that there is a difference between the three groupings applied to the spectra. The data has been normalised to account for intensity errors that occur due to changes in lighting conditions or changes in the reflectance of the ham caused by different pigment or fat concentrations, for example. The spectra show the greatest variation in the 570nm region in accordance with other research on ham colour fading [7, 11]. 1 1 N o F a d e M o d e r a t e F a d e 0 . 8 0 . 5 C o m p l e t e F a d e y t i s n e t 0 . 6 0 n I 2
d C e s P i l 0 . 4 - 0 . 5 a m r o N 0 . 2 N o F a d e - 1 M o d e r a t e F a d e C o m p l e t e F a d e 0 - 1 . 5 4 4 0 5 7 0 7 0 0 - 4 - 2 0 2 4 6 8 W a v e l e n g t h ( n m ) P C 1 (a) (b)
Figure 2 (a) Spectral reflections from the S2000 and (b) the corresponding P.C.s The P.C.s are displayed in Figure 2(b). From this figure, it is clear that there is a gradual trend in the colour of ham from no fading to complete fading. This fact explains the somewhat overlapping nature of the the groupings. In particular, the Moderate Fade group overlaps both the other groups. The P.C.s were then used as inputs to an MLP. The MLP was trained with a learning-rate parameter (η ) of 0.3 and a momentum term ( α ) of 0.4 using the backpropagation algorithm with a network size of 3:3:3. The classifier was trained and tested using separate datasets and the test results are presented in Table 2. Table 2: confusion matrix of the results for the MLP classifier Input Output Class Class No Moderate Complete Total Fade Fade Fade No Fade 290 0 0 290 Moderate Fade 40 170 80 290 Complete Fade 0 0 290 290 Total 600
The test set was accurately classified in each case. The confusion seen in categorising samples from the Moderate Fade class is can be attributed to samples that are on the border of either the Moderate and Complete Fade classes or the Moderate and No Fade classes. The overlapping nature of the three classes arise as a consequence of the gradual nature of the colour fading that occurs in ham Inevitably, a small number of samples on the border between two classes have been assigned to the incorrect category.
4. Conclusion The fading of the surface colour of sliced ham products when exposed to light in retail outlets has been shown to be measurable using an optical fibre based probe. The results shown here are preliminary but are required to demonstrate that the fading process of the ham can be (i) measured and (ii) classified. The results show that the most effective way to measure the ham colour fading is to examine the spectral reflection of light from the ham surface using principal component analysis and an MLP classifier. This method has been shown to produce better results than that of CIE L*a*b* values due to the difficulty with the reproducibility of the L*a*b* values. The reflection spectra are normalised, thus accounting for differences in the reflected light intensity. One problem with the classifier is the closeness of each classification to each other but this is unavoidable because the colour fading is a gradual and slight process. The goal of this study was to establish if ham colour fading could be measured; this goal has been achieved. The next stage of the research will involve having more control over the influencing factors that induce colour fading, such as illumination, OTR of packaging and product to headspace ratio. Combining information from these controlled factors and an accurate classifier it is envisaged to be able to predict a colour “sell-by” date.
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