Received 22 January 2007; Revised 16 April 2007; Accepted 18 April 2007

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1 QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL Qual. Reliab. Engng. Int. (in press) Published online in Wiley InterScience ( Special Issue Analysing Multivariate Data from Designed Experiments: a Case Study of Photo-oxidation in Sour Cream Frøydis Bjerke 1,,, Hanne Larsen 1 and Siri Geiner Tellefsen 2, 1 Matforsk-Norwegian Institute for Food Research, Osloveien 1, NO-1430 Aas, Norway 2 Buskerudveien 254, NO-3057 Solbergelva, Norway Exposure to light induces photo-oxidation in dairy products, causing undesired offtaste/rancidity. In this case study we present the results of a designed experiment in four factors: illumination source, type of packaging, distance to illumination source and exposure time, in order to observe the resulting oxidation of low-fat sour cream, measured by fluorescence spectroscopy. Such spectroscopic data yield highly correlated response variables. A novel multivariate method, MANOVA, is applied for the statistical analysis. The main objective is to present and interpret complex and comprehensive data results to researchers and practitioners in an informative way. The experimentation revealed that several factors and interactions must be taken into consideration when designing optimal environment for sour cream storage. Copyright 2007 John Wiley & Sons, Ltd. Received 22 January 2007; Revised 16 April 2007; Accepted 18 April 2007 KEY WORDS: designed experiment; case study; multivariate analysis; MANOVA; spectroscopy data; dairy products; fluorescence 1. INTRODUCTION When dairy products are exposed to light, e.g. in supermarket shelves, it triggers an oxidation process where certain constituents are degraded, causing rancidity. Former studies of dairy products have shown that the deterioration of sensory properties during commercial light exposure is rapid (see e.g., Andersen et al. 1 ). A few hours of exposure is enough to alter the product significantly with respect to flavour and odour. Hence, for the dairy industry and retailers, knowledge of lighting conditions and light-protecting packaging is important in order to maintain freshness of products during shelf storage in shops. Correspondence to: Frøydis Bjerke, Matforsk-Norwegian Institute for Food Research, Osloveien 1, NO-1430 Aas, Norway. froydis.bjerke@matforsk.no Student UMB (Norwegian University of Life Sciences). Copyright q 2007 John Wiley & Sons, Ltd.

2 F. BJERKE, H. LARSEN AND S. G. TELLEFSEN Photo-oxidation has two main causes. The first is due to UV radiation: direct sunlight causes immediate flavour decay. The second, which is the subject of this study, occurs in the presence of certain constituents, photosensitizers, in products exposed to visible light. Typical photosensitizers in foods are riboflavin, chlorophyll, porphyrins, amino acids, vitamins, nucleine acids, flavonoids and antocyanins 2,and several of these are present in dairy products. In our experiment, sour cream (18% fat) was exposed to fluorescent tube light under various conditions in order to determine the most influential factors for light oxidation. Fluorescence spectroscopy was used to measure degradation of the photosensitizers in order to understand and quantify the oxidation 3.These measurements were used as response variables. Within certain wavelength ranges the response variables are expected to relate to specific chemical constituents of the products, like riboflavin and chlorophyll. The experiment was part of the experimental work of Siri Geiner s 4 Master thesis, where an objective was to better understand the underlying mechanisms of oxidation by exposure to light. In Section 2, the experiment is described in terms of factors, design and response measurements, before the statistical modelling in Section 3 is introduced. Here, the MANOVA method is applied, and the results are visualized through several graphs. 2. FACTORS, DESIGN AND RESPONSES A designed experiment in four factors: illumination source (tube type), type of packaging, distance to illumination source, and exposure time was performed, and the resulting oxidation of low-fat sour cream was measured by fluorescence spectroscopy (emission spectrum, nm). The responses are intensity spectra of highly correlated multiple variables. The factors considered in the experiment were: Illumination source (four types of commercially available fluorescence tubes). Distance to illumination source (10 cm vs 30 cm). Exposure time (12 h vs 36 h). Packaging material (white vs transparent cups with lids). The experiment followed a design where two cups of sour cream were measured per condition, in total = 64 cups of sour cream, plus two reference samples stored in darkness. The temperature was controlled and equal for all samples. A former study by Andersen et al. 1 showed that temperature had no effect, as opposed to light intensity and exposure time, on light-induced oxidation of semi-hard cheese. Also, practical experience (not published) has shown that sour cream stored in cold darkness for up to two months does not deteriorate, i.e. no changes in fluorescence spectra are observed. The specific fluorescent tubes were 76 Natura de Luxe, 827 Interna, 830 Warm White, and 950 Daylight, all of 58 W. These four tubes were mounted vertically in four separate shelf racks containing eight shelves per tube. The factor tube type can be regarded as a whole-plot factor in four levels, as all cups are placed within one of the four shelf racks (illumination treatments). All cups were from the same production batch of a commercial producer. They were treated equally before and after the experiment, i.e. stored in darkness in a common refrigerator. The reference samples were not placed in the shelves, but were analysed together with the design samples afterwards. Regarding the last three factors, the cups were placed in the shelves according to the following restrictions: only two cups per shelf, and only one distance per shelf. Also, the pair of cups having identical treatments were not placed on the same shelf. The 36-h cups were placed on their respective shelves 24 h before the 12-h cups. Shelf was considered a block factor of eight levels in the experimental design. The design is outlined in Table I. Note that within each light source, the layout of samples is balanced with respect to all three factors. Ideally, the layouts should not have been identical for all four tube types, but that was what happened in practice. The responses considered were fluorescence emission spectra measured non-destructively directly in intact sour cream. All fluorescence measurements were done within a day, and the cups were analysed in random

3 MULTIVARIATE DATA FROM DoE: PHOTO-OXIDATION IN SOUR CREAM Table I. Complete layout of samples Time (36 h) Time (12 h) Cup Cup Light source Shelf no. Distance (cm) White Transparent White Transparent 76 Natura de Luxe Natura de Luxe Natura de Luxe Natura de Luxe Natura de Luxe Natura de Luxe Natura de Luxe Natura de Luxe Interna Interna Interna Interna Interna Interna Interna Interna Warm White Warm White Warm White Warm White Warm White Warm White Warm White Warm White Daylight Daylight Daylight Daylight Daylight Daylight Daylight Daylight Note: The numbers 1 8 relate to the standard order of the design points (factors: distance, time, cup). order. The exposed sour cream samples were prepared in cuvettes before measuring. The samples were exposed to excitation light for 1 s at 380 nm (nanometer) by a Xenon light source with excitation filter, and then their resulting fluorescence emission spectra were measured within the wavelengths of nm. (Such a short time 1 s of light exposure does not affect the samples.) The resulting response data were obtained in a two-way matrix of intensity values of the emitted fluorescence spectra. The number of recorded wavelengths, which is the number of variables (195 in total), largely exceeds the number of samples, although the variables are highly correlated. The response variables are named by their corresponding wavelength. All samples were measured twice, by rotating the cuvette 90 between measurings. The average spectrum from these two measurements was considered as the sample response. Further description of the fluorescence

4 F. BJERKE, H. LARSEN AND S. G. TELLEFSEN Figure 1. Typical fluorescence sample spectrum peaks corresponding to photosensitive constituents. Product: Sour cream sample stored in darkness (reference spectrum) measurements can be found, e.g. in Wold et al. 5, which also demonstrates that fluorescence spectroscopy is a very sensitive method, being both non-destructive and relatively inexpensive. Previous work with fluorescence of dairy products has shown that certain wavelength intervals correspond to specific constituents (see e.g., Wold et al. 5,6 ). Figure 1 gives an example of a typical fluorescence spectrum curve from sour cream, although all constituents are not yet fully identified. Identification of more constituents is still in progress, see e.g., Wold et al. 6. After analysing the whole spectra ( nm), we decided to divide the response variables into wavelength sectors related to the main known active constituent within that sector. Sector 1: nm. Sector 2: nm (riboflavin). Sector 3: nm (porphyrins). Sector 4: nm (chlorophyll). These sectors could then be analysed separately, in order to understand more of how the experimental factors affect each constituent or photosensitizer more specifically. We concentrated on the two sectors reflecting mostly riboflavin and mostly chlorophyll, i.e. sectors 2 and MODELLING AND RESULTS 3.1. Statistical tools: Manova In cases where an experiment is run by a factorial design giving multiple correlated responses, the MANOVA method 7 is very well suited MANOVA is a generalization of classical MANOVA, utilizing principal component analysis (PCA) on the multivariate response matrix, and is particularly useful in situations where ordinary MANOVA fails, i.e. when the response variables are highly correlated and/or the number of variables exceeds the number of samples. The MANOVA performs a multivariate test for several principal components (PCs), in order to reveal both effects along the single PCs and effects that are spread among several PCs. The criterion for choosing the number of PCs for the test is based on explained variances corresponding to the PCA decomposition. However, the correct number of components is uncertain and therefore a group of buffer components is introduced. The inclusion of buffer components

5 MULTIVARIATE DATA FROM DoE: PHOTO-OXIDATION IN SOUR CREAM reduces the negative effect (power loss) of choosing too few components for testing. Another case study, explaining MANOVA in further details, is given in Bjerke et al Data pre-treatment In order to better visualize the individual spectra, the effect analysis is performed on difference spectra measuring decay, that is, the difference between the dark reference spectrum (Figure 1) and the sample spectrum. The objective of this transformation, which does not affect computation, is to magnify the effects (compared to raw spectral data) for improved visualization. Note that the transformation implies that high response values equal large degradation, while for the raw spectra, the opposite holds. (Low intensity equals large degradation.) 3.3. Statistical modelling A first-order linear model with interactions can be obtained from the two-level design Model: Y = L + D + T + P + LD+ LT + LP + DT + DP + TP+ block + error where L is the light source, D the distance, T the time, and P the packaging. Y is a multiple response matrix, in this case the spectrum. A first analysis (not shown) reveals that block (shelf level) is not significant. The analysis is then continued using 32 averaged response spectra, in order to increase robustness. The model Y = L + D + T + P + LD+ LT + LP + DT + DP + TP+ error yields the following output from the specialized MANOVA software: MANOVA ver objects responses: Source DF exvarss p-value L D T P LD LT LP DT DP TP Error In the ANOVA-like output above, the p-values are adjusted for testing multiple responses simultaneously. ExVarSS describes the relative contribution from sums of squares to the explained variation, and add up to 1. The analysis reveals that all main effects and several two-factor interactions are significant. Note that some care has to be taken in interpreting the p-values. The tube type (L) may be considered a wholeplot factor and D, T and P sub-plot factors. As there are no degrees of freedom for the whole-plot error, the L-factor cannot be tested. Analysing the experiment assuming only one error stratum may cause the p-values of factor L to be too low. The subplot factors D, T, P, and all interactions belongs to the subplot

6 F. BJERKE, H. LARSEN AND S. G. TELLEFSEN stratum hence their respective p-values are correct. Also the lack of randomization of the layouts within each tube may influence the analysis. It is, however, unlikely that these deviations from an ideal completely randomized experiment should cause or have any serious impact on the clear patterns observed in the frequency spectra Curve plots and interactions plots The output above only yields information on significant and non-significant terms. However, from a practitioner s point of view, the magnitude and direction of significant effects are also of interest. In order to visualize effects, graphs of averaged difference spectra could be compared, as shown in Figure 2, where the main effects of all four factors are illustrated. These curve plots show that the largest effect is related to the factor distance; samples 10 cm from the light source are far more decayed than samples 30 cm from the source. Regarding tube type, the figure shows that 827 Interna and 830 Warm White are quite similar, and cause less decay than do 76 Natura de Luxe and 950 Daylight. This information is particularly useful, as the formal testing for tube types was not available. Exposure time also affects decay, the longer the more, as expected. The two packaging materials show some differences in the riboflavin sector ( nm), where the transparent cups are decaying most, but are quite similar in other regions of the spectrum. The negative difference values in the nm region are assumed to relate to the formation of oxidation products, as recently described by Veberg et al. 9, which is a consequence of the degradation of the photosensitizers observed in the other regions of the spectrum. Figure 2. Main effects plots: the curves are the differences between Figure 1 curve and the actual treatment mean curve for the whole spectrum

7 MULTIVARIATE DATA FROM DoE: PHOTO-OXIDATION IN SOUR CREAM Figure 3. PT interaction plot (packaging time): differences between the reference curve and the actual treatment combination mean curve for the whole spectrum Figure 4. DT interaction plot (distance time): differences between the reference curve and the actual treatment combination mean curve for the whole spectrum Some interactions were also found significant, and Figure 3 shows the interaction of time and packaging (TP, p = ). In the riboflavin sector, there is no difference in decay caused by time for the white cups (solid lines), but for the transparent cups (dotted lines) time does certainly matter. Note that this interaction is less evident in the chlorophyll sector ( nm) of the spectrum. For comparison, a non-significant interaction between distance and time (p = ) is shown in Figure 4. For most of the wavelength sectors it is evident that the main effects cause the differences. However, for the peaks related to the chlorophyll sector, there seems to be a distance time interaction which literally drowns when all wavelengths are modelled together. Hence, in order to examine these interactions more closely, we

8 F. BJERKE, H. LARSEN AND S. G. TELLEFSEN run the Manova for two separate sectors (or sets) of wavelengths; sector 2 (riboflavin, nm) and sector 4 (chlorophyll, nm) MANOVA ver objects--79responses: Sector Source DF exvarss p-value L D T P LD LT LP DT DP TP Error MANOVA ver objects--67responses: Sector Source DF exvarss p-value L D T P LD LT LP DT DP TP Error In both cases, the main effects are highly significant. However, the output reveals some differences with respect to significant interactions. In the riboflavin sector (2), the DT interaction is not significant, while in the chlorophyll sector (4) it is. The opposite yields e.g. for DP. This means that the two constituents or photosensitizers respond differently to the factors of the experiment; response in this case means degradation. 4. CONCLUSIONS MANOVA is a simple and convenient method for handling multivariate-correlated responses e.g. spectroscopic data when design of experiments is used for generating samples, and the curve plots make a valuable addition to the tabular output, which help communicating the results easily to the field experts

9 MULTIVARIATE DATA FROM DoE: PHOTO-OXIDATION IN SOUR CREAM and non-statisticians. This in turn facilitates the physical and contextual interpretations by the field experts, and also inspire them to initiate follow-up studies. We have observed that riboflavin responds to a distance package interaction, while chlorophyll responds to a distance time interaction, and curve plots for each sector and interaction were used to reveal the nature of these interactions; to observe direction and magnitude of the effects (plots not shown). The physical interpretation of these observations is not yet fully understood, and is a subject for further research. In a recent study of butter 6 it is shown that the various photosensitizers degrades in different ways when exposed to coloured light. In order to more fully understand the effects of photo-oxidation, a sensory analysis of the samples must be obtained to see how the degradation affects flavour and odour of the sour cream. Sensory assessments are not included here. However, the tables and curve plots clearly show that several factors and interactions must be taken into consideration when designing optimal environment for sour cream shelf storage. Even if this knowledge has been made known for producers and retailers, other issues are considered as important for them: fully lit refrigerators with transparent glass doors in the supermarkets, and transparent or white cups of packaging, in order to tempt the customers. Acknowledgements Dr Jens Petter Wold and Dr Annette Veberg, Matforsk, are thanked for their expertise contribution on fluorescence spectroscopy. Dr Øyvind Langsrud is thanked for discussions and feedback on the MANOVA analyses. Thanks to the editor Dr John Tyssedal and two anonymous referees who suggested several improvements to the manuscript. Downloadable MANOVA software can be found at: no/ola/index.html. REFERENCES 1. Andersen CM, Wold JP, Mortensen G. Light-induced changes in semi-hard cheese determined by fluorescence spectroscopy and chemometrics. International Dairy Journal 2006; 16: Bekbölet M. Light effects on food. Journal of Food Protection 1990; 53: Wold JP, Jørgensen K, Lundby F. Non-destructive measurement of light-induced oxidation in dairy products by fluorescence spectroscopy and imaging. Journal of Dairy Science 2002; 85: Geiner SBT. Optimal emballering av rømme/optimal packaging of sour cream (in Norwegian). IKBM-Department of Chemistry, Biotechnology and Food Science, UMB The Norwegian University of Life Sciences, Aas, Wold JP, Veberg A, Nilsen A, Juzenas P, Iani V, Moan J. The role of naturally occurring chlorophyll and porphyrins in light induced oxidation of dairy products. A study based on fluorescence spectroscopy and sensory analysis. International Dairy Journal 2005; 15: Wold JP, Bro R, Veberg A, Lundby F, Nilsen A, Juzenas P, Moan J. Active photosensitizers in butter detected by fluorescence spectroscopy and multivariate curve resolution. Agricultural and Food Chemistry 2006; 54: Langsrud Ø Multivariate analysis of variance for collinear responses. The Statistician 2002; 51: Bjerke F, Langsrud Ø, Aastveit AH. Restricted randomization and multiple responses in industrial experiments. Quality and Reliability Engineering International 2007; Veberg A, Olsen E, Nilsen A, Wold JP. Front face fluorescence measurement of photosensitizers and lipid oxidation products during photooxidation of butter. Journal of Dairy Science 2007; 90: Authors biographies Frøydis Bjerke holds a PhD in Applied Statistics, Norwegian University of Life Sciences, 2004, and a MSc in Industrial Mathematics Applied Statistics, from the Norwegian University of Science and Technology in Trondheim, Since 1996 a Research Scientist at Matforsk-Norwegian Food Research Institute. Her professional interests include statistical thinking and implementing these principles in industry, design of experiments, six sigma.

10 F. BJERKE, H. LARSEN AND S. G. TELLEFSEN Hanne Larsen holds a PhD in Food Science, 2002, and MSc in Food Science, 1985, both from the former Agricultural University of Norway (now: Norwegian University of Life Sciences). Since 1987 she is a Research Scientist at Matforsk, within the packaging research group. Her professional interests include the effects of light and gas barrier properties of the packaging materials on the shelf life of different foods, measurements of oxygen barrier properties of different packaging materials, modified atmosphere packaging of foods, shelf life studies, and effect of various packaging methods. Siri Geiner Tellefsen holds a MSc in Food Science, Norwegian University of Life Sciences, Since 2006 she is an inspector at Mattilsynet-Norwegian Food Safety Authority.

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