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Red discoloration of chicory under controlled atmosphere conditions
Postharvest Biology and Technology 26 (2002) 313– 322 www.elsevier.com/locate/postharvbio
Red discoloration of chicory under controlled atmosphere conditions E. Vanstreels *, J. Lammertyn, B.E. Verlinden, N. Gillis, A. Schenk, B.M. Nicolaı¨ Katholieke Uni6ersiteit Leu6en, Flanders Centre/Laboratory of Posthar6est Technology, W. De Croylaan 42, 3001 He6erlee, Belgium Received 9 November 2001; accepted 15 April 2002
Abstract Several types of discoloration can develop in the heads of chicory (Cichorium intybus L.) during postharvest storage and commercialisation, which considerably reduces their market value. One important disorder is the occurrence of red discoloration, typically found in the basal parts of the medial leaves. In this work factors influencing the development of red discoloration in chicory heads stored in controlled atmosphere conditions were investigated using multiple logistic regression analysis. It was found that the weight of the chicory head is a significant intrinsic parameter that affects the development of red discoloration. Atmospheric conditions with elevated CO2 and decreased O2 concentrations are favourable for avoiding red discoloration, while storage at 1 °C increases red discoloration. An atmospheric composition of 10% O2 and 10% CO2 in combination with a storage temperature of 5 °C was found optimal for prevention of red discoloration and other negative quality aspects, such as leaf edge discoloration. These results may be used for MA package design for chicory. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Cichorium intybus L.; Logistic regression; Controlled atmosphere; Red discoloration; Chicory quality
1. Introduction Chicory (Cichorum intybus L.) is a common vegetable in several West European countries. It is typically grown in a biennial cycle, with a tuberised root produced during the vegetative growth phase. The developed roots are harvested * Corresponding author. Tel.: +32-16-322-732; fax: + 3216-322-955 E-mail address: [email protected] (E. Vanstreels).
and cold-stored for varying periods. They are subsequently forced in darkness by traditional means or hydroponic systems to yield white etiolated buds, called chicons, which can be consumed raw as a salad or cooked, and which have a characteristic bitter taste. Quality assessment in chicory is based on a wide range of parameters, including pure biometric properties of the chicons as well as observation of, mainly visual, defects. During postharvest storage and commercialisation of chicory, several disorders may develop. One such important defect
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E. Vanstreels et al. / Posthar6est Biology and Technology 26 (2002) 313–322
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is red discoloration resulting from cell damage and subsequent oxidation of phenolic compounds (Martinez and Whitaker, 1995). Red spots typically develop a few days after harvest of the chicons, in the basal parts of the medial leaves. This red discoloration considerably affects the commercial value of the chicons. The exact mechanism that causes cell damage is still not entirely clear, but it has been observed that the occurrence of red discoloration seems to be correlated with the continuation of the growth of the floral stem during postharvest storage (Van Kruistum, 1997). Gillis et al. (2001) have shown that at the position which is coincident with the discoloured spots, the corresponding strain in the leaves causes a stress concentration, suggesting that cell damage could be a consequence of mechanical stress induced by floral stem growth during postharvest storage. Other negative quality features that may develop during postharvest storage and commercialisation of chicory are leaf edge, which can be recognised as brown necrotic areas at the edges of the outermost leaves, and the extensive growth of the internal core. The internal core or flower stem gives the chicory a bitter taste, and continuation of its growth during postharvest storage should be avoided as much as possible. Modified atmosphere (MA) packaging and subsequent storage at low temperatures has been developed over the last decades as a technique to retain high quality of vegetables and thus to prolong shelf life. Hertog et al. (1998) developed a model for respiration of chicory in closed packages as a function of temperature, O2 and CO2 concentrations. However, so far no publications have appeared in which the influence of modified or controlled gas conditions on the change in Table 1 Atmospheric conditions of the CA experiment conducted in 1999 (X) and in 2000 (+)
0% CO2 5% CO2 11% CO2 19% CO2
2% O2
5% O2
11% O2
X+ X+ X+ +
X X X X
X+ X+ +
+ + + +
20.8% O2 X + (air)
quality of chicory heads has been described. The objective of our research was, therefore, to evaluate the factors influencing the development of red discoloration during storage, including CO2 and O2 levels, storage temperature and duration, size and weight of the chicory head. These results may be used for simulation-based MA package design.
2. Materials and methods
2.1. Experimental design Two large-scale experiments with chicory under controlled atmosphere (CA) conditions were carried out in August 1999 and in August 2000. Chicory (C. intybus L., cv. Tabor) used for this experiment was grown hydroponically and harvested by a single grower. After harvest, the chicory heads were immediately transported to the experimental CA facility of the laboratory. The chicory heads were stored there in stainless steel controlled atmosphere containers, which were monitored continuously and individually. At regular time intervals (every 4–6 h) atmosphere samples were taken from the containers, and analysed for O2 and CO2 levels. If deviations from the set point larger than 0.2% were observed the composition of the atmosphere in the containers was adjusted. Oxygen concentrations which were too low were compensated by injection of air and those too high by injection of N2. Low CO2 concentrations were compensated by injection of CO2, while excessive CO2 was scrubbed with an active carbon scrubber. In this way the composition of the atmosphere inside the containers was kept constant. The set-up (atmospheric conditions) of both experiments is shown in Table 1. In addition, a number of different storage temperatures were evaluated. In 1999 temperatures of 5, 12 and 20 °C were tested. However, because of the rapid deterioration of chicory stored at 20 °C, in 2000 temperatures of 1, 5 and 12 °C were chosen. Quality of the chicory heads was evaluated six times over a period of 3 weeks. An initial evaluation was performed on 40 (1999) or 160 (2000) chicory heads, further evaluations were performed on 20 (1999) or 40 (2000) chicory
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heads per storage condition. In total, 4000 (1999) and 8800 (2000) chicory heads were evaluated. A third experiment was conducted for model validation purposes in January 2001. Two atmospheric conditions (10% O2 +10%CO2 and 20.8% O2 + 0.03% CO2) and two storage temperatures (5 and 12 °C) were tested. Quality was evaluated on 80 freshly harvested chicory heads and further evaluations were performed four times over a period of 3 weeks on 80 chicory heads per condition. In total, 1360 chicory heads were evaluated.
2.2. Quality e6aluations Six parameters were evaluated for quality assessment, namely weight, length, rot, leaf edge discoloration, red discoloration and stem length. Each chicory head was weighed and its length was measured with a ruler. The outside was scanned for signs of rot, or leaf edge discoloration and given a score of 0 or 1 for these parameters. The ten outermost leaves were peeled off and the number containing red spots was counted. The chicory heads were then cut in half and the length of the floral stem was measured. Extensively rotten chicory heads were excluded from the evaluations.
2.3. Statistical analysis Red discoloration was transformed into a binary score of 0 (0 or 1 leaf with red discoloration) or 1 (2 or more leaves with red discoloration). The rationale for doing so was to exclude possible misjudgements of the red discoloration in a single leaf; only when two leaves were scored as positive (i.e. red) was the chicory head regarded as having the disorder. The proportion of chicory heads with red discoloration per storage condition was calculated. The probability of red discoloration was then modelled as a function of the explanatory variables (storage conditions, biometric properties of the chicory head, relative stem length, etc.) by means of multiple logistic regression. In logistic regression analysis, the probability of a binary or ordinal response variable, in this case red discoloration or no red discoloration, is modelled as a function of one or more explanatory
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variables (Hosmer and Lemeshow, 1989; Collet, 1991). The dependence of the probability of disorder on explanatory variables is modelled as follows: logit(yi )= log
m yi = h+ % ij xij 1− yi j=1
where i is the number of observations, m is the number of explanatory variables in the model, yi is the probability of the disorder developing, given the set of m explanatory variables, h is the intercept parameter, ij is the slope parameter for the jth explanatory variable and xij is the ith observation for the jth explanatory variable. The logit transformation in logistic regression leads to coefficients i which can be interpreted in terms of odds ratios (Lammertyn et al., 2000; Verlinden et al., 2001). The data set for the year 1999 was restricted to the first 14 days of storage. This was done because of the rapid deterioration of the chicory heads at 20 °C after that time, which meant that for some storage conditions no or very few heads were left to examine for red discoloration. Similarly, the data set for the year 2000 was restricted to the first 17 days of storage, although the deterioration problem was less pronounced here. All statistical analyses were performed using the SAS/STAT software, version 6.12 or version 8 (SAS Institute, Cary, NC). The PROC LOGISTIC procedure was used for logistic regression analysis. Relevant parameters were automatically selected for entry into the model at the 0.05 significance level by means of a stepwise selection procedure for the year 2000 data set. Important direct effects such as temperature, storage time (here transformed to log(storage time)), percentage O2 and CO2 were forced manually into the model because of the hierarchical principle. For the year 1999 data set the structure of the model was kept the same as the one obtained for the data of the year 2000, i.e. the parameters that were found to be significant for the data of the year 2000, were forced manually into the model. Details on syntax and options in PROC LOGISTIC can be found in the manual (Logistic Regression Examples Using the SAS® System, Version 6, First Edition, Cary, NC, SAS Institute, 1995).
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Table 2 Summary of the logistic regression analysis of factors affecting red discoloration Explanatory variables
Parameter
Parameter estimate
Wald confidence limits Lower
Upper
A. 2000 data Intercept Log(Storage time) O2 CO2 Temperature Log(Storage time)*Temperature Log(Storage time)*CO2 Log(Storage time)*Log(Storage time) Temperature*O2 Temperature*CO2 Temperature*Temperature O*CO 2 2 CO*CO 2 2 Weight
h i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12 i13
−9.117 6.0360 0.0534 −0.0641 −0.353 −0.144 −0.0259 −1.0218 0.00814 −0.00840 0.0427 −0.00522 0.00886 0.0151
−10.484 5.0481 0.00790 −0.1580 −0.455 −0.170 −0.0473 −1.243 0.00428 −0.0110 0.0373 −0.00827 0.00582 0.0128
−7.750 7.0239 0.0988 0.0298 −0.251 −0.117 −0.00450 −0.8008 0.0120 −0.00586 0.0481 −0.00216 0.0119 0.0174
B. 1999 data Intercept Log(Storage time) O2 CO2 Temperature Log(Storage time)*Temperature Log(Storage time)*CO2 Log(Storage time)*Log(Storage time) Temperature*O2 Temperature*CO2 Temperature*Temperature O*CO 2 2 CO*CO 2 2 Weight
h i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12 i13
−4.298 0.442 −0.0359 0.00822 −0.0279 0.0668 −0.0617 0.107 0.00851 −0.00414 −0.00658 0.00182 0.00545 0.0144
−5.510 −0.0271 −0.0760 −0.0873 −0.146 0.0326 −0.0923 0.0822 0.00534 −0.00724 −0.0101 −0.00294 0.00260 0.0117
−3.0862 0.912 0.00419 0.104 0.0904 0.101 −0.0310 0.131 0.0117 −0.00104 −0.00302 0.00659 0.00830 0.0170
The relative stem length was calculated as length of the floral stem divided by the total length of the chicory head. Factors influencing the relative stem length were analysed by means of multivariate regression analysis.
3. Results and discussion
3.1. Logistic regression model for red discoloration The results of logistic regression analysis to model the probability of red discoloration using
the data of the year 2000 are summarised in Table 2A. Model parameter estimates and their 95% lower and upper Wald confidence limits are shown. The selected model is a second order model with the parameter ‘storage time’ logarithmically transformed. The model provides the best possible fit for the data as judged by the Akaike Information Criterion (AIC; Akaike, 1973) and by the Hosmer and Lemeshow Goodness-of-Fit test (Hosmer and Lemeshow, 1989). This last test only scored positive when the intrinsic explanatory variable ‘weight’ was included in the model (null hypothesis that the model provides a good fit for the data could not be rejected at the 0.05 level).
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Fig. 1. Probability of red discoloration after 10 days of storage at different temperatures, data set of year 2000. Bar diagrams represent the measured values of red discoloration (relative number of chicory heads with red spots). Predicted probability of red discoloration determined by regression is represented by a sloping grid. (A) Storage at 1 °C; (B) storage at 5 °C; (C) storage at 12 °C.
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All variables in the model, except for the variable ‘weight’, occur in interaction terms, making their interpretation not straightforward. The variable weight is the only directly interpretable variable; the probability of red discoloration increases with increasing weight of the chicory head. For this variable the odds ratio was calculated. The odds are defined as the ratio of the probability, y, that the disorder will occur and the probability that the disorder will not occur, 1−y. An odds ratio, in turn, is a ratio of odds. The odds ratio of an explanatory variable expresses by which factor the odds of the event increase or decrease for a unit change of the explanatory variable, while all other variables are kept constant (Agresti, 1996). Estimated odds ratios are computed by exponentiating the parameter estimates when the explanatory variable does not interact with any other variable. An odds ratio can range between zero and infinity. A value of 1 indicates that the variable has no influence on the incidence of the disorder. The odds of red discoloration increase by 1.5% for each 1 g increase in weight of the chicory head (odds ratio for explanatory variable weight=1.015). The importance of the influence of the factor weight becomes clear when calculating the odds ratio for e.g. a 20 g increase in weight of the head (1.353). The odds of red discoloration of a chicory head weighing 120 g are 35.3% higher then the odds of red discoloration of a chicory head weighing 100 g. Oxygen, CO2, temperature and storage time (transformed to log(storage time)), interact. The parameter estimate for the variable O2 depends on temperature and on the CO2 concentration: 0.0534+0.00814*temperature− 0.00522*CO2. It is in general positive in the range of the model, indicating that the odds of red discoloration increase with increasing O2, except for conditions with high CO2 concentration (19%) and low temperature (1 °C). The parameter estimate for log(storage time) is also always positive within the range of the model, indicating that the probability of red discoloration increases with increasing storage time. The parameter estimates for CO2 and for temperature are more difficult to interpret. In these cases a visual interpretation of the results has been made (Figs. 1 and 2).
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Fig. 2. Probability of red discoloration after 21 days of storage at 5 °C, data set of year 2000 (A) and of year 1999 (B). Bar diagrams represent the measured values of red discoloration (relative number of chicory heads with red spots). Predicted probability of red discoloration determined by regression is represented by a sloping grid. Note the different scale of X-axis in part B of the figure.
After 10 days of storage at a temperature of 1 °C, chicory heads showed a large amount of red discoloration, almost irrespective of the atmospheric conditions they were kept in. At temperatures of 5 or 12 °C there was considerably less red discoloration. At these temperatures a concentration of 10% CO2 seems to be best suited to prevent red discoloration, while the O2 concentration seems to be less important (Fig. 1). After 21 days of storage in CA conditions at 5 °C there was a spectacular decrease in red discoloration as compared to storage in air (20.8% O2, 0% CO2) (Fig. 2A).
Results of logistic regression analysis to model the probability of red discoloration using the data obtained in the year 1999 are shown in Table 2B. As mentioned before, the structure of the model was kept the same as the one obtained for the data of the year 2000, i.e. the parameters that were found to be significant for the data of the year 2000 (Table 2A and discussed above), were forced manually into the model. All parameters proved to be significant for entry into the model at the h= 0.05 level, except for the interaction effect of O2 and CO2 (P=0.4532). The selected model provided a good fit for the data, as judged by the Hosmer and Lemeshow Goodness-of-Fit test. The general conclusion that a combination of high CO2, low O2 and low storage temperature (5°C) is optimal for prevention of red discoloration could be drawn from both experiments (Fig. 2A and B). The odds ratio for the parameter weight calculated for the 1999 data set is 1.014, which confirms the conclusion that the odds of red discoloration increase with about 1.5% for each gram increase of weight. It has been suggested that the occurrence of red discoloration is correlated with the relative stem length of the chicory head (Van Kruistum and Embrechts, 1994; Gillis et al., 2001). In the logistic regression model presented here ‘relative stem length’ is not included as a factor, while, the factor ‘weight’ is. However, since weight and relative stem length are positively correlated (z= 0.08), this indirectly implies also an influence of the intrinsic parameter ‘relative stem length’.
3.2. Optimal storage conditions to pre6ent red discoloration In order to avoid production of off-flavours as a result of fermentation, the O2 concentration during storage should not be too low. For this reason, and based on our data, a concentration of 10% O2 was chosen as optimal. To determine the optimal CO2 concentration we used the developed logistic regression model. Derivatives with respect to CO2 for each storage condition (O2, temperature, storage time) and for both models (i.e. the model for the data of 1999 and for the data of 2000) were calculated and were set equal to zero
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( logit(yi ) =0 . ([CO2]
Note that because of the monotonous relationship between yi and logit(yi ) the position of the conditional minimum is the same for both expressions. Thus the calculated CO2 concentration is the optimal concentration to prevent red discoloration, given a certain O2 concentration, temperature and storage time. Results are shown in Fig. 3. Optimal CO2 concentrations are comparable for each storage time. The calculated optimal CO2 concentration is higher for the data of 2000 than for the data of 1999. There is a small difference in optimal CO2 concentrations at different temperatures, but this is negligible, especially for the data of 2000. In general, for all storage conditions and for both years, the optimal CO2 concentration at an O2 concentration of 10% ranges between 7 and
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11%. An atmospheric composition of 10% O2 and 10% CO2 is thus proposed as optimal and as the basis for the development of MA packaging for chicory. At these conditions, a storage temperature of 5 °C is best suited to prevent red discoloration.
3.3. Other quality aspects In the present study we focussed on the occurrence of red discoloration, although other quality parameters were assessed at the same time. A representation of results for the parameters ‘rot’, ‘leaf edge’ and ‘relative stem length’ is given in Fig. 4. Multivariate regression analysis for the parameter ‘relative stem length’ revealed that significant explanatory variables are storage time, temperature, O2 concentration and interactions
Fig. 3. CO2 concentration that corresponds with the minimal probability of red discoloration for different O2 concentrations and storage conditions. (A) Ten days storage time; (B) 14 days storage time; (C) 17 days storage time; (D) 21 days storage time.
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between these parameters. The CO2 concentration has no significant effect on the continuation of the stem growth, as can also be seen in Fig. 4A. Storage conditions that minimise the chance of the occurrence of red discoloration are also favourable to avoid rot, leaf edge and continuation of stem growth (Fig. 4). This implies that an optimisation of atmosphere conditions for optimal quality preservation in chicory is feasible.
3.4. Validation of the model and the proposed storage conditions
Fig. 4. Relative stem length after 21 days of storage at 5 °C (A), probability of leaf edge (B) and of rot (C) after 21 days of storage at 5 °C, data set of 2000. Bar diagrams represent measured values. Predicted relative stem length determined by regression is represented by a sloping grid. Notice the different scale of the Z-axis in part B and C of the figure.
The predictive quality of the model derived from the data of the year 2000 was evaluated by means of linear regression. The correlation between the observed percentage of red discoloration and the predicted probability of red discoloration was determined. This was done for the calibration data set (experiment of the year 2000), the data set of the year 1999 and for the validation data set of the year 2001. Calculated coefficients of determination (R 2), intercept and slope parameters are shown in Fig. 5. Theoretically, in the case of an ideal fit, the intercept should be zero and the slope parameter should be equal to one. The validation set of 2001 has a high R 2 and its slope parameter is close to one. The intercept parameter is relatively large: when there is no red discoloration observed, the model still predicts a percentage of 20% red discoloration. In contrast, the R 2 for the data set of the year 1999 is low, indicating a clear batch effect and influence of other factors that are not included in the model. Other factors affecting red discoloration might be the environmental conditions during forcing (e.g. temperature, composition of the nutrient solution), time of harvest of the chicory heads (developmental stage of the chicory head at the time of harvest) or physiological condition of the root. The general applicability of the chosen storage conditions was validated by conducting an experiment in which storage in the selected conditions was compared to storage in air (validation experiment of 2001, Fig. 6). Storage in 10% O2 + 10% CO2 resulted in a significant decrease in the number of red discoloured chicory heads. Storage
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at low temperature was more beneficial than storage at higher temperatures. However, the effect of the atmospheric conditions was more pronounced than the effect of temperature: storage in air at 5 °C resulted in a higher proportion of red discoloured chicory heads than storage in 10% O2 + 10% CO2 at 12 °C.
Fig. 6. Proportion of chicory heads with red discoloration for different storage conditions. Validation experiment of 2001.
4. Conclusion The development of physiological disorders that considerably affect the commercial value of chicory can be strongly influenced by storage conditions, as confirmed by two large scale CA experiments. Storage in CA conditions strongly reduced the occurrence of red discoloration. Other negative quality aspects were also considerably reduced. An atmospheric composition of 10% O2 and 10% CO2 and a temperature of 5 °C is considered optimal for storage and these conditions could be used to develop MA packaging for chicory.
Acknowledgements This research was conducted with financial support of the Belgian Ministry of Small Enterprises, Traders and Agriculture (project S-5947). J. Lammertyn is research assistant of the Fund of Scientific Research-Flanders (Belgium) (FWOVlaanderen).
References Fig. 5. Correlation between predicted and observed proportions of red discoloration. (A) Data set of 2000 calibration set; (B) data set of 2001 validation set; (C) data set of 1999.
Agresti, A. (Ed.), 1996. An Introduction to Categorical Data Analysis, second edition. Wiley, New York.
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Akaike, H., 1973. Information theory and extension of the maximum likelihood principle. In: Petrov, B.N., Csa`ki, F. (Eds.), Second International Symposium on Inference Theory. Akade´ mia Kiado, Budapest, pp. 267 –281. Collet, D.R. (Ed.), 1991. Modelling Binary Data. Chapman and Hall, London. Gillis, N., Verlinden, B.E., Van Hecke, P., De Baerdemaeker, J., Nicolaı¨, B.M., 2001. Finite element analysis of damage in chicory (Cichorium intybus, L.). In: Postharvest 2000, 4th International Conference on Postharvest Science, Jerusalem, Israel. Acta Hort., (ISHS) 553: 527 –530. Hertog, M.L.A.T.M., Peppelenbos, H.W., Evelo, R.G., Tijskens, L.M.M., 1998. A dynamic and generic model of gas exchange of respiring produce: the effects of oxygen, carbon dioxide and temperature. Postharvest Biol. Technol. 14, 335 – 349. Hosmer, D.W., Lemeshow, S. (Eds.), 1989. Applied Logistic Regerssion. Wiley, New York.
Lammertyn, J., Aerts, M., Verlinden, B.E., Schotsmans, W., Nicolaı¨, B.M., 2000. Logistic regerssion analysis of factors influencing core breakdown in ‘Conference’ pears. Postharvest Biol. Technol. 20, 25 – 37. Martinez, M.V., Whitaker, J.R., 1995. The biochemistry and control of enzymatic browning. Trends Food Sci. Technol. 6, 195 – 200. Van Kruistum, G., 1997. Productie van witlof en roodlof. Praktijkonderzoek voor de Akkerbouw en de Vollegrondsgroenteteelt. Lelystad, Nederland. Van Kruistum, G., Embrechts, A., 1994. Roodverkleuring harder aanpakken. Groenten + Fruit/Vollegrondsgroenten 9, 20 – 21. Verlinden, B.E., Lammertyn, J., Vanstreels, E., Gillis, N., Nicolaı¨, B.M., 2001. Logistic regression of categorical data for MA-package design of horticultural produce. In: CA2001, 8th International Controlled Atmosphere Research Conference, 8 – 13 July, 2001, Rotterdam, The Netherlands. Acta Hort., in press.
Red discoloration of chicory under controlled atmosphere conditions E. Vanstreels *, J. Lammertyn, B.E. Verlinden, N. Gillis, A. Schenk, B.M. Nicolaı¨ Katholieke Uni6ersiteit Leu6en, Flanders Centre/Laboratory of Posthar6est Technology, W. De Croylaan 42, 3001 He6erlee, Belgium Received 9 November 2001; accepted 15 April 2002
Abstract Several types of discoloration can develop in the heads of chicory (Cichorium intybus L.) during postharvest storage and commercialisation, which considerably reduces their market value. One important disorder is the occurrence of red discoloration, typically found in the basal parts of the medial leaves. In this work factors influencing the development of red discoloration in chicory heads stored in controlled atmosphere conditions were investigated using multiple logistic regression analysis. It was found that the weight of the chicory head is a significant intrinsic parameter that affects the development of red discoloration. Atmospheric conditions with elevated CO2 and decreased O2 concentrations are favourable for avoiding red discoloration, while storage at 1 °C increases red discoloration. An atmospheric composition of 10% O2 and 10% CO2 in combination with a storage temperature of 5 °C was found optimal for prevention of red discoloration and other negative quality aspects, such as leaf edge discoloration. These results may be used for MA package design for chicory. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Cichorium intybus L.; Logistic regression; Controlled atmosphere; Red discoloration; Chicory quality
1. Introduction Chicory (Cichorum intybus L.) is a common vegetable in several West European countries. It is typically grown in a biennial cycle, with a tuberised root produced during the vegetative growth phase. The developed roots are harvested * Corresponding author. Tel.: +32-16-322-732; fax: + 3216-322-955 E-mail address: [email protected] (E. Vanstreels).
and cold-stored for varying periods. They are subsequently forced in darkness by traditional means or hydroponic systems to yield white etiolated buds, called chicons, which can be consumed raw as a salad or cooked, and which have a characteristic bitter taste. Quality assessment in chicory is based on a wide range of parameters, including pure biometric properties of the chicons as well as observation of, mainly visual, defects. During postharvest storage and commercialisation of chicory, several disorders may develop. One such important defect
0925-5214/02/$ - see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 5 2 1 4 ( 0 2 ) 0 0 0 6 0 - 1
E. Vanstreels et al. / Posthar6est Biology and Technology 26 (2002) 313–322
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is red discoloration resulting from cell damage and subsequent oxidation of phenolic compounds (Martinez and Whitaker, 1995). Red spots typically develop a few days after harvest of the chicons, in the basal parts of the medial leaves. This red discoloration considerably affects the commercial value of the chicons. The exact mechanism that causes cell damage is still not entirely clear, but it has been observed that the occurrence of red discoloration seems to be correlated with the continuation of the growth of the floral stem during postharvest storage (Van Kruistum, 1997). Gillis et al. (2001) have shown that at the position which is coincident with the discoloured spots, the corresponding strain in the leaves causes a stress concentration, suggesting that cell damage could be a consequence of mechanical stress induced by floral stem growth during postharvest storage. Other negative quality features that may develop during postharvest storage and commercialisation of chicory are leaf edge, which can be recognised as brown necrotic areas at the edges of the outermost leaves, and the extensive growth of the internal core. The internal core or flower stem gives the chicory a bitter taste, and continuation of its growth during postharvest storage should be avoided as much as possible. Modified atmosphere (MA) packaging and subsequent storage at low temperatures has been developed over the last decades as a technique to retain high quality of vegetables and thus to prolong shelf life. Hertog et al. (1998) developed a model for respiration of chicory in closed packages as a function of temperature, O2 and CO2 concentrations. However, so far no publications have appeared in which the influence of modified or controlled gas conditions on the change in Table 1 Atmospheric conditions of the CA experiment conducted in 1999 (X) and in 2000 (+)
0% CO2 5% CO2 11% CO2 19% CO2
2% O2
5% O2
11% O2
X+ X+ X+ +
X X X X
X+ X+ +
+ + + +
20.8% O2 X + (air)
quality of chicory heads has been described. The objective of our research was, therefore, to evaluate the factors influencing the development of red discoloration during storage, including CO2 and O2 levels, storage temperature and duration, size and weight of the chicory head. These results may be used for simulation-based MA package design.
2. Materials and methods
2.1. Experimental design Two large-scale experiments with chicory under controlled atmosphere (CA) conditions were carried out in August 1999 and in August 2000. Chicory (C. intybus L., cv. Tabor) used for this experiment was grown hydroponically and harvested by a single grower. After harvest, the chicory heads were immediately transported to the experimental CA facility of the laboratory. The chicory heads were stored there in stainless steel controlled atmosphere containers, which were monitored continuously and individually. At regular time intervals (every 4–6 h) atmosphere samples were taken from the containers, and analysed for O2 and CO2 levels. If deviations from the set point larger than 0.2% were observed the composition of the atmosphere in the containers was adjusted. Oxygen concentrations which were too low were compensated by injection of air and those too high by injection of N2. Low CO2 concentrations were compensated by injection of CO2, while excessive CO2 was scrubbed with an active carbon scrubber. In this way the composition of the atmosphere inside the containers was kept constant. The set-up (atmospheric conditions) of both experiments is shown in Table 1. In addition, a number of different storage temperatures were evaluated. In 1999 temperatures of 5, 12 and 20 °C were tested. However, because of the rapid deterioration of chicory stored at 20 °C, in 2000 temperatures of 1, 5 and 12 °C were chosen. Quality of the chicory heads was evaluated six times over a period of 3 weeks. An initial evaluation was performed on 40 (1999) or 160 (2000) chicory heads, further evaluations were performed on 20 (1999) or 40 (2000) chicory
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heads per storage condition. In total, 4000 (1999) and 8800 (2000) chicory heads were evaluated. A third experiment was conducted for model validation purposes in January 2001. Two atmospheric conditions (10% O2 +10%CO2 and 20.8% O2 + 0.03% CO2) and two storage temperatures (5 and 12 °C) were tested. Quality was evaluated on 80 freshly harvested chicory heads and further evaluations were performed four times over a period of 3 weeks on 80 chicory heads per condition. In total, 1360 chicory heads were evaluated.
2.2. Quality e6aluations Six parameters were evaluated for quality assessment, namely weight, length, rot, leaf edge discoloration, red discoloration and stem length. Each chicory head was weighed and its length was measured with a ruler. The outside was scanned for signs of rot, or leaf edge discoloration and given a score of 0 or 1 for these parameters. The ten outermost leaves were peeled off and the number containing red spots was counted. The chicory heads were then cut in half and the length of the floral stem was measured. Extensively rotten chicory heads were excluded from the evaluations.
2.3. Statistical analysis Red discoloration was transformed into a binary score of 0 (0 or 1 leaf with red discoloration) or 1 (2 or more leaves with red discoloration). The rationale for doing so was to exclude possible misjudgements of the red discoloration in a single leaf; only when two leaves were scored as positive (i.e. red) was the chicory head regarded as having the disorder. The proportion of chicory heads with red discoloration per storage condition was calculated. The probability of red discoloration was then modelled as a function of the explanatory variables (storage conditions, biometric properties of the chicory head, relative stem length, etc.) by means of multiple logistic regression. In logistic regression analysis, the probability of a binary or ordinal response variable, in this case red discoloration or no red discoloration, is modelled as a function of one or more explanatory
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variables (Hosmer and Lemeshow, 1989; Collet, 1991). The dependence of the probability of disorder on explanatory variables is modelled as follows: logit(yi )= log
m yi = h+ % ij xij 1− yi j=1
where i is the number of observations, m is the number of explanatory variables in the model, yi is the probability of the disorder developing, given the set of m explanatory variables, h is the intercept parameter, ij is the slope parameter for the jth explanatory variable and xij is the ith observation for the jth explanatory variable. The logit transformation in logistic regression leads to coefficients i which can be interpreted in terms of odds ratios (Lammertyn et al., 2000; Verlinden et al., 2001). The data set for the year 1999 was restricted to the first 14 days of storage. This was done because of the rapid deterioration of the chicory heads at 20 °C after that time, which meant that for some storage conditions no or very few heads were left to examine for red discoloration. Similarly, the data set for the year 2000 was restricted to the first 17 days of storage, although the deterioration problem was less pronounced here. All statistical analyses were performed using the SAS/STAT software, version 6.12 or version 8 (SAS Institute, Cary, NC). The PROC LOGISTIC procedure was used for logistic regression analysis. Relevant parameters were automatically selected for entry into the model at the 0.05 significance level by means of a stepwise selection procedure for the year 2000 data set. Important direct effects such as temperature, storage time (here transformed to log(storage time)), percentage O2 and CO2 were forced manually into the model because of the hierarchical principle. For the year 1999 data set the structure of the model was kept the same as the one obtained for the data of the year 2000, i.e. the parameters that were found to be significant for the data of the year 2000, were forced manually into the model. Details on syntax and options in PROC LOGISTIC can be found in the manual (Logistic Regression Examples Using the SAS® System, Version 6, First Edition, Cary, NC, SAS Institute, 1995).
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Table 2 Summary of the logistic regression analysis of factors affecting red discoloration Explanatory variables
Parameter
Parameter estimate
Wald confidence limits Lower
Upper
A. 2000 data Intercept Log(Storage time) O2 CO2 Temperature Log(Storage time)*Temperature Log(Storage time)*CO2 Log(Storage time)*Log(Storage time) Temperature*O2 Temperature*CO2 Temperature*Temperature O*CO 2 2 CO*CO 2 2 Weight
h i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12 i13
−9.117 6.0360 0.0534 −0.0641 −0.353 −0.144 −0.0259 −1.0218 0.00814 −0.00840 0.0427 −0.00522 0.00886 0.0151
−10.484 5.0481 0.00790 −0.1580 −0.455 −0.170 −0.0473 −1.243 0.00428 −0.0110 0.0373 −0.00827 0.00582 0.0128
−7.750 7.0239 0.0988 0.0298 −0.251 −0.117 −0.00450 −0.8008 0.0120 −0.00586 0.0481 −0.00216 0.0119 0.0174
B. 1999 data Intercept Log(Storage time) O2 CO2 Temperature Log(Storage time)*Temperature Log(Storage time)*CO2 Log(Storage time)*Log(Storage time) Temperature*O2 Temperature*CO2 Temperature*Temperature O*CO 2 2 CO*CO 2 2 Weight
h i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12 i13
−4.298 0.442 −0.0359 0.00822 −0.0279 0.0668 −0.0617 0.107 0.00851 −0.00414 −0.00658 0.00182 0.00545 0.0144
−5.510 −0.0271 −0.0760 −0.0873 −0.146 0.0326 −0.0923 0.0822 0.00534 −0.00724 −0.0101 −0.00294 0.00260 0.0117
−3.0862 0.912 0.00419 0.104 0.0904 0.101 −0.0310 0.131 0.0117 −0.00104 −0.00302 0.00659 0.00830 0.0170
The relative stem length was calculated as length of the floral stem divided by the total length of the chicory head. Factors influencing the relative stem length were analysed by means of multivariate regression analysis.
3. Results and discussion
3.1. Logistic regression model for red discoloration The results of logistic regression analysis to model the probability of red discoloration using
the data of the year 2000 are summarised in Table 2A. Model parameter estimates and their 95% lower and upper Wald confidence limits are shown. The selected model is a second order model with the parameter ‘storage time’ logarithmically transformed. The model provides the best possible fit for the data as judged by the Akaike Information Criterion (AIC; Akaike, 1973) and by the Hosmer and Lemeshow Goodness-of-Fit test (Hosmer and Lemeshow, 1989). This last test only scored positive when the intrinsic explanatory variable ‘weight’ was included in the model (null hypothesis that the model provides a good fit for the data could not be rejected at the 0.05 level).
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Fig. 1. Probability of red discoloration after 10 days of storage at different temperatures, data set of year 2000. Bar diagrams represent the measured values of red discoloration (relative number of chicory heads with red spots). Predicted probability of red discoloration determined by regression is represented by a sloping grid. (A) Storage at 1 °C; (B) storage at 5 °C; (C) storage at 12 °C.
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All variables in the model, except for the variable ‘weight’, occur in interaction terms, making their interpretation not straightforward. The variable weight is the only directly interpretable variable; the probability of red discoloration increases with increasing weight of the chicory head. For this variable the odds ratio was calculated. The odds are defined as the ratio of the probability, y, that the disorder will occur and the probability that the disorder will not occur, 1−y. An odds ratio, in turn, is a ratio of odds. The odds ratio of an explanatory variable expresses by which factor the odds of the event increase or decrease for a unit change of the explanatory variable, while all other variables are kept constant (Agresti, 1996). Estimated odds ratios are computed by exponentiating the parameter estimates when the explanatory variable does not interact with any other variable. An odds ratio can range between zero and infinity. A value of 1 indicates that the variable has no influence on the incidence of the disorder. The odds of red discoloration increase by 1.5% for each 1 g increase in weight of the chicory head (odds ratio for explanatory variable weight=1.015). The importance of the influence of the factor weight becomes clear when calculating the odds ratio for e.g. a 20 g increase in weight of the head (1.353). The odds of red discoloration of a chicory head weighing 120 g are 35.3% higher then the odds of red discoloration of a chicory head weighing 100 g. Oxygen, CO2, temperature and storage time (transformed to log(storage time)), interact. The parameter estimate for the variable O2 depends on temperature and on the CO2 concentration: 0.0534+0.00814*temperature− 0.00522*CO2. It is in general positive in the range of the model, indicating that the odds of red discoloration increase with increasing O2, except for conditions with high CO2 concentration (19%) and low temperature (1 °C). The parameter estimate for log(storage time) is also always positive within the range of the model, indicating that the probability of red discoloration increases with increasing storage time. The parameter estimates for CO2 and for temperature are more difficult to interpret. In these cases a visual interpretation of the results has been made (Figs. 1 and 2).
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Fig. 2. Probability of red discoloration after 21 days of storage at 5 °C, data set of year 2000 (A) and of year 1999 (B). Bar diagrams represent the measured values of red discoloration (relative number of chicory heads with red spots). Predicted probability of red discoloration determined by regression is represented by a sloping grid. Note the different scale of X-axis in part B of the figure.
After 10 days of storage at a temperature of 1 °C, chicory heads showed a large amount of red discoloration, almost irrespective of the atmospheric conditions they were kept in. At temperatures of 5 or 12 °C there was considerably less red discoloration. At these temperatures a concentration of 10% CO2 seems to be best suited to prevent red discoloration, while the O2 concentration seems to be less important (Fig. 1). After 21 days of storage in CA conditions at 5 °C there was a spectacular decrease in red discoloration as compared to storage in air (20.8% O2, 0% CO2) (Fig. 2A).
Results of logistic regression analysis to model the probability of red discoloration using the data obtained in the year 1999 are shown in Table 2B. As mentioned before, the structure of the model was kept the same as the one obtained for the data of the year 2000, i.e. the parameters that were found to be significant for the data of the year 2000 (Table 2A and discussed above), were forced manually into the model. All parameters proved to be significant for entry into the model at the h= 0.05 level, except for the interaction effect of O2 and CO2 (P=0.4532). The selected model provided a good fit for the data, as judged by the Hosmer and Lemeshow Goodness-of-Fit test. The general conclusion that a combination of high CO2, low O2 and low storage temperature (5°C) is optimal for prevention of red discoloration could be drawn from both experiments (Fig. 2A and B). The odds ratio for the parameter weight calculated for the 1999 data set is 1.014, which confirms the conclusion that the odds of red discoloration increase with about 1.5% for each gram increase of weight. It has been suggested that the occurrence of red discoloration is correlated with the relative stem length of the chicory head (Van Kruistum and Embrechts, 1994; Gillis et al., 2001). In the logistic regression model presented here ‘relative stem length’ is not included as a factor, while, the factor ‘weight’ is. However, since weight and relative stem length are positively correlated (z= 0.08), this indirectly implies also an influence of the intrinsic parameter ‘relative stem length’.
3.2. Optimal storage conditions to pre6ent red discoloration In order to avoid production of off-flavours as a result of fermentation, the O2 concentration during storage should not be too low. For this reason, and based on our data, a concentration of 10% O2 was chosen as optimal. To determine the optimal CO2 concentration we used the developed logistic regression model. Derivatives with respect to CO2 for each storage condition (O2, temperature, storage time) and for both models (i.e. the model for the data of 1999 and for the data of 2000) were calculated and were set equal to zero
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( logit(yi ) =0 . ([CO2]
Note that because of the monotonous relationship between yi and logit(yi ) the position of the conditional minimum is the same for both expressions. Thus the calculated CO2 concentration is the optimal concentration to prevent red discoloration, given a certain O2 concentration, temperature and storage time. Results are shown in Fig. 3. Optimal CO2 concentrations are comparable for each storage time. The calculated optimal CO2 concentration is higher for the data of 2000 than for the data of 1999. There is a small difference in optimal CO2 concentrations at different temperatures, but this is negligible, especially for the data of 2000. In general, for all storage conditions and for both years, the optimal CO2 concentration at an O2 concentration of 10% ranges between 7 and
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11%. An atmospheric composition of 10% O2 and 10% CO2 is thus proposed as optimal and as the basis for the development of MA packaging for chicory. At these conditions, a storage temperature of 5 °C is best suited to prevent red discoloration.
3.3. Other quality aspects In the present study we focussed on the occurrence of red discoloration, although other quality parameters were assessed at the same time. A representation of results for the parameters ‘rot’, ‘leaf edge’ and ‘relative stem length’ is given in Fig. 4. Multivariate regression analysis for the parameter ‘relative stem length’ revealed that significant explanatory variables are storage time, temperature, O2 concentration and interactions
Fig. 3. CO2 concentration that corresponds with the minimal probability of red discoloration for different O2 concentrations and storage conditions. (A) Ten days storage time; (B) 14 days storage time; (C) 17 days storage time; (D) 21 days storage time.
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between these parameters. The CO2 concentration has no significant effect on the continuation of the stem growth, as can also be seen in Fig. 4A. Storage conditions that minimise the chance of the occurrence of red discoloration are also favourable to avoid rot, leaf edge and continuation of stem growth (Fig. 4). This implies that an optimisation of atmosphere conditions for optimal quality preservation in chicory is feasible.
3.4. Validation of the model and the proposed storage conditions
Fig. 4. Relative stem length after 21 days of storage at 5 °C (A), probability of leaf edge (B) and of rot (C) after 21 days of storage at 5 °C, data set of 2000. Bar diagrams represent measured values. Predicted relative stem length determined by regression is represented by a sloping grid. Notice the different scale of the Z-axis in part B and C of the figure.
The predictive quality of the model derived from the data of the year 2000 was evaluated by means of linear regression. The correlation between the observed percentage of red discoloration and the predicted probability of red discoloration was determined. This was done for the calibration data set (experiment of the year 2000), the data set of the year 1999 and for the validation data set of the year 2001. Calculated coefficients of determination (R 2), intercept and slope parameters are shown in Fig. 5. Theoretically, in the case of an ideal fit, the intercept should be zero and the slope parameter should be equal to one. The validation set of 2001 has a high R 2 and its slope parameter is close to one. The intercept parameter is relatively large: when there is no red discoloration observed, the model still predicts a percentage of 20% red discoloration. In contrast, the R 2 for the data set of the year 1999 is low, indicating a clear batch effect and influence of other factors that are not included in the model. Other factors affecting red discoloration might be the environmental conditions during forcing (e.g. temperature, composition of the nutrient solution), time of harvest of the chicory heads (developmental stage of the chicory head at the time of harvest) or physiological condition of the root. The general applicability of the chosen storage conditions was validated by conducting an experiment in which storage in the selected conditions was compared to storage in air (validation experiment of 2001, Fig. 6). Storage in 10% O2 + 10% CO2 resulted in a significant decrease in the number of red discoloured chicory heads. Storage
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at low temperature was more beneficial than storage at higher temperatures. However, the effect of the atmospheric conditions was more pronounced than the effect of temperature: storage in air at 5 °C resulted in a higher proportion of red discoloured chicory heads than storage in 10% O2 + 10% CO2 at 12 °C.
Fig. 6. Proportion of chicory heads with red discoloration for different storage conditions. Validation experiment of 2001.
4. Conclusion The development of physiological disorders that considerably affect the commercial value of chicory can be strongly influenced by storage conditions, as confirmed by two large scale CA experiments. Storage in CA conditions strongly reduced the occurrence of red discoloration. Other negative quality aspects were also considerably reduced. An atmospheric composition of 10% O2 and 10% CO2 and a temperature of 5 °C is considered optimal for storage and these conditions could be used to develop MA packaging for chicory.
Acknowledgements This research was conducted with financial support of the Belgian Ministry of Small Enterprises, Traders and Agriculture (project S-5947). J. Lammertyn is research assistant of the Fund of Scientific Research-Flanders (Belgium) (FWOVlaanderen).
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