Modeling changes of sensory attributes for individual and mixed fresh-cut leafy vegetables

Postharvest Biology and Technology 38 (2005) 202–212

Modeling changes of sensory attributes for individual andmixed fresh-cut leafy vegetables

Andrea M. Piagentini, Julio C. Mendez, Daniel R. Guemes, Marıa E. Pirovani∗

Instituto de Tecnologıa de Alimentos, Facultad de Ingenierıa Quımica, Universidad Nacional del Litoral,C.C. 266-3000 Santa Fe, Argentina

Received 10 August 2004; accepted 2 July 2005

Abstract

Changes of the main sensory attributes of three fresh-cut leafy vegetables (Iceberg and Romaine lettuce and chicory) wereinvestigated at selected temperatures (2–20◦C). The aim of this work was to develop and apply a kinetic approach to modelsensory quality changes in order to establish the appropriate function that describes the time–temperature dependence of eachattribute. The changes of the sensory characteristics followed first order reaction kinetics and the temperature dependence ofrate constants followed the Arrhenius relationship. The limiting quality factor, which determined the sensory shelf life forthe three fresh-cut vegetables assayed, at any temperature, was the general appearance of the products. The activation energiesobtained for general appearance were 71.1 kJ mol−1 for fresh-cut Iceberg lettuce, 69.5 kJ mol−1 for fresh-cut Romaine lettuce and65.7 kJ mol−1 for fresh-cut chicory. Additional experimental tests showed that the predicted and experimental sensory shelf lifefor individual fresh-cut vegetables at constant temperature were not different (P > 0.05). Under dynamic temperature conditions( e also notd of qualityc erimentalv lts showedt oduct.©

K

1

m

con-our,a ofo-

t

0

sequence of different temperatures), the predicted and experimental values of browning, wilting, and off-odour werifferent (P > 0.05), but the general appearance loss model overestimated the quality loss from 10 to 30%. The modelshange for individual vegetables were used to predict the sensory shelf life of fresh-cut mixed vegetables. The expalidation tests proved that these models provide a good approach to evaluate the shelf life of the mixed product. Resuhat the general appearance of fresh-cut Iceberg and Romaine lettuce dominated the sensory perception of mixed pr

2005 Elsevier B.V. All rights reserved.

eywords: Sensory quality; Shelf life; Iceberg lettuce; Romaine lettuce; Chicory

. Introduction

Leafy vegetables are a well-recognized source ofinerals, vitamins, and dietary fibre. The desire of

∗ Corresponding author.E-mail address: [email protected] (M.E. Pirovani).

consumers for fresh-cut vegetables due to theirvenience and fresh-like properties (texture, flavand appearance) has led to a relatively new arefood preservation named minimally or lightly prcessed (King and Bolin, 1989; Cantwell, 1992), IVGamme (Carlin et al., 1990), ready-to-use (Francis eal., 1999), or fresh-cut fruits and vegetables (Gorny,

925-5214/$ – see front matter © 2005 Elsevier B.V. All rights reserved.doi:10.1016/j.postharvbio.2005.07.001

A.M. Piagentini et al. / Postharvest Biology and Technology 38 (2005) 202–212 203

1996). These types of products do not need additionalpreparation at home and have been slightly modified intheir fresh characteristics. In Argentina, their demandhas increased markedly in the last years and industryhas expectations of further growth. Although it is arelatively new market it represents 20% of the total ofvegetable sales in supermarkets. Therefore, this kind ofproduct appears destined to become an important com-ponent of the food industry but efforts must be taken toensure products of high quality. Fresh-cut leafy vegeta-bles have a short shelf life and are exposed to conditionsthat can destroy their superior quality especially duringtransport and retailing. Temperature conditions primar-ily determine the rate of quality degradation and theshelf life of the product causing changes in sensorycharacteristics, which can influence consumer accept-ability. Appearance is the most important attribute eval-uated by the consumer in the decision to accept apackaged leafy vegetable (IFT, 1990). Subsequently,when the package is open, aroma or the off-odours, ifpresent, would be also important attributes.

Several studies on quality changes of different fresh-cut vegetables for specific storage conditions havebeen reported (Bolin et al., 1977; Barriga et al., 1991;Heimdal et al., 1995; Pirovani et al., 1998; Artes et al.,1999) but little is available on sensory quality modelingto predict quality degradation as a function of temper-ature and time, simultaneously (Vankerschaver et al.,1996; Piagentini et al., 2004). The aim of this workwas to develop and apply a kinetic approach to models pro-p tured leafyv con-s ma-t ringc pri-a ifee ndr

2

2

R

and chicory (Cichorium intybus L.) were obtainedfrom a local farm near Santa Fe (Argentina). Uponarrival, the vegetables were stored at 4◦C and 90% RH,and processed separately 1 day after harvest. Outer,damaged and yellowed leaves, roots, and stems wereremoved. The remaining leaves were cut in shreds of5 mm width with a sharp stainless steel knife. Thecut vegetable was washed for 4 min in cold water(4–6◦C) containing 100 mg/L available chlorine assodium hypochlorite and pH 6.8, with a water-to-produce ratio of 18 L/kg. Then, it was rinsed withrunning tap water (0.2 mg/L total chlorine) for 4 min.The vegetable was centrifuged in a basket-type cen-trifuge at 540 min−1 for 4 min. Each fresh-cut veg-etable sample (70 g for Iceberg and Romaine lettuce,50 g for chicory) was placed in a semi-rigid polyethy-lene tray (130 mm× 100 mm× 40 mm) overwrappedwith a plasticized PVC film 13�m thick, with a sur-face area of 0.013 m2. Gas transmission rates of PVCfilm for O2, CO2, and water vapour were 8.71× 10−11

and 7.96× 10−10 mol m−2 s−1 Pa−1 (both at 23◦C and1.01× 105 Pa) and 2.96× 10−4 mol m−2 s−1 (at 37◦Cand 90% RH), respectively. Finally, samples werestored at 90% RH and: 1.7, 4.7, 8.9, or 20.3◦C forIceberg lettuce; 1.4, 4.3, 8.9, or 20.3◦C for Romainelettuce; 1.6, 4.5, 8.9, or 20.3◦C for chicory. The exper-iment was repeated three times for each vegetable.

2.2. Sensory analysis

per-t test.A revi-o ali-t uteso y tos ribedb -i anda Theye ance,w est-i

ual-i ne,w nd.T buteb cale

ensory quality changes in order to establish the apriate function that describes the time–temperaependence of each attribute for three fresh-cutegetables. The kinetic parameters, namely ratetants and activation energies, provide useful inforion on the quality changes that could occur duommercial handling. By establishing the approte quality function, a quantitative tool for shelf lstimation during different conditions of transport aetailing management can be obtained.

. Materials and methods

.1. Preparation of fresh-cut vegetables

Iceberg lettuce (Latuca sativa L. var. capitata L.),omaine lettuce (L. sativa L. var. longifolia Lam.),

The evaluation of the characteristic sensorial proies of the samples was performed by a descriptivetrained sensory panel of 6–8 judges, which had pusly participated in evaluating fresh vegetable qu

ies, was required to evaluate sensory quality attribf fresh-cut vegetable samples. The methodologelect and train panellists was the same descy Pirovani et al. (1998). During the specific train

ng (5× 30 min sessions), the panellists discussedgreed on sensory attributes and anchored terms.valuated off-odour development, general appearilting, and browning of fresh-cut vegetable each t

ng day.The judges indicated their perception of each q

ty attribute intensity on a 150 mm unstructured liith anchored terms located 15 mm from either ehey scored the perceived intensity of each attriy placing a vertical line across the unstructured s

204 A.M. Piagentini et al. / Postharvest Biology and Technology 38 (2005) 202–212

line. Quantitation was accomplished by measuring thedistance from the left end (0.00) to the vertical line,reporting measurements in centimeters. The anchoredterms were indicated from left to right as: none andsevere for off-odour; very poor and excellent for gen-eral appearance; none and very severe for wilting andbrowning.

The panellists were instructed to open the sealedsamples and evaluate the off-odour first, and then theother attributes. Each panellist performed the sensorytest individually in separate booths with white incan-descent lighting (sufficient to provide 700 lx). Sampleswere coded with three-digit random numbers. The cen-tral point of the scale was established as the cut-offscore. Therefore, the fresh-cut vegetable was consid-ered as unacceptable when a mean score below 7.5was reached for general appearance or above 7.5 for theother sensory attributes (Barriga et al., 1991; Piagentiniet al., 1997, 2004; Jacxsens et al., 2002).

2.3. Weight loss

Weight loss was determined on all samples at eachtesting day and was expressed as a percentage of initialfresh weight.

2.4. Mathematical modeling of quality changes

The rate of quality changes of foods can be describedby the following general equation:

±

w -t fort ithi ndo s-i

odss ared nc-t 7T netico oef-fi st ints)a eter-

mination of the reaction order. Then, the effect of tem-perature on quality attribute changes is modeled.

The influence of a constant storage temperature onthe reaction rate constant can be described using theArrhenius equation (Saguy and Karel, 1980; Taoukiset al., 1997):

kq(T ) = k0 exp

[−Ea

RT

](2)

wherekq(T) is rate constant for each quality atribute;k0 the pre-exponential factor;Ea the activationenergy (J mol−1); R the universal gas constant(8.3145 J K−1 mol−1); T is the absolute temperature(K). The Ea for each quality attribute is obtained byregression analysis.

Another parameter that is often used to describethe relationship between temperature and reaction rateconstant is theQ10 value.Q10 is defined as follows:

Q10 = reaction rate at temperature (T + 10)

reaction rate at temperature (T )(3)

Equivalently,Q10 has been defined as the change ofshelf life (ts), i.e., the time for the failure attribute toreach an unacceptable level when the food is stored ata 10◦C higher temperature. It can be shown thatQ10and activation energy,Ea, are related by the followingexpression (Singh and Heldman, 1993):

ln Q10 =(

Ea

R

) [10

T (T + 10)

](4)

it ofa aver-a bove7 edff istic( e tor ndt

2

t tov am-p n thes t dif-f fe ofe ilure

dQ

dt= kq[Q]n (1)

hereQ is quality attribute;t the time; n the reacion order;kq is the quality change rate constanthe attributeQ. The sign (+) refers to attributes wncreasing values during time (browning, wilting, aff-odour) and the sign (−) to attributes with decrea

ng values (general appearance).Traditionally, quality change processes of fo

tored under controlled environmental conditionsescribed with zero order and/or first order rate fu

ions (Saguy and Karel, 1980; Taoukis et al., 199).he regression analysis is used to determine the kirder of the quality change rate. An analysis of the ccient of determination (R2), residuals (ei , defined ahe difference between observed and fitted data pond estimated value of the intercept would enable d

The time to reach a sensory characteristic limcceptability at a specified storage temperature (ge score below 7.5 for general appearance or a.5 for browning, wilting, and off-odour) was deriv

rom the prediction models (Eqs.(1) and(2)). For eachresh-cut vegetable, the limiting sensory characterfailure attribute) was obtained comparing the timeach the limit of acceptability of each attribute aemperature.

.5. Validation of the models

Three confirmatory experiments were carried oualidate the fitted models at constant temperature. Sles of each fresh-cut vegetable were prepared iame way as previously described and stored aerent constant temperatures. The sensory shelf liach sample was determined by evaluating the fa

A.M. Piagentini et al. / Postharvest Biology and Technology 38 (2005) 202–212 205

attribute. Experimental data were compared to valuespredicted from the models by at-test analysis.

The combined temperature–quality change mod-els, obtained under constant conditions, were testedto verify their adequacy under a variable temperaturesequence. Therefore, these models were used to predictthe evolution of sensory attributes of fresh-cut Iceberglettuce stored at two different temperature profiles. Pro-file A: 2 h at 21.4◦C; 46 h at 2.3◦C, and finally, 94 h at5.5◦C. Profile B: 2 h at 22◦C; 46 h at 9.7◦C, and finally94 h at 5.8◦C. Experiments were repeated twice foreach temperature profile. The observed and predictedfailure attribute values of fresh-cut Iceberg lettuce werecompared.

The models to predict changes of general appear-ance obtained for each vegetable were also tested onmixed vegetable samples. Fresh-cut vegetables wereprepared in the way previously described. The mixedvegetable sample was prepared by putting 20 g of eachvegetable (Iceberg and Romaine lettuce, and chicory)together in the same tray, wrapping with PVC film andstoring at 1.3, 4.3, 8.1, or 20.4◦C. Data from threeconfirmatory experiments were compared to valuespredicted by model equations obtained for individualfresh-cut vegetable.

2.6. Statistical analysis

All data were analysed using STATGRAPHICS Plus(Manugistics, Inc., Rockville, MD, USA). The aver-a andi thet d tot naly-s ls ofe men-t entsw tionb

3

3a

gen-e for

Fig. 1. Experimental and predicted values of sensory attributes forfresh-cut Iceberg lettuce throughout storage at different tempera-tures. Bars indicate S.D.

the three packaged fresh-cut leafy vegetables through-out storage at different temperatures are presented inFigs. 1–3. They showed that browning, wilting, andoff-odour scores did not change as rapidly as generalappearance scores. Panellists scored general appear-ance as the overall visual impact of the samples. During

ge and standard deviation of weight loss valuesndividual scores of the sensory attribute given byrained panel were calculated. The data were fittehe corresponding models and the regression aes were carried out. The 95% confidence intervastimated parameters were calculated. The experi

al data obtained during the confirmatory experimere compared to values predicted by model equay at-test analysis.

. Results and discussion

.1. Sensory characteristics of fresh-cut Icebergnd Romaine lettuces, and chicory

The mean scores of sensory quality attributes (ral appearance, browning, wilting, and off-odour)

206 A.M. Piagentini et al. / Postharvest Biology and Technology 38 (2005) 202–212

Fig. 2. Experimental and predicted values of sensory attributes forfresh-cut Romaine lettuce throughout storage at different tempera-tures. Bars indicate S.D.

the process of language development, they agreed thatwilting, browning, and all other unexpected visualquality factors would contribute to the general appear-ance score. At the end of experiments, some panellistsindicated that colour changes or decay were includedin general appearance evaluation.

Fig. 3. Experimental and predicted values of sensory attributes forfresh-cut chicory throughout storage at different temperatures. Barsindicate S.D.

As storage time and temperature increased, pan-ellists gave lower scores for general appearance andhigher ones for browning, wilting, and off-odour forall the samples.

General appearance and browning changed the mostduring storage for both types of lettuce and for chicory

A.M. Piagentini et al. / Postharvest Biology and Technology 38 (2005) 202–212 207

Fig. 4. Weight losses of fresh-cut vegetables during storage at dif-ferent temperatures.

at all temperatures assayed.Jacxsens et al. (2002)foundthat general appearance (freshness) and the colour werethe properties of packaged mixed lettuce that presentedthe largest changes.

In the present work, wilting, and off-odour wereimportant defects only in samples stored at higher tem-peratures (8.9 and 20.3◦C).

Weight loss of fresh-cut Iceberg and Romaine let-tuce and chicory was less than 1.7% after 6 days ofstorage for temperatures lower than 4.7◦C (Fig. 4).When storage temperature was 8.9◦C, the weight lossafter 6 days of storage was approximately 2–2.5% forboth types of lettuce but significantly higher (3.7%) for

chicory. This is in accordance with sensory evaluationof wilting (score around four for both lettuce and sixfor chicory).

3.2. Modeling sensory characteristics of fresh-cutleafy vegetables

The first step in selecting an appropriate model torepresent the quality changes was to perform regressionanalysis to determine the kinetic order. The perfor-mance of the fitted models was analysed. Based onthe best coefficient of determination (R2), first orderwas the apparent order of the quality change reactionsin the majority of the cases (Table 1). Further analy-sis determined the estimated value of the intercept ofthe first order model. Finally, the plots of residuals ver-sus predicted values (not shown) for each model (zeroand first order) indicated that the distribution aroundzero was more random for the first order model. There-fore, all these results were taken into account to selectthe first order reaction model to describe the qualitychanges of these leafy vegetables. Other researchers(Vankerschaver et al., 1996; Piagentini et al., 2004)also found that first order was the most adequate kineticmodel for fitting sensory characteristic changes in min-imally processed vegetables. Curves inFigs. 1–3showthe evolution of each predicted attribute using the fittedmodels.

Tables 2–4show the rate constants for each attributeand temperature and the activation energies for pack-a andc thea per-are pre-s

nce( d6 et-t aredt hesea . Ont ings ute( el em-p ilting

ged fresh-cut Iceberg and Romaine lettuce,hicory, respectively. Results indicated that for allttributes the dependence of rate constants on temture followed the Arrhenius relationship (R2 valuesanged between 0.700 and 0.998). TheQ10 values forach attribute and fresh-cut leafy vegetable areented inTable 5.

The activation energies of general appeara71.1 and 69.6 kJ mol−1) and wilting (66.9 an5.1 kJ mol−1) for fresh-cut Iceberg and Romaine l

uce, respectively, showed the highest values compo other sensory characteristics indicating that tttributes are the most influenced by temperature

he other side, lower activation energies of brownignify smaller temperature sensitivity for this attribEa = 47.5 and 39.6 kJ mol−1 for Iceberg and Romainettuce, respectively). Therefore, a reduction in terature would benefit general appearance and w

208 A.M. Piagentini et al. / Postharvest Biology and Technology 38 (2005) 202–212

Table 1Coefficient of determination (R2) for zero and first order models for sensory attribute of fresh-cut Romaine and Iceberg lettuces and chicory

Attribute Temperature (◦C)a Iceberg lettuce Romaine lettuce Chicory

Zero First Zero First Zero First

General appearance A 0.8500 0.9239 0.9595 0.9370 0.4316 0.4198B 0.9849 0.9937 0.7799 0.7513 0.8178 0.7879C 0.9341 0.9731 0.8101 0.9445 0.9466 0.7812D 0.9165 0.9974 0.8597 0.9892 0.9164 0.9133

Wilting A 0.7814 0.8868 0.7821 0.7874 0.0070 0.0105B 0.9687 0.977 0.5614 0.6277 0.3351 0.3477C 0.9271 0.9153 0.7081 0.7626 0.8923 0.9119D 0.9843 0.9706 0.9674 0.8888 0.8228 0.893

Browning A 0.7902 0.9349 0.9313 0.9714 0.2810 0.3212B 0.9853 0.9570 0.7017 0.7286 0.6551 0.6917C 0.9408 0.8550 0.8154 0.9140 0.9169 0.9555D 0.9532 0.8227 0.9304 0.7671 0.9016 0.8948

Off-odour A 0.6264 0.8284 0.7715 0.7920 0.5152 0.5768B 0.8539 0.8615 0.8069 0.8795 0.5477 0.6691C 0.8113 0.8331 0.652 0.7522 0.8930 0.9216D 0.9649 0.9832 0.8899 0.8109 0.9078 0.9066

a Fresh-cut Iceberg lettuce: A = 1.7◦C, B = 4.7◦C, C = 8.9◦C, D = 20.3◦C; fresh-cut Romaine lettuce: A = 1.4◦C, B = 4.3◦C, C = 8.9◦C,D = 20.3◦C; fresh-cut chicory: A = 1.6◦C, B = 4.5◦C, C = 8.9◦C, D = 20.3◦C.

Table 2Rate constant (kq) from sensory attributes of fresh-cut Iceberg lettuce for the first order model and the respective activation energies (Ea) fromthe Arrhenius equation

Attribute Temperature (◦C) Rate constanta, kq (day−1) Coefficient of determination (R2)

General appearance 1.7 0.065± 0.019 0.92394.7 0.161± 0.013 0.99378.9 0.235± 0.054 0.9731

20.3 0.576± 0.090 0.9974

Ea (kJ mol−1) 71.1 0.9168

Wilting 1.7 0.081± 0.026 0.88684.7 0.118± 0.018 0.97708.9 0.185± 0.078 0.9153

20.3 0.528± 0.280 0.9706

Ea (kJ mol−1) 66.9 0.9979

Browning 1.7 0.178± 0.042 0.93494.7 0.227± 0.048 0.95708.9 0.287± 0.164 0.8550

20.3 0.671± 0.484 0.8227

Ea (kJ mol−1) 47.5 0.9968

Off-odour 1.7 0.094± 0.038 0.82844.7 0.104± 0.042 0.86158.9 0.172± 0.107 0.8331

20.3 0.535± 0.213 0.9832

Ea (kJ mol−1) 65.3 0.9888

a kq ± confidence interval at 95%.

A.M. Piagentini et al. / Postharvest Biology and Technology 38 (2005) 202–212 209

Table 3Rate constant (kq) from sensory attributes of fresh-cut Romaine lettuce for the first order model and the respective activation energies (Ea) fromthe Arrhenius equation

Attribute Temperature (◦C) Rate constanta, kq (day−1) Coefficient of determination (R2)

General appearance 1.4 0.075± 0.015 0.93704.3 0.123± 0.058 0.75138.9 0.132± 0.047 0.9445

20.3 0.579± 0.183 0.9892

Ea (kJ mol−1) 69.6 0.9585

Wilting 1.4 0.053± 0.021 0.78744.3 0.089± 0.056 0.62778.9 0.105± 0.052 0.7626

20.3 0.367± 0.155 0.8888

Ea (kJ mol−1) 65.1 0.9744

Browning 1.4 0.125± 0.016 0.97144.3 0.140± 0.070 0.72868.9 0.198± 0.072 0.9140

20.3 0.373± 0.222 0.7671

Ea (kJ mol−1) 39.6 0.9962

Off-odour 1.4 0.078± 0.030 0.79204.3 0.131± 0.040 0.87958.9 0.147± 0.075 0.7522

20.3 0.373± 0.204 0.8109

Ea (kJ mol−1) 51.4 0.9632

a kq ± confidence interval at 95%.

more than it would reduce browning development onboth types of packaged fresh-cut lettuce. With respectto the losses of quality due to the development ofoff-odour, the Iceberg lettuce is more sensitive to tem-perature than Romaine lettuce.

In the case of chicory, wilting is the most tem-perature sensitive attribute (Ea = 92.9 kJ mol−1) andQ10 = 4.0), followed by off-odour, browning, and gen-eral appearance.

As it was previously mentioned, the shelf life ofa fresh-cut vegetable was defined as the time of stor-age at which any one of the sensory attributes scored7.5. Once the estimates of the quality rate constantsand their temperature dependency were obtained, thetime to reach the cut-off score could be predicted forany temperature within the experimental range (Fig. 5).When several reactions with differentEa are impor-tant to food quality, it is possible that each of themwill predominantly define quality for a different tem-perature range (Taoukis et al., 1997). For the threefresh-cut vegetables assayed, the limiting quality fac-

tor at any temperature was the visual impact evaluatedby the panellists as general appearance. However, itshould be noted that any of the attributes could be con-sidered the failure attribute between 15 and 20◦C forchicory.

Finally, curves from the general appearance modelscould be used for shelf-life prediction of these fresh-cut vegetables as a function of temperature (Fig. 5). Itcan be seen that fresh-cut chicory has the longest shelflife at low temperature.

3.3. Validation of the fitted models

Three challenge tests were carried out at constanttemperature for each fresh-cut vegetable to validatethe shelf-life prediction models from the kinetics ofgeneral appearance loss. Adequate agreement betweenpredicted values and experimental results was found,validating the models (Table 6).

The possible utilization of these quality loss modelswhen the fresh-cut vegetables are stored under dynamic

210 A.M. Piagentini et al. / Postharvest Biology and Technology 38 (2005) 202–212

Table 4Rate constant (kq) from sensory attributes of fresh-cut chicory for the first order model and the respective activation energies (Ea) from theArrhenius equation

Attribute Temperature (◦C) Rate constanta, kq (day−1) Coefficient of determination (R2)

General appearance 1.6 0.017± 0.009 0.41984.5 0.037± 0.019 0.78798.9 0.156± 0.060 0.9573

20.3 0.219± 0.103 0.9133

Ea (kJ mol−1) 65.7 0.6990

Wilting 1.6 0.004± 0.019 0.01504.5 0.037± 0.025 0.34778.9 0.180± 0.078 0.9119

20.3 0.405± 0.213 0.893

Ea (kJ mol−1) 92.9 0.8298

Browning 1.6 0.036± 0.026 0.32124.5 0.068± 0.045 0.69178.9 0.261± 0.078 0.9555

20.3 0.422± 0.220 0.8948

Ea (kJ mol−1) 69.0 0.7650

Off-odour 1.6 0.080± 0.068 0.57684.5 0.078± 0.055 0.66918.9 0.313± 0.127 0.9216

20.3 0.640± 0.313 0.9066

Ea (kJ mol−1) 81.2 0.8337

a kq ± confidence interval at 95%.

temperature conditions was also evaluated. Succes-sive temperature changes are usual during commercialtransporting and retailing even though a cold distri-bution chain is intended. Two additional experimentalassays with fresh-cut lettuce were done. Panellists eval-uated quality losses at days 3 and 6. Simultaneously,the models were used to predict the attributes of thesamples based on the time–temperature storage pro-files experimentally measured (Table 7). The predictedvalues for browning, wilting, and off-odour showedadequate agreement with the experimental results. Inthe case of general appearance, the predicted values

Table 5Q10 values for each attribute and fresh-cut leafy vegetables over thetemperature range 1–20◦C

Attribute Iceberg lettuce Romaine lettuce Chicory

General appearance 2.9 2.9 2.7Wilting 2.7 2.7 4.0Browning 2.0 1.8 2.8Off-odour 2.6 2.2 3.4

were lower than the experimental ones; therefore, itcould be assumed that the model slightly overestimatesthe general appearance losses when dynamic tempera-ture conditions are used.

Table 6Predicted and experimental shelf life of fresh-cut vegetables

Type of vegetable Temperature(◦C)

Sensory shelf life (days)

Predicted Experimental(range)

Iceberg lettuce 1.7 6.4 84.3 4.8 4–68.9 2.9 2–3

20.3 0.9 0.5–1

Romaine lettuce 1.4 7.2 8–94.3 5.2 2–58.9 3.2 2–5

20.3 1.0 1–2

Chicory 4.5 9.2 9–108.9 5.9 4–6

20.3 2.0 2–3

Initial general appearance quality (Q0) for Iceberg lettuce: 13.3, forRomaine lettuce: 12.9 and for chicory: 12.5.

A.M. Piagentini et al. / Postharvest Biology and Technology 38 (2005) 202–212 211

Table 7Experimental and predicted attributes values for fresh-cut Iceberg lettuce under dynamic temperature conditions

Temperature profile Attribute Day 3 Day 6

Experimentala Predicted Experimentala Predicted

A General appearance 11.6± 0.9 9.1 8.5± 1.5 6.1Browning 2.5± 0.8 3.0 5.5± 1.1 6.0Wilting 2.0 ± 0.9 2.5 3.7± 0.7 3.6Off-odour 2.8± 0.2 3.9 4.2± 0.5 5.6

B General appearance 8.1± 1.8 7.2 6.0± 1.0 4.8Browning 5.7± 1.7 3.9 8.4± 1.9 7.8Wilting 2.7 ± 1.1 3.0 4.1± 1.3 4.4Off-odour 3.5± 1.9 4.6 5.6± 1.9 6.9

A: 2 h at 21.4◦C + 46 h at 2.3◦C + 94 h at 5.5◦C; B: 2 h at 22.0◦C + 46 h at 9.7◦C + 94 h at 5.8◦C.a Mean value± S.D.

Fig. 5. Influence of temperature on the predicted time to reach thelimit of acceptability (7.5) for all sensory attributes.

Fig. 6. Experimental shelf life of mixed vegetables and predictedshelf life of individual fresh-cut vegetables at different temperatures.Bars indicate S.D.

Another possible application of these models is forthe prediction of shelf life of fresh-cut mixed vegetablesfrom the individual vegetable quality change mod-els. The superposition of individual shelf-life curves(Fig. 6) for the three products showed that Iceberg andRomaine lettuce would be the first to fail in generalappearance during storage. The experimental resultswith mixed vegetables validated this conclusion show-ing that these two models provide an adequate tech-nique for evaluating the shelf life of the mixed products.

4. Conclusions

The present work allows prediction of sensorychanges of selected fresh-cut leafy vegetables storedat different temperatures by means of their kinetic

212 A.M. Piagentini et al. / Postharvest Biology and Technology 38 (2005) 202–212

constants and their temperature relationship. Takinginto account that visual attributes and off-odour areimportant components of fresh-cut leafy vegetablequality and that sensory testing is expensive and timeconsuming, these mathematical models will be a use-ful tool to predict quality loss or shelf life under abroad range of storage temperatures (2–20◦C, approxi-mately) often observed in the distribution chain of theseproduct. However, shelf life prediction under dynamictemperature conditions should be taken as a first esti-mate of the likely behaviour of the product.

Acknowledgements

This study was partly supported by CAI + D of Uni-versidad Nacional del Litoral (Santa Fe, Argentina).The authors thank Mr. Miguel Ranieri and Mrs. SilvinaLassa (Monte Vera, Santa Fe, Argentina) for providingthe raw vegetables used in the experiments.

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