Dehumidifier Assisted Drying of a Model Fruit Pulp-Based Gel and Sensory Attributes

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Dehumidifier Assisted Drying of a Model FruitPulp-Based Gel and Sensory AttributesShipra Tiwari, Ramasamy Ravi, and Suvendu Bhattacharya

Abstract: Model fruit pulp-based gels were prepared by varying mango pulp (0% to 50%), sucrose (0% to 20%), andagar (1% to 3%) and according to a response surface experimental design followed by drying at a low temperature of40 ◦C upto 15 h in a tray dryer assisted by a dehumidifier. The moisture content, shrinkage (SHR), and rheologicalparameters (failure strain, failure stress (FS), firmness, and energy for compression) were determined as a function of dryingtime. The composition of gel, particularly the agar content had a prominent effect on the characteristics of the driedgel. Detailed descriptive sensory analysis employing principle component analysis (PCA) biplot indicated two distinctgroups of attributes; the first group comprised initial and final moisture contents, extent of moisture removal (EMR),and shrinkage. The fracture stress and energy formed the second group. The analysis of variance for failure stress showedthat it depended only on the positive linear and quadratic effects of agar (significant at P ≤ 0.01 and 0.05, respectively).The theoretically predicted extent of moisture removal at 95.6% could be achieved when the level of agar was 1.2%; pulpand sucrose levels were also close to their lowest levels of 3.6% and 0.04%, respectively.

Keywords: drying, gel, moisture content, principle component analysis, rheology, shrinkage

Practical Application: Scope exists to develop gel-based fruit analogues wherein an appropriate hydrocolloid can beemployed along with fruit juice/pulp. To provide a reasonable shelf-life of the developed intermediate moisture containingproduct, dehumidifier assisted drying is a pragmatic approach that affects sensory and rheological attributes of the driedfruit analogue.

IntroductionFruit juices and pulps can serve as a base raw material for de-

veloping fruit analogues wherein hydrocolloids has an importantrole to play. Multicomponent gels including binary gels possessseveral technological advantages wherein more than one gellingbiopolymers and co-solutes are incorporated to obtain the de-sirable rheological and sensory attributes (Walstra 2003). A fewstudies have indicated the possibility of developing fabricated gelsusing fruit juices/pulps from orange, mango, pineapple, and so on(Genovese and others 2010; Saha and Bhattacharya 2010). How-ever, selection of appropriate raw materials and suitable conditionfor gelling are mandatory to obtain a gel with desirable sensoryand rheological attributes.

Several aspects of gel have been studied that include gellingcharacteristics of pectin-agar gels (Genovase and others 2010),structural changes including the shrinkage (SHR) of alginategels (Rassis and others 2002), freeze-drying of agar-fruit gels(Nussinovitch and others 2004), migration of sucrose (Warin andothers 1997), and rheological characterization of gel (Labropoulosand others 2002; Saha and Bhattacharya 2010). A fruit gel usuallyshows syneresis and it may continue for a long time making theproduct less attractive in addition to a decline in shelf-life due to

MS 20111049 Submitted 8/31/2011, Accepted 4/11/2012. SuvenduBhattacharya and Shipra Tiwari are with Food Engineering Dept., and RamasamyRavi is with Sensory Science Dept, Central Food Technological Research Inst. (Coun-cil of Scientific and Industrial Research), Mysore 570020, India. Direct inquiries toauthor Bhattacharya (E-mail: [email protected]).

microbial growth and enzyme action. An appropriate solution tothis problem may be the partial drying of the formed gels such thatthe phenomena of syneresis is reduced or eliminated in additionto a decrease in moisture content with simultaneous increase inshelf-life of the resultant partially dried gel product. In this sit-uation, a low temperature of drying is to be maintained as hightemperature usually melts the most of the thermo-reversible gelsleading to a damage of the integrity, size and shape. The gel set-ting/melting temperatures for common hydrocolloids are low. Forexample, these temperatures for agar gels are 32 to 40 ◦C and85 to 90 ◦C, respectively (Nussinovitch 1997).

Drying of food gels is a subject of limited studies (Drouzasand others 1999; Rassis and others 2002; Nussinovitch and others2004); the latter two studies deal with microwave and/or vacuumdrying. However, the present authors have not come across anystudy that deals with drying of mango pulp-based fabricated gels.Low-temperature drying in the temperature range 10 to 50 ◦Cis thus favorable to limit product deterioration such as brown-ing, shrinkage, and loss of heat sensitive nutrients. This process isexpensive, requires a long operational time, and is complex if ap-plied under vacuum and/or refrigeration conditions (Djaeni andothers 2009). Changing the moisture content of the drying air isan attractive option by using a desiccant like zeolites (Djaeni andothers 2007). The use of a dehumidifier is possibly a good optionto reduce the time of drying. However, investigations on dryingin combination with a dehumidifier are scarce.

The objectives of the present study are to determine (1) thesensory and rheological attributes due to dehumidifier assisteddrying of a model food gel system that has been developed by

C© 2012 Institute of Food Technologists R©doi: 10.1111/j.1750-3841.2012.02760.x Vol. 00, Nr. 0, 2012 � Journal of Food Science S1Further reproduction without permission is prohibited

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Attributes of dried gels . . .

varying the contents of gel forming ingredients like mango pulp,agar, and sucrose, and (2) the interrelationships among the responsefunctions.

Materials and Methods

MaterialsAgar powder (Loba Chemie, Mumbai, India), sucrose (procured

from a local supermarket), and canned sweetened mango pulp(Allahabad Canning Co., Allahabad, India) having soluble solidcontent of 26o Brix were used for the present study. As per themanufacturer, agar powder contains about 1% acid insoluble ash,5% sulphated ash, and 1% foreign insoluble matter.

MethodsProximate composition. Mango pulp samples were char-

acterized for their proximate composition including moisturecontent, total ash, crude fat, crude fiber, protein (N X 6.25),and carbohydrate content (by difference) by following the AOACmethods (2005) on triplicate samples. Initial moisture (IM) con-tent was determined as per the AOAC (2005); the method #925.49 was used to determine the moisture content, a methodsuggested for confectionary products (vacuum drying method).The samples were dried for 2 h at about 60 ◦C) under a pres-sure less than 50 mm of mercury. During the subsequent stagesof drying, moisture content was calculated by knowing the initialmoisture content and the change in the mass of sample due to dry-ing. The reported results are the mean of triplicate measurements.The major sugars like sucrose, fructose, and glucose in the samplewere determined by following the chemical method mentionedby Ranganna (1987).

Experimental design and statistical analysis. A centralcomposite rotatable design (CCRD) of experiments was employedto determine the effect of 3 independent variables of gel-formingingredients such as the concentrations of agar (X1), mango pulp(X2), and sucrose (X3) following the response surface method-ology (RSM) (Myers 1971; Khuri and Cornell 1989). The re-sponse functions were the fracture strain, fracture stress, energy forpenetration-shearing, and firmness of the formed gel after 16 h ofdrying. The independent variables had 5 levels each (−1.682, −1,0, 1, and 1.682) in coded levels of variables. The experimentaldesign in the actual (X) and coded (x) levels of variables is shownin Table 1. The response functions (Yijk) were approximated bya second-degree polynomial (Eq. 1) with linear, quadratic, andinteraction effects.

yijk = b0 +n∑

i=1b i xi +

n∑

i=1

n∑

j = 1i ≤ j

b i j xi x j + ∈i j k . . . (1)

The number of variables was denoted by n and j, while k andi were the integers. The coefficients of the polynomials wererepresented by bo, bi, and bij, and ∈ijk was the random error;when i < j, bij represented the interaction effects of the variablesxi and xj. The response surface graphs were obtained from theregression equations. The detailed analysis of variance (ANOVA)was conducted (in coded level of variables) to know the effects ofindividual variables; the significance of F values was determinedat P ≤ 0.10, 0.05, and 0.01.

Gel preparation. Requisite amount of agar powder, after dis-persing in distilled water for 1 h at room temperature (about25 ◦C), was heated in a water bath maintained at 90 ◦C for

1 h. Sucrose and mango pulp were then added to agar disper-sions as per the experimental design (Table 1). The hot disper-sions were then poured into petri plates (10 mm in height and47 mm in diameter) and allowed to set at room temperature for16 h after which they were manually removed for further testing(Banerjee and Bhattacharya 2011). The process of gel preparationwas repeated twice.

Drying of gel. Gels prepared with different proportions ofagar, pulp, and sucrose (Table 1) were dried at a low temperature of40 ◦C upto 16 h in a tray dryer, assisted by a dehumidifier (Model# FFB 170, Bry Air, Mumbai, India). The RH of dehumidified(drying) air was 12% to 14%, air velocity above the samples wasabout 10 cm/s, sample loading rate was 0.5 kg per batch, and thegel samples were distributed in 3 horizontal trays. Samples werewithdrawn at regular intervals to determine their masses in orderto calculate the moisture content during drying. The extent ofmoisture removal (EMR) was calculated as the difference betweenthe contents (dry basis) of initial and final moistures divided by theinitial moisture content (dry basis), and expressed as per cent basis.Shrinkage and rheological measurements were also performed ongels at different intervals on triplicate samples.

Shrinkage of gels was expressed in terms of the percentagechange in the volume of dried gels compared to its initial volumeby using Eq. (2).

Shrinkage (%) = (Vo − V)Vo

× 100 . . . (2)

where, V o and V are the initial and the final volumes of gel at theend of 16 h. Volume of samples were calculated by measuring thediameter and height of 5 samples.

Rheological measurement. A cylindrical ebonite probe(10 mm in diameter and 50 mm in height) was allowed to passthrough the gel sample, that is, penetration-shearing. The gelsample was placed on a sample holder having a hollow portion of25 mm in diameter. A texture measuring system (Model TAHD,Stable Microsystems, Surrey, U.K.) was employed; a load cell of50 N and the crosshead speed of 1 mm/s was employed to attain

Table 1–Design of experiments in coded (x) and actual level (X)of variables.

Actual level ofvariablesCoded level of variables

Experiment Agar Pulp Sugarno. Agar Pulp Sugar %) (%) (%)

1 0 0 0 2 25 102 0 0 0 2 25 103 0 0 0 2 25 104 0 −1.682 0 2 0 105 1.682 0 0 3 25 106 −1.682 0 0 1 25 107 0 1.682 0 2 50 108 0 0 0 2 25 109 0 0 −1.682 2 25 010 −1 −1 1 1.41 10.14 15.9511 1 1 −1 2.60 39.86 4.0612 −1 1 −1 1.41 39.86 4.0613 0 0 1.682 2.00 25.00 20.0014 −1 −1 −1 1.41 10.14 4.0615 1 1 1 2.60 39.86 15.9516 0 0 0 2.00 25.00 10.0017 1 −1 1 2.60 10.14 15.9518 −1 1 1 1.41 39.86 15.9519 1 −1 −1 2.60 10.14 4.0620 0 0 0 2.00 25.00 10.00

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complete shearing-penetration of gel samples. The rheological pa-rameters determined from the force-deformation curves were thefailure strain, failure stress (FS), energy for penetration-shearing,and firmness. The failure stress is the ratio of the maximum forceoffered by the sample at the time of failure and the cross-sectionalarea of the probe. Firmness was obtained from the slope of theinitial linear portion of the force-deformation curve (Szczesniak1983). Failure strain, as evidenced by a significant decrease in forcedue to failure (complete penetration) of sample, was calculated asthe distance the probe that had moved at the time of failure di-vided by the initial height of sample (Ravi and others 2007). Thearea under the force-deformation curve till failure was taken as theenergy required for penetration-shearing. Ten samples were testedeach time for all rheological measurements.

Sensory analysis. Quantitative descriptive analysis (QDA) wasused to measure the sensory profile of the gel samples that wereearlier prepared according to the experimental design. The tex-ture profile method (Skinner 1988) was adapted to follow thegeneric consensus method as described by Lawless and Heymann(1998). A total of 14 trained panelists (both male and female) be-tween the ages of 25 and 58 y having experience of evaluatinggel samples were selected. For panelist selection, a screening testwas performed prior to the descriptive analysis. Those panelistswho scored perfect scores in taste sensitivity test and able to iden-tify 5 of 7 commonly found food flavors (Meilgaard and others1991) were selected. The other criteria for panel selection were theexperience and performance in sensory analysis, interest to par-ticipate, and availability for all sessions, and the ability to commu-nicate verbally the observations regarding the product perceptions(Plemmons and Resurreccion 1998). Panelists orientation, train-ing, and calibration consisted of 4 training sessions during 4 d, witheach training session lasted for 2.5 h. Quantitative descriptive anal-ysis (Tragon Corporation, Redwood City, Calif., U.S.A.) methodwas adapted for training and evaluation sessions as described byGrosso and Resurreccion (2002). A 150 mm unstructured linescale was used for evaluation (Stone and Sidel 1985). A list of defi-nitions and a sheet with reference intensity ratings were developedduring the training sections (Grosso and Resurreccion 2002). Thedefinition of the attributes and gel lexicon were developed basedon the earlier guidelines (Civille and Lyon 1996; Gwartney andothers 2004). All samples were evaluated in partitioned boothsunder fluorescent light at room temperature (22 ◦C) as per theASTM standards. Ten gram of the product samples were placedinto porcelain cups with lids coded with 3-digit random numbers,and the samples were served to the panelists using a randomizedcomplete block design. Panelists evaluated 4 samples per sessionand the warm-up sample per day. Every evaluation day was di-vided in 2 sessions of 2.5 h each (morning 2.5 h and afternoon 2.5h). Before beginning the evaluation of the samples, the panelistsretested all reference samples. The reference intensity ratings anddefinitions were posted in the booths for all test sessions. The scoreswere recorded on a paper-based score card and the generated datawere subjected to further analysis.

In session one, panelists were asked to develop a list of texturalattributes to describe the gel samples that were representative ofthe experimental set. During second session, panelists were askedto work as a group to generate and define textural attributes todescribe the complete texture profile of the gel samples. In total,panelists developed a list of 24 textural attributes (Table A1). Inthe third and final training session, panelists were familiarized withthe usage of line scale and practiced using the attributes developedearlier.

A length of 15 cm was used to record the sensory perceptions.Any perception that was low was likely to be close to zero whilehigh intensity attained the maximum of the scale. Panelists wereasked to rinse their mouth with water in between testing twosamples.

Optimization. Optimization was conducted by employingcanonical analysis (Khuri and Cornell 1989) wherein the codedlevels of the variables (x1, x2, x3, and x4), within the experimen-tal range, were determined to obtain the maximum values of theresponse functions. The roots (λ1, λ2, λ3, and λ4) of the auxiliaryequation (λ2– λ + 1 = 0) were calculated initially to know thenature of optimum. A situation of a saddle point occurred whenthe roots have positive and negative values.

The occurrence of saddle point was observed in the presentstudy and hence optimization was conducted by employing canon-ical analysis. This optimization method consisted of the translationof the response function (yk) from the origin to the stationarypoints (Myers 1971). Then the response function was expressed interms of the new variables, the axes of which corresponded to theprincipal axes of the contour system. Optimization was conductedon coded level of variables and the results were expressed in termsof coded as well as actual level of variables.

In addition, simultaneous optimization technique was also used.Conventionally, either graphical methods or numerical methodsare employed to locate the optimum condition in a multiresponsesystem. In graphical method, response surface or contour plotsare generated, and superimposed to locate regions(s) where all re-sponses are simultaneously optimized or the specific criteria aremet (Floros and Chinnan 1988). Often, a number of trial anderror approaches is required to determine which factors to keepconstant and what levels are to be selected to obtain the bestview of the surface in order to locate the optimum condition(Myers and Montgomery 2002). On the other hand, numericaloptimization techniques help in arriving specific goal even withseveral constraints are imposed on the system. Derringer and Suich(1980) reported the modified desirability function analysis to solvemultiresponse system linking the overall desirability (D) and theindividual desirability (d ). A high value of D (maximum possi-ble value is 1) indicates the more desirable and best functions ofthe system, which is considered as the optimal solutions of theparticular system. In the present situation, the optimum values ofthe factors were determined from the values of individual desiredfunctions that maximized overall desirability values.

Principal component analysis. The methodology of prin-cipal component analysis (PCA) was employed (Lawless andHeymann 1998) for analysis of response functions using the sta-tistical software Statistica ‘99 (StatSoft, Tulsa, Okla., U.S.A.). Themain purpose of application of PCA is to explore the relationamong the variables and response functions and to classify thembased on rheological attributes and moisture content.

Results

Product attributesThe pH of sweetened mango pulp is 4.1, and its proximate is:

moisture 81.2%, protein (NX6.25) 0.6%, fat 0.4%, ash 0.5%, andcarbohydrate (by difference): 17.3%. Further, the sample contains6.9% sucrose, 2.0% glucose, and 2.2% fructose. The pectin contentof pulp, as reported as calcium pectate, is 0.5%.

The sample compression curves for gels (before and after dry-ing) are shown in Figure 1. The gels prior to drying show lowerresistive forces when subjected to compression; they also fail ear-lier during penetration-shearing compared to the corresponding

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dried samples. The latter samples do not shear easily as it sticksto the penetration probe. This trend is true for gels made withand without mango pulp (Figure 1). The general features of thesecurves are that initially the sample offers a good resistance nearlyin a linear manner to display firmness/toughness. This portion isfollowed by a nonlinear portion wherein the sample undergoescomplete shearing and force decreases abruptly.

The response functions studied in the present study can begrouped into 3 categories such as the indices related to moisturecontent (initial and final moisture contents, and the extent ofmoisture removal), size characteristics during drying (shrinkage),and the rheological attributes (failure stress and failure energy)of the resultant dried gels (Table 2). All these response functionscan be related to the 3 independent variables (contents of agar,mango pulp, and sucrose) by second order polynomials (0.915 ≤R ≤ 0.998, P ≤ 0.01) containing linear, quadratic, and interactionterms.

Moisture contentThe initial moisture content of the samples are between 269%

and 1209% (dry basis), and depends on the proportions of agar,pulp, and sucrose contents. Their linear effects are significant atP ≤ 0.01 and possess negative effects as is also reflected by theresponse surface plot (Figure 2). The quadratic effects of pulp andsucrose, and their interaction effect have positive effects (significantat P ≤ 0.01) meaning that a curvilinear effect is expected for pulpand sucrose content.

The final moisture (FM) content is primarily affected by thenegative linear effect of sucrose while its quadratic effect is pos-itive (significant at P ≤ 0.01 and 0.05, respectively). This meansthat an increase in sucrose content decreases the final moisturecontent while a very high level of sucrose increases the final mois-ture content (Figure 2). It is possible that sucrose binds with waterwhen sucrose is at its low level. On the other hand, a too highlevel of sucrose may have a detrimental effect on gel formation toyield a weak structure. In addition, the mango pulp has a nega-tive linear effect while its quadratic effect is positive (significant atP ≤ 0.05 and 0.10, respectively) indicating a curvilinear relation-ship (Figure 2).

The extent of moisture removal is between 66% and 91%, anddepends mainly on the linear effect of sucrose followed by mangopulp (significant at P ≤ 0.01). An increase in pulp and/or sucrosecontent decreases the extent of moisture removal (Figure 2). Thecurvilinear effect of pulp is reflected by its marginal quadratic effectas it is significant at P ≤ 0.05; similarly, agar possesses a marginalnegative linear effect.

At the end of 16 h of drying, the extent of volume shrinkagevaries between 55.7% and 87.3% (Table 2). Among the lineareffects, sucrose imparts the maximum effect followed by pulp and

agar contents; all these variables offer a negative effect (Table 3).However, their quadratic effects are also significant indicating acomplex trend (Figure 3). Interestingly, the interaction term ofpulp and sucrose is also significant at P ≤ 0.01 meaning that theeffect of mango pulp on shrinkage depends on the level of sucroseused. It is worth mentioning that mango pulp also contains a goodquantity of sucrose (about 7%) that can bind adequate amount ofwater to reduce shrinkage.

Rheological characteristicsThe failure stress and failure energy (FE) are the two rheological

indices that have been determined for the developed gels. Thefailure stress indicates the maximum possible stress that the samplecan offer or withstand prior to complete penetration-shearing.The wide variations in failure stresses between 24.5 and 150.9 kPa(Table 2) mean that the dried gels, based on preliminary sensoryresults, can be categorized into very soft, moderately soft, andsoft products. The analysis of variance for failure stress (Table 3)shows that it depends only on the positive linear and quadraticeffects of agar (significant at P ≤ 0.01 and 0.05, respectively)(Figure 3). On the other hand, failure energy is an indication ofenergy required for complete penetration-shearing of products;it varies between 5.3 and 58.0 mJ (Figure 3). Like failure stress,failure energy also depends only on the agar content as its linearas well as the quadratic effects is significant.

Table 2–Results of experiments based on experimental design.

Extent ofInitial Final moisture Failure Failure

Experiment moisture moisture removal Shrinkage stress energyno (%, db) (%, db) (%) (%) (kPa) (mJ)

1 475.7 97.0 79.6 73.5 44.5 16.32 501.5 102.7 79.5 72.6 59.7 21.23 475.7 107.1 77.5 73.3 43.8 15.94 809.1 114.2 85.9 82.2 70.0 26.15 444.2 103.0 76.8 67.5 150.9 58.06 510.7 96.9 81.0 71.6 31.3 6.67 321.1 110.0 65.7 67.2 65.9 22.88 475.5 97.1 80.2 72.4 45.8 16.69 1094.0 141.9 87.0 84.3 82.1 26.910 445.3 117.3 73.7 66.4 24.5 5.311 509.4 109.5 78.5 74.7 100.8 38.712 557.0 111.6 80.0 74.4 45.6 16.313 279.2 90.7 67.5 55.7 75.6 19.614 1208.9 115.0 90.5 87.3 39.3 15.815 268.9 91.4 66.0 58.8 68.9 22.516 482.1 100.4 79.0 73.6 47.9 17.117 412.0 94.1 77.2 65.5 107.3 42.018 285.8 92.3 67.7 59.3 30.9 9.719 1032.5 135.0 86.9 86.5 73.2 19.820 491.0 98.1 79.4 73.0 43.3 16.4

Experimental conditions are shown in Table 1.

Figure 1–Sample penetration-shearing curves forgels made with (A) 2% agar, 0% pulp, and 10%sugar (experiment # 4, Table 1), and (B) 1.4%agar, 39.9% pulp, and 4.1% sugar (experiment# 12, Table 1). Top curves denote 16 h dried gelswhile bottom curves are for same samples beforedrying.


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Optimization. The λ values, roots of the auxiliary equations(Section sensory analysis) concerning the optimization of responsefunctions, indicate the nature of optimization (Table 4). Theresponse functions, with the exception of failure stress, offer asituation of saddle point because the roots of λ are a mixture ofpositive and negative values. The occurrence of all three positiveλ values for failure stress indicates it to be a situation of minimiza-tion. The situation of saddle point means that the true optimumcondition (minimization/maximization) is not present in the zoneof experimentation. Hence, the method of canonical analysis hasbeen applied to find the status of minimization/maximization in-side the experimental range of independent variables. The opti-mum levels of independent variables in coded and actual levels areshown in Table 4. As the drying process is primarily associatedwith the removal of moisture, the desirable condition is to attainits maximum level. The theoretically predicted extent of moistureremoval at 95.6% can be achieved when the level of agar is closeto its lowest level, that is, at 1.2%; pulp and sucrose levels are alsoclose to their lowest levels, that is, at 3.6% and 0.04%, respectively.This means that a low concentration of agar in absence of addedsucrose and/or pulp undergoes the highest extent of moisture re-moval. On the contrary, the lowest extent of moisture removal of57.8% is achieved when these independent variables are close totheir higher levels (3.0%, 49.9%, and 17.3%, respectively). It in-dicates that solids present in pulp and sucrose binds water capably

such that the removal of moisture becomes difficult. The responsefunctions related to moisture contents and moisture removal bearsome similarities. However, the 6 response functions do not yielda common situation wherein general minimization/maximizationis possible. It is thus inferred that the extent of moisture removal,shrinkage, and rheological status of products are all interrelatedthough may not follow the same trend. The other inference is thata wide variation in the extent of moisture removal, shrinkage, andrheological attributes indicate the possibility of obtaining prod-ucts having different characteristics by varying the contents of gelforming ingredients such as agar, mango pulp, and sucrose.

Interrelationships. The interrelationships among the 6 re-sponse functions are presented as the lower-half correlation matrix(Figure 4). As expected, a very high correlation coefficient (r) of≥ 0.97 has been observed (significant at P ≤ 0.01) between initialand final moisture contents, and between failure stress and energy.Interestingly, a low r value of 0.59 (significant only at P ≤ 0.05)exists between the extent of moisture removal and final mois-ture content. Shrinkage in volume is highly correlated (r ≥ 0.93)with the extent of moisture removal and initial moisture contentindicating a strong interdependence. On the other hand, the rhe-ological indices such as failure stress and failure energy do notpossess any significant correlation with other response functions.It means that the rheological status of the dried products hardlydepends on the moisture-related indices as well as shrinkage.

Figure 2–Initial moisture (A, B), final moisturecontent (C, D), and extent of moisture removal (E,F) as a function of independent variables.


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Tab

le3–

Anal

ysis

of

vari

ance

(AN

OV

A)fo

rre

sponse

funct

ions

inco

ded

leve

lof

vari

able

s.

Initia

lm

ois

ture

(%)

Fin

alm

ois

ture

(%)

Ext

ent

ofm

ois

ture

rem

oval

(%)

Shri

nka

ge(%

)Fai

lure

stre

ss(k

Pa)

Fai

lure

ener

gy(m

J)

Coef

fici

ents

Coef

fici

ents

Coef

fici

ents

Coef

fici

ents

Coef

fici

ents

Coef

fici

ents

Sourc

eof

of

of

of

of

of

of

vari

atio

npoly

nom

ial

Fva

lue

poly

nom

ial

Fva

lue

poly

nom

ial

Fva

lue

poly

nom

ial

Fva

lue

poly

nom

ial

Fva

lue

poly

nom

ial

Fva

lue

Con

stan

t48

3.35

–10

0.44

–79

.20

–73

.06

–48

.13

–17

.45

–Li

near

effe

ctA

gar

−28.

2613

.07∗

∗∗0.

290.

02N

S−0

.75

3.47

∗−0

.64

8.74

∗∗30

.10

53.8

0∗∗∗

11.9

040

.55∗

∗∗Pu

lp−1

68.2

946

3.48

∗∗∗

−4.6

65.

12∗∗

−5.1

316

0.32

∗∗∗

−4.6

746

6.54

∗∗∗

−0.3

60.

01N

S−0

.10

0.00

NS

Suga

r−2

39.1

693

6.06

∗∗∗

−11.

8633

.21∗

∗∗−6

.17

232.

02∗∗

∗−8

.85

1678

.53∗

∗∗−2

.80

0.46

NS

−1.7

10.

84N

S

Qua

drat

icef

fect

Aga

r2−0

.49

0.00

NS

−0.5

20.

07N

S−0

.03

0.01

NS

−1.2

032

.42∗

∗∗11

.22

7.89

∗∗4.

064.

98∗

Pulp

230

.48

16.0

5∗∗∗

3.79

3.57

∗−1

.13

8.29

∗∗0.

659.

46∗∗

3.03

0.58

NS

1.28

0.50

NS

Suga

r273

.44

93.1

6∗∗∗

5.26

6.89

∗∗−0

.61

2.43

NS

−1.0

223

.72∗

∗∗6.

902.

98N

S0.

870.

23N

S

Inte

ract

ion

effe

ctA

gar×

pulp

18.1

33.

15N

S0.

030.

00N

S−0

.39

0.53

NS

0.17

0.36

NS

−2.9

40.

30N

S−0

.68

0.08

NS

Aga

r×

suga

r21

.74

4.53

∗−5

.24

3.80

∗0.

852.

61N

S−0

.09

0.11

NS

3.97

0.55

NS

2.90

1.41

NS

Pulp

×su

gar

109.

0511

3.99

∗∗∗

0.16

0.00

NS

0.23

0.18

NS

1.35

22.9

7∗∗∗

−8.2

42.

36N

S−4

.32

3.13

NS

Mul

tiple

corr

elat

ion

coef

ficie

nt(R

)0.

997∗

∗∗0.

916∗

∗∗0.

988∗

∗∗0.

998∗

∗∗0.

933∗

∗∗0.

915∗

∗∗

Vari

able

s:x1

=ag

ar;x

2=

man

gopu

lp;x

3=

suga

r.∗ S

igni

fican

tat

P≤

0.10

.∗∗ S

igni

fican

tat

P≤

0.05

.∗∗∗

Sign

ifica

ntat

P≤

0.01

.NS:

Non

signi

fican

tat

P=

0.10

.

The descriptive sensory scores (Table A1 and A2) for differentsensory attributes were normalized and subjected into principalcomponent (PC) analysis for exploring underlying relationshipsbetween sensory descriptors and instrumental values. It also helpsto reduce the difficulty of discussing the each/individual scores(which may also be erroneous and can mislead the conclusions).The PCA plot among the major sensory attributes is shown inFigure 5. This plot accounts for a total of 80% of the variationby the principal components PC1 and PC2. The sensory attributelike shrinkage appears to behave differently from rest of the sensoryattributes. Cohesiveness, leathery mouth feel, and chewiness showtheir good interrelations due to their presence in the first quadrant.

The PCA biplots (Figure 6) also help to visualize multidimen-sional parameters into a few axis, which also enhances the un-derstanding of variables and their interactions simultaneously. Theprincipal component 1 (PC1), plotted on the x-axis, accountsfor the 57.2% of the variation in data while PC2, placed on they-axis, can explain only 33.0% of the total variation; hence, a totalof 90.2% total variation can be accomplished by PC1 and 2. Thedifferent experimental points (1 to 20, Table 1) have been placedin the PCA plot (Figure 5) along with the 7 response functions(Table 2). The response functions like initial moisture content,final moisture content, shrinkage, and the extent of moisture re-moval apparently form one cluster. However, fracture stress andenergy for fracture form the other cluster; this latter group be-haves in a different manner than the earlier group as they are wideaway. The 1st cluster is located in the positive side of PC1. Frac-ture stress (FS) and fracture energy (FE) are closely associated witheach other and are located on the negative side of the PC2 axis.Experiment #12 and 14, placed in the 1st quadrant, are associatedpositively with initial moisture content, final moisture content,extent of moisture removal, and shrinkage as they are located inthe positive side of PC1 axis; this situation arises when the agar islow like 1.4% such that less water binding by hydrocolloids causesincreased loss of moisture due to drying (Table A3). On the otherhand, experiment #11 possesses a high value of fracture stress andenergy; this situation occurs when the agar and pulp contents arehigh (2.6% and 39.9%, respectively). Hence, the application ofPCA can categorize the dried gels in terms of textural attributesand moisture content related parameters.

Discussion

Moisture removal and shrinkageFracture stress/energy shows that these 2 rheological attributes

increases with an increase in agar content; the effect of the pulpis nonsignificant. However, Genovase and others (2010) haveindicated that increasing concentrations of microsized fruit pulpreinforces rigidity of fruit jams obtained as pectin-agar gels.Nussinovitch and others (2004) have reported that the fractalindex of freeze-dried agar-fruit gels is sensitive to the pulpcontent of the product. For the present study, pulp content has amarked effect on the extent of moisture removal and shrinkage.Rassis and others (2002) have observed that immersion of gel insucrose solution decreases shrinkage during the subsequent dryingprocess. In our present study, we have observed that the presenceof sucrose reduces shrinkage as its linear effect is negative.

During the drying process, moisture escapes from the outer sur-face and increases the concentration of sucrose at the boundaries.As sucrose can bind moisture effectively, it can resist shrinkage.Hence, less shrinkage is expected for gels containing high sucroseconcentrations. The effect of sweetened mango pulp is similar to


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that of sucrose as the sample contains about 7% sucrose. The crys-tallization of sugar at the outer surface of sample possibly reducesshrinkage and offers a better mechanical strength.

The process of drying is affected by the moisture binding ca-pacities of food constituents particularly the different sugars andorganic acids present in ripe fruits. These sugars possess low glasstransition temperatures and are hygroscopic in their amorphous

state (Jaya and Das 2009). Water acts as a plasticizer and decreasesthe glass transition temperature of the product with an increase inmoisture content. The water binding effect of 2 or more types ofsugars (sucrose, glucose, and fructose) in mango pulp thus affectsthe drying phenomenon, rheological behavior, and the sensoryattributes. Hence, the drying rate decreases with increased level ofsugar addition.

Figure 3–Shrinkage (A, B), failure stress (C, D), andfailure energy (E, F) as a function of independentvariables.

Table 4–Results of the optimization study in coded and actual levels of variables.

Initial Final Extent of moisture Failure FailureParameters moisture (%db) moisture (%db) removal (%) Shrinkage (%) stress (kPa) energy (mJ)

Roots of the auxiliaryequation

λ1 112.319 6.270 0.205 0.888 12.976 5.022

λ2 −1.855 3.784 −0.749 −1.152 7.762 2.424λ3 −7.031 −1.532 −1.235 −1.310 0.410 −1.239

Nature of optimum Saddle point Saddle point Saddle point Saddle point Minimization Saddle pointOptimum conditions in x1 Minimization −1.680 1.678 1.678 −0.606 −1.422 −1.680

coded level of variables Maximization 1.647 −0.754 −1.372 0.917 1.678 1.679x2 Minimization 1.481 0.587 1.672 1.523 1.066 −0.274

Maximization −1.014 −1.679 −1.440 −1.225 −1.604 −0.935x3 Minimization 0.831 1.596 1.235 1.679 1.248 0.836

Maximization −1.623 −1.677 −1.676 1.681 −0.246 −0.509Optimum conditions in X1 (%) Minimization 1.004 3.000 3.000 1.640 1.154 1.000

actual level of variables Maximization 2.980 1.551 1.184 2.546 3.000 3.000X2 (%) Minimization 47.012 33.725 49.851 47.636 40.844 20.928

Maximization 9.929 0.045 3.597 6.793 1.160 11.103X3 (%) Minimization 14.940 19.488 17.342 19.982 17.420 14.970

Maximization 0.351 0.030 0.036 19.994 8.538 6.974Optimum conditions of Minimization 257.866 78.632 58.712 53.056 24.797 4.817

response function Maximization 1309.789 146.928 95.552 92.869 142.730 47.696

Variables: X1/x1 = agar; X2/x2 = mango pulp; and X3/x3 = sugar.


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Rheological characteristicsRassis and others (2002) have observed that increased filler con-

centration made with cellular solids such as corn starch generallyincreases the Young’s modulus and the initial slope; the latter termmay be denoted as firmness. However, the present study dealingwith agar-mango pulp-sucrose gels shows that the effect of su-

crose on rheological properties is marginal; fracture stress/energyincreases markedly with an increase in agar content. Genovase andothers (2010) have observed that an increase in the concentrationof small particles (<125 μm) produces a signifiacnt increase in theelastic modulus of the composite gel, made with pectin and appleparticles. In the present study, the sucrose and solids present in

Figure 5–PCA plot among the sensory attributes.

Figure 6–Principal component analysis plotamong the response functions. Numbers inside theplot indicate the experiment number(corresponding to Table 1).


S:Se

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mango pulp act as fillers into the cavities of agar gel matrix. How-ever, all these 3 components (agar, sucrose, mango pulp) competeeach other for water. Adding filler either creates a new matrixor the true matrices may co-exist. Further, there is a chance thatthe agar gel matrix is incomplete because of high content of su-crose and/or mango pulp. The addition of fruit particles into thegels increases the dry matter content of the dried product. Inter-estingly, the positive linear effect of agar on fracture stress/strainindicates that it is the agar that forms the gel matrix while solu-ble solids from sucrose/pulp just act as fillers, and increases these2 rheological parameters.

DryingThe drying capacity of the air in conventional convective dryers

depends on the temperature and the moisture content of air. Op-tions to increase the drying capacity are (1) increasing temperatureat constant absolute moisture content, (2) lowering the absolutemoisture content at a given temperature, or (3) a combination ofboth. Because of heat sensitivity of many food products, increas-ing the temperature is not a good solution to improve efficiency.Changing the water content of the air is an attractive option.Zeolites have a high potential for water adsorption and can beapplied in dryers to improve the water uptake capacity of air atlow temperatures. (Djaeni and others 2007). Drouzas and others(1999) have observed that the drying rate of sugar containing foodmaterials is reduced in the last stages of drying due to the collapseof the porous structure created earlier in the drying process; thisphenomenon is also true for the present study.

ConclusionsA wide variation in the extent of moisture removal, shrinkage,

failure stress, and failure energy has been obtained on the modelgel that indicates that the variation in the gel forming ingredientslike mango pulp, sucrose, and agar can lead to products with widelyvarying characteristics. The final moisture content and the extentof moisture removal are markedly affected by the levels of sucroseand mango pulp. The rheological attributes such as failure stressand failure energy mostly depend on the linear positive effect ofagar; these two rheological indices do not have any significantrelationships with shrinkage or the extent of moisture removalas they appear in separate zones in the PCA biplot. The sensoryshrinkage appears to behave differently from rest of the sensoryattributes. However, sensory cohesiveness, leathery mouth feel,and chewiness show their good interrelations due to their presencein the 1st quadrant. The response functions can be related tothe independent variables by second-order polynomials (0.915 ≤r ≤ 0.997). The minimization and maximization of these responsefunctions have been conducted. Dehumidifier assisted drying is asuitable option to develop fruit-based dried gels in which a lowtemperature of drying like 40 ◦C can be employed.

AcknowledgmentThe work has been funded under the Network Project

(SIP 002) of Council of Scientific and Industrial Research (CSIR),New Delhi, India.

NomenclatureIM initial moistureFM final moistureEMR extent of moisture removalSHR shrinkageFS failure stressFE failure energy

ReferencesAOAC Intl. 2005. Official methods of analysis. In: Horwitz W, editor. Official methods of

analysis of Association of Official Analytical Chemists. 18th ed. Gaithersburg, Md.: AOACIntl. chap. 44, p. 24.

Banerjee S, Bhattacharya S. 2011. Compressive textural attributes, opacity and syneresis of gelsprepared from gellan, agar and their mixtures. J Food Eng 102:287–92.

Civille GV, Lyon B. 1996. ASTM lexicon vocabulary for descriptive analysis. Philadelphia:American Society for Testing and Materials.

Derringer G, Suich R. 1980. Simultaneous optimization of several response variables. J QualityTechnol 12:214–9.

Djaeni M, Bartels P, Sanders J, van Straten G, van Boxtel AJB. 2007. Process integration forfood drying with air dehumidified by zeolites. Drying Technol 25:225–39.

Djaeni M, van Straten G, Bartels PV, Sanders JPM, van Boxtel AJB. 2009. Energy effi-ciency of multi-stage adsorption drying for low-temperature drying. Drying Technol 27:555–64.

Drouzas AE, Tsami E, Saravacos GD. 1999. Microwave/vacuum drying of model fruit gels.J Food Eng 39:117–22.

Floros JD, Chinnan MS. 1988. Computer graphics assisted optimization for product and processdevelopment. Food Technol 42:72–84.

Genovase DB, Aiqain Ye, Singh H. 2010. High methoxy pectin/apple particles composite gels:effect of particle size and particle concentration on mechanical properties and gel structure.J Text Studies 41:171–89.

Grosso NR, Resurreccion AVA. 2002. Predicting consumer acceptance ratings of cracker-coatedand roasted peanuts from descriptive analysis and hexanal measurements. J Food Sci 67:1530–7.

Gwartney EA, Larick DK, Foegeding EA. 2004. Sensory texture and mechanical properties ofstranded and particulate whey protein emulsion gels. J Food Sci 69:S333–9.

Jaya S, Das H. 2009. Glass transition and sticky point temperatures and stability/mobility diagramof fruit powders. Food Bioprocess Technol 2:89–95.

Khuri AI, Cornell JA. 1989. Response surfaces: designs and analyses. New York: Marcel Dekker.p. 19–69.

Labropoulos KC, Niesz DE, Danforth SC, Kevrekidis PG. 2002. Dynamic rheology of agargels: theory and experiments. Part I. Development of a rheological model. Carbo Poly 50:393–406.

Lawless HT, Heymann H. 1998. Sensory evaluation of food: principles and practices. New York:Chapman & Hall. p. 606–8.

Meilgaard M, Civille GV, Carr BT. 1991. Sensory evaluation techniques. 2nd ed. Boca Raton,FL.: CRC Press. p. 135–83.

Myers RH, Montgomery DC. 2002. Response surface methodology. New York: Wiley.p. 1–57.

Myers RH. 1971. Response surface methodology. Boston: Allyn and Bacon. p. 61–106.

Nussinovitch A, Jaffe N, Gillilov M. 2004. Fractal pore-size distribution on freeze-dried agar-texturized fruit surfaces. Food Hydrocolloids 18:825–35.

Nussinovitch A. 1997. Agar. Hydrocolloid applications: gum technology in the food and otherindustries. 1st ed. London: Blackie Academic and Professional. p. 1–16.

Plemmons LE, Resurreccion AVA. 1998. A warm-up sample improves reliability of responses indescriptive analysis. J Sen Studies 13:359–76.

Rassis DK, Saguy IS, Nussinovitch A. 2002. Collapse, shrinkage and structural changes in driedalginate gels containing fillers. Food Hydrocolloids 16:139–151.

Ranganna, S. (1987). Hand book of analysis and quality control for fruit and vegetable products.New Delhi: Tata McGraw-Hill Publications.

Ravi R, Roopa BS, Bhattacharya S. 2007. Texture evaluation by uniaxial compression of somesnack foods. J Text Studies 38:135–152.

Saha D, Bhattacharya S. 2010. Characteristics of gellan gum based food gel. J Text Studies41:459–71.

Skinner EZ. 1988. The texture profile method. In: Moskowitz H, editor. Applied sensory analysisof foods. Boca Raton: CRC Press. p. 89–107.

Stone H, Sidel JL. 1985. Sensory evaluation practices. Florida: Academic Press INC. p. 194–226.

Szczesniak AS. 1983. Physical properties of foods: what they are and their relation to otherfood properties. In: Peleg M, Bagley EB, editors. Physical properties of foods. Westport: AVIPublication. p. 1–41.

Walstra P. 2003. Soft solids. Physical chemistry of foods. New York: Marcel Dekker. p. 683–771.

Warin F, Gekas V, Voirin A, Dejmek P. 1997. Sugar diffusivity in agar gel/milk bilayer systems.J Food Sci 62:454–6.


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Appendix

Table A1–Sensory descriptors and definitions used for gel samples.

Textural attributes Description

1 Color Degree of visible color imparted by the product2 Moisture Amount of moisture in the mouth after complete mastication3 Shrinkage The extent of reduction in size and shape4 Surface smoothness Degree to which the sample piece was perceived as smooth when evaluated prior to mastication with the tongue5 Surface slipperiness Degree to which the sample piece was perceived as slippery when evaluated prior to mastication with the tongue6 Stickiness Tendency to adhere to contacting surfaces especially to palate, teeth, and tongue during mastication7 Chewiness The number of chews that were required before the sample is ready for swallowing8 Leathery Extent to which the sample was felt as leathery during mastication9 Springiness Degree to which the sample returned to the original shape after partial compression between the tongue and hard

palate10 Firmness Force required to fracture the sample with the molars11 Cohesiveness of mass Degree to which the sample mass stayed together as chewing progresses12 Compressibility Degree to which the sample was deformed or compressed before fracture using the tongue and hard palate13 Moisture release Extent to which moisture was released from the sample during the 1st bite with the molars14 Crumbliness Degree to which the sample was fractured into pieces on the 1st bite with the molars15 Sweetness Degree of sweetness perceived by the tongue during eating16 Mango flavor/like Degree of mango pulp flavor in the product17 Particle size Size of breakdown particles after 8 to 10 chews (small to large)18 Particle size distribution Degree of homogeneity in the particle size distribution after 8 to 10 chews19 Particle shape Degree of irregular particle shape after 8 to 10 chews (irregular means the particles had distinct edges)20 Smoothness Degree to which the mass of particles was felt smooth after 8 to 10 chews21 Rate of sample breakdown Rate at which the sample was broken into smaller and smaller particles (slow to fast)22 Chalkiness Extent to which the sample was felt chalky during mastication23 Adhesiveness Degree to which the sample was sticking to teeth during mastication24 Time The time required to make the sample ready for swallowing25 Fibrous Manifested by the presence of readily separated filamentous elements


S:Se

nsory

&Fo

odQu

ality


Tab

leA

2–D

escr

ipti

vese

nso

rypro

filing

of

gel

sam

ple

ssh

ow

ing

mea

nsc

ore

sof

senso

ryat

trib

ute

s.

Sen

sory

attr

ibute

s

Surf

ace

Surf

ace

Mois

ture

Man

goO

vera

llSl.

No.

Colo

rM

ois

tnes

sShri

nka

gesm

ooth

nes

sslip

per

ines

sStick

ines

sC

hew

ines

sLea

ther

ySpri

ngin

ess

Fir

mnes

sC

ohes

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ess

rele

ase

Cru

mblines

sSw

eetn

ess

like

qual

ity

110

.8±

0.5

4.8

±0.

38.

8±

0.6

8.0

±0.

43.

6±

0.2

4.5

±0.

36.

0±

0.4

6.4

±0.

34.

6±

0.3

8.0

±0.

67.

2±

0.5

4.2

±0.

25.

1±

0.3

5.3

±0.

44.

8±

0.2

6.7

±0.

32

11.2

±0.

84.

5±

0.1

8.9

±0.

76.

8±

0.6

3.9

±0.

14.

9±

0.2

6.1

±0.

27.

6±

0.8

4.8

±.2

8.2

±0.

67.

7±

0.6

3.9

±0.

24.

4±

0.2

4.4

±0.

34.

6±

0.2

7.6

±0.

73

10.8

±0.

64.

7±

0.1

9.5

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66.

2±

0.2

3.1

±0.

25.

4±

0.3

6.3

±0.

47.

5±

0.7

4.6

±0.

27.

4±

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7.9

±0.

45.

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5.1

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25.

0±

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5.0

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27.

3±

0.3

42.

6±

0.1

3.5

±0.

110

.0±

0.5

4.5

±0.

12.

9±

0.1

6.3

±0.

27.

9±

0.7

7.3

±0.

35.

8±

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8.6

±0.

97.

3±

0.7

3.2

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14.

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3.1

±0.

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7.0

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0.7

2.0

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23.

0±

0.2

2.7

±0.

14.

3±

0.2

7.4

±0.

77

11.2

±1.

03.

5±

0.1

5.3

±0.

18.

3±

0.9

4.0

±0.

24.

0±

0.2

9.3

±0.

58.

3±

0.7

6.5

±0.

77.

4±

0.7

8.0

±0.

32.

0±

0.1

3.0

±0.

22.

5±

0.2

4.3

±0.

26.

8±

0.4

88.

6±

0.7

4.5

±0.

25.

3±

0.3

5.5

±0.

24.

5±

0.2

5.3

±0.

38.

7±

0.6

9.3

±0.

67.

5±

0.6

7.7

±0.

68.

0±

0.6

2.0

±0.

13.

0±

0.2

2.7

±0.

15.

3±

0.2

7.3

±0.

39

6.0

±0.

25.

2±

0.3

5.3

±0.

13.

8±

0.2

3.5

±0.

25.

3±

0.3

9.0

±0.

79.

3±0.

55.

5±

0.2

6.4

±0.

58.

0±

0.7

2.0

±0.

23.

0±

0.2

2.7

±0.

24.

6±

0.3

6.9

±0.

510

7.0

±0.

64.

7±

0.2

8.7

±0.

79.

5±

0.9

8.7

±0.

75.

6±

0.2

9.1

±0.

68.

5±

0.6

5.0

±0.

34.

6±

0.2

4.7

±0.

24.

4±

0.3

9.5

±0.

68.

7±

0.7

9.5

±0.

79.

0±

0.7

118.

0±

0.9

9.5

±0.

75.

6±

0.2

6.2

±0.

38.

7±

0.6

6.2

±0.

39.

5±

0.8

6.2

±0.

39.

5±

0.6

9.0

±0.

66.

2±

0.3

5.1

±0.

36.

2±

0.2

5.6

±0.

36.

2±

0.3

5.6

±0.

212

6.0

±0.

36.

2±

0.3

9.1

±0.

73.

1±

0.1

5.6

±0.

34.

5±

0.2

6.2

±0.

44.

5±

0.3

6.2

±0.

38.

7±

0.7

4.5

±0.

34.

6±

0.4

3.1

±0.

19.

1±

0.8

3.1

±0.

28.

6±

0.6

135.

0±

0.3

3.1

±0.

18.

5±

0.6

5.4

±0.

39.

1±

0.7

7.2

±0.

63.

1±

0.2

7.2

±0.

73.

1±

0.2

5.6

±0.

37.

2±

0.6

4.6

±0.

45.

4±

0.7

8.5

±0.

65.

4±

0.3

9.4

±0.

714

8.0

±0.

75.

4±

0.3

5.0

±0.

26.

3±

0.3

8.5

±0.

65.

8±

0.2

5.4

±0.

25.

8±

0.3

5.4

±0.

39.

1±

0.7

5.8

±0.

33.

0±

0.2

6.3

±0.

35.

0±

0.2

6.3

±0.

79.

4±

0.6

159.

0±

0.6

6.3

±0.

24.

6±

0.3

7.5

±0.

65.

0±

0.3

8.3

±0.

96.

3±

0.6

8.3

±0.

96.

3±

0.3

8.5

±0.

68.

3±

0.6

3.0

±0.

27.

5±

0.6

4.6

±0.

37.

5±

0.6

10.5

±0.

716

9.0

±0.

87.

5±

0.7

4.7

±0.

34.

6±

0.3

4.6

±0.

25.

5±

0.5

7.5

±0.

75.

5±

0.3

7.5

±0.

25.

0±

0.3

5.5

±0.

33.

0±

0.2

4.6

±0.

34.

7±

0.3

4.6

±0.

411

.0±

0.9

179.

5±

0.6

4.6

±0.

34.

5±

0.1

7.4

±0.

64.

7±

0.2

3.8

±0.

24.

6±

0.3

3.8

±0.

24.

6±

0.2

4.6

±0.

43.

8±

0.2

3.0

±0.

27.

4±

0.6

8.5

±0.

67.

4±

0.5

9.4

±0.

518

8.9

±0.

78.

5±

0.9

5.0

±0.

37.

9±

0.6

4.9

±0.

39.

5±

0.6

7.4

±0.

69.

5±

0.7

6.4

±0.

34.

7±

0.3

9.5

±0.

79.

5±

0.9

7.9

±0.

77.

0±

0.3

7.9

±0.

76.

8±

0.3

196.

7±

0.3

6.5

±0.

66.

0±

0.3

5.7

±0.

28.

5±

0.4

5.6

±0.

37.

9±

0.7

6.5

±0.

37.

8±

0.7

6.3

±0.

36.

2±

0.5

6.2

±0.

45.

7±

0.3

8.0

±0.

75.

7±

0.4

8.4

±0.

720

9.5

±0.

79.

0±

0.8

7.0

±0.

66.

5±

0.2

6.2

±0.

38.

4±

0.6

5.7

±0.

35.

0±

0.2

6.2

±0.

28.

0±

0.7

4.2

±0.

28.

5±

0.6

6.4

±0.

48.

0±

0.6

8.4

±0.

56.

4±

0.5

Tab

leA

3–R

esult

of

sim

ult

aneo

us

opti

miz

atio

nof

vari

able

s.

Var

iable

sLev

els

Sen

sory

des

crip

tors

Pre

dic

ted

senso

rysc

ore

Pre

dic

ted

obje

ctiv

esc

ore

s

1A

gar

(%)

3.0

Moi

stur

e4.

43M

oist

ure

60.5

%2

Pulp

(%)

32.5

Shri

nkag

e6.

47Sh

rink

age

50.7

%3

Suga

r(%

)8.

0St

icki

ness

5.66

Failu

rest

ress

45.9

kPa

Che

wy

8.97

Failu

reen

ergy

29.6

mJ

Spri

ngin

ess

7.91

Firm

ness

9.08

Coh

esiv

enes

s7.

59C

rum

bly

6.10

Ove

rall

qual

ity7.

08

Ove

rall

desir

abili

ty0.

74


Dehumidifier Assisted Drying of a Model Fruit Pulp-Based Gel and Sensory Attributes

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Transcript of Dehumidifier Assisted Drying of a Model Fruit Pulp-Based Gel and Sensory Attributes