The paper introduces a multi-criteria assessment system that can be used for sensory analysis by fuzzy-Eckenrode and fuzzy-TOPSIS methods. Respondents evaluated the sensory characteristics of Cucumis melo (L.), which included aroma, colour, taste, texture, and overall acceptance, after six days of storage. The product was stored under three different temperature conditions: 10 ° C (B1), 14 ° C (B2), and room temperature (27-30 ° C) (B3). The product was also stored at three types of packaging: unpackaged stem (A1), packaged fruit with one layer of banana stem (A2), and packaged fruit with two layers of banana stem (A3). The best result was demonstrated by the Cucumis melo that was stored at 14 ° C and packaged in a two-layered banana stem (A3B2). Both fuzzy-Eckenrode and fuzzy-TOPSIS method provided an easy, fast, and unambiguous calculation of multi-criteria sensory assessment.

Анотація наукової статті за медичними технологіями, автор наукової роботи - Fadhil Rahmat, Agustina Raida


Область наук:
  • Медичні технології
  • Рік видавництва: 2019
    Журнал: Foods and Raw materials

    Наукова стаття на тему 'A MULTI-CRITERIA SENSORY ASSESSMENT OF CUCUMIS MELO (L.) USING FUZZY-ECKENRODE AND FUZZY-TOPSIS METHODS'

    Текст наукової роботи на тему «A MULTI-CRITERIA SENSORY ASSESSMENT OF CUCUMIS MELO (L.) USING FUZZY-ECKENRODE AND FUZZY-TOPSIS METHODS»

    ?A multi-criteria sensory assessment of Cucumis melo (L.) using fuzzy-Eckenrode and fuzzy-TOPSIS methods

    Rahmat Fadhil *, Raida Agustina

    Universitas Syiah Kuala, Banda Aceh, Indonesia * e-mail: Ця електронна адреса захищена від спам-ботів. Вам потрібно увімкнути JavaScript, щоб побачити її.

    Received May 26, 2019; Accepted in revised form June 17, 2019; Published October 21, 2019

    Abstract: The paper introduces a multi-criteria assessment system that can be used for sensory analysis by fuzzy-Eckenrode and fuzzy-TOPSIS methods. Respondents evaluated the sensory characteristics of Cucumis melo (L.), which included aroma, colour, taste, texture, and overall acceptance, after six days of storage. The product was stored under three different temperature conditions: 10 ° C (B1), 14 ° C (B2), and room temperature (27-30 ° C) (B3). The product was also stored at three types of packaging: unpackaged stem (A1), packaged fruit with one layer of banana stem (A2), and packaged fruit with two layers of banana stem (A3). The best result was demonstrated by the Cucumis melo that was stored at 14 ° C and packaged in a two-layered banana stem (A3B2). Both fuzzy-Eckenrode and fuzzy-TOPSIS method provided an easy, fast, and unambiguous calculation of multi-criteria sensory assessment.

    Keywords: Banana stem, hedonic scale, Cucumis melo (L.), sensory assessment, TOPSIS, Eckenrode

    Please cite this article in press as: Fadhil R, Agustina R. A multi-criteria sensory assessment of Cucumis melo (L.) using fuzzy-Eckenrode and fuzzy-TOPSIS methods. Foods and Raw Materials. 2019; 7 (2): 339-347. DOI: http://doi.org/10.21603/2308-4057-2019-2-339-347.

    E-ISSN 2310-9599 ISSN 2308-4057

    Research Article Open Access

    Check for updates

    DOI: http://doi.org/10.21603/2308-4057-2019-2-339-347 Available online at http: jfrm.org.ua

    INTRODUCTION

    Cucumis melo L. is a tropical and sub-tropical fruit that easily decays and rots because of its high-water content (70-95%). For the fruit to maintain its quality and freshness, it has to be handled properly during and after harvesting. A good quality fruit should be fresh, with a smooth, undamaged, and flawless skin. Compared to other cucumbers (Cucumis), Cucumis melo has a greener colour, more crunchy texture, higher water content, and sweeter taste. In addition, Cucumis melo can be harvested at an earlier stage.

    Packaging is extremely important in post-harvest handling. It creates proper condition for the fruit to maintain its quality during the desired period. Packaging is a container or wrapper that can help to prevent or reduce damage to the packaged / wrapped object. The main functions of packaging are to keep food products from contamination, to protect them from physical damage, and to inhibit their quality degradation.

    In the Province of Aceh (Indonesia), Cucumis melo is usually packaged in traditional manner by using banana stem, because banana leaves are cheap, easy to find, and eco-friendly. The fruit is placed in the middle

    part of banana stem, which are then folded into two parts (Fig. 1). Banana stem are able to protect the fruit from shocks and damage during transportation from producer to consumer. When ripe, the epidermis of Cucumis melo cracks, and banana stem help keep its shape and texture. Usually, Cucumis melo is protected with a single layer of banana stem.

    According to Lukman [1], banana stem is part of banana pseudo stem [1]. Its structure is very different from that of woody plants, because it is an apparent trunk formed by tightly packed, over-leaping stem. The fibre of banana stem are strong and waterproof to both fresh and salt water. The packaging of Cucumis melo with a various amount of banana stem is necessary to preserve its wholeness and texture, because this fruit is easily broken when ripe. The storage temperature varies from room temperature to cold temperature, which is also expected to prolong the shelf life of Cucumis melo.

    A quick method to find out consumer acceptance towards the food product is to perform a sensory assessment by collecting respondents 'opinions on the product. This multi-criteria assessment method was completed with a weighting assessment approach,

    Copyright © 2019, Fadhil et al. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.

    O a b c d

    Figure 1 Cucumis melo packaged in banana stem

    Figure 2 Membership function

    which is usually used in decision making. Therefore, this article introduces a multi-criteria assessment system that performs a sensory analysis by using fuzzy-Eckenrode and the fuzzy-TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) methods. According to the system, the respondents evaluated each product and rated its level of acceptance according to a multi-criteria sensory assessment, which included aroma, colour, taste, texture, and overall acceptance.

    Fuzzy logic. Fuzzy logic is a development of the set theory, where each member has a degree of membership that ranges in value between 0 and 1. It means that fuzzy sets can represent interpretation of each value according to the opinion, or decision, and its probability. Rating 0 represents 'wrong', rating 1 represents 'right', and there are still other numbers between the 'right' and 'wrong' [2, 3].

    In fuzzy sets, there are two attributes. The first one is linguistic attribute: it is a naming of a group which represents a certain situation or condition by using a natural language such as 'cold', 'cool', 'normal', or 'warm'. The second attribute is numeric: it is a value (number) which shows a measure of a variable, such as 10, 30, 50, etc. [4]. Membership function is a curve that defines how each point in the input room is mapped into the membership value (degree of membership between 0 and 1). If U states universal sets and A is fuzzy function sets in U, so A can be stated as sorted pair as following [2]:

    M (x)

    A = {(X Ma (x)} x e U}

    (1)

    where fiA (x) is a membership function that gives value of degree of membership x to fuzzy set A, which is:

    (2)

    Ma: U ^ 10,

    In a fuzzy set, there are several membership functions of a new fuzzy set, which result from basic operation of the fuzzy set, i.e .:

    Intersection: A n B = min (| A [x], Mc [y]) (3)

    Union: A u B = max (| BA [x], | B (y])

    (4)

    Complement: ~ A = 1 - | A [x] (5)

    Membership function is statedas follows:

    0; x < a or x > c

    (B - a) / (x - a); a < x <b

    (B - x) / (c - b) -, b <x <c

    (6)

    In a fuzzy system, there is a linguistic variable. This is a variable that has a value in verbal form in a natural language. Each linguistic variable is related to a certain membership function. Figure 2 gives an example of membership function.

    Fuzzy-Eckenrode. The Eckenrode method was initially known as a weighting multiple criteria method, which was introduced by Robert T. Eckenrode from Dunlop and Association, Inc. in 1965 and has been widely used until today [5-8]. The Eckenrode method is simpler and more efficient in determining the importance weight in a decision [9-11]. The Eckenrode weighting analysis method is one of weighing methods used in determining the degree of importance, or Weight (B), from each Criteria (K) and Sub-criteria (SK), which have been set in decision making [12]. This weight determination is perceived as very important because it affects the final total value of each chosen decision. The concept used in this weighting method is by doing a change of order to value where, for instance, first order (1) has the highest rate (value) and the fifth order (5) has the lowest rate.

    Fuzzy-TOPSIS. TOPSIS belongs to the Multiple Attribute Decision Making (MADM), which was firstly introduced by Yoon, Yoon et al. and Hwang et al. [13-15]. It has been widely applied in various studies related to decision making, such as Kumar et al., Han et al., Tyagi, Estrella et. al., Roszkowska et al., Selim et al. [16-21]. TOPSIS can only be implemented for a criterion whose weight has been known or calculated before, because there is a step in TOPSIS which involves the process of multiplication of criterion weight and the alternative value of the criterion.

    In many situations, the data available is insufficient for a real life problem, because human assessment, which is considered as preference, is unclear, and the preference can not be estimated with exact numeric value. The verbal expression, e.g. 'Low', 'medium', 'high', etc., is considered as a representation of the decision maker. Thus, fuzzy logic is necessary in making a structured decision of the preference maker.

    Table 1 Attributes of multi-criteria sensory assessment of Cucumis melo

    Multi-Criteria Sensory Assessment of Cucumis melo L

    Attribute

    Assessment consideration

    Aroma (C1)

    Colour (C2)

    Taste (C3)

    Texture (C4)

    Overall acceptance (C5)

    Typical, no sour smell

    Yellowish-green

    Sweet and not sour

    Solid, not watery, no wrinkles

    Yellowish-green in colour, solid,

    and sweet

    The Fuzzy theory helps to measure the uncertainty associated with human judgement, which is subjective. Therefore, evaluation is necessary to be done in an environment. According to Ningrum et al. and Fadhil et al., fuzzy logic can help improve failure, which happens when only Eckenrode or TOPSIS method is used [4, 22].

    STUDY OBJECTS AND METHODS

    This study used Cucumis melo (L.) which was

    harvested in two months after planting. The harvested

    Cucumis melo was cleaned by washing and then stored under three different conditions: without banana stem packaging (A1), with one layer of banana stem packaging (A2), and with two layers of banana stem packaging (A3). Cucumis melo was then stored for six days under three temperature regimes: 10 ° C (B1), 14 ° C (B2), and at room temperature (27-30 ° C) (B3).

    Procedure of assessment. The multi-criteria sensory assessment of Cucumis melo included aroma, colour, taste, texture, and overall acceptance (Table 1). The attribute weight of respondents 'assessment toward the multi-criteria was determined according to the hedonic scale. The hedonic scale is a preference of respondent's opinion based on likes or dislikes that are converted into number (Table 2).

    The framework of this study included four steps: (1) selection of respondents and criteria, (2) determination of criterion weight of the assessment by using the fuzzy-Eckenrode method, (3) determination of the best alternative of all treatments by using fuzzy -TOPSIS, and (4) recommendation of the best acceptance from all respondents. Figure 3 shows the complete framework.

    Combinations of storage conditions were as follows: A1B1: without banana stem-packaging at 10 ° C; A1B2: without banana stem-packaging at 14 ° C; A1B3: without banana stem-packaging at 27-30 ° C; A2B1: with one layer of banana stem-packaging at 10 ° C;

    Table 2 Assessment of preference according to hedonic scale

    Score

    Preference

    Like very much Like

    Neither like nor dislike Dislike

    Dislike very much

    Determination of respondent * Determination of attribute weight Determination of the best alternative

    ?

    Respondents 'criteria Fuzzy-Eckenrode Fuzzy-Topsis

    V + J

    Recommendation of the best acceptance

    Figure 3 Research framework

    A2B2: with one layer of banana stem-packaging at 14 ° C; A2B3: with one layer of banana stem-packaging at 27-30 ° C;

    A3B1: with two layers of banana stem-packaging at 10 ° C;

    A3B2: with two layers of banana stem-packaging at 14 ° C;

    A3B3: with two layers of banana stem-packaging at 27-30 ° C.

    Fuzzy-Eckenrode method. According to the Eckenrode weight calculation method, the respondents were asked to make a rating (eg from R1 until Rn, where n ranking, j = 1, 2, 3, ..., n, ranking j = R) for each criterion ( criterion i is notated with Ki, which is presented in a number of n criteria, i = 1, 2, 3,., n) [11]. Table 3 shows the obtained data. Next, N was calculated based on P .. and R ..

    lj n-j

    Ni = G., P .. x R, j = 1, 2, 3,., N. (7)

    j = 1 rij n-j 'J |>|>|>|> \ J

    Total Score = Gw N, i = 1, 2, 3,., N. (8)

    Then, criterion weight Bi (which are B1, B2, B3, ..., Bn) was calculated, where i = 1, 2, 3,., 3, by using the following formula:

    Tabel 3 Calculation of criterion weight according to the Eckenrode method

    Criteria Rank Score Weight

    ~ R.1 R2 ...... R ...... R

    _1 2_j_n_

    K P11 P12 ............ P1n N1 BT

    K2 P21 P22 ............ P2n N2 B2

    k. p..

    i j

    K Pi P0 ............ P N B

    n n1 n2 mn n n

    Multiplier Rn-1 Rn-2 ...... Rn-j Rn-n Total 1.00

    factor_Score_

    Rj = ranking order at j, j = 1, 2, 3, ..., n Ki = criterion type i, i = 1, 2, 3,., N

    Pij = number of respondents who chose ranking j for criterion i Rn-j = multiplier factor j, which was obtained from the reduction of number of criteria or number of ranking (which is n) with the rank order on the column. For instance, if there are five criteria, so the multiplier factor for column of 3rd rank (if j = 3) is n-j = 5-3 = 2 Bi = weight of criterion i.

    Table 4 Scale of weighting comparison among criteria of fuzzy-Eckenrode method

    -1

    Scale Annotation TFN membershipfunction

    ~ 1 Very unimportant 1, 1, 2

    ~ 2 Less important 1, 2, 3

    ~ 3 Neutral 2, 3, 4

    ~ 4 Important 3, 4, 5

    ~ 5 Very important 4, 5, 5

    B = (N / Total Score)

    (9)

    To find the level of importance of each sub-criterion within a criterion, the respondents were also asked to rank each sub-criterion within a criterion. Then, by using the same procedure, the weight of each sub-criterion was calculated (Bu, the weight of sub-criterion 1 in criterion i). Thus, the weighted weight (Bn) from sub-criterion 1 in criterion i was obtained, which was BT = B B. Then, to find the score of each criterion, the respondents were asked to rate eacg sub-caheoiae wt-hin each criterion [23].

    The assessment of each sub-criterign was calcuiited by using a geometric mean formula according -o the assessment result from all respondfnts, wiiich = as multiplied with the weighted weighf of eanC cub-criterion. Each criterion (K1 to K5) w = s calculaief by summing up the total score of ad sub-criterig en eacli criterion. To assess the weighting by the resptndhrts, age fuzzy-Eckenrode method was applied with tire vaiuh of preference, as shown in Table 4.

    Fuzzy-TOPSIS method. The analysis with thu fuzzy-TOPSIS method included the fol lowing tasks [24]:

    To rank the fuzzy from each decision made, Dk; (K = 1, 2, 3, ..., k) can be reprcsenUed cn Cdmngulcr fuzzy number ~ Rk; (K = 1, 2, 3,., K) with membership function (x).

    To produce an appropriate alternative, to dcteeminc the criteria of evaluation, and ho ougacciee the gronp of decision-maker. It was assumed that there were m alternatives, n criteria of evaluatign, and gecision k.

    To choose the linguistic variable accoedicg Co the weight of criterion importance = (wj = Z-, ^? Jc mh and alternative linguistic rankings on cridecion =%) fin Triangular Fuzzy Number (TFN).

    To do a weight aggregation of eamh criterion to obtain fuzzy weight aggregate he, -) incriterion C. and to determine the fuzzy aggregate value from citernative yg on each criterion C.

    |.? Y = 1 [-. (Y + -.y + | ". + - ^? Y]

    i = 1, 2, .., m; and j = 1, 2, ..., n

    Wj = - [w.1 + w.2 + .... K

    j = 1,2, ..., n To build a fuzzy decisionmatrix.

    w / j

    (10)

    (11)

    ?i 5 = ^ 2

    A

    '1 in

    X21 -.22 |:) cm1

    ^ ln

    x2n

    Xmn

    W = [we, w2 ... w "] (12)

    To do noemalieatkcn of the decision matrix, where:

    lyjmxn

    i = 11, 2V .., m. and j = 1, 2, ..., n (13)

    C_cutorn- Ir-mxn ca2b1 done _ith:

    + Ma; +? A

    (14)

    where tt | = Max u ^-.

    To determine the weight noimalisation of the fuzzy decision malrig. Ba + ed on different importance o + each criterion, 2he deciskm of the weighted

    normalisatifn C2nbe arawged as:

    ^ [(0a] __ i

    with, t = \ t t>2___, m; 2ind j = 1, b, ..., n

    where:

    Vij = f? JI® W? Y

    with, 1 = 1, +2, ..., - ansl j = 1, 2, ..., n

    (15)

    (16)

    bo determine fuzzy poiitive) daal solution (FbIS) S + and. fuzzy negative idea. sdlutian {FNI S) Si-:

    + = U2Г, 22 ~ '....' in; 0

    (17)

    (18)

    whene: h = mee "(/ {} znd ah" = min {m_} i} with H ^ are T FNnnrmali sation ^ eighn.

    To calcu2ote the intarvoC li ttween each alternative value aeid tlie vaUua of FP.S (Fuzzy Positive Ideal Solution) and FNIS (fuzzr negft} vz Mealsolution).

    d Wi, ^) = ^^ g.S ^ i - rnie) 2 +

    + (Is - ite) 2 (19) ctf = mn = in (viy, ir /), i = 1,2, ..., m (20) df = hy ^ O ^ y, 2 / "), i = 1,) ..... m (21)

    To calculate the closeness coefficient (CCi) and the ranking accordin- m the coefficient value oit) ined using the following equation:

    d, "

    CC: - -r1-y t = d'2y _.ym

    1 W + '' ''

    (22)

    To rate each alternative by the respondents, we used the fuzzy-TOPSIS method with preference value, as in Table5.

    Figure4 illustrates the procedure of the analysis. Selection of respondents. A total of 10 respondents were chosen todo a multi-criteria sensory assessment of

    Table 5 Comparison scale of determination of the fuzzy-TOPSIS method alternative

    Table 6 Respondents 'weighting score of criteria based on the fuzzy-Eckenrode method

    Scale TFN Linguistics No Criteria Order

    Dislike very much (STS) 1, 1, 2 1 2 3 4 5

    Dislike (TS) 1, 2, 3 1 C1 ~ 4 ~ 3 ~ 1 ~ 1 ~ 1

    Neither like nor dislike (N) 2, 3, 4 2 C2 ~ 3 ~ 4 ~ 1 ~ 1 ~ 1

    Like (S) 3, 4, 5 3 C3 ~ 3 ~ 1 ~ 4 ~ 1 ~ 1

    Like very much (SS) 4, 5, 5 4 C4 ~ 2 ~ 2 ~ 4 ~ 1 ~ 1

    5 C5 ~ 5 ~ 2 ~ 1 ~ 1 ~ 1

    Value 4 3 2 1 0

    |S c Determination of N

    Problem -> material treatment

    Statement alternative to be

    J < considered

    Choosing linguistic variable with weight of criterion importance

    Formulating fuzzy decision matrix and weight normalisation of fuzzy decision matrix

    Aggregation of fuzzy criteria weights and fuzzy alternative ranking

    I

    Determining alternative linguistic ranking towards criteria on TFN

    I

    r

    Determining

    FPIS and

    FNIS

    v <

    Calculating interval

    between each alternative value and FPIS & FNIS value

    Calculate the

    closeness coefficient and ranking

    Figure 4 Steps of the fuzzy-TOPSIS method analysis

    Cucumis melo. The respondents were selected according to several criteria. The potential respondents had to:

    1. like Cucumis melo, raw or processed;

    2. be experienced in sensory assessment;

    3. be healthy as flu, cough, mouth ulcers, etc. can bother the sensory assessment process;

    4. be able to distinguish colours.

    RESULTS AND DISCUSSION Determination of assessment criteria weight.

    A hedonic scale was used to evaluate the results of determination of respondents 'assessment of criteria weight towards multi-criteria which were considered in the sensory assessment. After that, they were translated into fuzzy logic functions (Table 6).

    As for the data of respondents 'assessment towards criteria of importance weight determination from each sensory attribute, the values ​​of lower bound

    (X criteria-order)

    (Low), middle (medium), and upper bound (upper) were arranged as summarised on Table 7. The next step was to calculate the score and the weight of each criterion. Figure 5 represents a radar diagram.

    According to the respondents 'assessment of the criteria with the help of the fuzzy-Eckenrode method, the order of criteria weight was obtained from the highest to the lowest: (1) overall acceptance, 0.216; (2) colour, 0.211; (3) aroma, 0.203; (4) taste, 0.191; and (5) texture 0.176.

    Determination of the best alternative. The

    priority of the best alternative from the multi-criteria sensory assessment of Cucumis melo was determined by summarising all respondents 'preferences. The preferences were chosen based on the mode number, i.e. the value that appears most often from each choice of material treatment. The mode number was chosen by the respondents. The next step was to arrange the matrix of the respondents 'assessment on all alternatives (Table 8). The data of respondents 'assessment was then transformed into TFN linguistic data, as presented in Table 9.

    After that, we formulated the normalised weight matrix on each alternative. The value normalisation can be done by using Eqs. (13) and (14). Table 10 shows the results of the TFN value normalisation.

    Then, we arranged the matrix of multiplication between criteria weights and normalisation value of each alternative. This process can be done by using Eqs. (15) and (16). Table 11 summarises the results of the matrix multiplication.

    Table 7 TFN value of experts 'weighting on criteria of the fuzzy-Eckenrode method

    No Criteria 1 2 3 4 5 Score Weight

    l m u l m u l m u m u l m u

    1 C1 4 5 5 1 2 3 1 1 2 1 2 1 1 2 82 0.206

    2 C2 3 4 5 12 3 1 2 3 1 21 12 84 0.211

    3 C3 1 2 3 11 2 3 4 5 2 31 12 76 0.191

    4 C4 1 1 2 12 3 3 4 5 2 31 12 70 0.176

    5 C5 3 4 5 1 2 3 1 1 2 2 3 1 1 2 86 0.216

    Value (X criteria-order) 4 3 2 0 398 1,000

    l = lower, m = middle, u = upper

    Aroma

    Texture '..... "' Taste

    Figure 5 Radar diagram of criteria weight

    Table 8 Matrix of experts 'assessment on alternatives

    Alternatives Criteria

    C1 C2 C3 C4 C5

    A1B1 3 2 1 1 3

    A1B2 3 2 2 1 3

    A1B3 2 + 2 2 Будiвництво 1 1

    A2B1 4 3 3 2 4

    A2B2 4 4 3 2 4

    A2B3 2 + 2 2 Будiвництво 1 1

    A3B1 5 4 5 4 5

    A3B2 5 5 5 5 5

    A3B3 1 1 1 1 1

    The next step was to determine the positive ideal solution value (FPIS) S + and the negative ideal solution value (FNIS) S ". When determining both values, the characteristic of data available should be taken into consideration. To obtain both groups of values, one can use Eqs. (17) and (18). Table 12 demonstrates FPIS and FNIS values.

    After that, the interval between each alternative value and FPIS and FNIS was calculated by using Eqs. (19), (20), and (21). The results of the interval calculation between alternative value toward FPIS and FNIS can be observed from Table 13 and Table 14.

    We evaluated the criteria distance value to the fuzzy positive ideal solution (FPIS) and the fuzzy negative ideal solution (FNIS) according to comparison of d + and d-. It showed preference of product acceptance on a radar diagram (Fig. 6). For instance, the treatment of Cucumis melo without packaging at temperature of 10 ° C (A1B1) had such d + and d- values ​​that showed the biggest distance from the positive ideal and the negative ideal.

    The final step was to calculate the closeness coefficient (CCi) of each alternative by using Eq. (22). From the calculation result, we obtained ranking from the highest to the lowest (Fig. 7). The biggest coefficient value was the main alternative, which was suggested to be chosen or prioritised, compared to other alternatives based on respondents 'preference (product acceptance).

    According to the closeness coefficient (CCi), an alternative ranking can be arranged from the biggest to the lowest as follows: two-layer banana stem-packaging at 14 ° C (A3B2), two-layer banana stem-packaging at 10 ° C (A3B1), one-layer banana stem-packaging at 14 ° C (A2B2), one-layer banana stem-packaging at 10 ° C (A2B1), without banana stem packaging at 14 ° C (A1B2), one-layer banana stem-packaging at room temperature (A2B3), without banana stem packaging at 10 ° C (A1B1), without banana stem packaging at room temperature (A1B3), and two-layer banana stem-packaging at room temperature (A3B3) (Fig. 7).

    The analysis with fuzzy-TOPSIS approach showed

    Table 9 Matrix of respondents 'assessment on alternative in TFN scale

    Alternatives Criteria

    Aroma (0.191, 0.206, 0.211) Colour (0.206, 0.211, 0.216) Taste (0.176, 0.191, 0.206) Texture (0.176, 0.176, 0.191) Overall acceptance (0.204, 0.216, 0.216)

    A1B1 (2, 3, 4) (1, 2, 3) (1, 1, 2) (1, 1, 2) (2, 3, 4)

    A1B2 (2, 3, 4) (1, 2, 3) (1, 2, 3) (1, 1, 2) (2, 3, 4)

    A1B3 (1, 2, 3) (1, 2, 3) (1, 2, 3) (1, 1, 2) (1, 1, 2)

    A2B1 (3, 4, 5) (2, 3, 4) (2, 3, 4) (1, 2, 3) (3, 4, 5)

    A2B2 (3, 4, 5) (3, 4, 5) (2, 3, 4) (1, 2, 3) (3, 4, 5)

    A2B3 (1, 2, 3) (1, 2, 3) (1, 2, 3) (1, 1, 2) (1, 1, 2)

    A3B1 (4, 5, 5) (3, 4, 5) (4, 5, 5) (3, 4, 5) (4, 5, 5)

    A3B2 (4, 5, 5) (4, 5, 5) (4, 5, 5) (4, 5, 5) (4, 5, 5)

    A3B3 (1, 1, 2) (1, 1, 2) (1, 1, 2) (1, 1, 2) (1, 1, 2)

    A1B1: without banana stem-packaging at 10 ° C A1B2: without banana stem-packaging at 14 ° C A1B3: without banana stem-packaging at 27-30 ° C A2B1: with one layer of banana stem-packaging at 10 ° C A2B2 : with one layer of banana stem-packaging at 14 ° C A2B3: with one layer of banana stem-packaging at 27-30 ° C A3B1: with two layers of banana stem-packaging at 10 ° C A3B2: with two layers of banana stem-packaging at 14 ° C A3B3: with two layers of banana stem-packaging at 27-30 ° C

    Fadhil R. et al. Foods and Raw Materials, 2019, vol. 7, no. 2, pp. 339-347 Table 10 Matrix of TFN scale normalisation

    Alternative Criteria

    Aroma Colour Taste Texture Overall acceptance

    (0.191, 0.206, 0.211) (0.206, 0.211, 0.216) (0.176, 0.191, 0.206) (0.176, 0.176, 0.191) (0.204, 0.216, 0.216)

    A1B1 (0.40, 0.60, 0.80) (0.20, 0.40, 0.60) (0.20, 0.20, 0.40) (0.20, 0.20, 0.40) (0.40, 0.60, 0.80)

    A1B2 (0.40, 0.60, 0.80) (0.20, 0.40, 0.60) (0.20, 0.40, 0.60) (0.20, 0.20, 0.40) (0.40, 0.60, 0.80)

    A1B3 (0.20, 0.40, 0.60) (0.20, 0.40, 0.60) (0.20, 0.40, 0.60) (0.20, 0.20, 0.40) (0.20, 0.20, 0.40)

    A2B1 (0.60, 0.80, 1.00) (0.40, 0.60, 0.80) (0.40, 0.60, 0.80) (0.20, 0.40, 0.60) (0.60, 0.80, 1.00)

    A2B2 (0.60, 0.80, 1.00) (0.60, 0.80, 1.00) (0.40, 0.60, 0.80) (0.20, 0.40, 0.60) (0.60, 0.80, 1.00)

    A2B3 (0.20, 0.40, 0.60) (0.20, 0.40, 0.60) (0.20, 0.40, 0.60) (0.20, 0.20, 0.40) (0.20, 0.20, 0.40)

    A3B1 (0.80, 1.00, 1.00) (0.60, 0.80, 1.00) (0.80, 1.00, 1.00) (0.60, 0.80, 1.00) (0.80, 1.00, 1.00)

    A3B2 (0.80, 1.00, 1.00) (0.80, 1.00, 1.00) (0.80, 1.00, 1.00) (0.80, 1.00, 1.00) (0.80, 1.00, 1.00)

    A3B3 (0.20, 0.20, 0.40) (0.20, 0.20, 0.40) (0.20, 0.20, 0.40) (0.20, 0.20, 0.40) (0.20, 0.20, 0.40)

    Table 11 Matrix of multiplication of criteria weights and alternative normalisation values

    Alternatives Criteria

    Aroma Colour Taste Texture Overall acceptance

    (0.203, 0.204, 0.209) (0.196, 0.203, 0.204) (0.188, 0.196, 0.203) (0.188, 0.188, 0.196) (0.204, 0.209, 0.209)

    A1B1 (0.08, 0.12, 0.17) (0.04, 0.08, 0.13) (0.04, 0.04, 0.08) (0.04, 0.04, 0.08) (0.08, 0.13, 0.17)

    A1B2 (0.08, 0.12, 0.17) (0.04, 0.08, 0.13) (0.04, 0.08, 0.12) (0.04, 0.04, 0.08) (0.08, 0.13, 0.17)

    A1B3 (0.04, 0.08, 0.13) (0.04, 0.08, 0.13) (0.04, 0.08, 0.12) (0.04, 0.04, 0.08) (0.04, 0.09, 0.13)

    A2B1 (0.11, 0.16, 0.21) (0.08, 0.13, 0.17) (0.07, 0.11, 0.16) (0.04, 0.07, 0.11) (0.12, 0.17, 0.22)

    A2B2 (0.11, 0.16, 0.21) (0.12, 0.17, 0.22) (0.07, 0.11, 0.16) (0.04, 0.07, 0.11) (0.12, 0.17, 0.22)

    A2B3 (0.04, 0.08, 0.13) (0.04, 0.08, 0.13) (0.04, 0.08, 0.12) (0.04, 0.04, 0.08) (0.04, 0.04, 0.09)

    A3B1 (0.15, 0.21, 0.21) (0.12, 0.17, 0.22) (0.14, 0.19, 0.21) (0.11, 0.14, 0.19) (0.16, 0.22, 0.22)

    A3B2 (0.15, 0.21, 0.21) (0.16, 0.21, 0.22) (0.14, 0.19, 0.21) (0.14, 0.18, 0.19) (0.16, 0.22, 0.22)

    A3B3 (0.04, 0.04, 0.08) (0.04, 0.04, 0.09) (0.04, 0.04, 0.08) (0.04, 0.04, 0.08) (0.04, 0.04, 0.09)

    Table 12 Positive ideal solution and negative ideal solution values

    Criteria Aroma Colour Taste Texture Overall acceptance

    S (+) (0.21, 0.21, 0.21) (0.22, 0.22, 0.22) (0.21, 0.21, 0.21) (0.19, 0.19, 0.19) (0.22, 0.22, 0.22) S (-) _ (0.04, 0.04, 0.04 ) (0.04, 0.04, 0.04) (0.04, 0.04, 0.04) (0.19, 0.19, 0.19) (0.22, 0.21, 0.22)

    Table 13 Intervals between criteria value and FPIS

    FPIS _Criteria_d +

    (D +) Aroma Colour Taste Texture Overall

    acceptance

    A1B1 0.096 0.136 0.156 0.143 0.095 0.626

    A1B2 0.096 0.136 0.133 0.143 0.095 0.603

    A1B3 0.134 0.136 0.133 0.143 0.135 0.681

    A2B1 0.062 0.096 0.097 0.122 0.059 0.436

    A2B2 0.062 0.060 0.097 0.122 0.059 0.400

    A2B3 0.134 0.136 0.133 0.143 0.160 0.706

    A3B1 0.034 0.060 0.039 0.057 0.030 0.219

    A3B2 0.034 0.030 0.039 0.030 0.030 0.162

    A3B3 0.158 0.161 0.156 0.143 0.160 0.778

    Table 14 Interval between criteria value and FNIS

    FPIS _Criteria_d-

    (D-) Aroma Colour Taste Texture Overall

    acceptance

    A1B1 0.093 0.057 0.027 0.143 0.095 0.416

    A1B2 0.093 0.057 0.056 0.143 0.095 0.445

    A1B3 0.057 0.057 0.056 0.143 0.135 0.449

    A2B1 0.131 0.094 0.090 0.122 0.059 0.496

    A2B2 0.131 0.134 0.090 0.122 0.059 0.536

    A2B3 0.057 0.057 0.056 0.143 0.160 0.474

    A3B1 0.154 0.134 0.147 0.057 0.030 0.521

    A3B2 0.154 0.158 0.147 0.030 0.030 0.518

    A3B3 0.027 0.026 0.027 0.143 0.160 0.384

    that the respondents preferred Cucumis melo stored in a two-layer banana stem packaging at 14 ° C (A3B2). Since the scores were fairly close between Cucumis melo stored in a two-layer banana stem packaging at 14 ° C (A3B2) and Cucumis melo stored in a two-layer banana stem packaging at 10 ° C (A3B1), both products were favored by consumers (respondents 'preferences).

    CONCLUSION

    According to the consumer assessment of all types of the six-day storage of Cucumis melo, the optimal storage conditions involved packaging with two layers of banana stem at the temperature of 14 ° C (A3B2). The fuzzy-Eckenrode and fuzzy-TOPSIS methods were very helpful in calculating the results of the multi-criteria

    A1B1

    Figure 6 Evaluation of d + and d-

    sensory assessment through weighing. They made the process of determining consumers 'acceptance easier, faster, and more certain.

    0.724 ° -785

    A1B1 A1B2 A1B3 A2B1 A2B2 A2B3 A3B1 A3B2 A3B3

    Figure 7 Alternative ranking of Cuciimis melo product acceptance

    CONFLICT OF INTEREST

    The authors declare no conflict of interest.

    ACKNOWLEDGEMENTS

    We thank our laboratory assistants at the Post-Harvest Engineering Laboratory, Faculty of Agriculture, Syiah Kuala University, especially Riza Rahmah, for their support and assistance in organising respondents and material preparation for this research.

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    ORCID IDs

    Rahmat Fadhil https://orcid.org/0000-0002-8124-1599


    Ключові слова: BANANA STEM / HEDONIC SCALE / CUCUMIS MELO (L.) / SENSORY ASSESSMENT / TOPSIS / ECKENRODE

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