2016 年 22 巻 5 号 p. 583-593
Fuzzy mathematics was applied to comprehensively evaluate the sensory quality of instant sea cucumber. Color, flavor, and fleshy elasticity were the evaluation factors considered and given with appropriate weights. The optimal conditions for instant sea cucumber were determined using single-factor and response surface methodology. The optimum processing parameters were as follows: cooking time, 60 min; cooking temperature, 83°C; water-swelling time, 42.5 h; and seasoning concentration, 100%. Under these conditions, the sensory evaluation score was 94.28, which matched well with the predicted value. Moreover, instant sea cucumber can keep its various nutritional values better. It is also found to have a better and nutritionally more beneficial amino acid composition than commercial sea cucumber.
Most sea cucumbers originated in the sea area of Bohai belong to families Echinopermata, Holothuroidea, Aspidochirotida, and Stichopodidae, which are Chinese traditional sea cucumber treasures (Bai et al., 2013; Eriksson and Clarke, 2015). The number of recorded sea cucumber species worldwide is more than 1100; approximately 140 kinds of sea cucumbers have been identified, and more than 20 species are considered edible in China (Conand and Byrne, 1993). Sea cucumber is a seafood with high-protein, low-fat, and low-cholesterol levels (Wen et al., 2010). Many recent studies found that sea cucumbers contain various bioactive constituents (Mamelona et al., 2009; Lawrence et al., 2010; Bordbar et al., 2011) that exhibit antitumor, anticoagulant, anti-aging, and antifungal properties, as well as contribute in blood fat reduction, diabetes control, and rheumatoid arthritis prevention and treatment. Oral administration of sea cucumber can prevent gastric ulcer and alleviate ethanol-induced histological damage (Wang et al., 2012). The sea cucumber body wall contains hemolytic enzymes, which lead to the melting of sea cucumber skin when the organism is exposed at high temperatures or when it encounters certain substances such as oil, minerals or strong oxidants. Therefore, storage of fresh sea cucumbers is difficult. Moreover, sea cucumber products through traditional processing technologies have an important impact on its nutritional value and may involve a very complicated consumption process (Conand and Byrne, 1993). With the generally accelerated pace and quality improvement of human life, the demand for high-quality instant food is becoming increasingly important (Charunuch et al., 2011). To meet the consumption needs of the modern society, studying superior-quality on instant sea cucumber production provides much significance. In judging the merits and demerits of the food, the evaluation results of the same samples were not consistent with the different judges due to the subjective factor. For similar samples, traditional sensory evaluation method can not give a clear difference. In addition, it is difficult to accurately express the deserved value of a certain index with the simple mean. Overall, the traditional sensory evaluation methods present a series of defects (Duan et al., 2010), which lead to incorrect or undesirable evaluation results. Using the fuzzy mathematics comprehensive evaluation, the samples were divided into different evaluation domains with respective fuzzy weight and were respectively evaluated. Given every comment domain appropriate membership, more accurate evaluation values can be obtained. Hence, the fuzzy evaluation method was applied to assess instant sea cucumber, aiming to develop an instant sea cucumber product with high nutritional value and visual appeal in the present study. Moreover, the nutritional values associated with physical and chemical indexes, as well as bioactive constituents of the commercial sea cucumber and the new one presented in this study were compared.
Processing parameters were optimized in this study by single-factor and response surface methodologies. Fuzzy mathematics was applied to comprehensively evaluate the instant sea cucumber product and reduce the effects of subjective assumption.
(1) Materials All sea cucumbers harvested from the sea area of Bohai were placed in foam boxes, covered with ice and cold-chain transported to the food laboratory. All ingredients used in this study were commercially available. All the reagents and solvents used in the experiment were of analytical grade and obtained from Baoding, Hebei Province, China.
(2) Preparation of instant sea cucumber All Fresh sea cucumbers of uniform quality underwent organ removal through cutting a quarter to a third of the sea cucumber body from the abdomen close to the anus. Afterward, dirt was washed away with distilled water and fresh sea cucumbers were placed into a water bath at 85 to 95°C for 2 min to blanch and finalize the design, then the gutted sea cucumbers were put into a −20°C refrigerator to spare. Pretreated sea cucumbers were then boiled in flavoring liquid (the ratio of material to water is 1:3, and 1 kg of the 100% flavoring liquid contains 35 g sugar, 5 g monosodium glutamate, 20 mL soybean sauce, 20 g salt, 3 g ginger powder, 3 g five-spice powder, 4 g octagon, 5 mL white vinegar, 8 mL cooking wine, and 5 g chicken essence). Then the boiled sea cucumbers were swollen in distilled water. Subsequently, the sea cucumbers were placed into the long neck of a Buchner funnel suction filter and maintained for 30 s to remove surface moisture, packed with vacuum packing machine (DZ, Aixun Packing Instrument Co. Ltd, Shanghai, China), and then sterilized by UV (HDL, Donglian Har Instrument Manufacture Co. Ltd, Beijing, China) for 30 min.
(3) Determination of rehydration ratio The boiled sea cucumbers were placed into the water with a temperature of 4°C and the rehydration ratio was measured each 12 h. Subsequently, the sea cucumbers were transferred into a long neck Buchner funnel suction filter and filtered for 30 s to remove surface moisture. Quickly afterward, the sea cucumbers were weighed. Rehydration ratio was calculated using the following formula:
 |
Where Mi is the weight after i hour (g), and M0 is the original weight.
(4) Sensory evaluation method Ten whole sea cucumber samples (weight 35 ± 1 g, volume 31 ± 1 cm3) were placed in some identical sensory panels coded with randomly selected 3-digit numbers prior to random monadic presentation to each of the evaluators. Through a series of training, testing and screening, choosing 10 qualified evaluators from all of the trainees as sensory panel member. The sensory panel members had a minimum of 2 and a maximum of 20 years of experience in food sensory evaluation. Sensory panel members individually evaluated instant sea cucumber samples simultaneously in random order and ranked them according to sea cucumber quality characteristics of the gradual transition from ‘very good’ to ‘poor’. There were four levels of satisfaction that the panelists could choose ranging from ‘very good’ (value = A) to ‘poor’ (value = D) depending on a four-point grading scale (Table 1). Evaluators eat tasteless bread to avoid cross-contamination between samples. Sensory evaluations were conducted in a food laboratory where lighting and ventilation conditions were deemed appropriate for such tests.
| Comment domain | Evaluation domain | |||
|---|---|---|---|---|
| Mark | Color | Flavor | Flesh elasticity | |
| very good | A | black | rich seafood flavor | flesh elasticity and hardness is better |
| fine | B | brown | seafood flavor | flesh elasticity, palatability is a bit poor |
| ordinary | C | yellowish brown | slight fishy smell | generally fleshy palate and hardness is not appropriate |
| poor | D | yellow | fishy smell | flesh inelastic, the hardness is worse |
Traditional evaluation results are not exact because they are obtained only by a single sensory evaluation index without any data processing, so some subjective factors could directly affect the sensory evaluation results such as personal preference, psychological factors and personal experience. However, fuzzy mathematics used grading scales to evaluate that producing the best discrimination between samples and the most reproducible results were chosen. Fuzzy weight vector was used to distribute accurately the weight of different factors in fuzzy mathematics comprehensive evaluation, which is a necessary factor for the validity of final results of sensory evaluation. In the process of fuzzy comprehensive evaluation, sensory panel members need to analyze sensory factors affecting the quality of instant sea cucumber, also the evaluation factor criterion layer, scheme and membership function, coupled with the weight of each factor and the membership degree to determine the level of instant sea cucumbers. Thereby fuzzy mathematics could increase the accuracy of results and reduce the influence of subjective factors. Therefore, the sensory evaluation of sea cucumber was performed by using fuzzy mathematics comprehensive evaluation method.
Fuzzy mathematics comprehensive evaluation changes the traditional average number system (Liu et al., 2012), and the method requires a choice for the evaluation domain U = (U1 U2 U3) = (color flavor fleshy elastic), comment domain V = (V1 V2 V3 V4) = (very good fine ordinary poor), fuzzy weight vector X = (X1 X2 X3) = (0.45 0.2 0.35), grade evaluation matrix P = (P1 P2 P3 P4) = (100 80 60 40), and sensory evaluation indexes (Table 1). In this study, fuzzy mathematics comprehensive evaluation was performed through a three-step membership grade: (i) the sensory evaluation results of every evaluation domain were calculated to build multiple objective evaluation membership matrix Ri = (ri1 ri2 ri3 ri4) and obtain the fuzzy evaluation matrix R = (Ri1 Ri2 Ri3)T; (ii) the comprehensive grading vector was Bk = X×R = (X1 X2 X3)(Ri1 Ri2 Ri3)T, and the specific operations of (i) and (ii) were conducted by referring to the available literature (Gao and Fu, 2011; Liu et al., 2012; Liu et al., 2014); (iii) the comprehensive evaluation score of each sample was Sj = P×BkT =(P1 P2 P3 P4)(Bk1 Bk2 Bk3 Bk4)T, which can be applied to rank competition of sea cucumber. In this study, the weight of the sea cucumber's color, flavor, and flesh elasticity represents the average of the mandatory scoring, sequential evaluation, and statistical evaluation methods.
(5) Experimental design and statistical analysis Response surface optimization is better than the traditional parameter optimization because the former can save time, space, and raw materials. The Box—Behnken Design (BBD) for statistical screening was applied to monitor the quality of the sea cucumber, optimize the conditions of the sea cucumber processing technology, and evaluate the main effects, interaction effects, and quadratic effects of the sensory evaluation formulation (Bordbar et al., 2011; Song et al., 2012; Jentzer et al., 2015; Madani et al., 2015). The main factors including cooking time, cooking temperature, water-swelling time, and seasoning concentration can significantly influence the sensory evaluation of sea cucumbers. Therefore, these four parameters were monitored through a single-factor experiment. For statistical calculations, the relationships between the coded values and the actual values were described through the following equation:
 |
Where Xi is the coded value of the independent variable, Ai is the actual value of the independent variable, Ao is the actual value of Ai at the center point, and ΔA is the step-change value.
On the basis of the single-factor experimental data, a second-order polynomial model corresponding to the BBD was used to correlate the relationship between the independent variables and the response values to predict the optimized conditions. The nonlinear computer-generated quadratic model was given as follows:
 |
Where Y is the response value; βo is a constant; βi, βii and βij represent the linear, quadratic, and interactive coefficients, respectively; Xi is the coded level of the independent variables; and XiYj and Xi2 represent the interaction and quadratic terms, correspondingly.
(6) Determination of physical, chemical, and bioactive constituents Physical and chemical indexes of sea cucumber were determined by referring to national standards, whereas the sea cucumber bioactive constituents were determined by referring to published works; in particular, collagen was determined by referring to Zhu et al. (2012); saponins were determined by referring to Zhao et al. (2010); polysaccharide was determined by referring to Yang et al. (2015); and cerebrosides was determined by referring to La et al. (2012). These bioactive constituents were all obtained through crude extraction.
(7) Determination and analysis of amino acids In this work, amino acids were quantified using Waters HPLC system (Waters Corporation, Milford, USA). The chromatographic system consisted of a Waters 1525 Binary HPLC pump and Waters 2489 UV/Visible detector. Chromatographic separation was performed on an Eclipse XDB-C18 column (250 mm × 4.6 mm, 5 µm, Agilent Technologies, Palo Alto, USA) with an oven temperature of 40°C. The detection wavelength was set at 360 nm. The mobile phase involved a mixture solvent (A: chromatographic grade acetonitrile; B: 2.5 g of sodium acetate and 1.5 mL triethylamine dissolved into 1 L of water, with the solution pH at 5.25). The flow rate was set at 1 mL/min. The injection solution volume was set at 10 µL with a Waters 2707 autosampler. Each sample was analyzed thrice, and the running time was set at 50 min. The gradient elution program is presented in Table 2 (Cheng et al., 2009).
| Time/min | Mobile phase | |
|---|---|---|
| A% | B% | |
| 0.00 | 18.00 | 82.00 |
| 10.00 | 18.00 | 82.00 |
| 15.00 | 20.00 | 80.00 |
| 30.00 | 34.00 | 66.00 |
| 35.00 | 45.00 | 55.00 |
| 38.00 | 55.00 | 45.00 |
| 42.00 | 60.00 | 40.00 |
| 45.00 | 18.00 | 82.00 |
| 48.00 | 18.00 | 82.00 |
| 49.00 | 18.00 | 82.00 |
One gram of sea cucumber was hydrolyzed with 10 mL of 6 M HCl in a sealed ampoule for 24 h at 110°C under vacuum. The acid hydrolysate was evaporated until dried by using a Speedvac concentrator (ZLS-1, Hexi instrument machine Co.Ltd, Hunan, China), and the dry residue was resolved in 2 mL of 0.1 M HCl. The sample was filtered through a 0.45 mm nylon filter before being derived (Sujak et al., 2006). Up to 50 µL of the sample was added into 200 µL of the Na2CO3—NaHCO3 solution at pH 9.0, and 100 µL of 2, 4-dinitrochlorobenzene was added to a water box under a constant temperature of 90°C. The mixture was maintained for 90 min without sunlight. The pH was adjusted to neutral by adding acetic acid at 25°C, and then the derivatives were dissolved in 1 mL of purified water. The sample was filtered through a 0.45 mm organic filter before being analyzed with HPLC (Kang et al., 2006).
The protein quality was estimated by determining the total amino acids (TAA), nonessential amino acids (NEAA) and essential amino acids (EAA). According to the FAO/WHO model (1973) and the whole egg model, the calculative results of ratio of amino acid (RAA), chemical score (CS), ratio coefficient (RC), and essential amino acid index (EAAI) can be used to evaluate the sea cucumber value.
(8) Statistical analysis The analyses were repeated ten times and results were expressed as mean values ± standard deviation (SD) (n=10) in the part of the nutritional exploration, others were mean values (n=3). All data sets were analyzed statistically by using SPSS 17.0, and differences in the concentration of nutritional elements between instant or commercial sea cucumber were tested by independent sample t-test. Differences were considered to be significant when P < 0.05.
(1) Effects of sea cucumber processing parameters on evaluation index
a) Effects of sea cucumber processing parameters on sensory evaluation Sensory evaluation is influenced by various factors. Fig. 1a indicates the effect of cooking time and other processing conditions, which were fixed as follows: the water-swelling time was 48 h, the seasoning concentration was 100%, and the cooking temperature was 95°C. As shown in Fig. 1a, the sensory evaluation score rapidly increased when the cooking time ranged from 30 to 60 min. During the early stages of processing, the sea cucumber collagen fiber gradually shrunk, and the internal components became denatured, which resulted in enhanced color, flavor, and flesh elasticity. However, the sensory evaluation score rapidly decreased when the cooking time went beyond 60 min because the sea cucumber gelatinized in a stepwise manner, and the exchange of internal and external moisture made the sea cucumber body wall appearance seems blistered. Therefore, the sensory evaluation score of sea cucumber was significantly higher than other parameters when the cooking time reached 60 min.
Effects of cooking time(a), cooking temperature(b), water-swelling time(c) and seasoning concentration(d) on sensory evaluation
Cooking temperature is another important factor that can influence sensory evaluation. The effect of cooking temperature on sensory evaluation was studied at a cooking time of 60 min, water-swelling time of 48 h, and seasoning concentration of 100%. The cooking temperatures were set at 60, 70, 80, 90 and 100°C. The results (Fig. 1b) showed that the sensory evaluation score of sea cucumber initially increased then decreased after peaking at 80°C. Research showed that hydrogen bonds and ionic bonds between collagen molecules and peptide bonds were opened with increasing cooking temperature. When the temperature reached up to 80°C, irreversible changes in collagen occurred, with the molecules regularly restructuring into a 3D network structure. However, the exorbitant temperature can cause collagen melting, and the sea cucumber surface became adhesive. Therefore, the optimal sensory evaluation was detected when the temperature was 80°C.
To study the effects of different water-swelling times on the sensory evaluation of sea cucumber, water swelling was conducted at various durations: 12, 24, 36, 48 and 60 h. The cooking time was fixed at 60 min, cooking temperature at 80°C, and seasoning concentration at 100%. As shown in Fig. 1c, the water-swelling time played an important role in the sensory evaluation of sea cucumber. The graph appeared as a wave, with the sensory evaluation score peaking at 12 h. This phenomenon may be ascribed to the initial superior flexibility, color, and taste of the sea cucumber. However, a small sea cucumber body considerably affected its commercial value. Hence, the 12 h water-swelling time was not selected. When the time ranged from 24 to 60 h, the graph exhibited an initial rising trend, which subsequently declined. This phenomenon may be attributed to the uneven initial water swelling, ultimately leading to worse taste and flavor compared with other water-swelling durations. The internal moisture of the sea cucumber gradually became well-distributed, and the sea cucumber assumed a full and smooth body with continuous moisture absorption. Hence, at these durations, the sea cucumber obtained better sensory evaluation scores. Nevertheless, when the water-swelling time exceeded 48 h, the sensory evaluation score rapidly decreased because the sea cucumber body wall exhibited peeling upon further water absorption, even leading to the separation of the sea cucumber body wall. Thus, the optimum water-swelling time was set at 36 to 48 h.
Seasoning concentration is another factor that can influence sensory evaluation. The effect of seasoning concentration was studied with the cooking time set at 60 min, cooking temperature at 80°C, and water-swelling time at 42 h. As shown in Fig. 1d, the sensory evaluation evidently changed slowly with increasing seasoning concentration. When the seasoning concentration ranged from 100% to 110%, the sensory evaluation results improved. Statistical analysis with SPSS 17.0 showed no significant difference between 100% and 110%. Therefore, 100% was selected as the optimum seasoning concentration to avoid waste. Based from our experience and according to previously published data, no interactive effects exist between seasoning concentration and the other parameters mentioned above with respect to the sensory evaluation results. Hence, seasoning concentration was not the test factor of response surface.
b) Effects of boiling time on rehydration ratio of sea cucumber Pretreated sea cucumbers were randomly distributed into seven portions and then marked. These sea cucumbers were placed in water at 95°C to boil for 30, 40, 50, 60, 70, 80 and 90 min. After 12 h, the sea cucumber weight was immediately measured to study the influence of different boiling times on rehydration ratios. The sea cucumber rehydration ratios generally increased with increasing boiling time (Fig. 2).
Effects of boiling time on sea cucumber rehydration ratio
The rehydration ratio within 24 to 48 h was also significantly better than those at 0 to 24 h and 48 to 72 h. Moisture penetration was impeded within 0 to 24 h because the sea cucumber collagen fiber structure packed tightly. Within the 24 to 48 h period, the sea cucumber internal structure gradually became loose, and the rehydration ratio increased with the constant infiltration of moisture. However, the water-absorbing ability gradually approached saturation; hence, at 48 to 72 h, this ability gradually declined, and the excessive water absorption probably affected the sensory evaluation score, as well as the sea cucumber quality and structure. At cooking times of 60, 80, and 90 min, the rehydration ratio was relatively higher than those at other durations. As shown in Fig. 1a and Fig. 2, the sea cucumber demonstrated relatively optimal sensory evaluation and rehydration ratio when the cooking time was 60 min, which was also considered as a better cooking time system.
c) Effects of boiling temperature on sea cucumber rehydration ratio Pretreated sea cucumbers were randomly distributed into five portions and then marked. These sea cucumbers were placed in water at temperatures of 60, 70, 80, 90 and 100°C to boil for 60 min. After 12 h, the weight of the sea cucumbers was immediately measured to study the influence of different boiling temperatures on the rehydration ratio. Sea cucumbers at temperatures range from 60 to 80°C yielded the highest rehydration ratio (Fig. 3). However, excessively high temperatures affected this ratio. The cause was predicted to be that the hydrogen bonds and ionic bonds between collagen molecules and peptide bonds became being opened with increasing cooking temperature, thereby enhanced the water-holding capacity of the sea cucumber. Nevertheless, the collagen structure being damaged at excessively high temperatures, leading to poor water-holding capacity. As shown in Fig. 1b and Fig. 3, 80°C was selected as the optimum cooking temperature for the sea cucumbers.
Effects of boiling temperature on sea cucumber rehydration ratio
(2) Optimization of processing parameters for sensory evaluation On the basis of the preliminary results, a suitable level was selected to conduct the response surface experiment. The range and center point values of three independent variables are presented in Table 3. A total of 17 runs were performed to optimize the three individual parameters in the current BBD. The design matrix of the variables coded with the predicted and experimental values is displayed in Table 4. The data were analyzed through multiple regressions by using Design-Expert 8.0.6. The sensory evaluation score was taken as the dependent variable or the response value.
| Variables | Coded levels | ||
|---|---|---|---|
| −1 | 0 | +1 | |
| Cooking temperature X1/°C | 70 | 80 | 90 |
| Cooking time X2/min | 50 | 60 | 70 |
| Water-swelling time X3/h | 36 | 42 | 48 |
| Test number | X1 | X2 | X3 | Experimental | Predicted |
|---|---|---|---|---|---|
| 1 | 0 | −1 | 1 | 79.67 | 79.26 |
| 2 | 1 | 0 | −1 | 83.85 | 84.31 |
| 3 | 0 | −1 | −1 | 80.57 | 79.91 |
| 4 | 0 | 0 | 0 | 94.10 | 94.09 |
| 5 | 0 | 1 | 1 | 80.95 | 81.61 |
| 6 | 1 | −1 | 0 | 84.37 | 84.57 |
| 7 | −1 | 0 | −1 | 78.97 | 78.76 |
| 8 | 0 | 0 | 0 | 94.70 | 94.09 |
| 9 | 0 | 0 | 0 | 94.50 | 94.09 |
| 10 | 1 | 0 | 1 | 85.09 | 85.30 |
| 11 | 0 | 0 | 0 | 93.50 | 94.09 |
| 12 | 0 | 0 | 0 | 93.67 | 94.09 |
| 13 | 0 | 1 | −1 | 76.17 | 76.58 |
| 14 | −1 | 1 | 0 | 79.94 | 79.74 |
| 15 | 1 | 1 | 0 | 87.08 | 86.22 |
| 16 | −1 | 0 | 1 | 82.61 | 82.15 |
| 17 | −1 | −1 | 0 | 81.50 | 82.37 |
Through the multiple regression fitting of experimental data, the sensory evaluation results of sea cucumber were concluded to be in line with the quadratic polynomial regression model, considering different factors. The following polynomial equation was derived to predict the sensory evaluation results of sea cucumber as a function of the tested independent variables. Y is the sea cucumber sensory evaluation result, and X1, X2, and X3 are the coded values for cooking temperature, cooking time, and water-swelling time, respectively. The quadratic regression equation is calculated as follows:
 |
The statistical significance of this equation was determined by F-test and ANOVA for the response surface quadratic model. The results are summarized in Table 5 Considering the model factor result P < 0.0001 and the lack of fit P = 0.1028, the model fitting was evidently better, allowing prediction of the effect of processing parameters on the sensory evaluation of sea cucumber (Madani et al., 2015). In addition, the results in Table 5 show that the factors with extremely significant effects on the sensory evaluation results represent the line term of cooking temperature, cooking time, the quadratic term of cooking time and water-swelling time, and the square term of the three factors. The quadratic term of cooking temperature and cooking time also significantly influenced the sensory evaluation results. Table 6 shows that the coefficient of determination (R2) of the regression equation is 0.9932, the Adj R2 is 0.9845, the score of Coefficient of variation (CV) is 0.92%, and the model can explain 98.45% change in response. The R2 is the proportion of variability in the data explained or accounted for by the model (Xu et al., 2013). As shown in Table 6, the experimental data were reliable, the test error was small, and the fitting and stability of the model were excellent. The results suggested that the recommended equation ensured an appropriate estimation to determine the relationship between the independent variables and response variables. Therefore, the model provided a reliable basis for the sensory assessment of sea cucumber.
| Source | S.S. | D.F. | M.S. | F-value | P-value | Significance |
|---|---|---|---|---|---|---|
| Model | 636.32 | 9 | 70.70 | 113.86 | <0.0001 | ** |
| X1 | 37.71 | 1 | 37.71 | 60.74 | 0.0001 | ** |
| X2 | 0.49 | 1 | 0.49 | 0.78 | 0.4061 | NS |
| X3 | 9.59 | 1 | 9.59 | 15.45 | 0.0057 | ** |
| X1X2 | 4.56 | 1 | 4.56 | 7.34 | 0.0302 | * |
| X1X3 | 1.44 | 1 | 1.44 | 2.32 | 0.1716 | NS |
| X2X3 | 8.07 | 1 | 8.07 | 12.99 | 0.0087 | ** |
| X12 | 60.50 | 1 | 60.50 | 97.44 | <0.0001 | ** |
| X22 | 211.10 | 1 | 211.10 | 339.97 | <0.0001 | ** |
| X32 | 247.91 | 1 | 247.91 | 399.24 | <0.0001 | ** |
| Residual | 4.35 | 7 | 0.62 | |||
| Lack of fit | 3.28 | 3 | 1.09 | 4.11 | 0.1028 | NS |
| Pure error | 1.06 | 4 | 0.27 | |||
| Cor total | 640.66 | 16 |
“**” shows the significantly different (P < 0.01), “*” represents significantly different (P < 0.05) and “NS” means not significant difference.
| Item | Value | Item | Value |
|---|---|---|---|
| Standard deviation | 0.79 | Adj R2 | 0.9845 |
| CV% | 0.92 | Pre R2 | 0.9154 |
| R2 | 0.9932 | Adeq Precision | 28.981 |
As shown in Fig. 4, the 3D graphs exhibited a parabolic surface, and each graph presented an optimum point; the corresponding contour plots also displayed clear peaks. The quadratic polynomial regression model and response surface analysis results showed that the stable point existed in the regression model (Jentzer et al., 2015). Significant interaction existed between cooking temperature and cooking time, and highly significant interactions were found between cooking time and water-swelling time. The sea cucumber quality gradually became close to the optimal level with the enhancement of the relevant processing parameters. Nevertheless, excessive cooking and high temperature made sea cucumber gradually lose its flesh elasticity, as well as its unique color and flavor, which led to the decrease in the sensory evaluation value. Cooking temperature and cooking time affected the interior structure of the sea cucumber, and the internal network structure affected water swelling. An appropriate water-swelling time made sea cucumber gain good flavor and high commercial value. The correlation analysis results of the mathematical regression model showed that the experimental data can accurately fit with the quadratic polynomial model. The experiment was performed thrice to test the reliability of the experimental method. The processing conditions were cooking temperature, 83°C; cooking time, 60 min; water-swelling time, 42.5 h; and seasoning concentration, 100%. Under these conditions, the mean value of the sensory evaluation was 94.28, which matched well with the predicted value.
Respomse surface(3D) showing the effects of experimental conditions on sensory evaluation
(3) Chemical composition and bioactive compounds
a) Basic nutrients and bioactive constituents Commercial and instant sea cucumbers were compared, and the test results are listed in Table 7; the results are similar to those of a published work (Öera et al., 2004). As shown in Table 7, the instant sea cucumber was better than that of commercial sea cucumber except for moisture and ash contents with the concentrations of protein and crude saponin no significantly difference between instant and commercial sea cucumber. This finding suggests that the instant sea cucumber can keep the bioactive components better. And these bioactive components have been discovered gradually and proved that has a certain functional role.
| Main index (%) | Instant | Commercial | Significance |
|---|---|---|---|
| Moisture | 88.31±0.55 | 91.06±0.60 | * |
| Crude Protein | 6.31±0.08 | 6.26±0.03 | NS |
| Crude Fat | 0.45±0.04 | 0.36±0.04 | * |
| Ash | 0.83±0.02 | 1.03±0.04 | * |
| Crude collagen | 3.53±0.02 | 2.96±0.03 | * |
| Crude Polysaccharide | 0.36±0.01 | 0.28±0.01 | * |
| Crude Saponin | 0.21±0.01 | 0.21±0.03 | NS |
| Crude Cerebroside | 0.05±0.00 | 0.04±0.00 | * |
All results were mean ± standard deviation values from 10 samples (n=10).
“*” represents significantly different (P<0.05) and “NS” represents not significantly different.
b) Amino acid analysis In contrast to plants, humans and animals can only synthesize nonessential amino acids. The biosynthesis of the EAA is not possible without their continuous supply through food consumption (Sujak et al., 2006). Therefore, foods rich in exogenous amino acids are desirable. Modern nutrition theories show that the nutritional value of protein is closely related to the amino acid composition (Wong and C.K, 2001). A close amino acid composition to that of humans indicates a high nutritional value (Li et al., 2013). Such foods can be digested and absorbed easily by the human body and can yield high nutritional value. In recent years, the evaluation patterns of amino acids have become increasingly reported (Zarkadas et al., 2000; Gilani, 2012). The current work used the 1973 FAO/WHO requirement pattern (WHO/FAO/UNU, 2002) and the whole egg pattern to evaluate the sea cucumber protein's nutritional value, which proves to be the highly approved among different patterns.
The amino acid compositions of instant and commercial sea cucumbers are listed in Tables 8–10. Table 8 reveals that 18 of the amino acids in instant and commercial sea cucumbers exhibit significantly (P < 0.05) difference. The most abundant amino acids were glycine, glutamic acid, aspartic acid, and arginine, which agrees with previous reports (Wen et al., 2010). According to the protein nutritional evaluation criteria, when the numerical values of RAA, CS, RC, and EAAI are closer to 1, the protein's nutritional value is higher (Constantinos et al., 1995; Sujak et al., 2006). Although both of them there was no significant difference in protein, the nutritional value of amino acid in instant sea cucumber was significantly (P < 0.05) higher than that in commercial sea cucumber (Tables 9–10). In the current study, the EAAI score of instant sea cucumber was higher than that of the commercial sea cucumber in both the FAO/WHO model and whole egg model. This finding signifies that the instant sea cucumber was superior to the commercial sea cucumber and further verified that the nutritional value of the instant sea cucumber was higher than that of the commercial sea cucumber. Through the RC score, the restrictive amino acid in the food can be discerned. Data in Table 9 suggest that the first restrictive amino acid in the sea cucumber is tryptophan, followed by phenylalanine and lysine. The method failed to detect tyrosine in this study, which may explain why the tyrosine and phenylalanine contents were low. Generally, the amino acids of instant sea cucumber were better than those of commercial sea cucumber, suggesting that the former is a good protein source. Hence, the processing technology of instant sea cucumber was an effective production mechanism.
| Amino acids | Instant | Commercial | Significance | |
|---|---|---|---|---|
| Aspartic acid | 11.05±0.03 | 8.00±0.02 | * | |
| Glutamic acid | 16.80±0.07 | 12.50±0.05 | * | |
| Histidine | 1.31±0.00 | 0.80±0.01 | * | |
| NEAA | Serine | 5.80±0.04 | 4.15±0.01 | * |
| Arginine | 7.94±0.03 | 5.87±0.02 | * | |
| Glycine | 25.05±0.08 | 17.34±0.07 | * | |
| Alanine | 6.55±0.05 | 5.20±0.01 | * | |
| Proline | 8.58±0.06 | 6.00±0.02 | * | |
| Cysteine | 2.73±0.03 | 1.24±0.01 | * | |
| Valine | 4.65±0.04 | 3.26±0.02 | * | |
| Methionine | 2.00±0.03 | 1.27±0.00 | * | |
| Threonine | 5.77±0.05 | 3.92±0.01 | * | |
| EAA | Isoleucine | 3.90±0.04 | 2.70±0.00 | * |
| Leucine | 5.61±0.05 | 3.90±0.02 | * | |
| Tryptophan | 0.37±0.01 | 0.34±0.00 | * | |
| Phenylalanine | 3.58±0.02 | 2.33±0.01 | * | |
| Lysine | 4.90±0.03 | 3.80±0.01 | * | |
| Taurine | 0.06±0.00 | 0.06±0.00 | * | |
| TAA | 116.59±0.20 | 83.28±0.28 | * | |
| EAA | 33.53±0.10 | 22.82±0.07 | * | |
| EAA/TAA | 0.29±0.00 | 0.27±0.00 | * | |
| EAA/NEAA | 0.40±0.00 | 0.38±0.00 | * |
All results were mean ± standard deviation values from 10 samples (n=10).
“*” represents significantly different (P < 0.05) and “NS” represents not significantly different.
| Amino acids | RAA% | CS% | Reported composition/g·100g Crude Protein−1 | |||||
|---|---|---|---|---|---|---|---|---|
| Instant | Commercial | Significance | Instant | Commercial | Significance | FAO/WHO | Egg | |
| Thr | 1.442±0.013 | 0.991±0.006 | * | 1.131±0.010 | 0.777±0.005 | * | 4 | 5.1 |
| Val | 0.931±0.007 | 0.660±0.006 | * | 0.637±0.005 | 0.452±0.001 | * | 5 | 7.3 |
| Met+Cys | 1.351±0.014 | 0.725±0.003 | * | 0.860±0.009 | 0.461±0.002 | * | 3.5 | 5.5 |
| Ile | 0.974±0.010 | 0.687±0.012 | * | 0.590±0.006 | 0.416±0.007 | * | 4 | 6.6 |
| Leu | 0.801±0.008 | 0.566±0.008 | * | 0.637±0.006 | 0.451±0.007 | * | 7 | 8.8 |
| Phe+Tyr | 0.597±0.004 | 0.396±0.005 | * | 0.358±0.002 | 0.238±0.003 | * | 6 | 10 |
| Trp | 0.369±0.012 | 0.344±0.005 | * | 0.231±0.008 | 0.215±0.003 | * | 1 | 1.6 |
| Lys | 0.886±0.005 | 0.697±0.003 | * | 0.761±0.005 | 0.599±0.003 | * | 5.5 | 6.4 |
All results were mean ± standard deviation values from 10 samples (n=10).
“*” represents significantly different (P < 0.05) and “NS” represents not significantly different.
| Evaluation | FAO/WHO | whole Egg | |||||
|---|---|---|---|---|---|---|---|
| index | Instant | Commercial | Significance | Instant | Commercial | Significance | |
| RC | Thr | 1.570±0.145 | 1.585±0.009 | * | 1.740±0.016 | 1.746±0.010 | NS |
| Val | 1.014±0.007 | 1.056±0.010 | * | 0.981±0.008 | 1.015±0.010 | * | |
| Met+Cys | 1.472±0.015 | 1.159±0.040 | * | 1.322±0.013 | 1.036±0.004 | * | |
| Ile | 1.061±0.011 | 1.100±0.020 | * | 0.908±0.009 | 0.935±0.016 | * | |
| Leu | 0.872±0.008 | 0.906±0.014 | * | 0.980±0.009 | 1.012±0.015 | * | |
| Phe+Tyr | 0.650±0.004 | 0.633±0.008 | * | 0.551±0.004 | 0.534±0.007 | * | |
| Trp | 0.402±0.013 | 0.550±0.008 | * | 0.355±0.011 | 0.484±0.007 | * | |
| Lys | 0.965±0.006 | 1.116±0.006 | * | 1.171±0.007 | 1.347±0.007 | * | |
| EAAI | 0.851±0.004 | 0.603±0.003 | * | 0.590±0.003 | 0.418±0.002 | * |
All results were mean ± standard deviation values from 10 samples (n=10).
“*” represents significantly different (P < 0.05) and “NS” represents not significantly different.
This work described the application of fuzzy mathematics for comprehensive and objective sensory evaluation of sea cucumber. The sensory evaluation of instant sea cucumber was performed using three factors. BBD was based on response surface methodology. Through single-factor and response surface analyses, the optimal process parameters of instant sea cucumber were determined as follows: cooking time, 60 min; cooking temperature, 83°C; water-swelling time, 42.5 h; and seasoning concentration, 100%. Under such conditions, the sensory evaluation score is 94.28, which matches well with the predicted value. Moreover, the instant sea cucumber processing technology can maintain various nutritional ingredients. Through the FAO/WHO and the whole egg patterns of assessing the nutrition of sea cucumber's protein, the protein of instant sea cucumber was determined to be better than that of commercial sea cucumber. Therefore, the instant sea cucumber is proven as a good protein source.
Acknowledgements This work was supported by Science and Technology Project of Hebei Province (No. 14273205D), Education Department of Hebei Province (No.YQ2014037), and the program of Young and Top Talents of Hebei Province. The authors declare that they have no competing financial interests.
