Optimization of fermentation technology of hawthorn-pear wine by uniform design and response surface design

Abstract

Uniform design methodology and response surface methodology were used to determine the optimum conditions for hawthorn-Yali pear wine. By using uniform design, the effects of fermentation temperature, sugar content, the ratio of hawthorn to pear, soaking time of hawthorn, additional volume of SO₂, and yeast dosage on sensory quality were investigated, which indicated that the first three aspects were of great significance to the sensory quality. By using three-factor, three-level response surface methodology, a prediction model was established in the form of quadratic polynomial regression equation, with the best processing conditions hereby determined under the conditions of fermentation temperature (25.18°C), sugar content (22.00%), the ratio of hawthorn to pear (21.19:100), and the alcohol degree (11.05 (V/V %)).

Keywords

hawthorn / pear wine / uniform design / response surface design

Introduction

In Hebei Province, the output of Yali pear (Pyrus. bretschneideri Rehd) is immense, which has suffered from great difficulties in market sales in recent years, with relatively lower income of pear growers. To multiply processing approaches, take full advantage of Yali pear, and thus get rid of the dilemma of pear production, it is advisable that high-quality pear wine be produced. The phenolic compounds can affect the taste of wine (Careri et al., 2003), but Yali pear alone can produce light-flavored perry poor in taste quality (Yuan et al., 2003). Hawthorn (Prunus salicina Bge.) contains high-level flavonoid suited best for cardiovascular hygienic care (Rigelsky and Sweet, 2002; Xu et al., 2009). Our previous results proved that the brownish red hawthorn-Yali pear wine was excellent in color, aroma, and flavor. In the present study, we wanted to optimize the fermentation process to improve the quality of hawthorn-Yali pear wine.

Uniform design is an experimental method only emphasizing the uniformly distributed testing points, which is applied to multivariable and multilevel experiments to find out optimal experimental conditions as well as reduces the test frequency substantially (Tang and Cai, 2006; Xing and Li, 2008; Jin and Zhou, 2009). Response surface analysis is based on the polynomial fitting of dependent variables and independent variables (Ratnam et al., 2003; Martendal et al., 2007), which is widely used in wine researches (Yannam et al., 2009; Zhao and Song, 2009; Zhou et al., 2011). Jointly using the uniform design and the response surface design, working out secondary factors via uniform analysis, and then optimizing the experimental conditions according to the response surface design will reduce testing frequency, conduct data conveniently, and improve experiment efficiency.

This article explored the effect on sensory quality and alcohol degree with factors, such as fermentation temperature, sugar content, and the ratio of hawthorn to pear, by both uniform design and response surface design, in order to optimize the fermentation conditions of hawthorn-Yali pear wine, which will promote the development of Hebei Yali pear industry.

Materials and methods

Materials

Yali pears were provided by an orchard at Shunping County, Hebei Province. Hawthorns were purchased from market; Danbaoli high-activity dry yeast for wine was provided by Danbaoli Yeast Co., Ltd.

Experimental methods

Sensory evaluation

A seven-member assessment panel graded the wines by color, aroma, taste, and typicality. The highest grade in four grades for these four aspects was 20, 30, 40, and 10, respectively. Grading standards are listed in Table 1 (Niu et al., 2009).

Alcohol degree

Gas chromatography (GC) is used to quantify alcohol degree by adding 0.2 mL 4-methyl-2-pentanol into 10 mL perry samples diluted 2 to 10 times as internal standard with column temperature 80°C rising to 180°C at 15°C/min. The temperature at injection port and detection port was 200°C and 220°C (GB/T, 2006), respectively.

Uniform design

The experiment factors consisted of fermentation temperature, sugar content, soaking time of hawthorn, the ratio of hawthorn to pear, SO₂ addition, and yeast inoculation at 5 × 10 × 10 × 10 × 10 × 10 level (Charoen et al., 1998; Torija et al., 2003; Serra et al., 2005; Wu et al., 2009). Hawthorn pulp was crushed into granular, and yeast was activated with 5% sugar water at 40°C for 30 min before SO₂, hawthorn pulp granular, sugar, and activated yeast were added into 600 mL extracted Yali pear juice in 1 L conical flasks for main fermentation. Seven days after main fermentation, the dregs were filtered out from the mixture followed by 20 days post-fermentation and 3 months aging when the wine was evaluated by the seven-member assessment panel, with the main effective factors of wine quality determined according to the results of sensory evaluation.

Response surface design

A three-factor, three-level response surface experiment was designed for optimizing the fermentation condition in order to obtain the high alcohol degree. Each experiment was performed twice.

Results and analysis

Initial screening of the fermentation factors by uniform design

To get the best sensory quality, uniform design experiments of 6 factors at 5 × 10 × 10 × 10 × 10 × 10 level were performed. The factors and levels are listed in Table 2. The sensory evaluation results in Table 3 showed that No. 3 treatment won the highest score (90.1) followed by No. 8 treatment (88.3). The various factors in the two treatments were fermentation temperature of 21°C and 29°C, sugar content of 19.8% and 21.3%, hawthorn soaking time of 8 h and 22 h, the hawthorn-to-pear ratio of 20∶100 and 35∶100, SO₂ of 50 and 95 mg/L, and yeast dosage of 0.3 and 0.8 g/L, respectively, which are more appropriate conditions for each factor.

According to the visual analysis, it was suggested that fermentation temperature, sugar content, and the ratio of hawthorn to pear were important impacts to the next response surface test, with the hawthorn soaking time, the dosage of SO₂, and yeast as the less important impacts.

Optimization of process conditions by response surface method

Experimental design

According to the analysis of uniform experiments and available data (Constantinos and Olga, 1994), a three-factor, three-level response surface design was applied for optimizing the fermentation condition in order to obtain the high alcohol degree of hawthorn-pear wine, with the three independent variables of fermentation temperature (A, °C), sugar content (B, %), and ratio of hawthorn to pear juice (C, g:g) at three levels each (Table 4). A total of 15 experiments were designed (Table 5). The average alcohol degree (V/V %) was taken as the response, Y.

Models

On the basis of the response surface analysis, the predicted second-order polynomial model was

(1)Y=\mathrm{9.16}-\mathrm{0.19}A+\mathrm{0.88}B-\mathrm{0.22}C+\mathrm{0.042}AB+\mathrm{0.015}AC-\mathrm{0.028}BC-\mathrm{0.51}{A}^{\mathrm{2}}+\mathrm{0.048}{B}^{\mathrm{2}}-\mathrm{0.3}{C}^{\mathrm{2}}.

The results indicated that the sugar content (B) was the major factor contributing to the efficiency of alcohol degree (Y), while the fermentation temperature value and the ratio of hawthorn to pear juice were insignificant.

The analysis of variance for the response surface design is shown in Table 6. The model P-value of 0.0004 implied the significance of model (P<0.01) (Li et al., 2011). The coefficient of determination (R²) of the model (0.9856) indicated that 98.56% of the variations could be explained by the structured model, which adequately represented the real relationship between the parameters chosen.

Response surface plotting

To determine optimal levels of the variables for the alcohol degree of hawthorn-pear wine, three-dimensional surface plots were constructed according to Eq. (1). Fig. 1 shows the effect of sugar content and fermentation temperature on the alcohol degree at a fixed ratio of hawthorn to pear of 25∶100. Fig. 2 shows the effect of ratio of hawthorn to pear and fermentation temperature at a fixed sugar content of 20%. Fig. 3 shows the effect of ratio of hawthorn to pear and sugar content on the alcohol degree at a fixed fermentation temperature of 25°C.

Higher sugar content was found beneficial to the alcohol degree. At the same time, the high temperature and the ratio of hawthorn to pear could lead to a decrease in the alcohol degree. Therefore, the extraction temperature and the ratio of hawthorn to pear should be controlled strictly.

According to the actual production situation, the sugar content determined should be at 22.00%, the best condition determined by the software consisted of fermentation temperature 25.18°C, sugar content 22.00%, and the ratio of hawthorn to pear 21.19∶100. Under the condition, the alcohol degree of 10.11 (V/V %) was predicted.

Verification results

The suitability of the model equation for predicting the optimum response values was tested using the recommended optimum conditions. The experimental value was 11.05 (V/V %), which was in agreement with the predicted ones (Table 7).

Conclusions

In these experiments, the optimal fermentation conditions of hawthorn-pear wine were studied. Through uniform design experiment, the effects of fermentation temperature, sugar content, the ratio of hawthorn to pear, soaking time of hawthorn, SO₂ addition, and yeast inoculation on sensory quality were investigated, and the first three factors were determined as major ones to sensory quality.

On this basis, the prediction model of fermentation temperature (A), sugar content (B), and the ratio of hawthorn to pear (C) to alcohol degree was performed with a three-variable, three-level response surface design. The experiment results showed that the sugar content was a major contributing factor to the alcohol degree. The best condition was at 25.18°C fermentation temperature, 22.00% sugar content, and 21.19∶100 ratio of hawthorn to pear, with the optimal alcohol degree of 11.05%. The adequacy of this model needs to be confirmed by further experiments.

References

[1] Careri M, Corradini C, Elviri L, Nicoletti I, Zagnoni I (2003). Direct HPLC analysis of quercetin and trans-resveratrol in red wine, grape, and winemaking byproducts. J Agric Food Chem, 51(18): 5226–5231

[2] Charoen C, Graham H F, Paul A H (1998). Effects of temperature, ph, and sugar concentration on the growth rates and cell biomass of wine yeasts. Am J Enol Vitic, 49(3): 283–288

[3] Constantinos D, Olga L (1994). Effects of pH, sulphur dioxide, alcohol content, temperature and storage time on colour composition of a young Portuguese red table wine. J Sci Food Agric, 65(4): 477–485

[4] GB/T 15038 (2006) Analytical methods of wine and fruit wine (in Chinese)

[5] Jin R C, Zhou S T (2009). Uniform design compare with orthogonal design on Astragalus extract active components. J of biomathematies, 24(3):515–522 (in Chinese)

[6] Li S S, Xu H D, Li Y J, Yan C (2011). Optimization of fermentation process for onion wine by response surface methodology. Food Science, 32(6): 135–138 (in Chinese)

[7] Martendal E, Budziak D, Carasek E (2007). Application of fractional factorial experimental and Box-Behnken designs for optimization of single-drop microextraction of 2,4,6-trichloroanisole and 2,4,6-tribromoanisole from wine samples. J Chromatogr A, 1148(2): 131–136

[8] Niu G C, Zhu D, Wang J, Fan Z J, Li Z J (2009). Screening and molecular identification of superior yeasts for Hippophae rhamnoides L. wine. Journal of Chinese Institute of Food Science and Technology, 9(6): 60–65 (in China)

[9] Ratnam B V V, Narasimha R M, Damodar R M, Subba R S, Ayyanna C (2003). Optimization of fermentation conditions for the production of ethanol from sago starch using response surface methodology. World J Microbiol Biotechnol, 19(5): 523–526

[10] Rigelsky J M, Sweet B V (2002). Hawthorn: pharmacology and therapeutic uses. Am J Health Syst Pharm, 59(5): 417–422

[11] Serra A, Strehaiano P, Taillandier P (2005). Influence of temperature and pH on Saccharomyces bayanus var. uvarum growth; impact of a wine yeast interspecific hybridization on these parameters. Int J Food Microbiol, 104(3): 257–265

[12] Tang C H, Cai S X (2006). Application of the combined use of uniform experimental design and orthogonal experimental design in biomedical engineering. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, 23(6): 1228–1231 (in Chinese)

[13] Torija M J, Beltran G, Novo M, Poblet M, Guillamón J M, Mas A, Rozès N (2003). Effects of fermentation temperature and Saccharomyces species on the cell fatty acid composition and presence of volatile compounds in wine. Int J Food Microbiol, 85(1-2): 127–136

[14] Wu Y, Yang Q L, Aikebaier 000, Li J Y, Yang H Y (2009). Study on brewing process of wine with dried jujube from hami area of xinjiang. Food Science, 30(2): 283–285 (in Chinese)

[15] Xing J, Li Z G (2008). Uniform design in optimizing waler extraction technology of schisandra chinensis. China Pharmaceuticals, 17(10): 51–52 (in Chinese)

[16] Xu H, Xu H E, Ryan D (2009). A study of the comparative effects of hawthorn fruit compound and simvastatin on lowering blood lipid levels. Am J Chin Med, 37(5): 903–908

[17] Yannam S K, Reddy S P, Obulam V S R (2009). Optimisation of fermentation conditions for mango (Mangifera indica L.) wine production by employing response surface methodology. Int J Food Sci Technol, 44(11): 2320–2327

[18] Yuan L, Wang J, Zhang W, Li C W (2003). Problems in the brewing of juicy pear wine and the relative solutions. Liquor-making Science & Technology, 1: 72–73 (in Chinese)

[19] Zhao S Y, Song K (2009). Development of soft pear fruit wine. Liquor-Making Science & Technology, 1: 87–88 (in Chinese)

[20] Zhou Y, Xu H D, Mi L F, Cai S (2011). Optimizing the fermentation process for Radix Astragali Wine by response surface methodology. Food Science, 32(4): 293–296 (in Chinese)

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