Effects of frozen storage on the quality characteristics of frozen cooked noodles

Food Chemistry 283 (2019) 522–529

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Food Chemistry journal homepage: www.elsevier.com/locate/foodchem

Effects of frozen storage on the quality characteristics of frozen cooked noodles

T

⁎

Qian Liua,b, Xiao-Na Guoa,b, , Ke-Xue Zhua,b a b

State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, Jiangsu Province, People’s Republic of China School of Food Science and Technology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, Jiangsu Province, People’s Republic of China

A R T I C LE I N FO

A B S T R A C T

Keywords: Frozen cooked noodles Quality Frozen storage Water mobility Microstructure

The effects of frozen storage on the quality of frozen cooked noodles were investigated. Texture analysis showed hardness and tensile force reduced during frozen storage. An increasing cooking loss and a decreasing water uptake ratio were determined when testing cooking qualities. As storage time prolonged, differential scanning calorimetry (DSC) detected more freezable water, and an increased relaxation time was recorded by nuclear magnetic resonance (NMR). Magnetic resonance imaging (MRI) revealed water distribution was more heterogeneous. A ruptured microstructure with large pores of frozen cooked noodles was observed by scanning electron microscopy (SEM). The confocal laser scanning microscope (CLSM) photographs demonstrated gluten network lost its integrity and compactness. Size-exclusion high performance liquid chromatography (SE-HPLC) indicated the amount of SDS-soluble proteins increased. The present study showed water characteristics and protein network underwent some changes during frozen storage, which had major effects on the quality of frozen cooked noodles.

1. Introduction Noodle, consumed as a stable food for thousands of years, enjoyed a considerable welcome by people all over the world due to its variety, convenience, and nutritional values. Frozen cooked noodle (FCN), originated in Japan, was designed as a partially ready meal to save time and energy for consumers or retailers. Generally, frozen cooked noodle was made through a precooking process, cooling process and freezing process, and it only requires a minimal reheating time (Hatcher, 2004). Frozen food has drawn great attention these years with the development of economy and technology, responding to the pursuit of convenient, healthy and good quality foods among consumers (Ahlgren, Gustafsson, & Hall, 2005). And the rapidly developing freezing preservation technology provides a favorable condition for its prosperity. Currently, starch-based frozen foods such as frozen dough and frozen bread occupied an enormous market all over the world, and frozen dumpling was produced on a large scale in China. Frozen food can keep the taste, texture and nutritional value of food effectively and has a much longer shelf-life compared with fresh foods (Xu, Zhang, Mujumdar, & Adhikari, 2017). Silvas-García et al. (2016) found more starch damaged and the microstructure of frozen dough changed with the extension of frozen

storage, which had a detrimental influence on the final product quality. Wang et al. (2014) observed the glutenin macropolymer (GMP) depolymerized during the frozen storage, leading to a less thermal stable and disordered structure. These quality deteriorations imposed challenges to the industry. As for the frozen cooked noodle which is a relatively new staple product, it has attracted the attention of some researchers. Yue, Guo, and Zhu (2017) studied on the effect of different wheat flours on the quality of noodles, and found out that flour with high-gluten strength is more suitable for making frozen cooked noodles. Olivera and Salvadori (2009) compared the influence of two different freezing conditions on the quality of frozen cooked pasta, and found out that cryogenic freezing is an effective way to obtain frozen pasta which has close quality to the fresh cooked product. Compared with the process and formulation, frozen storage is equally important for product quality, closely related to the production and consumption of frozen cooked noodles. However, beyond these researches, limited work has been done on the effects of frozen storage on the quality of frozen cooked noodles. Eckardt et al. (2013) did a survey on the effect of longterm storage on wheat bread and dough, finding that ice imposed a negative effect on volume of bread and compactness of the crumb, especially on sensory characteristics. Water plays a complex and important role in frozen foods, revealing the status and movement of

⁎ Corresponding author at: State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, Jiangsu Province, People’s Republic of China. E-mail address: [email protected] (X.-N. Guo).

https://doi.org/10.1016/j.foodchem.2019.01.068 Received 23 October 2018; Received in revised form 11 January 2019; Accepted 11 January 2019 Available online 19 January 2019 0308-8146/ © 2019 Elsevier Ltd. All rights reserved.

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2.4. Cooking properties of frozen cooked noodles

water will lead to a holistic view and a better understanding of frozen foods. The influence of frozen storage on the quality of frozen cooked noodles has not been fully investigated, especially for long-term storage. Therefore, the objectives of this work were to: determine the texture and cooking properties of frozen cooked noodles during frozen storage for 12 weeks; investigate water characteristics by DSC and NMR; observe microstructure changes by SEM and CLSM; and determine the amount of SDS-soluble proteins by SE-HPLC.

Cooking properties include cooking loss and water uptake ratio of frozen cooked noodles. Cooking loss was determined according to AACC Method 66–50 (AACC, 2000) with small modifications. Precooked frozen noodles whose original uncooked weight (W1) were recorded were brought out of −18 °C freezer and immediately reheated in 500 mL boiling water for 90 s. Then the reheated noodles were taken out and measured weight (W2) after using 5 pieces of filter paper to absorb excess water. Meanwhile, the cooking water and rinse water were collected in a volumetric flask and adjusted volume to 500 mL with distilled water. Then 100 mL was transferred to a beaker (washed and pre-dried to a constant weight W3) and evaporated most of the water on the infrared heater. Next, it was transferred to an air oven at 105 °C until reached to a constant weight (W4). Each experiment was carried out in triplicate. The water uptake ratio (W %) is calculated according to Eq. (1)

2. Materials and methods 2.1. Materials Wheat flour was provided by Hai Jia flour industry Co., Ltd., Fujian, China. The flour component were analyzed according to the AACC (2000) methods, was 12.10% moisture (AACC method 44-01), 0.42% ash (AACC method 08-12), 11.40% protein (AACC method 39-11). Other chemical reagents used in the experiments were at least analytical grade.

W% =

(W2 − W1) × 100% W1

(1)

where W1 is the initial uncooked noodle weight, W2 is the final sample weight and W is the percentage moisture content, in wet basis. And the cooking loss (M%) of the sample is calculated according to Eq. (2)

2.2. Preparation of frozen cooked noodles The basic formula of frozen cooked noodle consisted of 600 g of flour, 276 g distilled water in which 9 g of sodium chloride was dissolved and then hydrated. The dough was formed by a vacuum dough mixer (HWJZ-5, Nanjing Yangzi Grain and Oil Instrument Co., Ltd., Nanjing, China) and mixed for 5 min at −0.08 MPa. Then the prepared dough was placed to rest in a sealed plastic bag for 15 min. After this process, the dough was rolled continuously by the tablet machine (YJ241, China) for 12 times. Then the dough was sheeted through a semiautomatic strand machine (SK-240, Chengdu Solatek Instrument Co., Ltd., Taiwan, China) at the roll gap of 3.0 mm and 2.0 mm for 3 times, respectively. After that, the sheet was cut into fresh noodles with the dimension of 20 cm × 0.20 cm × 0.20 cm. After making fresh noodles, 30 g noodles were weighed and immediately boiled in 500 mL water until the optimal cooking time (3.5 min), which was determined according to AACC 66-50 method (AACC, 2000). Then the precooked noodles were immersed in 500 mL water (4 °C) for 1 min to cool down and drained for 1 min sequentially. Next, the products were frozen in a −40 °C refrigerator for 1 h and transfered to a −18 °C freezer. In this survey, the whole storage duration was 12 weeks under −18 °C, and the sampling time was 1 weeks, 3 weeks, 6 weeks, 9 weeks, 12 weeks.

M% = 5 ×

W4 − W3 × 100% W1

(2)

where W3 is the empty pre-dried beaker weight, W4 is the residue and beaker weight and M is the percentage of cooking loss, in wet basis. 2.5. Differential scanning calorimetry (DSC) measurement for freezable water The freezable water content was evacuated by a differential scanning calorimeter (DSC8500, PerkinElmer, USA) in a N2 flow. The measurement method was recorded by Tran, Thitipraphunkul, Piyachomkwan, and Sriroth (2008) with a slight modification. A small piece of frozen cooked noodles of approximately 10 mg was cut out and placed into the aluminum pans and hermetically sealed together with an empty pan for reference. Each sample was weighed in triplicate. Before the measurement, the system was calibrated with indium. Then the frozen noodle piece was holding for 5.0 min at −20 °C for equilibrating and heating subsequently from −20 °C to 40 °C at a rate of 10 °C/min. The onset temperature (To), peak temperature (Tp), conclusion temperature (Tc), and enthalpy (ΔHw) were recorded. The freezable water content (FW%) is calculated according to Eq. (3)

ΔHw × 100% ΔHi×Tw

2.3. Texture analysis of noodle samples

FW% =

Texture properties were measured by a TA-XT2i Texture Analyzer (Stable Micro Systems, London, England) and the test method was according to a previous study described by Luo, Guo, and Zhu (2015). The frozen cooked noodles were reheated in boiling water for 90 s and the measurements were carried out at room temperature within 15 min after reheating. The instrument was calibrated using 1 kg load cell. For texture profile analysis (TPA), a HDP/PFS probe was used and the distance calibration was conducted with a return trigger path at 15 mm. And the settings were: pre-test, 0.8 mm/s; test speed, 0.8 mm/s; posttest speed, 0.8 mm/s; strain, 75%; interval time, 2 s. The experiment was repeated for 10 times. For tensile strength analysis, an A/SPR probe was applied in the mode of “Measure Force in Tension” where the distance calibration was performed with a return trigger path at 60 mm. And the testing parameters were: pre-test, 2.0 mm/s; test speed, 2.0 mm/s; post-test speed, 10.0 mm/s; distance, 90 mm. The experiment was repeated for 10 times.

where ΔHw is the enthalpy of ice fusion of frozen cooked noodle, ΔHi is the latent heat of ice fusion which is 334 J/g and TW is the total water content in samples.

(3)

2.6. Water distribution and migration Water distribution and migration were determined by nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI). The changes were evaluated according to the method by Carini, Vittadini, Curti, Antoniazzi, and Viazzani (2010). The tests were performed on a MesoMR23-060V-I Series NMR Analyzer (Shanghai Niumag Co., Ltd, Shanghai, China) equipped with standard micro imaging accessories. During the tests, the frozen cooked noodle samples were sealed in PET/PE bags after reheating to avoid the interference of air and moisture. For NMR test, the transverse relaxation curves were acquired by 523

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supernatants were loaded (20 μL) on a size exclusion analytical column (TSK G4000-SWXL, 7.8 mm × 300 mm, (Tosoh Corporation, Tokyo, Japan). The column was eluted with 0.05 mol/L sodium phosphate buffer (pH 7.0) containing 2.0% SDS at ambient temperature. All extractions were performed at least in triplicate. Chromatography conditions included a flow rate of 0.7 mL/min and temperature of 28 °C with protein detection at 214 nm. Measurements were performed in triplicate and the total peak area was recorded. Protein extractability in SDS containing medium of both non-reducing and reducing samples was calculated from its peak area and was converted to percentage of total extractability (SDSEP). The peak area of frozen cooked noodles flour under reducing conditions was the total extractability.

using a CPMG (Carr-Purcell-Meiboom-Gill) pulse sequence. The pulsespacing was selected to be as short as possible (0.5 ms) to minimize exchange and diffusion effects on the decay curves. For the measurements, the setting parameters were as follows: echo time (TE) = 0.300 ms, the number of sampling points (TD) = 194992, the interval time of sampling (TW) = 3500 ms, scanning frequency (SF) = 100 kHz, NECH = 6500 and number of slice (NS) = 2. For the measurement of MRI, a radiofrequency (RF) coil (15 mm) was selected and a standard SPIN-ECHO (SE) sequence was used to produce images (TE = 20.000 ms, TR = 500.00 ms, Averages = 2). The images were recreated on a 128 × 128 matrix for 2D images 2.7. Microstructure transformation

2.9. Statistical analysis 2.7.1. Scanning electron microscopy (SEM) The surface and section profile of frozen cooked noodles with different frozen storage were analysed using SEM (S-4800, Japan Electron Optics Laboratory CO., Ltd., Tokyo, Japan), based on the method of Li et al. (2012). Firstly, the frozen cooked noodles were soaked in glutaraldehyde with a solution of 2.5% (w/w) for 4 h for chemical fixation. Then rinsed with cold phosphate buffer (pH 6.8, 0.1 mol/L) for 4 times. After that, the samples were eluted in graded ethanol series (30%, 50%, 70%, 90%, and 100%) for 15 min at each gradation. The noodles were freeze-dried afterwards and the dehydrated samples were coated with gold particles for 4 min. The external and internal images of noodles were observed at 300 times and 600 times respectively at an accelerating voltage of 1.0 kV.

All the determinations and analysis were performed in triplicate at least. One-way analysis of variance (ANOVA) and Duncan’s test were conducted by using the software SPSS 19.0 for windows (SPSS Inc., Chicago, IL, USA). Significance was defined at P < 0.05. 3. Results and discussion 3.1. Texture analysis The texture characteristics of frozen cooked noodles play an important role in quality, which determines the acceptance of consumers on the product (Olivera & Salvadori, 2009). Firmness and adhesiveness are regarded as the most pivotal parameters for cooking quality, and the tensile properties representing the breaking strength are the major attributes to noodles (Sozer, Dalgıç, & Kaya, 2007). In this study, textural profile analysis (TPA) and tensile test were used to evaluate the quality of frozen cooked noodles. The textural parameters of frozen cooked noodles during frozen storage were measured and summarized in Table 1. TPA results showed hardness gradually decreased from 3631.13 g to 3005.97 g (P < 0.05) during the 12 weeks frozen storage while adhesiveness increased from 107.15 g·s to 154.42 g·s (P < 0.05). Olivera and Salvadori (2009) found the cooked pasta that frozen in air tunnel presented less hardness, less consistency than the unfrozen product. During the frozen storage, the sample hardness might be influenced by the ice growing and recrystallization which caused great damage on the gluten network (Park & Baik, 2009). Adhesiveness or stickiness is related with the amount of starch and starch gelatinization. During cooking, amylose and soluble protein in the noodle are dissolved into the water. The higher adhesiveness of frozen cooked noodles may attribute to more soluble substance on the noodle surface, which leads to the damaged gluten network leading to the leakage when reheating. For tensile test, the tensile force and distance showed a slight decrease during the first 3 week, but a significant (P < 0.05) decreasing tendency was observed in further frozen storage. The tensile force declined from 16.33 g to 12.73 g meanwhile tensile distance declined from 49.38 mm to 33.04 mm. The changes in texture properties may be due to the structure changes of frozen cooked noodles during the frozen storage, and may also be related to water characteristics changes. As ice growed, the bigger ice caused more damage to noodles and weakened

2.7.2. The confocal laser scanning microscope (CLSM) The morphology of gluten network was observed on CLSM (Model LSM 710, Leica, Germany) using the method described by Silva, Birkenhake, Scholten, Sagis, and van der Linden (2013) with some modifications. A piece of frozen cooked noodle was wrapped in Leica gel and cut into 20 μm sections with a steel knife in a freezing microtome (PM2245, Leica). The sections were transferred onto glass slides then and post-stained with a solution of 0.025% (w/w) Rhodamin B in water, which will preferentially stain protein. After dyeing for 1 min, a small amount of deionized water was used to rinse and excess liquid was suck away by filter paper. CLSM images, acquired in 1024 × 1024 pixel resolution, were analyzed using a ZEN2012 software to determine the gluten network of frozen cooked noodle samples. The excitation wavelengths for Rhodamin B was 561 nm. 2.8. Size-exclusion high performance liquid chromatography (SE-HPLC) SE-HPLC was carried on a LC (Liquid Chromatogram) system (Shimadzu, Kyoto, Japan). The protein was extracted according to a modified method of Wang et al. (2014). The freeze-dried frozen cooked noodle samples (containing 1.0 mg protein) were accurately weighted and extracted with 1 mL of a 0.05 mol/L sodium phosphate buffer (pH 7.0), containing 2.0% sodium dodecyl sulphate (SDS). To determine the protein extractability under reducing conditions, samples were extracted with the SDS phosphate buffer containing 1.0% (w/v) dithiothreitol (DTT). After vortex mixed, centrifugated at 8000 r/min for 10 min, and filtrated over polyethersulfone (Millex-HP, 0.45 μm), the

Table 1 Texture analysis of frozen cooked noodles with different storage time (1, 3, 6, 9, and 12 week). Time 1 week 3 week 6 week 9 week 12 week

Hardness/g 3631.13 3581.91 3417.54 3301.84 3005.97

± ± ± ± ±

Adhesiveness/g·s a

71.48 70.38a 101.48b 40.97b 91.15c

107.15 121.20 126.13 142.04 154.42

± ± ± ± ±

a

4.90 10.29b 6.43b 9.32c 9.85d

Means with different small letter superscripts within each column are significantly different at P < 0.05. 524

Force/g 17.50 17.49 16.33 15.20 12.73

± ± ± ± ±

Distance/mm a

0.48 0.53a 0.71b 0.64c 0.62d

55.24 54.59 49.38 41.36 33.04

± ± ± ± ±

3.10a 2.17a 2.44b 2.77c 3.27d

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Cooking loss (%) Water uptake ratio (%)

100

D

a

b

C 90

c 1.4

d

B

A

A

Freezable water content凚 %凛 c

80

Freezable water content凚 %凛

a

1.6

100

Water uptake ratio(%)

Cooking loss (%)

1.8

1.2 70

a

e

d

b

80

60 60

1.0 1

3

6

9

12

Frozen storage time凚 week凛

0

Fig. 1. Changes in cooking properties of frozen cooked noodles during frozen storage.

3

6

9

12

Frozen storage time (week) Fig. 2. Effect of frozen storage on the content of freezable water in frozen cooked noodles.

the gluten network, putting a negative impact on the tensile quality. This was supported by Edwards, Izydorczyk, Dexter, and Biliaderis (1993).

products. Since the content of freezable water plays a critical role in the amount, size and distribution of ice crystals, it is quite necessary to do a survey on the freezable water (Kontogiorgos, Goff, & Kasapis, 2008). In the DSC measurement, the enthalpy of ice melting derived from the heat flow curve was recorded and converted to the percentage of freezable water via the Eq. (3). Fig. 2 summarized the enthalpy of ice melting and the percentage of freezable water inside frozen cooked noodles during frozen storage. With the frozen storage time prolonged, the content of freezable water significantly (P < 0.05) increased from 84% on 1st week to 95% on the 12th week, and it was also noticed that the growth rate increased from 3th week to 6th week. During the frozen storage, the amount of freezable water of frozen dough increased, which leads to the decline of sensory and texture properties of wheat bread (Eckardt et al., 2013). Xuan et al. (2017) also reported that the increment of freezable water might mainly be caused by ice growth and recrystallization which yield greater damage to the product, generating a loose, broken structure. In this article, the amount of freezable water of frozen cooked noodles increased which may be caused by part of water bounded to gluten or starch became freer during frozen storage. So the waterholding capacity of frozen cooked noodles could be weakened during the frozen storage, which is in accordance with the decrease of water uptake ratio.

3.2. Changes in cooking properties Cooking properties can be determined in terms of cooking loss and water uptake ratio. Cooking loss is defined as the total amount of solid substances left in the cooking water, which may be attributed to the leak of amylose and solubilization of some soluble proteins (Petitot, Boyer, Minier, & Micard, 2010). It is extensively used as a good indicator for the overall cooking performance of noodles. As shown in Fig. 1, the cooking loss showed no significant change (P < 0.05) during first 3 weeks which suggested that the frozen cooked noodles quality maintained well during initial storage. In addition, an increase cooking loss was observed in the next 9 weeks. As the storage time extended, the solid content in boiled water increased which indicated the structure of frozen cooked noodles may suffer some destruction during frozen storage. Zweifel, Handschin, Escher, and Conde-Petit (2003) found that protein aggregation can promote a strong protein network preventing the leak of solid content. Nouviaire, Lancien, and Maache-Rezzoug (2008) also reported that the heat treatment of pasta causes denaturation of proteins, leading to a stiffening of pasta structure, preventing leaching of starch and thus decreasing cooking loss. The increasing cooking loss of frozen cooked noodles may be explained by the ice growth and recrystallization. The ice transformation caused the bigger holes inside the noodle structure and poor protein network making it hard to trap the low molecular effectively. Water uptake ratio shows the capacity of noodle to absorb water, serving as an indicator of water holding capacity of the product (Yue et al., 2017). From Fig. 1, the water absorption ratio slightly decreased (P > 0.05) at the first 3 weeks. However, after 6 weeks, a significant (P < 0.05) descending trend in prolonged frozen storage was noticed. From the results above, we assumed the decline of water uptake ratio was related with the water-holding capacity changes of frozen cooked noodles during frozen storage.

3.4. Water distribution and migration 3.4.1. T2 distributions and changes Water, playing as an active substance, is regarded as a critical role in the quality of food especially for the frozen food. As frozen cooked noodles possess a high water content of over 60%, and most of water can transform into ice crystals (Carini et al., 2010). Therefore, it is quite necessary to study the water characterization in frozen cooked noodles during frozen storage. NMR and MRI were applied in order to understand the behavior of water distribution and migration of frozen cooked noodles further. In this study, the transverse relaxation time T2 and the proportion of different type of water were evaluated to study changes of frozen cooked noodles during frozen storage. The results were presented in Fig. 3(A). Two distributions can be distinguished with one at relatively shorter relaxation time broadly ranging 0.1–10 ms (T21), and the other at longer time broadly ranging 10–100 ms (T22), indicating two types of water were found in frozen cooked noodles. Similar trends were

3.3. Differential scanning calorimetry (DSC) measurement for freezable water Thermal characteristics were determined by DSC which has been used to confirm the presence of freezable water in frozen cooked noodles quantitatively (Tran et al., 2008). Frozen products were in poor uniformity because of the ice crystal distributing unevenly inside the 525

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˄A˅ Time

T21

T22

A21

A22

1week

2.0092±0.0001a

54.3442±0.5586a

0.0982±0.0030a

0.9019± 0.0040a

3week

2.0097±0.0007a

54.7619±0.0322a

0.0980±0.0050a

0.9023±0.0040a

6week

2.1547±0.0071b

57.1597±0.0905b

0.0970±0.0060a

0.9052±0.0030b

9week

2.2996±0.0148c

62.3483±0.5091c

0.0925±0.0020b

0.9069± 0.0001c

12week

2.6146±0.0587d

66.9289±0.1301d

0.0898±0.0070c

0.9092±0.0011d

˄B˅

(a)

(b)

(c)

Fig. 3. Water distribution and migration determined by NMR. (A) Values from T2 profiles of frozen cooked noodles with different frozen storage time. (B) MRI images of frozen cooked noodles with different storage time, (a) 1 week, (b) 8 week, (c) 12 week.

reported by Bosmans et al. (2012), who did 2H NMR measurements using starch, gluten and flour model systems, and suggested that the transverse relaxation time represented the mobility of water. The first distribution with less mobility was assigned to those bound water strongly associated with gluten and starch (Kontogiorgos et al., 2008). The second abundant distribution was recognized as water exhibiting weak interactions with gluten and starch, or the non-specific diffusive exchange of water molecules with various components of the matrix (Lodi, Abduljalil, & Vodovotz, 2007). Water with high relaxation time was more mobile and active than those with low relaxation time. A longer T21 and T22 recorded during frozen storage were observed, which indicated the combination of water with other components in frozen cooked noodles was weakened with the prolonged storage time Fig. 3(A). Besides, a decreased A21 and an increased A22 were measured, suggesting more mobile water that can be frozen into ice was detected, which is in line with the DSC results. These changes above could be attributed to the structural changes of gluten in frozen cooked noodles whose compact and continuous structure was destroyed during frozen storage.

3.4.2. MRI images changes MRI is suitable for depicting the internal structure of food samples and tracking physicochemical changes during processing and storage (Liu et al., 2013). It has been considered as a great potential tool with the advantages of non-destructiveness, accuracy, and high resolution. In this study, the MRI was performed to evaluate the water distribution and migration of frozen cooked noodles during storage. MRI pseudocolor images corresponding to different storage time were shown in Fig. 3(B). Under optimal cooking time, the frozen cooked noodles quickly absorbed water and swelled, and water gradually penetrated from the surface toward the core, forming a concentric moisture distributions. As thus, it exhibited a water distribution with surface possessing high content of water and the core with lower content of water. Reflected in pseudo-color images, the color of frozen cooked noodles section changed from red to yellow and then to green against the deep blue anhydrous background. The color changes from red to yellow to blue indicated the gradual decrease in the proton density with the water distribution changes. Horigane et al. (2006) revealed that water penetrated concentrically toward the center of spaghetti with boiling time, 526

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(a)

(b)

(d)

(e)

(g)

(h)

(c)

(f)

(i)

Fig. 4. SEM and CLSM micrographs of frozen cooked noodles with different frozen storage time. SEM: (a) 1 week, surface; (b) 8 week, surface; (c) 12 week, surface; (d) 1 week, section; (e) 8 week, section; (f) 12 week, section. CLSM: (g) 1 week; (h) 8 week; (i) 12 week;

differences can be noticed after 8 weeks (Fig. 4b) and 12 weeks (Fig. 4c), when the surface became rougher along with large pores, which may be related to the ice crystals formation. From the section profile of frozen cooked noodles (Fig. 4d–f), a relatively intact and coherent network can be observed at the very start, then more ruptured structure can be noticed, in the meantime large pores appeared and distributed irregularly as frozen storage time prolonged. The SEM results showed that the continuous, compact structure of frozen cooked noodle suffered from destruction of the mechanical damage during the frozen storage. The gluten matrix of frozen wheat dough became less continuous and more ruptured when it stored for extended time (Sharadanant & Khan, 2006). The structure damage of frozen cooked noodle may be related to water characteristic and ice crystals. So it is important to prevent water migration and ice growth for frozen foods during frozen storage. Adding some additives like gums which could help to immobilize water and reduce water migration from forming large ice crystals may be effective to improve the product quality. The laser scanning confocal microscopy (CLSM) was applied to observe the gluten network change of frozen cooked noodle during frozen storage. The protein network presented compact and integrated on the first week (Fig. 4g). However, after 4 weeks storage, the gluten network became more loose with the increase of small fragments

and the moisture content map obtained by MRI clearly showed the change in the moisture distribution toward homogenization maintaining the concentric distributions. As Fig. 3(B)-a shown, at the initial time of 1 week storage, an integrated highly hydrated circle (red area) on the outside could be observed. Nevertheless, as the storage time extended, the high moisture area gradually disappeared reflecting in red area could be hardly observed after 12 weeks (Fig. 3(B)-b & c). The results may have connection with the decrease of water uptake ratio of frozen cooked noodles, they are corresponding to the cooking quality results, in which water uptake ratio reduced with extension of frozen storage time. These changes further confirmed that frozen storage weakened the water holding capacity of the system, which was detrimental to the noodle quality. 3.5. Microstructure change during frozen storage To have tangible evidence of microstructure changes of frozen cooked noodles during the frozen storage visually, the scanning electron microscopy (SEM) was employed. The surface and cross-section of frozen cooked noodles with different storage time were exhibited in Fig. 4. The surface of frozen cooked noodle stored after 1 week presented a smooth and continuous status (Fig. 4a). However, clear 527

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SDSEP (%)

14

a

b

d

frozen cooked noodles. Water mobility increased and water distribution tended to be heterogeneous, suggesting the water-holding capacity was weakended, which promoted to the growth of ice crystals causing more damage to the frozen cooked noodles. An increased SDS-soluble proteins content were determined, indicating more protein depolymerized during frozen storage. The results provided a better understanding on the changes of water characteristics and gluten network structure during the frozen storage, which were related to the quality of frozen cooked noodles. For further research, some measures can be taken to immobilize water and reduce protein network structure damage for improving the quality of frozen cooked noodles during frozen storage.

c

b

SDSEP (%)

12

10

8

Acknowledgements This work was financed by the National Natural Science Foundation of China (Grant No. 31571871), the program for National Top Youth Talent of Grain industry, the program for Distinguished Talents of Six Domains in Jiangsu Province, and the program of Collaborative Innovation Center for Modern Grain Circulation and Safety in Jiangsu Province.

6 0

3

6

9

12

Frozen storage time (week) Fig. 5. Protein extractability in sodium dodecyl sulfate of frozen cooked noodles with different frozen storage time.

Declaration of interests

(Fig. 4h–i) which could be related with ice crystals growth during the frozen storage. In previous study, large ice crystals were observed in the pores of fermented bread dough by cryo-scanning electron microscopy (Baier-Schenk et al., 2005).

None. References

3.6. Determination of size-exclusion high performance liquid chromatography (SE-HPLC)

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SE-HPLC is an excellent method for size fractionation of wheat storage proteins (Cornec, Popineau, & Lefebvre, 1994). During the processing, the gluten proteins form a three-dimensional network upon hydration. Gluten strength and the quality of the protein play a key role in identifying acceptable wheat flour for frozen noodles (Hatcher, 2004). The extractability of proteins by SDS solutions gives a good indication of the degree of crosslinking of protein (Hayta & Schofield, 2004). Moreover, protein aggregation behaviour and extractability differences have been connected to quality of final product. Therefore, the SE-HPLC was designed for studying the SDSEP changes of frozen cooked noodles upon long-term frozen storage. As shown in Fig. 5, the protein extractability of frozen cooked noodles increased as storage time prolonged. And this increasing trend was more pronounced after 6 weeks which may be caused by protein depolymerization. Wang et al. (2014) reported that depolymerization of glutenin macropolymer leaded to the increase of SDS-soluble proteins during the frozen storage. Sharadanant and Khan (2006) reported the gluten matrix of frozen dough became less continuous, more ruptured with extended frozen storage time, and the amount of SDS-soluble proteins increased. In combination with the results of SEM and CLSM, it can be inferred that ice formation and recrystallization yielded the morphology destruction of frozen cooked noodles, leading to the depolymerization and the shedding of small fragments of gluten. For frozen cooked noodles, the addition of hydrophilic gums could be used to immobilize water and reduce ice formation which would promote the continuous gluten structure and benefit the quality of frozen cooked noodles. 4. Conclusion The present study indicated the texture and cooking qualities of frozen cooked noodles were deteriorated during frozen storage. A rougher structure with large pores and an uncontinuous gluten network with small fragments were observed in micromorphology study, which had negative effects on textural properties and cooking qualities of 528

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