Table of Contents Author Guidelines Submit a Manuscript
Journal of Chemistry
Volume 2018, Article ID 7464297, 8 pages
https://doi.org/10.1155/2018/7464297
Research Article

Relationship of Moisture Status and Quality Characteristics of Fresh Wet Noodles Prepared from Different Grade Wheat Flours from Flour Milling Streams

1Province Key Laboratory of Transformation and Utilization of Cereal Resource, Henan University of Technology, Zhengzhou, Henan 450001, China
2College of Grain and Food Science, Henan University of Technology, Zhengzhou, Henan 450001, China

Correspondence should be addressed to Sen Ma; nc.ude.tuah@nesam and Xiaoxi Wang; moc.621@tuahgnawxx

Received 6 August 2017; Revised 8 December 2017; Accepted 18 May 2018; Published 5 August 2018

Academic Editor: Qingbin Guo

Copyright © 2018 Li Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This study was performed to investigate the quality of fresh wet noodles made from flour milling streams. The basic composition, texture properties, cooking characteristics, and moisture status were measured to evaluate the qualities of noodles. The results indicated that as storage time increased, the springiness of fresh wet noodles gradually decreased, but the hardness increased. Additionally, the cooking loss rate was increased obviously, and the water absorption rate generally decreased. The relaxation times T21 and T22, analyzed by low-field nuclear magnetic resonance, showed a downward trend that proton mobility became poor and bound water changed into intermediate water. Noodles made from reduction flour exhibited better quality. Compared to that with ambient temperature storage, the wet noodles under frozen storage showed better quality. The relaxation time T21, and T22 showed a positive correlation with noodle quality.

1. Introduction

Free water, intermediate water, and bound water are three forms of water in food [1]. As an important component of many foods, water has a decisive influence on food’s rheological characteristics and its chemical and physical properties [2, 3]. The presence, distribution, and concentration of water strongly influence the processing characteristics, stability, and preservation properties of food [4].

Low-field nuclear magnetic resonance (NMR) technology is an effective tool to study the water status of food, mainly by determination of the proton relaxation behavior [5, 6]. The relaxation process occurs through fluctuations in the magnetic field caused by random molecular motions, both rotational and translational. The rate and the characteristics of these motions both affect the decay of the NMR signal, which is observed by the T1 and T2 relaxation times [7]. The NMR signal is commonly analyzed in terms of two main parameters, T1 and T2. The spin-lattice (T1) relaxation involves the transfer of energy between the spin system and the environment, and spin-spin relaxation (T2) processes involve the dephasing of nuclear spins, which are entropic processes [8]. The T2 can be used to analyze the interactions between water and dough. The T2 value is sensitive to water distribution with different mobility states [9]. Two transverse relaxation time constants, T21 and T22, are spin-spin relaxation time constants and were identified from the NMR experiments using the Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence. The existence of these time constants indicates the presence of two distinct fractions of water. T21 is the portion of water that is strongly associated with other molecules by hydrogen bonding, almost “bound” water. However, T22 is more mobile water with a high molecular mobility. T21 and T22 have different relaxation rates and degrees of mobility. Generally, shorter T21 values indicate less mobile water, and longer T22 values indicate more mobile water [10].

A model of water distribution is useful to enable real-time monitoring and control of food quality during production and storage [11, 12]. Water mobility is mainly depended on the changes of hydrogen bonding structure. Hydrophilic materials such as proteins and carbohydrates can form hydrogen bonds with water molecules to influence the water mobility. Higher contents of proteins or carbohydrates decrease water mobility and vice versa [13].

Compared with dry noodles, wet noodles are fresher, with stronger boiling fastness, stronger gluten, better taste, and better flavor. However, the high moisture of fresh wet noodles can easily lead to spoilage, browning, rancidity, and deterioration, damaging appearance, quality, and flavor. At the same time, the change of both content and distribution of water during the milling of wheat flour contribute to the loss and migration of moisture and changes in flour characteristics. When noodles were stored at different temperatures (37°C, 45°C, or 55°C), T21, T22, and the content of free water increased, which indicated that migration and redistribution of water occurred [10]. Lai and Hwang found that the T2 changed regularly with the moisture distribution and movement in noodles during cooking and storage. Additionally, the surface and the interior water showed different migration behaviors during storage, and moisture migration played a decisive role in the hardening of noodles [14]. Sekiyama et al. found that when the storage time was between 10 min and 120 min, the T2 value on the surface of noodles decreased gradually with the extension of storage time, which was completely contradictory with the T2 value of the center of noodles. This difference was mainly attributed to the redistribution of water. After 120 minutes, the water in different regions presented a certain moisture gradient, and the relaxation time T2 was related to the microstructure and the degree of starch gelatinization of noodles [15].

The objectives of this research were to study the quality of fresh wet noodles made from different grade wheat flours of flour milling streams under different storage conditions, and the influence of storage temperature on the change of water status were observed by low-field NMR. Our finding was able to correlate water status and noodle quality, suggesting water status can be used to predict changes in noodle quality.

2. Materials and Methods

2.1. Materials

Wheat powders were obtained from the flour production workshop of Henan Zhonghe Co. Ltd. (Henan, China). Representative online wheat milling streams of break flour (2B, the number “2” represents the second time of milling, similarly hereinafter), reduction flour (1M, 2M, and 3M), and sizing flour (1S) were selected for the study. The basic physical and chemical indicators of the wheat milling streams are shown in Table 1.

Table 1: The basic physical and chemical indicators of the wheat milling streams.
2.2. Noodle Preparation

Fresh wet noodles were prepared as described previously [16]. Briefly, 100 parts of wheat flour and 35 parts of deionized water were mixed for 7 min using a pin mixer. The dough pieces were then hand kneaded into a stiff mass and passed through a laboratory noodle machine 4-5 times to form and compound a noodle sheet at a gap setting of 3.5 mm. The dough was then sheeted through five different roll gaps (3.0, 2.5, 2.0, 1.5, and 1.0 mm). Next, the sheet was cut into fresh noodle strands (15.0 cm length, 2.0 cm width, and 1.0 cm thickness) with cutting rollers.

2.3. Noodle Storage

Noodles were stored at room temperature (25°C) or in the cold (4°C) and were covered with a plastic wrap. Samples were then removed regularly and subjected to testing.

2.4. Chemical Analysis

Moisture and protein were measured following standard AACC methods (AACC, 2000). The starch content was determined by 1% hydrochloric acid polarimetry. Damaged starch (DS) content was determined using the SDmatic procedure [17]. The farinograph test was performed according to standard AACC methods (2000). The whiteness was determined according to GB/T 12097. The falling number was measured following GB/T10361-2008. D50 was determined using a laser particle size analyzer (BT-2002, Dandong BT Instrument Co. Ltd.). All analyses were performed in triplicate.

2.5. Texture Properties

The TA-XT2i type texture analyzer (Stable Micro Systems, UK) was used for texture property analysis (TPA), and a set of three strands of cooked noodles were placed parallel to each other on a flat metal plate. Hardness and springiness was determined. The experimental parameters were set as follows: pretest speed: 2 mm/s, test speed: 0.8 mm/s, posttest speed: 0.8 mm/s, minimum inductive force: 5 g, compression rate: 70%, and the time interval between two compression tests: 1 s.

2.6. Water Absorption

20 g of noodles was cooked in 500 mL of boiling distilled water until the white core of the noodles disappeared, and a colander was used to separate the noodles from the water. The noodles were transferred to a filter paper, drained for 5 min at room temperature, and then weighed. The final results are the mean of triplicate determinations. The formula of water absorption index of dry matter was calculated according to the Chinese Standard Method GB 5497–1985.

2.7. Cooking Loss Ratio

Noodles and cooking water were cooled to room temperature and then transferred to a 500 mL volumetric flask and measured. Next, 50 mL of the above solution was poured into a 250 mL beaker of constant mass and then evaporated to dryness over a water bath. This evaporation procedure was performed as described above four times, drying a total 200 mL of the above solution. The dried material was then transferred to a hot air oven that was maintained at 105 ± 2°C and dried to constant mass. The cooking loss rate of dry matter (%) was calculated according to Zhang’s method [18].

2.8. Nuclear Magnetic Resonance (NMR)

NMR (NMR variable temperature analysis system, VTMR20-010V, Shanghai NM electronic Science & Technology Co., Ltd.) was used to assess the water properties of the noodles. The Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence was used to determine the transverse relaxation time T2 of samples. The test parameters were set as follows: number of echo (NECH) = 2000, number of scans (NS) = 16, and echo time (ET) = 0.1 ms. The NMR spectra and T2 of samples were processed with T2-FitFrm software.

2.9. Statistical Analysis

All the data obtained in the study were expressed as the mean of at least two determinations. Analysis of variance was performed, and the data were analyzed using Duncan’s test (level of significance, ) with SPSS software (SPSS Institute, Cary, NC, USA).

3. Results and Discussion

3.1. Textural Properties

As shown in Table 2, the hardness of fresh wet noodles showed an overall upward trend as the storage time was extended. When stored for the same length of time, the hardness of noodles stored at room temperature was greater than that of noodles subjected to cold storage. It was probably because the noodles stored under normal temperature were more likely to loss moisture, causing noodles to become relatively dry and hard [19]. Noodles made from 1M showed the largest hardness value, followed by 2M; the hardness of 1s was close to 3M, and noodles made from 2B showed the lowest hardness value. This was probably due to the variation of starch content in the noodle. Previous studies reported that starch content was positively related to hardness [20], and our experimental results are consistent with this finding, as shown in Table 1. The variation of protein content might also affect the hardness of the noodle. Flour with higher protein content had higher water holding capacity, preventing the moisture loss of the noodle during storage [21].

Table 2: Hardness of fresh wet noodles made from different flours during storage.

Table 3 shows that the springiness of fresh wet noodles decreased as storage time increased under different storage conditions. Additionally, the springiness of noodles stored at cold temperature was lower than for those stored at room temperature. This was probably caused by the further development of the gluten network at room temperature, but in the cold condition, this development process was more restricted [22]. The springiness of sizing and the reduction flour was greater than that of the break flour, due to the higher content of bran speck in break flour, which hindered the formation of the gluten network structure. The reduction and sizing flour properties were determined by the presence of the endosperm, evaluation of the flour quality, and more complete formation of the gluten network structure [23]. Li et al. studied the textural properties of noodles during storage and found that the hardness increased and the springiness decreased from 0 h to 24 h, and then the springiness decreased again at 36 h and 48 h, consistent with our results [24]. Other reports suggested that with the extension of storage time, the brittleness increased, and that had an effect on springiness. With storage time being increased, the alpha helix content in the gluten protein secondary structure decreased and random coil content increased, resulting in a decrease of springiness and cohesiveness in noodles [25, 26].

Table 3: Springiness of fresh wet noodles made from different flours during storage.
3.2. Cooking Properties

Table 4 shows that under different storage conditions, the cooking loss rate of all fresh wet noodles showed an obvious increasing trend with the extension of storage time. This change may be due to the starch retrogradation process and gluten network disruption during storage [27, 28]. This can lead to loosening of the starch and other small molecular compounds and the dissolution of small particles embedded in the gluten protein network, resulting in increased cooking loss rate. Noodles stored in the cold showed lower cooking loss rate than that stored at room temperature. As seen from Table 5, the water absorption rate of dry matter showed a downward trend during storage. The water absorption rate of dry matter was greater in the cold, which indicated the cold storage was helpful to maintain noodle quality. With increased storage time, the binding force between starch and the gluten network structure in fresh wet noodles weakened gradually. Subsequently, the dissolution of starch in the cooking process increased, leading to a gradual increase of cooking loss rate and a decrease of water absorption rate as storage time increased [16]. The water absorption rate of 1S presented the opposite trend under different storage temperatures. We speculated that, at higher temperature, there was more extensive contact between molecules, facilitating the formation of intermolecular chemical bonds. Moreover, the high content of damaged starch in 1S has great ability of combining with water. During cooking, the noodles showed a greater ability to swell, and the water absorption ability increased.

Table 4: Cooking loss rate of fresh wet noodles made from different flours during storage.
Table 5: Water absorption rate of fresh wet noodles made from different flours during storage.

For the same amount of storage time, the largest cooking loss rate was exhibited for noodles made with 1M, followed by 3M noodles. Noodles made from 2B flour showed the smallest cooking loss rate, probably because in the reduction flour, the protein colloidal particles failed to fully contact water molecules during dough kneading and fermentation due to the relatively limited amount of water. Therefore, the gluten network structure was unable to fully form in the reduction flour. Furthermore, the cooking loss rate was negatively correlated with protein content and wet gluten content. The higher the protein content, the lower the loss rate of cooking [29].

3.3. Water Properties

The relaxation time was positively correlated with the mobility of water molecules. The T21 expressed by the relaxation time of water was related to the presence of nonaqueous material, including gluten protein, starch, and other macromolecular substances, also known as “deep binding water” [30, 31]. The T22 represents the water associated with the starch/arabinoxylans, as the gelatinization process includes the absorption of water [32]. Figures 1(a) and 1(b) shows that, under different storage conditions, the relaxation time T21 of the bound-water of fresh wet noodles decreased as storage time increased. The T21 values of noodles stored at cold temperature were generally larger than those for noodles stored at room temperature, probably because the noodles stored at room temperature suffered a greater loss of moisture content, the mobility of protons decreased, and there was diminished signal amplitude of the corresponding protons [33]. For the fresh wet noodles made from different flours, 2B showed the maximum relaxation time T21, and 3M showed the minimum value. This difference was likely due to the high moisture content of 2B and the low moisture content of 3M.

Figure 1: The water status of fresh wet noodles during storage. Relaxation time T21 at 25°C (a); T21 at 4°C (b); T22 at 25°C (c); T22 at 4°C (d).

Figures 1(c) and 1(d) show that, under different storage conditions, the extension of storage time for all fresh wet noodles gradually decreased the relaxation time T22 of the intermediate state water, and the water transformed from the combinative state to the intermediate state, for an overall decrease of total water content. Wang et al. also determined the water status in the noodle drying process using a low-field nuclear magnetic resonance analyzer, and reported that the weakly bonded water with transverse relaxation time T22 accounted for the largest proportion of water and the T22 value decreased gradually with drying time [34]. The relaxation time T22 was larger for noodles subjected to cold storage compared to noodles stored at room temperature. These differences are likely because of the higher moisture content, stronger proton mobility, and larger proton signal amplitude of the noodles stored in the cold. The largest relaxation time T22 was for 2B, and the 3M flour noodles showed the smallest value of T22, in agreement with the observed relaxation time T21.

The relaxation time T2 reflects the number of water molecules with spin-spin relaxation time in the range of proton mobility. The decrease of relaxation time T22 and T21 is related to the migration and redistribution of water molecules in different states. For fresh wet noodles stored under different storage conditions, the hardness, acidity, and cooking loss rate increased with the decrease of the relaxation time T21 and T22, and the whiteness, cohesiveness, springiness, and the water absorption of dry matter decreased. He et al. reported changes in the NMR parameters related to aging and moisture redistribution in steamed bread, and changes in moisture distribution were key to the aging process of steamed bread [35].

3.4. Correlation Analysis

The correlation analysis of quality characteristics and NMR parameters of fresh wet noodles made from different flours and stored under different storage conditions were determined and are shown in Table 6. The hardness values of all system powders were significantly negatively correlated with T21 and T22. Thus, the lower the water content (including both the bound water and the mobile water), the greater the hardness of noodles. The springiness showed positive correlation with both T21 and T22 values for 2B and 3M, with significant correlation. This result indicated that sufficient moisture content in noodles can help maintain the springiness of noodles. The cooking loss rate of all fresh wet noodles showed a highly significant negative correlation with T21 and T22, and the correlation coefficients were above 0.9. Under room temperature storage, most water absorption rates for the powders showed a significant positive correlation with T21 and T22. However, the correlations between the water absorption of dry matter and T21 and T22 were not significant during cold storage. Thus, under different storage conditions, the water status was significantly correlated with the quality of the flour in fresh wet noodles and the migration changes of different states of water affected the quality characteristics of noodles. Therefore, it may be feasible to predict changes in noodle quality through changes in the state of water.

Table 6: Correlation analysis between moisture and quality indicators of wheat flour.

4. Conclusions

Overall, the noodles made from 2B flour had lower whiteness, hardness, springiness, cooking loss rate, and water absorption rate of dry matter, compared with noodles made from reduction flour or sizing flour. With extended storage time, the relaxation time T21 and T22 decreased over 24 hours for all noodles. The relaxation time of noodles after storage at cold temperature was greater than that for noodles stored at room temperature. Noodles made from 2B flour showed the maximum relaxation time, and 3M noodles exhibited the minimum value. Clearly, the migration changes of different states of water influenced the quality characteristics of the noodles. In sum, 2M is the best type of flour for making wet noodles.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The present research was financially supported by the State Key Research and Development Plan “Modern Food Processing and food Storage and Transportation Technology and Equipment” (2017YFD0400200), National Natural Science Foundation of China (nos. 31571873 and U1704118), Henan Province Colleges and Universities Young Backbone Teacher Plan (2016GGJS-070), Key Scientific and Technological Project of Henan Province (172102110008), National University Students’ Innovation and Entrepreneurship Training Program (201710463002), and Opening Foundation of Province Key Laboratory of Transformation and Utilization of Cereal Resource in Henan University of Technology (PL2016003).

References

  1. M. Kortesniemi, A. L. Vuorinen, J. Sinkkonen et al., “NMR metabolomics of ripened and developing oilseed rape (Brassica napus) and turnip rape (Brassica rapa),” Food Chemistry, vol. 172, pp. 63–70, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. C. P. Sherwin, T. P. Labuza, A. McCormick, and B. Chen, “Cross-polarization/magic angle spinning NMR to study glucose mobility in a model intermediate-moisture food system,” Journal of Agriculture and Food Chemistry, vol. 50, no. 26, pp. 7677–7683, 2002. View at Publisher · View at Google Scholar · View at Scopus
  3. P. Zhou, X. Liu, and T. P. Labuza, “Moisture-induced aggregation of whey proteins in a protein/buffer model system,” Journal of agriculture and food chemistry, vol. 56, no. 6, pp. 2048–2054, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. Q. Rao, J. R. Rocca-Smith, and T. P. Labuza, “Storage stability of hen egg white powders in three protein/water dough model systems,” Food Chemistry, vol. 138, no. 2-3, pp. 1087–1094, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. P. D. Williams, M. H. Oztop, M. J. McCarthy, K. L. McCarthy, and Y. M. Lo, “Characterization of water distribution in xanthan-curdlan hydrogel complex using magnetic resonance imaging, nuclear magnetic resonance relaxometry, rheology, and scanning electron microscopy,” Journal of Food Science, vol. 76, no. 6, pp. 472–478, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. R. Godelmann, F. Fang, E. Humpfer et al., “Targeted and nontargeted wine analysis by 1H NMR spectroscopy combined with multivariate statistical analysis: differentiation of important parameters: grape variety, geographical origin, year of vintage,” Journal of Agriculture and Food Chemistry, vol. 61, no. 23, pp. 5610–5619, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. B. Blümich, F. Casanova, and S. Appelt, “NMR at low magnetic fields,” Chemical Physics Letters, vol. 477, no. 4–6, pp. 231–240, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Sangpring, M. Fukuoka, N. Ban, H. Oishi, and N. Sakai, “Evaluation of relationship between state of wheat flour-water system and mechanical energy during mixing by color monitoring and low-field 1 H NMR technique,” Journal of Food Engineering, vol. 211, pp. 7–14, 2017. View at Publisher · View at Google Scholar · View at Scopus
  9. C. Huang, Q. Zhou, S. Gao, Q. Bao, F. Chen, and C. Liu, “Time-domain nuclear magnetic resonance investigation of water dynamics in different ginger cultivars,” Food Chemistry, vol. 64, pp. 470–477, 2015. View at Google Scholar
  10. C. I. Cheigh, H. W. Wee, and M. S. Chung, “Caking characteristics and sensory attributes of ramen soup powder evaluated using a low-resolution proton NMR technique,” Food Research International, vol. 44, no. 4, pp. 1102–1107, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. R. N. M. Pitombo and G. A. M. R. Lima, “Nuclear magnetic resonance and water activity in measuring the water mobility in Pintado (Pseudoplatystoma corruscans) fish,” Journal of Food Engineering, vol. 58, no. 1, pp. 59–66, 2003. View at Publisher · View at Google Scholar · View at Scopus
  12. L. Mannina, A. P. Sobolev, and S. Viel, “Liquid state 1H high field NMR in food analysis,” Progress in Nuclear Magnetic Resonance Spectroscopy, vol. 66, pp. 1–39, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. F. Hao, L. Lu, and C. Ge, “Effect of fat content on water sorption properties of biscuits studied by nuclear magnetic resonance,” Food Nutrition Research, vol. 2, no. 11, pp. 814–818, 2014. View at Publisher · View at Google Scholar
  14. H. M. Lai and S. C. Hwang, “Water status of cooked white salted noodles evaluated by MRI,” Food Research International, vol. 37, no. 10, pp. 957–966, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Sekiyama, A. K. Horigane, H. Ono, K. Irie, T. Maeda, and M. Yoshida, “T2 distribution of boiled dry spaghetti measured by MRI and its internal structure observed by fluorescence microscopy,” Food research International, vol. 48, no. 2, pp. 374–379, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. L. Fan, S. Ma, X. X. Wang, and X. L. Zheng, “Improvement of Chinese noodle quality by supplementation with arabinoxylans from wheat bran,” International Journal of Food science and Technology, vol. 51, no. 3, pp. 602–608, 2016. View at Publisher · View at Google Scholar · View at Scopus
  17. D. G. Medcalf and K. A. Gilles, “Determination of starch damage by rate of iodine absorption,” Cereal Chemistry, vol. 42, pp. 546–557, 1965. View at Google Scholar
  18. J. Zhang, M. Q. Li, X. Z. Gong, and L. Y. Jiang, “Relationship between wheat flour traits and quality indexes of fresh-wet noodles,” Journal of Chinese Cereal Oil Association, vol. 23, no. 2, pp. 20–24, 2008. View at Google Scholar
  19. Y. Sangpring, M. Fukuoka, and S. Ratanasumawong, “The effect of sodium chloride on microstructure, water migration, and texture of rice noodle,” LWT–Food Science and Technology, vol. 64, no. 2, pp. 1107–1113, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. S. B. Zhang and Q. Y. Lu, “Effects of amylose content of flour pasting properties and noodle qualities,” Journal of Henan University of Technology, vol. 26, no. 6, pp. 9–12, 2005. View at Google Scholar
  21. E. N. Greer and B. A. Stewart, “The water absorption of wheat flour: relative effects of protein and starch,” Journal of the Science of Food and Agriculture, vol. 10, no. 4, pp. 248–252, 1959. View at Publisher · View at Google Scholar · View at Scopus
  22. Q. Y. Lu and L. L. Yao, “Change of chemical compositions during frozen noodle storage under low temperature conditions,” Food Science and Technology, vol. 2, pp. 82–84, 2005. View at Google Scholar
  23. B. Iuliana, S. Georgeta, I. Violeta, and A. Iuliana, “Physicochemical and rheological analysis of flour mill streams,” Cereal Chemistry, vol. 87, no. 2, pp. 112–117, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Li, M. Ma, K. X. Zhu, X. N. Guo, and H. M. Zhou, “Delineating the physico-chemical, structural, and water characteristic changes during the deterioration of fresh noodles: understanding the deterioration mechanisms of fresh noodles,” Food Chemistry, vol. 216, pp. 374–381, 2017. View at Publisher · View at Google Scholar · View at Scopus
  25. G. S. Song, J. Hu, X. Shen, S. Q. Hu, and L. Li, “Effects of ultrasound-assisted freeze on secondary structure of gluten protein,” Modern Food Science and Technology, vol. 25, no. 8, pp. 860–864, 2009. View at Google Scholar
  26. W. Zhang, Y. Wei, S. Wei, and B. Guo, “Quality changes and shelf life of fresh cooked noodles during storage,” Journal of the Chinese Cereals and Oils Association, vol. 32, no. 4, pp. 11–17, 2017. View at Google Scholar
  27. R. Liu, Y. Zhang, and B. Zhang, “Physicochemical and cooking properties of commercial dried Chinese noodles in China,” Journal of Chinese Institute of Food Science and Technology, vol. 15, no. 6, pp. 212–219, 2015. View at Google Scholar
  28. C. Betchaku and R. Niihara, “Changes in the rheological properties and degree of gelatinization of cooked noodles after frozen storage: effects of freezing and thawing conditions,” Journal of Home Economics of Japan, vol. 50, no. 11, pp. 1183–1188, 1999. View at Google Scholar
  29. C. Wang, K. Mip, D. B. Fowler, and R. Holley, “Effects of protein content and composition on white noodle making quality: color,” Cereal Chemistry, vol. 81, no. 6, pp. 777–784, 2003. View at Google Scholar
  30. R. Roger, X. A. Wang, L. C. Paul et al., “Study of water in dough using nuclear magnetic resonance,” Trend in Food Technology, vol. 76, no. 2, pp. 213–320, 1999. View at Google Scholar
  31. V. Gallo, P. Mastrorilli, I. Cafagna et al., “Effects of agronomical practices on chemical composition of table grapes evaluated by NMR spectroscopy,” Journal of Food Composition and Analysis, vol. 35, no. 1, pp. 44–52, 2014. View at Publisher · View at Google Scholar · View at Scopus
  32. S. B. Engelsen, M. K. Jensen, H. T. Pedersen, L. Norgaard, and L. Munck, “NMR-baking and multivariate prediction of instrumental texture parameters in bread,” Journal of Cereal Science, vol. 33, no. 1, pp. 59–69, 2001. View at Publisher · View at Google Scholar · View at Scopus
  33. Y. Li, X. Lin, J. Wu, and R. Ruan, “Monitoring the quality of kelp fresh noodles during the storage using the low-field NMR,” Journal of Chinese Institute of Food Science and Technology, vol. 15, no. 5, pp. 254–260, 2015. View at Google Scholar
  34. Z. H. Wang, B. Zhang, Y. Q. Zhang, and Y. M. Wei, “Review of moisture and heat transfer mechanism during drying process of noodles,” Transactions of the Chinese society of agricultural Engineering, vol. 32, no. 13, pp. 310–314, 2016. View at Google Scholar
  35. C. Y. He, X. Y. Lin, and Y. Zhang, “NMR Study on the water mobility of steamed bread,” Food Science, vol. 30, no. 13, pp. 143–146, 2009. View at Google Scholar