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Dynamic characteristics of dough during the fermentation process of Chinese steamed bread
Journal Pre-proofs Dynamic characteristics of dough during the fermentation process of Chinese steamed bread Xianhui Chang, Xingyi Huang, Xiaoyu Tian, Chengquan Wang, Joshua H. Aheto, Bonah Ernest, Ren Yi PII: DOI: Reference:
S0308-8146(19)32196-X https://doi.org/10.1016/j.foodchem.2019.126050 FOCH 126050
To appear in:
Food Chemistry
Received Date: Revised Date: Accepted Date:
16 July 2019 27 November 2019 10 December 2019
Please cite this article as: Chang, X., Huang, X., Tian, X., Wang, C., Aheto, J.H., Ernest, B., Yi, R., Dynamic characteristics of dough during the fermentation process of Chinese steamed bread, Food Chemistry (2019), doi: https://doi.org/10.1016/j.foodchem.2019.126050
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Dynamic characteristics of dough during the fermentation process of Chinese steamed
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bread
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Xianhui Chang, Xingyi Huang*, Xiaoyu Tian, Chengquan Wang, Joshua H. Aheto, Bonah
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Ernest, Ren Yi
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School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang
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212013, Jiangsu, P. R. China
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*Corresponding author
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Prof. Xingyi Huang
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School of Food and Biological Engineering
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Jiangsu University, Xuefu Road 301, Zhenjiang 212013, Jiangsu, P. R. China
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Tel: +86-51188792368
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Email: h_xingyi@ ujs.edu.cn
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Abstract
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The fermentation process is crucial to the production of Chinese steamed bread (CSB). In
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order to select suitable indicators as the basis for further research of establishing intelligent
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monitoring method for dough fermentation state, this study investigated the dynamic
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characteristics of dough during fermentation. Indicators included water mobility and distribution,
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starch-pasting properties, content of free amino acid (FAA), volatile organic compounds (VOCs)
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and electronic nose (E-nose) response value. Starch-pasting properties of dough and relaxation
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time (T21, T22) did not change significantly during the fermentation process (p < 0.05). The
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VOCs and FAAs of the dough had significant differences (p < 0.05) in different fermentation
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times, but no rule was established. The E-nose response value to headspace was most suitable to
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monitor the fermentation of dough. Principal component analysis (PCA) was performed on E-
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nose data from 75 samples and the results indicated that samples of different fermentation states
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were accurately classified.
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Keywords
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Dough; Dynamic characteristics; Fermentation state; Monitoring; PCA.
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1. Introduction
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As a traditional Chinese staple food, CSB has been popular with Chinese consumers since
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its origin more than 1,700 years ago (G. H. Zhang et al., 2016). CSB forms an important part in
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the diet of many Chinese, especially in northern China(Guo Hua Zhang, Zhou, & He, 2012)
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because the ingredients and processing methods of CSB are simple, and its flavor is pleasant and
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unique. CSB is generally processed by mixing flour, yeast, and water into a dough, which is then
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fermented and steamed (F. Zhu, 2014). This traditional approach is believed to play a significant
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part in the final products’ unique flavor, soft crumb, smooth white skin, and pleasant aroma,
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making it entirely different from western baked breads (Wang, Yang, Gu, Xu, & Jin, 2017b).
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Nowadays, CSB has become popular among other Asian countries (X. Liu et al., 2018), and
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various studies have been conducted on it.
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Similar to other fermented flour products, such as bread and pizza, fermentation is a crucial
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step in the processing of CSB. Different leavening agents, fermentation methods, and
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fermentation time affect the aroma, taste, appearance, texture and other quality parameters of the
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product. The main leavening agents used for dough fermentation are active dry yeasts(Liu,
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Chang, Li, & Liu, 2012; F. Zhu, 2014; Fan Zhu, 2016), distiller’s yeast (Liu, Wang et al 2012),
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sour dough(Kim, Huang, Zhu, & Rayasduarte, 2009; T. Liu et al., 2018; Rinaldi, Paciulli,
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Caligiani, Scazzina, & Chiavaro, 2017; G. H. Zhang et al., 2016), chemical leavening agents
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(Wang, Yang, Gu, Xu, & Jin, 2017a) , and strains, such as lactobacillus (Di Renzo, Reale,
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Boscaino, & Messia, 2018). The aroma characteristics of CSB are always different, when
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different leavening agents are used during the fermentation process. The fermentation method
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also affects the quality of CSB. As a national standard method (GBT35991-2018), single
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fermentation is the most preferred method among producers at present because of its simple
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operation. Remixed fermentation is a traditional CSB processing method that improves the
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hardness and chewiness of CSB, and is more relished by consumers in northern China (Li, Deng,
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Li, Liu, & Bian, 2015; Kim et al., 2009; Maeda et al., 2009). In recent years, some researchers
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have introduced multi-technology fusion methods into dough fermentation, such as ultrasound-
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assisted dough fermentation (Luo et al., 2018). These studies were mainly directed at studying
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the influence of fermentation on the quality of CSB, and fermentation process optimization. No
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systematic study has been performed on the dynamic changes in various dough indicators during
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fermentation.
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Similar to the development of baked bread processing in the western countries, industrial
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processing of steamed bread will be the trend of CSB production in the future. More so, with the
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rapid development of artificial intelligence (AI) technology, the process control and monitoring
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of CSB will be integrated with AI technology in the foreseeable future.
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In recent years, food-processing industries have adopted food process monitoring
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technologies that combine intelligent sensing technology with chemometrics to achieve real-time
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quality and safety monitoring. Intelligent sensing technologies, such as computer vision, spectral
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technology, E-nose, and E-tongue, are nondestructive and can provide information in real time.
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Such monitoring techniques have been widely applied in monitoring the fermentation of black
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tea (Pan, Zhao, Chen, & Yuan, 2015), protein feed (H & Sensors, 2014; Jiang, Chen, & Liu,
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2014), rice wine (Ouyang, Zhao, Pan, & Chen, 2016), ethanol (Jiang et al., 2018), and wheat
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straw (Mei, Yang, Shu, Jiang, & Liu, 2015) as well as the freshness of fish during storage (Ding
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et al., 2014; Huang et al., 2016). Monitoring models must be established in these studies by
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combining intelligent sensor signals with chemical, physical, or artificial evaluation.
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As described above, fermentation is a critical link in CSB processing. The appropriate
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fermentation level products acceptable flavor, surface, and texture characteristics for the final
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product (Maeda et al., 2009). The crucial premise of establishing AI monitoring model is the
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assessment of various dough indexes during fermentation. The main components of dough are
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water, starch and protein. During the fermentation of dough, the metabolism of yeast produces a
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large amount of gas, while the degradation of proteins produces amino acids.
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Therefore, the objectives of this study are to measure the dough indicators (water mobility
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and distribution, starch-pasting properties, FAAs, VOCs and E-nose response value) and their
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dynamic changes during fermentation and select suitable characteristic indexes for further
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research of establishing monitoring models of the dough fermentation state. The results of this
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study will provide a theoretical basis for intelligent dough monitoring. This study is significant to
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the industrialization and prospects of CSB.
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2. Materials and methods
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2.1. Materials
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Instant dry yeast (Angel®, Angel Yeast Co., Ltd, China) and medium gluten wheat flour
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(Wonder Farm Brand®, Yihai Kerry (Kunshan) Food Industry Co., Ltd., China) were purchased
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from a local supermarket. Flour moisture was determined by using national standard method (GB
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5009.3-2016). Water absorption and rheological properties of flour were obtained according to
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the national standard method with a Brabender farinograph (GB/T 14614-2006).
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2.2. Sample preparation
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The dough was prepared mixing flour, dry yeast and water. The additive amount of yeast is
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0.8% of flour. The amount of water was 80% of the optimum water absorption determined by
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Brabender farinograph. After mixing, the dough was placed into a sealed container and allowed
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to rest for 10 min at room temperature. Thereafter, the dough was divided into pieces on the
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basis of the experimental requirement and then kneaded to smooth. The dough was fermented at
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30 °C under 80% to 90% relative humidity for 0, 15, 30, 45, 60, 75, 90, 105 and 120 min.
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The fermentation state of dough was determined by well-trained professionals in the field of
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CSB processing.
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2.3. Low-field nuclear magnetic resonance (LF-NMR)
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Water mobility and the distribution of fermented dough ware measured by using a Niumag
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NMR Analyzer (NMI20-030V-I-25mm, Suzhou Niumag Analytical Instrument Corporation)
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with a magnetic field strength of 0.54 T and a corresponding resonance frequency for protons of
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22 MHz. Approximately 8g of dough was placed into a 25 mm glass tube, fermented for 0, 15,
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30, 45, 60, 75, 90, 105, 120min, and inserted in the NMR probe. Transverse relaxation time(T2)
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was measured by using the Carr-Purcell-Meiboom-Gill pulse sequence. The optimal pulse
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parameters were: SW = 100kHz, SF = 22MHz, P1 = 5.50μs, DRG1 = 3, TD = 200002, TW =
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1000,000ms, NS = 8, P2=11.00, and NECH = 4000. Relaxation time of all samples was
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measured at 30 °C.
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2.4. Pasting property identification via the rapid viscosity analyzer (RVA)
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The dough(50g) fermented at different times (0, 15, 30, 45, 60, 75, 90, 105, 120min) was
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freeze-dried and ground in a mortar for testing. Determining the pasting properties was
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conducted by using the RVA (TechMaster, Perten Instruments, Macquarie Park, Australia). The
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sample (3.5 g, 14%) was mixed with deionized water (25 mL) and homogenized to form
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suspension in an aluminum barrel. The barrel was subjected to the RVA for testing, and the test
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parameters ware as follows: maintained at 50 °C for 1 min, then heated to 95 °C at 12 °C/min,
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held at 95 °C for 2.5 min, and cooled to 50 °C at 12 °C/min for 2 min. The initial stirring speed
134
of the analyzer was at 960 rpm for 10 s and then maintained at 160 rpm (Yang et al.,2018). The
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peak viscosity, trough (minimum viscosity), breakdown, peak time, and pasting temperature
136
were recorded for analysis.
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2.5. FAA determination
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FAA was determined by using an S-433D automatic amino acid analyzer (Sykam,
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Germany). The freeze-dried dough was weighted accurately at 1.0 g (up to the last four decimal
140
places and the quality was recorded in detail) and carefully place in a 25 mL volumetric flask.
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Trichloroacetic acid solution (15mL, 5%) was added to dissolve the sample, which was shaken
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and dispersed. This solution was added again to fix the volume. Ultrasound was performed at
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room temperature for 20 min and again for 2h. A pinhead filter was used to strain the filtrate.
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The filtrate was collected in a 1.5 mL centrifuge tube and centrifuged at 15,000r /min for 30min.
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The supernatant (400 μL) was determined in a liquid sample bottle by using an automatic amino
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acid analyzer.
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Analysis method: The method used was the precolumn derivatization of OPA FMOC. The
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chromatographic condition included the use of an Agilent 1100 liquid chromatograph. The
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chromatographic column was 250 × 1.6 mm, (5 μm, ODS HYPERSH). The column temperature
150
was 40 °C.
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Mobile phase: A phase: Crystallized sodium acetate (8.0 g) was weighed in a 1,000 mL
152
beaker, and 1000 mL water was added and stirred until all the crystals dissolved. Triethylamine
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(225 L) was then added and stirred. Acetic acid (5%) was added, and the pH was adjusted to 7.20
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± 0.05. Tetrahydrofuran (5 mL) was added, and the solution was mixed and reserved.
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B phase: Crystallized sodium acetate (8.0 g) was weighed and mixed in an 800 mL beaker
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with 400 mL of water until all crystals were dissolved. The pH was adjusted to 7.20 ± 0.05 with
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2% acetic acid. The solution was added to 800 mL acetonitrile and 800 mL methanol for mixed
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use.
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Flow rate: The flow rate was 1.0 mL/min. UV detection was conducted at 338 nm.
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2.6. Volatile organic compound (VOC) analysis via gas chromatography–ion mobility
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spectrometry (GC–IMS)
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IMS analysis was performed by using a gas chromatography (GC) coupled with an IMS
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instrument (Flavourspec®, G.A.S. Dortmund Company, Germany). Briefly, the dough (1.0 g)
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fermented at different times (0, 15, 30, 45, 60, 75, 90, 105, 120 min) was directly placed in a 20
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mL headspace vial and incubated at 60 °C for 10 minutes. Following the incubation period,
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100μL of sample headspace was automatically injected injection speed of 30 mL min-1 and the
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injector temperature was maintained at 85°C. The chromatographic separation was performed on
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a SE-54-CB-1 (5% phenyl–1% vinyl–94% methylpolysiloxane) capillary column (15 m × 0.53
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mm, 1 μm film thickness) and maintained at 85 °C, with nitrogen as the carrier gas. The GC run
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time was set to 30 min after a preliminary time of 20 min, resulting in poor compound
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separation. A flushing time of 5 min was allowed to prevent carryover effects. During the
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analysis of GC-IMS data, an area set capable of integrating all marker spots was created by using
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G.A.S. software suite called Laboratory Analytical Viewer (LAV). This software was used to
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illustrate the IMS topographic plots.
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2.7. Dynamic headspace analysis: via E-nose
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In this study, the dynamic headspace during fermentation was detected by using an E-nose
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instrument (PEN3, Airsense Analytics GmbH, Germany). The E-nose had an array of 10
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different metal oxide sensors positioned in a small steel chamber (V = 1.8 mL) and used a pattern
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recognition software (Win Muster v.3.0).
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The dough(100g) was placed into a 1,000 mL sealed beaker and then into a fermentation
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tank. When testing, two Luer Lock needles were inserted into the beaker. One was connected to
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a clean air source (charcoal filter), and the other needle was connected to the Teflon tubing (3
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mm) of the E-nose. The “Automatic Measurement” mode was adopted in this study, and the test
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was conducted simultaneously with dough fermentation. The E-nose response data of headspace
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were collected every 5 minutes. The detection parameters included measurement and flush times
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of 120 and 180 s, respectively. The dough was fermented for 120 min, and 25 groups of data
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were collected. A total of 75 groups of data were collected in three parallel tests.
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2.8. Data processing method
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All the experiments were arranged in at least three parallels and the results were expressed
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as mean value ± standard deviation via SPSS 21.0 (SPSS Inc., Chicago, USA). The data were
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plotted by using Origin 8.5 (Origin Lab Corp., Northampton, MA, USA). PCA of E-nose data
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was performed on MATLAB R2014a (Mathworks, USA).
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3. Results and discussion
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3.1. Flour properties and optimum fermentation time of dough
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Flour moisture was 13.0%. Farinograph water absorption, development time, stability time,
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and mixing tolerance index were 61.6%, 2.0 min, 3.9 min, and 68 FU. The flour was suitable for
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processing CSB, and the appropriate amount of water to make the dough was 49.3% (80% of
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water absorption).
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According to the judgment of professionals, the optimum fermentation time of the dough
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was 45–75min. On the basis of the fermentation state, the dough samples could be divided into
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three groups, namely, insufficiently fermented (0–30 min), moderately fermented (45–75 min),
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and over–fermented (90–120 min) samples.
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3.2. Water mobility and distribution in dough during fermentation
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Water accounts for more than 60% of the dough and is crucial in CSB production.
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Fermentation is a complex process that involves water migration in dough. The distribution and
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migration of water were studied extensively by using LF-NMR, in which these parameters are
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measured by using the spin–spin relaxation time (T2) and signal amplitude of protons. (Bosmans
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et al. 2012, Huang et al. 2016). Figure 1 shows the proton signal amplitude and T2 of dough at
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different fermentation times. Two peaks of T21 (1–36 ms) and T22 (38–270 ms), which
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represented the bound and weak bound waters, were observed in each curve, respectively (Li, Z,
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Deng, C et al., 2015). Figure 1 illustrates that the peak area (M) represents the proton signal
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intensities that can be used to represent the relative content of water in each part.
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Table 1 lists the T2 (T21, T22), proton signal intensities (M21, M22), and fractions of M21, M22
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of the dough samples fermented at different times. T2 (T21, T22) of fermented dough samples
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were significantly shorter than those of their unfermented counterparts. This change might be
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due to the starch–water and gluten–water models of dough samples that tightened under warm
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conditions. With the increase in fermentation time, T21 and T22 of the dough samples showed a
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shortening trend. However, such trend was insignificant (p < 0.05). Given that starch absorbed
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most of the water in the dough, the status of starch did not change significantly (p < 0.05) during
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the fermentation process; the starch only acted as an inert filling component of the dough,
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thereby limiting the migration of water in the dough (Bosmans et al., 2012). In combination with
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the fermentation state of the dough, the differences in T21 and T22 were insignificant among
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different fermentation states of dough samples. Therefore, T2 was an unsuitable indicator in
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determining the fermentation state of dough.
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Table 1 also lists the area and area fraction of each peak that could be used to identify the
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changes in the relative content and proportion of water in the dough during the fermentation
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process. The bound water demonstrated a decreasing trend with the extension of fermentation
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time, whereas the weak bound water exhibited an increasing trend. The competition for water
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caused the water absorption of starch to be less than that of gluten in the dough (Bosmans et al.,
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2012). As the fermentation time progressed, the gluten network was further strengthened, while
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a portion of the carbohydrate and protein were consumed by yeast growth. All of these
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conditions might lead to the decrease in bound water (M21) in the dough. At the same time, the
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degradation of carbohydrates resulted in the formation of water, most of which remained in the
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dough as a form of weakly bound water. Therefore, the weakly bound water (M22) in the dough
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tended to increase. In this study, M showed a clear trend of change (M21 decreased, M22
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increased). Given that the appropriate fermentation time was 45–75 min (Section 3.1), the
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threshold could be set in accordance with the M21 and M22 values of the dough fermented for 45–
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75 min to determine its fermentation state. According to table 1, when the dough was fermented
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for 45 minutes and 75 minutes, the mean values of M21 were 24485.26 and 23113.81,
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respectively. Therefore, the threshold of M21 could be set as [24485.26, 23113.81]，Within this
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range, the dough can be judged to be moderately fermented. When M21 > 24485.26, the dough
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can be judged as insufficiently fermented. while when M21 < 23113.81, the dough can be judged
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as over fermented. Similarly, when dough is fermented for 45 minutes and 75 minutes, the mean
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value of M22 is 352.07 and 404.17, respectively, so the threshold value of M22 can be set as
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[352.07, 404.17].
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3.3. Change in starch-pasting properties during dough fermentation
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Starch is the most abundant component in wheat flour, and the change in starch-pasting
249
properties will likely affect the quality of dough during fermentation. In this study, the peak
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viscosity, trough viscosity, final viscosity, peak time, and pasting temperature were measured by
251
using the RVA method (Fig. 2). The pasting temperature of starch in the dough changed slightly
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with the extension of fermentation time from 86.43 °C to 87.25 °C given that no rule is available
253
to follow (Fig. 2A). The measurement of peak time showed a similar phenomenon from 5.86 min
254
to 6.00 min (Fig. 2B), and the value was similar to the farinograph result (Section 3.1,
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development time was 2.0 min, stability time was 3.9 min). This result might be due to the
256
fermentation of dough at low temperatures. The water content in the dough was limited, and the
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water absorption rate of starch in the dough was lower than that of gluten (Section 2) due to the
258
competition for water content, which inhibited the gelatinization of starch in the dough.
259
Therefore, the physicochemical properties of starch did not change during dough fermentation,
260
and the changes in pasting temperature and peak time were insignificant (p < 0.05). The results
261
were comparable to those of soybean flour, as reported by Olanipekun et al. (Olanipekun,
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Otunola, Adelakun, & Oyelade, 2009).
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Figure 2C shows that the changes in peak, trough, and final viscosities were also
264
insignificant. With the extension of fermentation time, peak and trough viscosities demonstrated
265
a slightly increasing trend from 1498.5 cP to 1568.5 cP and from 883.5 cP to 927.0 cP,
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respectively. On the contrary, the final viscosity decreased slightly from 1808.5 cP to 1698.5 cP.
267
Research has shown that the viscosity of paste is affected by multiple factors, such as starch and
268
protein contents and their components and hydrocolloids(Kowalczewski, Rozanska, Makowska,
269
Jezowski, & Kubiak, 2019; G. Zhang, Hamaker, & Chemistry, 2003). Therefore, the increase in
270
peak and trough viscosities might be due to the expansion of the gluten network in the dough
271
after fermentation. However, in the process of RVA determination, the paddle was constantly
272
stirring. The long fermentation time expanded the gluten sufficiently that was easily destroyed by
273
the paddle. As a result, the final viscosity showed a downward trend.
274
According to the results of pasting temperature, peak time, peak viscosity, trough viscosity,
275
and final viscosity determined by the RVA, no significant difference (p < 0.05) occurred among
276
the samples at different fermentation times. Therefore, starch-pasting properties were unsuitable
277
indicators to determine the fermentation state of dough.
278
3.4. FAA analysis
279
During dough fermentation, the metabolism of yeast consumes amino acids. Hence, the
280
FAA content in dough might be varied at different fermentation times. Changes in FAAs, such as
281
in flavoring substances (Chen et al., 2019), might also be helpful in studying the flavor change of
282
food. Therefore, FAAs in dough samples with different fermentation times were determined, and
283
their change rules were expounded in this study. Table 2 lists the 18 FAAs detected in the dough
284
samples.
285
Table 2 indicates that the total FAA content of dough is significantly different at different
286
fermentation times (p < 0.05). In the fermentation stage of 0–90 min, the content of total FAA
287
decreased gradually. The total FAA content of the dough fermented for 90 min was 45.37%
288
lower (decreased by 954.67 μg/g) than that of the dough fermented for 0 min. The total FAA
289
content of dough increased when fermented for 105 min and decreased again at 120 min. This
290
result might be due to the consumption of FAAs from the metabolism of yeast during dough
291
fermentation that subsequently decreased the total FAA content. With the extension of
292
fermentation time, the metabolism of yeast slowed down, and the amino acid consumption
293
decreased. The proteins in the dough were degraded by endogenous enzymes to produce FAAs.
294
Hence, the total FAA content rebounded in the later stage of fermentation. This trend was similar
295
to the result of Collar’s research (C Collar et al., 1992).
296
The variations in each individual FAA were different in the fermentation process. The
297
contents of Asp and Glu decreased gradually during the fermentation stage of 0–105 min but
298
slightly increased when fermented for 120 min. The contents of Ser, His, Gly, Thr, Arg, and Ala
299
did not change significantly when the dough was fermented for 0–45 min, decreased
300
significantly after the dough was fermented for 60 min, and maintained a relatively low level
301
during 60–90 min. The content of these FAAs in the dough increased when the dough was
302
fermented for 105 min and decreased again at 120 min. A significant decrease in Tyr content
303
appeared at 45 min, and the subsequent change trend was similar to that of Ser and His. The
304
other FAAs in the dough demonstrated fluctuating changes during the fermentation process, and
305
the rules were unclear. Notably, Lys, Phe, Ile, and Leu were almost exhausted by the time the
306
dough was fermented for 30 min, and the subsequent change trend was undulating. This result
307
suggested that their assimilation by yeast was high at the beginning of fermentation. These
308
phenomena were consistent with Kokawa’s research (Kokawa et al., 2017).
309
3.5. Comparative profiling of VOCs changes via GC–IMS
310
The results of some studies have shown that the VOCs in fermented flour products are
311
mainly alcohols, aldehydes, ketones, and esters (Di Renzo et al., 2018; G. H. Zhang et al., 2016;
312
Guo Hua Zhang et al., 2012). The GC–IMS technology combines the high separation capacity of
313
GC and high sensitivity of IMS, that enables the rapid monitoring of trace VOCs (Becher,
314
Purkhart, Hillmann, Graupner, & Werner, 2014; Denawaka, Fowlis, & Dean, 2014). In this study,
315
GC-IMS technology was used to detect the fingerprints information of VOCs in dough samples
316
fermented at different times.
317
In all the samples of this study, 22 characteristic ion transport peaks of VOCs were selected
318
for analysis. The gallery plots shown in Figure 3A illustrates the vertical cross-section view
319
corresponding to the characteristic ion transport peaks of 22 VOCs detected and the horizontal
320
cross-section view corresponding to the dough samples fermented at different times. In the
321
NIST2014 spectral database of the instrument used in this study, only a part of characteristic ion
322
transport peaks could be identified. Out of the 22 characteristic peaks screened in this study, 17
323
types of component information could be determined, including 8 alcohols, 5 ketones, 3 esters,
324
and 1 aldehyde.
325
Signature plots were used to describe the relative IMS signal intensity of the measurement
326
in all areas to compare the change in VOCs in the dough samples under investigation. Figure 3B
327
illustrates that the signature plots generated by LAV analysis software correspond to the gallery
328
plots. The gray, dark, and bright yellow boxes indicate the single, high, and low signature values
329
of the corresponding area and measurement, respectively. The changes in various VOCs could be
330
observed among the signature intensities of measurements combining their gallery plots and
331
signature plots.
332
Figures 3A and 3B depict clear differences in the types and signal intensities of VOCs
333
between the unfermented (0 min) and fermented (15–120 min) dough samples. In the
334
unfermented and fermented dough samples, 17 and 20 VOC characteristic peaks were detected,
335
respectively. The contents of 1-butanol, 1-pentanol, hexanol, phenethyl alcohol, and α-terpineol
336
in the fermented dough samples were obviously higher than those in their unfermented
337
counterparts, but the fermentation time had a small effect on their contents. Propanol and linalool
338
changed slightly in all the samples. Some ketones, such as 2-butanone, 2,3-butanedione, and 2-
339
decanone, had similar changes to that of 1-butanol. By contrast, the contents of 2-pentanedione
340
and 2,3-pentanedione in the fermented dough samples were slightly less than those in their
341
unfermented counterparts. However, a minimal change in their content occurred as the
342
fermentation time increased. The content of esters in the fermented dough was clearly higher
343
than that in the unfermented dough, but the content also had no relationship with the
344
fermentation time. Heptanal showed no significant change before and after fermentation. The
345
content of heptanal was similar in all samples.
346
3.6. Changes in E-nose response value of dough during fermentation
347
3.6.1. E-nose response to headspace gas produced by dough fermentation
348
E-nose has been widely used in the research of fermentation process monitoring, including
349
submerged and solid-state fermentation, as a quality assurance monitoring method due to its
350
rapid and accurate characteristics (Jiang et al., 2015). This research intended to use the E-nose
351
technology to monitor the change in dynamic headspace during the dough fermentation process
352
to estimate whether it could be used as the monitoring technology for dough fermentation. In this
353
study, the E-nose included 10 sensors, each sensor of the E-nose responded every 1 second, and
354
each sample was tested in 120 s. The test curve is shown in figure 4A (with samples fermented
355
for 75 minutes as an example). Five sensors (W5S, W1S, W1W, W2S, and W2W) had clear
356
responses to the fermentation headspace that were sensitive to nitrogen oxides, methyl class
357
material, sulfide, alcohol, and aromatics, respectively. This result was consistent with the
358
detection results of FAAs and VOCs in Sections 3.4 and 3.5, respectively.
359
The average value of the corresponding relatively stable sensor response is usually used
360
for analysis. Figure 4A shows the response values of sensors in the E-nose were relatively
361
stable from 60–120 s. Therefore, the average response value of 60–120s of each sensor was
362
used as the analysis variable. The curve in Figure 4B represents the variation trend of the
363
average response values of each sensor in the fermentation process.
364
As shown in figure 4B, the response values of W5S, W1S, W1W, W2S, and W2W all
365
showed an increasing trend. Combined with the results of the fermentation state in Section 3.1,
366
the growth rate of each sensor in the 0–40 min stage (insufficient fermentation stage) was
367
relatively fast. At this stage, the dough was rich in nutrients, the conditions were suitable for
368
yeast growth, and the yeast metabolism was active and enhanced logarithmically. Therefore, the
369
gas production rate was rapid. In the second stage, the rate of increase in the E-nose response
370
value became slow with fermentation for 45–80 min that might be because the metabolism of
371
yeast slowed down and resulted in the slow gas production rate of the dough. The disulfide bond
372
(-s-s -) in the dough had strong force at this stage. Consequently, the gluten network was tight,
373
the dough had strong gas holding power, and substantial metabolized gas was wrapped in the gas
374
cells of the dough. These conditions led the slow increase in response value of the E-nose. The
375
results of the two stages were consistent with the study of gas production of dough in the
376
fermentation process reported by Song et al. (2015). In the third stage of fermentation, gas
377
production was stable and even decreased, as demonstrated by the research of Song et al. (2015).
378
However, according to the change curve of E-nose response values of this study, the response
379
value of E-nose still increased after the dough was fermented for 80 min, and the increasing rate
380
accelerated again. This result might be due to the fact that after the dough was fermented for 85
381
min, it was overfermented and some of the disulfide bonds in the dough were ruptured, leading
382
to the breakage of the gluten network and the escape of gas that was originally encased in the gas
383
cells from the dough. As a result, the E-nose response continued to increase and grow rapidly.
384
The results of Kang et al. (2019) showed that the dough began to leak after fermenting for 90
385
min, and this result was similar to that of the current study.
386
3.6.2. Classification analysis of samples using PCA
387
The three stages of the response value change of the E-nose sensors basically coincided
388
with those of dough fermentation. This study also performed PCA on the E-nose data of 75
389
headspace samples collected during the dough fermentation process to prove whether E-nose
390
could be applied to the monitoring of the dough fermentation process (25 headspace samples
391
were collected during each dough fermentation process, and 3 dough samples were tested). The
392
average response values of the 10 sensors of 75 samples could obtain a 75 × 10 matrix. The PCA
393
results showed that the cumulative contribution rate of the first three principal components was
394
99.8%. Figure 4C presents the distribution of 75 headspace samples in the 3D coordinates of the
395
three principal components. The red, green, and blue symbols denoted the samples collected in
396
the insufficiently fermented (0–40 minutes of fermentation), moderately fermented (45–80
397
minutes), and over fermented (85–120 minutes) stages. These results were consistent with those
398
of professional judgments made in Section 3.1, thereby indicating that the E-nose technology
399
could be used in the monitoring of the dough fermentation state.
400
4. Conclusions
401
The dynamic changes in various indicators of CSB dough were investigated to select
402
suitable characteristic indexes for the in-depth research on establishing a monitoring method for
403
dough fermentation. Three major results were obtained. First, pasting properties of starch in
404
dough (peak viscosity, minimum viscosity, final viscosity, peak time, and gelatinization
405
temperature) and relaxation time (T21, T22) did not change significantly during the fermentation
406
process (p < 0.05). The VOCs and FAAs of the dough samples had significant differences (p <
407
0.05) in different fermentation times, but no rule was established. Therefore, these indicators
408
were unsuitable for the monitoring of dough fermentation state. Second, the proton signal
409
intensities (M21, M22) of dough samples fermented at different times demonstrated a clear trend
410
of change (M21 decreased, M22 increased). The threshold of M21 and M22 values could be set
411
based on the moderate fermentation stage (45–75 min) and used as the basis for determining
412
whether the dough fermentation is moderate. Third, the curve of E-nose response to headspace
413
during fermentation could be divided into three stages. The time nodes of these stages were
414
consistent with those of the three fermentation stages of dough. The dough samples of different
415
fermentation states could be accurately classified by using the PCA of E-nose response.
416
417
Author Contributions Section
418 419
1. Xianhui Chang performed the test and data collection, drafted and wrote the manuscript with support from all authors.
420
2. Xingyi Huang, in addition to conceiving the original idea for the study also verified the key
421
steps taken to perform the experiment. She also helped to supervise the research and
422
contributed to the final manuscript.
423 424
3. Xiaoyu Tian and Chengquan Wang helped to supervise the research and check the manuscript.
425
4. Joshua H. Aheto and Bonah Ernest worked on the main modification of the manuscript.
426
5. Ren Yi aided in performing some software analysis.
427 428 429 430
Acknowledgement This study was supported by the National Key R&D Program of China
431
(2017YFD0400100, 2017YFD0400102), Postgraduate Research & Practice Innovation
432
Program of Jiangsu Province (KYCX18_2276).
433 434 435 436 437 438 439 440
Conflict of interest statement The authors have no conflict of interest to declare
441 442
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559 560 561
562
Figure Captions:
563
Figure 1. The relaxation time(T2) of the dough fermented for different time
564
Figure 2. RVA parameters of dough fermented for different time
565 566
Figure 3: (A) gallery plots of the doughs fermented for different times versus 22 areas; (B) Signature plots corresponding to (A)
567 568 569 570
Figure 4: The E-nose response to headspace gas produced by dough fermentation. (A) The response curve of E-nose; (B) Average values of 10 E-nose sensors response to headspace gas samples produced by doughs during fermentation;(C) PCA plot of data showing the doughs in different fermentation states.
571 572 573
574 575 576 577
Fig 1 The relaxation time(T2) of the dough fermented at different times (0, 15, 30, 45, 60, 75, 90, 105, 120min)
578
B
7 6
a
a
a
a
a
a
a
a
a
0
15
30
45
60
75
90
105
120
Peak time (min)
5 4 3 2 1 0 -15
135
Fermentation time (min)
579
C
2000 a
ab
1800 1600
bc
abc
bc bc
bc abc
c abc
c
c
c ab
ab
c a
ab
viscosity(cP)
1400 1200 1000
abc
abc
ab
ab
ab
ab
ab
ab
a
800 600 400 200 0 -15
0
15
30
45
60
75
90
105
120
135
fermentation time(min) 580 581 582 583
Trough viscosity
Peak viscosity
Final viscosity
Fig. 2 RVA parameters of dough fermented for different time, A: pasting temperature; B: peak time; C: peak viscosity, trough viscosity and final viscosity. Vertical bar indicates standard deviation of the mean (n=3). Different letters represent significant difference (p < 0.05).
584
A
585 586 587
B
588 589 590 591 592
Fig. 3: (A) gallery plots of the doughs fermented for different times versus 22 areas; (B) Signature plots corresponding to (A)
A Sensor response(G/G0)
80
60
W1C W5S W3C W6S W5C W1S W1W W2S W2W W3S
40
20
0
593
40
20
0
60
80
120
100
Exposure time (s)
B
100
sensors response(G/G0)
80
W1C W5S W3C W6S W5C W1S W1W W2S W2W W3S
60
40
20
0 0
594 595
20
40
60
80
fermentation time(min)
100
120
596 597 598 599 600 601 602
Fig. 4: The E-nose response to headspace gas produced by dough fermentation. (A) The response curve of E-nose; (B) Average values of 10 E-nose sensors response to headspace gas samples produced by doughs during fermentation;(C) PCA plot of data showing the doughs in different fermentation states.
603
Table Captions:
604
Table 1. The spin-spin relaxation times(T2), signal intensities and fraction of peak area
605
Table 2. individual FAA content (of dry weight basis) of doughs fermented for different time
606 607 608 609
610 611 612 613 614 615 616
Table 1 The spin-spin relaxation times(T2), signal intensities(M) and fraction of peak area T2
M
fraction of peak area(%)
fermentation time(min)
T21
T22
M21
M22
M21
M22
0
9.89±0.40a
144.81±0.00a
25778.55±1991.50a
241.30±44.36g
99.07±0.21a
0.93±0.16g
15
9.23±0.37b
129.06±5.23b
25691.87±1852.68a
285.68±33.39fg
98.90±0.10a
1.10±0.10fg
30
9.23±0.37b
123.22±4.88bc
25355.34±1607.33a
330.04±32.98ef
98.72±0.08b
1.28±0.08ef
45
9.01±0.00b
120.40±4.88cd
24485.26±1463.53ab
352.07±27.70de
98.58±0.04bc
1.42±0.04e
60
9.01±0.00b
120.40±4.88cd
23866.29±1084.93abc
371.70±22.91cde
98.47±0.04c
1.53±0.04d
75
8.81±0.35bc
117.59±0.00cd
23113.81±925.37bc
404.17±25.85bcd
98.28±0.04d
1.72±0.04cd
90
8.41±0.00c
117.59±0.00cd
22587.46±739.87bc
425.74±32.90abc
98.15±0.09de
1.85±0.09c
105
8.41±0.00c
114.96±4.55d
22395.22±898.50bc
456.45±33.60ab
98.00±0.08ef
2.00±0.08b
120
8.41±0.00c
117.59±0.00cd
22150.76743.73c
482.36±30.26a
97.87±0.08f
2.13±0.08a
Note: In the same column, different letters indicate significant differences(n=3,p＜0.05).
Table 2 Individual FAA content (of dry weight basis) of doughs fermented for different time Na me (μg/ g) Asp Glu Ser His Gly Thr Arg Ala Tyr Cys -s Val Met Trp Phe Ile Leu Lys Pro Tot al
0
15
30
222.87±3. 57a 416.37±10 .64a 44.46±0.9 4a 63.84±3.8 2ab 72.37±1.6 0a 78.72±2.4 5a 207.47±15 .39a 249.91±1. 80a 261.74±12 .95b 11.32±2.0 4ab 71.01±3.5 8bc 35.13±1.5 7d 40.66±2.0 8c 40.73±1.2 5c 30.74±1.6 9d 41.19±0.4 1b 29.96±1.7 9d 185.41±9. 26ab 2103.89±3 7.98a
226.06±9. 18a 399.34±21 .07a 42.05±1.6 0a 65.68±2.3 4a 68.96±2.0 1a 78.23±1.6 7a 206.65±5. 90a 246.64±10 .34a 332.01±43 .35a 12.98±1.0 9ab 76.80±3.5 2b 34.57±1.5 2d 30.91±5.6 5cd 2.76±1.78 d 3.68±1.54 e 2.34±1.39 e 4.08±1.99f
208.69±17 .45ab 357.16±15 .09b 38.93±0.7 2a 62.30±1.8 5ab 72.76±0.4 8a 78.17±1.7 1a 190.52±1. 59ab 230.77±11 .63a 63.60±8.9 7c 13.16±1.8 0ab 85.45±3.7 0a 46.21±1.1 3bc 69.79±4.8 6a 75.07±3.0 7ab 62.48±1.5 0ab 74.59±1.0 7a 38.60±2.5 1c 194.7210. 43a 1962.99±2 1.76b
175.08±7. 57abc 2008.83±8 0.46b
Fermentation time (min) 45 60 75 204.07±6. 24ab 325.77±8. 50c 40.57±3.1 4a 59.80±2.7 0ab 70.10±0.7 9a 75.02±1.6 0a 180.85±7. 13b 222.72±5. 88a 46.31±3.6 1c 14.48±1.9 5a 91.33±3.6 0a 41.74±1.4 3c 54.65±5.3 5b 72.69±1.6 9b 54.87±1.6 9bc 78.12±1.9 3a 14.28±1.2 7e 152.12±11 .89c 1799.49±3 0.57c
198.90±2. 22ab 319.77±12 .77c 17.28±1.3 1c 17.20±1.4 8c 40.04±1.1 3b 34.77±2.0 2c 96.29±1.3 6c 136.94±8. 90bc 44.92±2.0 7c 11.48±1.5 5ab 63.63±6.5 0c 35.09±0.2 2d 36.34±1.6 3cd 35.91±2.0 1c 33.12±4.1 2d 39.12±4.7 4b 17.75±1.1 6e 160.75±11 .72bc 1339.30±6 .45f
203.35±1. 22ab 304.52±9. 08c 16.59±0.7 2c 15.66±0.4 9c 41.71±2.2 6b 29.78±0.9 3c 95.63±3.8 2c 133.896.4 6bc 37.25±1.0 9c 15.35±0.5 3a 9.70±1.36 e 70.83±3.4 4a 54.74±4.9 2b 75.51±8.8 7ab 62.08±1.1 8ab 79.691.51 a 13.32±1.3 2e 127.95±7. 76d 1387.5±1 1.89 f
90
105
120
185.95±8. 09b 301.55±1 2.40c 19.41±2.3 0c 13.26±1.3 2c 39.24±1.6 9b 30.42±2.2 8c 91.75±1.7 2c 117.84±7. 67c 47.98±6.3 6c 5.60±1.65 cd 6.67±1.07 e 49.93±1.9 8b 24.86±3.5 2d 11.08±1.7 6d 59.53±4.5 0ab 14.84±2.2 0c 12.46±1.6 0e 116.86±9. 08d 1149.22± 7.11g
185.69±6. 25b 291.8.56± 6.52c 30.61±3.2 3b 58.22±1.7 1b 73.08±1.6 0a 69.80±1.4 1b 176.48±10 .55b 157.55±17 .32b 254.19±5. 45b 2.86±1.65 d 9.36±1.40 e 21.76±3.0 7e 65.26±3.9 4ab 9.03±0.92 d 50.90±2.3 6c 18.97±0.4 8c 51.89±4.8 3b 108.96±5. 04d 1643.15±1 5.13d
201.22±13 .75ab 313.12±14 .30c 16.16±1.2 4c 13.86±0.1 2c 40.92±2.1 8b 32.57±3.8 6c 104.79±11 .65c 122.19±4. 44c 51.05±3.8 4c 8.08±0.39 bc 50.05±2.5 7d 29.11±1.5 2d 60.60±4.6 7ab 83.91±1.2 6a 66.26±0.9 1a 73.91±0.3 0a 69.16±3.7 7a 161.34±4. 69bc 1498.30±4 8.16e
Note: In the same column, different letters indicate significant differences(n=3, p＜0.05).
Highlights
Investigated the changes in various indicators of dough during fermentation.
There was no significant change in starch-paste properties, T21, and T22 of dough.
VOCs and FAAs of the dough change significantly, but no rule was established.
The change of headspace was most suitable for monitoring the fermentation of dough.
3 fermentation states of dough could be accurately identified by using E-nose.
S0308-8146(19)32196-X https://doi.org/10.1016/j.foodchem.2019.126050 FOCH 126050
To appear in:
Food Chemistry
Received Date: Revised Date: Accepted Date:
16 July 2019 27 November 2019 10 December 2019
Please cite this article as: Chang, X., Huang, X., Tian, X., Wang, C., Aheto, J.H., Ernest, B., Yi, R., Dynamic characteristics of dough during the fermentation process of Chinese steamed bread, Food Chemistry (2019), doi: https://doi.org/10.1016/j.foodchem.2019.126050
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© 2019 Published by Elsevier Ltd.
1
Dynamic characteristics of dough during the fermentation process of Chinese steamed
2
bread
3 4
Xianhui Chang, Xingyi Huang*, Xiaoyu Tian, Chengquan Wang, Joshua H. Aheto, Bonah
5
Ernest, Ren Yi
6
School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang
7
212013, Jiangsu, P. R. China
8 9 10 11 12 13
*Corresponding author
14
Prof. Xingyi Huang
15
School of Food and Biological Engineering
16
Jiangsu University, Xuefu Road 301, Zhenjiang 212013, Jiangsu, P. R. China
17
Tel: +86-51188792368
18
Email: h_xingyi@ ujs.edu.cn
19
20 21
Abstract
22
The fermentation process is crucial to the production of Chinese steamed bread (CSB). In
23
order to select suitable indicators as the basis for further research of establishing intelligent
24
monitoring method for dough fermentation state, this study investigated the dynamic
25
characteristics of dough during fermentation. Indicators included water mobility and distribution,
26
starch-pasting properties, content of free amino acid (FAA), volatile organic compounds (VOCs)
27
and electronic nose (E-nose) response value. Starch-pasting properties of dough and relaxation
28
time (T21, T22) did not change significantly during the fermentation process (p < 0.05). The
29
VOCs and FAAs of the dough had significant differences (p < 0.05) in different fermentation
30
times, but no rule was established. The E-nose response value to headspace was most suitable to
31
monitor the fermentation of dough. Principal component analysis (PCA) was performed on E-
32
nose data from 75 samples and the results indicated that samples of different fermentation states
33
were accurately classified.
34 35
Keywords
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Dough; Dynamic characteristics; Fermentation state; Monitoring; PCA.
37 38 39 40
41 42
1. Introduction
43
As a traditional Chinese staple food, CSB has been popular with Chinese consumers since
44
its origin more than 1,700 years ago (G. H. Zhang et al., 2016). CSB forms an important part in
45
the diet of many Chinese, especially in northern China(Guo Hua Zhang, Zhou, & He, 2012)
46
because the ingredients and processing methods of CSB are simple, and its flavor is pleasant and
47
unique. CSB is generally processed by mixing flour, yeast, and water into a dough, which is then
48
fermented and steamed (F. Zhu, 2014). This traditional approach is believed to play a significant
49
part in the final products’ unique flavor, soft crumb, smooth white skin, and pleasant aroma,
50
making it entirely different from western baked breads (Wang, Yang, Gu, Xu, & Jin, 2017b).
51
Nowadays, CSB has become popular among other Asian countries (X. Liu et al., 2018), and
52
various studies have been conducted on it.
53
Similar to other fermented flour products, such as bread and pizza, fermentation is a crucial
54
step in the processing of CSB. Different leavening agents, fermentation methods, and
55
fermentation time affect the aroma, taste, appearance, texture and other quality parameters of the
56
product. The main leavening agents used for dough fermentation are active dry yeasts(Liu,
57
Chang, Li, & Liu, 2012; F. Zhu, 2014; Fan Zhu, 2016), distiller’s yeast (Liu, Wang et al 2012),
58
sour dough(Kim, Huang, Zhu, & Rayasduarte, 2009; T. Liu et al., 2018; Rinaldi, Paciulli,
59
Caligiani, Scazzina, & Chiavaro, 2017; G. H. Zhang et al., 2016), chemical leavening agents
60
(Wang, Yang, Gu, Xu, & Jin, 2017a) , and strains, such as lactobacillus (Di Renzo, Reale,
61
Boscaino, & Messia, 2018). The aroma characteristics of CSB are always different, when
62
different leavening agents are used during the fermentation process. The fermentation method
63
also affects the quality of CSB. As a national standard method (GBT35991-2018), single
64
fermentation is the most preferred method among producers at present because of its simple
65
operation. Remixed fermentation is a traditional CSB processing method that improves the
66
hardness and chewiness of CSB, and is more relished by consumers in northern China (Li, Deng,
67
Li, Liu, & Bian, 2015; Kim et al., 2009; Maeda et al., 2009). In recent years, some researchers
68
have introduced multi-technology fusion methods into dough fermentation, such as ultrasound-
69
assisted dough fermentation (Luo et al., 2018). These studies were mainly directed at studying
70
the influence of fermentation on the quality of CSB, and fermentation process optimization. No
71
systematic study has been performed on the dynamic changes in various dough indicators during
72
fermentation.
73
Similar to the development of baked bread processing in the western countries, industrial
74
processing of steamed bread will be the trend of CSB production in the future. More so, with the
75
rapid development of artificial intelligence (AI) technology, the process control and monitoring
76
of CSB will be integrated with AI technology in the foreseeable future.
77
In recent years, food-processing industries have adopted food process monitoring
78
technologies that combine intelligent sensing technology with chemometrics to achieve real-time
79
quality and safety monitoring. Intelligent sensing technologies, such as computer vision, spectral
80
technology, E-nose, and E-tongue, are nondestructive and can provide information in real time.
81
Such monitoring techniques have been widely applied in monitoring the fermentation of black
82
tea (Pan, Zhao, Chen, & Yuan, 2015), protein feed (H & Sensors, 2014; Jiang, Chen, & Liu,
83
2014), rice wine (Ouyang, Zhao, Pan, & Chen, 2016), ethanol (Jiang et al., 2018), and wheat
84
straw (Mei, Yang, Shu, Jiang, & Liu, 2015) as well as the freshness of fish during storage (Ding
85
et al., 2014; Huang et al., 2016). Monitoring models must be established in these studies by
86
combining intelligent sensor signals with chemical, physical, or artificial evaluation.
87
As described above, fermentation is a critical link in CSB processing. The appropriate
88
fermentation level products acceptable flavor, surface, and texture characteristics for the final
89
product (Maeda et al., 2009). The crucial premise of establishing AI monitoring model is the
90
assessment of various dough indexes during fermentation. The main components of dough are
91
water, starch and protein. During the fermentation of dough, the metabolism of yeast produces a
92
large amount of gas, while the degradation of proteins produces amino acids.
93
Therefore, the objectives of this study are to measure the dough indicators (water mobility
94
and distribution, starch-pasting properties, FAAs, VOCs and E-nose response value) and their
95
dynamic changes during fermentation and select suitable characteristic indexes for further
96
research of establishing monitoring models of the dough fermentation state. The results of this
97
study will provide a theoretical basis for intelligent dough monitoring. This study is significant to
98
the industrialization and prospects of CSB.
99 100
2. Materials and methods
101
2.1. Materials
102
Instant dry yeast (Angel®, Angel Yeast Co., Ltd, China) and medium gluten wheat flour
103
(Wonder Farm Brand®, Yihai Kerry (Kunshan) Food Industry Co., Ltd., China) were purchased
104
from a local supermarket. Flour moisture was determined by using national standard method (GB
105
5009.3-2016). Water absorption and rheological properties of flour were obtained according to
106
the national standard method with a Brabender farinograph (GB/T 14614-2006).
107
2.2. Sample preparation
108
The dough was prepared mixing flour, dry yeast and water. The additive amount of yeast is
109
0.8% of flour. The amount of water was 80% of the optimum water absorption determined by
110
Brabender farinograph. After mixing, the dough was placed into a sealed container and allowed
111
to rest for 10 min at room temperature. Thereafter, the dough was divided into pieces on the
112
basis of the experimental requirement and then kneaded to smooth. The dough was fermented at
113
30 °C under 80% to 90% relative humidity for 0, 15, 30, 45, 60, 75, 90, 105 and 120 min.
114
The fermentation state of dough was determined by well-trained professionals in the field of
115
CSB processing.
116
2.3. Low-field nuclear magnetic resonance (LF-NMR)
117
Water mobility and the distribution of fermented dough ware measured by using a Niumag
118
NMR Analyzer (NMI20-030V-I-25mm, Suzhou Niumag Analytical Instrument Corporation)
119
with a magnetic field strength of 0.54 T and a corresponding resonance frequency for protons of
120
22 MHz. Approximately 8g of dough was placed into a 25 mm glass tube, fermented for 0, 15,
121
30, 45, 60, 75, 90, 105, 120min, and inserted in the NMR probe. Transverse relaxation time(T2)
122
was measured by using the Carr-Purcell-Meiboom-Gill pulse sequence. The optimal pulse
123
parameters were: SW = 100kHz, SF = 22MHz, P1 = 5.50μs, DRG1 = 3, TD = 200002, TW =
124
1000,000ms, NS = 8, P2=11.00, and NECH = 4000. Relaxation time of all samples was
125
measured at 30 °C.
126
2.4. Pasting property identification via the rapid viscosity analyzer (RVA)
127
The dough(50g) fermented at different times (0, 15, 30, 45, 60, 75, 90, 105, 120min) was
128
freeze-dried and ground in a mortar for testing. Determining the pasting properties was
129
conducted by using the RVA (TechMaster, Perten Instruments, Macquarie Park, Australia). The
130
sample (3.5 g, 14%) was mixed with deionized water (25 mL) and homogenized to form
131
suspension in an aluminum barrel. The barrel was subjected to the RVA for testing, and the test
132
parameters ware as follows: maintained at 50 °C for 1 min, then heated to 95 °C at 12 °C/min,
133
held at 95 °C for 2.5 min, and cooled to 50 °C at 12 °C/min for 2 min. The initial stirring speed
134
of the analyzer was at 960 rpm for 10 s and then maintained at 160 rpm (Yang et al.,2018). The
135
peak viscosity, trough (minimum viscosity), breakdown, peak time, and pasting temperature
136
were recorded for analysis.
137
2.5. FAA determination
138
FAA was determined by using an S-433D automatic amino acid analyzer (Sykam,
139
Germany). The freeze-dried dough was weighted accurately at 1.0 g (up to the last four decimal
140
places and the quality was recorded in detail) and carefully place in a 25 mL volumetric flask.
141
Trichloroacetic acid solution (15mL, 5%) was added to dissolve the sample, which was shaken
142
and dispersed. This solution was added again to fix the volume. Ultrasound was performed at
143
room temperature for 20 min and again for 2h. A pinhead filter was used to strain the filtrate.
144
The filtrate was collected in a 1.5 mL centrifuge tube and centrifuged at 15,000r /min for 30min.
145
The supernatant (400 μL) was determined in a liquid sample bottle by using an automatic amino
146
acid analyzer.
147
Analysis method: The method used was the precolumn derivatization of OPA FMOC. The
148
chromatographic condition included the use of an Agilent 1100 liquid chromatograph. The
149
chromatographic column was 250 × 1.6 mm, (5 μm, ODS HYPERSH). The column temperature
150
was 40 °C.
151
Mobile phase: A phase: Crystallized sodium acetate (8.0 g) was weighed in a 1,000 mL
152
beaker, and 1000 mL water was added and stirred until all the crystals dissolved. Triethylamine
153
(225 L) was then added and stirred. Acetic acid (5%) was added, and the pH was adjusted to 7.20
154
± 0.05. Tetrahydrofuran (5 mL) was added, and the solution was mixed and reserved.
155
B phase: Crystallized sodium acetate (8.0 g) was weighed and mixed in an 800 mL beaker
156
with 400 mL of water until all crystals were dissolved. The pH was adjusted to 7.20 ± 0.05 with
157
2% acetic acid. The solution was added to 800 mL acetonitrile and 800 mL methanol for mixed
158
use.
159
Flow rate: The flow rate was 1.0 mL/min. UV detection was conducted at 338 nm.
160
2.6. Volatile organic compound (VOC) analysis via gas chromatography–ion mobility
161
spectrometry (GC–IMS)
162
IMS analysis was performed by using a gas chromatography (GC) coupled with an IMS
163
instrument (Flavourspec®, G.A.S. Dortmund Company, Germany). Briefly, the dough (1.0 g)
164
fermented at different times (0, 15, 30, 45, 60, 75, 90, 105, 120 min) was directly placed in a 20
165
mL headspace vial and incubated at 60 °C for 10 minutes. Following the incubation period,
166
100μL of sample headspace was automatically injected injection speed of 30 mL min-1 and the
167
injector temperature was maintained at 85°C. The chromatographic separation was performed on
168
a SE-54-CB-1 (5% phenyl–1% vinyl–94% methylpolysiloxane) capillary column (15 m × 0.53
169
mm, 1 μm film thickness) and maintained at 85 °C, with nitrogen as the carrier gas. The GC run
170
time was set to 30 min after a preliminary time of 20 min, resulting in poor compound
171
separation. A flushing time of 5 min was allowed to prevent carryover effects. During the
172
analysis of GC-IMS data, an area set capable of integrating all marker spots was created by using
173
G.A.S. software suite called Laboratory Analytical Viewer (LAV). This software was used to
174
illustrate the IMS topographic plots.
175
2.7. Dynamic headspace analysis: via E-nose
176
In this study, the dynamic headspace during fermentation was detected by using an E-nose
177
instrument (PEN3, Airsense Analytics GmbH, Germany). The E-nose had an array of 10
178
different metal oxide sensors positioned in a small steel chamber (V = 1.8 mL) and used a pattern
179
recognition software (Win Muster v.3.0).
180
The dough(100g) was placed into a 1,000 mL sealed beaker and then into a fermentation
181
tank. When testing, two Luer Lock needles were inserted into the beaker. One was connected to
182
a clean air source (charcoal filter), and the other needle was connected to the Teflon tubing (3
183
mm) of the E-nose. The “Automatic Measurement” mode was adopted in this study, and the test
184
was conducted simultaneously with dough fermentation. The E-nose response data of headspace
185
were collected every 5 minutes. The detection parameters included measurement and flush times
186
of 120 and 180 s, respectively. The dough was fermented for 120 min, and 25 groups of data
187
were collected. A total of 75 groups of data were collected in three parallel tests.
188
2.8. Data processing method
189
All the experiments were arranged in at least three parallels and the results were expressed
190
as mean value ± standard deviation via SPSS 21.0 (SPSS Inc., Chicago, USA). The data were
191
plotted by using Origin 8.5 (Origin Lab Corp., Northampton, MA, USA). PCA of E-nose data
192
was performed on MATLAB R2014a (Mathworks, USA).
193 194
3. Results and discussion
195
3.1. Flour properties and optimum fermentation time of dough
196
Flour moisture was 13.0%. Farinograph water absorption, development time, stability time,
197
and mixing tolerance index were 61.6%, 2.0 min, 3.9 min, and 68 FU. The flour was suitable for
198
processing CSB, and the appropriate amount of water to make the dough was 49.3% (80% of
199
water absorption).
200
According to the judgment of professionals, the optimum fermentation time of the dough
201
was 45–75min. On the basis of the fermentation state, the dough samples could be divided into
202
three groups, namely, insufficiently fermented (0–30 min), moderately fermented (45–75 min),
203
and over–fermented (90–120 min) samples.
204
3.2. Water mobility and distribution in dough during fermentation
205
Water accounts for more than 60% of the dough and is crucial in CSB production.
206
Fermentation is a complex process that involves water migration in dough. The distribution and
207
migration of water were studied extensively by using LF-NMR, in which these parameters are
208
measured by using the spin–spin relaxation time (T2) and signal amplitude of protons. (Bosmans
209
et al. 2012, Huang et al. 2016). Figure 1 shows the proton signal amplitude and T2 of dough at
210
different fermentation times. Two peaks of T21 (1–36 ms) and T22 (38–270 ms), which
211
represented the bound and weak bound waters, were observed in each curve, respectively (Li, Z,
212
Deng, C et al., 2015). Figure 1 illustrates that the peak area (M) represents the proton signal
213
intensities that can be used to represent the relative content of water in each part.
214
Table 1 lists the T2 (T21, T22), proton signal intensities (M21, M22), and fractions of M21, M22
215
of the dough samples fermented at different times. T2 (T21, T22) of fermented dough samples
216
were significantly shorter than those of their unfermented counterparts. This change might be
217
due to the starch–water and gluten–water models of dough samples that tightened under warm
218
conditions. With the increase in fermentation time, T21 and T22 of the dough samples showed a
219
shortening trend. However, such trend was insignificant (p < 0.05). Given that starch absorbed
220
most of the water in the dough, the status of starch did not change significantly (p < 0.05) during
221
the fermentation process; the starch only acted as an inert filling component of the dough,
222
thereby limiting the migration of water in the dough (Bosmans et al., 2012). In combination with
223
the fermentation state of the dough, the differences in T21 and T22 were insignificant among
224
different fermentation states of dough samples. Therefore, T2 was an unsuitable indicator in
225
determining the fermentation state of dough.
226
Table 1 also lists the area and area fraction of each peak that could be used to identify the
227
changes in the relative content and proportion of water in the dough during the fermentation
228
process. The bound water demonstrated a decreasing trend with the extension of fermentation
229
time, whereas the weak bound water exhibited an increasing trend. The competition for water
230
caused the water absorption of starch to be less than that of gluten in the dough (Bosmans et al.,
231
2012). As the fermentation time progressed, the gluten network was further strengthened, while
232
a portion of the carbohydrate and protein were consumed by yeast growth. All of these
233
conditions might lead to the decrease in bound water (M21) in the dough. At the same time, the
234
degradation of carbohydrates resulted in the formation of water, most of which remained in the
235
dough as a form of weakly bound water. Therefore, the weakly bound water (M22) in the dough
236
tended to increase. In this study, M showed a clear trend of change (M21 decreased, M22
237
increased). Given that the appropriate fermentation time was 45–75 min (Section 3.1), the
238
threshold could be set in accordance with the M21 and M22 values of the dough fermented for 45–
239
75 min to determine its fermentation state. According to table 1, when the dough was fermented
240
for 45 minutes and 75 minutes, the mean values of M21 were 24485.26 and 23113.81,
241
respectively. Therefore, the threshold of M21 could be set as [24485.26, 23113.81]，Within this
242
range, the dough can be judged to be moderately fermented. When M21 > 24485.26, the dough
243
can be judged as insufficiently fermented. while when M21 < 23113.81, the dough can be judged
244
as over fermented. Similarly, when dough is fermented for 45 minutes and 75 minutes, the mean
245
value of M22 is 352.07 and 404.17, respectively, so the threshold value of M22 can be set as
246
[352.07, 404.17].
247
3.3. Change in starch-pasting properties during dough fermentation
248
Starch is the most abundant component in wheat flour, and the change in starch-pasting
249
properties will likely affect the quality of dough during fermentation. In this study, the peak
250
viscosity, trough viscosity, final viscosity, peak time, and pasting temperature were measured by
251
using the RVA method (Fig. 2). The pasting temperature of starch in the dough changed slightly
252
with the extension of fermentation time from 86.43 °C to 87.25 °C given that no rule is available
253
to follow (Fig. 2A). The measurement of peak time showed a similar phenomenon from 5.86 min
254
to 6.00 min (Fig. 2B), and the value was similar to the farinograph result (Section 3.1,
255
development time was 2.0 min, stability time was 3.9 min). This result might be due to the
256
fermentation of dough at low temperatures. The water content in the dough was limited, and the
257
water absorption rate of starch in the dough was lower than that of gluten (Section 2) due to the
258
competition for water content, which inhibited the gelatinization of starch in the dough.
259
Therefore, the physicochemical properties of starch did not change during dough fermentation,
260
and the changes in pasting temperature and peak time were insignificant (p < 0.05). The results
261
were comparable to those of soybean flour, as reported by Olanipekun et al. (Olanipekun,
262
Otunola, Adelakun, & Oyelade, 2009).
263
Figure 2C shows that the changes in peak, trough, and final viscosities were also
264
insignificant. With the extension of fermentation time, peak and trough viscosities demonstrated
265
a slightly increasing trend from 1498.5 cP to 1568.5 cP and from 883.5 cP to 927.0 cP,
266
respectively. On the contrary, the final viscosity decreased slightly from 1808.5 cP to 1698.5 cP.
267
Research has shown that the viscosity of paste is affected by multiple factors, such as starch and
268
protein contents and their components and hydrocolloids(Kowalczewski, Rozanska, Makowska,
269
Jezowski, & Kubiak, 2019; G. Zhang, Hamaker, & Chemistry, 2003). Therefore, the increase in
270
peak and trough viscosities might be due to the expansion of the gluten network in the dough
271
after fermentation. However, in the process of RVA determination, the paddle was constantly
272
stirring. The long fermentation time expanded the gluten sufficiently that was easily destroyed by
273
the paddle. As a result, the final viscosity showed a downward trend.
274
According to the results of pasting temperature, peak time, peak viscosity, trough viscosity,
275
and final viscosity determined by the RVA, no significant difference (p < 0.05) occurred among
276
the samples at different fermentation times. Therefore, starch-pasting properties were unsuitable
277
indicators to determine the fermentation state of dough.
278
3.4. FAA analysis
279
During dough fermentation, the metabolism of yeast consumes amino acids. Hence, the
280
FAA content in dough might be varied at different fermentation times. Changes in FAAs, such as
281
in flavoring substances (Chen et al., 2019), might also be helpful in studying the flavor change of
282
food. Therefore, FAAs in dough samples with different fermentation times were determined, and
283
their change rules were expounded in this study. Table 2 lists the 18 FAAs detected in the dough
284
samples.
285
Table 2 indicates that the total FAA content of dough is significantly different at different
286
fermentation times (p < 0.05). In the fermentation stage of 0–90 min, the content of total FAA
287
decreased gradually. The total FAA content of the dough fermented for 90 min was 45.37%
288
lower (decreased by 954.67 μg/g) than that of the dough fermented for 0 min. The total FAA
289
content of dough increased when fermented for 105 min and decreased again at 120 min. This
290
result might be due to the consumption of FAAs from the metabolism of yeast during dough
291
fermentation that subsequently decreased the total FAA content. With the extension of
292
fermentation time, the metabolism of yeast slowed down, and the amino acid consumption
293
decreased. The proteins in the dough were degraded by endogenous enzymes to produce FAAs.
294
Hence, the total FAA content rebounded in the later stage of fermentation. This trend was similar
295
to the result of Collar’s research (C Collar et al., 1992).
296
The variations in each individual FAA were different in the fermentation process. The
297
contents of Asp and Glu decreased gradually during the fermentation stage of 0–105 min but
298
slightly increased when fermented for 120 min. The contents of Ser, His, Gly, Thr, Arg, and Ala
299
did not change significantly when the dough was fermented for 0–45 min, decreased
300
significantly after the dough was fermented for 60 min, and maintained a relatively low level
301
during 60–90 min. The content of these FAAs in the dough increased when the dough was
302
fermented for 105 min and decreased again at 120 min. A significant decrease in Tyr content
303
appeared at 45 min, and the subsequent change trend was similar to that of Ser and His. The
304
other FAAs in the dough demonstrated fluctuating changes during the fermentation process, and
305
the rules were unclear. Notably, Lys, Phe, Ile, and Leu were almost exhausted by the time the
306
dough was fermented for 30 min, and the subsequent change trend was undulating. This result
307
suggested that their assimilation by yeast was high at the beginning of fermentation. These
308
phenomena were consistent with Kokawa’s research (Kokawa et al., 2017).
309
3.5. Comparative profiling of VOCs changes via GC–IMS
310
The results of some studies have shown that the VOCs in fermented flour products are
311
mainly alcohols, aldehydes, ketones, and esters (Di Renzo et al., 2018; G. H. Zhang et al., 2016;
312
Guo Hua Zhang et al., 2012). The GC–IMS technology combines the high separation capacity of
313
GC and high sensitivity of IMS, that enables the rapid monitoring of trace VOCs (Becher,
314
Purkhart, Hillmann, Graupner, & Werner, 2014; Denawaka, Fowlis, & Dean, 2014). In this study,
315
GC-IMS technology was used to detect the fingerprints information of VOCs in dough samples
316
fermented at different times.
317
In all the samples of this study, 22 characteristic ion transport peaks of VOCs were selected
318
for analysis. The gallery plots shown in Figure 3A illustrates the vertical cross-section view
319
corresponding to the characteristic ion transport peaks of 22 VOCs detected and the horizontal
320
cross-section view corresponding to the dough samples fermented at different times. In the
321
NIST2014 spectral database of the instrument used in this study, only a part of characteristic ion
322
transport peaks could be identified. Out of the 22 characteristic peaks screened in this study, 17
323
types of component information could be determined, including 8 alcohols, 5 ketones, 3 esters,
324
and 1 aldehyde.
325
Signature plots were used to describe the relative IMS signal intensity of the measurement
326
in all areas to compare the change in VOCs in the dough samples under investigation. Figure 3B
327
illustrates that the signature plots generated by LAV analysis software correspond to the gallery
328
plots. The gray, dark, and bright yellow boxes indicate the single, high, and low signature values
329
of the corresponding area and measurement, respectively. The changes in various VOCs could be
330
observed among the signature intensities of measurements combining their gallery plots and
331
signature plots.
332
Figures 3A and 3B depict clear differences in the types and signal intensities of VOCs
333
between the unfermented (0 min) and fermented (15–120 min) dough samples. In the
334
unfermented and fermented dough samples, 17 and 20 VOC characteristic peaks were detected,
335
respectively. The contents of 1-butanol, 1-pentanol, hexanol, phenethyl alcohol, and α-terpineol
336
in the fermented dough samples were obviously higher than those in their unfermented
337
counterparts, but the fermentation time had a small effect on their contents. Propanol and linalool
338
changed slightly in all the samples. Some ketones, such as 2-butanone, 2,3-butanedione, and 2-
339
decanone, had similar changes to that of 1-butanol. By contrast, the contents of 2-pentanedione
340
and 2,3-pentanedione in the fermented dough samples were slightly less than those in their
341
unfermented counterparts. However, a minimal change in their content occurred as the
342
fermentation time increased. The content of esters in the fermented dough was clearly higher
343
than that in the unfermented dough, but the content also had no relationship with the
344
fermentation time. Heptanal showed no significant change before and after fermentation. The
345
content of heptanal was similar in all samples.
346
3.6. Changes in E-nose response value of dough during fermentation
347
3.6.1. E-nose response to headspace gas produced by dough fermentation
348
E-nose has been widely used in the research of fermentation process monitoring, including
349
submerged and solid-state fermentation, as a quality assurance monitoring method due to its
350
rapid and accurate characteristics (Jiang et al., 2015). This research intended to use the E-nose
351
technology to monitor the change in dynamic headspace during the dough fermentation process
352
to estimate whether it could be used as the monitoring technology for dough fermentation. In this
353
study, the E-nose included 10 sensors, each sensor of the E-nose responded every 1 second, and
354
each sample was tested in 120 s. The test curve is shown in figure 4A (with samples fermented
355
for 75 minutes as an example). Five sensors (W5S, W1S, W1W, W2S, and W2W) had clear
356
responses to the fermentation headspace that were sensitive to nitrogen oxides, methyl class
357
material, sulfide, alcohol, and aromatics, respectively. This result was consistent with the
358
detection results of FAAs and VOCs in Sections 3.4 and 3.5, respectively.
359
The average value of the corresponding relatively stable sensor response is usually used
360
for analysis. Figure 4A shows the response values of sensors in the E-nose were relatively
361
stable from 60–120 s. Therefore, the average response value of 60–120s of each sensor was
362
used as the analysis variable. The curve in Figure 4B represents the variation trend of the
363
average response values of each sensor in the fermentation process.
364
As shown in figure 4B, the response values of W5S, W1S, W1W, W2S, and W2W all
365
showed an increasing trend. Combined with the results of the fermentation state in Section 3.1,
366
the growth rate of each sensor in the 0–40 min stage (insufficient fermentation stage) was
367
relatively fast. At this stage, the dough was rich in nutrients, the conditions were suitable for
368
yeast growth, and the yeast metabolism was active and enhanced logarithmically. Therefore, the
369
gas production rate was rapid. In the second stage, the rate of increase in the E-nose response
370
value became slow with fermentation for 45–80 min that might be because the metabolism of
371
yeast slowed down and resulted in the slow gas production rate of the dough. The disulfide bond
372
(-s-s -) in the dough had strong force at this stage. Consequently, the gluten network was tight,
373
the dough had strong gas holding power, and substantial metabolized gas was wrapped in the gas
374
cells of the dough. These conditions led the slow increase in response value of the E-nose. The
375
results of the two stages were consistent with the study of gas production of dough in the
376
fermentation process reported by Song et al. (2015). In the third stage of fermentation, gas
377
production was stable and even decreased, as demonstrated by the research of Song et al. (2015).
378
However, according to the change curve of E-nose response values of this study, the response
379
value of E-nose still increased after the dough was fermented for 80 min, and the increasing rate
380
accelerated again. This result might be due to the fact that after the dough was fermented for 85
381
min, it was overfermented and some of the disulfide bonds in the dough were ruptured, leading
382
to the breakage of the gluten network and the escape of gas that was originally encased in the gas
383
cells from the dough. As a result, the E-nose response continued to increase and grow rapidly.
384
The results of Kang et al. (2019) showed that the dough began to leak after fermenting for 90
385
min, and this result was similar to that of the current study.
386
3.6.2. Classification analysis of samples using PCA
387
The three stages of the response value change of the E-nose sensors basically coincided
388
with those of dough fermentation. This study also performed PCA on the E-nose data of 75
389
headspace samples collected during the dough fermentation process to prove whether E-nose
390
could be applied to the monitoring of the dough fermentation process (25 headspace samples
391
were collected during each dough fermentation process, and 3 dough samples were tested). The
392
average response values of the 10 sensors of 75 samples could obtain a 75 × 10 matrix. The PCA
393
results showed that the cumulative contribution rate of the first three principal components was
394
99.8%. Figure 4C presents the distribution of 75 headspace samples in the 3D coordinates of the
395
three principal components. The red, green, and blue symbols denoted the samples collected in
396
the insufficiently fermented (0–40 minutes of fermentation), moderately fermented (45–80
397
minutes), and over fermented (85–120 minutes) stages. These results were consistent with those
398
of professional judgments made in Section 3.1, thereby indicating that the E-nose technology
399
could be used in the monitoring of the dough fermentation state.
400
4. Conclusions
401
The dynamic changes in various indicators of CSB dough were investigated to select
402
suitable characteristic indexes for the in-depth research on establishing a monitoring method for
403
dough fermentation. Three major results were obtained. First, pasting properties of starch in
404
dough (peak viscosity, minimum viscosity, final viscosity, peak time, and gelatinization
405
temperature) and relaxation time (T21, T22) did not change significantly during the fermentation
406
process (p < 0.05). The VOCs and FAAs of the dough samples had significant differences (p <
407
0.05) in different fermentation times, but no rule was established. Therefore, these indicators
408
were unsuitable for the monitoring of dough fermentation state. Second, the proton signal
409
intensities (M21, M22) of dough samples fermented at different times demonstrated a clear trend
410
of change (M21 decreased, M22 increased). The threshold of M21 and M22 values could be set
411
based on the moderate fermentation stage (45–75 min) and used as the basis for determining
412
whether the dough fermentation is moderate. Third, the curve of E-nose response to headspace
413
during fermentation could be divided into three stages. The time nodes of these stages were
414
consistent with those of the three fermentation stages of dough. The dough samples of different
415
fermentation states could be accurately classified by using the PCA of E-nose response.
416
417
Author Contributions Section
418 419
1. Xianhui Chang performed the test and data collection, drafted and wrote the manuscript with support from all authors.
420
2. Xingyi Huang, in addition to conceiving the original idea for the study also verified the key
421
steps taken to perform the experiment. She also helped to supervise the research and
422
contributed to the final manuscript.
423 424
3. Xiaoyu Tian and Chengquan Wang helped to supervise the research and check the manuscript.
425
4. Joshua H. Aheto and Bonah Ernest worked on the main modification of the manuscript.
426
5. Ren Yi aided in performing some software analysis.
427 428 429 430
Acknowledgement This study was supported by the National Key R&D Program of China
431
(2017YFD0400100, 2017YFD0400102), Postgraduate Research & Practice Innovation
432
Program of Jiangsu Province (KYCX18_2276).
433 434 435 436 437 438 439 440
Conflict of interest statement The authors have no conflict of interest to declare
441 442
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562
Figure Captions:
563
Figure 1. The relaxation time(T2) of the dough fermented for different time
564
Figure 2. RVA parameters of dough fermented for different time
565 566
Figure 3: (A) gallery plots of the doughs fermented for different times versus 22 areas; (B) Signature plots corresponding to (A)
567 568 569 570
Figure 4: The E-nose response to headspace gas produced by dough fermentation. (A) The response curve of E-nose; (B) Average values of 10 E-nose sensors response to headspace gas samples produced by doughs during fermentation;(C) PCA plot of data showing the doughs in different fermentation states.
571 572 573
574 575 576 577
Fig 1 The relaxation time(T2) of the dough fermented at different times (0, 15, 30, 45, 60, 75, 90, 105, 120min)
578
B
7 6
a
a
a
a
a
a
a
a
a
0
15
30
45
60
75
90
105
120
Peak time (min)
5 4 3 2 1 0 -15
135
Fermentation time (min)
579
C
2000 a
ab
1800 1600
bc
abc
bc bc
bc abc
c abc
c
c
c ab
ab
c a
ab
viscosity(cP)
1400 1200 1000
abc
abc
ab
ab
ab
ab
ab
ab
a
800 600 400 200 0 -15
0
15
30
45
60
75
90
105
120
135
fermentation time(min) 580 581 582 583
Trough viscosity
Peak viscosity
Final viscosity
Fig. 2 RVA parameters of dough fermented for different time, A: pasting temperature; B: peak time; C: peak viscosity, trough viscosity and final viscosity. Vertical bar indicates standard deviation of the mean (n=3). Different letters represent significant difference (p < 0.05).
584
A
585 586 587
B
588 589 590 591 592
Fig. 3: (A) gallery plots of the doughs fermented for different times versus 22 areas; (B) Signature plots corresponding to (A)
A Sensor response(G/G0)
80
60
W1C W5S W3C W6S W5C W1S W1W W2S W2W W3S
40
20
0
593
40
20
0
60
80
120
100
Exposure time (s)
B
100
sensors response(G/G0)
80
W1C W5S W3C W6S W5C W1S W1W W2S W2W W3S
60
40
20
0 0
594 595
20
40
60
80
fermentation time(min)
100
120
596 597 598 599 600 601 602
Fig. 4: The E-nose response to headspace gas produced by dough fermentation. (A) The response curve of E-nose; (B) Average values of 10 E-nose sensors response to headspace gas samples produced by doughs during fermentation;(C) PCA plot of data showing the doughs in different fermentation states.
603
Table Captions:
604
Table 1. The spin-spin relaxation times(T2), signal intensities and fraction of peak area
605
Table 2. individual FAA content (of dry weight basis) of doughs fermented for different time
606 607 608 609
610 611 612 613 614 615 616
Table 1 The spin-spin relaxation times(T2), signal intensities(M) and fraction of peak area T2
M
fraction of peak area(%)
fermentation time(min)
T21
T22
M21
M22
M21
M22
0
9.89±0.40a
144.81±0.00a
25778.55±1991.50a
241.30±44.36g
99.07±0.21a
0.93±0.16g
15
9.23±0.37b
129.06±5.23b
25691.87±1852.68a
285.68±33.39fg
98.90±0.10a
1.10±0.10fg
30
9.23±0.37b
123.22±4.88bc
25355.34±1607.33a
330.04±32.98ef
98.72±0.08b
1.28±0.08ef
45
9.01±0.00b
120.40±4.88cd
24485.26±1463.53ab
352.07±27.70de
98.58±0.04bc
1.42±0.04e
60
9.01±0.00b
120.40±4.88cd
23866.29±1084.93abc
371.70±22.91cde
98.47±0.04c
1.53±0.04d
75
8.81±0.35bc
117.59±0.00cd
23113.81±925.37bc
404.17±25.85bcd
98.28±0.04d
1.72±0.04cd
90
8.41±0.00c
117.59±0.00cd
22587.46±739.87bc
425.74±32.90abc
98.15±0.09de
1.85±0.09c
105
8.41±0.00c
114.96±4.55d
22395.22±898.50bc
456.45±33.60ab
98.00±0.08ef
2.00±0.08b
120
8.41±0.00c
117.59±0.00cd
22150.76743.73c
482.36±30.26a
97.87±0.08f
2.13±0.08a
Note: In the same column, different letters indicate significant differences(n=3,p＜0.05).
Table 2 Individual FAA content (of dry weight basis) of doughs fermented for different time Na me (μg/ g) Asp Glu Ser His Gly Thr Arg Ala Tyr Cys -s Val Met Trp Phe Ile Leu Lys Pro Tot al
0
15
30
222.87±3. 57a 416.37±10 .64a 44.46±0.9 4a 63.84±3.8 2ab 72.37±1.6 0a 78.72±2.4 5a 207.47±15 .39a 249.91±1. 80a 261.74±12 .95b 11.32±2.0 4ab 71.01±3.5 8bc 35.13±1.5 7d 40.66±2.0 8c 40.73±1.2 5c 30.74±1.6 9d 41.19±0.4 1b 29.96±1.7 9d 185.41±9. 26ab 2103.89±3 7.98a
226.06±9. 18a 399.34±21 .07a 42.05±1.6 0a 65.68±2.3 4a 68.96±2.0 1a 78.23±1.6 7a 206.65±5. 90a 246.64±10 .34a 332.01±43 .35a 12.98±1.0 9ab 76.80±3.5 2b 34.57±1.5 2d 30.91±5.6 5cd 2.76±1.78 d 3.68±1.54 e 2.34±1.39 e 4.08±1.99f
208.69±17 .45ab 357.16±15 .09b 38.93±0.7 2a 62.30±1.8 5ab 72.76±0.4 8a 78.17±1.7 1a 190.52±1. 59ab 230.77±11 .63a 63.60±8.9 7c 13.16±1.8 0ab 85.45±3.7 0a 46.21±1.1 3bc 69.79±4.8 6a 75.07±3.0 7ab 62.48±1.5 0ab 74.59±1.0 7a 38.60±2.5 1c 194.7210. 43a 1962.99±2 1.76b
175.08±7. 57abc 2008.83±8 0.46b
Fermentation time (min) 45 60 75 204.07±6. 24ab 325.77±8. 50c 40.57±3.1 4a 59.80±2.7 0ab 70.10±0.7 9a 75.02±1.6 0a 180.85±7. 13b 222.72±5. 88a 46.31±3.6 1c 14.48±1.9 5a 91.33±3.6 0a 41.74±1.4 3c 54.65±5.3 5b 72.69±1.6 9b 54.87±1.6 9bc 78.12±1.9 3a 14.28±1.2 7e 152.12±11 .89c 1799.49±3 0.57c
198.90±2. 22ab 319.77±12 .77c 17.28±1.3 1c 17.20±1.4 8c 40.04±1.1 3b 34.77±2.0 2c 96.29±1.3 6c 136.94±8. 90bc 44.92±2.0 7c 11.48±1.5 5ab 63.63±6.5 0c 35.09±0.2 2d 36.34±1.6 3cd 35.91±2.0 1c 33.12±4.1 2d 39.12±4.7 4b 17.75±1.1 6e 160.75±11 .72bc 1339.30±6 .45f
203.35±1. 22ab 304.52±9. 08c 16.59±0.7 2c 15.66±0.4 9c 41.71±2.2 6b 29.78±0.9 3c 95.63±3.8 2c 133.896.4 6bc 37.25±1.0 9c 15.35±0.5 3a 9.70±1.36 e 70.83±3.4 4a 54.74±4.9 2b 75.51±8.8 7ab 62.08±1.1 8ab 79.691.51 a 13.32±1.3 2e 127.95±7. 76d 1387.5±1 1.89 f
90
105
120
185.95±8. 09b 301.55±1 2.40c 19.41±2.3 0c 13.26±1.3 2c 39.24±1.6 9b 30.42±2.2 8c 91.75±1.7 2c 117.84±7. 67c 47.98±6.3 6c 5.60±1.65 cd 6.67±1.07 e 49.93±1.9 8b 24.86±3.5 2d 11.08±1.7 6d 59.53±4.5 0ab 14.84±2.2 0c 12.46±1.6 0e 116.86±9. 08d 1149.22± 7.11g
185.69±6. 25b 291.8.56± 6.52c 30.61±3.2 3b 58.22±1.7 1b 73.08±1.6 0a 69.80±1.4 1b 176.48±10 .55b 157.55±17 .32b 254.19±5. 45b 2.86±1.65 d 9.36±1.40 e 21.76±3.0 7e 65.26±3.9 4ab 9.03±0.92 d 50.90±2.3 6c 18.97±0.4 8c 51.89±4.8 3b 108.96±5. 04d 1643.15±1 5.13d
201.22±13 .75ab 313.12±14 .30c 16.16±1.2 4c 13.86±0.1 2c 40.92±2.1 8b 32.57±3.8 6c 104.79±11 .65c 122.19±4. 44c 51.05±3.8 4c 8.08±0.39 bc 50.05±2.5 7d 29.11±1.5 2d 60.60±4.6 7ab 83.91±1.2 6a 66.26±0.9 1a 73.91±0.3 0a 69.16±3.7 7a 161.34±4. 69bc 1498.30±4 8.16e
Note: In the same column, different letters indicate significant differences(n=3, p＜0.05).
Highlights
Investigated the changes in various indicators of dough during fermentation.
There was no significant change in starch-paste properties, T21, and T22 of dough.
VOCs and FAAs of the dough change significantly, but no rule was established.
The change of headspace was most suitable for monitoring the fermentation of dough.
3 fermentation states of dough could be accurately identified by using E-nose.