Journal of Cereal Science 80 (2018) 44e49
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Sorption isotherm and state diagram for indica rice starch with and without soluble dietary ﬁber Jie Wan, Yueping Ding, Guohui Zhou, Shunjing Luo, Chengmei Liu*, Fei Liu State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang, 330047, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 28 September 2017 Received in revised form 31 December 2017 Accepted 6 January 2018
Moisture sorption isotherms and state diagrams for indica rice starch (IRS) and indica rice starch-soluble dietary ﬁber (IRS-SDF) were developed to investigate the effect of SDF on the stability of IRS. Sorption isotherms of IRS and IRS-SDF were determined by the static gravimetric method and the data were modeled by GuggenheimeAndersonede Boer (GAB) model. The GAB monolayer moisture contents were calculated to be 7.43 and 8.37 g/100g (dry basis) for IRS and IRS-SDF, respectively. The state diagram was composed of the glass transition line and freezing curve, which were ﬁtted according to GordoneTaylor and Chen models, respectively. The ultimate maximum-freeze-concentration conditions were calculated as characteristic glass transition temperature (Tg’) of 42.5 C and 31.5 C with characteristic solids content (Xs') being 0.71 and 0.72 g/g (wet basis), and characteristic temperature of end point of freezing (Tm’) being 18.2 C and 13.8 C for IRS and IRS-SDF, respectively. The state diagrams and sorption isotherms of IRS and IRS-SDF have great signiﬁcance for evaluating storage stability, optimizing drying and freezing processes. © 2018 Published by Elsevier Ltd.
Keywords: Sorption isotherm State diagram Indica rice starch Soluble dietary ﬁber
1. Introduction The storage stability of foods is signiﬁcantly inﬂuenced by water content and temperature. After the concept of water activity (aw) was put forward, a great deal of further research was reported on the relationship between water activity and food storage stability (Al-Muhtaseb et al., 2002). At present, water activity is used as a reliable assessment of the storage stability (lipid oxidation, microbial growth, non-enzymatic and enzymatic activities) and the texture of foods (Yu and Li, 2012). However, the limitations of using water activity to evaluate of foods quality and stability have been pointed out, meanwhile, the concept of the glass transition was proposed, which indicates that foods are most stable at or below the glass transition (Sablani et al., 2004). The temperature at which an amorphous system changes from a glassy to a rubbery state is considered as the glass transition temperature (Tg) (Roos and Karel, 1991a; Shi et al., 2012). The relationship between the glass transition temperature and food processing or storage stability has been described in detail (Bhandari and Howes, 1999; Rahman, 2009). In the literature, all of the experimental results could not be explained
* Corresponding author. State Key Laboratory of Food Science and Technology Nanchang University, 330047 Jiangxi, China. E-mail address: [email protected]
(C. Liu). https://doi.org/10.1016/j.jcs.2018.01.003 0733-5210/© 2018 Published by Elsevier Ltd.
by water activity or glass transition concepts, respectively, and all foods are beyond their prediction range (Rahman and Al-Saidi, 2017). In addition, Rahman and Al-Saidi (2017) indicated that a combination of water activity and glass transition could be a powerful tool in determining food stability. The concept of water activity was added along with the complementary concept of glass transition in order to better understand the factors governing the stability of foods (Bhandari and Howes, 1999). State diagram is a map of different states of a food as a function of water or solids content and temperature (Rahman, 2004); and it has been used as an effective tool for foods researchers to determine the best methods of food processing and storage. Moreover, state diagram provides useful information regarding storage stability and shelf life for low-moisture-content and frozen foods (Roos and Karel, 1991a). State diagram usually consists of the freezing curve, glass transition line, and maximal-freezeconcentration, and it was mainly researched by differential scanning calorimetry (DSC) (Sablani et al., 2009). Rice is an important agricultural crop worldwide; and rice starch has received extensive attention due to its smooth texture, digestible and hypoallergenic qualities. However, rice starch also has some negative aspects, such as being susceptible to retrogradation and having a tendency to produce undesirable weak-bodied, cohesive, rubbery pastes or gels under extended cooking and high-
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shear conditions (Viturawong et al., 2008). Therefore, various types of hydrocolloids are used to overcome these shortcomings, especially soluble dietary ﬁber (SDF). Banchathanakij and Suphantharika (2009) and Krystyjan et al. (2013) studied the storage stability of starch-hydrocolloid composite gel. They found that the addition of SDF could increase the strength of the starch gel and decrease the aging rate during storage, as well as maintain the relative stability of rheological and textural properties. Due to the functionality of SDF, such as water-holding capacity, gel-forming ability, fat-mimetic properties, and thickening effects, the texture, shelf-life and sensory characteristics of starch-based foods can be improved by adding SDF (Lai et al., 2011). The oat hydrocolloidal ﬁber component was added to the formula to improve the functionality of rice noodles (Inglett et al., 2005). Furthermore, SDF could avert cardiovascular disease and colon cancer, in addition to lowing serum cholesterol and absorption of released glucose (Sowbhagya et al., 2007). Therefore, SDF is widely used in starchbased food systems. It's signiﬁcant to study the stability of starchbased food systems during processing and storage. The effects of temperature and water content on the physical state and physicochemical properties during processing and storage could be obtained from state diagram, which provides the evaluation of thermal and physical changes in food systems (Rahman, 2006). In recent years, the number of studies into state diagrams for starchbased foods has gradually increased. For example, Sablani et al. (2009) studied the thermal transition properties and state diagrams of rice noodles. Additionally, the state diagram for extruded instant artiﬁcial rice (Herawat et al., 2014) has been reported. Although the effect of dietary ﬁber on the physicochemical properties of starch has been investigated (Lai et al., 2011), less research into phase diagrams for IRS-SDF is available. Therefore, the aim of this work is to obtain the storage stability criteria for IRS and IRS-SDF as functions of temperature and moisture content according to the development of state diagrams and the moisture sorption isotherms. 2. Materials and methods 2.1. Materials Indica rice starch (IRS, 0.53% protein, 0.42% fat, 0.21% ash, 8.30% moisture, 25.64% amylose and 64.12% amylopectin) was obtained from kunming Pueryongji Group (Kunming, China). Dietary ﬁber from soybean was provided by Zhengzhou Linuo Biotech Co Ltd (Zhengzhou, China). Soluble dietary ﬁber (SDF, 3.5% protein, 5.5% ash, 0.32% fat) from dietary ﬁber of soybean were prepared based on AOAC Ofﬁcial Method 991.43 and Guo and Beta (2013). 2.2. Preparation of soluble dietary ﬁber-rice starch blends IRS-SDF blends were prepared by replacing IRS at 5.0% with SDF. The slurry (90% moisture content) of starch and SDF was stirred at 800 rpm for 60 min. Then, the sample was freeze-dried and immediately ground to ﬁne powder. Finally, the powder was screened through mesh size of 100. The IRS dispersions (90% moisture content) were also prepared and freeze-dried as controls. 2.3. Measurement and modeling of water sorption isotherms The moisture sorption isotherms for IRS and IRS-SDF were determined by a static gravimetric method and water activity ranging from 0.11 to 0.90 at 25 C based on sorption theory (Rahman, 2006). The IRS-SDF and IRS powders were placed in airtight desiccator with phosphorus oxide. Then, dry samples (~1.000 g) were put in an weighing bottles (25 mm 40 mm)
stored in different desiccators while maintaining different relative humidity environments created using different saturated salt solutions at 25 C. The salts maintaining equilibrium relative humidity employed were LiCl, CH3COOK, MgCl2$6H2O, K2CO3$2H2O, Mg (NO3)2$6H2O, NaNO2, NaCl, KCl, and BaCl2$2H2O with equilibrium relative humidities of 11%, 23%, 33%, 43%, 53%, 67%, 76%, 84%, and 90%, respectively (Greenspan, 1977). Thymol in a 5 ml beaker was placed inside the airtight containers for higher aw (aw0.75) to prevent mold growth in samples during storage. Samples were weighed periodically for approximately 5 weeks until a constant mass was reached (the weight difference of samples between two successive measurements being less than 0.001 g). The moisture content values of equilibrated samples were determined gravimetrically by drying samples placed inside an oven at 105 C for at least 18 h. The samples with moisture contents higher than 0.2 g/g were prepared by adding pre-calculated amount of distilled water to the dried IRS and IRS-SDF powders. Then, the samples were sealed and equilibrated at 4 C for 24 h before DSC analyses. Water absorption data were modeled with GuggenheimAndersen-de Boer (GAB) model. The GAB equation is:
Xm CKaw ð1 Kaw Þð1 Kaw þ CKaw Þ
Where Xws is the water content in dry basis (g/100 g dry-solids); Xm is GAB monolayer moisture content which was considered as secure water content of freeze-dried foods (g/100 g dry-solids); C and K are GAB parameters related to the heat of adsorption of monolayer and multilayer, respectively. 2.4. Measurement and modeling of glass transition by DSC The thermal characteristics of samples were determined by modulated DSC (Q2000, TA Instruments, New Castle, DE, USA) equipped with mechanical refrigerated cooling system (cooling the samples to 90 C). The distilled water (melting point 0.0 C, DHm ¼ 333 J/g) and indium standard (melting point 156.5 C, DHm ¼ 28.44 J/g) were used to calibrate the heat ﬂow and temperature of the instrument. Aluminum pans of 30 ml with lid were used in all experiments and an empty sealed aluminum pan was used as a reference in each test. Nitrogen was used as the carrier gas (50 ml/ min). The sealed pans with samples (~10 mg) containing unfrozen water were cooled to 90 C at 5 C/min, and then equilibrated for 10 min. They were then scanned from 90 C to 50 C at a heating rate of 5 C/min. Thermograms were analyzed for the initial (Tgi), mid (Tgm) and end-points (Tge) of the glass transitions, and Tgi was considered as the characteristic temperature of the transition (Rahman, 2006). Three replicates were used for the determination of glass transition temperature at each water content/water activity. A different procedure was used for samples with higher water content (moisture content: 0.28e0.72 g/g, web basis) and these samples contained freezable water. In order to achieve maximalfreeze-concentration conditions and eliminate the exothermic peak, the process of annealing is necessary before Tg can be measured. Optimum annealing for 30 min should be performed in order to acquire maximum ice formation (Telis and Sobral, 2001). Tm’ is the end point of freezing or the start of the melting of ice crystals (Rahman, 2004). (Tm’)n was deﬁned as the apparent maximal-freeze-concentration condition. Annealing for 30 min at [(Tm’)n1]◦C was used to obtain the annealed maximal freezeconcentration condition. The annealing procedure was as follows: Samples (~10 mg) of the powder in a sealed aluminum pan (30 ml) were cooled from 20 C to 90 C at 5 C/min, and held for 10 min.
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After equilibrium, the sample was heated at 5 C/min to (Tm’)n1 ((Tm’)n can be measured according to the above procedure), annealed for 30 min at (Tm’)n1, then cooled to 90 C at 5 C/min, and equilibrated for 10 min. Finally, the sample scanned from 90 C to 50 C at 5 C/min, then, annealed maximal-freezeconcentration temperatures were determined. (Shi et al., 2012). The water content is the main factor which affects the glass transition temperatures of foods. The inﬂuence of water content on glass transition temperature was ﬁtted with the Gordon and Taylor equation (Gordon and Taylor, 1952):
Xs Tgs þ kXw Tgw ¼ Xs þ kXw
Where Tgm, Tgs and Tgw are the glass transition temperatures of the mixture, dry-solids and water, respectively; Xw and Xs are the mass fractions of water and solids, respectively; k is the GordonTaylor parameter. The model parameters (k and Tgs) of Eq. (2) were estimated using non-linear regression analysis. The freezing curves of IRS and IRS-SDF with change in water content were modeled using the Chen model based on the extended ClausiuseClapeyron equation (Yu and Li, 2015). The Chen model is expressed as:
b 1 Xs BXs d ¼ ln lw 1 Xs BXs þ EXs
for IRS and IRS-SDF. The model constants C and K for IRS were 26.87 and 0.78, respectively; whereas for IRS-SDF the values were 31.72 and 0.75, respectively. Moreover, Sablani et al. (2009) reported that the C and K values of basmati rice were 25.40 and 0.45, respectively. The monolayer moisture content (X0) is an important experimental result for determining suitable storage conditions, providing the longest time period with minimum quality loss at a ﬁxed temperature (Shi et al., 2012). The monolayer moisture content (X0) obtained from the GAB model at 25 C was 0.74 and 0.84 (dry basis) for IRS and IRS-SDF, respectively. The X0 for the IRS-SDF is higher than IRS, indicating that the safest aw level of IRS-SDF is higher than that of IRS at a given temperature. 3.2. Thermal transitions by DSC 3.2.1. Samples containing un-freezable water The glass transition temperatures for IRS and IRS-SDF powders decrease with an increased in moisture content, as shown in Table 2. The depression in glass transition with increasing water content is the result of the plasticization effect of water on the amorphous parts of the matrix (Rahman, 2006). At the same water activity, the Tgi, Tgm and Tge of IRS were larger than those for IRS-SDF. This may be because the plasticization of water held by SDF and constituents of the IRS system was changed by adding SDF.
Where d is the freezing point depression (TweTF), TF is the freezing point of the food material ( C), Tw is the freezing point of water ( C); lw is the molecular mass of water, Xs is the solids mass fraction; b is the molar freezing point constant of water (1860 kg K/ kg mol), B is the un-freezable water (g/g dry-solid), and E is the molecular weight ratio of water and solids (lw/ ls). The values of B and E also were estimated by non-linear regression analysis using Matlab 7.0 software. 2.5. Statistical analysis All experiments were conducted at least three times and average and standard deviations of selected data points are presented. The statistical signiﬁcance was assessed with one-way Analysis of Variance (ANOVA) using the Origin 8.5 (OriginLab Inc., Northampton, MA, USA). Differences were considered at signiﬁcant level of 95% (p < .05). 3. Results and discussion 3.1. Moisture sorption isotherm The moisture sorption isotherms for IRS and IRS-SDF at 25 C are shown in Fig. 1. All of them showed a typical type II curve, which is referred to as a sigmoidal shape (Erbas¸ et al., 2005). This indicates the possible existence of multilayer adsorption on the surface of starch granules. These results could be attributed to the microporous structure; monolayers and multilayers of moisture are formed on granular surfaces when the relative humidity is high enough (Saripella et al., 2014). According to Table 2, the equilibrium moisture content increased as water activity increased. In addition, Table 2 shows that the equilibrium moisture contents for IRS-SDF were slightly higher than IRS at ﬁxed relative humidity, which implies the higher hydroscopicity of IRS-SDF may be due to the addition of SDF (Lecumberri et al., 2007). The adsorption isotherm data were ﬁtted by non-linear regression with the GAB equation and model parameters are presented in Table 1. We can conclude that the GAB equation ﬁts well with the experimental data according to the values of R2 and RMSE
3.2.2. Samples containing freezable water For IRS and IRS-SDF samples containing freezable water (0.208e0.714 g/g, wet basis) were scanned without annealing to determine the (Tm’)n. The average values of (Tm’)n for IRS and IRSSDF without annealing were 17.64 C and 20.12 C, respectively. Optimum annealing conditions were obtained when IRS and IRS-SDF were scanned at 19 C and 21 C (z(Tm’)ne1), respectively. The Tg of samples containing freezable water with annealing are shown in Table 3. Moreover, when the water content was greater than 0.60 g/g sample (wet basis), the glass transition was gone and only the melting peak could be found, as shown in Table 3. This indicates that the endothermic shift was beyond the detectable range because of the glass transition (Zhao et al., 2015). We found that the average values of Tg tend to be constant when moisture content was higher than 0.47 g/g (wet basis). Roos and Karel (1991b) obtained similar results. Tg appeared less visible just before the ice melted and tended towards an equilibrium value at high moisture contents (Telis and Sobral, 2001). The glass transition was modeled by the GordoneTaylor equation using nonelinear regression. The results are listed in Table 4, and show that the GordoneTaylor equation is a good ﬁt for the experimental data. The model parameters Tgs and k of Eq. (2) were estimated as 56.45 C and 2.62 for IRS, and 33.85 C and 1.63 for IRS-SDF. The value of Tgs obtained using the GordoneTaylor equation for IRS-SDF was lower than for IRS, due to the differences in components of IRS-SDF and IRS. The parameter k is related to the strength of the interaction between water and foods solids and higher values indicate a greater plasticizing effect of water on solids (Shi et al., 2015). The higher k value suggested that IRS is easily plasticized by water. Moreover, the values of glass transition temperature were inﬂuenced by the moisture content and chemical composition of materials (Sablani et al., 2009). Herawat et al. (2014) found k value of 6.4 and Tgs value of 75.5 C for formulated rice. 3.2.3. Freezing point The freezing points for IRS and IRS-SDF are listed in Table 3. The Chen model parameter E and B for predicting freezing point were estimated using Matlab 7.0 software. The results indicate that accurate ﬁtting can be done using Eq. (3) according to the evaluation
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Fig. 1. Sorption isotherms of IRS and IRS-SDF at 25 C. (
glass transition), Tm’ (characteristic end point of freezing) and Xs' (characteristic solids content). For IRS and IRS-SDF, the curve AB representing the freezing curve (i.e., equilibrium between the solution and the ice formed) was modeled using the Chen model (Eq. (3)). The line CD represents the glass transition curves (glass transition temperature versus solids content) by ﬁtting the GordoneTaylor equation (Eq. (2)). Point M is the maximal freeze concentration point, at which the values of Tm’ for IRS and IRS-SDF are equal to 18.2 C and 13.8 C, respectively. The values of Xs' determined by extending the freezing curve were 0.71 and 0.72 g/g (wet basis) for IRS and IRS-SDF, respectively. In addition, the moisture contents Xw’, corresponding to Xs', were calculated as 0.29 g/g (wet basis) for IRS and 0.28 g/g (wet basis) for IRS-SDF. Xw’ considered as the un-freezable water, was higher than the value of B as evaluated by Eq (3). Similar result was observed by Yu and Li (2012). The ultimate maximal-freeze-concentration glass transition temperature (Tg’) was identiﬁed as the intersection of a vertical extrapolation from point M on the glass transition curve CD, which was 42.5 C for IRS and 31.5 C for IRS-SDF (at point P), and its solid content was the same as Xs' at point M. In the
Table 1 Model ﬁtting for sorption experimental data. Sample
indicators (MSD, R2, and RMSE) listed in Table 4. The Chen model parameters E and B were estimated as 0.0262 and 0.1244 for IRS, and 0.0278 and 0.1949 for IRS-SDF, respectively. From the values of E, the effective molecular weights of solids were estimated as 687.1 g/mol for IRS, and 647.5 g/mol for IRS-SDF, respectively.
3.3. State diagraj Fig. 2 presents the state diagrams for IRS and IRS-SDF, showing the freezing curve, the glass transition curve, the maximal-freezeconcentration conditions, and the values of Tg’ (characteristics Table 2 Glass transition temperature of samples (with no freezable water). aw
Moisture content (g/g w.b.) Tgi ( C) 0.11 0.23 0.33 0.43 0.53 0.67 0.76 0.84 0.90
0.0565 0.0772 0.0864 0.0935 0.1102 0.1216 0.1429 0.1605 0.2068
Tgm ( C) a*
20.65 ± 1.38 9.86 ± 1.71b 0.24 ± 1.56c 2.26 ± 1.24cd 6.53 ± 1.72de 10.52 ± 1.65e 15.84 ± 1.59f 19.30 ± 2.01fg 21.87 ± 1.78gi
Tge ( C) a
24.92 ± 2.31 11.18 ± 1.64b 0.13 ± 0.96c 0.24 ± 1.02cd 3.13 ± 0.89cd 8.01 ± 1.42e 13.32 ± 0.79f 16.04 ± 1.08fg 19.86 ± 1.96gi
Moisture content (g/g w.b.) Tgi ( C) a
26.74 ± 2.01 13.23 ± 2.35b 2.16 ± 1.42bc 0.24 ± 1.41cd 1.53 ± 0.92cd 6.52 ± 1.36e 11.62 ± 0.86f 14.02 ± 1.16fg 17.83 ± 1.62gi
0.0618 0.0826 0.1101 0.1203 0.1332 0.1440 0.1562 0.1771 0.2015
Tgm ( C) a
9.46 ± 1.84 1.04 ± 1.32b 2.53 ± 1.05bc 3.93 ± 0.93c 6.31 ± 1.72c 12.68 ± 1.91d 17.32 ± 2.10de 20.42 ± 1.78ef 26.87 ± 2.31fg
Note: DSC heating rate: 5 C/min. Two means followed by the same letter in the same column are not signiﬁcantly (p > 0.05) different.
Tge ( C) a
12.02 ± 1.92 2.07 ± 1.47b 0.97 ± 1.78bc 3.54 ± 2.01c 5.65 ± 0.99c 11.54 ± 1.65d 15.24 ± 1.72de 18.45 ± 0.96ef 24.56 ± 2.26fg
15.23 ± 2.14a 5.62 ± 1.72b 3.12 ± 2.01bc 1.78 ± 0.93bc 2.85 ± 1.56c 9.36 ± 0.82d 13.52 ± 1.63de 15.74 ± 1.72ef 21.51 ± 2.26fg
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Table 3 Glass transition temperature and freezing point of sample (with freezable water). Xw (g/g, w.b.)
21.74 ± 1.03 29.98 ± 1.45b 33.06 ± 0.94bc 37.46 ± 1.85cd 39.05 ± 2.63de 41.57 ± 2.91de 43.85 ± 1.87ef Nd Nd
0.2088 0.2857 0.3750 0.4118 0.4736 0.5454 0.6000 0.6552 0.7143
Nd 16.4 ± 2.13a 12.1 ± 1.88b 9.53 ± 2.02bc 6.87 ± 0.98cd 5.42 ± 1.45cde 3.61 ± 1.06def 1.80 ± 0.73ef 0.59 ± 0.38f
27.92 ± 1.93 35.18 ± 2.11b 39.21 ± 1.84bc 41.93 ± 2.63cd 45.82 ± 2.41de 49.37 ± 3.78de 52.47 ± 3.72de Nd Nd
Nd 12.6 ± 1.54a 8.75 ± 0.93b 6.83 ± 1.42bc 3.22 ± 1.07cd 1.15 ± 1.92cde 0.93 ± 0.83de 0.69 ± 1.47e 0.50 ± 0.23e
Note: DSC heating rate: 5 C/min, Nd: not detected. Two means followed by the same letter in the same column are not signiﬁcantly (p > 0.05) different.
Table 4 Parameters of thermal analysis of rice starch and starch-dietary ﬁber composite system. System
Tgs (◦C) k R2 MSD (%) RMSE E B R2 MSD (%) RMSE Tg’’ (◦C) Tg’ (◦C) Tm’ (◦C) Xs'’ Xs'
56.45 2.6202 0.9627 9.3482 0.7538 0.0262 0.1244 0.9887 10.8453 0.3642 22.30 42.50 18.20 0.79 0.71
33.85 1.6308 0.9426 12.6956 0.9947 0.0278 0.1949 0.9349 16.3428 0.8761 19.60 31.50 13.80 0.77 0.72
Fig. 2b. State diagrams of IRS (b) (AB: freezing curve; CD: glass line modeled using GordoneTaylor equation; M: end point of freezing; P: glass transition point of maximal freeze-concentration.
Fig. 2a. State diagrams of IRS (a) and IRS-SDF.
literature, the Tg’’ value was deﬁned as the intersection of extending the AB line to the glass line while maintaining the curvature of the freezing curve (Rahman, 2004; Rahman et al., 2005). The values of Tg’’ and Xs'’, corresponding to Tg’’, are shown in Fig. 2. Based on the state diagram, the best storage conditions for IRS and IRS-SDF systems could be obtained. It was concluded that the IRS-SDF system is more difﬁcult to store than the IRS system under the same water activity. Temperatures below Tg’ are recommended for the safe storage of high moisture content food systems (Shi et al., 2015).
The state diagrams for IRS and IRS-SDF were developed by determining the glass line, the freezing curve, and the ultimate maximal-freeze-concentration conditions. The state diagrams provide the characteristic glass transition Tg’ and the corresponding total solids content Xs' of 42.5 C and 0.71 g/g (wet basis) for IRS, and 31.5 C and 0.72 g/g (wet basis) for IRS-SDF, respectively. The values for the characteristic end point of freezing (Tm’) were 18.2 C and 13.8 C for IRS and IRS-SDF, respectively. The differences in the experimental data for IRS and IRS-SDF are the result of the hydroscopicity and water-holding capacity of SDF, which could improve the moisture content of the system. The water adsorption isotherms for IRS and IRS-SDF were also ﬁtted using the GAB model, and the values for the monolayer moisture content were 7.43 and 8.37 g/100g (dry basis) for IRS and IRS-SDF, respectively. These results indicate that the monolayer moisture content of IRS was increased by the addition of SDF.
Acknowledgements This study was supported by the National Natural Science Foundation of China (Nr 31360407), and the Research Foundation for Young Scientists of State Key Laboratory of Food Science and Technology, Nanchang University, China (No. SKLF-QN-201510).
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