蘑菇渗透脱水规律的研究

通过多因素正交试验，得出影响蘑菇渗透脱水因素的主次顺序及因素间交互作用的关系；应用均匀试验，建立了蘑菇渗透脱水的回归数学模型，其理论值与实测值具有很好的一致性；利用回归模型，建立了不同条件下的渗透脱水规律预测表。     

莴苣真空渗透脱水的二次回归正交旋转组合试验

为获得莴苣真空渗透脱水的较优工艺参数,以莴苣为原料,通过试验研究了渗透温度、切片厚度、真空度和蔗糖浓度4个因素对莴苣真空渗透脱水过程的影响;其失水率和固形物增加率随着切片厚度和真空绝对压力的增大而减少,随着渗透温度和蔗糖浓度的增大而增大。运用二次回归正交旋转组合设计方法建立了莴苣真空渗透脱水的失水率和固形物增加率回归数学模型,模型相关系数值分别为0.912和0.904。最后利用多目标非线性优化分析法对莴苣真空渗透脱水工艺进行了综合优化;优化结果表明,在试验范围内,莴苣真空渗透脱水的较优工艺参数为:切片厚度2 mm、渗透温度28℃、蔗糖浓度47%和真空度22 kPa,此时,失水率为72.16%,固形物增加率为11.82%。     

雪莲果超声波辅助渗透脱水工艺参数的优化

以渗透脱水温度、时间、蔗糖质量分数、超声波功率和处理时间为因素,以失水率(water loss,WL)和固形物增加率(sugar gain,SG)为指标,通过单因素试验,研究雪莲果的渗透脱水工艺参数。以渗透脱水温度、时间、蔗糖质量分数、超声波处理时间为因素,以WL、SG和二者比值(WL/SG)为指标,通过二次回归正交旋转组合试验设计建立雪莲果超声波辅助渗透脱水过程中各响应值(WL、SG和WL/SG)与各因素之间的回归方程,并得到超声波辅助渗透脱水的最优工艺参数。结果表明,超声波辅助处理可显著提高雪莲果渗透脱水效果;影响WL的因素主次顺序是温度＞时间＞蔗糖质量分数＞超声波处理时间;影响SG的因素主次顺序依次是渗透脱水时间＞超声波处理时间＞温度＞蔗糖质量分数;影响SG/WL的因素主次顺序是渗透脱水时间＞蔗糖质量分数＞超声波处理时间＞温度。雪莲果超声波辅助渗透脱水的最佳工艺参数为渗透脱水温度41℃、时间1.7h、蔗糖质量分数60.18%、超声波处理时间35min。在此组合参数条件下,SG/WL平均值为0.059。     

常压、真空及脉冲真空环境对双孢蘑菇中乳酸钙渗透规律的影响

研究常压、真空和脉冲真空环境对双孢蘑菇不同部位(菇盖、褶皱、菇柄)中乳酸钙的渗透规律,为双孢蘑菇保鲜机能提供理论依据。试验结果表明,乳酸钙量随渗透时间的延长而增加,其中脉冲真空下渗透量最大。对双孢蘑菇而言,菇盖、褶皱乳酸钙量高于菇柄。渗透率随渗透时间的延长呈逐渐下降的趋势,前30 min渗透率达到最大值后逐渐趋于平衡。回归分析结果显示,3个环境下双孢蘑菇不同部位的乳酸钙渗透量与渗透时间呈显著的二次曲线关系。构建了以渗透时间为自变量,渗透速率为因变量的多项式回归模型,验证了模型的可行性。     

食品蛋白电渗透脱水条件与模型解析

电渗透脱水的条件影响及其数学模型解析是将这一研究转化为应用技术的重要条件，本文通过对豆腐渣、大豆分离蛋白和玉米蛋白电渗透脱水试验分析，探讨了食品蛋白电渗透脱水的各种条件影响和加快脱水速率的方法。同时对照建立的可压缩理想浆料电渗透脱水理论模型进行了推导和分析。     

二次回归正交旋转设计优化鸭肉渗透传质模型

为了研究禽类食品原料在不同的渗透处理条件下的传质和脱水规律,实验选择鸭肉片作为研究对象,采用食盐和蔗糖溶液作为渗透液,在单因素试验的基础上,应用二次回归正交旋转设计研究厚度、渗透液浓度、温度、时间等各因素对鸭肉的影响。结果表明鸭肉渗透脱水的最优加工条件为:盐浓度为9.50%～9.91%,糖浓度为7.81%～8.12%,时间为3h,厚度为2.80～2.92mm,温度为18.22～18.93℃。     

南瓜馅料渗透脱水工艺研究

为探索不同参数对南瓜渗透脱水的影响规律,寻找较好南瓜馅料加工方法,选当季新鲜南瓜为试验材料,用不同浓度的麦芽糖浆为渗透液,通过恒温水浴锅创造稳定的温度,对南瓜进行脱水处理。以脱水率和脱水后炒制成的南瓜馅料感官性状为标准,分析渗透脱水过程,比较出渗透液种类、渗透液浓度、处理温度、南瓜条大小等因素对南瓜渗透脱水的影响。脱水后的南瓜馅料进行炒制得到较好的脱水方案:渗透液为30%麦芽糖溶液+10%NaCl,渗透温度为45℃,渗透时间为6 h,南瓜条宽2 mm~3 mm,厚1 mm~2 mm。馅料炒制后水分活度为0.73,有利于馅料的保存。南瓜馅料渗透脱水工艺简洁,易于操作。制品色泽金黄、香味浓郁、半固体状态、口感细腻润滑。     

热带亚热带果蔬渗透脱水研究进展

从渗透脱水的影响因素、渗透脱水对热带亚热带果蔬的影响以及渗透脱水传质动力学三个方面综述了果蔬渗透脱水的研究进展,其中重点介绍了渗透脱水的影响因素,同时对果蔬渗透脱水技术的局限性及其应用前景进行了探讨。     

辣椒渗透脱水特性及渗透工艺优化

以脱水率、固形物获取率、脱水率与固形物获取率比值、有效水分扩散系数等为考察指标,通过渗透脱水试验和正交试验,考察了辣椒的渗透脱水特性,并优化了渗透脱水工艺方案。结果表明:随着渗透温度的增加或渗透液中食盐含量的增加,辣椒的脱水率和固形物获取率增大,经温度为45℃渗透液(45%蔗糖+10%食盐)渗透脱水2,4,6h,辣椒的脱水率与固形物获取率比值分别为4.21±0.12,5.11±0.10,5.56±0.13;在渗透脱水初期,脱水率和固形物获取率增加较快;在渗透脱水后期,脱水率和固形物获取率增加趋缓;经温度为45℃渗透液(45%蔗糖+10%食盐)渗透脱水6h,辣椒的有效水分扩散系数为1.57×10~(-10) m~2/s。     

超声波辅助渗透脱水处理及其对西兰花冻结品质的影响

以西兰花为试材,在单因素试验的基础上,以水分流失和西兰花冻结-解冻后硬度为考察指标,通过正交试验优化西兰花冷冻前超声波辅助渗透脱水工艺条件。结果表明,渗透脱水最佳工艺条件为:超声波功率240 W,超声时间30 min,渗透液质量分数50%,在此工艺条件下,西兰花的水分流失为1.26,硬度为39.59 N。在此工艺条件的基础上,对比了普通渗透脱水(OD)和超声波辅助渗透脱水(UOD)对西兰花冻结效率和品质的影响。结果表明:UOD可以在短时间内达到OD较长时间的渗透效果,提高西兰花的冷冻效率,减少西兰花冻藏期的汁液流失,抑制抗坏血酸含量的减少以及色泽的变化。     

Evaluation of Mass Exchange During Osmotic Dehydration of Plum Using Response Surface Methodology

Water loss (WL), solid gain (SG), weight reduction (WR) and shrinkage were quantitatively investigated during osmotic dehydration of plum using response surface methodology with the sucrose concentration (30–60g/100 g sample), temperature of sucrose solution (40–60°C) and immersion time (60–240 min). Experiments were designed according to Central Composite Rotatable Design with these three factors. For each response, second order polynomial models were developed using multiple linear regression analysis. With respect to water loss, solid gain, weight reduction and shrinkage, both linear and quadratic effects of four variables were found to be significant. In most cases, an increase of sucrose concentration, temperature and immersion time increased WL, SG, WR and shrinkage, except the increasing of immersion time for osmotic treatment has no effect on shrinkage. It was found that immersion time and temperature were the most significant factors affecting the WL during osmotic dehydration of plum followed by concentration of sucrose solution. This was also true for WR. Effect of temperature and time were more pronounced for SG than the concentration of sucrose solution.     

Prediction of mass transfer kinetics during osmotic dehydration of apples using neural networks

An artificial neural network (ANN) model was developed for the prediction of water loss (WL) and solid gain (SG) in osmotic dehydration of apple cylinders using a wide variety of data from the literature to make it more general. This model mathematically correlates six processing variables (temperature and concentration of osmotic solution, immersion time, surface area, solution to fruit mass ratio and agitation level) with WL and SG. The optimal ANN consisted of one hidden layer with four neurons. This model was able to predict WL and SG in a wide range of processing variables with a mean square error of 13.9 and 4.4, and regression coefficient of 0.96 and 0.89, respectively, in testing step. This ANN model performs better when compared to linear multi-variable regression. The wide range of processing variables considered for the formulation of this model, and its easy implementation in a spreadsheet using a set of equations, make it very useful and practical for process design and control.     

OSMOTIC DEHYDRATION OF APPLES BY IMMERSION IN CONCENTRATED SUCROSE/MALTODEXTRIN SOLUTIONS

The effect of sucrose:maltodextrin (S:M100) ratios on solids gain (SG) and water loss (WL) was investigated during the osmotic dehydration of apple disks. Concentrated solutions were prepared at 40C with 100:0, 90:10, 70:30, 50:50, 30:70 and 10:90 ratios of (S:M100). The highest score in sensory evaluation was achieved with 90:10 ratio of (S:M100). Three stages of osmotic dehydration can be observed by plotting the shrinkage of apple disks vs the moisture content. The result is that a higher maltodextrin concentration favors volume loss and enhances water loss, increasing the duration of stage 2 osmotic dehydration.     

不同小分子糖渗透草莓的传质动力学及对真空冷冻干燥草莓品质的影响

为系统地了解不同小分子糖特别是低聚糖和糖醇对草莓的渗透行为以及不同小分子糖对真空冷冻干燥草莓品质的影响,本研究利用两种数学模型对10 种常见小分子糖（白利度为40 °Brix）的渗透动力学进行拟合,并进一步对渗糖处理后真空冷冻干燥草莓的理化特性进行表征。结果表明,Weibull模型更适用于描述渗糖处理后草莓的可溶性固形物增量（solid gain,SG）,而Peleg模型可以更好地描述草莓的水分去除量（water loss,WL）。经不同糖渗透处理结束后,草莓的SG差异较大,山梨糖醇可以使草莓的SG达到6.84 g/100 g,是低聚异麦芽糖的6.16 倍。此外,渗糖处理的草莓硬度得到普遍提高（94.58%～223.23%）;葡萄糖、果糖、山梨糖醇渗透处理组的脆度分别降低了16.70%、20.74%、41.45%,低聚果糖渗透处理后的草莓质构特性与蔗糖最为接近。综合考虑渗透效率、感官营养品质和生产成本,低聚果糖是蔗糖在果蔬渗透处理方面的一种潜在替代品。     

芒果片真空预处理联合超声辅助渗透脱水的传质动力学及品质分析

为解决芒果果脯生产过程中传质效率低、加工时间长的问题,该文研究了脉冲真空预处理联合超声辅助渗透脱水对芒果传质动力学、质量特性和微观结构的影响。结果表明：脉冲真空预处理联合超声辅助渗透脱水组的芒果失水率（54.43%）最高,较常规渗透脱水、脉冲真空预处理渗透脱水、超声辅助渗透脱水组分别高45.85%、14.06%、29.38%,增固率（12.81%）较常规渗透脱水、超声辅助渗透脱水、脉冲真空预处理渗透脱水组分别高90.03%、53.43%、32.06%。用Azuara模型拟合渗透脱水过程中失水率和增固率的变化,高回归系数（R2>0.97）和低RMSE表明Azuara模型可以较好拟合芒果渗透脱水过程,预测脉冲真空预处理联合超声辅助渗透脱水组的平衡脱水率、增固率最高,分别为65.06%和23.35%。测定色泽和质构,发现超声辅助渗透脱水组、脉冲真空预处理联合超声辅助渗透脱水组芒果硬度值显著低于常规渗透脱水组和脉冲真空预处理渗透脱水组（p<0.05）,而芒果色泽得到了较好保护。通过扫描电镜的观察,发现超声处理使芒果细胞壁塌陷与变形、细胞横截面积变小、微孔增多。此外,真空对芒果硬度和微观结构的影响均较小。综上,脉冲真空预处理联合超声辅助渗透脱水通过改变芒果细胞结构、增加传质微通道,提高了渗透脱水的效率,缩短加工时间,可以较好保护芒果色泽,但会导致硬度的下降。     

Effect of stirring in the osmotic dehydration of chestnut using glycerol solutions

The use of osmotic dehydration like a previous step to drying process could be an interesting option in order to reduce costs or to preserve the characteristics of food material. This work studies the osmotic dehydration operation of chestnuts (Castanea sativa M.) with aqueous solutions of glycerol (25, 35, 45 and 60 g/100 g) and glycerol and salt (35 and 9.7 g/100 g, respectively), at different soaking times (1, 2, 4 and 8 h) at 20 °C. In order to evaluate the hydrodynamic effect of osmotic medium on the mass transfer rate, static and dynamic conditions have been tested (0, 40 and 110 rpm). The process was monitored employing several parameters related to moisture content and solid gain changes.Water loss rate using binary solutions increases with glycerol concentration and no dependency was detected with stirring level using glycerol concentration higher than 35 g/100 g. WL/SG ratio increases with glycerol concentration and linear behaviour between WL/SG and water activity of solution was established. NMC contents decrease with glycerol concentration up to 0.78 at 8 h of osmotic dehydration and NSC contents increase with glycerol concentration up to 1.12.WL kinetics using ternary solutions composed by glycerol and sodium chloride solutions were similar to the obtained with glycerol solutions at the same water activity range, but SG kinetics is improved by the acquisition of sodium chloride by the chestnut. In this way, the WL/SG ratio and NMC are lower.Model proposed by Peleg was satisfactorily employed to fit the experimental NMC and NSC data for both binary and ternary solutions considering that equilibrium value is no dependent on stirring level.     

Mass transfer kinetics and regressional‐desirability optimisation during osmotic dehydration of pumpkin,kiwi and pear

Mass transfer kinetics and optimisation of osmotic dehydration (OD) of fruits and vegetables with diverse structures were studied. Different concentrations of sucrose (20–60 °Brix) and process times (0–24 h) were used. Magee’s model was appropriate for predicting water loss (WL), while Azuara’s model fitted well solids gain (SG) data and represented more accurately the evolution of the complete process close to equilibrium. Polynomial equations for each kinetic variable [WL, SG and weight reduction (WR) – for pumpkin, kiwi and pear] using multiple linear regression were fitted for a selected range of experimental data (30–240 min, 20–60 °Brix). A complete solution algorithm for desirability function was coded in Matlab^® 7.2 (Mathworks, Natick, MA, USA) with the aim to optimise osmotic dehydration process in terms of WL, SG and WR; optimal conditions were found for each fruit. Besides, an optimal common zone was identified for OD corresponding to process time from 114 to 240 min and sucrose concentration from 54 to 60 °Brix.     

Modelling of mass transfer during osmotic dehydration of apple tissue pre-treated by pulsed electric field

The osmotic dehydration mechanism of apple sample pre-treated by pulsed electric field (PEF) was investigated over a range of 44.5-65 ° Brix sucrose concentrations. Apple disks treated by PEF field of 0.90 kV/cm during a constant time were immersed in sucrose solution at ambient temperature with 3:1 syrup to apple ratio (w/w). Increase of the initial solute concentration and the PEF treatment resulted in acceleration of the osmotic dehydration (OD). The PEF-treated samples had a higher water loss (WL) and higher solid gain (SG) than the untreated samples. The osmotic dehydration kinetics was studied on the basis of two approaches: Fick's unsteady state diffusion equation and a two-exponential kinetic model. The coefficients of effective diffusion of water and solute were calculated. Their values were higher for samples pre-treated electrically. The effect of PEF was more pronounced for the WL comparatively to the SG. The two-exponential kinetic model permits evaluating of both convective and diffusion stages of OD. The PEF pre-treatment accelerates the kinetics of water and solute transfer during convective and diffusion stages of OD.     

Genetic Algorithm�CArtificial Neural Network Modeling of Moisture and Oil Content of Pretreated Fried Mushroom

In this research, the effect of different pretreatments (osmotic dehydration and gum coating) on moisture and oil content of fried mushroom was investigated, and artificial neural network and genetic algorithm were applied for modeling of these parameters during frying. Osmotic dehydration was performed in solution of NaCl with concentrations of 5% and 10%, and methyl cellulose was used for gum coating. Either pretreated or control samples were fried at 150, 170, and 190 °C for 0.5, 1, 2, 3, and 4 min. The results showed that osmotic dehydration and gum coating significantly decreased (0–84%, depending upon the processing conditions) oil content of fried mushrooms. However, moisture content of fried samples diminished as result of osmotic pretreatment and increased by gum coating. An artificial neural network was developed to estimate moisture and oil content of fried mushroom, and genetic algorithm was used to optimize network configuration and learning parameters. The developed genetic algorithm–artificial neural network (GA–ANN) which included 17 hidden neurons could predict moisture and oil content with correlation coefficient of 0.93 and 96%, respectively. These results indicating that GA–ANN model provide an accurate prediction method for moisture and oil content of fried mushroom.