What is the difference between moisture content and water activity

Goto our Water Activity resource page and maybe get some understandable descriptions. With that understanding, here are a couple of simple definitions. Moisture content is how much water there is in a given material. Water activity is how difficult it is to remove the water. There are two basic direct techniques to measure moisture.

Loss-on drying drives off the moisture by applying heat energy. Karl Fischer deconstructs the chemistry to free the moisture. Additionally, there are many indirect methods that must refer to the direct measurements. Water activity is measured by letting a product sample reach equilibrium relative humidity in a closed temperature-controlled chamber. This allows water that is naturally released at that temperature to form a vapor and stabilize.

When the resulting vapor pressure stops changing, no further moisture releases from the sample. When the temperature and the RH within the container were stabilized, the vapor pressure of the samples and the interstitial air in the vessel reached the equilibrium state.

The RH and temperature values were determined. To ensure the equilibrium state, each temperature level was maintained for 12 h, then adjusted to next temperature level. This technique has been used to determinate sorption isotherm for autoclaved aerated concrete [ 24 ], sweet potato slices [ 25 ], pea seeds [ 26 ] and Oolong tea [ 27 ]. The six datasets from five countries used to evaluate the factors affecting the regression parameters between Aw and MC are in Table 1.

The published models used, along with the data from the literature and seven other published models, are displayed in Table 2. Selected studies on the relationship between water activity and moisture content in honey.

If the influencing factor has several levels of qualitative categories, the significance of the qualitative treatment could be tested by t -test or F-test. To evaluate the effect of categorical variables such as type or state of honey, an indicator variable is used. The equation for the regression line relating two types of datasets that differ in both intercept and slope are as follows:. To test the hypothesis that two regression lines have the same slope or intercept, we could use the t -test.

For three treatments, the regression equations relating datasets that differ in both intercept and slope are as follows:. If the qualitative variables have two qualitative factors e. The results of the estimated parameters and comparison statistics for the linear equation at different temperature are in Table 3. The effect of temperature on parameters A and B is shown in Figure 3 and Figure 4.

The equation for A and B was expressed as:. Three forms of the linear equation that incorporated the temperature term were proposed as follows:. Two types of honey flower and honeydew [ 5 ] were used to evaluate the factors affecting the relationship between Aw and MC by Equation 1.

The data distribution and predicted lines are in Figure 6. The relationship between water activity Aw and moisture content MC of flower and honeydew honey in Slovenia [ 5 ].

The numbers in parentheses below the estimated values of parameters are t -test values for the estimated value. Type had no significant effect on the Aw and MC relationship. From Equation 17 — 19 , we found no significant difference in the slope of the linear equation for flower and honeydew honey. However, the intercept significantly differed with two types of honey. The datasets for Glitter et al. Two indicator variables were analyzed by Equation 8.

The results indicated a significant difference in the intercept. With the same crystallized state, flower and honeydew honey had a similar slope, 0. With the same liquid state, the slope was 0. That is, the state not the type of honey significantly affects the slope parameter of the Aw linear equation.

The prediction lines of two states and two types of honey are in Figure 7. The prediction equations between water activity and moisture content including different type flower and honeydew and state liquid and crystallized of honey [ 8 ]. The datasets from different countries with the liquid state are in Figure 8.

The relationship between water activity and moisture content of flower and honeydew honey from Argentinian [ 7 ] and Slovenia [ 5 ]. Two datasets for Slovenia honey were pooled and evaluated by Equation 4. The regression equation was as follows:. The adequate linear equation was as follows:. The datasets for Aw and MC for the two countries had the same slope, but a different intercept.

The datasets from Germany pooled flower and honeydew, liquid state [ 8 ] and Slovenia liquid state honey [ 5 ] are in Figure 9 and were used for assessing the influencing factors. The relationship between water activity and moisture content for datasets for honey from Germany pooled of the flower and honeydew, liquid state [ 8 ] and Slovenia liquid state [ 5 ]. The adequate equation is as follows:. The type flower or honeydew did not significantly affect the slope.

The datasets from different types and states [ 13 ] and Slovenian honey pooled liquid states: flower and honeydew [ 5 ] are evaluate in Figure The relationship between water activity and moisture content for datasets for honey of different types and states [ 13 ] and Slovenia pooled data of liquid states: flower and honeydew [ 5 ]. Both datasets had different slopes and intercepts.

These results could be explained by the source of the honey. The Argentinian honey was liquid, the mixed honey included liquid, crystallized and partially crystallized states. The datasets from Spain flower honey, unknown state [ 6 ] and Slovenia pooled data of liquid states, flower and honeydew [ 5 ] are in Figure The relationship between water activity and moisture content for datasets for honey from Spain flower honeys, unknown state [ 6 ] and Slovenia pooled data of liquid states, flower and honeydew [ 5 ].

The datasets from Gleiter et al. The relationship between water activity and moisture content for datasets for honey from Germany [ 8 ] and Slovenia [ 5 ]. The linear equations for the two datasets had different slopes and intercepts. The slope of the three datasets was identical, and the intercepts significantly differed. The slope of the three datasets was identical and the intercepts significantly differed. Our study confirms these results. The linear equation was as follows:.

The slopes and intercepts of the three datasets significantly differed. The intercept and slope of the equation for the dataset of flower honey from La Palma Island, Spain, significantly differed from those in other datasets [ 9 ]. Outlier data The original linear equation proposed by the authors was as follows:. The comparison between Equations 32 and 33 is shown in Figure The comparison between two equations with and without outliers in the datasets of Sanjuan et al.

After deleting this data point, the slope, intercept and coefficient of determination changed obviously. If we compare all data from Sanjuan et al.

If the outlier was deleted from the datasets of Sanjuan et al. The slope of the two datasets was identical. From the results of Equations 37 and 38 , the outlier significantly affected the comparison results for the two datasets.

With modern regression analysis, more useful information on correlation between Aw and MC could be found. The results indicated the importance of finding the correct equation with modern regression. Based on the study of the datasets of Gleiter et al.

The slope parameter could be classified into two categories: liquid and crystallized. Thus, the slope for the linear equation was affected only by the state of the honey. The type of honey, flower and honeydew, and other factors did not affect the slope but did affect the intercept.

The authors developed an Aw equation from the effect of the osmotic concertation on the osmotic coefficient, which was as follows:. The same practice is followed to study curves relating water activity under equilibrium conditions to water content. Two basic methods can be used to obtain the constant temperature sorption curves.

In the first method, food of known moisture content is allowed to come to equilibrium with a small headspace in a tight enclosure and partial pressure of water activity is measured manometrically, or relative humidity is measured using a hyqrometer.

Relative humidity sensors of great variety are available for this purpose, including electric hygrometers, dewpoint cells, psychrometers, and others. A second basic method for preparing isotherms is the exposure of a small sample of food to various constant humidity atmospheres. After equilibrium is reached, the moisture content is determined gravimetrically or by other methods.

A number of saturated salt solutions are available for this purpose. Saturated salt solutions have the advantage of maintaining a constant humidity as long as the amount of salt present is above saturation level. Salt slushes and solutions of glycerol or sulfuric acid are among those commonly used. Knowledge of sorption behavior of food is useful in concentration and dehydration processes for two reasons:.

Products containing free water give off moisture in vapor form to the air in the environment, only when the vapor pressure in the air is below that of the product.

The vapor pressure of a salt or sugar solution is reduced in comparison to that of pure water. The amount of vapor in the surrounding air generally is measured as relative humidity. At the equilibrium point, water is neither given off nor absorbed. All the food preservation techniques implies reduction in moisture content and so does water activity.

Generally people think that if the moisture content of a food commodity is reduced obviously the water available for the microorganisms will also be reduced and hence the food commodity will be less prone for the growth of microorganisms. Higher moisture content does not necessarily means high water activity.