Geometric mean diameter
The mean geometric dimensions (L, B, T) of the 50 grains measured at different MC (10.58 to 45.45% d.b.), along with the standard errors, are shown in Table 1. The values indicate a rise of 12.30, 8.45 and 9.50% in the dimensions (L, B and T) with an increase in MC, respectively. The coefficients and Regression model values (Eqs. 9 to 37) are shown in Table 2. The GMD (D
gm) of green gram was computed and the variation in GMD over the change in moisture content was studied using Regression analysis. The trend (Fig 1) shows the mean diameter of green gram increased (3.75 to 4.12 mm) with an increase in moisture content (10.58 to 45.45%, d.b.) and the Regression equations 9 and 10represent the best fit curves for the data.
There is a 9.87% increase in the value of GMD with an increase in moisture content. This was due to increase in the overall dimension of the grain with additional gain of moisture. The high R
2 value and low standard errors (0.01 and 0.003) imply the goodness of fit among the data and the variation is statistically significant (p<0.05) for the range of moisture studied. Several researchers like
Alibas and Koksal, 2015 (soybean) and
Nimkar and Chattopadhyay. 2001,
Pandiselvam et al., 2017 and
Unal et al., 2008 (green gram) observed similar trends in their work.
Sphericity
The sphericity of green gram was calculated and shown in Fig 1. Equation 11 and 12 shows the relationship between MC and sphericity of grain. The data initially increased from 0.833 to 0.844 and then reduced to 0.819. Data fitted well with cubic and quadratic curves (Se=0.003 and 0.002). The variation was non-significant (p=0.208) for cubic function. Very less variation was observed in the sphericity of the grain within the MC studied.
The results of this study are in agreement with studies by
Nimkar and Chattopadhyay. 2001 and
Unal et al., 2008 who observed similar results for green grams through their work. Zewdu and Solomon. 2007 detected low sphericity values at higher MC for Tef seed in his study.
Surface area
The surface area of green gram changed from 44.13 to 53.45 mm
2 (Fig 2), with an increase in moisture content (10.58 to 45.45%). The relation between them is established using Regression analysis (Eqs. 13 and 14).
Both the curves fitted well for the surface area values with low Se and a high coefficient of determination (R
2) and the effect was statistically significant (p<0.05). It indicates that with an increase in moisture content, the grain dimensions increased significantly, resulting in a higher surface area. Similar results have been reported by
Sacilik et al., 2003 (Hempseed),
Sharon et al., 2015 (Black gram),
Singh et al., 2010 (Barnyard millet) through their works.
Mass of 1000 grains
With an increase in moisture content from 10.58 to 45.45% (d.b.), the mass of 1000 grains varied from 44.13 g to 53.45 g (Fig 2). The equations (15 and 16) displaying the relationship between grain mass and MC are given below.
Within MC’s range, the grain mass increased by 20.39% and the variation in mass was statistically significant (p<0.05). The increase in 1000 grain weight was due to absorption of moisture by the molecules of the grain. The low Se values predict the closeness among the data. Many researchers have reported a surge in the mass of 1000 grains with a rise in MC
(Nimkar et al., 2005, Pandiselvam et al., 2017, Singh et al., 2010 and
Sharon et al., 2015).
Bulk density
The bulk density of green gram reduced significantly (p< 0.05) from 860 kg m
-3 to 670 kg m
-3 (Fig 3) with an increase in moisture content from 10.58 to 45.45%. The bulk density and MC display the following relationship (Eqs.17 and 18). The respective standard errors are 0.013 and 0.016, which reflect close fit among the values.
There was a reduction in bulk density by 22.09% with MC and it may be due to an increase in its size and dimensions, causing more inter-granular space between the bulk. The corresponding increase in mass was less pronounced than the grains’ volumetric expansion, resulting in lower bulk density.
Balasubramanian and Viswanathan. 2010 (Minor millets),
Nimkar and Chattopadhyay. 2001 (Green gram),
Sharon et al., 2015 (Black gram),
Shelke et al., 2019 (Black gram) and
Unal et al., 2008 (Mung bean) have observed a decrease in bulk density values with rising in MC in their respective works.
True density (Toluene displacement method)
True density of green gram varied from 1330 kg m
-3 to 1240 kg m
-3 (Fig 3) with rise in moisture content from 10.58 to 45.45%. The variation in true density was statistically significant (p<0.05) with low Se values and the relation is represented by Regression equations below.
Relatively higher true grain volume against its rising weight is attributed to a decline in the grain’s true density. The results were in accordance with several other researchers, including
Nimkar and Chattopadhyay. 2001 (green gram),
Nimakar et al., 2005 (Moth gram) and
Sharon et al., 2015 (black gram).
Porosity
The porosity of grains is computed from the data of bulk and true densities. It was observed, with an increase in moisture content from 10.58 to 45.45%, the porosity increased from 35.75 to 46.38% (Fig 4). Regression equations 21 and 22 signify the relation between porosity and MC of grains.
The increase in porosity is well defined from the bulk and true density values, which declined with rising MC. The low p-values and low S
e values portray the statistical significance of MC’s variation and a high correlation among the values is also observed.
Nimkar and Chattopadhyay. 2001 (Green gram),
Pandiselvam et al., 2017 (Green gram),
Sharon et al., 2015 (Black gram) and
Unal et al., 2008 (Mung bean) also reported this incline in porosity in their studies.
Terminal velocity
The terminal velocity of green gram varied significantly (p<0.05) from 9.20 to 11.10 m s
-1 with an increase in moisture content from 10.58 to 45.45%. The linear and quadratic regression equations (23, 24) represent a high coefficient of determination with the goodness of fit with 0.049 and 0.057 S
e values, respectively.
The mean values of terminal velocity at different moisture levels are shown in Fig 4. The increase in terminal velocity value is due to a rise in grain mass due to moisture absorption.
Nimkar and Chattopadhyay. 2001 (green gram),
Nimakar et al., 2005 (Moth gram),
Singh et al., 2010 (Barnyard millet and kernel) and
Unal et al., 2008 (mung bean) obtained similar results during their studies on grains.
Dynamic angle of repose
The experimental values (Fig 5) confirmed that with a rise in MC, the angle of repose increased significantly from 30.95 to 46.57° (50.46% increase). It may be due to higher internal friction at elevated MC resulting from more contact surface area of the grains.
The regression equations (25 and 26) with higher R2 values, low p and Se values indicate good dependency and statistical significance among the data. Various other researchers reported similar outcomes from their study (
Baryeh. 2002,
Pandiselvam et al., 2017, Singh et al., 2010 and
Unal et al., 2008).
Coefficient of internal friction
There was an increase in the mean value of coefficient internal friction from 0.78 to 0.90 (Fig 5) with increase in MC of green gram. The variation in the values were represented using the equations (27 and 28) obtained through Regression analysis. Data shows good dependency (high R
2 and low S
e) and the values are statistically significant.
The increased coefficient of internal friction at higher MC may be due to increased cohesion among the grains.
Balasubramanian and Viswanathan. 2010 and
Singh et al., 2010 observed similar trends for minor millets and barnyard millets, respectively.
Coefficient of static friction
The effect of different surfaces and MC (10.58 to 45.45% d.b.) on the static coefficient of friction of grain was studied using Regression and it showed a linear relationship (0.859% R
2 value) and the equation (29) is presented below. The combined effects of both surface and MC were statistically significant (p<0.05). The mean values of the coefficient of static friction are presented in Table 3.
Further, the impact of moisture on the friction coefficient on different surfaces was studied and the Regression equations (30 to 37) are presented below. The coefficient data of green gram shows, there is a change from 0.35 to 0.64 (82.86%) for mild steel (Fig 6), 0.29 to 0.54 (86.20%) for stainless steel (Fig 6), 0.26 to 0.56 (115.38%) for plywood (Fig 6) and from 0.26 to 0.60 (130.77%) for galvanised iron surface (Fig 6). The MS surface had the highest coefficient of static friction values at 45.45% MC, followed by GI, plywood and stainless steel. The highest percentage of increase (130.77%) in the coefficient of friction is observed for the GI surface and the least increase is detected for MS (82.86%) surface within the range of MC studied. It might be because the mild steel surface offered the highest resistance among all the surfaces to the grain movement, while stainless steel had the least resistance due to its roughness. The static coefficient of friction for green gram increased for all the surfaces with an increase in MC due to increased adhesive force between grains and the surface. Similar results were obtained by
Nimkar and Chattopadhyay. 2001,
Pandiselvam et al., 2017 and
Singh et al., 2010 during their research work. The relationship between moisture and coefficient of friction of grain at different surfaces are presented below.
Mild steel (MS)
Stainless steel (SS)