Mechanical properties of maize kernel horny endosperm, floury endosperm and germ

Bo, Wang; Wang, Jun

doi:10.1080/10942912.2019.1614050

Cited by 24 publications

(17 citation statements)

References 27 publications

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“…Moreover, varietal differences also influence the breakage behaviour of grains due to their differences in composition [ 78 , 79 , 80 , 81 , 82 , 83 , 84 ]. For example, in corn grains, the horny endosperm show lower deformation and can withstand higher forces when compared with floury endosperm and germs [ 85 ].…”

Section: Resultsmentioning

confidence: 99%

“…Corn has a harder exterior seed coat, while its interior structure is more mealy and soft (floury), which means comminution can take place when material merely hits the disc (with lower impact forces, velocities, energies), even without cutting. The coarse particles are then produced from the bran and the horny endosperm, while the finer from floury endosperm [ 85 ]. The increasing velocities and, in consequence, the energy of impacts results in the higher fragmentation degree of harder parts of corn grains [ 85 ].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Energy-Dependent Particle Size Distribution Models for Multi-Disc Mill

et al. 2022

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Comminution is important in the processing of biological materials, such as cereal grains, wood biomass, and food waste. The most popular biomaterial grinders are hammer and roller mills. However, the grinders with great potential in the processing of biomass are mills that use cutting, e.g., disc mills. When it comes to single-disc and multi-disc grinders, there are not many studies describing the relationships between energy, motion, material, and processing or describing the effect of grinding, meaning the size distribution of a product. The relationship between the energy and size reduction ratio of disc-type grinder designs has also not been sufficiently explored. The purpose of this paper was to develop models for the particle size distribution of the ground product in multi-disc mills depending on the variable process parameters, i.e., disc rotational velocity and, consequently, power consumption, and the relationship between the grinding energy and the shape of graining curves, which would help predict the product size reduction ratio for these machines. The experiment was performed using a five-disc mill, assuming the angular velocity of the grinder discs was variable. Power consumption, product particle size, and specific comminution energy were recorded during the tests. The Rosin–Rammler–Sperling–Bennet (RRSB) distribution curves were established for the ground samples, and the relationships between distribution coefficients and the average angular velocity of grinder discs, power consumption, and specific comminution energy were determined. The tests showed that the specific comminution energy increases as the size reduction ratio increases. It was also demonstrated that the RRSB distribution coefficients could be represented by the functions of angular velocities, power consumption, and specific comminution energy. The developed models will be a source of information for numerical modelling of comminution processes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Energy-Dependent Particle Size Distribution Models for Multi-Disc Mill

et al. 2022

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“…Higher temperatures increased the drying rate, as well as the susceptibility to breakage, an effect similar to that caused by the use of continuous drying or shorter rest periods. Wang and Wang (2019) found a positive correlation between increased susceptibility to breakage and the presence of cracks. Shirmohammadia, Charraulta, and Blencowe (2018) also found that the resting and drying periods promoted changes in the mechanical properties of grains and reduced potential damage.…”

Section: Resultsmentioning

confidence: 92%

Microscopy of maize grains subjected to continuous and intermittent drying

et al. 2022

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Drying is an important step in the post-harvest processes as a way of product conservation and quality preservation. In this context, this study aimed to evaluate the effect of continuous and intermittent drying of maize grains with different rest periods on the integrity of their micro- and macroscopic structures. Maize grains were harvested with a moisture content of 0.3399 ± 0.001 dry basis (db) and subjected to continuous and intermittent drying with 4, 8, 12, and 16 hours of rest period. An experimental fixed-bed dryer, with controlled drying air conditions at a temperature of 100 °C and air flow of 1.5 m3 min.−1 m−2 (12 m3 min.−1 m−3), was used. Continuous drying was completed with a moisture content of 0.1628 ± 0.0003 db, whereas intermittent drying was interrupted with 0.2195 ± 0.0002 db and resumed after rest. The drying rate, integrity through grain images, the conformation of particles through scanning electron microscopy, and cell membrane integrity were evaluated. The drying rate increased with an increase in the rest period, the increase in rest period reduced the intensity of cracks, and the reduction in rest period led to higher dispersion and reduction in the size of starch granules and lower integrity of cell membranes.

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“…Their data showed that when kernels were at the same moisture content, the BS showed significant variance. This is because different varieties of maize are composed of different amount of starch, protein, and oil and it can affect BS (Wang & Wang, 2019). As a result, predicting BS by moisture content must consider the components in maize.…”

Section: Introductionmentioning

confidence: 99%

“…It was concluded that the horny endosperm was higher in strength than floury endosperm. The ratio of starch to protein in horny endosperm and floury endosperm was reported as 11:1 and 6:1 (Wang & Wang, 2019). Similarly, they declared that the horny endosperm is stronger than floury endosperm in terms of rupture force, toughness and firmness.…”

Section: Introductionmentioning

confidence: 99%

Feature selection, artificial neural network prediction and experimental testing for predicting breakage rate of maize kernels based on mechanical properties

Xia

Yang

et al. 2020

J Food Process Engineering

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The kernel breakage in harvest process is a significant problem which causes massive grain losses. In order to solve this problem, a possible solution is to predict the breakage rate and give suitable harvest time thus reducing grain losses. This study focused on using mechanical properties to predict breakage rate. Compression tests, compression–relaxation tests, insertion–retraction and stretch tests were conducted and 20 parameters were derived. Threshing tests were conducted to get breakage rate. As these parameters might be related and it is not practical to use massive parameters for prediction, the feature selection using neighborhood component analysis (FSNCA) was used for picking important parameters. The variables were compacted to an acceptable level by the FSNCA while various aspects of mechanical properties were included. The FSNCA determined the hardness of floury endosperm, rupture energy in vertical direction (x‐axis), allowable stress in lateral direction (z‐axis), hysteresis in x‐axis and elastic modulus in z‐axis as predictors. The artificial neural network (ANN) was built for prediction. The built ANN model showed an agreement in prediction results and real breakage rate (R = 0.98). The prediction error was ±1.7%.Practical ApplicationsThe mechanical properties were illustrated as potential indexes which can be utilized for predicting breakage rate of maize kernels in threshing process. The selection method reduced the dimension of predictors, which means fewer tests are required for prediction. As a result, this research proposed a possible solution for the prediction of maize breakage in harvest. Moreover, the prediction model can give guidance for optimal harvest time and thus reduce breakage rate in harvest.

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Mechanical properties of maize kernel horny endosperm, floury endosperm and germ

Cited by 24 publications

References 27 publications

Energy-Dependent Particle Size Distribution Models for Multi-Disc Mill

Energy-Dependent Particle Size Distribution Models for Multi-Disc Mill

Microscopy of maize grains subjected to continuous and intermittent drying

Feature selection, artificial neural network prediction and experimental testing for predicting breakage rate of maize kernels based on mechanical properties

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