Mansoureh Mozaffari; Asghar Mahmoudi; Bahareh Jamshidi
Abstract
The present paper investigated the feasibility of using texture-based features of laser backscattering images in monitoring and modeling of apple slices shrinkage during hot air drying. The backscattering imaging was performed at three wavelengths (650, 780 and 880 nm) in the visible and near-infrared ...
Read More
The present paper investigated the feasibility of using texture-based features of laser backscattering images in monitoring and modeling of apple slices shrinkage during hot air drying. The backscattering imaging was performed at three wavelengths (650, 780 and 880 nm) in the visible and near-infrared regions. The acquired images were subjected to four texture analysis methods including first-order statistics of image histogram, co-occurrence matrix, gray level run-length matrix and wavelet transform. Stepwise multiple linear regressions was used to develop models and determine the most effective features by using individual types of feature sets and their combinations as inputs of calibration models. The results showed the capability of the texture features extracted from the laser backscattering images in the near-infrared wavelength range for prediction of apple slices shrinkage; by using homogeneity feature of co-occurrence matrix-90˚ at 880 nm (with Rp2=0.95, RMSEp=5.15) and fusion of the four feature sets extracted from different texture analysis methods at 780nm (with Rp2=0.94, RMSEp=5.61), could make models with high accuracy. This study showed that Laser backscattering imaging technique can be used as a non-destructive, rapid and low-cost method for prediction of the shrinkage in the process of hot air drying of apple slices.
Abstract
Temperature management is an important subject in maintaining the quality of horticultural products after harvest. One of the methods in suitable control of temperature, is precooling that is conducted before storing of the product and increases shelf life and storage time the fruits. On the other hand, ...
Read More
Temperature management is an important subject in maintaining the quality of horticultural products after harvest. One of the methods in suitable control of temperature, is precooling that is conducted before storing of the product and increases shelf life and storage time the fruits. On the other hand, estimation of cooling parameters (half and seven-eighths cooling times) need precise sensors and time consuming in precooling operations. So, in this research, airflow velocities as an effective factor in cooling at three levels of 0.5, 1, and 1.3 m s-1 was considered. Parameters including lag factor, cooling coefficient and half and seven-eighths cooling times were calculated based on recorded data of the temperature sensors. Finally, using the dimensionless numbers, Fourier and Reynolds, the estimation of regression models obtained for cooling times and compared with experimental data. With increasing airflow velocity, cooling times decreased and convective heat transfer coefficient enhanced up to 58.46%. The overall results showed that for sphere products like pomegranate, using Fo-Re correlation, cooling times are estimated with suitable precision (maximum error for half and seven-eighths cooling times 11.46 and 10.83, respectively) and without using complex equations.
Abstract
This study was conducted to achieve effective and low-cost technology for non-destructive grading of unshelled almonds in real time. A laboratory prototype of an intelligent online impact-acoustic system composed of a feeding unit, acoustical recognition unit, and pneumatic separator with an electronic ...
Read More
This study was conducted to achieve effective and low-cost technology for non-destructive grading of unshelled almonds in real time. A laboratory prototype of an intelligent online impact-acoustic system composed of a feeding unit, acoustical recognition unit, and pneumatic separator with an electronic controller unit was constructed and tested. To evaluate system operation according to almond variety and class (hard, semi-soft, and soft), the effect of an acoustic signal generated by dropping the nuts onto a steel plate was captured by microphone and the amplitude, phase, and power spectral density were extracted from analysis of the sound signal in the time and frequency domains by means of fast Fourier transform. A multilayer perceptron neural network with a LM training function were used in all experiments. The classification accuracy using validation data was about 96.2% in the offline mode, but accuracy decreased to 88% in the online mode. This decrease in accuracy was probably the result of a difference in size and mass of the almond samples in the hard and semi-soft classes.
