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Apples have been constantly damaged in collecting, transporting, and processing, leading research focus on apples' mechanical‐structural damage behavior. To research apples' mechanical‐structural damage behavior during collision, a dropping collision damage testing device was self‐established, with PLC control, data acquisition‐processing, in situ high‐speed observation. The effect of impact material, drop height, impact orientation on apple deformation and bruise area was investigated with self‐established device, considering three typical kinds of apples. The results indicated that apple dropping collision can be divided into two stages: dropping down contact deformation stage and recovering contact deformation stage. Three kinds of apples demonstrate the largest deformation and bruise area when the impact material is steel and acrylic plate. The deformation is similar when apples collide with soil and foam, apples have no bruise area when the impact material is foam. The correlation between apple deformation, bruising area, and drop height was established, reflecting the relationship between drop height and apples' mechanical‐structural damage behavior. Yellow Marshal apple deformation is the largest compared to other two kinds of apples under the same collision condition. Red Fuji apple bruise area is the largest compared to other two kinds of apples. The largest bruise area of Yellow Marshal apple and Guoguang apple are in apple transverse, and Red Fuji apple is in apple top. The study can provide basic theoretical and practical guidance for apples postharvest work. [ABSTRACT FROM AUTHOR]

ABSTRACTEfforts have been made to streamline the abstract as follows. If further changes are made we are concerned that the integrity of the content may be compromised. We hope you will understand. This study aimed to investigate the impact damage experienced by Pucheng crisp pears during various stages, including harvest, transportation, and processing. Drop impact tests were conducted on pears, considering drop height and collision contact material as factors influencing damage. The bruise area was used as an indicator for damage evaluation. Additionally, the pressure-sensitive film technique was employed to measure the contact stress distribution characteristics of Pucheng crisp pears after the drop impact. The relationship between contact stress distribution and damage area was examined, and the stress area range close to the damage area was determined. The results revealed that the damage area of pears increased linearly with the increasing drop height. Among the four contact materials, foam board exhibited the best cushioning properties, with a maximum safe drop height of 20 cm. The pressure distribution tended to follow a normal distribution, with a pressure range of 0.2-0.3 MPa covering the largest area, corresponding to the majority of the bruise area on the pears. Furthermore, the pressure area and average pressure were found to be related to the severity of bruising. When pears were dropped onto steel, rubber plate and corrugated board, for the former two the stress area exceeding 0.2 MPa closely approximated the damage area, with an average relative error of 7.2%. For the latter, the closest range was above 0.3 MPa, with an average relative error of 3.6%. However, when dropped onto foam board, the average relative error was 75%. This research provides valuable insights for designing packaging tools to minimize damage and serves as a theoretical basis for predicting and assessing pear bruise area using finite element analysis.

Achieving fast and accurate prediction of the fruit mechanical damage area is important to improve the accuracy and efficiency of apple quality grading. In this paper, the spectral data of all samples in the wavelength range from 376 to 1011 nm were collected, the sample set was divided by the physicochemical coeval distance method, and the spectral preprocessing methods were evaluated by establishing a full-wavelength artificial neural network model. The wavelength selection of spectral data was performed by competitive adaptive reweighted sampling, L1 parameter method, and the Pearson correlation coefficient method, and the partial least squares, artificial neural network, and support vector machine (Gaussian kernel) prediction models were established to predict the fruit bruise area size. The surface fitting was performed using the actual apple bruise area, and the regression surface equation of the damage time and damage height of the fruit was established. The results showed that (1) the preprocessing method of first-order difference + SG smoothing can make the prediction model more accurate; (2) the CARS-ANN prediction model has better prediction performance and higher operation efficiency, with the prediction set root mean square error of prediction and R-value of 0.1150 and 0.8675, respectively; (3) the sparrow search algorithm was used to optimize the model, which improved the accuracy of the prediction model. The root mean square error of prediction reached 0.0743 and The R-value reached 0.9739. (4) The relationship between spectral information, bruise area, damage time, and damage degree was obtained by combining the establishment of the fitted surface of the apple bruise area with the prediction model. This study is of application and extension value for the rapid nondestructive prediction of fruit bruise area. [ABSTRACT FROM AUTHOR]

Efforts have been made to streamline the abstract as follows. If further changes are made we are concerned that the integrity of the content may be compromised. We hope you will understand. This study aimed to investigate the impact damage experienced by Pucheng crisp pears during various stages, including harvest, transportation, and processing. Drop impact tests were conducted on pears, considering drop height and collision contact material as factors influencing damage. The bruise area was used as an indicator for damage evaluation. Additionally, the pressure-sensitive film technique was employed to measure the contact stress distribution characteristics of Pucheng crisp pears after the drop impact. The relationship between contact stress distribution and damage area was examined, and the stress area range close to the damage area was determined. The results revealed that the damage area of pears increased linearly with the increasing drop height. Among the four contact materials, foam board exhibited the best cushioning properties, with a maximum safe drop height of 20 cm. The pressure distribution tended to follow a normal distribution, with a pressure range of 0.2-0.3 MPa covering the largest area, corresponding to the majority of the bruise area on the pears. Furthermore, the pressure area and average pressure were found to be related to the severity of bruising. When pears were dropped onto steel, rubber plate and corrugated board, for the former two the stress area exceeding 0.2 MPa closely approximated the damage area, with an average relative error of 7.2%. For the latter, the closest range was above 0.3 MPa, with an average relative error of 3.6%. However, when dropped onto foam board, the average relative error was 75%. This research provides valuable insights for designing packaging tools to minimize damage and serves as a theoretical basis for predicting and assessing pear bruise area using finite element analysis. [ABSTRACT FROM AUTHOR]

Achieving fast and accurate prediction of the fruit mechanical damage area is important to improve the accuracy and efficiency of apple quality grading. In this paper, the spectral data of all samples in the wavelength range from 376 to 1011 nm were collected, the sample set was divided by the physicochemical coeval distance method, and the spectral preprocessing methods were evaluated by establishing a full-wavelength artificial neural network model. The wavelength selection of spectral data was performed by competitive adaptive reweighted sampling, L1 parameter method, and the Pearson correlation coefficient method, and the partial least squares, artificial neural network, and support vector machine (Gaussian kernel) prediction models were established to predict the fruit bruise area size. The surface fitting was performed using the actual apple bruise area, and the regression surface equation of the damage time and damage height of the fruit was established. The results showed that (1) the preprocessing method of first-order difference + SG smoothing can make the prediction model more accurate; (2) the CARS-ANN prediction model has better prediction performance and higher operation efficiency, with the prediction set root mean square error of prediction and R-value of 0.1150 and 0.8675, respectively; (3) the sparrow search algorithm was used to optimize the model, which improved the accuracy of the prediction model. The root mean square error of prediction reached 0.0743 and The R-value reached 0.9739. (4) The relationship between spectral information, bruise area, damage time, and damage degree was obtained by combining the establishment of the fitted surface of the apple bruise area with the prediction model. This study is of application and extension value for the rapid nondestructive prediction of fruit bruise area.