Search Results (19)

Using eye-tracking technology, the proposed study investigates how customers visually evaluate apples varieties and apple defects and how these evaluations affect their purchasing decisions. Three aspects were examined in this study: apple variety, defect severity, and cultural background. Idared, Golden Delicious Yellow, and Golden Delicious Green apple varieties with increasing degrees of bruising were shown to Chinese and Hungarian participants. The findings show that apple variety had no significant effect on gaze patterns, whereas cultural background had a considerable impact on visual attention measures. The most important element in grabbing and retaining customer attention was the severity of the defect, which was measured by area. The “Threshold of Rejection”, which characterizes consumer tolerance for Apple defects, is introduced in the study. Furthermore, a polynomial regression model was created to predict the probability of repurchasing an apple depending on its visual quality (level of bruising). These results provide useful information for marketing plans, quality assurance, and comprehending customer behavior in the fresh produce sector. Full article

Lycium barbarum L. (goji), as an economic crop, has a high added value. However, the tender and fragile fruits are easily damaged during harvesting and transportation, leading to fruit bruising, which can cause rotting or black–brown spots after drying, seriously affecting the quality and price. In this study, two varieties of goji were used to determine and evaluate fruit bruising using a pendulum impact test, and the impact process was recorded using a high-speed camera and impact force sensor. This study discussed the energy changes during the impact process of fruits and conducted a correlation analysis of the impact energy, absorbed energy, restitution coefficient, impact force, and other indicators, analyzing the changes in each indicator with the falling height. The results showed that 0.2 m could be considered a critical height for damaging the fruit of goji. Furthermore, this study calculated the bruise susceptibility of the different varieties at different heights, which can be used for predicting bruising during the harvesting and collection of goji berries and ultimately for estimating the damage caused by mechanical harvesting. Full article

Falling damage is the most common form of damage sustained by kiwifruit during the process of picking and post-processing, and it is difficult to conduct a quantitative analysis of this phenomenon through traditional experimental methods. In order to deeply understand the sensitivity of kiwifruit to falling collision damage, the finite element numerical simulation method was used to evaluate and predict the sensitivity of kiwifruit to falling collision damage during harvesting. First, we obtained the appearance characteristics of kiwifruit through reverse engineering technology and determined the geometric and mechanical property parameters of kiwifruit through physical mechanics experiments. Then, according to the characteristics of fruit tissue structure, a multiscale finite element model, including the skin, pulp, and core, was constructed to simulate the effects of different falling heights, collision angles, and contact surface materials on fruit damage, and the accuracy of the model was verified through falling experiments. Finally, based on the simulation results, the Box–Behnken design was employed within the response surface methodology to establish a sensitivity prediction model for the drop damage sensitivity of kiwifruit across different contact materials. The results showed that the maximum relative error between the speed change obtained using finite element simulation and the speed obtained by the high-speed camera was 5.19%. The model showed high rationality in energy distribution, with the maximum value of hourglass energy not exceeding 0.08% of the internal energy. On the contact surface material with a large elastic modulus, a higher falling height and larger collision angle will significantly increase the risk of fruit bruise. When the contact surface material was a steel plate, the falling height was 1 m, and the collision angle was 90°; the maximum bruise sensitivity of kiwifruit reached 6716.07 mm³ J⁻¹. However, when the contact surface material was neoprene, the falling height was 0.25 m, and the collision angle was 0°, the damage sensitivity was the lowest, at 1570.59 mm³ J⁻¹. The multiscale finite element model of kiwifruit falling collision constructed in this study can accurately predict the damage of kiwifruit during falling collision and provide an effective tool for the quantitative analysis of kiwifruit falling collision damage. At the same time, this study can also provide guidance for the design and optimization of the loss reduction method of the harvesting mechanism, which has important theoretical significance and practical value. Full article

As a common vegetable variety, asparagus is rich in B vitamins, vitamin A, and trace elements such as folate, selenium, iron, manganese, and zinc. With the increasing market demand, China has become the world’s largest cultivated area for asparagus production and product exportation. However, traditional asparagus grading mostly relies on manual visual judgment and needs a lot of manpower input to carry out the classification operation, which cannot meet the needs of large-scale production. To address the high labor cost and labor-intensive production process resulting from the large amount of manpower input and low accuracy of existing asparagus grading devices, this study proposed an improved asparagus grading system and method based on TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) objective evaluation and SVM (support vector machine) prediction. The key structure of classification device was analyzed first, the key components were designed, and the structural parameters were determined by theoretical calculation. Through analysis of the factors affecting asparagus quality, three key attributes were determined: length, diameter, and bruises, which were used as reference attributes to conduct experimental analysis. Then, the graded control groups were set up, combining the TOPSIS principle with weighting, and a score for each asparagus sample was determined. These scores were compared with those of a graded control group to derive the grade of each asparagus, and these subsets of the dataset were used as the training set and the test set, excluding the error caused by the subjectivity of the manual judgment. Based on a comparison of the accuracies of different machine learning models, the support vector machine (SVM) was determined to be the most accurate, and four SVM methods were used to evaluate the test set: linear SVM, quadratic SVM, cubic SVM, and medium Gaussian SVM. The test results showed that the grading device was feasible for asparagus. The bruises had a large influence on asparagus quality. The training accuracy of the medium Gaussian SVM method was high (96%), whereas its test accuracy was low (86.67%). The training accuracies and test accuracy of the quadratic and cubic SVM methods were 93.34%. The quadratic SVM and cubic SVM were demonstrated to have better generalization ability than the medium Gaussian SVM method for predicting unknown grades of asparagus and meeting the operational requirements of the asparagus grading. Full article

Mechanical damage and bruising of fruit is a critical problem in the food industry. Minimizing brusing and damage can be achieved by designing energy-absorbing structures and packaging systems in order to ensure the long-term quality of fresh produce. The aim of this study is to investigate the response and bruise susceptibility of pears under impact loading conditions through finite element analysis (FEA) methods. In this paper, three impact heights (0.25 m, 0.5 m, and 1.0 m), four impact material surfaces (poplar wood, rubber, cardboard, and acrylonitrile butadiene styrene (ABS) plastic), two packaging sizes (standard 0.22″ and sandwich lattice 2.1″), and three impact design structures (rigid, corrugated, and honeycomb) are considered. Based on mesh sensitivity analysis, a mesh element of 1.5 mm was adopted for all simulations, assuring the accuracy of results and considering the trade-off between mesh size and computational time. The response surface analysis approach was utilized in order to develop predictive empirical models related to pear bruising. Results revealed that the rubber-based impact platform yielded minimal bruise susceptibility at all heights, while standard-sized corrugated cardboard performed best at a height of 0.25 m. Furthermore, single, double, and triple layers of packaging cardboard were tested. We observed that adding a second soft layer of corrugated cardboard reduced the stress on the pear by around 33%. However, adding a third layer only reduced stress by 5%. The 3D-printed honeycomb ABS has potential as protective packaging but would require further investigations and parameter optimization. Stacking multiple layers of cardboard on top of each other is a cost-effective solution that could improve damping and, therefore, ensure good quality and increase the shelf life of the fresh produce. This study will help decision-makers select the optimal energy-absorbing material for cushioning and packaging designs in order to improve the handling and post-harvesting logistics of fresh produce. Full article

The pressure distribution characteristics of an apple subjected to compressive loading were investigated using the pressure-sensitive film (PSF) technique combined with apple bruise measurements. Pressure was unevenly distributed in the elliptical contact region. The average pressure had no effect on bruising because it changed slightly in the range of 0.26–0.31 MPa with increasing load. Pressures of 0.20–0.40 MPa accounted for 72% of the total pressure area. Comparatively, the area where pressure over 0.50 MPa was distributed could be ignored and showed little contribution to the bruise area. The contact edge subjected to pressure below 0.10 MPa showed that no bruising occurred. As a result, the relationship between the ≥0.10 MPa pressure area strongly correlated with the bruise area according to a linear equation, with a correlation coefficient of ≥0.99. When this relationship was applied to determine the bruise area with FE, satisfactory predicted results were obtained with minor error rates of 0–7.89% for loads of 54–80 N. But larger prediction errors occurred when the load was above 90 N, suggesting that the linear elastic FE model may not be appropriate for accurately predicting apple bruising. Full article

The study of apple damage mechanisms is key to improving post-harvest apple treatment techniques, and the evolution pattern of damaged tissue is fundamental to the study of apple damage mechanisms. In the study, ‘Red Delicious’ apples were used to explore the relationship between damage and time. A cell death zone was found in the pulp of the damaged tissue after the apple had been bruised. The tissue damage was centered in the cell death zone and developed laterally, with the width of the damage increasing with injury time. The extent of tissue damage in the core and pericarpal directions varied. About 60% of the damaged tissue developed in the core direction and 40% in the pericarpal direction, and the damage ratios in both directions remained consistent throughout the injury. The depth of damage and the rate of damage were influenced by the impact force size and the difference in the size of the damaged part of the apple, but the damage development pattern was independent of the impact force size and the difference in the damaged part. The maximum damage rate was reached at about 30 min, and the depth of damage was stabilized at about 72 min. By studying the evolution pattern of the damaged tissue of the bruised ‘Red Delicious’ apple, it provides the research idea and theoretical basis for enhancing the prediction accuracy and robustness of early stage damage in apples. Full article

Prediction models of damage severity are crucial for the damage expression of fruit. In light of issues such as the mismatch of existing models in actual damage scenarios and the failure of static models to meet research needs, this article proposes a dynamic prediction model for damage severity throughout the entire process of apple damage and studies the relationship between the initial bruise form and impact energy distribution of apple damage. From the experiments, it was found that after impact a “cell death zone” appeared in the internal pulp of the damaged part of Red Delicious apples. The reason for the appearance of the cell death zone was that the impact force propagated in the direction of the fruit kernel in the form of stress waves; the continuous action of which continuously compressed the pulp’s cell tissue. When the energy absorbed via elastic deformation reached the limit value, intercellular disadhesion of parenchyma cells at the location of the stress wave peak occurred to form cell rupture. The increase in intercellular space for the parenchyma cells near the rupture site caused a large amount of necrocytosis and, ultimately, formed the cell death zone. The depth of the cell death zone was closely related to the impact energy. The correlation coefficient r between the depth of the cell death zone and the distribution of impact energy was slightly lower at the impact height of 50 mm. As the impact height increased, the correlation coefficient r increased, approaching of value of 1. When the impact height was lower (50 mm), the correlation coefficient r had a large distribution range (from 0.421 to 0.983). As the impact height increased, the distribution range significantly decreased. The width of the cell death zone had a poor correlation with the pressure distribution on the impact surface of the apples that was not related to the impact height. In this article, the corresponding relationship between the form and impact energy distribution of the internal damaged tissues in the initial damage of Red Delicious apples was analyzed. This analysis aimed to provide a research concept and theoretical basis for more reliable research on the morphological changes in the damaged tissues of apples in the future, further improving the prediction accuracy of damage severity. Full article

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. Full article

Spectroscopy data are useful for modelling biological systems such as predicting quality parameters of horticultural products. However, using the wide spectrum of wavelengths is not practical in a production setting. Such data are of high dimensional nature and they tend to result in complex models that are not easily understood. Furthermore, collinearity between different wavelengths dictates that some of the data variables are redundant and may even contribute noise. The use of variable selection methods is one efficient way to obtain an optimal model, andthis was the aim of this work. Taking advantage of a non-contact spectrometer, near infrared spectral data in the range of 800–2500 nm were used to classify bruise damage in three apple cultivars, namely ‘Golden Delicious’, ‘Granny Smith’ and ‘Royal Gala’. Six prominent machine learning classification algorithms were employed, and two variable selection methods were used to determine the most relevant wavelengths for the problem of distinguishing between bruised and non-bruised fruit. The selected wavelengths clustered around 900 nm, 1300 nm, 1500 nm and 1900 nm. The best results were achieved using linear regression and support vector machine based on up to 40 wavelengths: these methods reached precision values in the range of 0.79–0.86, which were all comparable (within error bars) to a classifier based on the entire range of frequencies. The results also provided an open-source based framework that is useful towards the development of multi-spectral applications such as rapid grading of apples based on mechanical damage, and it can also be emulated and applied for other types of defects on fresh produce. Full article

Mechanical damage is a threat to both food security and sustainability. Bruising is the most common type of mechanical damage, and it causes a huge economic loss due to rejection of fresh produce and downgrading of the appearance quality by consumers. Therefore, this study aims to examine the effect of bruising during postharvest handling using a pendulum test technique. Pomegranate fruit were bruised once at two impact levels (1.189 ± 0.109 and 2.298 ± 0.239 J) and then stored (at 5 °C ± 1 °C and 22 °C ± 1 °C) for 28 days. The study evaluated the effect of impact bruising, storage temperature, and duration on the bruise magnitude and quality attributes of the bruised and non-bruised pomegranates. The results showed that the investigated factors affect the bruise size of bruised pomegranates. Increasing storage temperature from 5 to 22 °C and impact level from 1.189 to 2.298 J increased the bruise area, bruise volume, and bruise susceptibility over time. Alterations in total soluble solids (TSS) and titratable acidity (TA%) were statistically (p < 0.05) induced by bruising, particularly at a high impact. The total soluble solids (TSS) content was reduced in all tested pomegranate fruit (bruised and non-bruised) and recorded the highest percentage decline in those impacted at a high level and stored at 22 °C, at 16.81%. The combination of both studied factors did not affect the water activity (A_w) of aril or the mesocarp of bruised or non-bruised fruit. Bruising parameters and quality attributes were strongly correlated in this study, excluding water activity (A_w). The regression models showed a good determination coefficient (R²) between the predicted and measured values of bruise susceptibility (BS), total soluble solids (TSS), titratable acidity (TA%), and sugar: acid ratio (TSS:TA). The study demonstrates that bruising at a high impact level and long-term storage both affected the susceptibility of pomegranates to bruise, and altered fruit quality. Thus, these factors need to be considered during the postharvest supply chain. Full article

Bruising is one of the main problems in the post-harvest grading and processing of ‘Zaozhong 6’ loquats, reducing the economic value of loquats, and even food quality and safety problems are caused by it. Therefore, one of the main tasks in the post-harvest processing of loquats is to detect whether loquats are bruised, as well as the degree of bruising of loquats, to reduce the loss by proper treatment. An appropriate dimensionality reduction method can be used to reduce the redundancy of variables and improve the detection speed. The multispectral analysis method (MAM) has the advantage of accurate, rapid, and nondestructive detection, which was proposed to identify the different bruising degrees of loquats in this study. Firstly, the visible and near-infrared region (Vis–NIR, 400–1000 nm), the visible region (Vis, 400–780 nm), and the near-infrared region (NIR, 781–1000 nm) were analyzed using principal component analysis (PCA) to obtain the spectral regions and PC vectors, which could be used to effectively distinguish bruised loquats from normal loquats. Then, based on the selected second PC (PC2) score images, a morphological segmentation method (MSM) was proposed to distinguish bruised loquats from normal loquats. Furthermore, the weight coefficients of corresponding wavelength points of different degrees of bruising of loquats were analyzed, and the local extreme points and both sides of the interval were selected as the characteristic wavelength points for multi-spectral image processing. A gray level co-occurrence matrix (GLCM) was used to extract texture features and gray information from two-band ratio images K_782/999. Finally, the MAM was proposed to detect the degree of bruising of loquats, which included the spectral data of three characteristic wavelength points in the NIR region coupled with texture features of the two-band ratio images, and the classification accuracy was 91.3%. This study shows that the MAM can be used as an effective dimensionality reduction method. The method not only improves the effect of prediction but also simplifies the process of prediction and ensures the accuracy of classification. The MSM can be used for rapid detection of normal and bruised fruits, and the MAM can be used to classify the degree of bruising of bruised fruits. Consequently, the processed methods are effective and can be used for the rapid and nondestructive detection of the degree of bruising of fruit. Full article

From the producers’ point of view, there is no universal and quick method to predict bruise area when dropping an apple from a certain height onto a certain type of substrate. In this study the authors presented a very simple method to estimate bruise volume based on drop height and substrate material. Three varieties of apples were selected for the study: Idared, Golden Delicious, and Jonagold. Their weight, turgor, moisture, and sugar content were measured to determine morphological differences. In the next step, fruit bruise volumes were determined after a free fall test from a height of 10 to 150 mm in 10 mm increments. Based on the results of the research, linear regression models were performed to predict bruise volume on the basis of the drop height and type of substrate on which the fruit was dropped. Wood and concrete represented the stiffest substrates and it was expected that wood would respond more subtly during the free fall test. Meanwhile, wood appeared to react almost identically to concrete. Corrugated cardboard minimized bruising at the lowest discharge heights, but as the drop height increased, the cardboard degraded and the apple bruising level reached the results as for wood and concrete. Contrary to cardboard, the foam protected apples from bruising up to a drop height of 50 mm and absorbed kinetic energy up to the highest drop heights. Idared proved to be the most resistant to damage, while Golden Delicious was medium and Jonagold was least resistant to damage. Numerical models are a practical tool to quickly estimate bruise volume with an accuracy of about 75% for collective models (including all cultivars dropped on each of the given substrate) and 93% for separate models (including single cultivar dropped on each of the given substrate). Full article

Simulated impact damage testing was investigated by fractal image analysis using response surface methodology (RSM) with a central composite design (CCF) on quality of ‘Glom Sali’ guava for drop heights (0.2, 0.4, and 0.6 m), number of drops (1, 3, and 5) and storage temperature conditions (10, 20, and 30 °C). After 48 h, impacted fruit were determined and analyzed for bruise area (BA), bruise volume (BV), browning index (BI), total color difference (∆E), image analysis for bruise area (BAI), and fractal dimension (FD) at the bruising region on peeled guava. Results showed that the correlation coefficient (r = −0.6055) between ∆E and FD value was higher than ∆E and either BA (r = 0.3132) or BV (r = 0.2095). The FD variable was determined as a better indicator than conventional measurement (BA or BV) for pulp browning and impact bruising susceptibility. The FD variable also exhibited highest R²_adj value (81.69%) among the other five variables, as the highest precision model with high determination coefficient value (R²_adj) (>0.8) for impact bruising prediction. Recommended condition of the FD variable to minimize impact bruising was drop height of 0.53 m for five drops under storage at 30 °C. FD variable assessed by image analysis was shown to be a highly capable measurement to determine impact bruising susceptibility in guava fruit. Full article

Bruise damage is a very commonly occurring defect in apple fruit which facilitates disease occurrence and spread, leads to fruit deterioration and can greatly contribute to postharvest loss. The detection of bruises at their earliest stage of development can be advantageous for screening purposes. An experiment to induce soft bruises in Golden Delicious apples was conducted by applying impact energy at different levels, which allowed to investigate the detectability of bruises at their latent stage. The existence of bruises that were rather invisible to the naked eye and to a digital camera was proven by reconstruction of hyperspectral images of bruised apples, based on effective wavelengths and data dimensionality reduced hyperspectrograms. Machine learning classifiers, namely ensemble subspace discriminant (ESD), k-nearest neighbors (KNN), support vector machine (SVM) and linear discriminant analysis (LDA) were used to build models for detecting bruises at their latent stage, to study the influence of time after bruise occurrence on detection performance and to model quantitative aspects of bruises (severity), spanning from latent to visible bruises. Over all classifiers, detection models had a higher performance than quantitative ones. Given its highest speed in prediction and high classification performance, SVM was rated most recommendable for detection tasks. However, ESD models had the highest classification accuracy in quantitative (>85%) models and were found to be relatively better suited for such a multiple category classification problem than the rest. Full article