Quantitative evaluation of mechanical damage to fresh fruits

Detection of early bruises in apples using hyperspectral imaging and an improved MobileViT network

Apples are susceptible to postharvest bruises, leading to a shortened shelf life and significant waste. Therefore, accurate detection of apple bruises is crucial to mitigate food waste. This study proposed an improved lightweight network based on MobileViT for detecting early-stage bruises in apples, utilizing hyperspectral imaging technology from 397.66 to 1003.81 nm. After acquiring hyperspectral images, the Otsu threshold algorithm was employed for mask extraction, and principal component analysis was used for feature image extraction. Subsequently, the improved MobileViT network (iM-ViT) was implemented and compared with traditional algorithms, utilizing depthwise separable convolutions for parameter reduction and integrating local and global features to enhance bruise detection capability. The results demonstrated the superior performance of iM-ViT in accurately detecting apple bruises, showing significant improvements. The F1 score and test accuracy for detecting apple bruises using iM-ViT reached 0.99 and 99.07%, respectively. The fivefold cross-validation strategy was used to assess the stability and robustness of iM-ViT, and ablation experiments were performed to explore the effects of depthwise separable convolutions and local features on parameter reduction and classification accuracy improvement for early-stage bruise detection in apples. The results demonstrated that iM-ViT effectively reduced parameters and improved the ability to detect early bruises in apples. PRACTICAL APPLICATION: This study proposed an improved lightweight network to detect early bruises in apples, providing a reference for quick detection of bruises caused in the production process. Potential insights into the nondestructive detection of apple bruises using lightweight networks have been presented, which might be applied to mobile or online devices.

Quantitative impact damage of apple based on hyperspectral imaging combined with mechanical parameters and size correction

In order to solve the problem of decreasing the accuracy of quantitative prediction of damage of fruits resulting in the size difference of fruits, the spectral correction method based on the size difference of fruits was adopted. To provide richer theoretical knowledge for the quality detection of fruits and the design of damage reduction programs in reality. First, the undamaged spectra of the group of apples with better performance of the model were selected as the reference spectra by analyzing and comparing the modeling results of the prediction models of mechanical parameters with the single fruit diameter groups. The spectral correction coefficient was calculated with the formulas, and the damage spectra of three groups of apples were size-corrected by this coefficient to build the mechanical parameter models. Finally, the corrected spectra were screened for characteristic wavelengths by competitive adaptive reweighting and uninformative variable elimination algorithms. The results of study showed that the correlation coefficients of the prediction set of the models were improved by 2.1%-13% and the root mean square errors were reduced by 16%-51% with the spectrally corrected models compared with the precorrection models. Therefore, the size correction method can be used to eliminate the effect of size difference on the mechanical parameter models to improve the applicability of the quantitative damage prediction models, and it can provide the theoretical guidance to design the loss-reducing protective measures and the agricultural mechanized operation process.

Fast detection of moisture content and freshness for loquats using optical fiber spectroscopy

Detection of the moisture content (MC) and freshness for loquats is crucial for achieving optimal taste and economic efficiency. Traditional methods for evaluating the MC and freshness of loquats have disadvantages such as destructive sampling and time-consuming. To investigate the feasibility of rapid and non-destructive detection of the MC and freshness for loquats, optical fiber spectroscopy in the range of 200-1000 nm was used in this study. The full spectra were pre-processed using standard normal variate method, and then, the effective wavelengths were selected using competitive adaptive weighting sampling (CARS) and random frog algorithms. Based on the selected effective wavelengths, prediction models for MC were developed using partial least squares regression (PLSR), multiple linear regression, extreme learning machine, and back-propagation neural network. Furthermore, freshness level discrimination models were established using simplified k nearest neighbor, support vector machine (SVM), and partial least squares discriminant analysis. Regarding the prediction models, the CARS-PLSR model performed relatively better than the other models for predicting the MC, with R 2 P and RPD values of 0.84 and 2.51, respectively. Additionally, the CARS-SVM model obtained superior discrimination performance, with 100% accuracy for both calibration and prediction sets. The results demonstrated that optical fiber spectroscopy technology is an effective tool to fast detect the MC and freshness for loquats.

Combining transfer learning and hyperspectral imaging to identify bruises of pears across different bruise types

Mechanical bruise is one of the most crucial factors affecting the quality of pears, which has a huge influence on postharvest transportation, storage, and sale of pears. To rapidly detect early bruises of pears across different bruise types, hyperspectral imaging technology coupled with transfer learning methods was performed in this study. Two transfer learning methods, that is, transfer component analysis (TCA) and manifold embedded distribution alignment (MEDA), were applied for two tasks (impact bruise → crush bruise, crush bruise → impact bruise). Supporting vector machine (SVM) was set as a baseline to conduct analysis and comparison of the transferability of the models. The result showed that, for task 1 (impact bruise → crush bruise), MEDA and TCA-SVM model achieved a classification accuracy of 93.33% and 91.11% in target domain, individually. For task 2 (crush bruise →impact bruise), MEDA and TCA-SVM model achieved an accuracy of 88.89% and 85.19% in target domain, respectively. Both the two models improved the accuracy compared with SVM models (84.44% for task 1; 77.04% for task 2). Overall, the results indicated that transfer learning approaches could perform pear bruise detection across different bruise types. Hyperspectral imaging in combination with transfer learning methods is a promising possibility for the efficient and cost-saving field detection of fruit bruises among different bruise types. PRACTICAL APPLICATION: The production and export of pears are faced with problems of mechanical damage due to vibration, collision, impact, and other factors, which cause chemical changes in color, odor, and taste. Sometimes the bruise was too slight to be ignored which would infect with other fruits in the future. In this study, we used hyperspectral imaging combined with transfer learning method could detect these slight bruises caused by different factors. Distinguishing different types of damage can provide a reference for quick judgment of the process causing damage and take prompt measures to reduce economic losses.

The Fungal Diversity and Potential Pathogens Associated with Postharvest Fruit Rot of 'Huangguan' Pear (Pyrus bretschneideri) in Hebei Province, China

Postharvest fruit rot caused by pathogens is a serious problem in the pear industry. This study investigated the fungal diversity and main pathogens and identified a new pathogen in the stored 'Huangguan' pear (Pyrus bretschneideri Rehd.), the dominant pear variety in northern China. We sampled 20 refrigeration houses from five main producing regions in Hebei Province and used Illumina sequencing technology to detect the fungal composition. Alternaria (56.3%) was the most abundant fungus, followed by Penicillium (9.2%) and Monilinia (6.2%). We also isolated and identified nine strains of Alternaria and four strains of Penicillium. Moreover, we observed a new postharvest fruit disease in 'Huangguan' pear caused by Stemphylium eturmiunum, which was confirmed by phylogenetic analysis by combining the sequences of three conserved genes, including internal transcribed spacer, gapdh, and calmodulin. This study marks the first documentation of S. eturmiunum causing fruit rot in 'Huangguan' pears, offering valuable insights for identifying and controlling this newly identified postharvest disease.

Modelling of longitudinally cut carrot curling induced by the vascular cylinder-cortex interference pressure

Cut carrot pieces are popular convenience foods, which enable the use of misshapen or physiologically imperfect produce. Cut carrots curl due to residual stress, which limits their shelf life and causes unnecessary food waste. The aim of this study is to identify the geometrical and environmental factors which have the most influence on their longevity. An analytical equation was developed using compound cylinder solutions, and this was used to define carrot-specific finite element (FE) models. Over 100 longitudinally cut Lancashire Nantes carrot halves were characterized, each was modelled analytically and verified using FE models. This model was evaluated by comparing predicted curvatures to ones experimentally measured over a week. The average radius of curvature decreased from 1.61 to 1.1 m a week after. A 1.32× reduction in the elastic modulus after 7 days was observed. The moisture content reduction relates to 22% weight loss, correlating to the decreasing radius of curvature. Subsequently, carrots are recommended to be stored in humidity-controlled environments. The experimental results from this study match the predictions made using mechanical principles. The research provides a methodology to predict the deformation of cut root vegetables, and the procedure is likely to be applicable to other plant structures.

Mechanisms of the response of apple fruit to postharvest compression damage analyzed by integrated transcriptome and metabolome

Apple fruit is susceptible to compression damage within the postharvest supply chain given its thin peels and brittle texture, which can result in decay and deterioration and have a substantial impact on its marketability and competitiveness. Thorough bioinformatics investigations are lacking on postharvest compression damage stress-induced alterations in genes and metabolic regulatory networks in fruits. In the present study, a comprehensive analysis of both the transcriptome and metabolome was conducted on 'Red Fuji' apples experiencing compression-induced damage. During the storage after damage has occurred, the gene expression of MdOFUT19, MdWRKY48, MdCBP60E, MdCYP450 and MdSM-like of the damaged apples was consistently higher than that of the control group. The damaged apples also had higher contents of some metabolites such as procyanidin A1, Dl-2-Aminooctanoic acid, 5-O-p-Coumaroyl shikimic acid and 5,7-Dihydroxy-3',4',5'-trimethoxyflavone. Analysis of genes and metabolites with distinct expressions on the common annotation pathway suggested that the fruit may respond to compression stress by promoting volatile ester and lignin synthesis. The above results can deepen the comprehension of the response mechanisms in apple fruits undergoing compression-induced damage.

Effect on physiological properties of banana fruit based on pendulum impact test and storage

This study investigated the effect of mechanical damage of different impact energies (0.074 J, 0.160 J, 0.273 J) generated by a pendulum impactor and evaluated the bruise intensity and quality changes of bananas after 12 days of storage at 5, 13, and 22 °C. The bruise area (BA) and bruise susceptibility (BS) in the banana fruit were significantly increased as impact damage increased. The results of weight loss % and electrolyte leakage (EL%) were highly dependent on all studied factors; in the order of storage temperature > storage days > impact level. Storage temperature and impact damage accelerated the incidence of transpiration rate (TRm) during storage. Regarding color measurements, the results of image processing emphasized that storage at 13 °C exhibited the ideal color change in bruised fruit, which revealed a slow increment in lightness (L*) and yellowness (b*) until the last day of storage. The ethylene production rate (EPR) was recorded at its maximum peak on days 2 and 12 with values of 3.85 × 10-3 and 3.08 × 10-3 mg kg-1h-1 in banana fruit bruised by high-impact (0.273 J) and stored at 22 °C. Regardless of impact level, all size measurements reduction % including, surface area (As), geometric mean diameter (Dg), and fruit volume (Vf) of bruised bananas were higher at ambient storage conditions than those stored at 5 and 13 °C. The results of regression analysis can confirm the possibility of bruise susceptibility (BS) to estimate the quality changes of bruised fruit during storage.

Detection of early bruises on apples using hyperspectral reflectance imaging coupled with optimal wavelengths selection and improved watershed segmentation algorithm

BACKGROUND

Bruises caused by mechanical collision during the harvesting and storage and transportation period are difficult to detect using traditional machine vision technologies because there is no obvious difference in appearance between bruised and sound tissues. As a result of its fast and non-destructive characteristics, hyperspectral imaging technology is a potential tool for non-destructive detection of fruit surface defects.

RESULTS

In the present study, visible near infrared hyperspectral reflectance images of healthy apples and bruised apples at 6, 12 and 24 h were obtained. To reduce hyperspectral data dimension, optimal wavelength selection algorithms including principal component analysis (PCA) and band ratio methods were utilized to select the effective wavelengths and enhance the contrast between bruised and sound tissues. Then pseudo-color image transformation technology combining with improved watershed segmentation algorithm (IWSA) were employed to recognize the bruise spots. The result obtained showed that band ratio images obtained better detection performance than that of PCA. The G component derived from pseudo-color image ofλ 821 - λ 752 / λ 821 + λ 752 followed by IWSA obtained the best segmentation performance for bruise spots. Finally, a multispectral imaging system for the detection of bruised apple was developed to verify the effectiveness of the proposed two-band ratio algorithm, obtaining recognition rates of 93.3%, 92.2% and 92.5% for healthy, bruised and overall apples, respectively.

CONCLUSION

The bruise detection algorithm proposed in the present study has potential to detect bruised apple in online practical applications and hyperspectral reflectance imaging offers a useful reference for the detection of surficial defects of fruit. © 2023 Society of Chemical Industry.

Sodium alginate coating of Ginkgo biloba leaves extract containing phenylpropanoids as an ecofriendly preserving agent to maintain the quality of peach fruit

Plant constituents are of great interest in the food processing industry as potential natural preservative agents for controlling foodborne pathogens. In this study, the 95% EtOH/H2 O extract of Ginkgo biloba leaves was separated using polarity extraction solvents with petroleum ether (PE), ethyl acetate (EA), n-butanol (nB), and water (W) by the principle of similarity and compatibility. Through TLC and NMR analysis of these extracts, it can be concluded that the main component of PE extract were organic acids, for EA extract were flavonoids, for nB extract were phenylpropanoids, and water extract were oligosaccharides. Twelve monomer compounds were separated from the extracts to verify the composition of each extraction stage. Results of morphological and molecular identification revealed that Monilinia fructicola and Rhizopus stolonifer were the main fungi causing peach rot. After evaluating the antifungal activity and peach quality of the four extract/sodium alginate coatings, it was found that the n-butanol extract/sodium alginate coating containing phenylpropanoids had the lowest decay index and the best preservation effect, providing a sustainable alternative to reduce the harm to the environment of synthetic preservatives. PRACTICAL APPLICATION: The abuse of synthetic preservatives poses a threat to the ecological environment and physical health. Therefore, this study developed sodium alginate coating of Ginkgo biloba leaves extract containing phenylpropanoids, which has good effects on the preservation of peaches. The agent is a promising environmentally friendly alternative for synthetic preservatives.

Effect of Calcium Foliar Spray Technique on Mechanical Properties of Strawberries

The calcium fertilization of strawberry plants (Fragaria × ananassa Duchesne) was evaluated using two types of nozzles, with two liquid pressure levels and two driving speeds. The calcium content of the leaves and fruit were analyzed via flame photometry. Higher leaf calcium content was found in plots sprayed with standard nozzles, while higher fruit calcium content was observed for those sprayed with air induction nozzles. The fruit quality was assessed by determining the basic physical and mechanical properties, using uniaxial compression tests integrated with surface pressure measurements. Different spraying techniques influenced the mechanical resistance of the fruit. A spraying speed of 5 km/h and an operating pressure of 0.4 MPa significantly increased the firmness of the fruit by ~66%, the critical load level by 36%, and the maximum surface pressure by up to 38%, but did not increase the geometrical parameters of the strawberries. Regular foliar feeding during harvest could improve the mechanical strength of strawberries. An appropriate spraying technique with a calcium agent could effectively improve the mechanical properties of the delicate fruit, which is particularly important for limiting losses during harvesting, transportation, and storage.

Chitosan-based nanocomposites as coatings and packaging materials for the postharvest improvement of agricultural product: A review

The perishability nature of harvested fruits and vegetables, along with the effect of environmental factors, storage conditions, and transportation, reduce the products' quality and shelf-life. Considerable efforts have been allocated to alternate conventional coatings based on new edible biopolymers for packaging. Chitosan is an attractive alternative to synthetic plastic polymers due to its biodegradability, antimicrobial activity, and film-forming properties. However, its conservative properties can be improved by adding active compounds, limiting microbial agents' growth and biochemical and physical damages, and enhancing the stored products' quality, shelf-life, and consumer acceptability. Most of the research on chitosan-based coatings focuses on antimicrobial or antioxidant properties. Along with the advancement of polymer science and nanotechnology, novel chitosan blends with multiple functionalities are required and should be fabricated using numerous strategies, especially for application during storage. This review discusses recent developments in using chitosan as a matrix to fabricate bioactive edible coatings and their positive impacts on increasing the quality and shelf-life of fruits and vegetables.

Recent advance in quality preservation of non-thermal preservation technology of fresh mushroom: a review

Fresh mushrooms have a long history of cultivation and consumption, but high postharvest losses are a concern in the commercial production of mushrooms worldwide. Thermal dehydration is widely used in the preservation of commercial mushrooms, but the flavor and taste of mushrooms are significantly altered after dehydration. Non-thermal preservation technology, which effectively maintains the characteristics of mushrooms, is a viable alternative to thermal dehydration. The objective of this review was to critically assess the factors affecting fresh mushroom quality after preservation is remarkable, with the ultimate goal of developing and promoting non-thermal preservation technology for preserving fresh mushroom quality, effectively extending the shelf life of fresh mushrooms. The factors influencing the quality degradation process of fresh mushrooms discussed herein include the internal factors associated with the mushroom itself and the external factors associated with the storage environment. We present a comprehensive discussion of the effects of different non-thermal preservation technologies on the quality and shelf life of fresh mushrooms. To prevent quality loss and extend the shelf life after postharvest, hybrid methods, such as physical or chemical techniques combined with chemical techniques, and novel nonthermal technologies are highly recommended.

Development of a new bio-microscope for 3D geometry characterization of fruit single cells

Fruit cells are living irregular three-dimensional (3D) transparent objects which makes them challenging to determine their real 3D size and shape through only two-dimensional (2D) images using the existing biological microscope. This study deals with a newly self-developed biological microscope including a microscope imaging system, a light source system, a stage and a support base for the 3D size characterization of fruit single cells. The main design concept is based on two optical path systems set up at the front (x-axis) and bottom (z-axis) directions of a transparent chamber containing single cells that allow the front view and bottom view of the single cell to be observed. Performance indicators such as mass, size, observation range, objective magnification, total magnification, focal range, focal accuracy, and resolution of the developed biological microscope were estimated. Finally, the 3D geometry size of single tomato cells was measured by the new biological microscope to demonstrate the relative ease at which accurate real 3D geometry information of single fruit cells could be obtained, which echoes its scientific value.

Post-Harvest Problems of Strawberry and Their Solutions

Strawberry is a fruit with a short season of harvest. Strawberry is well-known among people all over the world for its distinct flavour, nutritional value, and delicacy. While on the other hand, preserving strawberry and shelf life extension has been a huge difficulty due to their perishable nature. Making effective and sustainable use of already available food processing and preservation technology needs time. Researchers must use advanced techniques like a cool store, modified atmospheric packaging (MAP), cool store, controlled atmospheric storage (CA), various packaging methods, and a variety of chemical and physical treatments to retain commodities for a longer period due to strategic market sales following harvest. Except for the preserving techniques, there is some polysaccharide-based edible coating which has a crucial role in delaying fruit softening, fruit decay, maintaining the increased levels of ascorbic acid and phenols, enhancing the activities of antioxidant enzymes, and reducing membrane damage. During the postharvest stages, there are numerous threats to keep in view regarding the safety and quality of strawberries. In this chapter, we will discuss the benefits and drawbacks of some of the various preservation technologies, as well as how they might be utilised to preserve and a prolonged period of freshly harvested strawberries.

Feature Reduction for the Classification of Bruise Damage to Apple Fruit Using a Contactless FT-NIR Spectroscopy with Machine Learning

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.

Surface Defect Detection of Cabbage Based on Curvature Features of 3D Point Cloud

The dents and cracks of cabbage caused by mechanical damage during transportation have a direct impact on both commercial value and storage time. In this study, a method for surface defect detection of cabbage is proposed based on the curvature feature of the 3D point cloud. First, the red-green-blue (RGB) images and depth images are collected using a RealSense-D455 depth camera for 3D point cloud reconstruction. Then, the region of interest (ROI) is extracted by statistical filtering and Euclidean clustering segmentation algorithm, and the 3D point cloud of cabbage is segmented from background noise. Then, the curvature features of the 3D point cloud are calculated using the estimated normal vector based on the least square plane fitting method. Finally, the curvature threshold is determined according to the curvature characteristic parameters, and the surface defect type and area can be detected. The flat-headed cabbage and round-headed cabbage are selected to test the surface damage of dents and cracks. The test results show that the average detection accuracy of this proposed method is 96.25%, in which, the average detection accuracy of dents is 93.3% and the average detection accuracy of cracks is 96.67%, suggesting high detection accuracy and good adaptability for various cabbages. This study provides important technical support for automatic and non-destructive detection of cabbage surface defects.

Intelligent detection of hard seeds of snap bean based on hyperspectral imaging

As a common problem in snap beans, hard seed has seriously affected the large-scale industrial planting and yield of snap bean. To realize accurate, quick and non-destructive identifying the hard seeds of snap bean is of great significance to avoiding the effects of hard seeds on germination and growth. This research was based on hyperspectral imaging (HSI) to achieve accurate detection of hard seeds of snap bean. This study obtained the characteristic spectra from the hyperspectral image of a single seed, and then combined the synthetic minority over-sampling technique (SMOTE) and Tomek links to balance the numbers of hard and non-hard seed samples. The characteristic wavelengths were extracted from the average spectrum. Then the average spectrum was processed by first derivative (1D). After that, the characteristic wavelengths could be extracted using successive projections algorithm (SPA). Finally, a radial basis function-support vector machine (RBF-SVM) model was established to realize the intelligent detection of hard seeds, and the detection accuracy rate reached 89.32%. The research results showed that HSI technology could achieved accurate, fast and non-destructive testing of the hard seeds of snap bean, which is of great significance to the large-scale and standardized planting of snap bean and increase the yield per unit area.

Research on Damage Properties of Apples Based on Static Compression Combined with the Finite Element Method

Apples are easily damaged during transportation due to extrusion and collision, resulting in structural damage and deterioration. To better understand apples’ mechanical–structural damage behavior, a texture analyzer platform combined with in situ observation was established. The effects of extrusion distance, speed, working temperature, and typical kinds of apple were considered for damage mechanisms. Apple damage was analyzed via the finite element method (FEM). The results indicated that the apple extrusion behavior can be divided into elastic interaction and plastic damage. Compression displacement effects were obviously significant in terms of structural damage, and apple samples were in an elastic stage with displacement of less than 2.3 mm, and no structural damage. The peak force energy-displacement mathematical model was established, showing an “s” shape and upward parabolic shape. The critical compression energy was around 100N·mm during elastic interaction. The damaged area was positively correlated with the compression energy. The FEM simulation results were consistent with the damage distribution of apples. The effects of speed on the three apple types were different. Red Fuji apples with a bruised area were not sensitive to pressure speed. The effect on the crack forming of Ralls apples was significant. Golden Delicious apples with a bruised area and crack formation showed an intermediate effect. The peak force–temperature fitting curve showed a downward parabolic shape and an R² determination factor of 0.99982. Apple squeeze damage mechanisms provide theoretical guidance for apple damage control.

Mechanical damages and packaging methods along the fresh fruit supply chain: A review

Mechanical damage of fresh fruit occurs throughout the postharvest supply chain leading to poor consumer acceptance and marketability. In this review, the mechanisms of damage development are discussed first. Mathematical modeling provides advanced ways to describe and predict the deformation of fruit with arbitrary geometry, which is important to understand their mechanical responses to external forces. Also, the effects of damage at the cellular and molecular levels are discussed as this provides insight into fruit physiological responses to damage. Next, direct measurement methods for damage including manual evaluation, optical detection, magnetic resonance imaging, and X-ray computed tomography are examined, as well as indirect methods based on physiochemical indexes. Also, methods to measure fruit susceptibility to mechanical damage based on the bruise threshold and the amount of damage per unit of impact energy are reviewed. Further, commonly used external and interior packaging and their applications in reducing damage are summarized, and a recent biomimetic approach for designing novel lightweight packaging inspired by the fruit pericarp. Finally, future research directions are provided.HIGHLIGHTSMathematical modeling has been increasingly used to calculate damage to fruit.Cell and molecular mechanisms response to fruit damage is an under-explored area.Susceptibility measurement of different mechanical forces has received attention.Customized design of reusable and biodegradable packaging is a hot topic of research.

Postharvest vibration-induced apple quality deterioration is associated with the energy dissipation system

Transit vibration is a potential risk that may cause fruit deterioration. Regulating energy metabolism is recognized for attenuating fruit abiotic/abiotic stresses. To explore the role of energy metabolism in the response of fruit to vibration stress, this research investigated the effects of exogenous treatment with adenosine triphosphate (ATP) and 2,4-dinitrophenol (DNP) on fruit after simulated vibration stress. The results demonstrated that DNP treatment induced significant energy depletion, which exacerbated the adverse physiological responses induced by vibration stress. In contrast, ATP regulated higher fruit energy levels and significantly alleviated fruit quality deterioration. This is achieved by supplying direct energy substances, maintaining higher energy charges, inhibiting ethylene biosynthesis, elevating the antioxidant system, and suppressing cell oxidative damage. The results demonstrated the positive role of fruit energy metabolism response to vibration stress. Ensuring sufficient energy level may be a promising strategy for controlling vibration-induced adverse physiological responses and a potential method to maintain fruit quality.

Non-Destructive Detection of Damaged Strawberries after Impact Based on Analyzing Volatile Organic Compounds

Strawberries are susceptible to mechanical damage. The detection of damaged strawberries by their volatile organic compounds (VOCs) can avoid the deficiencies of manual observation and spectral imaging technologies that cannot detect packaged fruits. In the present study, the detection of strawberries with impact damage is investigated using electronic nose (e-nose) technology. The results show that the e-nose technology can be used to detect strawberries that have suffered impact damage. The best model for detecting the extent of impact damage had a residual predictive deviation (RPD) value of 2.730, and the correct rate of the best model for identifying the damaged strawberries was 97.5%. However, the accuracy of the prediction of the occurrence time of impact was poor, and the RPD value of the best model was only 1.969. In addition, the gas chromatography-mass spectrophotometry analysis further shows that the VOCs of the strawberries changed after suffering impact damage, which was the reason why the e-nose technology could detect the damaged fruit. The above results show that the mechanical force of impact caused changes in the VOCs of strawberries and that it is possible to detect strawberries that have suffered impact damage using e-nose technology.

Simple Method for Apples' Bruise Area Prediction

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).

Design, development and performance evaluation of transportation container for sapota fruit

A transportation container was developed to minimize transportation losses of sapota fruit. The container was made of corrugated polypropylene (PP) sheet. The container is completely foldable, reusable and enclosed condition for protecting produce from adverse climate. The adjustable cells were made to enhance the safety of fruits. Separation sheets were provided in a container to support the fruits. Perforation was provided for proper respiration of the fruits. Velcro feature was provided to erect and fold the container. Freshly harvested and uniformly matured and graded sapota fruits were transported in seven types of containers or bags with 10 kg capacity stacked in six layers viz.; gunny bag, gunny bag lined with bubble sheet, perforated PP bag, foldable plastic container, egg tray in corrugated fiberboard box (CFB) carton, plastic crate and CFB carton. The fruits were transported from Junagadh to Jamnagar and returned from Jamnagar to Junagadh by road approximately 350 km in goods rickshaw. Effect of different containers on quality parameters of sapota fruits viz., hardness (30.09 kg/cm²), firmness (12.63 kgf) and rupture force (100.2 kgf) were found maximum and weight loss (1.01%), deformation (10.9 mm) and total soluble solids (16.40°Brix) were found minimum in the fruits transported in foldable plastic container. Bruising, cracking and impact damage were not observed on the fruits transported in foldable plastic container. Maximum marketable fruits (98.37%) were observed in foldable plastic container. Transportation losses of sapota fruits in foldable plastic container were minimized 8.65% and 2.85% as compared to gunny bag and plastic crate, respectively.

Classification Learning of Latent Bruise Damage to Apples Using Shortwave Infrared Hyperspectral Imaging

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.

Soft Grippers for Automatic Crop Harvesting: A Review

Agriculture 4.0 is transforming farming livelihoods thanks to the development and adoption of technologies such as artificial intelligence, the Internet of Things and robotics, traditionally used in other productive sectors. Soft robotics and soft grippers in particular are promising approaches to lead to new solutions in this field due to the need to meet hygiene and manipulation requirements in unstructured environments and in operation with delicate products. This review aims to provide an in-depth look at soft end-effectors for agricultural applications, with a special emphasis on robotic harvesting. To that end, the current state of automatic picking tasks for several crops is analysed, identifying which of them lack automatic solutions, and which methods are commonly used based on the botanical characteristics of the fruits. The latest advances in the design and implementation of soft grippers are also presented and discussed, studying the properties of their materials, their manufacturing processes, the gripping technologies and the proposed control methods. Finally, the challenges that have to be overcome to boost its definitive implementation in the real world are highlighted. Therefore, this review intends to serve as a guide for those researchers working in the field of soft robotics for Agriculture 4.0, and more specifically, in the design of soft grippers for fruit harvesting robots.

Immobilization of Crude Polyphenol Oxidase Extracts from Apples on Polypyrrole as a Membrane for Phenol Removal

This research aims to make a polypyrrole (PPy) membrane and crude extract of polyphenol oxidase (PPO) as a membrane of mPPy/PPO apple extracts. The membrane of PPy/PPO-apple extract has been synthesized by the electrodeposition method. The electrolyte composition consists of a mixture of 0.10-0.20 M pyrrole (Py) and 50-100% PPO apple extract, which is stable using 50 mM of phosphate buffer solution at pH 6.80-7.00 and room temperature. The electrodeposition process is used 400 mesh steel gauze anode ST-304 and carbon plate cathode. Electrodeposition is carried out at potential = 5.00-6.00 V; current = 0.02-0.25 A; the distance from both electrodes = 1.00-2.00 cm for 300-500 seconds. The results from the deposition of PPy/PPO apple extract of the anode are a membrane of mPPy/PPO-apple extract, with total enzyme activity (U) = (957,1441, 2287 and 1754) using 2.00-5.00 mM phenol as a substrate which is measured based on the UV-visible spectrophotometric method. PPy and mPPy/PPO-apple extracts were characterized by SEM and SEM-EDS. The membrane of mPPy/PPO-apple extract can be used to remove phenol in industrial wastewater samples is 50-65% with a filtration capacity of 500 mL for 2 hours.

Effect of peroxyacetic acid treatment and bruising on the bacterial community and shelf-life of baby spinach

The importance of leaf integrity, i.e. the effects of bruising (mechanical damage), and sanitisation with peroxyacetic acid (PAA) on bacterial communities of ready-to-eat baby spinach remains unclear. Two shelf-life studies were conducted at 4 °C to investigate the effect of bruising and sanitisation on the growth of spoilage microorganisms. In the first experiment, both bruising treatments (100% and 40% of leaves) halved shelf life to 12 d, whereas intact leaves had a shelf-life of 23 d. Bruising had no influence on bacterial diversity during shelf-life, though some differences in the relative abundance of minor genera were observed. Pseudomonas and Pantoea were the most dominant bacterial genera, regardless of bruising treatment. High throughput amplicon sequencing also identified other spoilage bacteria including Chryseobacterium, Stenotrophomonas, Bacillus, Sphingobacterium, Erwinia and Flavobacterium. In the second experiment, washing of intact baby spinach with a sanitiser (80 mg/L: PAA) reduced microbial load as determined by aerobic plate count but did not immediately affect the presence/relative abundance of most of the genera of spoilage bacteria observed. During shelf-life, the bacterial diversity of sanitised leaves was significantly lower than on water-washed leaves. Although sanitisation resulted in a higher initial log reduction in microbial load compared to control (portable tap water), sanitisation did not extend the shelf life of baby spinach (23 d). Sanitised spinach had reduced bacterial diversity however, by the end of shelf life, both sanitised and water-washed spinach was dominated by Pseudomonas and Pantoea spoilage bacteria. This study demonstrated for the first time that the shorter shelf life of bruised leaves was related to faster microbial growth rather than changes in bacterial diversity or community composition.

Preparation of modified polyvinyl formal vibration-damping material and its application in strawberry

Mechanical damage and microbial infestation were critical issues during strawberry transportation. This study prepared a modified polyvinyl formal (PVFM) vibration-damping material and investigated its application in strawberry transportation. This modified PVFM was primarily composed of polyvinyl alcohol, starch, formaldehyde, and allyl isothiocyanate. Our results showed that the modified PVFM had good vibration-damping and antifungal properties with 0.137 g/m³ of density, a water absorption ratio value of 6.24, 278 Pa of tensile strength, 3.86 Pa of elastic modulus, a buffer coefficient of 0.318, and antifungal rates of 68.8% and 80.6% against Bacillus anthracis and Botrytis cinerea, respectively. Moreover, this modified PVFM could enhance the storage life of strawberry by improving PAL activity from 376 to 3,125 U/g, inhibiting POD activity with a maximum value of 3.1 U/g, and decreasing CAT activity from 324 to 35 U/g. The modified PVFM presented a good vibration-damping capacity and the potential to extend strawberry shelf life. PRACTICAL APPLICATIONS: Berry fruits were vulnerable to mechanical damage caused by vibration during transportation, resulting in quality deterioration, rot, and aging. In this study, modified polyvinyl formal (PVFM) was prepared and was of low density and displayed good mechanical, vibration-damping, and antifungal properties. The modified PVFM could improve the storage life of strawberry. Thus, the modified PVFM could have potential application in berry fruits transportation.

Multimodal Deep Learning and Visible-Light and Hyperspectral Imaging for Fruit Maturity Estimation

Fruit maturity is a critical factor in the supply chain, consumer preference, and agriculture industry. Most classification methods on fruit maturity identify only two classes: ripe and unripe, but this paper estimates six maturity stages of papaya fruit. Deep learning architectures have gained respect and brought breakthroughs in unimodal processing. This paper suggests a novel non-destructive and multimodal classification using deep convolutional neural networks that estimate fruit maturity by feature concatenation of data acquired from two imaging modes: visible-light and hyperspectral imaging systems. Morphological changes in the sample fruits can be easily measured with RGB images, while spectral signatures that provide high sensitivity and high correlation with the internal properties of fruits can be extracted from hyperspectral images with wavelength range in between 400 nm and 900 nm—factors that must be considered when building a model. This study further modified the architectures: AlexNet, VGG16, VGG19, ResNet50, ResNeXt50, MobileNet, and MobileNetV2 to utilize multimodal data cubes composed of RGB and hyperspectral data for sensitivity analyses. These multimodal variants can achieve up to 0.90 F1 scores and 1.45% top-2 error rate for the classification of six stages. Overall, taking advantage of multimodal input coupled with powerful deep convolutional neural network models can classify fruit maturity even at refined levels of six stages. This indicates that multimodal deep learning architectures and multimodal imaging have great potential for real-time in-field fruit maturity estimation that can help estimate optimal harvest time and other in-field industrial applications.

Application of electronic nose and GC–MS for detection of strawberries with vibrational damage

Objectives

This study evaluated the potential of using electronic nose (e-nose) technology to non-destructively detect strawberry fruits with vibrational damage based on their volatile substances (VOCs).

Materials and methods

Four groups of strawberries with different durations of vibrations (0, 0.5, 1, and 2 h) were prepared, and their e-nose signals were collected at 0, 1, 2, and 3 days after vibration treatment.

Results

The results showed that when the samples from all four sampling days during storage were used for modelling, both the levels of vibrational damage and the day after the damage happened were accurately predicted. The best models had residual prediction deviation values of 2.984 and 5.478. The discrimination models for damaged strawberries also obtained good classification results, with an average correct answer rate of calibration and prediction of 99.24%. When the samples from each sampling day or vibration time were used for modelling, better results were obtained, but these models were not suitable for an actual situation. The gas chromatography–mass spectrophotometry results showed that the VOCs of the strawberries varied after experiencing vibrations, which was the basis for e-nose detection.

Limitations

The changes in VOCs released by other forces should be studied in the future.

Conclusions

The above results showed the potential use of e-nose technology to detect strawberries that have suffered vibrational damage.

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery

Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R² = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea.