Internal Bruising Research Articles

Internal bruise damage in machine-harvested blueberries

Internal bruise damage in machine-harvested blueberries

Determining the instantaneous bruising pattern in a sample potato tuber subjected to pendulum bob impact through finite element analysis

AbstractPotato bruising resulting from mechanical impact during production operations including harvest and postharvest is a significant concern within the potato production sector, leading to consumer complaints and economic losses. The detection of instantaneous internal bruising poses a particular challenge as it can progress over time during storage or transportation, making it difficult to identify immediately after external impact. This study aims to investigate the progression of bruising and accurately represent the instantaneous dynamic deformation behavior of potato tubers under four pendulum bob impact cases (pendulum arm angles of 30°, 45°, 60°, and 90°). To analyze the dynamic impact deformation characteristics of the tubers, solid modeling based on a reverse engineering approach and explicit dynamic engineering simulations were employed. The simulation results yielded valuable numerical data and visual representation of the deformation progression. The loading conditions considered in this study indicated that the maximum stress values, reaching 0.818 MPa at a pendulum arm angle of 90°, remained below the bio‐yield stress point of the tuber flesh (1.05 MPa) determined through experimental compression tests. Therefore, it was concluded that the impact did not cause permanent deformation (i.e., permanent bruising) in the tuber. However, the numerical analysis clearly demonstrated the sequence of stress occurrences, which is a key contributing factor to potential permanent bruising. In this regard, the bruising energy threshold of 318.314 mJ (R2: 0.96) was extrapolated. The numerical findings presented in this study can aid in evaluating the susceptibility of tuber samples to bruising. By employing nonlinear explicit dynamics simulations, this research contributes to the advancement of understanding complex deformation and bruising in solid agricultural products. Moreover, the application of these techniques holds significant industrial implications for enhancing the handling and transportation of agricultural produce.Practical applicationsThis research aims to tackle the challenge of accurately representing the immediate internal bruising pattern in potato tubers resulting from mechanical impact. Conventional methods, such as physical or analytical expressions, may not fully capture the distribution of bruising experienced by the tubers. To overcome this limitation, an engineering simulation approach is proposed to provide a more precise depiction of the instantaneous bruising pattern. By advancing the understanding of complex deformation and bruising in solid agricultural products, this research contributes to improving the efficiency and quality of agricultural production in the industry. Additionally, this study offers a step‐by‐step guide on how to conduct these simulations effectively.

Morphometry, gross morphology and available histopathology in North Atlantic right whale (Eubalaena glacialis) mortalities (1970-2002)

Fifty-four right whale mortalities have been reported from between Florida, USA and the Canadian Maritimes from 1970 to 2002. Thirty of those animals were examined: 18 adults and juveniles, and 12 calves. Morphometric data are presented such that prediction of body weight is possible if the age, or one or more measurements are known. Calves grew approximately linearly in their first year. Total length and fluke width increased asymptotically to a plateau with age, weight increased linearly with age, weight and snout to blowhole distance increased exponentially with total length, whereas total length was linearly related to fluke width and flipper length. Among the adults and juveniles examined in this study, human interaction appeared to be a major cause of mortality, where in 14/18 necropsies, trauma was a significant finding. In 10/14 of these, the cause of the trauma was presumed to be vessel collision. Entanglement in fishing gear accounted for the remaining four cases. Trauma was also present in 4/12 calves. In the majority of calf mortalities (8/12) the cause of death was not determined. Sharp ship trauma included propeller lacerations inducing multiple, deep lacerations that often incised vital organs including the brain, spinal cord, major airways, vessels and musculature. Blunt ship trauma resulted in major internal bruising and fractures often without any obvious external damage. In at least two cases fatal gear entanglements were extremely protracted: where the entanglements took at least 100 and 163 days respectively to be finally lethal. The sum of these findings show two major needs: (1) that extinction avoidance management strategies focused on reducing trauma to right whales from ship collisions and fishing gear entanglement are highly appropriate and need to be continued and; (2) that as mitigation measures continue to be introduced into shipping and fishing industry practices, there is a strong effort to maximise the diagnostic quality of post-mortem examination of right whale mortalities, to ensure an optimal understanding of resultant trends.

A deep learning-based web application to quantify blueberry internal bruising

A deep learning-based web application to quantify blueberry internal bruising

A deep learning-based web application for segmentation and quantification of blueberry internal bruising

Blueberries have become an important fruit crop around the world. Most blueberries are hand-harvested to maintain high quality before the packing process for fresh market distribution. However, in the last 10 years increasing acreage has been machine harvested to reduce labor costs although machine harvesting causes bruising in more than 20% of blueberries. Non-bruised blueberries remain firmer and can be cold stored longer than bruised blueberries, while the bruised fruit cannot be sold, leading to a substantial economic loss. Current packing line sorting technology can sort soft berries but is unable to detect and sort out bruised fruit. Blueberry bruise assessment is necessary for providing the means to improve the harvester efficiency and fruit sorting process in the packing house. The goal of this study was to develop a web browser-based application (Web App) that users can access easily and determine the blueberry bruises accurately and quickly. We annotated 1725 blueberries to train MobileNet SSD and MobileNet-UNet, two deep learning models, generating a berry detection model, a berry segmentation model, and a bruise segmentation model. The average precision (AP) for the berry detection model was 0.977. The mean intersection over union (IoU) for berry segmentation and bruise segmentation was 0.979 and 0.773, respectively. Bruises were assessed for 56 images with 50 sliced blueberries in each image using the trained models that implemented in the Web App we developed. The bruise ratio data obtained from three different hardware devices were compared with the results that were manually annotated. Linear regression analyses showed a high correlation between the results from the deep learning models and the ground truth. The bruise ratio prediction using three hardware devices achieved an accuracy of 78.7%, 79.0%, and 78.9%, respectively, indicating that the model performance was satisfactory regardless of the hardware configuration. The average processing time for each image under three hardware configurations revealed that our Web App was superior to the manual method. The Web App reduced the time needed for assessing and tabulating bruise damage for 50 fruit samples from about 15 min needed for manual visual method to less than 30 s with comparable accuracy. This Web App is a robust tool for blueberry breeders, farmers and packers for evaluating berry bruises.

Pruning Method and Trellising Impact Hand- and Machine-harvested Yield and Costs of Production in ‘Legacy’ Highbush Blueberry

A 6-year trial was established in Oct. 2015 in western Oregon to evaluate the effects of pruning and trellising on yield, hand- and machine-harvest efficiency, fruit quality, and costs of pruning and harvest of ‘Legacy’ highbush blueberry (complex hybrid based largely on Vaccinium corymbosum L. and Vaccinium darrowii Camp.). Pruning treatments began in Winter 2017–18 (before year 3) and continued each year through 2020–21 (year 6). Treatments included 1) recommended pruning for ‘Legacy’, removing less wood and leaving more short, thin laterals and a denser bush than is typical for most northern highbush cultivars (“control” with standard T-trellis), 2) control pruning and training to a V-trellis (“V”), and 3) standard northern highbush style pruning (“HB” with standard T-trellis). Fruit were harvested solely by hand in 2017 and 2018, and by hand for early harvests followed by machine for later harvests from 2019 to 2021. In most years, more wood was removed from HB- than control-pruned plants. On average, HB-pruned plants had a lower yield (6.7 kg/plant) than control-pruned plants, particularly those trained to a V-trellis (7.5 kg/plant). There was little effect of pruning treatment on fruiting season and hand- (7% drop) or machine-harvest efficiency (23% drop). Pruning method had no effect on berry weight, diameter, total soluble solids, or firmness over the study period or percent internal bruising in 2019. All of the ‘Legacy’ pruning methods studied required more time (358 to 561 h·ha−1) than the industry standard, ‘Duke’ (247 h·ha−1). Control and HB pruning did not differ in time to prune per unit area; however, in 2 of the 4 years, adding a V-trellis increased pruning time. On average, control and HB pruning had a similar cost per harvested fruit ($0.20 to $0.21/kg), whereas control pruning with a V-trellis ($0.23/kg) cost more than HB pruning. All treatments required the same amount of time to harvest (12.7 and 0.5 min·kg−1 for hand and machine picking, respectively). Total cost to prune and harvest ranged from $1.63/kg in 2019 to $3.43/kg in 2021 but was most heavily influenced by harvest costs rather than pruning. The one-time installation cost of $637/ha for the V-trellis was not compensated for by increased yield or efficiency of pruning or harvest compared with the control method with a standard T-trellis. Pruning according to recommended methods for ‘Legacy’ (control) increased yield without having a negative effect on fruit quality and had similar or lower costs to prune per kg of fruit harvested as typical northern highbush pruning.

Soluble Solids Content prediction for Korla fragrant pears using hyperspectral imaging and GsMIA

As an effective non-destructive detection technology, hyperspectral imaging (HSI) is widely applied in evaluating the quality of fruit, such as soluble solids content (SSC) in apples, oranges, pears, and sugar content in grape berries, and internal bruising in blueberries. But due to the redundancy in hyperspectral data, the prediction performance significantly relies on the characteristic wavelength selection. Most previously published studies rarely simultaneously consider the correlations among different spectral bands and the extraction of characteristic bands from the original spectrum. To solve the problem, this study explores the application of hyperspectral technology to determine soluble solids content (SSC) in Korla pears. It focuses on reducing the hyperspectral data by applying a new effective wavelength selection method called Group sampling Margin Influence Analysis (GsMIA). It combines band-correlation and band-influence to enhance the selection of key bands. GsMIA contains three steps: grouping, margin influence analysis, and second selection. Combined with S-G smooth first derivative preprocessing method and support vector regression models, the proposed method can effectively yield good prediction performance with only 7.81 % of the original wavelengths, and the experimental results of comparison with several state-of-the-art methods on Korla fragrant pears SSC datasets further demonstrate the effectiveness and superiority of the proposed method.

LiBAlF6:Cr3+ (B = Ca, Sr) fluoride phosphors with ultra-broad near-infrared emission for NIR pc-LEDs

Compared with oxide-based NIR phosphors, fluoride phosphors have received great attention recently due to their good thermal resistance and high photoelectric efficiency. In this work, novel LiBAlF 6 :Cr 3+ (B = Ca, Sr) with low phonon energy and relatively weak electron-phonon coupling were synthesized through a facile hydrothermal approach. Benefiting from low Dq/B, an ultra-broad NIR emission extending from 650 nm to 1100 nm can be achieved in these phosphors under excitation of blue light. Among them, LiSrAlF 6 :Cr 3+ presents a FWHM of 155 nm. At 423 K, LiCaAl 0.4 F 6 : 0.6Cr 3+ and LiSrAl 0.4 F 6 : 0.6Cr 3+ maintain 66.63% and 55.47% of their initial intensities, respectively. Better thermal stability of LiCaAlF 6 : Cr 3+ could be attributed to the broad band gap and weak electron-phonon coupling effect. Photoelectric conversion efficiencies of 5.002% and 5.468% at 300 mA could be obtained for NIR pc-LEDs packaged with LiCaAl 0.4 F 6 : 0.6Cr 3+ and LiSrAl 0.4 F 6 : 0.6Cr 3+ , respectively. A slightly internal bruise inside apple and water level behind trademark in bottle could be clearly detected, revealing that LiBAlF 6 :Cr 3+ (B = Ca, Sr) phosphors have great application potential in infrared inspection.

Classification of Lingwu long jujube internal bruise over time based on visible near-infrared hyperspectral imaging combined with partial least squares-discriminant analysis

Early detection of internal bruise is one of the major challenges in postharvest quality sorting processes in Lingwu long jujube. In this study, the visible/near infrared (VIS/NIR) hyperspectral imaging system (400–1000 nm) was used to rapidly detect the intact and damaged jujube at five time points after mechanical damage (2 h, 4 h, 8 h, 12 h and 24 h). The region of interest of samples was selected by ENVI software, and the average spectrum was calculated for modelling. Different preprocessing methods were used to transform and enhance the spectral signal. Partial least squares-discriminant analysis (PLS-DA) classification models of the original and preprocessed spectra were established. The de-trending-PLS-DA model had the best effect, the accuracy of the calibration set and prediction set were 85.56% and 92.22%, respectively. The spectra were pre-processed by the de-trending algorithm, and the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), interval variable iterative space shrinkage approach (iVISSA), cluster analysis (CA), two-dimensional correlation spectra (2D-COS), UVE-SPA, CARS-SPA, iVISSA-SPA, CA-SPA and 2D-COS-SPA method were used to select characteristic variables. The PLS-DA model based on feature variables was established. The de-trending-CARS-PLS-DA model with 63 variables was found to be the optimal model. In the de-trending-CARS-PLS-DA model, the accuracy of calibration set and prediction set were 86.67% and 91.11%, respectively. It was found that the model accurately detected bruising in jujube at 8 h after bruising, and the accuracy of calibration set and prediction set were 100%. The results showed that VIS/NIR hyperspectral imaging technology combined with PLS-DA could discriminate different stages of bruising in Lingwu long jujube. This study may help develop an online detection system of bruising in Lingwu long jujube.

Effect of pressing and impacting bruises on optical properties of kiwifruit flesh

• Effect of mechanical stress on μ a and μ s ' of kiwifruit flesh were investigated. • μ a increased first then decreased, while μ s ' decreased first then increased after bruising. • μ a of bruised kiwifruit flesh was higher than that of non-bruised flesh. • μ s ' of bruised flesh was significantly smaller ( P ≤ 0.05) than that of non-bruised ones. • Scattering technique has great potential in identifying bruised from non-bruised kiwifruit. Near-infrared spectroscopy and hyperspectral imaging technologies have been proved to be able to detect invisible bruised kiwifruit. Therefore, it is doubted that the optical properties of kiwifruit are affected by bruises. However, it is not clear how bruises influence the optical properties of kiwifruit and whether different bruises have same effect on optical properties. To answer these questions, the kiwifruit with pressing and impacting bruises were used as samples, and the non-bruised kiwifruit were used as control to investigate the absorption coefficient ( μ a ) and reduced scattering coefficient ( μ s ' ) of kiwifruit flesh by using an integrating sphere system in 950−1650 nm in 16 d after bruising. Moreover, the soluble solids content (SSC), moisture content, firmness and color were measured, and the microstructure of flesh tissue was observed. The results showed that at the absorption peaks of 980 nm and 1175 nm, the μ a of used samples increased first, then decreased with time after bruising. At the same time, the bruised samples had higher μ a than non-bruised ones, but there was no significant difference ( P ≤ 0.05). After bruising, the μ s ' decreased first, then increased with time at a given wavelength between 950 nm and 1360 nm. The μ s ' of bruised samples was significantly smaller ( P ≤ 0.05) than that of non-bruised samples at the same time. Bruises caused cell rupture and reduced firmness. The results indicate that scattering technique has great potential in identifying bruised kiwifruit from non-bruised ones. This study is helpful to understand the effect of bruises on optical properties of kiwifruit and to better apply near-infrared spectroscopy and hyperspectral imaging technologies to detect the kiwifruit with internal bruises.

Fully convolutional networks for blueberry bruising and calyx segmentation using hyperspectral transmittance imaging

Early detection of internal bruises in blueberries is a significant challenge for the blueberry industry. The main goal of this study was to detect blueberries’ internal bruising accurately, after mechanical damage from hyperspectral transmittance images (HSTIs), using the deep learning-based method of fully convolutional networks (FCNs) for segmentation tasks. To improve detection accuracy, a total of three classes (bruised tissue, unbruised tissue, and calyx end of blueberries), were treated as segmentation targets. A near-infrared hyperspectral imaging system was used to acquire transmittance images of 1200 blueberries, and the images were divided randomly to form training, validation and testing sets. Three categories of input HSTIs were used to evaluate the FCN models using pre-trained weights (transfer learning) and random initialisation. Random forests and linear discriminant analysis were applied to generate 9-channel and 3-channel input images along with full-wavelength multi-channel images. The results indicate that when using the deep learning approach, blueberry bruises and calyx ends can be segmented from the blueberry fruit as early as 30 min after mechanical damage has been inflicted on the blueberries. The new full-wavelength model with random initialisation had the highest accuracy (81.2% over the entire test set), and can be used to research the resistance of blueberry fruit to mechanical damage. The new 3-channel and 9-channel models show potential for application to packing-line detection and online inspection.

Effects of different loading forces and storage periods on the percentage of bruising and its relation with the qualitative properties of pear fruit.

Nowadays, due to the necessity of increasing quality awareness in the food sector and its health, the non-destructive computed tomography (CT) method, which is one of the most widely used methods because of the ability to detect internal bruise in a non-destructive way, attracted so much attention. By using the non-destructive CT method a total of 81 healthy pears was selected and then subjected to quasi-static and dynamical loading. The experiment was performed on wide edge quasi-static pressure of 70, 100, 130 N and thin edge of 15, 20, 25 N and dynamic load of 300, 350, 400 g and storage period for 5, 10 and 15 days, to investigate the different effects of loading forces and storage periods on the percentage of the bruise and its relation with the qualitative properties such as phenol, antioxidant and vitamin C contents and firmness. The results of the experiments showed that the highest and lowest percentages of the bruise were related to a load of 400 N of 15 days and a 15 N 5-day thin line with values of 47.36 and 0.007, respectively. The highest and lowest physiological values were 15 N load of the 5-day thin edge and the 400 N of 15-day impact. Finally, the highest antioxidant content was 51.5% for 300 g dynamic loading force and 5- day storage, 28.86 mg/100g phenol for loading force of 70 N wide edge and 5 day storage and 7.4 mg/100ml vitamin C for loading force of 70 N wide edge and 5 day storage. Finally, according to the obtained results, there was an inverse relationship between the amount of bruising and chemical properties of pear.

Optical properties of blueberry flesh and skin and Monte Carlo multi-layered simulation of light interaction with fruit tissues

One of the major issues of fresh blueberry production is the excessive bruising caused by mechanical impact during harvesting and packaging, which substantially lowers fruit quality and therefore compromises consumer satisfaction as well as the profitability for growers. Spectroscopy and imaging techniques such as hyperspectral imaging have great potential to detect and quantify internal bruises in fruit. It is important to measure the fundamental optical properties of blueberry tissues to better employ spectroscopy or imaging techniques. The aim of this study was to obtain the absorption coefficient (μa), reduced scattering coefficient (μs′), and scattering anisotropy (g) of blueberry flesh and skin in the spectral regions of 500–800 nm and 930–1400 nm and investigate the light propagation model of blueberries using Monte Carlo multi-layered (MCML) simulation. The total reflectance, total transmittance, and collimated transmittance of blueberry flesh and skin with three treatments (non-bruised, 30-min bruised, and 24-h bruised) were collected using a single integrating sphere-based spectroscopic system. Using the collected spectra, the inverse adding-doubling (IAD) method was applied to calculate μa, μs′, and g. Results indicated that the differences between bruised (30 min and 24 h) and non-bruised flesh samples for both μs′ and g were significant from 930 nm to 1400 nm. Microscope images revealed that the differences were caused by the damaged and ruptured cellular structure of bruised flesh. Although μa, μs′, and g showed significant differences between non-bruised and bruised (30 min and 24 h) flesh in the spectral region of 400–700 nm, the MCML simulation results showed that this spectral region is not effective in detecting bruises due to strong absorption and backward scattering of the blueberry skin. In contrast, the absorption effect of the skin in the near infrared range (930–1400 nm) was small, allowing light to penetrate and interact with the flesh. Therefore, the near infrared spectral region is an effective spectral range for inspecting bruised blueberries using either reflectance or transmittance method. This study reported the optical properties of blueberry skin and flesh with varying degree of bruising for the first time and simulated photon interaction with fruit tissues for bruising detection using MCML. These findings would provide guidance to develop non-destructive sensing methods for blueberry internal bruising detection.

Data Fusion of Two Hyperspectral Imaging Systems with Complementary Spectral Sensing Ranges for Blueberry Bruising Detection.

Currently, the detection of blueberry internal bruising focuses mostly on single hyperspectral imaging (HSI) systems. Attempts to fuse different HSI systems with complementary spectral ranges are still lacking. A push broom based HSI system and a liquid crystal tunable filter (LCTF) based HSI system with different sensing ranges and detectors were investigated to jointly detect blueberry internal bruising in the lab. The mean reflectance spectrum of each berry sample was extracted from the data obtained by two HSI systems respectively. The spectral data from the two spectroscopic techniques were analyzed separately using feature selection method, partial least squares-discriminant analysis (PLS-DA), and support vector machine (SVM), and then fused with three data fusion strategies at the data level, feature level, and decision level. The three data fusion strategies achieved better classification results than using each HSI system alone. The decision level fusion integrating classification results from the two instruments with selected relevant features achieved more promising results, suggesting that the two HSI systems with complementary spectral ranges, combined with feature selection and data fusion strategies, could be used synergistically to improve blueberry internal bruising detection. This study was the first step in demonstrating the feasibility of the fusion of two HSI systems with complementary spectral ranges for detecting blueberry bruising, which could lead to a multispectral imaging system with a few selected wavelengths and an appropriate detector for bruising detection on the packing line.

Application of laser biospeckle technique for the analysis of artificially introduced local dynamics in apple fruit

Laser biospeckle technique has been extensively used as a significant characterization tool to analyze the complex inner dynamics involved in both biological and non-biological processes. The technique has been widely applied in the field of biology, medicine and agriculture as well as in the chemical and pharmaceutical industries. Among the challenges in biology or agriculture, one of the tasks is the assessment of the local dynamics of biological specimens. We report on an optical imaging technique, the laser biospeckle to assess the local dynamics introduced on selected biological specimens subjected to mechanical and thermal loads. This study also aimed to explore the technique in detecting the early stages of biological activities. An experimental configuration of the biospeckle laser imaging technique was used to assess the monitoring and prediction of the bruising on fruit surfaces within a specified time. The methods utilized to interpret the biospeckle data allowed pre-disposition detection of the formation of internal bruising in the early stages after the occurrence of a mechanical or thermal impact.

English

The internal bruising in potatoes is a kind of damage caused by the mechanical impact, which is the major cause of post-harvest losses. This kind of injury is hard to detect because it occurs due to the internal cellular rupture of the tuber. This research aims at evaluating the applicability of the biospeckle laser technique in order to detect and predict the internal bruising in potatoes caused by mechanical impacts during the early stages where there is no visual perception of the injury. The correlation of Pearson between a quantitative index generated from the biospeckle laser images (BSL values) and the enzymatic darkening in the region of the injury have been calculated. The BSL values was used to compare a group of potatoes subjected to mechanical impacts caused by a pendulum and a group of potatoes that did not suffer any sort of mechanical impacts. Comparison was performed by average and the standard error of the BSL values ​​was obtained: Before impact, immediately after, 2, 4, 6, 24, 48 and 72 h after the mechanical impact. Results showed that the BSL values correlated significantly with the progressive darkening of the potato tissue (r = - 0.79) and this allowed the detection of the formation of internal bruising from 4 hours after the occurrence of a mechanical impact. Key words: Mechanical impacts, injury detection, optical technique.

Detection of blueberry internal bruising over time using NIR hyperspectral reflectance imaging with optimum wavelengths

Early detection of internal bruising is one of the major challenges in blueberry postharvest quality sorting processes. The potential of using near infrared (NIR) hyperspectral reflectance imaging (950–1650nm) with reduced spectral features was investigated for blueberry internal bruising detection 30min to 12h after mechanical impact. A least squares support vector machine (LS-SVM) was used to develop classification models to compute the spatial distribution of bruising based on the spectra extracted from regions of interest (ROIs) at four measurement times (30min, 2h, 6h, and 12h after mechanical impact). Three feature selection methods were used to select optimum wavelengths or band ratio images for bruising detection. The classification model, developed using optimum wavelengths selected by competitive adaptive reweighted sampling (CARS) (CARS-LS-SVM model) and full spectra (full spectra-LS-SVM), had similar performance in the identification of bruised blueberries. Band ratio images (1235nm/1035nm) achieved a comparable accuracy with the CARS-LS-SVM model at 6h, and higher accuracy than CARS-LS-SVM and full spectra-LS-SVM models at 12h. The overall classification accuracies of 77.5%, 83.8%, 92.5%, and 95.0% were obtained by band ratio images for blueberries 30min, 2h, 6h, and 12h after impact, respectively. In order to evaluate the performance of the proposed methods, additional validation samples were processed by the detection algorithm. The overall discrimination accuracies for healthy and bruised blueberries in the validation set were 93.3% and 98.0%, respectively, for CARS-LS-SVM model, and 93.3% and 95.9%, respectively, for band ratio images. The overall results indicated that NIR reflectance imaging can detect blueberry internal bruising as early as 30min after mechanical impact, and band ratio images computed from two wavelengths showed great potential to detect blueberry internal bruising on the packing line.

Bruising susceptibility of Manzanilla de Sevilla table olive cultivar under Regulated Deficit Irrigation

The olive tree (Olea europaea L.) is a Mediterranean tree adapted to drought conditions and traditionally rainfed. For irrigated olive groves, there is extensive knowledge about the effects of Regulated Deficit Irrigation (RDI) scheduling on the sensibility of different phenological stages and about the importance of rehydration in the last part of the fruit growth stage. Water stress in this period could reduce the fruit size, but the exact extent of this reduction is still unknown. Additionally, table olive varieties may be damaged during harvesting or processing. This results in bruising, superficial browning injuries that progress through the mesocarp. Bruising susceptibility is an important quality parameter in table olives, however it has been little studied in RDI research. Hand-harvested fruits of ‘Manzanilla de Sevilla’ for green processing, treated with two types of RDI and full irrigation, were bruise-induced with a standardized drop to evaluate bruising susceptibility. Bruising index (BI) and external (BAE) and internal (BAI) bruising damage were measured 3 and 24h after the bruising treatment. A full irrigation treatment was set up in order to maintain the Ψh values higher than −1.2MPa before pit hardening and −1.4MPa until harvest. RDI-1 and RDI-2 were irrigated with a full irrigation treatment until 2 and 4 weeks before harvest, respectively. Then, water was withheld until −2.5MPa. RDI-2 can save 50% of the water applied without affecting the fruit weight, volume or pulp-to-pit ratio (fresh and dry). Fruits under RDI treatments are less susceptible to bruising than fruits with a full irrigation treatment, as evidenced by the lower bruising index and the smaller external and internal damaged area. As for the bruising time line, 3h after the bruise inducement, the bruising index and external damaged area had already developed, whereas internal bruising damage continued increasing up to 24h after the bruising treatment.

Detection of Internally Bruised Blueberries Using Hyperspectral Transmittance Imaging

Abstract. Internal bruise damage that occurs in blueberry fruit during harvest operations and postharvest handling lowers the overall quality and causes significant economic losses. The main goal of this study was to nondestructively detect internal bruises in blueberries after mechanical damage using hyperspectral transmittance imaging. A total of 600 hand-harvested blueberries were divided into 20 groups of four storage times (30 min, 3 h, 12 h, and 24 h), two storage temperatures (22°C and 4°C), and three treatments (stem bruise, equator bruise, and control). A near-infrared hyperspectral imaging system was used to acquire transmittance images from 970 to 1400 nm with 5 nm bandwidth. Images were acquired from three orientations (calyx-up, stem-up, and equator-up) for fruit in the control and stem bruise groups and from four orientations (calyx-up, stem-up, equator-up, and equator-down) in the equator bruise groups. Immediately after imaging, the fruit samples were sliced, and the sliced surfaces were photographed. The color images of sliced fruit were used as references. By comparing with the reference color images, the profiles of spatial and spectral intensities were evaluated to observe the effect of orientation and help extract regions of interest (ROIs) of bruised and healthy tissues. A support vector machine (SVM) classifier was trained and tested to classify pixels of bruised and healthy tissues. Classification maps were produced, and the bruise ratio was calculated to identify bruised blueberries (bruise ratio >25%). The average accuracy of blueberry identification was 94.5% with the stem-up orientation. The results indicate that detecting bruised blueberries as soon as 30 min after mechanical damage is feasible using hyperspectral transmittance imaging. Keywords: Blueberry, Bruise detection, Classification, Hyperspectral imagery, Transmittance mode.

Non-invasive hyperspectral imaging approach for fruit quality control application and classification: case study of apple, chikoo, guava fruits

Mechanical injuries to fruits are often caused due to hidden internal damages that results in bruising of fruit. This is a serious cause of concern to the fruit industry, as spoiled or bruised fruits directly impact the producers profit. Hyperspectral imaging method can provide the ability to identify these internal bruises to classify these fruits as normal and injured (bruised), reducing time and increasing efficiency over the sorting line in marketing chain. In this paper, we have used three types of fruits i.e., apple, chikoo & guava for experiments. The mechanical injury is introduced by manual impact on surface of the fruits sample and hyperspectral images were captured over nine narrow band pass filters to produce hyperspectral cubes for a fruit. Three types of methods were used for the data processing. First two are non-invasive in nature i.e., pixel signatures over hyperspectral cubes and second is prediction model for classification of fruits quality into normal and bruised using feed forward back propagation neural network. Finally, invasive method is used to confirm the said prediction model using parameters like firmness, Total Soluble Solid (TSS) and weight with Principal Component Analysis. Results obtained by hyperspectral imaging method indicate scope for non-invasive quality control over spectral wavelength range of 400–1000 nm.