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•SpectralCNN was used to detect apple bruises at pixel level based on hyperspectral data.•SpectralCNN outperformed the other three CNN approaches on raw spectra.•SpectralCNN achieved ...the best accuracy without spectral preprocessing.•The test set accuracy of the characteristic wavelengths reached 95.79%•The classification result was visualized pixel by pixel.
The timely detection of apple bruises caused by collision and squeeze is of great significance to reduce the economic losses of the apple industry. This study proposed a spectral analysis model (SpectralCNN) based on a one-dimensional convolutional neural network to detect apple bruises. The influences of six spectral preprocessing methods on the SpectralCNN model were firstly analyzed in this paper. Compared with traditional chemometric models, the SpectralCNN model had a better accuracy, which was demonstrated not depend on the spectral preprocessing method by experiment results. Then, 20 characteristic wavelengths could be extracted by successive projection algorithm. The SpectralCNN model could achieve an accuracy of 95.79% on the test set of characteristic wavelengths, indicating that the extracted characteristic wavelengths contain most of the features of bruised and healthy pixels.
•Cattle sex was the most influential bruise-causing variable.•Females are more prone to carcass bruising than males.•Inadequate cattle handling and loading facilities increase the chance of ...bruising.•Increasing truck load density negatively affects carcass bruising.•Total distance traveled is positively associated with mean number of bruises.
Animal transportation and pre-slaughter procedures are major components of the beef production system, but cattle are especially susceptible to stress during those events. In addition to stress-induced meat quality problems that might occur, such as higher pH and DFD meat, stressed animals are more prone to carcass bruising, which represents negative impact for the beef industry, from producers to meat packing plants. Therefore, this study was conducted to identify and quantify some risk factors for severe bruising in cattle carcasses. A total of 154,100 carcasses from 5028 loads of cattle purchased by a commercial slaughterhouse were assessed, and the following antemortem bruise-related variables were analyzed: sex, cattle handling procedures, loading facilities on the farm, type of vehicle used for transportation, distance traveled from the farm to the slaughterhouse, journey duration, truck load density, and season of the year at slaughter. Data were analyzed using the binary logistic regression model and Poisson regression model, assuming the presence or absence of severe bruises and total number of severe bruises per load as response variables, respectively. All analyzed variables showed to be potential factors for severe carcass bruising. Cattle sex was the most influential variable, and the likelihood of severe carcass bruising was greater for females (P < 0.001), as was the mean number of severe bruises per load (P < 0.05), when compared to male cattle. When handling conditions during the loading process or farm facilities worsened from ‘good’ to ‘poor’, there was an increase in the likelihood of severe bruising (P < 0.001) and in the mean severe bruise counts per load (P < 0.05). The season of the year at slaughter was also a potential carcass-bruising factor, as the chances of severe bruising and mean severe bruise number per load were greater (P < 0.001) for cattle slaughtered in the fall. In general, the likelihood of severe carcass bruising and the mean number of severe bruises per load increased (P < 0.05) when cattle were transported in larger trucks or when load density was greater than 431 kg/m2. Moreover, distances traveled on unpaved roads greater than 31 km increased the chances of severe bruising (P < 0.001), whereas total distance traveled greater than 151 km increased the mean number of severe bruises per load (P < 0.05). In conclusion, inadequate pre-slaughter conditions compromise carcass quality by increasing the susceptibility of cattle to bruising.
In this study, we aimed to examine the loss of strawberry fruit quality, including fruit firmness and number of microorganisms, after mechanical damage. Ripe greenhouse strawberries (Fragaria × ...ananassa ‘Tochiotome’) were subjected to drop-shock tests, categorized according to four different visual damage indexes, and stored for 7 days at either 5 or 15 °C. Fruit firmness and active microorganism proportion were investigated, and microorganism amplification and identification were performed. At 5 ℃, fruit firmness was effectively preserved until the end of the storage period, regardless of damage. The concentration of active microorganisms was determined based on adenosine triphosphate (ATP)-dependent luciferase activity and calculated as a relative value. Severely damaged fruit with effusion of juice stored at 15 °C showed the highest concentration of active microorganisms, compared to fruit stored at 5 °C, thus confirming the effectiveness of low-temperature storage for controlling microbial population size. According to the results of the amplification of the fungal 26 S ribosomal DNA and bacterial 16 S ribosomal RNA regions, and in agreement with the neighbor-joining consensus phylogram, the closest matches for bacterial and fungal sequences were Bacillus sp., Pseudomonas sp., and Curtobacterium sp., and Alternaria sp., Aspergillus sp., Cryptococcus sp., and Ustilago sp., respectively. The most abundant microorganisms from the fruit samples were Bacillus siamensis and Cryptococcus albidus. The reduction in fruit firmness during storage caused an increase in microbial concentration, especially in fruit stored at 15 °C, which may result in fruit softening and rapid microbial decay during storage.
•Drop-shock test was used to simulate mechanical damage in strawberry fruit.•Concentration of microorganisms on strawberry skin increased with mechanical damage.•Negative correlation between fruit firmness and microbial concentration was found.•Greater softening and faster microbial decay occurred in fruit stored at 15 °C.•Effusion from injured tissues invited saprohite/pathogen invasion.
Child protection is one of the most difficult aspects of paediatric practice. Physical abuse is the commonest form of maltreatment identified by clinicians. Suspicious injuries are rarely accompanied ...by an admission of inflicted harm, even when this is the cause. Clinicians therefore must have a high index of suspicion and where injuries appear consistent with physical abuse or are inadequately explained they must follow safeguarding procedures. However, as for any clinical problem, there is a differential diagnosis to consider. Medical conditions can sometimes mimic signs suggestive of physical abuse. Other conditions can increase a child's vulnerability to specific injuries. In this article we discuss the differential diagnosis of bruising, fractures and head injuries which are the three commonest presenting features of physical abuse. Awareness of the differential diagnosis and appropriate assessment of the injured child can assist in reaching the correct diagnosis and therefore protect children from harm.
Plums are widely consumed, both fresh and processed. During harvest, handling, or transportation, they are exposed to static and dynamic compression forces exceeding the critical stress for tissue ...damage. Compression-related damage typically develops further as the fruit ripens and softens, facilitating the occurrence of rot, which might cause significant losses in the supply chain. Early detection of these damages is crucial to sorting the damaged fruit out and deviating it to processing, thus preventing food waste. However, early-stage bruises or damages on plums are not visible, especially not in dark-skin cultivars. Therefore, this study aimed to explore the potential of hyperspectral imaging in the 430 to 1 000 nm range and deep learning algorithms to detect these invisible bruises at an early stage. To this end, 'Presenta' plums were impacted at three different levels to simulate varying degrees of damage. Images of both bruised and non-bruised plums were taken immediately after bruising and 24 and 48 h after bruise induction. Three distinct CNNs were trained to analyze the images. Two of these networks were implemented using transfer learning (ResNet and HSCNN), while the third was custom-designed for this specific purpose. The most informative wavelengths were identified as inputs for the CNNs employing PCA. F1 scores over 81% were obtained in all cases, and almost 100% accuracy was obtained in classifying the bruised plums with the highest impact energy of 0.50 J. Thus, detecting and classifying bruised plums using only three wavelengths is possible, paving the way for in-line sorting with multispectral cameras in packing houses.
•Invisible bruises can be detected using VIS-NIR hyperspectral imaging.•F1 scores higher than 87% were obtained for detecting invisible bruises.•Wavelength reduction to 3 bands obtained results higher than 86%.
•Peaches by a slight collision, its internal physiological indicators and external morphological changes, such as browning and softening, but the browning and softening will not appear immediately, ...but in a period of time after the bruise.•Peaches 12 h after bruising and peaches 24, 36 and 48 h after bruising also showed clustering phenomenon, which indicated that spectra or images could detect the difference between bruised and unbruised samples in terms of internal physiological indicators, and it was difficult to distinguish the bruise grade during the 24, 36 and 48 h after bruising.•The highest reflectance was obtained after 12 h of peach bruising between 650 and 764 nm. The reflectance gradually decreased as the post-bruising time increased, probably due to the loss of internal moisture as the post-bruising time increased, resulting in a gradual decrease in spectral reflectance.•The spectral reflectance of normal peaches is higher than that of bruised peaches. In contrast, the spectral reflectance of bruised peaches is similar at different times, decreased, the browning degree and total soluble solids/titratable acidity (TSS/TA) increased.•The peach surface contains multiple background colours, making it difficult for imaging techniques to detect early bruising. In contrast, multi-wavelength spectroscopy can improve the identification of early (2-day) bruising and predict the physical and biological characteristics of the fruit after mutation.
Peaches are sensitive to bruising from mechanical impact and compression. Peaches affected by bruises will tend towards fermentation, decay or mildew and infect other non-bruised ones after damage occurrence. A moderate amount of bruising is a barrier to purchase desire of consumers instead of price. For the early bruised peaches, softening and browning does not appear immediately (within 2 days), and it is difficult to distinguish the problem of fruit bruises. The hypersecretion imaging device was used to collect the data of normal peaches and the data of 12, 24, 36 and 48 h after the encounter, and the spectral characteristics of these five types of peaches were analyzed to establish the PCA classification model of physiological indexes of peaches after the collision. Extract the spectrum and image features of the discolored area and the normal area. Secondly, divide the collection of image data sets into the modelling set and the prediction set at a ratio of 3:1. Input the spectrum and image feature to establish a time discrimination model for peach bruises. The results show that it is based on the PLS-DA algorithm When the input variable is a spectral feature, the classification accuracy rates at 12, 24, 36, and 48 h after bruising are 96.67%, 96.67%, 93.33%, and 83.33%, respectively, and the correlation coefficient (Rc) of the modelling set reaches 0.928; Based on LS -SVM algorithm, when the input variable is a spectral feature, the kernel function RBF_Kernel classifies correctly to 80%, 96.67%, 100%, 100%. This study shows that the spectral feature of hypersecretion technology can identify early bruises of peaches, which are online testing provide a theoretical basis for predicting short-term bumps of peaches.
The incidence of non-ambulatory non-injured (NANI), non-ambulatory injured (NAI) and dead pigs on-arrival at three Brazilian slaughterhouses were evaluated in 37,962 pigs to identify risk factors ...linked to them, besides carcass bruises and limb fractures. Total pre-slaughtering losses were 1.18%, in which NAI (0.39%) and NANI (0.37%) incidences contributed the most. A positive relation between on farm steeper ramp slope >20° and the incidence of NAI, NANI and dead pigs at unloading was found. Farm size, pigs/pen, enthalpy at loading, transportation time, truck loading order, muscle thickness and carcass weight, were identified as risk factors for pre-slaughtering losses. Loading procedures influenced the occurrence of limb fractures and bruises (which are a welfare issue and should be reduced). Therefore, personal training on pre-slaughter handling is essential to reduce the risk factors to improve animal welfare and avoid losses during the pre-slaughter process.
•Total losses during transport of pigs were 1.18%.•The incidence of NAI and NANI pigs were 0.39 and 0.37%, respectively.•The NAI and NANI pigs were the causes that contributed mostly to transport losses.•Most of risk factors occurred either on the farm or during transport.
The study presents a novel veterinary forensic approach to analyse the bruising of horse carcasses, based on the nature of the bruises and how they are grouped in certain anatomical areas. Data on ...pre-slaughter logistics was obtained for 113 journeys with horses that travelled from Mexico and the USA to a Mexican abattoir. We found that carcass bruising was a highly prevalent problem (79% of carcasses had bruising) and was especially problematic in journeys lasting longer than 12 h, independently of the animal's country of origin, sex, age, lairage time or vehicle type. Multivariable logistic regression showed that the most severe bruises were not dispersed randomly on the carcass and that their distribution was associated with the presence of medium-sized bruises on the abdominal wall, front and rear limb. Cluster analysis suggested four damage patterns based on bruise location: severe and concentrated bruising, as well as non-severe bruises on the rear limb, thoracic-wall or more dispersed throughout the carcass.
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