Quality losses in fresh produce throughout the postharvest phase are often due to the inappropriate use of preservation technologies. In the last few decades, besides the traditional approaches, advanced postharvest physical and chemical treatments (active packaging, dipping, vacuum impregnation, conventional heating, pulsed electric field, high hydrostatic pressure, and cold plasma) and biocontrol techniques have been implemented to preserve the nutritional value and safety of fresh produce. The application of these methodologies after harvesting is useful when addressing quality loss due to the long duration when transporting products to distant markets. Among the emerging technologies and contactless and non-destructive techniques for quality monitoring (image analysis, electronic noses, and near-infrared spectroscopy) present numerous advantages over the traditional, destructive methods. The present review paper has grouped original studies within the topic of advanced postharvest technologies, to preserve quality and reduce losses and waste in fresh produce. Moreover, the effectiveness and advantages of some contactless and non-destructive methodologies for monitoring the quality of fruit and vegetables will also be discussed and compared to the traditional methods.

Lentil (Lens culinaris Medik.) is one of the major pulse crops cultivated worldwide. However, in the last decades lentil cultivation decreased in many areas surrounding the Mediterranean Countries due to low yields, new lifestyles and changed eating habits. Thus, many landraces and local varieties disappeared, while local farmers are the only custodians of the treasure of lentil genetic resources. Recently, lentil has been rediscovered to meet the needs of a more sustainable agriculture and food systems. Here we propose an image analysis approach that besides being rapid and non-destructive method can characterize seed size grading and seed coat morphology. Results indicated that image analysis can give much more detailed and precise descriptions of grain size and shape characteristics than can be practically achieved by manual quality assessment. Lentil size measurements combined with seed coat descriptors and colour attributes of the grains allowed us to develop an algorithm able to identify 64 red lentil genotypes collected at ICARDA with an accuracy approaching 98% for seed size grading and close to 93% for classification of seed coat morphology respectively.

Fresh berries are a popular and important component of the human diet. The demand for high-quality berries and sustainable production methods is increasing globally, challenging breeders to develop modern berry cultivars that fulfill all desired characteristics. Since 1994, research projects have characterized genetic resources, developed modern tools for high-throughput screening, and published data in publicly available repositories. However, the key findings of different disciplines are rarely linked together, and only a limited range of traits and genotypes has been investigated. The Horizon2020 project BreedingValue will address these challenges by studying a broader panel of strawberry, raspberry and blueberry genotypes in detail, in order to recover the lost genetic diversity that has limited the aroma and flavor intensity of recent cultivars. We will combine metabolic analysis with sensory panel tests and surveys to identify the key components of taste, flavor and aroma in berries across Europe, leading to a high-resolution map of quality requirements for future berry cultivars. Traits linked to berry yields and the effect of environmental stress will be investigated using modern image analysis methods and modeling. We will also use genetic analysis to determine the genetic basis of complex traits for the development and optimization of modern breeding technologies, such as molecular marker arrays, genomic selection and genome-wide association studies. Finally, the results, raw data and metadata will be made publicly available on the open platform Germinate in order to meet FAIR data principles and provide the basis for sustainable research in the future.

Electronic nose (e-nose), attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy and image analysis (IA) were used to discriminate the ripening stage (half-red or red) of strawberries (cv Sabrosa, commercially named Candonga), harvested at three different times (H1, H2 and H3). Principal component analysis (PCA) performed on the e-nose, ATR-FTIR and IA data allowed us to clearly discriminate samples based on the ripening stage, as in the score space they clustered in distinct regions of the plot. Moreover, a correlation analysis between the e-nose sensor and 57 volatile organic compounds (VOCs), which were overall detected in all the investigated fruit samples by headspace solid-phase microextraction coupled to gas chromatographymass spectrometry (HS-SPME/GC-MS), allowed us to distinguish half-red and red strawberries, as the e-nose sensors gave distinct responses to samples with different flavours. Three suitable broad bands were individuated by PCA in the ATR-FTIR spectra to discriminate half-red and red samples: the band centred at 3295 cm?1 is generated by compounds that decline, whereas those at 1717 cm?1 and at 1026 cm?1 stem from compounds that accumulate during ripening. Among the chemical parameters (titratable acidity, total phenols, antioxidant activity and total soluble solid) assayed in this study, only titratable acidity was somehow correlated to ATR-FTIR and IA patterns. Thus, ATR-FTIR spectroscopy and IA might be exploited to rapidly assess titratable acidity, which is an objective indicator of the ripening stage.

Biological data sets are increasingly becoming information-dense, making it effective to use a computer science-based analysis. We used convolution neural networks (CNN) and the specific CNN architecture Unet to study sponge behavior over time. We analyzed a large time series of hourly high-resolution still images of a marine sponge, Suberites concinnus (Demospongiae, Suberitidae) captured between 2012 and 2015 using the NEPTUNE seafloor cabled observatory, off the west coast of Vancouver Island, Canada. We applied semantic segmentation with the Unet architecture with some modifications, including adapting parts of the architecture to be more applicable to three-channel images (RGB). Some alterations that made this model successful were the use of a dice-loss coefficient, Adam optimizer and a dropout function after each convolutional layer which provided losses, accuracies and dice scores of up to 0.03, 0.98 and 0.97, respectively. The model was tested with five-fold cross-validation. This study is a first step towards analyzing trends in the behavior of a demosponge in an environment that experiences severe seasonal and inter-annual changes in climate. The end objective is to correlate changes in sponge size (activity) over seasons and years with environmental variables collected from the same observatory platform. Our work provides a roadmap for others who seek to cross the interdisciplinary boundaries between biology and computer science.

In healthcare industry 4.0, a big role is played by radiomics. Radiomics concerns the extraction and analysis of quantitative information not visible to the naked eye, even by expert operators, from biomedical images. Radiomics involves the management of digital images as data matrices, with the aim of extracting a number of morphological and predictive variables, named features, using automatic or semi-automatic methods. Multidisciplinary methods as machine learning and deep learning are fully involved in this field. However, the large number of features requires efficient and effective core methods for their selection, in order to avoid bias or misinterpretations problems. In this work, the authors propose a novel method for feature selection in radiomics. The proposed method is based on an original combination of descriptive and inferential statistics. Its validity is illustrated through a case study on prostate cancer analysis, conducted at the university hospital of Palermo, Italy.

Radiographs of repacked cores of four different soils and of a reference impregnated soil block used for the validation of a new method of soil core volume measurement based on image analysis and Pappus 2nd centroid theorem. This dataset includes: images of a reference soil block which refers to a an accuracy test of the method, images of four oven-dried soil cores which refer to a precision test of the method, images of further four soil cores which refer to a shrinkage test performed considering 19 moisture ratios. Images are provided both entire and halved.

In green leafy vegetables, the retention of green colour is one of the most generally index used to evaluate the overall quality and freshness and it is associated to total chlorophyll content. The aim of this research is to demonstrate the relationship between changes in colour and total chlorophyll content during storage of fresh-cut rocket leaves. Fresh-cut rocket leaves (Eruca sativa Mill.) were put in open polypropylene bags containing about 150 g of product each and stored at three temperatures (20, 10 and 5°C) for 6, 12 and 16 days, respectively. Sixty-four bags were prepared for each storage temperature. At each storage time (0, 2, 5 and 6 days at 20°C; 0, 5, 8 and 12 days at 10°C; 0, 6, 13 and 16 days at 5°C) fresh-cut rocket samples were subjected to colour analysis by a computer vision system (CVS) and a colourimeter. Then were analysed for total chlorophyll content using a spectrophotometer method and a SPAD-meter. A part of data (70%) were subjected to regression analysis to build predictive models of total chlorophyll content using as predictors the colour data (L*, a*, b*) obtained by colourimeter and CVS. Unseen data (remaining 30%) were used to validate the models obtained. Results demonstrated that is possible to predict the total chlorophyll content starting by colour parameters, using CVS or colourimeter with similar performance in validation (mean R2=0.74). Moreover, similar performances were obtained using the SPAD-meter to predict the total chlorophyll of fresh-cut rocket leaves (R2=0.79).

Visualizing neuronal activation on a brain-wide scale yet with cellular resolution is a fundamental technical challenge for neuroscience. This would enable analyzing how different neuronal circuits are disrupted in pathology and how they could be rescued by pharmacological treatments. Although this goal would have appeared visionary a decade ago, recent technological advances make it eventually feasible. Here, we review the latest developments in the fields of genetics, sample preparation, imaging, and image analysis that could be combined to afford whole-brain cell-resolution activation mapping. We show how the different biochemical and optical methods have been coupled to study neuronal circuits at different spatial and temporal scales, and with cell-type specificity. The inventory of techniques presented here could be useful to find the tools best suited for a specific experiment. We envision that in the next years, mapping of neuronal activation could become routine in many laboratories, allowing dissecting the neuronal counterpart of behavior.

The qualitative and quantitative evaluation of nasal epithelial cells is interesting in chronic infectious and inflammatory pathologies of the nose and sinuses. Among the cells of the population of the nasal mucosa, ciliated cells are particularly important. In fact, the observation of these cells is essential to investigate primary ciliary dyskinesia, a rare and severe disease associated with other serious diseases such as respiratory diseases, situs inversus, heart disease, and male infertility. Biopsy or brushing of the ciliary mucosa and assessment of ciliary function through measurements of the Ciliary Beating Frequency (CBF) are usually required to facilitate diagnosis. Therefore, low-cost and easy-to-use technologies devoted to measuring the ciliary beating frequency are desirable. We have considered related works in this field and noticed that up to date an actually usable system is not available to measure and monitor CBF. Moreover, performing this operation manually is practically unfeasible or demanding. For this reason, we designed BeatCilia, a low cost and easy-to-use system, based on image processing techniques, with the aim of automatically measuring CBF. This system performs cell Region of Interest (RoI) detection basing on dense optical flow computation of cell body masking, focusing on the cilia movement and taking advantage of the structural characteristics of the ciliated cell and CBF estimation by applying a fast Fourier transform to extract the frequency with the peak amplitude. The experimental results show that it offers a reliable and fast CBF estimation method and can efficiently run on a consumer-grade smartphone. It can support rhinocytologists during cell observation, significantly reducing their efforts.

The concept of magnetic guidance is still challenging and has opened a wide range of perspectives in the field of tissue engineering. In this context, magnetic nanocomposites consisting of a poly(epsilon-caprolactone) (PCL) matrix and iron oxide (Fe3O4) nanoparticles were designed and manufactured for bone tissue engineering. The mechanical properties of PCL/Fe3O4 (80/20 w/w) nanocomposites were first assessed through small punch tests. The inclusion of Fe3O4 nanoparticles improved the punching properties as the values of peak load were higher than those obtained for the neat PCL without significantly affecting the work to failure. The effect of a time-dependent magnetic field on the adhesion, proliferation, and differentiation of human mesenchymal stem cells (hMSCs) was analyzed. The Alamar Blue assay, confocal laser scanning microscopy, and image analysis (i.e., shape factor) provided information on cell adhesion and viability over time, whereas the normalized alkaline phosphatase activity (ALP/DNA) demonstrated that the combination of a time-dependent field with magnetic nanocomposites (PCL/Fe3O4 Mag) influenced cell differentiation. Furthermore, in terms of extracellular signal-regulated kinase (ERK)1/2 phosphorylation, an insight into the role of the magnetic stimulation was reported, also demonstrating a strong effect due the combination of the magnetic field with PCL/Fe3O4 nanocomposites (PCL/Fe3O4 Mag).

Abstract The purpose of this study was to investigate the response of porcine corneal organ cultures to riboflavin/UV-A phototherapy in the injury healing of induced lesions. A porcine corneal organ culture model was established. Corneal alterations in the stroma were evaluated using an assay system, based on an automated image analysis method able to (i) localize the holes and gaps within the stroma and (ii) measure the brightness values in these patches. The analysis has been performed by dividing the corneal section in 24 regions of interest (ROIs) and integrating the data analysis with a "multi-aspect approach." Three group of corneas were analyzed: healthy, injured, and injured-and-treated. Our study revealed a significant effect of the riboflavin/UV-A phototherapy in the injury healing of porcine corneas after induced lesions. The injured corneas had significant differences of brightness values in comparison to treated (p < 0.00) and healthy (p < 0.001) corneas, whereas the treated and healthy corneas showed no significant difference (p = 0.995). Riboflavin/UV-A phototherapy shows a significant effect in restoring the brightness values of damaged corneas to the values of healthy corneas, suggesting treatment restores the injury healing of corneas after lesions. Our assay system may be compared to clinical diagnostic methods, such as optical coherence tomography (OCT) imaging, for in vivo damaged ocular structure investigations.

We performed a three-dimensional (3D) analysis of the microvascular network of the cerebral cortex of twitcher mice (an authentic model of Krabbe disease) using a restricted set of indexes that are able to describe the arrangement of the microvascular tree in CD31-stained sections. We obtained a near-linear graphical "fingerprint" of the microangioarchitecture of wild-type and twitcher animals that describes the amounts, spatial dispersion, and spatial relationships of adjacent classes of caliber-filtered microvessels. We observed significant alterations of the microangioarchitecture of the cerebral cortex of twitcher mice, whereas no alterations occur in renal microvessels, which is keeping with the observation that kidney is an organ that is not affected by the disease. This approach may represent an important starting point for the study of the microvascular changes that occur in the central nervous system (CNS) under different physiopathological conditions.

The AII amacrine cell is known as a key interneuron in the scotopic (night-vision) pathway in the retina. Under scotopic conditions, rod signals are transmitted via rod bipolar cells to AII amacrine cells, which split the rod signal into the OFF (via glycinergic synapses) and the ON pathway (via gap junctions). But the AII amacrine cell also has a "day job": at high light levels when cones are active, AII connections with ON cone bipolar cells provide crossover inhibition to extend the response range of OFF cone bipolar cells. The question whether AII cells contribute to crossover inhibition in primate fovea (where rods and rod bipolar cells are rare or absent) has not been answered. Here, immunohistochemistry and three-dimensional reconstruction show that calretinin positive cells in the fovea of macaque monkeys and humans have AII morphology and connect to cone bipolar cells. The pattern of AII connections to cone bipolar cells is quantitatively similar to that of AII cells outside the fovea. Our results support the view that in mammalian retina AII cells first evolved to serve cone circuits, then later were co-opted to process scotopic signals subsequent to the evolution of rod bipolar cells.

Early diagnosis is a corner stone for a successful treatment of most diseases including melanoma, which cannot be achieved by traditional histopathological inspection. In this respect, multimodal imaging, the combination of TPEF and SHG, features a high diagnostic potential as an alternative approach. Multimodal imaging generates molecular contrast, but to use this technique in clinical practice, the optical signals must be translated into diagnostic relevant information. This translation requires automatic image analysis techniques. Within this contribution, we established an analysis pipeline for multimodal images to achieve melanoma diagnostics of skin tissue. The first step of the image analysis was the pre-treatment, where the mosaicking artifacts were corrected and a standardization was performed. Afterwards, the local histogram-based first-order texture features and the local gray-level co-occurrence matrix (GLCM) texture features were extracted in multiple scales. Thereafter, we constructed a local hierarchical statistical model to distinguish melanoma, normal epithelium, and other tissue types. The results demonstrated the capability of multimodal imaging combined with image analysis to differentiate different tissue types. Furthermore, we compared the histogram and the GLCM-based texture feature sets according to the Fisher's discriminant ratio (FDR) and the prediction of the classification, which demonstrated that the histogram-based texture features are superior to the GLCM features for the given task. Finally, we performed a global classification to achieve a patient diagnostics with the clinical diagnosis as ground truth. The agreement of the prediction and the clinical results demonstrated the great potential of multimodal imaging for melanoma diagnostics.

The RIPS dataset has been created in order to evaluate and compare methods for the segmentation of pigment signs (PSs) in retinal images. It provides data and ground truth, so that everyone can test their own algorithm. On this page, instructions can be found on downloading the dataset.

Retinitis Pigmentosa is an eye disease that presents with a slow loss of vision and then evolves until blindness results. The automatic detection of the early signs of retinitis pigmentosa acts as a great support to ophthalmologists in the diagnosis and monitoring of the disease in order to slow down the degenerative process. A large body of literature is devoted to the analysis of Retinitis Pigmentosa. However, all the existing approaches work on Optical Coherence Tomography (OCT) data, while hardly any attempts have been made working on fundus images. Fundus image analysis is a suitable tool in daily practice for an early detection of retinal diseases and the monitoring of their progression. Moreover, the fundus camera represents a low-cost and easy-access diagnostic system, which can be employed in resource-limited regions and countries. The fundus images of a patient suffering from retinitis pigmentosa are characterized by an attenuation of the vessels, a waxy disc pallor and the presence of pigment deposits. Considering that several methods have been proposed for the analysis of retinal vessels and the optic disk, this work focuses on the automatic segmentation of the pigment deposits in the fundus images. The image distortions are attenuated by applying a local {\color{blue}pre-processing}. Next, a watershed transformation is carried out to produce homogeneous regions. Working on regions rather than on pixels makes the method very robust to the high variability of pigment deposits in terms of color and shape, so allowing the detection even of small pigment deposits. The regions undergo a feature extraction procedure, so that a region classification process is performed by means of an outlier detection analysis and a rule set. The experiments have been performed on a dataset of images of patients suffering from retinitis pigmentosa. Although the images present a high variability in terms of color and illumination, the method provides a good performance in terms of sensitivity, specificity, accuracy and the F-measure, whose values are 74.43, 98.44, 97.90, 59.04, respectively.

T4SA is intended for training and testing image sentiment analysis approaches. It contains little less than a million tweets, corresponding to about 1.5M images. We initially collected about 3.4M tweets corresponding to about 4M images. We classified the sentiment polarity of the texts (as described in Section 4) and we selected the tweets having the most confident textual sentiment predictions to build our Twitter for Sentiment Analysis (T4SA) dataset. The dataset is publicly available at: http://www.t4sa.it/

This paper reports the development and the results of a procedure aimed at measuring, modelling and validating the growth of surface molds in industrial salami ripening by relying on techniques of image analysis. The sausages under investigation were inoculated with fungal starters and ripened in a test carried out at SSICA (Parma) under closely monitored and controlled conditions in a pilot-scale ripening chamber based on the "Air flow from bottom upward" technology. The work has been carried out within the R&D PON01_01409 "Safemeat" project. Among the various investigations, digital images were purposely acquired in a standardized way throughout the experimental test of sausage ripening. The graphical procedures here discussed involve a bit of image pre-processing, a digital image analysis work and some data post-processing. A pre-processing software i ntroduces a black background around each photographed sausage. The open-source ImageJ software is used for recognizing and measuring the gut area covered by molds as a whole, distinguishing each individual mold colony, measuring its surface area and counting the overall number of colonies. Further data post- processing provides results in terms of percent surface covered by molds, number of mold colonies per unit gut surface and size distribution of colonies as a function of their individual area. Microbiological analyses confirmed that the fungal population established on the salami casing immediately after the surface inoculum was exactly corresponding to the mold starters. The developed methodology and the encouraging results obtained so far promise to be a rather simple and cheap way to control the onset and progress of the fungal colonization in industrial ripening chambers.

An alternative method to the traditional hydrated oocyte (HO) method has been evaluated for the Sicilian anchovy, Engraulis encrasicolus. The method is based on the processing of ovarian whole mount images and the identification of the spawning batch in oocyte size frequency distributions and shows the advantage that it can be applied to various oocyte stages rather than strictly to the HO stage. Despite the peculiar elliptical shape of anchovy oocytes, this image analysis technique was fully successful since the yolked stage appeared to perform equally to the HO stage for anchovy batch fecundity measurements.