Interactive Effect of Cultivars, Crop Years and Rootstocks on the Biochemical Traits of Prunus persica (L.) Batsch Fruits.

Peach fruit is one of the most economically widespread temperate fruits, whose productivity, and nutritional and sensory qualities are determined by interactions among several environmental and genetic factors, rootstocks, agronomic practices and pedo-climatic conditions. In recent years, climate change has prompted peach breeding programs to use specific rootstocks that are well adapted to unusual soil and climate characteristics, thus improving the plant's adaptability and fruit quality. The aim of this work was to assess the biochemical and nutraceutical profile of two different peach cultivars, considering their growth on different rootstocks over three crop years. An analysis was carried out evaluating the interactive effect of all factors (i.e., cultivars, crop years and rootstocks) revealing the advantages or disadvantages on growth of the different rootstocks. Soluble solids content, titratable acidity, total polyphenols, total monomeric anthocyanins and antioxidant activity in fruit skin and pulp were analyzed. An analysis of variance was performed to assess the differences between the two cultivars considering the effect of rootstock (one way) and crop years, rootstocks and their interaction (two ways). In addition, two principal component analyses were performed separately on the phytochemical traits of the two cultivars to visualize the distributions of the five peach rootstocks during the three crop years. The results showed that fruit quality parameters are strongly dependent on cultivars, rootstocks and climatic conditions. All these aspects could be useful for the choice of rootstock in relation to agronomic management, making this study a valuable tool for choosing the best rootstock, considering simultaneously more factors affecting peaches' biochemical and nutraceutical profile.

Different Traits, Different Evolutionary Pathways: Insights from Salamandrina (Amphibia, Caudata).

Species delimitation is often based on a single or very few genetic or phenetic traits, something which leads to misinterpretations and often does not provide information about evolutionary processes. Here, we investigated the diversity pattern of multiple phenetic traits of the two extant species of Salamandrina, a genus split only after molecular traits had been studied but the two species of which are phenetically very similar. The phenetic traits we studied are size, external body shape and head colour pattern, in a model comparison framework using non-linear mixed models and unsupervised and supervised clustering. Overall, we found high levels of intra-specific variability for body size and shape, depending on population belonging and habitat, while differences between species were generally lower. The habitat the salamanders dwell in also seems important for colour pattern. Basing on our findings, from the methodological point of view, we suggest (i) to take into account the variability at population level when testing for higher level variability, and (ii) a semi-supervised learning approach to high dimensional data. We also showed that different phenotypic traits of the same organism could result from different evolutionary routes. Local adaptation is likely responsible for body size and shape variability, with selective pressures more similar across species than within them. Head colour pattern also depends on habitat, differently from ventral colour pattern (not studied in this paper) which likely evolved under genetic drift.

An Open Source Low-Cost Device Coupled with an Adaptative Time-Lag Time-Series Linear Forecasting Modeling for Apple Trentino (Italy) Precision Irrigation.

Precision irrigation represents those strategies aiming to feed the plant needs following the soil's spatial and temporal characteristics. Such a differential irrigation requires a different approach and equipment with regard to conventional irrigation to reduce the environmental impact and the resources use while maximizing the production and thus profitability. This study described the development of an open source soil moisture LoRa (long-range) device and analysis of the data collected and updated directly in the field (i.e., weather station and ground sensor). The work produced adaptive supervised predictive models to optimize the management of agricultural precision irrigation practices and for an effective calibration of other agronomic interventions. These approaches are defined as adaptive because they self-learn with the acquisition of new data, updating the on-the-go model over time. The location chosen for the experimental setup is a cultivated area in the municipality of Tenna (Trentino, Alto Adige region, Italy), and the experiment was conducted on two different apple varieties during summer 2019. The adaptative partial least squares time-lag time-series modeling, in operative field conditions, was a posteriori applied in the consortium for 78 days during the dry season, producing total savings of 255 mm of irrigated water and 44,000 kW of electricity, equal to 10.82%.

A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots.

The degree of olive maturation is a very important factor to consider at harvest time, as it influences the organoleptic quality of the final product, for both oil and table use. The Jaén index, evaluated by measuring the average coloring of olive fruits (peel and pulp), is currently considered to be one of the most indicative methods to determine the olive ripening stage, but it is a slow assay and its results are not objective. The aim of this work is to identify the ripeness degree of olive lots through a real-time, repeatable, and objective machine vision method, which uses RGB image analysis based on a k-nearest neighbors classification algorithm. To overcome different lighting scenarios, pictures were subjected to an automatic colorimetric calibration method-an advanced 3D algorithm using known values. To check the performance of the automatic machine vision method, a comparison was made with two visual operator image evaluations. For 10 images, the number of black, green, and purple olives was also visually evaluated by these two operators. The accuracy of the method was 60%. The system could be easily implemented in a specific mobile app developed for the automatic assessment of olive ripeness directly in the field, for advanced georeferenced data analysis.

Application of near-infrared handheld spectrometers to predict semolina quality.

BACKGROUND: Durum wheat semolina is the best raw material for pasta production and its protein content and gluten strength are essential for cooking quality. The need to develop rapid methods to speed up quality control makes near-infrared spectroscopy (NIR) a useful method that is widely accepted in the cereal sector. In this study, two non-destructive and rapid technologies, a low-cost sensor providing a short wavelength NIR range (swNIR: 700-1100 nm) and a handheld NIR spectrometer (NIR: 1600-2400 nm), were employed to evaluate semolina quality. The spectra data were correlated with chemical (protein content) and rheological parameters (i.e., Gluten Index, Alveograph®, Sedimentation test, GlutoPeak®). A partial least squares (PLS) model was used to compare the efficacy of swNIR and NIR. RESULTS: The protein content was the reference parameter that correlated best with the spectra data and provided the best regression model (r model = 0.9788 for NIR and 0.9561 for swNIR). GlutoPeak indices also correlated well with spectral data, particularly with swNIR spectra. A provisional multivariate model was applied to classify semolina samples in quality classes by using their spectra. Better modeling efficiency was obtained for swNIR. CONCLUSION: The results highlighted the advantages of a pocket-sized low cost sensor (swNIR), which is easier to use directly at the sample source than laboratory instruments or more expensive portable devices. © 2020 Society of Chemical Industry.

An artificial class modelling approach to identify the most largely diffused cultivars of sweet cherry (Prunus avium L.) in Italy.

The nutritional and commercial value of the sweet cherry provides it a great economic importance in Italy. The aim of this study was to characterize 35 sweet cherry cultivars and one of sour cherry, by analyzing values of different pomological and nutraceutical traits, identifying cultivars with antioxidant activity and total anthocyanins content closest to those present in literature for Ferrovia (largely diffused in Italy). To this goal, a multivariate metric index through the Soft Independent Modeling of Class Analogy analyzing an artificial dataset and testing a real one, two hierarchical clustering and a principal component analysis, were performed. The multivariate analyses result simultaneously investigated all the variables highlighting cvs. Sylvia, Graffione nero Col di Mosso, Ferrovia, Mora della Punta, Bianchetta Nuchis and Sandra to be more similar to literature data of Ferrovia. This matrix index was a useful tool, to select the most commercial promising varieties.

An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer.

Extra virgin olive oil (EVOO) represents a crucial ingredient of the Mediterranean diet. Being a first-choice product, consumers should be guaranteed its quality and geographical origin, justifying the high purchasing cost. For this reason, it is important to have new reliable tools able to classify products according to their geographical origin. The aim of this work was to demonstrate the efficiency of an open source visible and near infra-red (VIS-NIR) spectrophotometer, relying on a specific app, in assessing olive oil geographical origin. Thus, 67 Italian and 25 foreign EVOO samples were analyzed and their spectral data were processed through an artificial intelligence algorithm. The multivariate analysis of variance (MANOVA) results reported significant differences (p < 0.001) between the Italian and foreign EVOO VIS-NIR matrices. The artificial neural network (ANN) model with an external test showed a correct classification percentage equal to 94.6%. Both the MANOVA and ANN tested methods showed the most important spectral wavelengths ranges for origin determination to be 308-373 nm and 594-605 nm. These are related to the absorption of phenolic components, carotenoids, chlorophylls, and anthocyanins. The proposed tool allows the assessment of EVOO samples' origin and thus could help to preserve the "Made in Italy" from fraud and sophistication related to its commerce.

Light Drone-Based Application to Assess Soil Tillage Quality Parameters.

The evaluation of soil tillage quality parameters, such as cloddiness and surface roughness produced by tillage tools, is based on traditional methods ranging, respectively, from manual or mechanical sieving of ground samples to handheld rulers, non-contact devices or Precision Agriculture technics, such as laser profile meters. The aim of the study was to compare traditional methods of soil roughness and cloddiness assessment (laser profile meter and manual sieving), with light drone RGB 3D imaging techniques for the evaluation of different tillage methods (ploughed, harrowed and grassed). Light drone application was able to replicate the results obtained by the traditional methods, introducing advantages in terms of time, repeatability and analysed surface while reducing the human error during the data collection on the one hand and allowing a labour-intensive field monitoring solution for digital farming on the other. Indeed, the profilometer positioning introduces errors and may lead to false reading due to limited data collection. Future work could be done in order to streamline the data processing operation and so to produce a practical application ready to use and stimulate the adoption of new evaluation indices of soil cloddiness, such as Entropy and the Angular Second Moment (ASM), which seem more suitable than the classic ones to achieved data referred to more extended surfaces.

Are the Innovative Electronic Labels for Extra Virgin Olive Oil Sustainable, Traceable, and Accepted by Consumers?

Traceability is the ability to follow the displacement of food through its entire chain. Extra virgin olive oil (EVOO) represents Italian excellence, with consumers' increased awareness for traceability. The aim of this work is to propose and analyze the economic sustainability and consumers' preference of three technological systems supporting traceability: Near Field Communication (NFC) based; tamper-proof device plus Radio Frequency Identification (RFID) and app; QR code tag plus "scratch and win" system and blockchain. An anonymous questionnaire to Italian consumers (n = 1120) was made to acquire consumers' acceptability of the systems and estimating their willingness to pay additional premium prices for these. An economic analysis estimated and compared the technology costs at different production levels. Results show that 94% of the consumer respondents are interested in the implementation of such technologies, and among them 45% chose QR-code protected by a "scratch-and-win" system with a blockchain infotracing-platform (QR-B). The consumers interested are willing to pay a mean premium price of 17.8% and economic analysis reported evidenced an incidence always lower than mid-/high-production levels. The success of the QR-B could be ascribed to different aspects: the cutting-edge fashion trend of blockchain in the food sector, the use of incentives, the easy-to-use QR-code, and the gamification strategy.

A review on blockchain applications in the agri-food sector.

BACKGROUND: Food security can benefit from the technology's transparency, relatively low transaction costs and instantaneous applications. A blockchain is a distributed database of records in the form of encrypted blocks, or a public ledger of all transactions or digital events that have been executed and shared among participating parties and can be verified at any time in the future. Generally, the robust and decentralized functionality of the blockchain is used for global financial systems, but it can easily be expanded to contracts and operations such as tracking of the global supply chain. In the precision agriculture context, Information and Communications Technology can be further implemented with a blockchain infrastructure to enable new farm systems and e-agriculture schemes. RESULTS: The purpose of this review is to show a panorama of the scientific studies (enriched by a terms mapping analysis) on the use of blockchain in the agri-food sector, from both an entirely computational and an applicative point of view. As evidenced by the network analysis, the reviewed studies mainly focused on software aspects (e.g. the architecture and smart contracts). However, some aspects regarding the different blockchain knots (computers always connected to the blockchain network) having the role to store and distribute an updated copy of each block in a food supply-chain, result of crucial importance. CONCLUSION: These technologies appear very promising and rich of great potential showing a good flexibility for applications in several sectors but still immature and hard to apply due to their complexity. © 2019 Society of Chemical Industry.

A Blockchain Implementation Prototype for the Electronic Open Source Traceability of Wood along the Whole Supply Chain.

This is the first work to introduce the use of blockchain technology for the electronic traceability of wood from standing tree to final user. Infotracing integrates the information related to the product quality with those related to the traceability [physical and digital documents (Radio Frequency IDentification-RFID-architecture)] within an online information system whose steps (transactions) can be made safe to evidence of alteration through the blockchain. This is a decentralized and distributed ledger that keeps records of digital transactions in such a way that makes them accessible and visible to multiple participants in a network while keeping them secure without the need of a centralized certification organism. This work implements a blockchain architecture within the wood chain electronic traceability. The infotracing system is based on RFID sensors and open source technology. The entire forest wood supply chain was simulated from standing trees to the final product passing through tree cutting and sawmill process. Different kinds of Internet of Things (IoT) open source devices and tags were used, and a specific app aiming the forest operations was engineered to collect and store in a centralized database information (e.g., species, date, position, dendrometric and commercial information).

Clasp2 ensures mitotic fidelity and prevents differentiation of epidermal keratinocytes.

Epidermal homeostasis is tightly controlled by a balancing act of self-renewal or terminal differentiation of proliferating basal keratinocytes. An increase in DNA content as a consequence of a mitotic block is a recognized mechanism underlying keratinocyte differentiation, but the molecular mechanisms involved in this process are not yet fully understood. Using cultured primary keratinocytes, here we report that the expression of the mammalian microtubule and kinetochore-associated protein Clasp2 is intimately associated with the basal proliferative makeup of keratinocytes, and its deficiency leads to premature differentiation. Clasp2-deficient keratinocytes exhibit increased centrosomal numbers and numerous mitotic alterations, including multipolar spindles and chromosomal misalignments that overall result in mitotic stress and a high DNA content. Such mitotic block prompts premature keratinocyte differentiation in a p53-dependent manner in the absence of cell death. Our findings reveal a new role for Clasp2 in governing keratinocyte undifferentiated features and highlight the presence of surveillance mechanisms that prevent cell cycle entry in cells that have alterations in the DNA content.

The potential reproductive contribution of Mediterranean migrating eels to the Anguilla anguilla stock.

The European eel is a highly migratory fish. After the reproduction in the Sargasso Sea early larval-stages start a passive ocean migration towards European and Mediterranean continental waters. After several years as yellow eels, mature adults change to silver stage and then start their return trip. The trajectory of their backward migration is unknown, because of low probability of capturing migrating individuals, having this capture never been reported in the Mediterranean. Recently, 8 silver eels were collected in the Strait of Sicily. Using literature information about possible individual route and speed, their geographical position was projected up to the spawning site during reproductive season. Despite using optimal and continuous migration swimming speed, none of the specimens may have been able to reach the Sargasso Sea in time for mating. Subsequently, to identify putative Mediterranean areas from which eels could have been reaching the spawning grounds on time, a backward scenario was postulated using the previous scientific assumptions. Our results suggests that just a small quota of Mediterranean silver males successfully reaches the Sargasso area, and only females from the westernmost and central parts of the basin could be able to fruitfully pond their eggs during the supposed spawning period.

Assessment of quality-assured Tarocco orange fruit sorting rules by combined physicochemical and sensory testing.

BACKGROUND: The aim of this study was to extract a sorting rule for Tarocco orange fruit from several physicochemical and sensory tests performed on a marketable lot of 399 Tarocco orange fruits. RESULTS: The elastic tension at 5% strain (T5 ) was found to be linearly correlated (r = 0.65) with the Magness-Taylor (MT) index. Thus T5 was regarded as a non-destructive parameter quantifying fruit firmness and used to categorise the aforementioned lot in three different firmness classes, high (HF), medium (MF) and low (LF). Only the MT index, fruit rind thickness near the fruit peduncle, lightness coefficient and yellow/blue hue component of the orange flesh, as well as total soluble solid content, confirmed the validity of this discrimination at the significance level of 5%. Sensory professionals recognised the greater compactness (7 ± 2) but lower ease of peeling (4 ± 2) and segment separation (4 ± 2) of the HF oranges with respect to the corresponding sensory attributes of orange fruits grouped in the MF and LF classes. CONCLUSION: To limit the costly rejection of Tarocco orange fruit considered too soft, especially after long-term shipping, it would be reasonable to select only fruits characterised by a compressive force or tension at 5% strain in the range 23-41 N or 300-540 N m⁻¹ respectively.

RGB color calibration for quantitative image analysis: the "3D thin-plate spline" warping approach.

In the last years the need to numerically define color by its coordinates in n-dimensional space has increased strongly. Colorimetric calibration is fundamental in food processing and other biological disciplines to quantitatively compare samples' color during workflow with many devices. Several software programmes are available to perform standardized colorimetric procedures, but they are often too imprecise for scientific purposes. In this study, we applied the Thin-Plate Spline interpolation algorithm to calibrate colours in sRGB space (the corresponding Matlab code is reported in the Appendix). This was compared with other two approaches. The first is based on a commercial calibration system (ProfileMaker) and the second on a Partial Least Square analysis. Moreover, to explore device variability and resolution two different cameras were adopted and for each sensor, three consecutive pictures were acquired under four different light conditions. According to our results, the Thin-Plate Spline approach reported a very high efficiency of calibration allowing the possibility to create a revolution in the in-field applicative context of colour quantification not only in food sciences, but also in other biological disciplines. These results are of great importance for scientific color evaluation when lighting conditions are not controlled. Moreover, it allows the use of low cost instruments while still returning scientifically sound quantitative data.

Electronic nose application for determination of Penicillium digitatum in Valencia oranges.

BACKGROUND: Penicillium digitatum and Penicillium italicum are responsible for one the most serious diseases occurring during storage of citrus fruits. Its early detection allows a relevant increase in shelf life, and in situ monitoring of fungal infections represents a very efficient tool to improve storage quality. In the case of metabolic alterations due to physiological or fungal pathologies, olfactometric analysis allows the detection of specific volatile biomarkers, thus providing an effective tool for postharvest quality control of fruits and vegetables. RESULTS: A total of 300 Valencia oranges were analysed with an electronic nose and results were screened by a multivariate classification technique, partial least squares discriminant analysis, in order to investigate whether the electronic nose could distinguish between Penicillium-infected and non-infected samples and to evaluate the efficiency of the group classifications. High percentages of correct classification were obtained at low levels of infection (100% for 2-5% infection in an independent test). CONCLUSION: The electronic nose may be successfully applied as a reliable, non-destructive and non-contact indirect technology for the identification of fungal strains in storage rooms, especially when the infection occurs in small percentages that are not easily identifiable by classic methodologies of inspection.

Nitrogen concentration estimation in tomato leaves by VIS-NIR non-destructive spectroscopy.

Nitrogen concentration in plants is normally determined by expensive and time consuming chemical analyses. As an alternative, chlorophyll meter readings and N-NO(3) concentration determination in petiole sap were proposed, but these assays are not always satisfactory. Spectral reflectance values of tomato leaves obtained by visible-near infrared spectrophotometry are reported to be a powerful tool for the diagnosis of plant nutritional status. The aim of the study was to evaluate the possibility and the accuracy of the estimation of tomato leaf nitrogen concentration performed through a rapid, portable and non-destructive system, in comparison with chemical standard analyses, chlorophyll meter readings and N-NO(3) concentration in petiole sap. Mean reflectance leaf values were compared to each reference chemical value by partial least squares chemometric multivariate methods. The correlation between predicted values from spectral reflectance analysis and the observed chemical values showed in the independent test highly significant correlation coefficient (r = 0.94). The utilization of the proposed system, increasing efficiency, allows better knowledge of nutritional status of tomato plants, with more detailed and sharp information and on wider areas. More detailed information both in space and time is an essential tool to increase and stabilize crop quality levels and to optimize the nutrient use efficiency.

Pomology observations, morphometric analysis, ultrastructural study and allelic profiles of "olivastra Seggianese" endocarps from ancient olive trees (Olea europaea L.).

Preliminary studies of historical sources and remote sensing were used to identify ancient olive trees near archaeological sites and heritage buildings in the Orcia Valley (Siena, Italy). Distinctive characters were assessed by traditional pomological observation. Trees with similar characters were selected on the basis of the features of endocarps, the only structure that survives aerobic deterioration and conserves useful botanical information for centuries. Non-invasive morphometric analysis of endocarp size and shape established morphological variations in individuals of different populations. Plastid organization in the endocarp and location of DNA in the endocarp tegument were detected by morphological and ultrastructural observations using light and electron microscopy. Cytoplasmic markers with high polymorphism were used to test similarity of endocarp and leaf DNA within individuals and to confirm low variability and minimal divergence between individuals. The ancient trees studied showed the same allelic profiles and therefore belonged to a distinct cultivar. The traditional pomological descriptions of the trees, leaves and fruits, morphometric analysis of size, and shape elliptic Fourier analysis of endocarp outline, ultrastructural observations and allelic profiles of endocarp tegument delineated the general species-specific qualities of the cultivar "olivastra Seggianese" of the Orcia Valley.

Development of a rapid soil water content detection technique using active infrared thermal methods for in-field applications.

The aim of this study was to investigate the suitability of active infrared thermography and thermometry in combination with multivariate statistical partial least squares analysis as rapid soil water content detection techniques both in the laboratory and the field. Such techniques allow fast soil water content measurements helpful in both agricultural and environmental fields. These techniques, based on the theory of heat dissipation, were tested by directly measuring temperature dynamic variation of samples after heating. For the assessment of temperature dynamic variations data were collected during three intervals (3, 6 and 10 s). To account for the presence of specific heats differences between water and soil, the analyses were regulated using slopes to linearly describe their trends. For all analyses, the best model was achieved for a 10 s slope. Three different approaches were considered, two in the laboratory and one in the field. The first laboratory-based one was centred on active infrared thermography, considered measurement of temperature variation as independent variable and reported r = 0.74. The second laboratory-based one was focused on active infrared thermometry, added irradiation as independent variable and reported r = 0.76. The in-field experiment was performed by active infrared thermometry, heating bare soil by solar irradiance after exposure due to primary tillage. Some meteorological parameters were inserted as independent variables in the prediction model, which presented r = 0.61. In order to obtain more general and wide estimations in-field a Partial Least Squares Discriminant Analysis on three classes of percentage of soil water content was performed obtaining a high correct classification in the test (88.89%). The prediction error values were lower in the field with respect to laboratory analyses. Both techniques could be used in conjunction with a Geographic Information System for obtaining detailed information on soil heterogeneity.

Automated image analysis for the detection of benthic crustaceans and bacterial mat coverage using the VENUS undersea cabled network.

The development and deployment of sensors for undersea cabled observatories is presently biased toward the measurement of habitat variables, while sensor technologies for biological community characterization through species identification and individual counting are less common. The VENUS cabled multisensory network (Vancouver Island, Canada) deploys seafloor camera systems at several sites. Our objective in this study was to implement new automated image analysis protocols for the recognition and counting of benthic decapods (i.e., the galatheid squat lobster, Munida quadrispina), as well as for the evaluation of changes in bacterial mat coverage (i.e., Beggiatoa spp.), using a camera deployed in Saanich Inlet (103 m depth). For the counting of Munida we remotely acquired 100 digital photos at hourly intervals from 2 to 6 December 2009. In the case of bacterial mat coverage estimation, images were taken from 2 to 8 December 2009 at the same time frequency. The automated image analysis protocols for both study cases were created in MatLab 7.1. Automation for Munida counting incorporated the combination of both filtering and background correction (Median- and Top-Hat Filters) with Euclidean Distances (ED) on Red-Green-Blue (RGB) channels. The Scale-Invariant Feature Transform (SIFT) features and Fourier Descriptors (FD) of tracked objects were then extracted. Animal classifications were carried out with the tools of morphometric multivariate statistic (i.e., Partial Least Square Discriminant Analysis; PLSDA) on Mean RGB (RGBv) value for each object and Fourier Descriptors (RGBv+FD) matrices plus SIFT and ED. The SIFT approach returned the better results. Higher percentages of images were correctly classified and lower misclassification errors (an animal is present but not detected) occurred. In contrast, RGBv+FD and ED resulted in a high incidence of records being generated for non-present animals. Bacterial mat coverage was estimated in terms of Percent Coverage and Fractal Dimension. A constant Region of Interest (ROI) was defined and background extraction by a Gaussian Blurring Filter was performed. Image subtraction within ROI was followed by the sum of the RGB channels matrices. Percent Coverage was calculated on the resulting image. Fractal Dimension was estimated using the box-counting method. The images were then resized to a dimension in pixels equal to a power of 2, allowing subdivision into sub-multiple quadrants. In comparisons of manual and automated Percent Coverage and Fractal Dimension estimates, the former showed an overestimation tendency for both parameters. The primary limitations on the automatic analysis of benthic images were habitat variations in sediment texture and water column turbidity. The application of filters for background corrections is a required preliminary step for the efficient recognition of animals and bacterial mat patches.