Assessing the Rheological Properties of Durum Wheat Semolina: A Review.

Empiric rheology is considered a useful tool for assessing the technological quality of wheat. Over the decades, several tests have been adapted from common to durum wheat, and new approaches have been proposed to meet the needs of the players of the durum wheat value chain. Breeders are looking for reliable methods to test the functional quality of wheat lines at early stages, where there are limited amounts of sample; millers need fast and reliable methods for checking wheat quality right at the point of the receiving station; and pasta-makers are looking for suitable methods to predict end product quality. This review provides an overview of the strengths and weaknesses of the rheological tests currently used to evaluate the quality of durum wheat semolina, with the emphasis on Europe. Moreover, the relationships among the parameters obtained from different rheological approaches are extrapolated from the literature and integrated with the data obtained from 74 samples of durum wheat semolina. Although numerous efforts have been made to propose rapid and reliable tests for semolina characterization, the ideal test has yet to be proposed, indicating that researchers and pasta companies need to focus on perfecting the way to assess the quality of durum wheat and pasta.

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.

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).

Plant Phenotyping Research Trends, a Science Mapping Approach.

Modern plant phenotyping, often using non-invasive technologies and digital technologies, is an emerging science and provides essential information on how genetics, epigenetics, environmental pressures, and crop management (farming) can guide selection toward productive plants suitable for their environment. Thus, phenotyping is at the forefront of future plant breeding. Bibliometric science mapping is a quantitative method that analyzes scientific publications throughout the terms present in their title, abstract, and keywords. The aim of this mapping exercise is to observe trends and identify research opportunities. This allows us to analyze the evolution of phenotyping research and to predict emerging topics of this discipline. A total of 1,827 scientific publications fitted our search method over the last 20 years. During the period 1997-2006, the total number of publications was only around 6.1%. The number of publications increased more steeply after 2010, boosted by the overcoming of technological bias and by a set of key developments at hard and software level (image analysis and data storage management, automation and robotics). Cluster analysis evidenced three main groups linked to genetics, physiology, and imaging. Mainly the model plant "Arabidopsis thaliana" and the crops "rice" and "triticum" species were investigated in the literature. The last two species were studied when addressing "plant breeding," and "genomic selection." However, currently the trend goes toward a higher diversity of phenotyped crops and research in the field. The application of plant phenotyping in the field is still under rapid development and this application has strong linkages with precision agriculture. EU co-authors were involved in 41.8% of the analyzed papers, followed by USA (15.4%), Australia (6.0%), and India (5.6%). Within the EU, coauthors were mainly affiliated in Germany (35.8%), France (23.7%), and United Kingdom (18.4%). Time seems right for new opportunities to incentivize research on more crops, in real field conditions, and to spread knowledge toward more countries, including emerging economies. Science mapping offers the possibility to get insights into a wide amount of bibliographic information, making them more manageable, attractive, and easy to serve science policy makers, stakeholders, and research managers.

Class-modeling approach to PTR-TOFMS data: a peppers case study.

BACKGROUND: Proton transfer reaction-mass spectrometry (PTR-MS), in its recently developed implementation based on time-of-flight mass spectrometry (PTR-TOFMS), was used to rapidly determine the volatile compounds present in fruits of Capsicum spp. RESULTS: We analyzed the volatile organic compounds emission profile of freshly cut chili peppers belonging to three species and 33 different cultivars. PTR-TOFMS data, analyzed with appropriate and advanced multivariate class-modeling approaches, perfectly discriminated among the three species (100% correct classification in validation set). VIP (variable importance in projection) scores were used to select the 15 most important volatile compounds in discriminating the species. The best candidates for Capsicum spp. were compounds with measured m/z of 63.027, 101.096 and 107.050, which were, respectively, tentatively identified as dimethyl sulfide, hexanal and benzaldehyde. CONCLUSIONS: Based on the promising results, the possibility of introducing multivariate class-modeling techniques, different from the classification approaches, in the field of volatile compounds analyses is discussed.

A new colorimetrically-calibrated automated video-imaging protocol for day-night fish counting at the OBSEA coastal cabled observatory.

Field measurements of the swimming activity rhythms of fishes are scant due to the difficulty of counting individuals at a high frequency over a long period of time. Cabled observatory video monitoring allows such a sampling at a high frequency over unlimited periods of time. Unfortunately, automation for the extraction of biological information (i.e., animals' visual counts per unit of time) is still a major bottleneck. In this study, we describe a new automated video-imaging protocol for the 24-h continuous counting of fishes in colorimetrically calibrated time-lapse photographic outputs, taken by a shallow water (20 m depth) cabled video-platform, the OBSEA. The spectral reflectance value for each patch was measured between 400 to 700 nm and then converted into standard RGB, used as a reference for all subsequent calibrations. All the images were acquired within a standardized Region Of Interest (ROI), represented by a 2 × 2 m methacrylate panel, endowed with a 9-colour calibration chart, and calibrated using the recently implemented "3D Thin-Plate Spline" warping approach in order to numerically define color by its coordinates in n-dimensional space. That operation was repeated on a subset of images, 500 images as a training set, manually selected since acquired under optimum visibility conditions. All images plus those for the training set were ordered together through Principal Component Analysis allowing the selection of 614 images (67.6%) out of 908 as a total corresponding to 18 days (at 30 min frequency). The Roberts operator (used in image processing and computer vision for edge detection) was used to highlights regions of high spatial colour gradient corresponding to fishes' bodies. Time series in manual and visual counts were compared together for efficiency evaluation. Periodogram and waveform analysis outputs provided very similar results, although quantified parameters in relation to the strength of respective rhythms were different. Results indicate that automation efficiency is limited by optimum visibility conditions. Data sets from manual counting present the larger day-night fluctuations in comparison to those derived from automation. This comparison indicates that the automation protocol subestimate fish numbers but it is anyway suitable for the study of community activity rhythms.

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.

A new laboratory radio frequency identification (RFID) system for behavioural tracking of marine organisms.

Radio frequency identification (RFID) devices are currently used to quantify several traits of animal behaviour with potential applications for the study of marine organisms. To date, behavioural studies with marine organisms are rare because of the technical difficulty of propagating radio waves within the saltwater medium. We present a novel RFID tracking system to study the burrowing behaviour of a valuable fishery resource, the Norway lobster (Nephrops norvegicus L.). The system consists of a network of six controllers, each handling a group of seven antennas. That network was placed below a microcosm tank that recreated important features typical of Nephrops' grounds, such as the presence of multiple burrows. The animals carried a passive transponder attached to their telson, operating at 13.56 MHz. The tracking system was implemented to concurrently report the behaviour of up to three individuals, in terms of their travelled distances in a specified unit of time and their preferential positioning within the antenna network. To do so, the controllers worked in parallel to send the antenna data to a computer via a USB connection. The tracking accuracy of the system was evaluated by concurrently recording the animals' behaviour with automated video imaging. During the two experiments, each lasting approximately one week, two different groups of three animals each showed a variable burrow occupancy and a nocturnal displacement under a standard photoperiod regime (12 h light:12 h dark), measured using the RFID method. Similar results were obtained with the video imaging. Our implemented RFID system was therefore capable of efficiently tracking the tested organisms and has a good potential for use on a wide variety of other marine organisms of commercial, aquaculture, and ecological interest.

The new Seafloor Observatory (OBSEA) for remote and long-term coastal ecosystem monitoring.

A suitable sampling technology to identify species and to estimate population dynamics based on individual counts at different temporal levels in relation to habitat variations is increasingly important for fishery management and biodiversity studies. In the past two decades, as interest in exploring the oceans for valuable resources and in protecting these resources from overexploitation have grown, the number of cabled (permanent) submarine multiparametric platforms with video stations has increased. Prior to the development of seafloor observatories, the majority of autonomous stations were battery powered and stored data locally. The recently installed low-cost, multiparametric, expandable, cabled coastal Seafloor Observatory (OBSEA), located 4 km off of Vilanova i la Gertrú, Barcelona, at a depth of 20 m, is directly connected to a ground station by a telecommunication cable; thus, it is not affected by the limitations associated with previous observation technologies. OBSEA is part of the European Multidisciplinary Seafloor Observatory (EMSO) infrastructure, and its activities are included among the Network of Excellence of the European Seas Observatory NETwork (ESONET). OBSEA enables remote, long-term, and continuous surveys of the local ecosystem by acquiring synchronous multiparametric habitat data and bio-data with the following sensors: Conductivity-Temperature-Depth (CTD) sensors for salinity, temperature, and pressure; Acoustic Doppler Current Profilers (ADCP) for current speed and direction, including a turbidity meter and a fluorometer (for the determination of chlorophyll concentration); a hydrophone; a seismometer; and finally, a video camera for automated image analysis in relation to species classification and tracking. Images can be monitored in real time, and all data can be stored for future studies. In this article, the various components of OBSEA are described, including its hardware (the sensors and the network of marine and land nodes), software (data acquisition, transmission, processing, and storage), and multiparametric measurement (habitat and bio-data time series) capabilities. A one-month multiparametric survey of habitat parameters was conducted during 2009 and 2010 to demonstrate these functions. An automated video image analysis protocol was also developed for fish counting in the water column, a method that can be used with cabled coastal observatories working with still images. Finally, bio-data time series were coupled with data from other oceanographic sensors to demonstrate the utility of OBSEA in studies of ecosystem dynamics.

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.

Accumulation of heavy metals to assess the health status of swordfish in a comparative analysis of Mediterranean and Atlantic areas.

During the last few decades, the combined effects of natural and human activities acting on the Mediterranean Sea basin have caused a reduction in the swordfish (Xiphias gladius, L. 1758) population. In this project, we investigated the accumulation of lead (Pb), cadmium (Cd) and mercury (Hg) levels in the Atlantic and Mediterranean populations of swordfish during a five-year survey. In the marine environment, top predators such as swordfish accumulate high concentrations of toxic metals, and thus, potentially incur a high toxicological risk. Furthermore, heavy metals, such as chemical pollutants, have strong long-term effects on fish, and thus, constitute a high risk for the resource and humans that consume it. The aim of this work is to contribute to the assessment of the state of European swordfish population health. We analyzed muscle tissue from 56 specimens captured in Mediterranean and Atlantic areas for trace elements. Mean concentrations of Hg, Cd, and Pb were in the following ranges: 0.66-2.41, 0.04-0.16, and 0.97-1.36 mg/kg ww, respectively. These data suggest a need for continuous monitoring to avoid reductions in the population of this fish species of high commercial and ecological interest.

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.

Activity rhythms in the deep-sea: a chronobiological approach.

Ocean waters deeper than 200 m cover 70% of the Earth's surface. Light intensity gets progressively weaker with increasing depth and internal tides or inertial currents may be the only remaining zeitgebers regulating biorhythms in deep-sea decapods. Benthopelagic coupling, exemplified by vertically moving shrimps within the water column, may also act as a source of indirect synchronisation to the day-night cycle for species living in permanently dark areas. At the same time, seasonal rhythms in growth and reproduction may be an exogenous response to spring-summer changes in upper layer productivity (via phytoplankton) or, alternatively, may be provoked by the synchronisation mediated by an endogenous controlling mechanism (via melatonin). In our review, we will focus on the behavioural rhythms of crustacean decapods inhabiting depths where the sun light is absent. Potential scenarios for future research on deep-sea decapod behaviour are suggested by new in situ observation technologies. Permanent video observatories are, to date, one of the most important tools for marine chronobiology in terms of species recognition and animals' movement tracking.

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.

A new morphometric implemented video-image analysis protocol for the study of social modulation in activity rhythms of marine organisms.

Video-image analysis can be an efficient tool for microcosm experiments portraying the modulation of individual behaviour based on sociality. The Norway lobster, Nephrops norvegicus is a burrowing decapod the commercial capture of which occurs by trawling only when animals are engaged in seabed excursions. Emergence behaviour is modulated by the day-night cycle but a further modulation occurs upon social interaction in a still unknown fashion. Here, we present a novel automated protocol for the tracking of the movement of different animals at once based on a multivariate morphometric approach. Four black and white tags were customized according to a precise geometric design. Shape Matching and Complex Fourier Descriptors analyses were used to track tag displacement through consecutive frames in a 7-day experiment under monochromatic blue light (480 nm)-darkness conditions. Shape Matching errors were evaluated in relation to tag geometry. Time series of centroid coordinates in pixels were transformed in centimetres. The FD analysis was slightly less efficient than the Shape Matching, although more rapid (i.e. up to 20 times faster). Nocturnal rhythms were reported for all animals. Waveform analysis indicated marked differences in the amplitude of activity phases as proof of interindividual interaction. Total diel activity presented a decrease in the rate of out of burrow locomotion as the testing progressed. N. norvegicus is a nocturnal species and present observations sustain the efficiency and fidelity of our automated tracking system.

A novel morphometry-based protocol of automated video-image analysis for species recognition and activity rhythms monitoring in deep-sea fauna.

The understanding of ecosystem dynamics in deep-sea areas is to date limited by technical constraints on sampling repetition. We have elaborated a morphometry-based protocol for automated video-image analysis where animal movement tracking (by frame subtraction) is accompanied by species identification from animals' outlines by Fourier Descriptors and Standard K-Nearest Neighbours methods. One-week footage from a permanent video-station located at 1,100 m depth in Sagami Bay (Central Japan) was analysed. Out of 150,000 frames (1 per 4 s), a subset of 10.000 was analyzed by a trained operator to increase the efficiency of the automated procedure. Error estimation of the automated and trained operator procedure was computed as a measure of protocol performance. Three displacing species were identified as the most recurrent: Zoarcid fishes (eelpouts), red crabs (Paralomis multispina), and snails (Buccinum soyomaruae). Species identification with KNN thresholding produced better results in automated motion detection. Results were discussed assuming that the technological bottleneck is to date deeply conditioning the exploration of the deep-sea.