Abstract
BACKGROUND: Freezing represents a common conservation practice regarding vegetal foodstuffs. Since compositional features need to be monitored during storage, the development of rapid monitoring tools suitable for assessing nutritional characteristics arises as a pertinent issue. In this study, cowpea (Vigna unguiculata L.) pods, both fresh and after 6 and 9 months of freezing at -18 degrees C, were evaluated by high-performance liquid chromatography for their content of protein as well as of essential and nonessential amino acids, while their Fourier transform infrared spectra in the mid infrared (MIR) and near infrared (NIR) ranges were concomitantly registered to assess the feasibility of this approach for the traceability of these frozen matrices. RESULTS: For the NIR interval, the application of the 1st derivative to the spectral data retrieved the best results, while for lower concentrations the application of the Savitzky-Golay algorithm was indispensable to achieve quantification models for the amino acids. MIR is also suitable for this purpose, though being unable to quantify amino acids with concentrations below 0.07 mmol g(-1) dry weight, irrespective of the data treatment used. CONCLUSIONS: The spectroscopic approach constitutes a methodology suitable for monitoring the impact of freezing on the nutritional properties of cowpea pods, allowing accurate quantification of the protein and amino acid contents, while NIR displayed better performance. (C) 2017 Society of Chemical Industry

Abstract
BACKGROUND: Freezing represents an important storage method for vegetal foodstuffs, such as cowpea pods, and thus the impact of this process on the chemical composition of these matrices arises as a prominent issue. In this sense, the phytochemical contents in frozen cowpea pods (i.e. at 6 and 9 months) have been compared with fresh cowpea pods material, with the samples being concomitantly assessed by Fourier-transform infrared spectroscopy (FTIR), both mid-infrared (MIR) and near infrared (NIR), aiming to evaluate the potential of these techniques as a rapid tool for the traceability of these matrices. RESULTS: A decrease in phytochemical contents during freezing was observed, allowing the classification of samples according to the freezing period based on such variations. Also, MIR and NIR allowed discrimination of samples: the use of the first derivative demonstrated a better performance for this purpose, whereas the use of the normalized spectra gave the best correlations between the spectra and specific contents. In both cases, NIR displayed the best performance. CONCLUSION: Freezing of cowpea pods leads to a decrease of phytochemical contents, which can be monitored by FTIR spectroscopy, both within the MIR and NIR ranges, whereas the use of this technique, in tandem with chemometrics, constitutes a suitable methodology for the traceability of these matrices. (C) 2017 Society of Chemical Industry

Abstract
Olive fruits, as well as their corresponding oil, represent an interesting source of phytochemicals, mainly phenolic compounds, which arise as secondary metabolites, resulting from the plant's response to biotic and abiotic stresses. Therefore, olive fruits from three distinct cultivars ("Cobrancosa," "Galega Vulgar," and "Picual") grown in Portugal and displaying different degree of resistance to biotic and abiotic stresses have been studied in relation to the concentration of total phenolic compounds, orthodiphenols and flavonoids, besides antioxidant capacity (DPPH, ABTS, FRAP, and ORACFL), in three maturation stages and two distinct harvest seasons (2012-2013). Generally, a decrease of all phenolic contents throughout the maturation stages has been observed, while, concerning cultivar, green olives of "Cobrancosa" showed the highest values for all contents assessed, denoting a strong influence of the genetic background. The same trend has not been observed regarding antioxidant activity, since Cobrancosa and Galega Vulgar cultivars presented the highest values only for the DPPH and ORACFL assays. Moreover, multivariate analyses pointed to the preponderance of the cultivars' phenolic composition in the semiripe stage for the resistance to biotic stress, with "Galega" the most susceptible cultivar, presenting the lowest contents at this maturation stage, whereas "Picual" displayed the most pronounced phytochemical response.

Abstract
Crop monitoring and harvesting by ground robots in steep slope vineyards is an intrinsically complex challenge, due to two main reasons: harsh conditions of the terrain and reduced time availability and unstable localization accuracy of the Global Positioning System (GPS). In this paper the use of agricultural wireless sensors as artificial landmarks for robot localization is explored. The Received Signal Strength Indication (RSSI), of Bluetooth (BT) based sensors/technology, has been characterized for distance estimation. Based on this characterization, a mapping procedure based on Histogram Mapping concept was evaluated. The results allow us to conclude that agricultural wireless sensors can be used to support the robot localization procedures in critical moments (GPS blockage) and to create redundant localization information.

Abstract
Reliable banknote recognition is critical for detecting counterfeit banknotes in ATMs and help visual impaired people. To solve this problem, it was implemented a computer vision system that can recognize multiple banknotes in different perspective views and scales, even when they are within cluttered environments in which the lighting conditions may vary considerably. The system is also able to recognize banknotes that are partially visible, folded, wrinkled or even worn by usage. To accomplish this task, the system relies on computer vision algorithms, such as image preprocessing, feature detection, description and matching. To improve the confidence of the banknote recognition the feature matching results are used to compute the contour of the banknotes using an homography that later on is validated using shape analysis algorithms. The system successfully recognized all Euro banknotes in 80 test images even when there were several overlapping banknotes in the same test image.

Abstract
In this article, a course that explores the potential of learning ROS using a collaborative game world is presented. The competitive mindset and its origins are explored, and an analysis of a collaborative game is presented in detail, showing how some key design features lead participants to overcome the challenges proposed through cooperation and collaboration. The data analysis is supported through observation of two different game simulations: the first, where all competitors were playing solo, and the second, where the players were divided in groups of three. Lastly, the authors reflect on the potentials that this course provides as a tool for learning ROS.