Antioxidant enzyme activity and hydrogen peroxide content during the drying of Arabica coffee beans

The development of the germination process and drought stress during the drying of coffee can generate reactive oxygen species, which can be neutralized by way of antioxidant mechanisms. No studies related to antioxidant enzymes during the drying of coffee were found in the literature, and considering their importance, the enzymatic activities of superoxide dismutase (SOD), guaiacol peroxidase (GPOX) and glutathione reductase (GR), and also the hydrogen peroxide content were evaluated during the drying of two types of coffee bean, one processed as natural coffee and the other as pulped natural coffee. The results showed a reduction in the SOD, GPOX and GR enzymatic activities of the natural coffee as compared to the pulped natural coffee during the drying period. Moreover, the hydrogen peroxide content of the natural coffee was greater than that of the pulped natural coffee. These results suggest the development of oxidative stress during the coffee drying process, controlled more efficiently in pulped natural coffee by the early action of GPOX during the drying process. Nevertheless, differential responses by SOD isoenzymes and possibly the role of other peroxidases also appear to be involved in the responses observed.     

Robust automated multiple view inspection

Recently, Automated Multiple View Inspection (AMVI) has been developed for automated defect detection of manufactured objects, and the framework was successfully implemented for calibrated image sequences. However, it is not easy to be implemented in industrial environments because the calibration is a difficult and an unstable process. To overcome these disadvantages, the robust AMVI strategy, which assumes that an unknown affine transformation exists between each pair of uncalibrated images, is proposed. This transformation is estimated using two complementary robust procedures: a global approximation of the affine mapping is computed by creating candidate correspondences via B-splines and selecting those which better satisfy the epipolar constraint for uncalibrated images. Then, we use this approximation as initial estimate of a robust intensity-based matching approach, which is applied locally on each potential defect. The result is that false alarms are discarded, and the defects of an industrial object are actually tracked along the uncalibrated image sequence. The method is successful as shown in our experiments on aluminum die castings.     

Automated fish bone detection using X-ray imaging

In countries where fish is often consumed, fish bones are some of the most frequently ingested foreign bodies encountered in foods. In the production of fish fillets, fish bone detection is performed by human inspection using their sense of touch and vision which can lead to misclassification. Effective detection of fish bones in the quality control process would help avoid this problem. For this reason, an X-ray machine vision approach to automatically detect fish bones in fish fillets was developed. This paper describes our approach and the corresponding experiments with salmon and trout fillets. In the experiments, salmon X-ray images using 10 × 10 pixels detection windows and 24 intensity features (selected from 279 features) were analyzed. The methodology was validated using representative fish bones and trouts provided by a salmon industry and yielded a detection performance of 99%. We believe that the proposed approach opens new possibilities in the field of automated visual inspection of salmon, trout and other similar fish.     

Threat Objects Detection in X-ray Images Using an Active Vision Approach

X-ray testing for baggage inspection has been increasingly used at airports, reducing the risk of terrorist crimes and attacks. Nevertheless, this task is still being carried out by human inspectors and with limited technological support. The technology that is being used is not always effective, as it depends mainly on the position of the object of interest, occlusion, and the accumulated experience of the inspector. Due to this problem, we have developed an approach that inspects X-ray images using active vision in order to automatically detect objects that represent a threat. Our method includes three steps: detection of potential threat objects in single views based on the similarity of features and spatial distribution; estimation of the best-next-view using Q-learning; and elimination of false alarms based on multiple view constraints. We tested our algorithm on X-ray images that included handguns and razor blades. In the detection of handguns we registered good results for recall and precision (Re = 67%, Pr = 83%) along with a high performance in the detection of razor blades (Re = 82%, Pr = 100%) taking into consideration 360 inspections in each case. Our results indicate that non-destructive inspection actively using X-ray images, leads to more effective object detection in complex environments, and helps to offset certain levels of occlusion and the internal disorder of baggage.     

Further studies on a DTBX prototype for the CMS muon detector at LHC

The performance of a small prototype chamber of the baseline project for the muon barrel detector for CMS has been studied in a muon beam. Its efficiency with different gases and wire diameters, the trigger possibilities and the response in presence of a large number of electromagnetic secondaries associated to the muon are evaluated. The results are compared with a full Monte Carlo simulation.     

Automated Design of a Computer Vision System for Visual Food Quality Evaluation

Considerable research efforts in computer vision applied to food quality evaluation have been developed in the last years; however, they have been concentrated on using or developing tailored methods based on visual features that are able to solve a specific task. Nevertheless, today’s computer capabilities are giving us new ways to solve complex computer vision problems. In particular, a new paradigm on machine learning techniques has emerged posing the task of recognizing visual patterns as a search problem based on training data and a hypothesis space composed by visual features and suitable classifiers. Furthermore, now we are able to extract, process, and test in the same time more image features and classifiers than before. Thus, we propose a general framework that designs a computer vision system automatically, i.e., it finds—without human interaction—the features and the classifiers for a given application avoiding the classical trial and error framework commonly used by human designers. The key idea of the proposed framework is to select—automatically—from a large set of features and a bank of classifiers those features and classifiers that achieve the highest performance. We tested our framework on eight different food quality evaluation problems yielding a classification performance of 95 % or more in every case. The proposed framework was implemented as a Matlab Toolbox available for noncommercial purposes.     

Quality classification of corn tortillas using computer vision

Computer vision is playing an increasingly important role in automated visual food inspection. However, quality control in tortilla production is still performed by human operators which may lead to misclassification due to their subjectivity and fatigue. In order to reduce the need for human operators and therefore misclassification, we developed a computer vision framework to automatically classify the quality of corn tortillas according to five hedonic sub-classes given by a sensorial panel. The proposed framework analyzed 750 corn tortillas obtained from 15 different Mexican commercial stores which were either small, medium or large in size. More than 2300 geometric and color features were extracted from 1500 images capturing both sides of the 750 tortillas. After implementing a feature selection algorithm, in which the most relevant features were selected for the classification of the five sub-classes, only 64 features were required to design a classifier based on support vector machines. Cross-validation yielded a performance of 95% in the classification of the five hedonic sub-classes. Additionally, using only 10 of the selected features and a simple statistical classifier, it was possible to determine the origin of the tortillas with a performance of 96%. We believe that the proposed framework opens up new possibilities in the field of automated visual inspection of tortillas.     

Sources and sinks of hydroxyl radicals upon irradiation of natural water samples

Hydroxyl radical formation rates, steady-state concentration, and overall scavenging rate constant were measured by irradiation of surface lake water samples from Piedmont (NW Italy) and nitrate-rich groundwater samples from Moldova (NE Romania). Dissolved organic matter (DOM) was the main source and sink of *OH upon lake water irradiation, with [*OH] being independent of DOM amount. Water oxidation by photoexcited DOM is a likely *OH source in the presence of very low levels of nitrate and dissolved iron. Under different circumstances it is not possible to exclude other processes, e.g., DOM-enhanced photo-Fenton reactions. Under the hypotheses of no interaction and absence of mutual screening of radiation, nitrate would prevail over DOM as *OH source for a NO3-/DOM ratio higher than 3.3 x 10(-5) (mol NO3-) (mg C)(-1), DOM prevailing for lower values. Substantial DOM photolability was observed upon irradiation of nitrate-rich groundwater, mainly due to the elevated *OH generation rate. For the first time to our knowledge, evidence was also obtained of the photoformation of potentially toxic and/or mutagenic nitroaromatic compounds upon irradiation of natural lake water and groundwater samples, proportionally to the nitrate levels.