Chemometric Studies on zNose™ and Machine Vision Technologies for Discrimination of Commercial Extra Virgin Olive Oils

The aim of this study was to classify Turkish commercial extra virgin olive oil (EVOO) samples according to geographical origins by using surface acoustic wave sensing electronic nose (zNose?) and machine vision system (MVS) analyses in combination with chemometric approaches. EVOO samples obtained from north and south Aegean region were used in the study. The data analyses were performed with principal component analysis class models, partial least squares‐discriminant analysis (PLS‐DA) and hierarchical cluster analysis (HCA). Based on the zNose? analysis, it was found that EVOO aroma profiles could be discriminated successfully according to geographical origin of the samples with the aid of the PLS‐DA method. Color analysis was conducted as an additional sensory quality parameter that is preferred by the consumers. The results of HCA and PLS‐DA methods demonstrated that color measurement alone was not an effective discriminative factor for classification of EVOO. However, PLS‐DA and HCA methods provided clear differentiation among the EVOO samples in terms of electronic nose and color measurements. This study is significant from the point of evaluating the potential of zNose? in combination with MVS as a rapid method for the classification of geographically different EVOO produced in industry.     

Computation of dynamic program slices for unstructured programs

A dynamic program slice is an executable part of the program whose behaviour is identical, for the same program input, to that of the original program with respect to a variable(s) of interest at some execution position. The existing algorithms of dynamic slice computation use data and control dependencies to compute dynamic slices. These algorithms are limited to structured programs because they may compute incorrect dynamic slices for unstructured programs, due to the limitations of control dependencies that are used to compute dynamic slices. In this paper, we present a novel approach to dynamic slice computation for unstructured programs. The approach employs the notion of a removable block in finding dynamic program slices. Dynamic slices are derived by identifying not only those parts of program execution that contribute to the computation of the value of a variable of interest, but also those parts of program execution that do not contribute to the computation of the variable value. Data dependencies are used to identify contributing computations, whereas removable blocks are used to identify noncontributing computations. We have proved that the presented dynamic slicing algorithms correctly compute dynamic slices. In addition, these algorithms may compute more accurate dynamic slices compared to existing algorithms that use control dependencies. The presented algorithms have been implemented in a tool that supports dynamic slicing for Pascal programs     

Thin film based semi-active resonant marker design for low profile interventional cardiovascular MRI devices

Objectives

A new microfabrication method to produce low profile radio frequency (RF) resonant markers on catheter shafts was developed. A semi-active RF resonant marker incorporating a solenoid and a plate capacitor was constructed on the distal shaft of a 5 Fr guiding catheter. The resulting device can be used for interventional cardiovascular MRI procedures.

Materials and methods

Unlike current semi-active device visualization techniques that require rigid and bulky analog circuit components (capacitor and solenoid), we fabricated a low profile RF resonant marker directly on guiding the catheter surface by thin film metal deposition and electroplating processes using a modified physical vapor deposition system.

Results

The increase of the overall device profile thickness caused by the semi-active RF resonant marker (130 µm thick) was lowered by a factor of 4.6 compared with using the thinnest commercial non-magnetic and rigid circuit components (600 µm thick). Moreover, adequate visibility performance of the RF resonant marker in different orientations and overall RF safety were confirmed through in vitro experiments under MRI successfully.

Conclusion

The developed RF resonant marker on a clinical grade 5 Fr guiding catheter will enable several interventional congenital heart disease treatment procedures under MRI.

     

Dynamic method for software test data generation

Test data generation in program testing is the process of identifying a set of test data which satisfies a given testing criterion. Existing pathwise test data generators proceed by selecting program paths that satisfy the selected criterion and then generating program inputs for these paths. One of the problems with this approach is that unfeasible paths are often selected; as a result, significant computational effort can be wasted in analysing those paths. In this paper, an approach to test data generation, referred to as a dynamic approach for test data generation, is presented. In this approach, the path selection stage is eliminated. Test data are derived based on the actual execution of the program under test and function minimization methods. The approach starts by executing a program for an arbitrary program input. During program execution for each executed branch, a search procedure decides whether the execution should continue through the current branch or an alternative branch should be taken. If an undesirable execution flow is observed at the current branch, then a real-valued function is associated with this branch, and function minimization search algorithms are used to locate values of input variables automatically, which will change the flow of execution at this branch.     

PELAS-program error-locating assistant system

Error localization in program debugging is the process of identifying program statements which cause incorrect behavior. A prototype of the error localization assistant system which guides a programmer during debugging of Pascal programs is described. The system is interactive: it queries the programmer for the correctness of the program behavior and uses answers to focus the programmer's attention on an erroneous part of the program (in particular, it can localize a faulty statement). The system differs from previous approaches in that it makes use of the knowledge of program structure, which is derived automatically. The knowledge of program structure is represented by the dependence network which is used by the error-locating reasoning mechanism to guide the construction, evaluation, and modification of hypothesis of possible causes of the error. Backtracking reasoning has been implemented in the reasoning mechanism     

Formal timing analysis for distributed real-time programs

A static analysis method for verifying timing properties of real-time distributed programs is presented. The goal is to calculate the worst-case response time of concurrent tasks which run mainly independently but share, and may have to wait for, logical or physical devices. For such tasks, the determination of the worst-case waiting time is a crucial problem because of the unpredictable order of synchronization events. We investigate the class of distributed Client-Server programs in which independent, time-critical tasks (clients) are synchronized only through additional server tasks, playing the role of monitors or resource managers. This model follows well-known real-time design guidelines for distributed ADA programs proposed to enhance schedulability and synchronization analysis. Our formal analysis approach is flow graph oriented. It leads to generating reduced program paths each of which represents a sequence of ordered local and global operations, thus transforming and reducing the original problem of computing the worst-case waiting time of a concurrent task into a graph-theoretic problem of calculating the maximal blocking time for one of its corresponding program paths. While local operations are completely independent global operations require mutually exclusive access to shared resources. We prove that computing the worst-case blocking time for a program path is NP-complete. Even for a reduced problem solution—which would yield a good upper bound for the worst-case blocking time—there was a conjecture maintained over many years that this problem was NP-complete. A major result of this paper is to show that this is wrong. Instead, we construct a polynomial solution algorithm, and we prove its correctness. The effectiveness and complexity of our method are discussed, with particular emphasis on distributed real-time debugging.     

Automated software test data generation

An alternative approach to test-data generation based on actual execution of the program under test, function-minimization methods and dynamic data-flow analysis is presented. Test data are developed for the program using actual values of input variables. When the program is executed, the program execution flow is monitored. If during program execution an undesirable execution flow is observed then function-minimization search algorithms are used to automatically locate the values of input variables for which the selected path is traversed. In addition, dynamic data-flow analysis is used to determine those input variables responsible for the undesirable program behavior, significantly increasing the speed of the search process. The approach to generating test data is then extended to programs with dynamic data structures and a search method based on dynamic data-flow analysis and backtracking is presented. In the approach described, values of array indexes and pointers are known at each step of program execution; this information is used to overcome difficulties of array and pointer handling     

Classification of Turkish Extra Virgin Olive Oils by a SAW Detector Electronic Nose

An electronic nose (e-nose), in combination with chemometrics, has been used to classify the cultivar, harvest year, and geographical origin of economically important Turkish extra virgin olive oils. The aroma fingerprints of the eight different olive oil samples [Memecik (M), Erkence (E), Gemlik (G), Ayvalık (A), Domat (D), Nizip (N), Gemlik–Edremit (GE), Ayvalık–Edremit (AE)] were obtained using an e-nose consisting a surface acoustic wave detector. Data were analyzed by principal component analysis (PCA) and discriminant function analysis (DFA). Classification of cultivars using PCA revealed that A class model was correctly discriminated from N in two harvest years. The DFA classified 100 and 97% of the samples correctly according to the cultivar in the 1st and 2nd harvest years, respectively. Successful separation among the harvest years and geographical origins were obtained. Sensory analyses were performed for determining the differences in the geographical origin of the olive oils and the preferences of the panelists. The panelists could not detect the differences among olive oils from two different regions. The cultivar, harvest year, and geographical origin of extra virgin olive oils could be discriminated successfully by the e-nose.     

Microbial and Sensory Assessment of Milk with an Electronic Nose

ABSTRACT: An electronic nose (e-nose) was used to assess milk odor inoculated with Pseudomonas fluorescens or Bacillus coagulans , and odors were correlated with microbial loads and sensory scores. Sterile whole, reduced-fat, and fat-free milk were inoculated, stored at 1.7, 7.2, and 12.8 °C, and evaluated at d 0, 3, 5, 7, and 10 by e-nose and sensory panel. Aerobic plate counts were performed. E-nose readings, microbial counts, and sensory data were analyzed using discriminant function analysis. The e-nose discriminated differences in odor due to microbial load and sensory data. This may lead to a rapid method for determining sensory evaluation and microbial loads of milk.