Phthalate Sample Preparation Methods and Analysis in Food and Food Packaging: a Review

A review on phthalate esters or phthalic acid esters (PAEs), chemicals of concern since a few decades ago that are widely used as plasticizers in food processing and packaging, is presented taking into account the background of such compounds, the metabolism, human exposure to PAEs, the sources and occurrence in food as well as the toxicological aspects and human health effects. In addition, 45 novel research articles that were published between 2002 and 2017 were identified and their results were tabulated showing the PAEs analysed, food matrix of PAEs, methods of sample preparation/extraction, methods of instrumental analysis and quantitation, percentage recovery and limit of detection (LOD) of the instrument for ease of comparison and referencing. In general, it was found that in the last 15 years, the number of PAEs analysed has increased from the commonly analysed 8 PAEs, namely dimethyl phthalate (DMP), diethyl phthalate (DEP), diisobutyl phthalate (DIBP), di-n-butyl phthalate (DBP), butyl benzyl phthalate (BBP), dicyclohexyl phthalate (DCHP), di-n-octyl phthalate (DNOP) and di-(2-ethylhexyl) phthalate (DEHP) to as many as 23 PAEs. The methods of sample preparation have also progressed from the simple liquid-liquid extraction using organic solvents to solid-phase microextraction techniques to the more recent head-space or direct immersion solid-phase microextraction methods. Whereas for the analysis of PAEs, gas chromatography and liquid chromatography are still the preferred methods with improved LOD of analysis ranging from approximately 10 ppm for fatty foods to 1–60 ppt for water, juices and cooking oil samples.     

Rapid Detection of Wheat Blast Pathogen Magnaporthe oryzae Triticum Pathotype Using Genome-Specific Primers and Cas12a-mediated Technology

Wheat blast, caused by the fungus Magnaporthe oryzae Triticum (MoT) pathotype, is a devastating disease persistent in South America and Bangladesh. Since MoT generally fails to cause visual symptoms in wheat until the heading stage when the infection would have advanced, disease control by fungicide application solely based on the detection of visual symptoms is ineffective. To develop an accurate and sensitive method to detect MoT at the seedling and vegetative stages for disease control, we sequenced the genomes of two MoT isolates from Brazil and identified two DNA fragments, MoT-6098 and MoT-6099, that are present in the MoT genome but not in the genome of the rice-infecting Magnaporthe oryzae Oryzae (MoO) pathotype. Using polymerase chain reaction (PCR), we confirmed the specificity of the two markers in 53 MoT and MoO isolates from South America and Bangladesh. To test the efficiency of the two markers, we first established a loop-mediated isothermal amplification (LAMP) method to detect MoT at isothermal conditions, without the use of a PCR machine. Following this, we used the Cas12a protein and guide RNAs (gRNAs) to target the MoT-6098 and MoT-6099 sequences. The activated Cas12a showed indiscriminate single-stranded deoxyribonuclease (ssDNase) activity. We then combined target-dependent Cas12a ssDNase activation with recombinase polymerase amplification (RPA) and nucleic acid lateral flow immunoassay (NALFIA) to develop a method that accurately, sensitively, and cost-effectively detects MoT-specific DNA sequences in infected wheat plants. This novel technique can be easily adapted for the rapid detection of wheat blast and other important plant diseases in the field.     

Epithelial Layer Estimation Using Curvatures and Textural Features for Dysplastic Tissue Detection

Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling process. The morphological pattern of an epithelial layer is greatly affected. Theoretically, the shape deformation model can normalise the distortions, but it needs a 2D image. Curvatures theory, on the other hand, is not yet tested on digital pathology images. Therefore, this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial layer. The boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being a straight line. The epithelial layer’s depth is estimated from the tissue edges and the connected nucleolus only. Then, the textural and spatial features along the estimated layer are used for dysplastic tissue detection. The proposed method achieved better performance compared to the whole tissue regions in terms of detecting dysplastic tissue. The result shows a leap of kappa points from fair to a substantial agreement with the expert’s ground truth classification. The improved results demonstrate that curvatures have been effective in reducing the boundary effects on the epithelial layer of tissue. Thus, quantifying and classifying the morphological patterns for dysplasia can be automated. The textural and spatial features on the detected epithelial layer can capture the changes in tissue.     

A non-contact method for part-based process performance monitoring in end milling operations

Surface response to excitation (SuRE) method was originally developed for structural health monitoring (SHM) applications. SuRE was used to evaluate the performance of completed milling operations. The method generates surface waves on the plate and studies the spectrum changes at selected points to detect defects and change of compressive forces. In this study, the length, depth, and width of a slot were changed step by step. The surface of the aluminum plate was excited in the 20–400 kHz range with a piezoelectric element. A laser scanning vibrometer was used to monitor the vibrations at the predetermined grid points after the dimensions of the slot were changed methodically. The frequency spectrums of measured vibrations were calculated by using the Fast Fourier Transformation (FFT). The sums of the squares of the differences (SSD) of the spectrums were calculated to evaluate the change of the spectrums. The SuRE method was able to determine if the dimensions were changed in each case at all the selected points. The scanning laser vibrometer is not feasible to be used at the shop floor. However, the study demonstrated that a piezoelectric element attached to any of the grid points would be able to evaluate the completed machining process.     

Computationally highly efficient mixture of adaptive filters

We introduce a new combination approach for the mixture of adaptive filters based on the set-membership filtering (SMF) framework. We perform SMF to combine the outputs of several parallel running adaptive algorithms and propose unconstrained, affinely constrained and convexly constrained combination weight configurations. Here, we achieve better trade-off in terms of the transient and steady-state convergence performance while providing significant computational reduction. Hence, through the introduced approaches, we can greatly enhance the convergence performance of the constituent filters with a slight increase in the computational load. In this sense, our approaches are suitable for big data applications where the data should be processed in streams with highly efficient algorithms. In the numerical examples, we demonstrate the superior performance of the proposed approaches over the state of the art using the well-known datasets in the machine learning literature.     

Hierarchical behavior categorization using correlation based adaptive resonance theory

This paper introduces a new model for robot behavior categorization. Correlation based adaptive resonance theory (CobART) networks are integrated hierarchically in order to develop an adequate categorization, and to elicit various behaviors performed by the robot. The proposed model is developed by adding a second layer CobART network which receives first layer CobART network categories as an input, and back-propagates the matching information to the first layer networks. The first layer CobART networks categorize self-behavior data of a robot or an object in the environment while the second layer CobART network categorizes the robot's behavior with respect to its effect on the object. Experiments show that the proposed model generates reasonable categorization of behaviors being tested. Moreover, it can learn different forms of the behaviors, and it can detect the relations between them. In essence, the model has an expandable architecture and it contains reusable parts. The first layer CobART networks can be integrated with other CobART networks for another categorization task. Hence, the model presents a way to reveal all behaviors performed by the robot at the same time.     

Effect of gamma irradiation on structural,morphological and optical properties of thermal spray pyrolysis deposited CuO thin film

This study reports the influences of gamma irradiation (GI) in the range of 20–100 kGy on CuO thin films via thermal spray pyrolysis technique on the glass substrates. The results demonstrate significant influences of GI on the crystallographic, microstructural and optical characteristics of CuO thin films. The obtained XRD results showed that the crystallinity of the films deteriorates by gradually decreasing crystallite size (from 59.13 to 46 nm) as applied gamma doses increases. However, the basic monoclinic crystal structure remains same. The dislocation density and lattice strain increased with the rise of GI absorbed dose due to the creation of defects. The values of number of crystallites per unit surface area increased as dose increased indicating the abundance of crystallization of nano CuO thin films. A UV–Vis–NIR spectrophotometer was utilized to determine the optical properties and obtained results indicated that the optical energy band gap (OBG) energies reduced from 2.00 to 1.72 eV as the doses increased from 0 to 100 kGy. No distinctions of the monoclinic phase of virgin CuO thin film have been perceived under applied absorbed doses, notwithstanding the slight deterioration of the crystallinity and narrowing the OBG.     

Identifying critical autonomous systems in the Internet

The Internet not only facilitates our daily activities, such as communication, entertainment and shopping but also serves as the enabling technology for many critical services, including finance, manufacturing, healthcare and transportation. On the other hand, a wide spectrum of attacks targets its communication infrastructure to disable or disrupt the network connectivity and traffic flow until recovery processes take place. Attacking all autonomous systems (ASes) in the Internet is typically beyond the capability of an adversary. Therefore, targeting a small number of ASes which results in the highest impact is the best strategy for attackers. Similarly, it is important for network practitioners to identify, fortify and secure those critical ASes to mitigate the impact of the attacks. In this study we introduce an intuitive and effective measure, IP address spatial path stress centrality, to assess and identify the critical ASes in the Internet. We compare IP address spatial path stress centrality to the three well-known and widely used centrality measures, namely customer-cone size, node degree and betweenness. We demonstrate that the proposed measure incorporates business relations and IP address spaces to achieve a better measure for identifying the critical ASes in the Internet.