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Dehradun, India

Russo M.V.,University of Molise | Avino P.,DIT | Perugini L.,University of Molise | Notardonato I.,University of Molise
RSC Advances

According to the Scopus database, using "phthalate" and "GC" as keywords, 758 papers have been found between 1990 and 2014, showing strong and increasing interest in this class of compounds from the scientific community. This review focuses attention on phthalate ester (PAE) extraction procedures, followed by GC-MS analysis, applied to food matrices and developed during the last 15 years (from 2000). In this area, 120 papers have been published, divided according to different sample preparation/extraction methods: liquid-liquid extraction (LLE), solid-phase (micro) extraction (SP(M)E), dispersive liquid-liquid (micro) extraction (DLL(M)E), headspace solid-phase micro-extraction (HS-SPME), microwave extraction, supercritical fluid extraction, ultrasonication extraction, thermal desorption extraction and Soxhlet extraction. Finally, for in-depth information, two important issues, phthalate toxicology and risk assessment and the blank problem in analytical determinations, are discussed. © The Royal Society of Chemistry 2015. Source

In this paper, we proposed a new model for recognizing various emotions of humans with different age groups and gender. Fuzzy is used for extracting more accurate region of interest, i.e., face. The dimensionality of face image is reduced by the Principal Component Analysis (PCA) [12] and finally emotion is recognized and classified using Euclidean Distance. Database is prepared and some performance metrics like recognition-rate v/s Eigen-range has been calculated. The proposed method was also tested on FACES Collection database [13]. The experiment results demonstrate that the emotion recognition system has been successful with average recognition rate of 96.66% (with both experiment databases) when approximately or more than 60% eigenfaces used. It is also shown that database can be easily expanded to classify faces and non faces images. © 2012 Springer-Verlag GmbH. Source

Notardonato I.,University of Molise | Avino P.,DIT | Cinelli G.,University of Molise | Russo M.V.,University of Molise
Food Analytical Methods

The acaricide use (Amitraz, Bromopropylate, Coumaphos, Tau-fluvalinate, and Fipronil) is a common worldwide practice in honey cultivation for protect the production. Nevertheless, their presence decreases honey safety and quality and affects the human health. Therefore, it is important to set up an easy, reliable, and rapid analytical method for determining such compounds in bee’s products (honey, molasses, and royal jelly). The method, based on solid-phase extraction using Carbograph 1 as sorbent and analysis by gas chromatography coupled with ion trap mass spectrometer detector (GC/IT-MS), allows very efficient recoveries ranging between 99 and 106 % with relative standard deviation (RSD) ≤5 % for the standard solution and between 98 and 102 % with RSD ≤9 % for samples spiked with 40 ng g−1 of each acaricide. The adsorption isotherms and breakthrough curves for Carbograph 1 sugary solution are reported. The instrumental analytical protocol has been found to yield a linear calibration in the range 10–300 ng g−1 with r2 ≥ 0.991. The limits of detection vary between 1.4 and 5.3 ng g−1 (RSD ≤4 %), whereas the limits of quantification range between 4.6 and 9.4 ng g−1 (RSD ≤6 %); the intra-day and inter-day repeatabilities as RSD were below 9 and 15 %, respectively. The analytical method developed has been applied to several commercial and home-made bee products. © 2015 Springer Science+Business Media New York Source

An ultrasound/vortex assisted dispersive liquid-liquid microextraction (USVADLLME) procedure coupled with gas chromatography-ion trap mass spectrometry (GC-IT-MS) is proposed for the rapid determination of seven phthalate esters in soft drinks and light alcoholic beverages (up to 6% alcohol by volume). Under the optimum conditions, the enrichment factors of the seven phthalate esters ranged from 205-fold to 315-fold for soft drinks and from 172-fold to 285-fold for light alcoholic beverages. The recoveries varied between 94.2% and 99.6% for soft drinks and 95.6% and 99.4% for light alcoholic beverages. The limits of detection were between 0.03 and 0.10 pg μL-1 and the limits of quantification were between 0.11 and 0.28 pg μL-1. The intra-day and inter-day precision expressed as the relative standard deviation varied between 2.9% and 5.1% and between 5.5% and 7.6%, respectively. The proposed USVADLLME-GC-IT-MS method was demonstrated to be simple, reproducible and practical for the determination of trace amounts of seven phthalate esters in soft drinks and light alcoholic beverages. © The Royal Society of Chemistry 2014. Source

Chandra M.,DIT | Gupta V.,R.K.G.I.T. | Paul S.K.,R.K.G.I.T.
Proceedings - 2011 International Conference on Communication Systems and Network Technologies, CSNT 2011

Automatic Document Summarization is a highly interdisciplinary research area related with computer science as well as cognitive psychology. This Summarization is to compress an original document into a summarized version by extracting almost all of the essential concepts with text mining techniques. This research focuses on developing a statistical automatic text summarization approach, K-mixture probabilistic model, to enhancing the quality of summaries. KSRS employs the K-mixture probabilistic model to establish term weights in a statistical sense, and further identifies the term relationships to derive the semantic relationship significance (SRS) of nouns. Sentences are ranked and extracted based on their semantic relationship significance values. The objective of this research is thus to propose a statistical approach to text summarization. We propose a K-mixture semantic relationship significance (KSRS) approach to enhancing the quality of document summary results. The K-mixture probabilistic model is used to determine the term weights. Term relationships are then investigated to develop the semantic relationship of nouns that manifests sentence semantics. Sentences with significant semantic relationship, nouns are extracted to form the summary accordingly. © 2011 IEEE. Source

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