Monitoring Chemical Changes in Cheddar Cheese During Aging by High Performance Liquid Chromatography and Gas Chromatography Techniques

The concentrations of several chemical metabolites in Cheddar cheese were monitored by various chromatographic techniques during the aging process to learn which metabolites were the best predictors of the glycolytic, lipolytic, and proteolytic age of the cheese. Pyruvic, lactic, acetic, and propionic acids were measured by ion-exchange high performance liquid chromatography; acetone, 2-butanone, ethanol, 2-pentanone, 2-butanol, and n-propanol were monitored by headspace gas chromatography; free fatty acids were quantitated (without derivatization) by gas chromatography; and free amino acids were determined as their o-phthaldehye derivatives by high performance liquid chromatography.The best predictors of the glycolytic age were propionic acid and acetic acid; the best predictors of lipolysis were the free fatty acids C₁₀, C₁₂, C₁₄, and C₁₆; and the best predictors of proteolysis were the free amino acids leucine, methionine, and glutamic acid. The volatile metabolites determined by headspace gas chromatography were not good indicators of aging; however, they did provide useful information related to flavor problems.Cheddar cheese aged at elevated temperatures produced propionic acid, acetic acid, and free amino acids at significantly faster rates than the other chemicals that were monitored.     

Selection of optimized features for fusion of palm print and finger knuckle‐based person authentication

The impact of digital technology in biometrics is much more efficient at interpreting data than humans, which results in completely replacement of manual identification procedures in forensic science. Because the single modality‐based biometric frameworks limit performance in terms of accuracy and anti‐spoofing capabilities due to the presence of low quality data, therefore, information fusion of more than one biometric characteristic in pursuit of high recognition results can be beneficial. In this article, we present a multimodal biometric system based on information fusion of palm print and finger knuckle traits, which are least associated to any criminal investigation as evidence yet. The proposed multimodal biometric system might be useful to identify the suspects in case of physical beating or kidnapping and establish supportive scientific evidences, when no fingerprint or face information is present in photographs. The first step in our work is data preprocessing, in which region of interest of palm and finger knuckle images have been extracted. To minimize nonuniform illumination effects, we first normalize the detected circular palm or finger knuckle and then apply line ordinal pattern (LOP)‐based encoding scheme for texture enrichment. The nondecimated quaternion wavelet provides denser feature representation at multiple scales and orientations when extracted over proposed LOP encoding and increases the discrimination power of line and ridge features. To best of our knowledge, this first attempt is a combination of backtracking search algorithm and 2D²LDA has been employed to select the dominant palm and knuckle features for classification. The classifiers output for two modalities are combined at unsupervised rank level fusion rule through Borda count method, which shows an increase in performance in terms of recognition and verification, that is, 100% (correct recognition rate), 0.26% (equal error rate), 3.52 (discriminative index), and 1,262 m (speed).     

III–V super-lattice SPST/SPMT pin switches for THz communication: theoretical reliability and experimental feasibility studies

Microsystem Technologies - A physics-based Quantum-Modified CLassical Drift–Diffusion (QMCLDD) non-linear mathematical model has been developed for design and characterisation of GaN/AlGaN...     

Design and characterization of asymetrical super-lattice Si/4H-SiC pin photo diode array: a potential opto-sensor for future applications in bio-medical domain

Microsystem Technologies - Photo-sensors are integral part of different bio-medical diagnostic equipment. Each type of bio-molecules possess unique spectral fingerprint in visible wavelength region...     

Favorable influence of ssDNA-functionalized SWCNT on the navigation pattern of C. elegans

Microsystem Technologies - Single-Walled Carbon Nanotubes (SWCNTs) are widely used as potential carriers in drug delivery systems. The objective of this work was to observe the effects of pristine,...     

Retraction of “Developing an integrated display of health data for aging in place”

The following article from Human Factors and Ergonomics in Manufacturing & Service Industries, “Developing an Integrated Display of Health Data for Aging in Place,” by Yang Gong and Arpita Chandra, published online on October 20, 2011 in Wiley Online Library ( www.onlinelibrary.wiley.com ), has been retracted by agreement between the authors, the journal Editors‐in‐Chief, Waldemar Karwowski and Gavriel Salvendy, and Publisher Wiley Periodicals, Inc. The retraction has been agreed as these articles were mistakenly published after being rejected.     

Symbolic dynamic filtering and language measure for behavior identification of mobile robots

This paper presents a procedure for behavior identification of mobile robots, which requires limited or no domain knowledge of the underlying process. While the features of robot behavior are extracted by symbolic dynamic filtering of the observed time series, the behavior patterns are classified based on language measure theory. The behavior identification procedure has been experimentally validated on a networked robotic test bed by comparison with commonly used tools, namely, principal component analysis for feature extraction and Bayesian risk analysis for pattern classification.