Aplication of LED technology for food quality control

Motivation: Currently, there are numerous chemical methods to analyze compounds, however, these involve a large investment and highly qualified personnel. In this work, Light Emission Diode has been used as a cheap and simple method to easily detect, the existence of: a) cheese whey in different representative samples of water b) rice syrup in different types of commercial honeys.Methods: Six different colour LEDs (orange, pink, ultraviolet, blue, green and white) were used in this research as a light source to measure the emission spectra of water and honey samples. The water and honey samples were prepared adding known concentrations of cheese whey( from 1% to 20%, 1800 samples) and rice syrup (1%-8%, 480 samples), to waters from five different rivers and reservoirs in Madrid; and to five different honeys, respectively. The phenomenon measured with this technology is fluorescence. The emited fluorescence is measured at a right angle from the light source, using a fiber spectrometer. The output of the spectrometer is collected in a computer. It is necessary to apply a linear regression model to obtain the concentration from the intensity values.This information is taken from a fluorescence emission spectrum.Results: In the whey emission spectra (for each LED), the increase in cheese whey concentrations were seen by an increase in its intensity. The honey spectra have different profiles as well as intensities for each honey, so the difference in syrup concentration is also detected by an intensity increase.In addition, the analysis of the measurements has obtained an efficiency of approximately 90%.Conclusions: It has been demonstrated that LED technology can be a potencial and important first approach to determine contaminants or adulterants in water and honey samples. It is also a cheap and user-friendly technique which could be useful in the food quality control sector

Silicon Nanowire Sensors Enable Diagnosis of Patients via Exhaled Breath

Two of the biggest challenges in medicine today are the need to detect diseases in a noninvasive manner and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularly modified silicon nanowire field effect transistor (SiNW FET) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma, and chronic obstructive pulmonary disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlate their sensitivity and selectivity toward volatile organic compounds (VOCs) linked with the various disease breathprints. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples show that the optimized SiNW FETs can detect and discriminate between almost all binary comparisons of the diseases under examination with >80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct harmless way, which can reassure patients and prevent numerous unpleasant investigations

Contribution of Genetic Background, Traditional Risk Factors, and HIV-Related Factors to Coronary Artery Disease Events in HIV-Positive Persons

We show in human immunodeficiency virus-positive persons that the coronary artery disease effect of an unfavorable genetic background is comparable to previous studies in the general population, and comparable in size to traditional risk factors and antiretroviral regimens known to increase cardiovascular ris

A highly sensitive diketopyrrolopyrrole-based ambipolar transistor for selective detection and discrimination of xylene isomers

An ambipolar poly(diketopyrrolopyrrole-terthiophene)-based <i>field-effect transistor (FET)</i> sensitively detects xylene isomers at low ppm levels with multiple sensing features. Combined with pattern-recognition algorithms, a sole ambipolar FET sensor, rather than arrays of sensors, can discriminate highly similar xylene structural isomers from one another

Silicon Nanowire Sensors Enable Diagnosis of Patients <i>via</i> Exhaled Breath

Two of the biggest challenges in medicine today are the need to detect diseases in a noninvasive manner and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularly modified silicon nanowire field effect transistor (SiNW FET) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma, and chronic obstructive pulmonary disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlate their sensitivity and selectivity toward volatile organic compounds (VOCs) linked with the various disease breathprints. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples show that the optimized SiNW FETs can detect and discriminate between almost all binary comparisons of the diseases under examination with >80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct harmless way, which can reassure patients and prevent numerous unpleasant investigations