Authenticity green coffee bean species and geographical origin using near-infrared spectroscopy combined with chemometrics

To prevent the adulteration of agricultural resources and provide a solution to enhance the green coffee bean supply chain, authentication using the near-infrared spectroscopy (NIRS) technique was investigated. Partial least square with discrimination analysis (PLS-DA) models combined with various preprocessing methods were built from NIR spectra of 153 Vietnamese green coffee samples. The model combined with the standard normal variate and the first order of derivative yielded excellent performance in predicting coffee species with the error cross-validation of 0.0261. PLS-DA model of mean centre and first-order derivative spectra also yielded good performance in verifying geographical indication of green coffee with the error of 0.0656. By contrast, the predicting abilities of post-harvest methods were poor. The overall results showed a high potential of the NIRS in online authentication practices.     

Parallel quicksort

A parallel version of quicksort on a CRCW PRAM is developed. The algorithm uses n processors and a linear space to sort n keys in the expected time O(log n) with large probability.     

Volatile organo-halogen trace analysis in beverages by thin-layer headspace enrichment and electron-capture gas-chromatography

Applicability of thin-layer headspace (TLHS) procedure giving an aqueous concentrate and also classical purge and trap (PT) in off-line mode to isolate and enrich volatile organohalogen compounds in common beverages was tested. Both enrichment tech-niques were used in combination with gas chromatography – electron capture detection (GC-ECD). TLHS, combined with direct aqueous injection (DAI)-GC-ECD, proved applicable in the determination of volatile organohalogen compounds in all studied beverages, while the PT in a version with no preliminary sample pre-treatment was of limited applicability. Detection limits of the TLHS-based procedure were in the order of 1 ppt. Content of volatile organohalogen compounds in a number of beverages available on the Polish market including mineral waters, beers, juices, carbonated and non-carbonated soft drinks, etc. was determined by means of TLHS-DAI-GC-ECD.     

Investigation and Elaboration of Methods of Multicolor Optical Thermometry

The various complex mathematical transformations previously proposed for eliminating methodological errors of multicolor radiation pyrometry are mainly reduced to the determination of such combinations of wavelengths or corrections of registered brightness for the spectral distribution of the radiating capacity of the object being thermometered at which its equivalent radiating capacity is equal to unity. Mathematical formulas of the determining parameters of multicolor pyrometry of radiation have been obtained. Engineering techniques for calculating the critical values of the determining and adjustable parameters at which the application of multicolor optical thermometry is excluded because of large errors have been developed. Algorithms of a priori and a posteriori calibration systems of multicolor pyrometry of radiation of objects with practically any spectral distribution of the radiating capacity are proposed.     

Thick film NO2 Sensor

 Thick film technology was used to manufacture a solid-state electrochemical nitrogen dioxide sensor. The prepared sensor has a linear dependence of electromotive force that is based in the NO₂ concentration level. This paper presents discussion on a thick-film sensor that has a much smaller dimension and shorter response time as compared to typical ceramic solid-state electrochemical devices. Received: 26 February 1996/Accepted: 27 June 1996     

Meningocerebral lesions in children with iatrogenic AIDS

This study is focused on structural modifications of leptomeninges and cerebral matter in children deceased because of iatrogenic AIDS. Although we do not evidence the specific lesions of AIDS, we have noticed edema, hyperemia, hematic extravasation, microlesions of the small vessel walls, perivascular infiltrations with lymphocytes, macrophages and even plasmocytes, moderate tigrolysis, mild demyelination, gliosis and sidero-calcic deposits. We do not consider these lesions as specific to HIV-infected patients.     

Automatic recognition of gait-related health problems in the elderly using machine learning

This paper proposes a system for the early automatic recognition of health problems that manifest themselves in distinctive form of gait. Purpose of the system is to prolong the autonomous living of the elderly at home. When the system identifies a health problem, it automatically notifies a physician and provides an explanation of the automatic diagnosis. The gait of the elderly user is captured using a motion-capture system, which consists of body-worn tags and wall-mounted sensors. The positions of the tags are acquired by the sensors and the resulting time series of position coordinates are analyzed with machine-learning algorithms in order to recognize a specific health problem. Novel semantic features based on medical knowledge for training a machine-learning classifier are proposed in this paper. The classifier classifies the user’s gait into: 1) normal, 2) with hemiplegia, 3) with Parkinson’s disease, 4) with pain in the back and 5) with pain in the leg. The studies of 1) the feasibility of automatic recognition and 2) the impact of tag placement and noise level on the accuracy of the recognition of health problems are presented. The experimental results of the first study (12 tags, no noise) showed that the k-nearest neighbors and neural network algorithms achieved classification accuracies of 100%. The experimental results of the second study showed that classification accuracy of over 99% is achievable using several machine-learning algorithms and 8 or more tags with up to 15 mm standard deviation of noise. The results show that the proposed approach achieves high classification accuracy and can be used as a guide for further studies in the increasingly important area of Ambient Assisted Living. Since the system uses semantic features and an artificial-intelligence approach to interpret the health state, provides a natural explanation of the hypothesis and is embedded in the domestic environment of the elderly person; it is an example of the semantic ambient media for Ambient Assisted Living.