1,721,046 research outputs found
Chemometrics. A report on the 2nd Colloquium Chemometricum Mediterraneum held in San Miniato, Italy, 21-24 October, 1991
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Selection of near infrared wavelengths using genetic algorithms for the determination of seed moisture content
No full textFor fast measurements of single seeds using near infrared (NIR) spectra for the prediction of seed moisture content, it may be necessary to reduce the spectra from over 1000 wavelengths to just a few narrow bands. This reduction makes it possible to utilise a few parallel and simultaneous NIR sensor measurements in seed sorting instead of scanning a few NIR bands that are sequential in time. Three different approaches of genetic algorithms (GA) were used to select wavelengths within the range 400-2500 nm. The GA models were compared for two different datasets: single seeds and bulk seeds of Scots pine. It was shown that GA and interval partial least squares combined with GA allowed a meaningful reduction in spectral content without any loss in predictive quality. The three models selected three to six wavelength regions mainly around the peak of the combination of the first O-H stretching over-tone and the O-H bending at 1190 nm and on the slopes of the first O-H stretching overtone at 1450 nm. For some of the GA models, the selected regions were subdivided into one to three more regions. In total six to eight narrow regions were used to simulate uniform density filters based on average absorbance within selected regions. The RMSEP values of the filter simulations were of at least the same quality as those for the whole wavelength range or the NIR range. The wavelength bands chosen for the single seeds were also applied for the bulk samples and vice versa with good result. The overall results illustrate the possibility of using GAs to select wavelengths in order to build filter spectrometers based on a few wavelength bands for the determination of seed moisture content
Use of experimental design to optimize the analysis of volatile compounds by dynamic headspace extraction followed by cold trapping and capillary GC
No full textA central composite design has been used to improve the extraction performance of a dynamic headspace method; extraction yield was evaluated on a homogeneous sample of cheese powder. The volatile compounds were stripped from the sample with nitrogen and adsorbed on a Tenax trap, analysis being performed with a thermal cold trapping injector connected directly to a capillary GC. Three variables (sample temperature, extraction time, and nitrogen flow rate) were investigated, and a quadratic model with interactions was postulated. Twenty experiments was performed, each producing five responses. It was shown that the best conditions for the extraction procedure were those characterized by the highest values of the three variables investigated. Copyright © 1994 Hüthig Gmb
Chemometric challenges in development of paper-based analytical devices: Optimization and image processing
No full textAlthough microfluidic paper-based analytical devices (μPADs) get a lot of attention in the scientific literature, they rarely reach the level of commercialization. One possible reason for this is a lack of application of machine learning techniques supporting the design, optimization and fabrication of such devices. This work demonstrates the potential of two chemometric techniques including design of experiments (DoE) and digital image processing to support the production of μPADs. On the example of a simple colorimetric assay for isoniazid relying on the protonation equilibrium of methyl orange, the experimental conditions were optimized using a D-optimal design (DO) and the impact of multiple factors on the μPAD response was investigated. In addition, this work demonstrates the impact of automatic image processing on accelerating color value analysis and on minimizing errors caused by manual detection area selection. The employed algorithm is based on morphological recognition and allows the analysis of RGB (red, green, and blue) values in a repeatable way. In our belief, DoE and digital image processing methodologies are keys to overcome some of the remaining weaknesses in μPAD development to facilitate their future market entry
Variable selection for multivariate calibration using a genetic algorithm: Prediction of additive concentrations in polymer films from Fourier transform-infrared spectral data
No full textVariable selection using a genetic algorithm is combined with partial least squares (PLS) for the prediction of additive concentrations in polymer films using Fourier transform-infrared (FT-IR) spectral data. An approach using an iterative application of the genetic algorithm is proposed. This approach allows for all variables to be considered and at the same time minimizes the risk of overfitting. We demonstrate that the variables selected by the genetic algorithm are consistent with expert knowledge. This very exciting result is a convincing application that the algorithm can select correct variables in an automated fashion. © 2002 Elsevier Science B.V. All rights reserved
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