Insights into information contained in multiplicative scatter correction parameters and the potential for estimating particle size from these parameters

This paper investigates the nature of information contained in scatter correction parameters. The study had two objectives. The first objective was to examine the nature and extent of information contained in scatter correction parameters. The second objective is to examine whether this information can be effectively extracted by proposing a method to obtain particularly the mean particle diameter from the scatter correction parameters. By using a combination of experimental data and simulated data generated using fundamental light propagation theory, a deeper and more fundamental insight of what information is removed by the multiplicative scatter correction (MSC) method is obtained. It was found that the MSC parameters are strongly influenced not only by particle size but also by particle concentration as well as refractive index of the medium. The possibility of extracting particle size information in addition to particle concentration was considered by proposing a two-step method which was tested using a 2-component and 4-component data set. This method can in principle, be used in conjunction with any scatter correction technique provided that the scatter correction parameters exhibit a systematic dependence with respect to particle size and concentration. It was found that the approach which uses the MSC parameters gave a better estimate of the particle diameter compared to using partial least squares (PLS) regression for the 2-component data. For the 4 component data it was found that PLS regression gave better results but further examination indicated this was due to chance correlations of the particle diameter with the two of the absorbing species in the mixture

Hypernetwork functional image representation

Motivated by the human way of memorizing images we introduce their functional representation, where an image is represented by a neural network. For this purpose, we construct a hypernetwork which takes an image and returns weights to the target network, which maps point from the plane (representing positions of the pixel) into its corresponding color in the image. Since the obtained representation is continuous, one can easily inspect the image at various resolutions and perform on it arbitrary continuous operations. Moreover, by inspecting interpolations we show that such representation has some properties characteristic to generative models. To evaluate the proposed mechanism experimentally, we apply it to image super-resolution problem. Despite using a single model for various scaling factors, we obtained results comparable to existing super-resolution methods

Solvent and Rotating Bed Reactor Extraction with One- and Two-Phase Solvents Applied to Bilberries (Vaccinium myrtillus) for Isolating Valuable Antioxidants

Extraction of antioxidants from bilberries using solvent extraction and the novel rotating bed reactor (RBR) both with one-phase (ethanol and water) and two-phase (ethanol and water +salt) solvents was studied. Solids, ethanol, and temperature settings in an experimental design were monitored for 1 h. The measured responses were (1) polyphenol concentration, (2) visible-near infrared spectra, and (3) HPLC measurement. The (1) responses were used for making response surfaces in time and the spectra (2) could confirm these results. The HPLC results (3) confirmed the results of 1 and 2 but were found unsuitable for online monitoring. The RBR was better than traditional extraction and 16 min sufficed. The response surfaces showed an optimal concentration of ethanol, temperatures above 50 degrees C gave the best results, and high loads of solid were beneficial. Two-phase extraction was less efficient. The methodology could be transferred to larger scale extraction systems to improve yield and save on reagents/energy cost

A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis

Prediction problems from spectra are largely encountered in chemometry. In addition to accurate predictions, it is often needed to extract information about which wavelengths in the spectra contribute in an effective way to the quality of the prediction. This implies to select wavelengths (or wavelength intervals), a problem associated to variable selection. In this paper, it is shown how this problem may be tackled in the specific case of smooth (for example infrared) spectra. The functional character of the spectra (their smoothness) is taken into account through a functional variable projection procedure. Contrarily to standard approaches, the projection is performed on a basis that is driven by the spectra themselves, in order to best fit their characteristics. The methodology is illustrated by two examples of functional projection, using Independent Component Analysis and functional variable clustering, respectively. The performances on two standard infrared spectra benchmarks are illustrated.Comment: A paraitr

Detection of visual defects in citrus fruits: multivariate image analysis vs graph image segmentation

¿The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40261-6_28This paper presents an application of visual quality control in orange post-harvesting comparing two different approaches. These approaches correspond to two very different methodologies released in the area of Computer Vision. The first approach is based on Multivariate Image Analysis (MIA) and was originally developed for the detection of defects in random color textures. It uses Principal Component Analysis and the T2 statistic to map the defective areas. The second approach is based on Graph Image Segmentation (GIS). It is an efficient segmentation algorithm that uses a graph-based representation of the image and a predicate to measure the evidence of boundaries between adjacent regions. While the MIA approach performs novelty detection on defects using a trained model of sound color textures, the GIS approach is strictly an unsupervised method with no training required on sound or defective areas. Both methods are compared through experimental work performed on a ground truth of 120 samples of citrus coming from four different cultivars. Although the GIS approach is faster and achieves better results in defect detection, the MIA method provides less false detections and does not need to use the hypothesis that the bigger area in samples always correspond to the non-damaged areaLópez García, F.; Andreu García, G.; Valiente González, JM.; Atienza Vanacloig, VL. (2013). Detection of visual defects in citrus fruits: multivariate image analysis vs graph image segmentation. En Computer Analysis of Images and Patterns. Springer Verlag (Germany). 8047:237-244. doi:10.1007/978-3-642-40261-6S237244804

Design av on-line transflektanscell för mätning av turbida material

En snabb och tillförlitlig teknik för att följa ändringar in en procesström är spektroskopi, i synnerhet nära infraröd (NIR). Online NIR-spektroskopi utförs med transmission (på homogena klara vätskor) eller med reflektion (på heterogena fasta material). För turbida vätskor behövs en prob som kan mäta integrerande över stora ytor och med olika inträngningsdjup i vätskan. Vi konstruerade en sådan prob och testade dess egenskaper med hjälp av statistisk försöksplanering och multivariat data analys. En jämförelse gjordes även med en kommersiell prob. Mejeriavfall som går till anaerob rötning är en typisk turbid vätska utan reproducerbara egenskaper. För att simulera mejeriavfallets turbiditet användes lösningar av reproducerbara mängder mjölkpulver. Som testfaktorer användes 1) olika mängder turbiditet (mjölkpulver koncentration), 2) temperatur och 3) mängden analyt. Jämförelsen av proberna visar att den konstruerade proben har nästan samma egenskaper som den kommersiella och att den går att använda för olika slags at-line och on-line process mätningar. Skillnaden är att den kommersiella proben är ämnad för immersion i vätskan medan den nya proben är till för mätning genom ett fönster i en vätska som blir pumpad. Försöksplaneringen ger en bra insyn i vilka faktorer som är viktiga för en stabil och reproducerbar mätning

Independent components in spectroscopic analysis of complex mixtures

We applied two methods of "blind" spectral decomposition (MILCA and SNICA) to quantitative and qualitative analysis of UV absorption spectra of several non-trivial mixture types. Both methods use the concept of statistical independence and aim at the reconstruction of minimally dependent components from a linear mixture. We examined mixtures of major ecotoxicants (aromatic and polyaromatic hydrocarbons), amino acids and complex mixtures of vitamins in a veterinary drug. Both MICLA and SNICA were able to recover concentrations and individual spectra with minimal errors comparable with instrumental noise. In most cases their performance was similar to or better than that of other chemometric methods such as MCR-ALS, SIMPLISMA, RADICAL, JADE and FastICA. These results suggest that the ICA methods used in this study are suitable for real life applications. Data used in this paper along with simple matlab codes to reproduce paper figures can be found at http://www.klab.caltech.edu/~kraskov/MILCA/spectraComment: 22 pages, 4 tables, 6 figure

Determining the end-date of long-ripening cheese maturation using NIR hyperspectral image modelling: A feasibility study

Near-infrared (874–1734 nm) hyperspectral (NIR-HS) imaging, coupled with chemometric tools, was used to explore the relationship between spectroscopic data and cheese maturation. A predictive tool to determine the end-date of cheese maturation (E-index, in days) was developed using a set of 425 NIR-HS images acquired during industrial-scale cheese production. The NIR-HS images were obtained by scanning the cheeses at 14, 16, 18 and 20 months of ripening, before a final sensorial assessment in which all cheeses were approved by 20 months. Regression modelling by partial least squares (PLS) was used to explore the relationship between average spectra and E-index. The best PLS model achieved 69.6% accuracy in the prediction of E-index when standard normal variate (SNV) correction and mean centring pre-processing were applied. Thus, NIR-HS image modelling can be useful as a complementary tool to optimise the logistics/efficiency of cheese ripening facilities by rapid and non-destructive prediction of the end-date of ripening for individual cheeses. However, the commercial application will require future improvements in the predictive capacity of the model, e.g. for larger datasets and repetitive scans of cheeses on random occasions

Use of near-infrared hyperspectral (NIR-HS) imaging to visualize and model the maturity of long-ripening hard cheeses

Spectroscopic measurements and imaging have great potential in rapid prediction of cheese maturity, replacing existing subjective evaluation techniques. In this study, 209 long-ripening hard cheeses were evaluated using a hyperspectral camera and also sensory evaluated by a tasting panel. A total of 425 NIR hyperspectral (NIR-HS) images were obtained during ripening at 14, 16, 18, and 20 months, until final sensorial approval of the cheese. The spectral data were interpreted as possible compositional changes between scanning occasions. Regression modelling by partial least squares (PLS) was used to explain the relationship between average spectra and cheese maturity. The PLS model was evaluated with whole cheeses (average spectrum), but also pixelwise, producing prediction images. Analysis of the images showed an increasing homogeneity of the cheese over the time of storage and ripening. It also suggested that maturation begins at the center and spreads to the outer periphery of the cheese

Bestämning av antocyaniner, Brix och torrhalt i blåbär med NIR-spektroskopi

Bärindustrin är beroende av insamling av plockade bär. Bären används färska eller processade i t.ex. läskedrycker, kosmetika och i näringssupplement. Bären som används varierar i sammansättning under den relativt korta växtsäsongen. Detta betyder att utbytet av slutprodukter varierar stort. Bärens kvalité kan mätas noggrant med våtkemiska metoder på laboratorium men dessa metoder är långsamma och dyra. En enkel och snabb mätmetod vore därför önskvärd. I projektet jämför vi laboratoriets analysmetoder med nära infraröd spektroskopi (NIR). Denna teknik kan göras bärbar för bruk i fält och ger snabba resultat (ofta inom 2 minuter). En systematisk studie utfördes under några månader sommaren 2014. Bär samlades in i Sverige och Finland med regelbundna intervall. NIR mättes på bären innan de skickades till ett laboratorium för analys. NIR mättes på färska, hela, homogeniserade bär samt på tinade bär efter infrysning. Resultaten från studien visade att vissa blåbäregenskaper kan mätas direkt med NIR på färska bär. För homogeniserade färska bär är det möjligt att beräkna laboratoriets resultat från NIR spektra. Mätningar på lagrade frysta bär gav inga användbara resultat. Detta betyder att det borde gå att bygga en kalibreringsmodell för homogeniserade färska bär som möjliggör NIR mätning och beräkning av viktiga egenskaper i fält väldigt snabbt