1,720,966 research outputs found
Analysing the water spectral pattern by near-infrared spectroscopy and chemometrics as a dynamic multidimensional biomarker in preservation: rice germ storage monitoring
No full textWater activity is an important phenomenon not yet explained in terms of water molecular structure. This paper aims to find the relationship between the water activity and water molecular structure of the rice germ, based on its spectral pattern which can be measured using non-destructive technology. Aquaphotomics near-infrared spectroscopy was used to study rice germ stored at different levels of water activity and atmosphere. The findings show that state of the rice germ is governed by the water activity upon storage, which is defined by the structure of water within germ matrix. The structure of water can be described solely by the absorbance spectral pattern at the following absorbance bands: proton hydrates, hydration shells and water vapor (1364, 1375 and 1382 nm), trapped water (1392 nm), free water (1410 nm), hydration water (1425 nm), adsorbed water (1455 nm), non-bonded hydroxyl (1436 nm) and bound water (1520 nm)
Qualitative pattern recognition in chemistry: Theoretical background and practical guidelines
No full textQualitative pattern recognition methods find important applications in the chemometric sector to extract structured information from complex experimental data. Two main strategies can be distinguished: unsupervised analysis, aimed at investigating on the presence of groupings within the samples analysed, and supervised analysis, aimed at predicting the class membership of new samples. Supervised qualitative methods are, in turn, divided in two families: discriminant and class-modelling methods. The first ones require at least two classes to be defined, while the second ones are suitable also for one-class classification. The features of each strategy, with a focus on advantages and limitations, are described and compared. New trends in the methods, as well as recent attempts to force discriminant methods to behave as class-modelling ones, and vice versa, are also critically presented
Pros and cons of image texture analysis on hyperspectral datacubes: the flour case-study
No full textNIR spectroscopy, an efficient tool for evaluating and enhancing the quality of extra virgin olive oil
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An analytical approach based on excitation-emission fluorescence spectroscopy and chemometrics for the screening of prostate cancer through urine analysis: a proof of concept study
No full textIn the present feasibility study, excitation-emission fluorescence spectroscopy has been investigated, as a rapid
and accurate analytical method for the development of a tentative model for the early screening of prostate
cancer directly through urine analysis in order to provide reliable results while improving patient compliance.
Sixty-nine urine samples (46 samples from patients with histologically proven prostate cancer and 23 from
healthy donors) were provided, by the University of Pisa, Urology Unit. The excitation-emission fluorescence
measurements were performed on centrifugated urine samples at room temperature on a Perkin-Elmer LS55B
luminescence spectrometer and the corresponding data array was analysed with parallel factor analysis
(PARAFAC).
From a synergistic analysis of the obtained results, four main fluorophores, corresponding to four selected
PARAFAC factors, were recognizable in the urine excitation-emission matrices (EEMs) and the respective species
could be potential markers in the differentiation among healthy and cancer samples. PARAFAC results, in terms
of extracted scores, coupled with discriminant algorithms, allowed to develop a first attempt of healthy/cancer
discrimination model. The chemometrics models show promising correlation between some of the depicted
fluorophores and the disease state. However, considering the limited cohort (not only in terms of number but also
of representativeness), this study must be considered as a proof of concept; a more sound and statistically
relevant sampling must be performed in order to consider the confounding factors in the cohort treated and to
develop an analytical approach applicable in real scenario
An in-depth study of cheese ripening by means of NIR hyperspectral imaging: Spatial mapping of dehydration, proteolysis and lipolysis
No full textCheese represents one of the most complex food matrices, for the high number of factors contributing to the chemical composition, and so its evaluation represents an important analytical challenge. The present study describes an innovative and non-destructive analytical approach, based on hyperspectral imaging in the near-infrared region (HSI-NIR) and multivariate pattern recognition, to study and monitor the extent – spatial and temporal – of biochemical phenomena responsible for cheese ripening. NIR spectral bands characterising dehydration, proteolysis and lipolysis were individuated and studied by exploiting a representative sample set of characteristic cheeses. The information obtained was employed to develop score maps based on principal component analysis (PCA), which permitted to monitor and visualise the ripening of Formaggetta, a commercial semi-hard cheese typical of Liguria, an Italian region, providing a deep understanding of the evolution of dehydration, proteolysis and lipolysis during the maturation period that precedes the placing on the market
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