Matteo Ottavian

Use of Near-Infrared Spectroscopy for Fast Fraud Detection in Seafood: Application to the Authentication of Wild European Sea Bass (Dicentrarchus labrax)

The possibility of using near-infrared spectroscopy (NIRS) for the authentication of wild European sea bass ( Dicentrarchus labrax ) was investigated in this study. Three different chemometric techniques to process the NIR spectra were developed, and their ability to discriminate between wild and farmed sea bass samples was evaluated. One approach used spectral information to directly build the discrimination model in a latent variable space; the second approach first used wavelets to transform the spectral information and subsequently derived the discrimination model using the transformed spectra; in the third approach a cascaded arrangement was proposed whereby very limited chemical information was first estimated from spectra using a regression model, and this estimated information was then used to build the discrimination model in a latent variable space. All techniques showed that NIRS can be used to reliably discriminate between wild and farmed sea bass, achieving the same classification performance as classification methods that use chemical properties and morphometric traits. However, compared to methods based on chemical analysis, NIRS-based classification methods do not require reagents and are simpler, faster, more economical, and environmentally safer. All proposed techniques indicated that the most predictive spectral regions were those related to the absorbance of groups CH, CH(2), CH(3), and H(2)O, which are related to fat, fatty acids, and water content.

A correlative study on data from pork carcass and processed meat (Bauernspeck) for automatic estimation of chemical parameters by means of near-infrared spectroscopy

The use of near-infrared spectroscopy (NIRS) is proposed in this study for the characterization of the quality parameters of a smoked and dry-cured meat product known as Bauernspeck (originally from Northern Italy), as well as of some technological traits of the pork carcass used for its manufacturing. In particular, NIRS is shown to successfully estimate several key quality parameters (including water activity, moisture, dry matter, ash and protein content), suggesting its suitability for real time application in replacement of expensive and time consuming chemical analysis. Furthermore, a correlative approach based on canonical correlation analysis was used to investigate the spectral regions that are mostly correlated to the characteristics of interest. The identification of these regions, which can be linked to the absorbance of the main functional chemical groups, is intended to provide a better understanding of the chemical structure of the substrate under investigation.

Adaptive blood glucose control for intensive care applications

Control of blood glucose concentration for patients in intensive care units (ICUs) has been demonstrated to be beneficial in reducing mortality and the incidence of serious complications, for both diabetic and non-diabetic patients. However, the high degree of variability and uncertainty characterizing the physiological conditions of critically ill subjects makes automated glucose control quite difficult; consequently, traditional, nurse-implemented protocols are widely employed. These protocols are based on infrequent glucose measurements, look-up tables to determine the appropriate insulin infusion rates, and bedside insulin administration. In this paper, a novel automatic adaptive control strategy based on frequent glucose measurements and a self-tuning control technique is validated based on a simulation study for 200 virtual patients. The adaptive control strategy is shown to be highly effective in controlling blood glucose concentration despite the large degree of variability in the blood glucose response exhibited by the 200 simulated patients.

Multivariate Image and Texture Analysis to Investigate the Erosion Mechanism of Film-coated Tablets: An Industrial Case Study

IntroductionThe Quality by Design initiative requires the design space of a process to be based on metrics that are robust and reproducible, and not on qualitative ones that might be easily biased by human perception. Hence, the use of image analysis is attractive for practical industrial applications where the quality assessment is still typically performed by a panel of trained experts.MethodsThe use of multivariate image and texture analysis is proposed in this study to quantitatively characterize the elegance of film-coated tablets. Four unsupervised metrics are developed to quantify both the color uniformity of tablet faces/bands and the surface erosion. To develop robust statistics, more than 7,000 tablets coated in nine different pilot-scale batches are considered. Latent variable modeling is used to regress the measured elegance against coating operating conditions to investigate the driving forces acting on the system and guide pharmaceutical manufacturing, consistently with the Quality by Design framework.ResultsThe model allows one to successfully investigate the causes leading to tablet erosion. Additionally, it is shown that the model space can be effectively used as a monitoring chart of the overall batch elegance in terms of color uniformity and surface erosion, since the batches are found to rank according to the surface roughness of the manufactured tablets.ConclusionsImage analysis has been shown to be an effective process analytical technology for the development of the design space of a film-coating process, where quality assessment on the final product is traditionally based on the judgment of panel of trained experts.

Multivariate Image and Texture Analysis for Film-Coated Tablets Elegance Assessment

Abstract The use of multivariate image and texture analysis is proposed in this study to quantitatively characterize the elegance of film-coated tablets. Four unsupervised metrics are developed to quantify both the color uniformity of tablet faces/bands and the erosion level inside and outside the tablet logo. Latent variable modeling is used to regress the measured elegance against coating operating conditions in order to investigate the driving forces acting on the system, consistently with the quality-by-design framework promoted by the Food and Drug Administration.