Development of a classification algorithm for efficient handling of multiple classes in sorting systems based on hyperspectral imaging

When dealing with practical applications of hyperspectral imaging, the development of efficient, fast and flexible classification algorithms is of the utmost importance. Indeed, the optimal classification method should be able, in a reasonable time, to maximise the separation between the classes of interest and, at the same time, to correctly reject possible outlier samples. To this aim, a new extension of Partial Least Squares Discriminant Analysis (PLS-DA), namely Soft PLS-DA, has been implemented. The basic engine of Soft PLS-DA is the same as PLS-DA, but class assignment is subjected to some additional criteria which allow samples not belonging to the target classes to be identified and rejected. The proposed approach was tested on a real case study of plastic waste sorting based on near infrared hyperspectral imaging. Household plastic waste objects made of the six recyclable plastic polymers commonly used for packaging were collected and imaged using a hyperspectral camera mounted on an industrial sorting system. In addition, paper and not recyclable plastics were also considered as potential foreign materials that are commonly found in plastic waste. For classification purposes, the Soft PLS-DA algorithm was integrated into a hierarchical classification tree for the discrimination of the different plastic polymers. Furthermore, Soft PLS-DA was also coupled with sparse-based variable selection to identify the relevant variables involved in the classification and to speed up the sorting process. The tree-structured classification model was successfully validated both on a test set of representative spectra of each material for a quantitative evaluation, and at the pixel level on a set of hyperspectral images for a qualitative assessment

Characterization of common wheat flours (Triticum aestivum L.) through multivariate analysis of conventional rheological parameters and gluten peak test indices

The GlutoPeak consists in high speed mixing of a small amount of wheat flour (<10 g) added with water, and in registering a torque vs. time curve in a very short time (<10 min). Peak torque, peak maximum time, and energy values are calculated from the curve, and used to estimate the aggregation behavior of gluten. The information brought by the GlutoPeak indices is still difficult to interpret correctly, also in relation to the conventional approaches in the field of cereal science. A multivariate approach was used to investigate the correlations existing between the GlutoPeak indices and the conventional rheological parameters. 120 wheat flours- different for protein, dough stability, extensibility, tenacity, and strength, and end-uses - were analyzed using the GlutoPeak and conventional instrumentation. The parameters were subjected to a data exploration step through Principal Component Analysis. Then, multivariate Partial Least Squares Regression (PLSR) models were developed using the GlutoPeak indices to predict the conventional parameters. The values of the squared correlation coefficients in prediction of an external test set showed that acceptable to good results (0.61 64 R2PRED 64 0.96) were obtained for the prediction of 18 out of the 26 conventional parameters here considered

Apparato e metodo per determinare parametri fisici e chimici di un campione disomogeneo tramite acquisizione ed elaborazione di immagini a colori del campione

L’invenzione consiste in un dispositivo portatile compatto, economico e di semplice utilizzo per il monitoraggio in campo del grado di maturazione fenolica dell’uva mediante l'analisi di immagini acquisite utilizzando uno smartphone

Screening of environmental yeasts for the fermentative production of arabitol from lactose and glycerol

Arabitol is a sugar alcohol, stereoisomer to xylitol, which is enlisted among the main target for biorefineries. It can serve as low calorie sweetener and as building block in the enantiopure synthesis of immunosuppressive glycolipids, herbicides, and drugs. Several studies described the fermentative production of arabitol by osmophilic yeasts, cultured with high concentrations of D-glucose. The utilization of cheaper carbon sources, such as glycerol or lactose, is of great interest for biorefinery implementation, but information on exploitation to arabitol production is still scarce. In the present study 50 yeasts belonging to 24 ascomycetous species were screened for the ability to grow and produce arabitol in presence of 80 g/L lactose or glycerol. Production from lactose was generally unsuccessful, the best producer being Kluyveromyces lactis WC 1401 with 0.94 g/L in 160 h. Production from glycerol was promising, with Zygosaccharomyces rouxii WC 1206, Pichia guilliermondii CBS 566, Hansenula anomala WC 1501, and Candida freyschussii ATCC 18737 yielding 3 to 4.5 g/L arabitol, with conversion yield (YP/S) ranging from 11 to 21.7%. Batch growth with high initial glycerol amount (160 g/L) resulted in higher production, with H. anomala WC 1501 yielding 10.0 g/L arabitol (YP/S = 12%) in 160 h. Preliminary bioreactor fermentations with H. anomala WC 1501 indicated that production is not growth associated and revealed some major parameters affecting production, such as the pH and the C:N ratio, that will be the target of following studies aiming at process optimization. Cultivation under controlled oxygenation (DOT = 20%) and pH (= 3.0) resulted in improvement in the performance of H. anomala WC 1501, yielding 16.1 g/L arabitol. Cultivation in a medium with high C:N ratio, lacking inorganic nitrogen yielded 17.1 g/L arabitol. Therefore, this strain was selected for the development of a fed-batch process, aiming to improve the efficiency of the biomass, generated in the growth phase, and increasing the production in the stationary phase

A feature selection strategy for the analysis of spectra from a photoacoustic sensing system

In the frame of the EU project CUSTOM, a new sensor system for the detection of drug precursors in gaseous samples is being developed, which also includes an External Cavity-Quantum Cascade Laser Photo Acoustic Sensor (ECQCLPAS). In order to define the characteristics of the laser source, the optimal wavenumbers within the most effective 200 cm -1 range in the mid-infrared region must be identified, in order to lead to optimal detection of the drug precursor molecules in presence of interfering species and of variable composition of the surrounding atmosphere. To this aim, based on simulations made with FT-IR spectra taken from literature, a complex multivariate analysis strategy has been developed to select the optimal wavenumbers. Firstly, the synergistic use of Experimental Design and of Signal Processing techniques led to a dataset of 5000 simulated spectra of mixtures of 33 different gases (including the 4 target molecules). After a preselection, devoted to disregard noisy regions due to small interfering molecules, the simulated mixtures were then used to select the optimal wavenumber range, by maximizing the classification efficiency, as estimated by Partial Least Squares - Discriminant Analysis. A moving window 200 cm -1 wide was used for this purpose. Finally, the optimal wavenumber values were identified within the selected range, using a feature selection approach based on Genetic Algorithms and on resampling. The work made will be relatively easily turned to the spectra actually recorded with the newly developed EC-QCLPAS instrument. Furthermore, the proposed approach allows progressive adaptation of the spectral dataset to real situations, even accounting for specific, different environments

Improved fed-batch processes with Wickerhamomyces anomalus WC 1501 for the production of D-arabitol from pure glycerol

D-Arabitol, a five-carbon sugar alcohol, represents a main target of microbial biorefineries aiming to valorize cheap substrates. The yeast Wickerhamomyces anomalus WC 1501 is known to produce arabitol in a glycerol-based nitrogen-limited medium and preliminary fed-batch processes with this yeast were reported to yield 18.0 g/L arabitol

Metronomic capecitabine versus best supportive care as second-line treatment in hepatocellular carcinoma: A retrospective study

Preliminary studies suggest that capecitabine may be safe and effective in HCC patients. The aim of this study was to retrospectively evaluate the safety and efficacy of metronomic capecitabine as second-line treatment. This multicentric study retrospectively analyzed data of HCC patients unresponsive or intolerant to sorafenib treatment with metronomic capecitabine or best supportive care (BSC).Median progression free survival was 3.1 months in patients treated with capecitabine (95%CI: 2.7-3.5). Median overall survival was 12.0 months (95% CI: 10.7-15.8) in patients receiving capecitabine, while 9.0 months (95% CI: 6.5-13.9) in patients receiving BSC. The result of univariate unweighted Cox regression model shows a 46% reduction in death risk for patients on capecitabine (95%CI: 0.357-0.829; p=0.005) compared to patients receiving BSC alone. After weighting for potential confounders, death risk remained essentially unaltered (45%; 95%CI: 0.354-0.883; p = 0.013). Metronomic capecitabine seems a safe second-line treatment for HCC patients in terms of management of adverse events, showing a potential anti-tumour activity which needs further evaluation in phase III studies