Pierantonio Facco

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.

General procedure to aid the development of continuous pharmaceutical processes using multivariate statistical modeling - an industrial case study.

Streamlining the manufacturing process has been recognized as a key issue to reduce production costs and improve safety in pharmaceutical manufacturing. Although data available from earlier developmental stages are often sparse and unstructured, they can be very useful to improve the understanding about the process under development. In this paper, a general procedure is proposed for the application of latent variable statistical methods to support the development of new continuous processes in the presence of limited experimental data. The proposed procedure is tested on an industrial case study concerning the development of a continuous line for the manufacturing of paracetamol tablets. The main driving forces acting on the process are identified and ranked according to their importance in explaining the variability in the available data. This improves the understanding about the process by elucidating how different active pharmaceutical ingredient pretreatments, different formulation modes and different settings on the processing units affect the overall operation as well as the properties of the intermediate and final products. The results can be used as a starting point to perform a comprehensive and science-based quality risk assessment that help to define a robust control strategy, possibly enhanced with the integration of a design space for the continuous process at a later stage.

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.

MULTIVARIATE STATISTICAL ESTIMATION OF PRODUCT QUALITY IN THE INDUSTRIAL BATCH PRODUCTION OF A RESIN

Abstract A multivariate statistical estimator of the product quality is developed for an industrial batch polymerization process producing a resin. It is shown that, for the purpose of quality estimation, the complex series of operating steps through which a batch is run can be simplified to a sequence of three estimation phases. For each phase, a PLS model for the estimation of the product quality is developed. Switching between one phase to the other one is triggered by easily detectable landmark events occurring in a batch. The performance of the resulting three-phase PLS estimator is very satisfactory.

Knowledge management in secondary pharmaceutical manufacturing by mining of data historians-A proof-of-concept study.

In this proof-of-concept study, a methodology is proposed to systematically analyze large data historians of secondary pharmaceutical manufacturing systems using data mining techniques. The objective is to develop an approach enabling to automatically retrieve operation-relevant information that can assist the management in the periodic review of a manufactory system. The proposed methodology allows one to automatically perform three tasks: the identification of single batches within the entire data-sequence of the historical dataset, the identification of distinct operating phases within each batch, and the characterization of a batch with respect to an assigned multivariate set of operating characteristics. The approach is tested on a six-month dataset of a commercial-scale granulation/drying system, where several millions of data entries are recorded. The quality of results and the generality of the approach indicate that there is a strong potential for extending the method to even larger historical datasets and to different operations, thus making it an advanced PAT tool that can assist the implementation of continual improvement paradigms within a quality-by-design framework.

Uncertainty back-propagation in PLS model inversion for design space determination in pharmaceutical product development

Abstract The inversion of latent-variable models is an effective tool to assist the determination of the design space (DS) of a new pharmaceutical product. A challenging issue in partial least-square (PLS) regression model inversion is to describe how the uncertainty on the model outputs (product quality) relates to the uncertainty on the model inputs (raw material properties and process parameters). In this study, a methodology to relate the uncertainty on the output of a PLS model to the uncertainty on the model inputs is proposed. Two uncertainty back-propagation models are formulated and critically compared. Frequentist confidence regions (CRs) for the solution of the inversion problem are built. These CRs represent a subspace of the historical knowledge space within which the DS of the product to be developed is likely to lie with assigned confidence level. The input combinations that belong to these CRs (and that are consistent with the historical calibration data set) should be primarily investigated when an experimental campaign is to be performed to determine the DS. The proposed methodology is tested on three different case studies, two of which involve experimental data taken from the literature, respectively, on a roller compactor and on a wet granulator. It is shown that both uncertainty back-propagation models are effective in bracketing the DS, with the second model outperforming the first one in terms of shrinkage of the space within which experiments should be carried out to identify the DS.

Using PLS and NIR spectra to model the first-breakage step of a grain milling process

Abstract In a wheat milling process, the product quality (i.e., particle size distribution) is highly affected by the process parameters and by the wheat characteristics. Therefore, both the process settings and the wheat properties must be accounted for to model the milling operation. In this study, the information coming from near-infrared (NIR) spectra is used to characterize the wheat feed material. Using experimental data, the information coming from the spectra is used to model the first-breakage operation in a wheat milling process. A partial least-squares model of the first-breakage milling operation is developed. The model takes into account the different wheat varieties and wheat characteristics, as well as the most significant process parameters, to determine the product quality and improve process understanding.

Vibrio Trends in the Ecology of the Venice Lagoon

ABSTRACT Vibrio is a very diverse genus that is responsible for different human and animal diseases. The accurate identification of Vibrio at the species level is important to assess the risks related to public health and diseases caused by aquatic organisms. The ecology of Vibrio spp., together with their genetic background, represents an important key for species discrimination and evolution. Thus, analyses of population structure and ecology association are necessary for reliable characterization of bacteria and to investigate whether bacterial species are going through adaptation processes. In this study, a population of Vibrionaceae was isolated from shellfish of the Venice lagoon and analyzed in depth to study its structure and distribution in the environment. A multilocus sequence analysis (MLSA) was developed on the basis of four housekeeping genes. Both molecular and biochemical approaches were used for species characterization, and the results were compared to assess the consistency of the two methods. In addition, strain ecology and the association between genetic information and environment were investigated through statistical models. The phylogenetic and population analyses achieved good species clustering, while biochemical identification was demonstrated to be imprecise. In addition, this study provided a fine-scale overview of the distribution of Vibrio spp. in the Venice lagoon, and the results highlighted a preferential association of the species toward specific ecological variables. These findings support the use of MLSA for taxonomic studies and demonstrate the need to consider environmental information to obtain broader and more accurate bacterial characterization.

Supporting the transfer of products between different equipment through latent variable model inversion

Abstract Product transfer is a problem commonly encountered in industry when a production has to be transferred from a source plant to a target plant. In this paper a strategy to assist product transfer is proposed. The strategy is based on latent variable models (LVMs) to relate the data available from one or more source plants with the (usually scarce) data available from the target plant, and on LVM inversion to estimate the target plant operating conditions. The inversion is performed within an optimization framework, which can handle constraints for both product quality variables and input variables. An experimental nanoparticle production process is used as a test bed to illustrate the benefits of the proposed strategy.

Data-based multivariate modeling of a grain comminution process

Abstract A grain comminution process is based on a gradual size reduction approach, through repeated milling and sieving units that increase the flour yield. In this study we use latent variable modeling techniques to link process parameters and grain properties to the final product quality. Using experimental data, it is shown how the use of models in their direct form allows one to improve process understanding and to predict the product quality from the process settings and grain properties. Additionally, it is shown that, by inverting the latent variable models, the optimal combination of process parameters and grain properties leading to a desired product quality can be determined.