LATENT VARIABLE MODELING TO ASSIST PRODUCT QUALITY CHARACTERIZATION IN THE FOOD AND PHARMACEUTICAL INDUSTRIES

The pressure of the global competition, continuously asking for lower costs and improved productivity, is forcing companies to seek global supply chains to cut production costs down. As a result, it is becoming more and more difficult to accurately monitor each step of a production process and to protect products from economically motivated fraud, adulterations and counterfeiting. In such context, traditional methods for product quality characterization, such as lab assays, are expensive, destructive, time-consuming, and for these reasons they have become inadequate in several applications. On the other hand, other approaches, such as absorption spectroscopy and computer vision, have been gaining much attention in the last decade, successfully contributing to speed up and automate the quality assessment exercise. Statistical modeling tools, particularly latent variable models (LVMs), are usually employed to exploit the information embedded in the large amount of highly correlated data (spectra and images) that absorption spectroscopy and computer vision generate. In the food and pharmaceutical sectors, product quality assessment still relies mainly on the judgment (of product color, odor, form, taste, etc.) of a panel of trained experts. Although the number of applications of LVMs as predictive tools for product quality monitoring is growing in these sectors, the use of LVMs for product quality assessment is usually tailored to each application, and general approaches to product quality assessment based on LVMs are lacking. The main objective of the research presented in this Dissertation is to overcome some of the limitations that hinder the diffusion of LVM tools in the food and pharmaceutical industrial practice. Three main strategies for product quality assessment are explored, namely the use of computer vision, the use of absorption spectroscopy, and the possibility of combining the information derived from different analytical instruments. With respect to the use of computer vision systems, the problem of maintaining such systems is discussed. Computer vision systems are deemed to be quick, accurate, objective and able to return reproducible results. However, likewise all other measurement systems, they need to be maintained. Alterations or failures (e.g. of the illuminating system or of the camera sensors) can dramatically affect measurement reproducibility, leading to a wrong product quality characterization. The problem of how to detect and manage these alterations or failures is discussed through a pharmaceutical engineering case study. General strategies are proposed to adapt a quality assessment model, which has been calibrated under certain environmental conditions, to new conditions. Results show that long downtime periods, which may be necessary to recalibrate the quality assessment model after a failure of the camera or of the lighting system, can be significantly reduced. Additionally, it is shown how image analysis can be effectively used not only to characterize the quality of a product, but also to improve the understanding on the production process (e.g., for troubleshooting or optimization purposes). In a specific pharmaceutical application, image analysis is used to investigate the causes leading to the erosion of tablets, allowing one to evaluate the effect of different physical phenomena occurring in the film-coating process. Additionally, the model relating the process conditions to the tablets quality is shown to be useful for process monitoring purposes. With respect to the use of absorption spectroscopy, a novel methodology to preprocess and classify spectral data is proposed. Traditionally, LVMs are built after some preprocessing of the raw spectra, and the optimal preprocessing strategy is chosen trough a time consuming trial-and-error procedure. Results from three different food engineering case studies show that the proposed methodology performs similarly to other existing approaches, but it uses a sequence of totally automated preprocessing steps, with no need for trial-and-error searches. Especially in the food industry, LVMs are usually tailored on the specific product being analyzed. For instance, for the detection of the fresh/frozen-thawed substitution fraud in fish fillets, a model is calibrated for each fish species possibly subject to substitution. This Dissertation considers a different approach: some strategies are proposed to design a multi-species, and possibly species-independent, classification model to detect this substitution fraud. The most promising strategy decomposes the information embedded in the spectral data using a single model, and it is shown to return the same overall accuracy of traditional approaches that employ one classification model for each species under investigation. Finally, with respect to the use of data fusion, it is shown how to effectively combine the information derived from different analytical instruments (such as spectrometers, digital cameras, texture analyzers, etc.) to enhance product quality characterization. Results on two food engineering case studies show that fusing the available information, rather than using them separately, improves the ability of assessing product quality.