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Ottavian, Matteo (2014) LATENT VARIABLE MODELING TO ASSIST PRODUCT QUALITY CHARACTERIZATION IN THE FOOD AND PHARMACEUTICAL INDUSTRIES. [Tesi di dottorato]

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Abstract (inglese)

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.

Abstract (italiano)

In un sistema economico globalizzato come quello in cui viviamo, garantire elevati standard in termini di qualità di prodotto costituisce per ogni azienda produttiva un fattore di successo. Monitorare in modo accurato la qualità del prodotto lungo tutti gli stadi della filiera produttiva, tuttavia, è divenuto progressivamente più complesso a causa della dimensione globale che quest’ultima ha assunto. É questo un effetto dei fenomeni di delocalizzazione della produzione, legati alla necessità delle aziende di non perdere quote di mercato a discapito di paesi emergenti caratterizzati da costi di produzione inferiori. In un tal sistema, aumenta anche il rischio di frodi, adulterazioni e contraffazioni del prodotto. Per certe categorie di prodotti, come quelli alimentari e farmaceutici, tali attività non solo danneggiano i consumatori dal punto di vista economico, ma possono anche causare seri problemi di salute.
Nonostante la grande importanza del monitorare la qualità di prodotto, a livello industriale si è ancora lontani da un sistema che permetta di caratterizzarla in modo rapido, economico, non invasivo (e quindi utilizzabile in tempo reale), riproducibile e multivariato (cioè in grado di quantificare contemporaneamente più parametri di qualità). Le tecniche che si sono dimostrate più promettenti in tal senso sono la spettroscopia d’assorbimento nella regione del visibile e del vicino infrarosso e l’analisi d’immagine. Per analizzare la moltitudine di dati (spettri e immagini) caratterizzati da forti correlazioni che queste generano, è necessario ricorrere a tecniche statistiche apposite, in particolare i modelli a variabili latenti (LVM, latent variable models). Tali tecniche, che sono pensate per trattare tali tipologie di dati, nascono dall’assunto che un sistema possa essere descritto mediante pochi fattori (detti anche variabili latenti) esprimibili come combinazione lineare delle variabili originali e interpretabili sulla base dei fenomeni chimico/fisici che interessano il sistema.
Il numero di applicazioni di LVM nel campo della caratterizzazione di prodotti alimentari e farmaceutici è cresciuto rapidamente negli ultimi anni. La maggior parte dei contributi pubblicati, tuttavia, offre soluzioni a specifici problemi anziché fornire approcci generali. L’obiettivo di questa Dissertazione è quello di superare alcune delle limitazioni esistenti al fine di favorire la diffusione di questi strumenti nella comune pratica industriale. La ricerca presentata si suddivide in tre macro aree di applicazione, che si differenziano a seconda della tecnica utilizzata per caratterizzare la qualità di prodotto, e cioè l’analisi d’immagine, la spettroscopia, e la fusione di dati (data fusion), cioè la combinazione delle informazioni provenienti da più strumenti analitici. Per ciascuna di queste aree, l’efficacia della modellazione a variabili latenti viene dimostrata applicando i modelli in diversi casi studio di tipo industriale o di laboratorio.
Con riferimento all’analisi d’immagine, vengono proposte applicazioni nel campo farmaceutico. Nel Capitolo 3, l’analisi d’immagine viene utilizzata per il miglioramento della comprensione di un processo industriale di rivestimento di compresse. In tale processo la qualità finale del prodotto, che è legata all’omogeneità del rivestimento e al grado di erosione superficiale, viene tradizionalmente valutata da un panel di esperti, che necessariamente fornisce un giudizio soggettivo e poco riproducibile. Inizialmente, il Capitolo discute come, a partire da immagini del prodotto finito, sia possibile valutare in modo quantitativo e riproducibile i parametri di qualità. Le metriche sviluppate vengono quindi utilizzate per il troubleshooting del processo stesso, con il fine di indagare il meccanismo che porta all’erosione superficiale. A tal scopo, le metriche vengono correlate ai parametri di processo tramite un modello a variabili latenti, e i parametri del modello vengono utilizzati per definire le condizioni operative ottimali da utilizzare per garantire un prodotto in specifica.
Il Capitolo 4, usando ancora come pretesto un processo di rivestimento di compresse, discute in modo critico il problema della riproducibilità dei risultati ottenuti tramite analisi d’immagine. Tale riproducibilità, infatti, è garantita solamente se le condizioni sperimentali utilizzate per raccogliere le immagini destinate alla calibrazione del modello di stima della qualità vengono mantenute inalterate. Tali condizioni includono il sistema di illuminazione e la fotocamera stessa. Viene proposto innanzitutto un modello per il monitoraggio dello stato dell’apparato sperimentale, da utilizzare ogniqualvolta viene avviata una campagna di controllo qualità e basato semplicemente sull’acquisizione di un’immagine di standard colorati. In caso venga rilevato un cambiamento, viene proposta una strategia per adattare il modello di stima della qualità alle nuove condizioni. I risultati dimostrano l’efficacia della strategia proposta, che si basa su una tecnica già nota nel contesto della sincronizzazione vocale e dell’allineamento di traiettorie temporali in processi produttivi di tipo batch.
Con riferimento alla spettroscopia d’assorbimento, le applicazioni presentate riguardano prodotti alimentari, con particolare attenzione alle tecnologie per la rilevazione rapida di frodi di sostituzione (di un prodotto avente un certo valore di mercato con uno a valore di mercato inferiore). Nel Capitolo 5, viene presentata una nuova tecnica per la classificazione di dati spettrali, che ha l’obiettivo di razionalizzare il pretrattamento cui i dati stessi sono generalmente sottoposti. I risultati dimostrano come la tecnica proposta garantisca di ottenere la stessa accuratezza di altri metodi, senza tuttavia ricorrere a procedure di tipo trial-and-error, onerose in termini computazionali, per la scelta del miglior pretrattamento.
Nel Capitolo 6, accanto a due applicazioni di autenticazione di prodotti alimentari (filetti pescati di branzino e formaggio Asiago d’allevo) tramite spettroscopia, viene presentata una tecnica multi specie per la rilevazione di una tipica frode del settore ittico, cioè la sostituzione di filetti freschi con filetti decongelati. Rispetto al tradizionale approccio di costruire un modello di rilevazione della frode per ciascuna specie, lavorare con un modello multi specie (e, magari, indipendentemente dalla specie) riduce notevolmente i tempi e i costi necessari nella fase di calibrazione. Delle tre strategie proposte, quella che fornisce risultati migliori lavora decomponendo l’informazione contenuta nei dati spettrali in due componenti, una legata alla specie e una legata allo stato fresco o decongelato. La tecnica, convalidata su un numero di spettri molto maggiore rispetto alla applicazioni riportate in letteratura, si è dimostrata efficace anche nell’autenticazione di campioni di specie non utilizzate nella fase di calibrazione.
Infine, con riferimento alla fusione di dati, il Capitolo 7 dimostra, attraverso due applicazioni in campo alimentare, come unire le informazioni ottenute da più strumenti analitici permetta di migliorare la caratterizzazione della qualità di un prodotto. La combinazione dei segnali a disposizione (detta low level, per distinguerla da altre tecniche di fusione di dati), opportunamente pesati, permette di ottenere risultati migliori rispetto all’utilizzo dei singoli segnali.

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Tipo di EPrint:Tesi di dottorato
Relatore:Barolo, Massimiliano
Dottorato (corsi e scuole):Ciclo 26 > Scuole 26 > INGEGNERIA INDUSTRIALE > INGEGNERIA CHIMICA, DEI MATERIALI E DELLA PRODUZIONE
Data di deposito della tesi:15 Gennaio 2014
Anno di Pubblicazione:15 Gennaio 2014
Parole chiave (italiano / inglese):Qualità del prodotto; analisi d'immagine; modellazione a variabili latenti; rilevazione frodi; fusione di dati; spettroscopia/ Product quality; image analysis; latent variable modeling; fraud detection; data fusion; spectroscopy
Settori scientifico-disciplinari MIUR:Area 09 - Ingegneria industriale e dell'informazione > ING-IND/26 Teoria dello sviluppo dei processi chimici
Struttura di riferimento:Dipartimenti > Dipartimento di Ingegneria Industriale
Codice ID:6252
Depositato il:19 Mag 2015 14:41
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