title: Statistical process control of cocrystallization processes: A comparison between OPLS and PLS.
abstract: Orthogonal partial least squares regression (OPLS) is being increasingly adopted as an alternative to partial least squares (PLS) regression due to the better generalization that can be achieved. Particularly in multivariate batch statistical process control (BSPC), the use of OPLS for estimating nominal trajectories is advantageous. In OPLS, the nominal process trajectories are expected to be captured in a single predictive principal component while uncorrelated variations are filtered out to orthogonal principal components. In theory, OPLS will yield a better estimation of the Hotelling's T2statistic and corresponding control limits thus lowering the number of false positives and false negatives when assessing the process disturbances. Although OPLS advantages have been demonstrated in the context of regression, its use on BSPC was seldom reported. This study proposes an OPLS-based approach for BSPC of a cocrystallization process between hydrochlorothiazide and p-aminobenzoic acid monitored on-line with near infrared spectroscopy and compares the fault detection performance with the same approach based on PLS. A series of cocrystallization batches with imposed disturbances were used to test the ability to detect abnormal situations by OPLS and PLS-based BSPC methods. Results demonstrated that OPLS was generally superior in terms of sensibility and specificity in most situations. In some abnormal batches, it was found that the imposed disturbances were only detected with OPLS.
authors.affiliation: LAQV/REQUIMTE, Departamento de Ciências Químicas, Faculdade de Farmácia, Universidade do Porto, Rua Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal; Laboratory of Pharmaceutical Process Analytical Technology, Ghent University, Ottergemsesteenweg 460, 9000 Ghent, Belgium.
authors.forename: Ana F T
authors.initials: AFT
authors.lastname: Silva
authors.affiliation: LAQV/REQUIMTE, Departamento de Ciências Químicas, Faculdade de Farmácia, Universidade do Porto, Rua Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal.
authors.forename: Mafalda Cruz
authors.initials: MC
authors.lastname: Sarraguça
authors.affiliation: NUPFARQ, Centro de Ciências Sociais, Saúde e Tecnologia, Universidade Federal do Maranhão (UFMA), CP:65.900-410, Imperatriz, MA, Brazil.
authors.forename: Paulo R
authors.initials: PR
authors.lastname: Ribeiro
authors.affiliation: NUPFARQ, Centro de Ciências Sociais, Saúde e Tecnologia, Universidade Federal do Maranhão (UFMA), CP:65.900-410, Imperatriz, MA, Brazil.
authors.forename: Adenilson O
authors.initials: AO
authors.lastname: Santos
authors.affiliation: Laboratory of Pharmaceutical Process Analytical Technology, Ghent University, Ottergemsesteenweg 460, 9000 Ghent, Belgium.
authors.forename: Thomas
authors.initials: T
authors.lastname: De Beer
authors.affiliation: Research Institute for Medicines (iMed.Lisboa), Faculdade de Farmácia, Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-003 Lisboa, Portugal. Electronic address: jlopes@ff.ulisboa.pt.
authors.forename: João Almeida
authors.initials: JA
authors.lastname: Lopes
mesh_terms: D010129:4-Aminobenzoic Acid / Q000737:chemistry; D003460:Crystallization / Q000379:methods*; D006852:Hydrochlorothiazide / Q000737:chemistry; D016018:Least-Squares Analysis; D011786:Quality Control; D019265:Spectroscopy, Near-Infrared
chemical_list: D006852:Hydrochlorothiazide; D010129:4-Aminobenzoic Acid
keywords: Batch statistical process control; Cocrystallization; Near infrared spectroscopy; Orthogonal partial least squares; Partial least squares