Monitoring Batch Cooling Crystallization Using NIR: Development of Calibration Models Using Genetic Algorithm and PLS

Li, Rui F.; Wang, Xue Z.; Abebe, Solomon B.

doi:10.1002/ppsc.200800029

Cited by 16 publications

(3 citation statements)

References 43 publications

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“…For more details, the interested reader is referred to (Goodarzi, Saeys, Deeb, Pieters, & Vander Heyden, 2013;R. F. Li, Wang, & Abebe, 2008;J Liu & Zhou, 2007;Chao Y. Ma & Wang, 2011;Reddy, Kumar, & Garg, 2010).…”

Section: Decision Trees (Dts) Automatic Generation Of Decision Treesmentioning

confidence: 99%

(Q)SAR modelling of nanomaterial toxicity: A critical review

et al. 2015

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There is an increasing recognition that nanomaterials pose a risk to human health, and that the novel engineered nanomaterials (ENMs) in the nanotechnology industry and their increasing industrial usage poses the most immediate problem for hazard assessment, as many of them remain untested. The large number of materials and their variants (different sizes and coatings for instance) that require testing and ethical pressure towards non-animal testing means that expensive animal bioassay is precluded, and the use of (quantitative) structure activity relationships ((Q)SAR) models as an alternative source of hazard information should be explored.(Q)SAR modelling can be applied to fill the critical knowledge gaps by making the best use of existing data, prioritize physicochemical parameters driving toxicity, and provide practical solutions to the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of (Q)SAR technologies to ENMs toxicity prediction, and summarizes the published nano-(Q)SAR studies and outlines the challenges ahead for nano-(Q)SAR modelling. It provides a critical review of (1) the present status of the availability of ENMs characterization/toxicity data, (2) the characterization of nanostructures that meets the need of (Q)SAR analysis, (3) the summary of published nano-(Q)SAR studies and their limitations, (4) the in silico tools for (Q)SAR screening of nanotoxicity and (5) the prospective directions for the development of nano-(Q)SAR models.

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Section: Decision Trees (Dts) Automatic Generation Of Decision Treesmentioning

confidence: 99%

(Q)SAR modelling of nanomaterial toxicity: A critical review

et al. 2015

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“…45 °C. According to the -LGA solubility equation given in the reference [29], the solubility is estimated by …”

Section: Experimental Testsmentioning

confidence: 99%

Investigation of the operating conditions to morphology evolution of β-l-glutamic acid during seeded cooling crystallization

Zhang

Liu

Huo

et al. 2017

Journal of Crystal Growth

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Abstract:In this paper the effects of operating conditions including cooling rate, initial supersaturation, and seeding temperature were investigated on the morphology evolution of -L-glutamic acid ( -LGA) during seeded cooling crystallization. Based on the results of in-situ image acquisition of the crystal morphology evolution during the crystallization process, it was found that the crystal products tend to be plate-like or short rod-like under a slow cooling rate, low initial supersaturation, and low seeding temperature. In the opposite, the operating conditions of a faster cooling rate, higher initial supersaturation, and higher seeding temperature tend to produce long rod-like or needle-like crystals, and meanwhile, the length and width of crystal products will be increased together with a wider crystal size distribution (CSD). The aspect ratio of crystals, defined by the crystal length over width measured from in-situ or sample images, was taken as a shape index to analyze the crystal morphologies. Based on comparative analysis of the experimental results, guidelines on these operating conditions were given for obtaining the desired crystal shapes, along with the strategies for obtaining a narrower CSD for better product quality. Experimental verifications were performed to illustrate the proposed guidelines on the operating conditions for seeded cooling crystallization of LGA solution.

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“…Fevotte et al 25 reported that depending on both qualitative and quantitative methods, fiber optic NIRS was used to investigate several key features of the polymorphic transitions during crystallization and filtration of an API. An application of genetic algorithm to select wavelength in PLS calibration model for monitoring batch cooling crystallization processes by NIRS was demonstrated by Li et al 26 Haag et al 27 investigated the general applicability of NIR and Raman spectroscopy to predict diphenhydramine hydrochloride contents in wafers with varying concentrations. Because of the availability of fiber optic probe, NIRS currently receives enormous interest in pharmaceutical manufacturing process.…”

Section: Introductionmentioning

confidence: 99%