MIA and NIR Chemical Imaging for pharmaceutical product characterization

[EN] This paper presents a three step methodology based on the use of chemical oriented models (MCR and CLS) for extracting out the chemical distribution maps (CDMs) from hyperspectral images, afterwards performing multivariate image analysis (MIA) on the CDMs, and !nally extracting 'channel' and textural features from the score images related to quality characteristics These features show complementary properties to those directly obtained from the CDMs, since they take advantage of their internal correlation structure. The approach has been successfully applied to the evaluation of homogeneity and cluster presence of API in a novel formulation developed to improve the dissolution of poorly soluble drugs. © 2012 Elsevier B.V. All rights reserved.Research in this study was partially supported by the Spanish Ministry of Science and Innovation and FEDER funds from the European Union through grant DPI2011-28112-C04-02, and also by NSF-Engineering Research Center for Structured Organic Particulate Systems (ERC-SOPS, EEC-0540855) and the program NSF-Major Research Instrumentation grant 0821113.Prats-Montalbán, JM.; Jerez-Rozo, J.; Romanach, R.; Ferrer Riquelme, AJ. (2012). MIA and NIR Chemical Imaging for pharmaceutical product characterization. Chemometrics and Intelligent Laboratory Systems. 117(117):240-249. https://doi.org/10.1016/j.chemolab.2012.04.002S24024911711

MCR-ALS on metabolic networks: Obtaining more meaningful pathways

[EN] With the aim of understanding the flux distributions across a metabolic network, i.e. within living cells, Principal Component Analysis (PCA) has been proposed to obtain a set of orthogonal components (pathways) capturing most of the variance in the flux data. The problems with this method are (i) that no additional information can be included in the model, and (ii) that orthogonality imposes a hard constraint, not always reasonably. To overcome these drawbacks, here we propose to use a more flexible approach such as Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) to obtain this set of biological pathways through the network. By using this method, different constraints can be included in the model, and the same source of variability can be present in different pathways, which is reasonable from a biological standpoint. This work follows a methodology developed for Pichia pastoris cultures grown on different carbon sources, lately presented in González-Martínez et al. (2014). In this paper a different grey modelling approach, which aims to incorporate a priori knowledge through constraints on the modelling algorithms, is applied to the same case of study. The results of both models are compared to show their strengths and weaknesses.Research in this study was partially supported by the Spanish Ministry of Science and Innovation and FEDER funds from the European Union through grants DPI2011-28112-C04-01 and DPI2011-28112-C04-02. The authors are also grateful to Biopolis SL for supporting this research.Folch-Fortuny, A.; Tortajada Serra, M.; Prats-Montalbán, JM.; Llaneras Estrada, F.; Picó Marco, JA.; Ferrer Riquelme, AJ. (2015). MCR-ALS on metabolic networks: Obtaining more meaningful pathways. Chemometrics and Intelligent Laboratory Systems. 142:293-303. https://doi.org/10.1016/j.chemolab.2014.10.004S29330314

In-Line estimation of the standard colour index of citrus fruits using a computer vision system developed for a mobile platform

The final publication is available at Springer via http://dx.doi.org/10.1007/s11947-012-1015-2A key aspect for the consumer when it comes to deciding on a particular product is the colour. In order to make fruit available to consumers as early as possible, the collection of oranges and mandarins begins before they ripen fully and reach their typical orange colour. As a result, they are therefore subjected to certain degreening treatments, depending on their standard colour citrus index at harvest. Recently, a mobile platform that incorporates a computer vision system capable of pre-sorting the fruit while it is being harvested has been developed as an aid in the harvesting task. However, due to the restrictions of working in the field, the computer vision system developed for this machine is limited in its technology and processing capacity compared to conventional systems. This work shows the optimised algorithms for estimating the colour of citrus in-line that were developed for this mobile platform and its performance is evaluated against that of a spectrophotometer used as a reference in the analysis of colour in food. The results obtained prove that our analysis system predicts the colour index of citrus with a good reliability (R2 = 0.925) working in real time. Findings also show that it is effective for classifying harvested fruits in the field according to their colour. © 2012 Springer Science+Business Media New York.This work was partially funded by the INIA through research project RTA2009-00118-C02-01 with the support of European FEDER funds, and by the project PAID-05-11-2745, Vicerectorat d'Investigacio, Universitat Politecnica de Valencia.Vidal, A.; Talens Oliag, P.; Prats-Montalbán, JM.; Cubero García, S.; Albert Gil, FE.; Blasco Ivars, J. (2013). In-Line estimation of the standard colour index of citrus fruits using a computer vision system developed for a mobile platform. Food and Bioprocess Technology. 6(12):3412-3419. https://doi.org/10.1007/s11947-012-1015-2S34123419612Arzate-Vázquez, I., Chanona-Pérez, J. J., Perea-Flores, M. J., Calderón-Domínguez, G., Moreno-Armendáriz, M. A., Calvo, H., Godoy-Calderón, S., Quevedo, R., & Gutiérrez-López, G. (2011). Image processing applied to classification of avocado variety Hass (Persea americana Mill.) during the ripening process. Food and Bioprocess Technology, 4(7), 1307–1313.Blasco, J., Aleixos, N., Cubero, S., Gómez-Sanchis, J., & Moltó, E. (2009). Automatic sorting of satsuma (Citrus unshiu) segments using computer vision and morphological features. Computers and Electronics in Agriculture, 66, 1–8.Campbell, B. L., Nelson, R. G., Ebel, C. E., Dozier, W. A., Adrian, J. L., & Hockema, B. R. (2004). Fruit quality characteristics that affect consumer preferences for satsuma mandarins. HortScience, 39(7), 1664–1669.Cavazza, A., Corradini, C., Rinaldi, M., Salvadeo, P., Borromei, C., & Massini, R. (2012). Evaluation of pasta thermal treatment by determination of carbohydrates, furosine, and color indices. Food and Bioprocess Technology. doi: 10.1007/s11947-012-0906-6 . In-press.Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., & Blasco, J. (2011). Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food and Bioprocess Technology, 4(4), 487–504.Cubero, S., Moltó, E., Gutiérrez, A., Aleixos, N., García-Navarrete, O. L., Juste, F., & Blasco, J. (2010). Real-time inspection of fruit on a mobile harvesting platform in field conditions using computer vision. Progress in Agricultural Engineering Science, 6, 1–16.Díaz, R., Faus, G., Blasco, M., Blasco, J., & Moltó, E. (2000). The application of a fast algorithm for the classification of olives by machine vision. Food Research International, 33, 305–309.DOGV (2006) Diari Oficial de la Comunitat Valenciana, 5346, 30321-30328.Gardner, J. L. (2007). Comparison of calibration methods for tristimulus colorimeters. Journal of Research of the National Institute of Standards and Technology, 112, 129–138.Hashim, N., Janius, R. B., Baranyai, L., Rahman, R. A., Osman, A., & Zude, M. (2011). Kinetic model for colour changes in bananas during the appearance of chilling injury symptoms. Food and Bioprocess Technology, 5(8), 2952–2963.HunterLab (2008): Applications note, 8(9), http://www.hunterlab.com/appnotes/an08_96a.pdf . Accessed September 2012.Hutchings, J. B., Luo, R., & Ji, W. (2002). Calibrated colour imaging analysis of food. In D. MacDougall (Ed.), Colour in Food (pp. 352–366). Cambridge: Woodhead Publishing.Jiménez-Cuesta MJ, Cuquerella J & Martínez-Jávega JM (1981) Determination of a color index for citrus fruit degreening. In Proc. of the International Society of Citriculture, Vol. 2, 750-753Kang, S. P., East, A. R., & Trujillo, F. J. (2008). Colour vision system evaluation of bicolour fruit: A case study with ‘B74’ mango. Postharvest Biology and Technology, 49, 77–85.Lang, C., & Hübert, T. (2011). A colour ripeness indicator for apples. Food and Bioprocess Technology, 5(8), 3244–3249.López-Camelo, A. F., & Gómez, P. A. (2004). Comparison of color indexes for tomato ripening. Horticultura Brasileira, 22(3), 534–537. http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-05362004000300006 .López-García, F., Andreu-García, A., Blasco, J., Aleixos, N., & Valiente, J. M. (2010). Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Computers and Electronics in Agriculture, 71, 189–197.Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., & Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology, 5(4), 1121–1142.Mendoza, F., Dejmek, P., & Aguilera, J. M. (2006). Calibrated color measurements of agricultural foods using image analysis. Postharvest Biology and Technology, 41, 285–295.Montgomery, D. C. (2005). Design and analysis of experiments, 6th ed. Tempe: Wiley.Noboru, O., & Robertson, A. R. (2005). Colorimetry. West Sussex: Wiley.Pathare, P. B., Opara, U. L., & Al-Said, F. A. (2012). Colour measurement and analysis in fresh and processed foods: a review. Food and Bioprocess Technology. doi: 10.1007/s11947-012-0867-9 . In-press.Quevedo, R. A., Aguilera, J. M., & Pedreschi, F. (2010). Colour of salmon fillets by computer vision and sensory panel. Food and Bioprocess Technology, 3, 637–643.Sahin, S., & Sumnu, S. G. (2006). Physical properties of foods. New York: Springer.Quevedo, R., Valencia, E., Alvarado, F., Ronceros, B., & Bastias, J. M. (2011). Comparison of whiteness index vs. fractal Fourier in the determination of bloom chocolate using image analysis. Food and Bioprocess Technology. doi: 10.1007/s11947-011-0729-x . In-press.Smith, T., & Guild, J. (1931). The C.I.E. colorimetric standards and their use. Transactions of the Optical Society, 33(3), 73–134.Yam, K. L., & Papadakis, S. E. (2004). A simple digital imaging method for measuring and analyzing color of food surfaces. Journal of Food Engineering, 61, 137–142

A three-dimensional discriminant analysis approach for hyperspectral images

Raman hyperspectral imaging is a powerful technique that provides both chemical and spatial information of a sample matrix being studied. The generated data are composed of three-dimensional (3D) arrays containing the spatial information across the x- and y-axis, and the spectral information in the z-axis. Unfolding procedures are commonly employed to analyze this type of data in a multivariate fashion, where the spatial dimension is reshaped and the spectral data fits into a two-dimensional (2D) structure and, thereafter, common first-order chemometric algorithms are applied to process the data. There are only a few algorithms capable of working with the full 3D array. Herein, we propose new algorithms for 3D discriminant analysis of hyperspectral images based on a three-dimensional principal component analysis linear discriminant analysis (3D-PCA-LDA) and a three-dimensional discriminant analysis quadratic discriminant analysis (3D-PCA-QDA) approach. The analysis was performed in order to discriminate simulated and real-world data, comprising benign controls and ovarian cancer samples based on Raman hyperspectral imaging, in which 3D-PCA-LDA and 3D-PCA-QDA achieved far superior performance than classical algorithms using unfolding procedures (PCA-LDA, PCA-QDA, partial lest squares discriminant analysis [PLS-DA], and support vector machines [SVM]), where the classification accuracies improved from 66% to 83% (simulated data) and from 50% to 100% (real-world dataset) after employing the 3D techniques. 3D-PCA-LDA and 3D-PCA-QDA are new approaches for discriminant analysis of hyperspectral images multisets to provide faster and superior classification performance than traditional techniques

A comparison of different discriminant analysis techniques in a steel industry welding process

The present work compares several statistical discriminant analysis techniques applied to a steel industry welding process. Data from 85 variables collected from 18,605 links, classified as Good (18,381), Defective (195) or Bad (29) from laboratory analysis, are available. Process engineers want to find out which variables explain the main differences between the three defined types, so they can implement effective action to reduce the percentage of Defective and Bad links. The approaches used are SIMCA, Global PCA, PLS-DA and Fisher's Linear Discriminant Analysis (LDA). The dataset comprises two kinds of variables, one for the chemical properties of the links, and the other related to the welding process. All the above approaches basically lead to the same results and match the ones derived from the more traditional Fisher s Linear Discriminant Analysis (LDA) technique. The pros and cons of the approaches used are discussed.The authors would like to thank to the referees for their helpful comments and suggestions that have improved the consistency of this work. This research was supported by the Spanish Government (Science and Technology Ministry) and the European Union (RDE funds) under the project DPI 2001-2749-C02. The authors want to thank Vicinay Cadenas S.A. for its support, providing the data and sharing its process knowledge.Peer reviewe

Maintenance of Machine Vision Systems for Product Quality Assessment. Part II. Addressing Camera Replacement

In the process industry, machine vision systems have been shown to be beneficial for the characterization of several products whose quality can be related to some visual features such as color, color uniformity, surface roughness, and presence of surface defects. With respect to the visual inspection traditionally carried out by a panel of trained experts, artificial vision systems can return a quick, accurate, and objective indication of the quality of the manufactured end product. However, reproducibility of the image analysis results is ensured only as long as the conditions, under which the images used for the calibration of the quality assessment model were collected, do not change during normal operation of the machine vision system. In this paper (Part II of this series following Part I, Ind. Eng. Chem. Res., 2013, 52, 12309−12318), we discuss a technology transfer problem for a machine vision system, namely the problem arising when replacing the camera, on which the system is centered, with a different one. In order to compensate for the differences between cameras, two strategies are tested. The first one, which is borrowed from the machine vision literature, aims at matching the color spaces of the two cameras through a linear or a nonlinear transformation. The second one, which was proposed in Part I to manage light alterations, aims at matching the color spaces of the cameras by synchronizing their projection onto a principal component analysis model. The superior performance of the latter strategy is demonstrated through two case studies involving images of pharmaceutical tablets and calibration standards of different colors, as well as images of film-coated tablets with different percentages of applied coating material

Multivariate locally stationary 2D wavelet processes with application to colour texture analysis

In this article we propose a novel framework for the modelling of non-stationary multivariate lattice processes. Our approach extends the locally stationary wavelet paradigm into the multivariate two-dimensional setting. As such the framework we develop permits the estimation of a spatially localised spectrum within a channel of interest and, more importantly, a localised cross-covariance which describes the localised coherence between channels. Associated estimation theory is also established which demonstrates that this multivariate spatial framework is properly defined and has suitable convergence properties. We also demonstrate how this model-based approach can be successfully used to classify a range of colour textures provided by an industrial collaborator, yielding superior results when compared against current state-of-the-art statistical image processing methods