Independent component analysis for the identification of sources of variation on an industrial nirs application

A Near Infrared Spectroscopy (NIRS) industrial application was developed by the LPF-Tagralia team, and transferred to a Spanish dehydrator company (Agrotécnica Extremeña S.L.) for the classification of dehydrator onion bulbs for breeding purposes. The automated operation of the system has allowed the classification of more than one million onion bulbs during seasons 2004 to 2008 (Table 1). The performance achieved by the original model (R2=0,65; SEC=2,28ºBrix) was enough for qualitative classification thanks to the broad range of variation of the initial population (18ºBrix). Nevertheless, a reduction of the classification performance of the model has been observed with the passing of seasons. One of the reasons put forward is the reduction of the range of variation that naturally occurs during a breeding process, the other is the variations in other parameters than the variable of interest but whose effects would probably be affecting the measurements [1]. This study points to the application of Independent Component Analysis (ICA) on this highly variable dataset coming from a NIRS industrial application for the identification of the different sources of variation present through seasons

Two Novel Methods For The Determination Of The Number Of Components In Independent Components Analysis Models

Independent Components Analysis is a Blind Source Separation method that aims to find the pure source signals mixed together in unknown proportions in the observed signals under study. It does this by searching for factors which are mutually statistically independent. It can thus be classified among the latent-variable based methods. Like other methods based on latent variables, a careful investigation has to be carried out to find out which factors are significant and which are not. Therefore, it is important to dispose of a validation procedure to decide on the optimal number of independent components to include in the final model. This can be made complicated by the fact that two consecutive models may differ in the order and signs of similarly-indexed ICs. As well, the structure of the extracted sources can change as a function of the number of factors calculated. Two methods for determining the optimal number of ICs are proposed in this article and applied to simulated and real datasets to demonstrate their performance

Comparison of multivariate calibration techniques applied to experimental NIR data sets

The present study compares the performance of different multivariate calibration techniques applied to four near-infrared data sets when test samples are well within the calibration domain. Three types of problems are discussed: the nonlinear calibration, the calibration using heterogeneous data sets, and the calibration in the presence of irrelevant information in the set of predictors. Recommendations are derived from the comparison, which should help to guide a nonchemometrician through the selection of an appropriate calibration method for a particular type of calibration data. A flexible methodology is proposed to allow selection of an appropriate calibration technique for a given calibration problem.54460862

Application of Fourier transform to multivariate calibration of near-infrared data

An approach for extracting the relevant features for multivariate calibration of the hydroxyl number in a polyol from a nearinfrared (NIR) spectroscopic data set by using the Fourier transform, is presented. It is carried out in the frequency domain starting from the frst 50 power-spectra (PS) coeffcients as the input to a genetic algorithm (GA). The appropriate PS coeffcients selected by the GA were used to build a multiple linear-regression (MLR) model. The performance of the new approach is compared with MLR after wavelength selection with GA, with the standard PCR and PLS methods applied to the wavelength domain, and PCR and PLS applied to the full PS domain. Furthermore, it was also compared to the `Uninformative Variable Elimination' (UVE) PLS method in the frequency domain. The results demonstrate that PS is a fast and powerful reduction method. The coeffcients selected are of two types: one that correlates with the characteristic investigated, and the other that takes into account different clusters. This also shows that the method can be used to investigate the structure of the data

Application Of Wavelet Transform To Extract The Relevant Component From Spectral Data For Multivariate Calibration

An approach aiming at extracting the relevant component for multivariate calibration is introduced, and its performance is compared with the "uninformative variable elimination" approach and with the standard PLS method for the modeling of near-infrared data. The extraction of the relevant component is carried out in the wavelet domain. The PLS results on these relevant features are better, and therefore, it seems that this approach can successfully be used to remove noise and irrelevant information from spectra for multivariate calibration.692143174323Martens, H., Naes, T., (1989) Multivariate Calibration, , Wiley: Chichester, UKJouan-Rimbaud, D., Walczak, B., Massart, D.L., Last, I.R., Prebble, K.A., (1995) Anal. Chim. Acta, 304, pp. 285-295Davies, A.M.C., (1995) Spectrosc. Eur., 7 (4), pp. 36-38Sutter, J.M., Kalivas, J.H., Lang, P.M., (1992) J. Chemom., 6, pp. 217-225Thomas, E.V., (1994) Anal. Chem., 66, pp. 795A-804ABrown, P.J., (1992) J. Chemom., 6, pp. 151-161Garrido Frenich, A., Jouan-Rimbaud, D., Massart, D.L., Kuttatharmmakul, S., Martínez Galera, M., Martínez Vidal, J.L., (1995) Analyst, 120, pp. 2787-2792Lindgren, F., Geladi, P., Rännar, S., Wold, S., (1994) J. Chemom., 8, pp. 349-363Lindgren, F., Geladi, P., Berglund, A., Sjostrom, M., Wold, S., (1995) J. Chemom., 9, pp. 331-342Centner, V., Massart, D.L., De Noord, O.E., De Jong, S., Vandeginste, B.M., Sterna, C., (1996) Anal. Chem., 68, pp. 3851-3858Mallat, S., (1989) IEEE Trans. Pattern Anal.Machine Intell., 11 (7), pp. 674-693Saito, N., Simultaneous noise suppression and signal compression using a library of orthonormal bases and the minimum description length criterion (1994) Wavelets in Geophysics, , Foufoula-Georgiou, F., Kumar, P., Eds.Academic Press: New YorkWalczak, B., Massart, D.L., (1997) Chemom. Intell. Lab. Syst., 36, pp. 81-94Rissanen, J., (1983) Ann. Stat., 11, pp. 416-431Rissanen, J., (1984) IEEE Trans. Inform. Theory, 30, pp. 629-636Rissanen, J., (1989) Stochastic Complexity in Statistical Inquiry, , World Scientific: SingaporeChui, C.K., (1991) Introduction to Wavelets, , Academic Press: Boston, MADaubechies, I., (1988) Commun. Pure Appl. Math., 41, pp. 909-996Beyer, W.H., (1991) CRC Standard Mathematical Tables and Formulae, 29th Ed., , CRC Press: Boca Raton, FLJouan-Rimbaud, D., Massart, D.L., Leardi, R., De Noord, O.E., (1995) Anal. Chem., 67, pp. 4295-4301Centner, V., Massart, D.L., De Noord, O.E., (1996) Anal. Chim. Acta, 330, pp. 1-17Barnes, R.J., Dhanoa, M.S., Lister, S.J., (1989) Appl. Spectrosc., 43, pp. 772-777Jouan-Rimbaud, D., Khots, M.S., Massart, D.L., Last, I.R., Prebble, K.A., (1995) Anal. Chim. Acta, 315, pp. 257-266Guchardi, R., (1996), Dissertation Thesis, State University of Campinas, BrazilMatlab, The MathWorks, Inc., 1992WavBox 3 by Carl Taswell, available via [email protected], R.R., Mayer, Y., Wickerhauser, V., (1993) Progress in Wavelet Analysis and Applications, pp. 77-93. , Meyer, Y., Roques, S., EdsEditions FrontieresCody, M.A., (1994) Dr. Dobb's J., 17, pp. 16-28Coifman, R.R., Wickerhauser, M.V., (1992) IEEE Trans. Inform. Theory, 38 (2), pp. 713-71

MBA-GUI: A chemometric graphical user interface for multi-block data visualisation, regression, classification, variable selection and automated pre-processing

In recent years, due to advances in sensor technology, multi-modal measurement of process and products properties has become easier. However, multi-modal measurements are only of use if the data from adding new sensors is worthwhile, especially in the case of industrial applications where financial justification is needed for new sensor purchase and integration, and if the multi-modal data generated can be properly utilised. Several multi-block methods have been developed to do this; however, their use is largely limited to chemometricians, and non-experts have little experience with such methods. To deal with this, we present the first version of a MATLAB-based graphical user interface (GUI) for multi-block data analysis (MBA), capable of performing data visualisation, regression, classification and variable selection for up to 4 different sensors. The MBA-GUI can also be used to implement a recent technique called sequential pre-processing through orthogonalization (SPORT). Data sets are supplied to demonstrate how to use the MBA-GUI. In summary, the developed GUI makes the implementation of multi-block data analysis easier, so that it could be used also by practitioners with no programming skills or unfamiliar with the MATLAB environment. The fully functional GUI can be downloaded from (https://github.com/puneetmishra2/Multi-block.git) and can be either installed to run in the MATLAB environment or as a standalone executable program. The GUI can also be used for analysis of a single block of data (standard chemometrics)

Recent trends in multi-block data analysis in chemometrics for multi-source data integration

In recent years, multi-modal measurements of process and product properties have become widely popular. Sometimes classical chemometric methods such as principal component analysis (PCA) and partial least squares regression (PLS) are not adequate to analyze this kind of data. In recent years, several multi-block methods have emerged for this purpose; however, their use is largely limited to chemometricians, and non-experts have little experience with such methods. In order to deal with this, the present review provides a brief overview of the multi-block data analysis concept, the various tasks that can be performed with it and the advantages and disadvantages of different techniques. Moreover, basic tasks ranging from multi-block data visualization to advanced innovative applications such as calibration transfer will be briefly highlighted. Finally, a summary of software resources available for multi-block data analysis is provided