Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

PAH diagnostic ratios for the identification of pollution emission sources

The common steps to calibrate and validate classification models based on partial least squares discriminant analysis are discussed in the present tutorial. All issues to be evaluated during model training and validation are introduced and explained using a chemical dataset, composed of toxic and non-toxic sediment samples. The analysis was carried out with MATLAB routines, which are available in the ESI of this tutorial, together with the dataset and a detailed list of all MATLAB instructions used for the analysis.

Classification tools in chemistry. Part 1: linear models. PLS-DA

Fluorescence as a potential monitoring tool for recycled water systems: a review.

Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. As a computational engine to data mining and analytics, machine learning serves as basic tools for information extraction, data pattern recognition and predictions. From the perspective of machine learning, this paper provides a review on existing data mining and analytics applications in the process industry over the past several decades. The state-of-the-art of data mining and analytics are reviewed through eight unsupervised learning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms. Several perspectives are highlighted and discussed for future researches on data mining and analytics in the process industry.

Data Mining and Analytics in the Process Industry: The Role of Machine Learning

Pre-processing of hyperspectral images. Essential steps before image analysis

Hyperspectral image analysis. A tutorial

Hyperspectral imaging techniques have widely demonstrated their usefulness in different areas of interest in pharmaceutical research during the last decade. In particular, middle infrared, near infrared, and Raman methods have gained special relevance. This rapid increase has been promoted by the capability of hyperspectral techniques to provide robust and reliable chemical and spatial information on the distribution of components in pharmaceutical solid dosage forms. Furthermore, the valuable combination of hyperspectral imaging devices with adequate data processing techniques offers the perfect landscape for developing new methods for scanning and analyzing surfaces. Nevertheless, the instrumentation and subsequent data analysis are not exempt from issues that must be thoughtfully considered. This paper describes and discusses the main advantages and drawbacks of the measurements and data analysis of hyperspectral imaging techniques in the development of solid dosage forms.

Practical issues of hyperspectral imaging analysis of solid dosage forms

1.3. Objectives of This Work 4584 2. Chromatographic Data 4584 2.1. The Chromatographic Signal 4584 2.2. Common Chromatographic Artifacts 4584 2.3. A Basic Model of Chromatographic Data 4585 3. Removing Artifacts by Preprocessing 4587 3.1. Baseline Offset/Background Contribution 4587 3.1.1. Curve-Fitting 4587 3.1.2. Factor Model Approach 4588 3.1.3. Limitations and Things To Consider 4589 3.2. Retention Time Shifts across Samples 4589 3.2.1. Synchronizing Signals 4589 3.2.2. Factor Model Approach 4592 3.2.3. Limitations and Things To Consider 4593 4. Co-eluting Peaks 4593 4.1. The Importance of Multichannel Detectors To Solve Overlapping Issues 4595

ChroMATHography: solving chromatographic issues with mathematical models and intuitive graphics.

Near-infrared spectroscopy chemical imaging (NIR-CI) is a powerful tool for providing a great deal of information on pharmaceutical samples, since the NIR spectrum can be measured for each pixel of the image over a wide range of wavelengths. Joining NIR-CI with chemometric algorithms (e.g., Principal Component Analysis, PCA) and using correlation coefficients, cluster analysis, classical least-square regression (CLS) and multivariate curve resolution-alternating least squares (MCR-ALS) are of increasing interest, due to the great amount of information that can be extracted from one image. Despite this, investigation of their potential usefulness must be done to establish their benefits and potential limitations. We explored the possibilities of different algorithms in the global study (qualitative and quantitative information) of homogeneity in pharmaceutical samples that may confirm different stages in a blending process. For this purpose, we studied four examples, involving four binary mixtures in different concentrations. In this way, we studied the benefits and the drawbacks of PCA, cluster analysis (K-means and Fuzzy C-means clustering) and correlation coefficients for qualitative purposes and CLS and MCR-ALS for quantitative purposes. We present new possibilities in cluster analysis and MCR-ALS in image analysis, and we introduce and test new BACRA software for mapping correlation-coefficient surfaces.

José Manuel Amigo

Papers

Pre-processing of hyperspectral images. Essential steps before image analysis

Hyperspectral image analysis. A tutorial

Practical issues of hyperspectral imaging analysis of solid dosage forms

ChroMATHography: solving chromatographic issues with mathematical models and intuitive graphics.

Study of pharmaceutical samples by NIR chemical-image and multivariate analysis