Tag: Multivariate Data Analysis

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Chemometrics and How to Use It?

Introduction

“Chemometrics” is a combination of two words “chemo” and “metrics” which signifies the application of computational tools to Chemical Sciences. Coined by a Swedish Scientist, Svante Wold, in 1972. Later in 1974, Svante Wold and Bruce Kowalski founded the International Chemometrics Society (ICS). ICS describes chemometrics as the chemical discipline that uses mathematical and statistical models to
a) design or select optimal measurement procedures and experiments, and
b) to provide maximum chemical information by analyzing chemical data.

How does Chemometrics help design optimal experiments

Classical chemistry depends on the conventional One-factor-at-a-time (OFAT) for building on the understanding of the process chemistry, performance of the process, and product characterizations. However, these conventional techniques suffer from many drawbacks such as:

  • OFAT studies are time-consuming and need a greater number of experimental
  • It does not give any information about potential interactions between the two or more factors, and
  • OFAT studies may or may not give the optimal settings for the process or the product attributes.

The chemometrics, in turn, employs multivariate mathematical and statistical tools in combination with computational techniques to investigate the effect of multiple factors on the optimality of the process and product attributes. The multivariate data is modeled into a mathematical equation that can predict the best optimal settings for the process and the effect of the excursions of the process parameters on the process performance and the product quality.

The outcome of the multivariate investigation allows identification of the multidimensional design space within which the process is not impacting the process performance and product quality attributes. Moreover, multivariate strategies cover multiple process insights into a single multivariate design of the experiment. The adoption of the multivariate design of experiments offers multiple advantages over the conventional OFAT like:

  • Reduces the product development timelines significantly,
  • Significantly reduce the product development costs in a highly competitive market.
  • Maximizes the total information obtained from the experiment.

How does Chemometrics help derive maximum information from the chemical data?

The multivariate analysis strategy in the analysis of the chemical data starts with the pretreatment of the chemical data, also known as data preprocessing. It involves the approaches, where:

  • The data is scaled and coded,
  • Cleaned for outliers,
  • Checked for errors and missing values, and
  • Transformed, if need be, into a format that is explicitly comprehensible by the statistical and mathematical algorithms.

After the preprocessing of the data, the chemometric tools look for the patterns and informative trends in the data. This is referred to as pattern recognition. Pattern recognition uses machine learning algorithms to identify trends and patterns in the data. These machine learning algorithms, in turn, employ the historical data stored in the data warehouses to predict the possible patterns in the new set of data. The pattern recognition ML tools use either supervised or unsupervised learning algorithms. The unsupervised algorithms include Hierarchical Cluster Analysis (HCA) and Principal Components Analysis (PCA) whereas supervised algorithms have K Nearest Neighbours (KNN).

What are the Different Tools and Techniques used in Chemometrics?

With advancements in time, chemometrics has added multiple feathers in its cap rather than being a single tool for its application in the Chemical Sciences. A wide variety of the disciplines that contributed to the advancements of the field of Chemometrics are shown in the figure below. It has been adding multiple techniques each time to expand its applicability in the Research & Development of the chemical sciences.

  • Multivariate Statistics & Pattern Recognition in the Chemometrics

Multivariate statistical analysis refers to the concurrent analysis of multiple factors to derive the totality of the information from the data. The information derived may be the effect of individual factors, the interaction between two or more factors, and the quadratic terms of the factors. As multivariate data analysis involves estimation of almost all the possible effects in the data, these analysis techniques have very high precision and help make highly predictable conclusions. The multivariate statistical tools and techniques find plenty of applications in following industries:

  • Pharma and Life Sciences
  • Food and Beverages
  • Agriculture
  • Chemical
  • Earth & Space
  • Business Intelligence

Some of the most popular and commonly used multivariate modelling approaches are described briefly below.

  • Principal Components Analysis

Data generated in chemometrics, particularly in spectroscopic analysis, is enormous. Such datasets are highly correlated and difficult to model. For that matter, Principal Components Analysis (PCA) creates new uncorrelated variables known as principal components. PCA is a dimensionality reduction technique that enhances the interpretability of large datasets by transforming large datasets into smaller variables without losing much of the information. Let’s Excel Analytics Solutions LLP offers a simple yet highly capable web-based platform for PCA, branded as the MagicPCA.

  • Linear Discriminant Analysis

Linear discriminant analysis is another multivariate technique that is dependent on dimensionality reduction. However, in LDA the dependent variables are categorical variables and the independent variables could be in the form of intervals. The LDA focuses on establishing a function that can distinguish between different categories of the independent variables. This helps identify the sources of maximum variability in the data. Our experts at Let’s Excel Analytics Solutions LLP have developed an application, namely niceLDA, that can solve your LDA problems.

  • Partial Least Squares

Partial Least Squares (PLS)  is a multivariate statistical tool that bears some resemblance with the Principal Components Analysis. It reduces the number of variables to a smaller set of uncorrelated variables and subsequently performs linear regression on them.  However, unlike linear regression, PLS fits multiple responses in a single model. Our programmers at Let’s Excel Analytics Solutions LLP have developed a user-friendly web-based application for partial least square regression, EasyPLS.

Application of Chemometrics in Analytical Chemistry

Chemometrics finds its application throughout the entire lifecycle of the Analytical Sciences right from the method development and validation, development of the sampling procedure, exploratory data analysis, model building and, predictive analysis. The analytical data generated has a multivariate nature and depends on the multivariate data analysis (MVDA) for the exploratory analysis and predictive modeling. The three main areas of the Analytical Sciences where Chemometrics has demonstrated its advantages over the conventional techniques include:

  1. Grouping or cluster analysis refers to a group of analyses where a data set is divided into various clusters in such a way that each cluster has a unique and peculiar property that differs from another set of clusters. A widely known example of cluster analysis is used in flow cytometric analysis of cell viabilities where cells are clustered based on the apoptotic markers. Principal Component Analysis can be used as a powerful tool for understanding the grouping patterns.
  2. Classification analysis is defined as a systematic categorization of chemical compounds based on known physicochemical properties. This allows for the exploration of the alternatives for a known chemical compound with similar physicochemical properties. For example, in the development of the HPLC method for polar and aromatic compounds, data mining for the corresponding solvents can be done by looking into polar and aromatic classes of the solvents. This can be done by building SIMCA models on top of the Principal Component Analysis.
  3. Calibration of the analytical methods: chemometrics-assisted calibration of analytical methods employ multivariate calibration models where multiple, sometimes hundreds, analytes are calibrated at the same time. These multivariate calibration models have many advantages over the conventional univariate calibration models. The major advantages include:
    1. significant reduction of noise,
    2. non-selectivity of the analytical methods,
    3. dealing with interferents and,
    4. outliers can be detected and excluded in the first place.
  4. The Principal Components Analysis and Partial Least Squares are the most commonly used chemometrics tools that are used for developing multivariate calibration models in the development of analytical methods for pharmaceuticals, foods, environmental monitoring, and forensic sciences. The chemometric tools have widely transformed the discipline of the Analytical Sciences by building highly reliable and predictive calibration models, providing tools that assist in their quantitative validations, and contributing to their successful application in highly sensitive chemical analyses.

Application of Chemometrics in Studying QSAR in Medicinal Chemistry

QSAR stands for “quantitative structure and activity relationship” and refers to the application of a wide variety of computational tools and techniques used to determine the quantitative relationship between the chemical structure of a molecule and its biological activities. It is based on the principle that each chemical moiety is responsible for a certain degree of biological activity in a chemical molecule and influences the activity of other moieties in the same molecule. In other words, the similarities in the structure of two chemical molecules could correspond to their similarities in biological activities. This forms a basis for predicting the biological activities of new drug molecules in medicinal chemistry.

For QSAR modeling, certain features of a chemical molecule that can potentially affect the biological activities are referred to as molecular descriptors. These molecular descriptors are classified into five major categories and include physicochemical, constitutional, geometric, topological, and quantum chemical descriptors. The biological activities of interest in QSAR correspond to the pharmacokinetic, pharmacodynamic, and toxicological properties of the molecule. Each of the molecular descriptors is referred to as the predictor and the corresponding biological activity as the response. The predictors are then modeled into a mathematical equation using multivariate statistical tools. There are two widely accepted statistical models used for predicting the QSAR of a new molecule and include regression and classification models. The regression models used are multiple linear regression (MLR), principal components regression (PCR), and Partial Least Square regression (PLS). Let’s Excel Analytics Solutions LLP has developed user-friendly interfaces for performing all these operations.

QSAR also has extended its approaches to other fields like chromatography (Quantitative Structure and Chromatography Relationship, QSCR), toxicology (Quantitative Structure and Toxicity Relationship, QSTR), biodegradability (Quantitative Structure and Biodegradability Relationship, QSBR), electrochemistry (Quantitative Structure and Electrochemistry Relationship, QSER) and so on.

Conclusion

Chemometrics has changed the way of designing and developing chemical processes. The information obtained from chemical data has maximized the degree to which processes can be optimized. It has also contributed significantly to the development of highly sensitive and accurate analytical methods by simplifying the complex amount of data generated during the development, calibration, and validation of the analytical data. In general, chemometrics is an ever-expanding domain that is constantly diversifying its applications in a wide variety of fields.

Let’s Excel Analytics Solutions LLP has a proven track record of developing highly reliable chemometric applications that can help you make better business decisions. If you are dealing with a complex problem and looking for the right solution, schedule a free consultation now!

Demystifying Data Science Terms
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Data Science: Demystifying the Terminologies

Introduction

Data Science related terminologies are buzzing around the internet. It marks the onset of the Industry 4.0 revolution. Data Science is a discipline that studies big data, uses modern tools and techniques for data mining and data analysis to find its applications across a wide variety of domains. For example, Google AI retinal scan collected retinal images from thousands of patients across South India. Finally, it analyzed the data to get information about the patients’ disposition to cardiovascular diseases in the next five years.

The statisticians, chemometricians and mathematicians have been breathing and living the data science concepts for years and may not be calling these terms with exactly the same buzz words. Perhaps the spread of the new terminologies is an outcome of massive online Data Science courses or the rebranding strategies of various companies that are trying to bank on the ‘Data Science’ capabilities. Whatever be the reason we need to prepare ourselves for the Industry 4.0 revolution, we should get familiar with these new terms. In this article, we broadly segregated the meaning of these terminologies based on interaction with various clients.

Big Data

Big data, put simply, refers to a collection of data from a wide variety of sources at a colossal scale. The data collected may be quantitative or qualitative, unknown or known, structured or unstructured and so on. As the scale of data collected is enormous, it is stored in specialized databases, known as big databases, that are developed using advanced computer programs such as SQL, MySQL etc. The collections of big data are also referred to as data warehouses. Many big databases are open source, e.g., Cassandra, HBase, MongoDB, Neo4j, CouchDB, OrientDB, Terrstore, etc. However, most of the popular databases are big-budgeted as well, e.g., Oracle, MySQL, Microsoft SQL, SAP HANA, etc. It is essential to state that the database choice is the fundamental and most critical step in the Data Science workflow. The storage requirements of the Big Data can range anywhere between MBs to TBs. Sometimes the data volume may be small, but the data complexity can be high. That is where data engineers pitch in to make things easy.

Data Engineering

The process of building a workflow to store the data in Big Data Warehouse and then extracting the relevant information is called Data Engineering.

Data Mining

Data mining is the process of extracting patterns from large datasets by combining methods from statistics and machine learning with database management. These techniques include association rule learning, cluster analysis, classification, and regression. Applications include mining customer data to determine segments most likely to respond to an offer, mining human resources data to identify characteristics of most successful employees or market basket analysis to model customers’ purchase behaviour.

Data Analysis

Data analysis is the exercise of analyzing, visualizing, and interpreting data to get relevant information that helps organizations make informed business decisions. It also involves data cleaning, outlier analysis, data preprocessing, and transformation to make data amenable to analysis. Data analysis is a very broad term that encompasses at least five different types of analyses. A data scientist chooses the most appropriate data analysis method based on the end goal of the analysis. Sometimes the same method of analysis can be used for the different end goal. Hence, another name may be used to call the technique despite involving the same mathematical and statistical concept. Therefore data analysis takes up various forms described briefly as below:

  • Descriptive statistical analysis is the fundamental step for performing any data analysis. It is also known as summary statistics and gives an idea of the basic structural features of the data like measures of central tendency, dispersion, skewness, etc.
  • Inferential statistical analysis is a type of statistical analysis that uses the information contained in a sampled data to make inferences about the corresponding larger population. It uses hypothesis testing of the data to draw statistically valid conclusions about the population. As the sampling process is always associated with an element of error, statistical analysis tools should also account for the sampling error so that a valid inference is drawn from the data.
  • Chemometrics is the science of extracting and analyzing Physico-chemical information by using spectroscopic sensors and other material characterization instruments. Chemometrics is interdisciplinary, using methods frequently employed in core data-analytic disciplines such as multivariate statistics, applied mathematics, and computer science to address problems in chemistry, biochemistry, medicine, biology, food, agriculture and chemical engineering. Chemometrics generally utilizes information from spectrochemical measurements such as FTIR, NIR, Raman and other material characterization techniques to control product quality attributes. It is being used for building Process Analytical Technology tools.
  • Predictive analysis models patterns in the big data to predict the likelihood of the future outcome. The models built are less likely to have 100% accuracy and are always associated with an intrinsic prediction variance. However, the data’s accuracy is refined each time more and more data is taken into account. Predictive analysis can be performed using linear regression, multiple linear regression, principal component analysis, principal component regression, partial least square regression, and linear discriminant analysis.
  • Diagnostic analysis, as the name suggests, is used to investigate what caused something to happen. The diagnostic analysis uses the historical data to look for the answers that caused the same something in the past. It is more of an investigative type of data analysis. This involves four main steps: data discovery, drill down, data mining and correlations. Data discovery is the process of identifying similar sources of data that underwent the same sequence of events in the past. Data drill down is about focusing on a particular attribute of the data that interests us. This is followed by data mining activity that ends with looking for strong correlations in the data to lead us to the event’s cause. Diagnostic analysis can be performed using all techniques mentioned for predictive analysis. However, the end goal of the diagnostic analysis is only to identify the root cause to improve the process or product.
  • Prescriptive analysis is the sum of all the data analysis techniques discussed above, but this form of analysis is more oriented towards making and influencing business decisions. Specifically, prescriptive analytics factors information about possible situations or scenarios, available resources, past performance, current performance and suggests a course of action or strategy. It can be used to make decisions on any time horizon, from immediate to long term.

Data Analytics

Data analytics is the sum of all the mentioned activities, right from big data, data engineering, data mining to the analysis of the data.

Machine Learning

Machine Learning creates new programs that can predict future events with little supervision by humans. Machine learning analytics is an advanced and automated form of data analytics. A Machine Learning algorithm is called Artificial Intelligence when the prediction accuracy is improved each time new data is added to it.

Machine learning algorithms that uses layered networks capable of unsupervised learning from the data are called Deep Learning algorithms. Examples of Deep Learning algorithms include Deep neural networks or Artificial Neural Networks inspired by the brain’s structure and function. These type of algorithms are designed to be analogous to human intelligence. The major difference between machine learning and deep learning is human supervision, i.e., deep learning algorithms are a completely unsupervised form of learning in contrast to machine learning algorithms.

Conclusion

The field of Data Science is more oriented towards the application side of the modern AI/ML tools that employ advanced algorithms to build predictive models that can transform the future of what we do and how we do it.

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