Castor oil, produced from castor beans, has long been considered to be of important commercial value primarily for the manufacturing of soaps, lubricants, and coatings, among others. Global castor oil production is concentrated primarily in a small geographic region of Gujarat in Western India. This region is favorable due to its labor-intensive cultivation method and subtropical climate conditions. Entrepreneurs and castor processors in the United States and South America also cultivate castor beans but are faced with the challenge of achieving high castor oil production efficiency, as well as obtaining the desired oil quality. In this manuscript, we provide a detailed analysis of novel processing methods involved in castor oil production. We discuss novel processing methods by explaining specific processing parameters involved in castor oil production.

A series of symmetrical chiral, liquid crystal dimeric molecules possessing ester- linked, biphenyl-naphthyl cores with varied spacer lengths and terminal vinyl groups have been synthesized using Naproxen as the synthetic precursor. The synthesized symmetrical chiral dimers were characterized by 1H NMR spectroscopy, and their liquid crystalline behavior was confirmed by DSC and HOPM studies. Structural effects on the mesomorphic and physicochemical properties were investigated in terms of variation of chiral chain length. The synthesized dimeric compounds exhibited SmX^*, SmC^*, SmA^*, N^*, BPI^*, and BPII^* mesophase sequences. An odd-even effect was observed in the dimers and the duration of the mesophase decreased with increasing spacer length. The synthesized vinyl substituted liquid crystalline dimers are particularly useful in understanding liquid crystal polymorphism and act as model compounds for liquid crystal polymers.

Modern instruments have the capacity to generate and store enormous volumes of data and the challenges involved in processing, analyzing and visualizing this data are well recognized. The field of Chemometrics (a subspecialty of Analytical Chemistry) grew out of efforts to develop a toolbox of statistical and computer applications for data processing and analysis. This chapter will discuss key concepts of Big Data Analytics within the context of Analytical Chemistry. The chapter will devote particular emphasis on preprocessing techniques, statistical and Machine Learning methodology for data mining and analysis, tools for big data visualization and state-of-the-art applications for data storage. Various statistical techniques used for the analysis of Big Data in Chemometrics are introduced. This chapter also gives an overview of computational tools for Big Data Analytics for Analytical Chemistry. The chapter concludes with the discussion of latest platforms and programming tools for Big Data storage like Hadoop, Apache Hive, Spark, Google Bigtable, and more.