Evaluation of the Miscibility of Novel Cocoa Butter Equivalents by Raman Mapping and Multivariate Curve Resolution–Alternating Least Squares

Cocoa butter (CB) is an ingredient traditionally used in the manufacturing of chocolates, but its availability is decreasing due to its scarcity and high cost. For this reason, other vegetable oils, known as cocoa butter equivalents (CBE), are used to replace CB partially or wholly. In the present work, two Peruvian vegetable oils, coconut oil (CNO) and sacha inchi oil (SIO), are proposed as novel CBEs. Confocal Raman microscopy (CRM) was used for the chemical differentiation and polymorphism of these oils with CB based on their Raman spectra. To analyze their miscibility, two types of blends were prepared: CB with CNO, and CB with SIO. Both were prepared at 5 different concentrations (5%, 15%, 25%, 35%, and 45%). Raman mapping was used to obtain the chemical maps of the blends and analyze their miscibility through distribution maps, histograms and relative standard deviation (RSD). These values were obtained with multivariate curve resolution–alternating least squares. The results show that both vegetable oils are miscible with CB at high concentrations: 45% for CNO and 35% for SIO. At low concentrations, their miscibility decreases. This shows that it is possible to consider these vegetable oils as novel CBEs in the manufacturing of chocolates.

Efficient Distributed Matrix Factorization Alternating Least Squares (EDMFALS) for Recommendation Systems Using Spark

With the emergence of e-commerce and social networking systems, the use of recommendation systems gained popularity to predict the user ratings of an item. Since the large volume of data is generated from various sources at high speed, predicting the ratings accurately in real-time adds enormous benefit to the users while choosing the correct item. So a recommendation system must be capable enough to predict the rating accurately when the data are large. Apache Spark is a distributed framework well suited for processing large datasets and real-time data streams. In this paper, we propose an efficient matrix factorisation algorithm based on Spark MLlib alternating least squares (ALS) for collaborative filtering. The optimisations used for the proposed algorithm using Tungsten improved the performance of the algorithm significantly while doing the predictions. The experimental results prove that the proposed work is significantly faster for top-N recommendations and rating predictions compared with the existing works.

Multivariate Curve Resolution Alternating Least Squares applied to Chromatographic Data: From the Basics to the Recent Advances

This revision presents applications of multivariate curve resolution alternating least squares (MCR-ALS) applied to chromatographic data. Initially, the fundamentals and recent advances of the MCR-ALS method will be presented. Several critical issues such as data organization, advantages of the modelling, constraints, evaluation of ambiguity and the use for mathematical separation is discussed. An extensive revision of the papers on MCR-ALS applied to chromatographic data reported up to 2020 is presented. A practical example of an innovative application of cholesterol lowering drugs using supercritical fluid chromatography (SFC) is described highlighting important aspects of the method. At the end, a list of links to MCR-ALS algorithms and graphical interfaces developed in Matlab, R and Python 3 is provided.