Predicting the evolution of pH and total soluble solids during coffee fermentation using near-infrared spectroscopy coupled with chemometrics.

Currently, coffee fermentation is visually operated, which results in incomplete or excessive processes and coffees with undesirable characteristics. In front of it, pH and total soluble solids (TSS) have been shown to be good fermentation indicators, although this requires rapid, accurate, and chemical-free measurement techniques such as NIR spectroscopy. However, the complexity of the NIR spectra requires optimization steps in which variable selection techniques simplify profiles and subsequent models.

Discrimination of foreign bodies in quinoa Willd.) grains using convolutional neural networks with a transfer learning approach.

The rising interest in quinoa ( Willd.) is due to its high protein content and gluten-free condition; nonetheless, the presence of foreign bodies in quinoa processing facilities is an issue that must be addressed. As a result, convolutional neural networks have been adopted, mostly because of their data extraction capabilities, which had not been utilized before for this purpose.

Competency analysis based on accounting career anchors using clustering techniques.

This research work aims to identify the prevalent anchors in the professional accounting career using the Schein scale and to describe the prevalent anchors by defining the values, attitudes, aptitudes, skills, and interests. Career anchors are defined by the competence, motivation, and values a person has to perform a particular job in an organization and are present throughout their working life. When determining the soft and hard competencies of the professional profile, universities must consider the career anchors essential for graduates' work performance.

An approach to fill in missing data from satellite imagery using data-intensive computing and DINEOF.

This paper proposes an approach to fill in missing data from satellite images using data-intensive computing platforms. The proposed approach merges satellite imagery from diverse sources to reduce the impact of the holes in images that result from acquisition conditions: occlusion, the satellite trajectory, sunlight, among others. The amount of computation effort derived from the use of large high-resolution images is addressed by data-intensive computing techniques that assume an underlying cluster architecture.

Amazonian cacao-clone nibs discrimination using NIR spectroscopy coupled to naïve Bayes classifier and a new waveband selection approach.

Near-Infrared Spectroscopy (NIRS) has shown to be helpful in the study of rice, tea, cocoa, and other foods due to its versatility and reduced sample treatment. However, the high complexity of the data produced by NIR sensors makes necessary pre-treatments such as feature selection techniques that produce compact profiles. Supervised and unsupervised techniques have been tested, creating different subsets of features for classification, which affect the performance of the classifiers based on such compact profiles.

Determination of the superficial citral content on microparticles: An application of NIR spectroscopy coupled with chemometric tools.

This work evaluates near-infrared () spectroscopy coupled with chemometric tools for determining the superficial content of citral ( ) on microparticles. To perform this evaluation, using spray drying, citral was encapsulated in a matrix of dextrin using twelve combinations of citral:dextrin ratios () and inlet air temperatures (). From each treatment, six samples were extracted, and their and absorption spectral profiles were measured.

Optimal shortening of uniform covering arrays.

Software test suites based on the concept of interaction testing are very useful for testing software components in an economical way. Test suites of this kind may be created using mathematical objects called covering arrays. A covering array, denoted by CA(N; t, k, v), is an N × k array over [Formula: see text] with the property that every N × t sub-array covers all t-tuples of [Formula: see text] at least once.

Multilayer perceptron architecture optimization using parallel computing techniques.

The objective of this research was to develop a methodology for optimizing multilayer-perceptron-type neural networks by evaluating the effects of three neural architecture parameters, namely, number of hidden layers (HL), neurons per hidden layer (NHL), and activation function type (AF), on the sum of squares error (SSE). The data for the study were obtained from quality parameters (physicochemical and microbiological) of milk samples. Architectures or combinations were organized in groups (G1, G2, and G3) generated upon interspersing one, two, and three layers.