Soft metrology has been defined as a set of measurement techniques and models that allow the objective quantification of properties usually determined by human perception such as smell, sound or taste. The development of a soft metrology system requires the measurement of physical parameters and the construction of a model to correlate them with the variables that need to be quantified. This paper presents a review of indirect measurement with the aim of understanding the state of development in this area, as well as the current challenges and opportunities; and proposes to gather all the different designations under the term soft metrology, broadening its definition. For this purpose, the literature on indirect measurement techniques and systems has been reviewed, encompassing recent as well as a few older key documents to present a time line of development and map out application contexts and designations. As machine learning techniques have been extensively used in indirect measurement strategies, this review highlights them, and also makes an effort to describe the state of the art regarding the determination of uncertainty. This study does not delve into developments and applications for human and social sciences, although the proposed definition considers the use that this term has had in these areas.

Flow rate is a necessary variable in industrial processes and, therefore, there is a wide variety of instruments designed to measure it. However, the most accepted measuring devices have the problem of being invasive or intrusive. The scientific and technological challenge is to achieve measurement by exploiting all the phenomenological possibilities using a non-intrusive, easy-to-install, portable and low-cost mechanism. This paper presents a literature review on the use of vibration analysis in flow rate metrological systems in order to identify research opportunities for the indirect measurement of this magnitude. A promising line of work was found based on soft flow rate sensors that use the analysis of pipeline vibrations integrated into computational intelligence routines, which allows inference of the flow rate value. The findings promote to continue with new technical and scientific challenges.

Analysis of flow-induced pipe vibrations has been applied in a variety of applications, such as flowrate inference and leak detection. These applications are based on a functional relationship between the vibration features estimated in the pipe walls and the dynamics related to the flow of the substance. The dataset described in this document is comprised of signals acquired using an accelerometer attached to a pipe conveying cold water at specific flowrate values. Tests were carried out under numerals of the ISO 4064-1/2: 2016 standard and were performed in two measurement benches designed for flowmeter calibration, and a total of 80 flowrate values, from 25 L/h to 20,000 L/h, were considered. For each flowrate value, 3 to 6 samples were taken, so that the resulting dataset has a total of 382 signals that contain acceleration values in three axes and a timestamp in microseconds.

El cáncer tiene alta incidencia y mortalidad a nivel mundial, y un diagnóstico temprano mejora significativamente la supervivencia. Por ello, se buscan herramientas no invasivas y económicas, como los soft sensores, para analizar Compuestos Orgánicos Volátiles (COV) que pueden actuar como biomarcadores de la enfermedad. Este artículo tuvo como objetivo revisar el estado del arte sobre el uso de narices y lenguas electrónicas como soft sensores para la detección de cáncer, junto con el procesamiento de datos mediante máquinas de aprendizaje. Se empleó una metodología cualitativa basada en la revisión de literatura científica publicada en bases de datos como ScienceDirect, IEEEXplore, Sage Journals y Scopus. Se seleccionaron 54 artículos relevantes, a partir de un proceso basado en la metodología PRISMA, publicados entre 2010 y 2024. Los resultados revelaron el uso de soft sensores para detectar cáncer de pulmón, próstata, vejiga, mama, ovario, colon, estómago y cavidad bucal, utilizando muestras como orina, aliento, saliva y sangre. La discusión incluyó comparaciones entre técnicas de análisis y decisiones, además se destacan tendencias, desafíos y oportunidades de investigación en el área. Se concluye que combinar la soft metrología, los soft sensores y el aprendizaje automático permite detectar biomarcadores del cáncer con una precisión alrededor del 90 %. No obstante, aún existen retos y oportunidades de investigación para optimizar las arquitecturas y lograr sistemas más confiables.