TY - JOUR

T1 - Curvas en S y análisis de cluster en ciclo de vida de la tecnología

T2 - Aplicación en 11 tecnologías en alimentos

AU - Zartha Sossa, Jhon Wilder

AU - Arango Álzate, Bibiana

AU - Hernández Zarta, Raúl

AU - Medina Henao, José Gamaliel

AU - Orozco Mendoza, Gina Lía

PY - 2015

Y1 - 2015

N2 - The paper is concerned curves in S and cluster analysis applied to data series extracted from articles and patents on time series of 11 unit operations in food technology. Nonlinear regression techniques were used to calculate the inflection point in the series of patents and papers by each unit operations or technologies. The statistics used to validate the results were adjusted by R2 adjusted, T-value, P-value and Durbin Watson. The data analyses were based on models of S curves with sigmaplot software, thirteen models were applied. The models used were the best statiscally adjusted ones, from which the inflection point was then calculated. Among the most important results inflection points are highlighted for items in 4 technologies from 2020, while the turning point in the same four patents for technologies were submitted before 2002. One of the main objectives of the study was the group classification of the 11 technologies through cluster analysis based on the inflection points obtained from analysis of articles and patents, as an attempt to know the state of the lifecycle: emerging, incoming, mature or declining. This cluster analysis was made through the nearest neighbor method taking as the Euclidean distance metric quadratic technologies were obtained 3 groups, the first is technology Emulsification is classified as emerging, the second includes technologies sedimentation, drying, grinding, sterilization, high pressure sieving and are classified as incoming or comprising key and the third centrifugation technologies, grinding, and cold plasma evaporation that could be classified as mature technologies. The inflection points calculated over the corresponding S-Curve allow for reduction of the uncertainty in making investment decisions regarding the use of food technologies. This reduction of the uncertainty can be useful to define the state of technologies (before and after their inflection points), to determine the correct moment to apply mechanisms of intellectual property and technology law.

AB - The paper is concerned curves in S and cluster analysis applied to data series extracted from articles and patents on time series of 11 unit operations in food technology. Nonlinear regression techniques were used to calculate the inflection point in the series of patents and papers by each unit operations or technologies. The statistics used to validate the results were adjusted by R2 adjusted, T-value, P-value and Durbin Watson. The data analyses were based on models of S curves with sigmaplot software, thirteen models were applied. The models used were the best statiscally adjusted ones, from which the inflection point was then calculated. Among the most important results inflection points are highlighted for items in 4 technologies from 2020, while the turning point in the same four patents for technologies were submitted before 2002. One of the main objectives of the study was the group classification of the 11 technologies through cluster analysis based on the inflection points obtained from analysis of articles and patents, as an attempt to know the state of the lifecycle: emerging, incoming, mature or declining. This cluster analysis was made through the nearest neighbor method taking as the Euclidean distance metric quadratic technologies were obtained 3 groups, the first is technology Emulsification is classified as emerging, the second includes technologies sedimentation, drying, grinding, sterilization, high pressure sieving and are classified as incoming or comprising key and the third centrifugation technologies, grinding, and cold plasma evaporation that could be classified as mature technologies. The inflection points calculated over the corresponding S-Curve allow for reduction of the uncertainty in making investment decisions regarding the use of food technologies. This reduction of the uncertainty can be useful to define the state of technologies (before and after their inflection points), to determine the correct moment to apply mechanisms of intellectual property and technology law.

KW - Cluster analysis

KW - Decision making

KW - Food technologies

KW - S-Curve

KW - Technology life cycle

UR - http://www.scopus.com/inward/record.url?scp=84935144206&partnerID=8YFLogxK

M3 - Artículo

AN - SCOPUS:84935144206

VL - 36

SP - 5

JO - Espacios

JF - Espacios

SN - 0798-1015

IS - 12

ER -