GAMLSS models applied in the treatment of agro-industrial waste

Abstract

In this paper, we present an application of GAMLSS (Generalized Additive Models for Location, Shape and Scale) to study bacterial cellulose production from agro-industrial waste. An experiment was conducted to research the effects of pH and cultivation time on bacterial cellulose yield obtained from discarded bananas. Several models were fitted to the collected data to determine an estimated expression for the mean and variance of bacterial cellulose yield. We found that the mean and variance of cellulose yield decrease as pH increases, while the opposite occurs as cultivation time increases.Palabras clave: GAMLSS models, Gamma distribution, linear regression, parameter estimation. ResumenEn este artículo se presenta una aplicación de los modelos GAMLSS (Generalized Additive Models for Location, Shape and Scale) para estudiar la producción de celulosa bacteriana a partir de residuos agroindustriales. El experimento fue realizado para investigar los efectos del pH y el tiempo de cultivo sobre el rendimiento de celulosa bacteriana obtenida a partir de residuos de banano. Varios modelos fueron ajustados a los datos recolectados para determinar expresiones estimadas 1 Hernández, F., Torres, M., Arteaga, L., Castro, C. (2015) 246Freddy Hernández, Mabel Torres, Lina Arteaga & Cristina Castro para la media y la varianza del rendimiento de celulosa bacteriana. Del mejor modelo obtenido se encontró que la media y la varianza del rendimiento de celulosa bacteriana disminuye a medida que el pH se incrementa, mientras al aumentar el tiempo de cultivo tanto la media como la varianza aumentan.

Production of New Nano-Bacterial Cellulose with Lactobacillus rhamnosus by Using Whey Waste as Substrate with Optimization Taguchi Method, which has the potential to be used in many biomedical products

“…In addition to the effects of other factors in obtaining bacterial cellulose, it is crucial to determine the optimum values of some factors such as fermentation time and pH that maximize the bacterial cellulose yield [12]. In the present study, bacterial nano-cellulose (BNC) production was optimized based from different concentration substrate, as well as temperature, pH, and incubation time.…”

Analysis of a longitudinal multilevel experiment using GAMLSSs

“…In fact, there are more than 90 distributions available in the current implementation of the gamlss.dist package in R and anyone can create a new one. While GAMLSSs are already well established in the literature for centile growth curve estimation (frequently used with this purpose by the WHO -World Health Organization) and have been gaining popularity in other areas as well (such as industrial (Barajas et al, 2015), medical (Petterle and Formiguieri, 2014), financial (Gilchrist et al, 2009), foresty (Hudson et al, 2009), etc), no applications where GAMLSSs were used for mixed modeling purposes have been found. Since the gamlss::gamlss() (R package::function within package) function allows an interface to be made with the well known nlme::lme() function, it can be used for repeated measurements, multilevel modeling, random intercept and slopes, etc, among other mixed model fit purposes.…”

GAMLSSs with applications to zero inflated and hierarquical data

“…In fact, there are more than 80 distributions available in the current implementation of the gamlss.dist package in R and anyone can create a new one. While GAMLSSs are already well established in the literature for centile growth curve estimation (frequently used with this purpose by the WHO -World Health Organization) and have been gaining popularity in other areas as well (such as industrial (Barajas et al, 2015), medical (Petterle and Formiguieri, 2014), financial (Gilchrist et al, 2009), foresty (Hudson et al, 2009), etc), no applications where GAMLSSs were used for mixed modeling purposes have been found. Since the gamlss::gamlss() (this means R package::function within package) function allows an interface to be made with the well known nlme::lme() function, it can be used for repeated measurements, multilevel modeling, random intercept and slopes, etc, among other mixed model fit purposes.…”