Assessment of table olive fermentation by functional data analysis

Ruiz-Bellido, M.A.; Romero‐Gil, Verónica; García-García, Pedro; Rodríguez‐Gómez, Francisco; Arroyo‐López, Francisco Noé; Garrido‐Fernández, Antonio

doi:10.1016/j.ijfoodmicro.2016.08.031

Cited by 4 publications

(4 citation statements)

References 37 publications

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“…Olives were harvested at the green ripe stage during the 2013/14 season (Valle del Guadalhorce, Málaga, Spain) and subjected to five different fermentation system: i) CC (usual brine, control cured olives): 7 g/100 ml NaCl, 0.1 g/100 ml citric acid (CA), 0.5 g/100 ml acetic acid (AA); ii) CI (highly acidified, cured olives): no salt, 0.1 g/100 ml CA, 1.6 g/100 ml AA; iii) CII (moderately acidified, cured olives): no salt, 0.1 g/100 ml CA, 1.0 g/100 ml AA; iv) CT (usual brine of cracked, traditional olives): 11 g/100 ml NaCl solution, and v) RT (usual brine, olives cracked after 72 h respiration at room temperature): brined in a 11 g/100 ml NaCl solution. For the rest of the details of the experimental design, and how microbiological and physicochemical data were acquired, please consult the paper by Ruiz-Bellido et al [1] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

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Data on the application of Functional Data Analysis in food fermentations

Ruiz-Bellido¹,

Romero‐Gil

García-García

et al. 2016

Data in Brief

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This article refers to the paper “Assessment of table olive fermentation by functional data analysis” (Ruiz-Bellido et al., 2016) [1]. The dataset include pH, titratable acidity, yeast count and area values obtained during fermentation process (380 days) of Aloreña de Málaga olives subjected to five different fermentation systems: i) control of acidified cured olives, ii) highly acidified cured olives, iii) intermediate acidified cured olives, iv) control of traditional cracked olives, and v) traditional olives cracked after 72 h of exposure to air. Many of the Tables and Figures shown in this paper were deduced after application of Functional Data Analysis to raw data using a routine executed under R software for comparison among treatments by the transformation of raw data into smooth curves and the application of a new battery of statistical tools (functional pointwise estimation of the averages and standard deviations, maximum, minimum, first and second derivatives, functional regression, and functional F and t-tests).

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Therefore, those interested in its application are kindly referred to their R routines and tutorial. Please, consult also [1] for detailed information of how raw data were processed and analysed.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Data on the application of Functional Data Analysis in food fermentations

Ruiz-Bellido¹,

Romero‐Gil

García-García

et al. 2016

Data in Brief

Self Cite

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“…Burfield et al [17] used FDA in characterizing the chemical data and conclude that it is a powerful technique to detect the function minima and maxima even though they argued that the computational part was more complex compared to classical multivariate analysis. Sierra et al [18] and Ruiz-bellido et al [19] shared the same thought that functional data analysis is a promising and valuable tool in their research. There are two main focuses in this research, the first this study focuses on using several two-parameter probability models such as Weibull, Gamma and Log Normal in simulating monthly rainfall based on 100 time simulation using the quantile function of probability models.…”

Section: Introductionmentioning

confidence: 95%

The Applied of Functional Data Analysis to Comparison 100 Times Simulated Monthly Rainfall Using Some Two Parameters Distribution

Yendra,

Desvina

et al. 2023

ijmcr

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Three probability models of mothly rainfall such as Gamma, Weibull and Log Normal distribution are evaluated in terms of their ability to reproduce the mean statistics derived from 100 times simulation of monthly rainfall in the Pekanbaru City, Indonesia. One of the important studies is to investigate and understand the simulate mean monthly rainfall patterns that occur throughout the year. To identify the pattern, it requires a rainfall curve to represent monthly observation of rainfall received during the year. Functional data analysis (FDA) methods are capable to convert discrete data into a function that can represent the rainfall curve and as a result, try to describe the hidden patterns of the rainfall. This study is focused on investigating 100 curve average monthly rainfall simulatated by three different quantile functions using the FDA. The mean and standard deviation of FDA for average monthly precipitation are obtained. Through these two statistics a the confidence interval curves of the mean curve are presented represent 95% pointwise confidence intervals. In this study, most of the monthly average rainfall from the actual data were around the FDA mean and the monthly average rainfall was within the FDA confidence interval. In this study, 100 times monthly rainfall simulations using the quantile function of the gamma and log normal distributions found that the mean FDA can capture most of the mean monthly rainfall from historical data, and within the FDA interval. The contradictory results shown by the monthly rainfall simulations using the Weibull distribution, most of the monthly average rainfall historical data cannot be captured by the FDA mean and are outside the FDA confidence interval. Based on the Mean Absolute Error (MAE) value of the average monthly rainfall of historical data and the monthly average of the FDA, it can be concluded that the gamma distribution can produce simulated rain better than the log normal distribution.

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“…Burfield et al [6] used FDA in characterizing the chemical data and conclude that it is a powerful technique to detect the function minima and maxima even though they argued that the computational part was more complex compared to classical multivariate analysis. Sierra et al [7] and Ruiz-bellido et al [8] shared the same thought that functional data analysis is a promising and valuable tool in their research.Chen et al [9]examined the distribution functions of GDP across different versions of the Penn World Tables and found the need to conduct appropriate analysis to check the robustness of the results. Nowadays, this approach has been widely explored and used in another statistical branch such as in nonparametric statistics (Ferraty and Vieu, [10]), functional analysis of variance (Cuevas et al, [11]) and functional clustering technique (Mizuta,[12]).…”

Section: Introductionmentioning

confidence: 95%

Estimation of Rainfall Curve by using Functional Data Analysis and Ordinary Kriging Approach

2018

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Rainfall is an interesting phenomenon to investigate since it is directly related to all aspects of life on earth. One of the important studies is to investigate and understand the rainfall patterns that occur throughout the year. To identify the pattern, it requires a rainfall curve to represent daily observation of rainfall received during the year. Functional data analysis methods are capable to convert discrete data intoa function that can represent the rainfall curve and as a result, try to describe the hidden patterns of the rainfall. This study focused on the distribution of daily rainfall amount using functional data analysis. Fourier basis functions are used for periodic rainfall data. Generalized cross-validation showed 123 basis functions were sufficient to describe the pattern of daily rainfall amount. North and west areas of the peninsula show a significant bimodal pattern with the curve decline between two peaks at the mid-year. Meanwhile,the east shows uni-modal patterns that reached a peak in the last three months. Southern areas show more uniform trends throughout the year. Finally, the functional spatial method is introduced to overcome the problem of estimating the rainfall curve in the locations with no data recorded. We use a leave one out cross-validation as a verification method to compare between the real curve and the predicted curve. We used coefficient of basis functions to get the predicted curve. It was foundthatthe methods ofspatial prediction can match up with the existing spatial prediction methods in terms of accuracy,but it is better as the new approach provides a simpler calculation.

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Assessment of table olive fermentation by functional data analysis

Cited by 4 publications

References 37 publications

Data on the application of Functional Data Analysis in food fermentations

Data on the application of Functional Data Analysis in food fermentations

The Applied of Functional Data Analysis to Comparison 100 Times Simulated Monthly Rainfall Using Some Two Parameters Distribution

Estimation of Rainfall Curve by using Functional Data Analysis and Ordinary Kriging Approach

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