Abstract
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).
Specifications Table
| Subject area | Food Technology |
| More specific subject area | Microbiology, Statistics |
| Type of data | Tables, figures |
| How data was acquired | Use of a titroprocessor mod 670 (Metrohm Instrument, Herisau, Switzerland) for determination of pH and titratable acidity values. Use of a Spiral System model dwScientific (Dow Whitley Scientific Limited, England) for determination of yeast counts on selective medium. |
| Data format | Raw, analyzed |
| Experimental factors | Five fermentation systems of cured and cracked Aloreña olives with different NaCl and acidification conditions. |
| Experimental features | Monitoring of fermentations, microbial and physicochemical analysis, transformation of data into smooth curves, functional data analysis |
| Data source location | Alozaina, Málaga, Spain. |
| Data accessibility | Data available within this article |
Value of the data
-
•
Use datasets as a benchmark for further functional data analysis or modelling of table olive fermentations.
-
•
Application of functional data analysis for the study of food fermentations.
-
•
Understand the influence of acidification and cracking of olives on the fermentation process of Aloreña olives by comparisons among different fermentation systems.
1. Data
The dataset provided in this article (Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7) and their corresponding Figures (Fig. 1, Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7, Fig. 8) represent the raw microbiological (yeast counts) and physicochemical (pH and titratable acidity) data, as well as their statistical analysis by the application and implementation of Functional Data analysis, of different olive fermentation systems using Aloreña de Málaga fruits.
Table 1.
Changes in yeast population (log10 cfu/ml) through the storage/fermentation process of Aloreña table olives. CC, control of acidified cured olives; CI, highly acidified cured olives; CII, intermediate acidified cured olives; CT, control of traditional (cracked) olives; RT, traditional olives, cracked after 72 h of exposure to air.
| Time (days) |
CC |
CI |
CII |
CT |
RT |
|||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1st Repl. | 2nd Repl. | 1st Repl. | 2nd Repl. | 1st Repl. | 2nd Repl. | 1st Repl. | 2nd Repl. | 1st Repl. | 2nd Repl. | |
| 1 | 3.45 | 5.48 | 1.78 | nda | 1.60 | nda | 3.03 | 3.03 | 3.20 | 3.20 |
| 15 | 5.06 | 5.11 | 3.62 | 5.26 | 5.19 | 5.55 | 5.87 | 5.58 | 5.84 | 4.08 |
| 38 | 4.95 | 4.94 | 4.70 | 4.97 | 5.13 | 4.66 | 5.02 | 4.99 | 5.42 | 5.15 |
| 52 | 4.73 | 5.13 | 5.00 | 5.12 | 4.99 | 5.18 | 3.30 | 3.90 | 4.34 | 5.18 |
| 80 | 4.36 | 5.24 | 4.68 | 4.46 | 4.01 | 4.35 | 4.06 | 3.20 | 3.95 | 4.48 |
| 137 | 5.58 | 5.41 | 5.70 | 5.73 | 4.90 | 5.30 | 6.02 | 5.48 | 5.15 | 4.20 |
| 250 | 4.79 | 5.85 | 5.06 | 4.81 | 4.90 | 4.75 | 5.39 | 4.45 | 4.70 | 4.92 |
| 380 | 3.82 | 3.74 | 2.20 | 1.78 | 4.62 | 2.93 | 5.10 | 4.25 | 3.78 | 4.62 |
Repl. stands for replicate.
nd, not detected (<1.3 log10 cfu/ml).
Table 2.
Changes in pH through the storage/fermentation process of Aloreña table olives. CC, control of acidified cured olives; CI, highly acidified cured olives; CII, intermediate acidified cured olives; CT, control of traditional (cracked) olives; RT, traditional olives, cracked after 72 h of exposure to air.
| Time (days) |
CC |
CI |
CII |
CT |
RT |
|||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1st Repl. | 2nd Repl. | 1st Repl. | 2nd Repl. | 1st Repl. | 2nd Repl. | 1st Repl. | 2nd Repl. | 1st Repl. | 2nd Repl. | |
| 1 | 2.71 | 2.71 | 2.37 | 2.37 | 2.40 | 2.40 | 2.71 | 2.71 | 2.71 | 2.71 |
| 15 | 3.79 | 3.73 | 3.32 | 3.32 | 3.33 | 3.35 | 4.32 | 4.39 | 4.42 | 4.48 |
| 38 | 4.21 | 4.17 | 3.91 | 3.79 | 3.96 | 3.99 | 4.34 | 4.40 | 4.35 | 4.45 |
| 52 | 4.26 | 4.21 | 3.97 | 3.95 | 4.17 | 4.06 | 4.36 | 4.43 | 4.35 | 4.33 |
| 80 | 4.46 | 4.30 | 4.13 | 3.91 | 4.12 | 4.08 | 4.34 | 4.44 | 4.40 | 4.36 |
| 137 | 4.41 | 4.15 | 4.22 | 4.13 | 4.21 | 4.17 | 4.38 | 4.35 | 4.32 | 4.31 |
| 250 | 4.62 | 4.25 | 4.28 | 4.08 | 4.28 | 4.23 | 4.30 | 4.30 | 4.31 | 4.36 |
| 380 | 4.43 | 4.00 | 4.08 | 4.01 | 4.14 | 4.09 | 4.55 | 4.21 | 4.20 | 4.24 |
Repl. stands for replicate.
Table 3.
Changes in titratable acidity (g lactic/100 ml brine) through the storage/fermentation process of Aloreña table olives. CC, control of acidified cured olives; CI, highly acidified cured olives; CII, intermediate acidified cured olives; CT, control of traditional (cracked) olives; RT, traditional olives, cracked after 72 h of exposure to air.
| Time (days) |
CC |
CI |
CII |
CT |
RT |
|||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1st Repl. | 2nd Repl. | 1st Repl. | 2nd Repl. | 1st Repl. | 2nd Repl. | 1st Repl. | 2nd Repl. | 1st Repl. | 2nd Repl. | |
| 1 | 0.61 | 0.61 | 2.40 | 2.40 | 1.60 | 1.60 | 0.61 | 0.61 | 0.61 | 0.61 |
| 15 | 0.49 | 0.49 | 1.59 | 1.87 | 1.26 | 1.33 | 0.49 | 0.49 | 0.44 | 0.44 |
| 38 | 0.37 | 0.38 | 1.53 | 1.50 | 1.20 | 1.18 | 0.40 | 0.40 | 0.4 | 0.31 |
| 52 | 0.43 | 0.43 | 1.55 | 1.48 | 1.12 | 1.14 | 0.43 | 0.43 | 0.41 | 0.41 |
| 80 | 0.49 | 0.47 | 1.51 | 1.37 | 1.06 | 1.12 | 0.39 | 0.41 | 0.39 | 0.34 |
| 137 | 0.77 | 0.81 | 1.91 | 1.92 | 1.68 | 1.59 | 0.54 | 0.53 | 0.54 | 0.54 |
| 250 | 0.44 | 0.71 | 1.75 | 2.02 | 1.47 | 1.55 | 0.46 | 0.50 | 0.44 | 0.38 |
| 380 | 0.94 | 1.11 | 2.36 | 2.45 | 1.89 | 2.01 | 0.43 | 0.59 | 0.53 | 0.48 |
Repl. stands for replicate.
Table 4.
Average areas (±SE) below the yeast, pH and titratable acidity curves, according to treatments. CC, control of acidified cured olives; CI, highly acidified cured olives; CII, intermediate acidified cured olives; CT, control of traditional (cracked) olives; RT, traditional olives, cracked after 72 h of exposure to air.
| Treatment | Yeast | pH | Titratable acidity |
|---|---|---|---|
| CC | 1808 (64) | 1618 (37) | 253 (16) |
| CI | 1505 (29) | 1529 (17) | 718 (13) |
| CII | 1882 (76) | 1553 (6) | 576 (5) |
| CT | 1820 (99) | 1637 (6) | 184 (5) |
| RT | 1726 (34) | 1627 (3) | 171 (5) |
Notes: One way ANOVA for the areas below the curves led to following p-values: 0.056, 0.003, and <0.001, for yeast, pH and titratable acidity, respectively.
Table 5.
Changes in pH during storage/fermentation process of Aloreña table olives. CC, control of acidified cured olives; CI, highly acidified cured olives; CII, intermediate acidified cured olives; CT, control of traditional (cracked) olives; RT, traditional olives, cracked after 72 h of exposure to air. Parameters (±SE) of the model fit over time (y=a+b(1−exp(−cx))).
| Treatment | a | b | C (days−1) |
|---|---|---|---|
| CC | 2.6±0.1 | 1.8±0.1 | (8.6±1.5)E-2 |
| CI | 2.2±0.1 | 1.9±0.1 | (6.6±0.7)E-2 |
| CII | 2.2±0.1 | 2.0±0.1 | (6.9±0.7)E-2 |
| CT | 2.0±0.9 | 2.4±0.9 | (0.35±0.38)⁎ |
| RT⁎ | --------⁎ | --------⁎ | --------⁎ |
a, intercept; b, overall change in pH; c, rate of pH change.
Non-significant parameters.
Table 6.
Pairwise comparison of pH values between the areas of the different storage/fermentation Aloreña table olive treatments (Fisher LSD method, ANOVA p-value=0.003). CC, control of acidified cured olives; CI, highly acidified cured olives; CII, intermediate acidified cured olives; CT, control of traditional (cracked) olives; RT, traditional olives, cracked after 72 h of exposure to air.
| Comparison | Diff of Means | LSD (alpha=0.050) | P | Diff≥LSD |
|---|---|---|---|---|
| CT vs. CI | 108 | 68 | 0.009 | Yes |
| CT vs. CII | 84 | 68 | 0.024 | Yes |
| CT vs. CC | 20 | 68 | 0.489 | No |
| CT vs. RT | 10 | 68 | 0.728 | No |
| RT vs. CI | 99 | 68 | 0.013 | Yes |
| RT vs. CII | 74 | 68 | 0.037 | Yes |
| RT vs. CC | 10 | 68 | 0.721 | No |
| CC vs. CI | 89 | 68 | 0.020 | Yes |
| CC vs.CII | 64 | 68 | 0.059 | No |
| CII vs. CI | 25 | 68 | 0.396 | No |
Table 7.
Pairwise comparison of titratable acidity values between the areas of the different storage/fermentation Aloreña table olive treatments (Fisher LSD method, ANOVA p-value<0.001). CC, control of acidified cured olives; CI, highly acidified cured olives; CII, intermediate acidified cured olives; CT, control of traditional (cracked) olives; RT, traditional olives, cracked after 72 h of exposure to air.
| Comparison | Diff of Means | LSD (α=0.050) | P | Diff≥LSD |
|---|---|---|---|---|
| CI vs. RT | 547 | 40 | <0.001 | Yes |
| CI vs. CT | 534 | 40 | <0.001 | Yes |
| CI vs. CC | 465 | 40 | <0.001 | Yes |
| CI vs. CII | 142 | 40 | <0.001 | Yes |
| CII vs. RT | 405 | 40 | <0.001 | Yes |
| CII vs. CT | 393 | 40 | <0.001 | Yes |
| CII vs. CC | 323 | 40 | <0.001 | Yes |
| CC vs. RT | 82 | 40 | 0.002 | Yes |
| CC vs. CT | 70 | 40 | 0.005 | Yes |
| CT vs. RT | 12 | 40 | 0.427 | No |
Fig. 1.
Changes in pH (panel A) and titratable acidity (panel B) over time, according to treatments (
and 
). CC, control of acidified cured olives; CI, highly acidified cured olives; CII, intermediate acidified cured olives; CT, control of traditional (cracked) olives; RT, traditional olives, cracked after 72 h of exposure to air.
Fig. 2.
Graphical presentation of some examples of yeast population smoothing; each row shows the two replicate of treatments CC (panel A), CI (panel B) and CII (panel C). CC, control of acidified cured olives; CI, highly acidified cured olives; CII, intermediate acidified cured olives.
Fig. 3.
Estimations of the average mean (panel A) and standard deviation (panel B) yeast in treatment CC, expressed as log10 cfu/ml, based on the yeast functional object obtained from smoothing. CC, control of acidified cured olives.
Fig. 4.
Functional regression, showing the overall trends obtained for all treatments assayed (top left), followed by the average (and their replicates) of the specific profiles for each of the treatments. CC, control of acidified cured olives; CI, highly acidified cured olives; CII, intermediate acidified cured olives; CT, control of traditional (cracked) olives; RT, traditional olives, cracked after 72 h of exposure to air.
Fig. 5.
Functional analysis of variance for the changes in pH (panel A) and titratable acidity (panel B) over time. Panel A: upper graph, predicted pH regression curves for the treatments assayed; bottom graph, pH permutation F-test for the curves above. Panel B: upper graph, regression predicted titratable acidity curves for the treatments assayed; bottom graph, permutation F-test for the above curves. In both permutation tests, the graphs show the observed F-value, together with its maximum (break line) and pointwise 0.05 critical values (dotted lines). CC, control of acidified cured olives; CI, highly acidified cured olives; CII, intermediate acidified cured olives; CT, control of traditional (cracked) olives; RT, traditional olives, cracked after 72 h of exposure to air.
Fig. 6.
Functional analysis of variance for first (pH, panel A upper graph; titratable acidity, panel B upper graph) and second derivatives (pH, panel C upper graph; titratable acidity, panel D upper graph), and their respective estimated permutation functional F-tests (bottom curves of panels). For the F-test, the pointwise F-values, together with its maximum (broken lines) and pointwise (dotted line) p=0.05 critical values are indicated. CC, control of acidified cured olives; CI, highly acidified cured olives; CII, intermediate acidified cured olives; CT, control of traditional (cracked) olives; RT, traditional olives, cracked after 72 h of exposure to air.
Fig. 7.
Functional permutation t-test for the comparison of yeast growth curves (CC vs. CI, CI vs. CII, and CT vs. RT). Graphs show the pointwise estimated t-test values together with their maxima (broken lines), and pointwise (dotted line) p=0.05 critical values. CC, control of acidified cured olives; CI, highly acidified cured olives; CII, intermediate acidified cured olives; CT, control of traditional (cracked) olives; RT, traditional olives, cracked after 72 h of exposure to air.
Fig. 8.
Permutation functional t-test for the comparison of pH changes in CT vs. RT (panel A) and titratable acidity changes in CI vs. CII (panel B). The graphs show the pointwise F-values, together with its maximum (broken lines) and pointwise (dotted line) p=0.05 critical values. CI, highly acidified cured olives; CII, intermediate acidified cured olives; CT, control of traditional (cracked) olives; RT, traditional olives, cracked after 72 h of exposure to air.
2. Experimental design, materials and methods
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].
The Functional Data Analysis was achieved using the R routines and “fda” functions for R software developed by Bi and Keusten [2] and Ramsay et al. [3]. 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.
Acknowledgements
The research leading to these results has received funding from the Junta de Andalucía through PrediAlo Project (AGR7755: www.predialo.science.com.es). Thanks to Copusan S.C.A (Alozaina, Málaga, Spain) for supplying the fruits and facilities for the development of experiments. AGF is particularly grateful for the kind attention of Dr. Jim Bi (Sensometric Research and Service, Richmon, VA, USA). FNAL wishes to thank the Spanish Government and CSIC for his Ramón y Cajal postdoctoral research contract, while VRG and MARB thank the AgriFood Campus of International Excellence (ceiA3), Bank of Santander, Spanish Government and ‘Aloreña de Málaga’ Olive Manufacturing Association, for their pre-doctoral fellowship (training program of Ph.D. in companies).
Footnotes
Transparency data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.dib.2016.09.013.
Transparency document. Supplementary material
Supplementary material
.
References
- 1.Ruiz-Bellido M.A., Romero-Gil V., García-García P., Rodríguez-Gómez F., Arroyo-López F.N., Garrido-Fernández A. Assessment of table olive fermentation by functional data analysis. Int. J. Food Microbiol. 2016;238:1–6. doi: 10.1016/j.ijfoodmicro.2016.08.031. [DOI] [PubMed] [Google Scholar]
- 2.Bi J., Kuesten C. Using functional data analysis (FDA) methodology and the R package “fda” for sensory time-intensity evaluation. J. Sens. Stud. 2013;28:474–482. [Google Scholar]
- 3.J.O. Ramsay, G. Hooker, S. Graves, R Package “fda” version 2.4.4. 〈http://cran.r-project.org/web/packages/fda/index.html〉. Last accessed March 12, 2015, 2014.
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary material