Elsevier

Food Microbiology

Volume 94, April 2021, 103659
Food Microbiology

Relating starter cultures to volatile profile and potential markers in green Spanish-style table olives by compositional data analysis

https://doi.org/10.1016/j.fm.2020.103659Get rights and content

Highlights

  • Microbial starters lead to different volatile profiles in concluded fermentations.

  • Starters were better related to volatiles by CoDa analysis than by standard techniques.

  • Strains were linked to characteristic volatiles and potential markers by CoDa tools.

  • Relating starters and volatiles promotes sensory controlled table olive production.

Abstract

This work relates native lactic acid bacteria (LAB) (Lactobacillus pentosus LPG1, L. pentosus Lp13, and Lactobacillus plantarum Lpl15) and yeast (Wickerhamomyces anomalus Y12) starters to the volatile components (VOCs) produced in green Spanish-style table olives. For this aim, the VOC profile was considered as compositional data (CoDa). The CoDa analysis generated new information on the relationship among inocula and VOCs through the tetrahedral plot, CoDa-biplot, variation array matrix, and CoDa dendrogram. The ilr (which includes pivot) coordinates (Euclidean space) from VOCs produced more reliable starters’ clustering than the original data. The potential VOC markers, identified by a test based on the pairwise comparison of the logratio variation arrays from the whole data set and the individual groups, were (starters in the parenthesis): 2-phenylethyl acetate (LPG1, Y12, Y12 + LAB), methanol (Lpl15), cis-2-penten-1-ol (LPG1, Y12, Y12 + LAB), 2-methyl-3-hexanol (LPG1, Y12), U (non-identified) C (m/z 83-112-97) (Y12) and UF (m/z 95-154-110) (LPG1, Y12 + LAB). Besides, some VOCs were partial/totally inhibited by specific starters: 2-methyl-1-propanol (Lp13, Y12 + LAB), 2-phenyl ethanol (Lp13), furfuryl methyl ether (Y12 + LAB), purpurocatechol (Y12, Y12 + LAB), 4-ethyl guaiacol (Lp13, Lpl15), 4-ethyl phenol (Lpl15), 5-tert-butylpyrogallol (Lp13, Lpl15), and UE (m/z 111–198) (Lp13). A better understanding of the relationship between starters and their VOC may facilitate modelling the flavour and quality of Spanish-style green table olive fermentations.

Introduction

Green Spanish-style represents 50–60% of the world table olives, estimated as 3.26·106 tonnes/year by the International Olive Council (IOC, 2019). Its processing consists of debittering of fruits with lye (NaOH solution), washing with tap water, and brining. Then, a spontaneous lactic fermentation produces numerous metabolites (Garrido-Fernández et al., 1997). Apart from the lactic, acetic and other minor acids, the volatile compounds (VOC) play an essential role in the sensory characteristics of the product. The introduction of the of GC/MS stimulated studies on the VOC profile, particularly on the effect of cultivar, growing area, packaging conditions or influence of inoculation (Cortés-Delgado et al., 2016; Sánchez et al., 2017, 2018; López-López et al., 2018; Benítez-Cabello et al., 2019). Several of these compounds were related to the “zapatería” spoilage (de Castro et al., 2018). These studies have systematically involved the application of standard statistics and multivariate methods. Moreover, the influence of starter cultures on the sensory characteristics of fermented olives was always obviated. However, those strains associated with the most favourable components could be used for improving the flavour and quality of the final products.

Compositional Data (CoDa) Analysis is a recent statistical methodology proposed initially by Aitchison (1986) to treat data expressed in proportions (e.g. mg/kg, or percentage) of the whole sample. Pawlowsky-Glahn et al. (2015) have also defined them as vectors with strictly positive components that carry relative information. Such structure has specific geometrical connotations because the same absolute difference may not reflect the real (relative) changes. Therefore, its study by multivariate tools, developed for data expressed in absolute values, may lead to useless conclusions (van den Boogaart and Tolosana-Delgado, 2013; Pawlowsky-Glahn et al., 2015; Filzmoser et al., 2018). For treating these data, Aitchison (1986) proposed the use of logratios, although other alternatives like additive (alr), centred (clr), or isometric logratio (ilr) transformations (Egozcue et al., 2003) are also suggested. Recently, pivot coordinates, a particular case of ilr transformation has also been introduced (Filzmoser et al., 2018). Simultaneously, tools for their treatment in-the-simplex (the sample space for compositions) was also developed. Nowadays, the proper application of CoDa analysis to these data includes stay-in-the-simplex techniques and their transformation into clr or ilr coordinates, followed by the study of these coordinates by the standard multivariate tools (Pawlowsky-Glahn et al., 2015; Filzmoser et al., 2018).

The CoDa analysis is common in geology (Tolosana-Delgado et al., 2011), genetic (Pierotti and Martín-Fernández, 2011), spatial exploration (Lammer et al., 2011), or lipid dynamics in pelagic amphipods (Kraft et al., 2015). Nevertheless, its use in foods is still scarce and related to wine (Hron et al., 2012), pig fat (Ros-Freixedes and Estany, 2014; Garrido Fernández and León Camacho, 2019) or table olives (Garrido Fernández et al., 2018). Recently, the standard multivariate techniques did not adequately segregate among Manzanilla treatments (Benítez-Cabello et al., 2019). In the study of the VOCs of coffee, compounds like acetic acid, 2-methyl pyrazine, furfural, 2-furfuryl alcohol, 2-6-dimethyl hydrazine, and 5-methyl furfural were chosen as relevant markers (Korhoňová et al., 2009). Therefore, the use of the new CoDa statistic to characterise the VOCs produced in green Spanish-style processing is challenging.

The work aims to relate the starter cultures used for the fermentation of green Spanish-style Manzanilla table olives to the formed VOCs, the selection of the most characteristic components, and the tentative identification of potential markers, using CoDa analysis.

The use of selected microorganisms may represent a good strategy for controlling the flavour of table olives and standardise their quality.

Section snippets

Olive processing

The olives were from the Manzanilla cultivar, harvested at the green maturation stage. Processing was carried out in cylindrical fermentation vessels (9.5 kg olives/5 L liquid) where the fruits were debittered using a lye solution containing: 32.4 g/L NaOH lye, 21.9 g/L NaCl and 8.9 g/L CaCl2 (97% purity). When the alkali reached 2/3 of the flesh (7 h), the olives were washed with fresh water for 5 h and, finally, brined in a solution having, per litre, 100 g NaCl, 14.2 g CaCl2 and 0.012 L of

Data set

The data set consisted of 12 rows (duplicate treatments) and a sub-composition of VOCs with significant differences between at least two treatments (Benítez-Cabello et al. (2019). Compounds not conclusively identified yet (21) are reported just as m/z values (see Table S1 in supplementary material). The profiles included acetates (3), acids (1), alcohols (19), aldehydes (2), sulfoxide (1), C13-norisoprenoid (1), ethyl ester (3), furan (1), ketones (3), methyl esters (3), phenols (8), terpenes

Conclusions

This study has demonstrated that applying CoDa analysis introduces new exploratory techniques like tetrahedral plot, biplot, CoDa-dendrogram, or variation array, which were useful for segregating processes according to inocula or studying relationships among VOCs and potential markers. Thus, the study opens the possibility of using specific starter cultures for the production of particular VOCs or the prevention of undesirable compounds in real fermentation conditions, i.e. for modelling the

Declaration of competing interest

As the corresponding author of the manuscript entitled “Relating starter cultures to volatile profile and potential markers in green Spanish-style table olives by Compositional Data Analysis” to be considered for publication in Food Microbiology, I warrant that all authors declare that they have no conflict of interest.

Acknowledgements

The research was funded by the Spanish Government (Project OliFilm AGL-2013-48300-R: www.olifilm.science.com.es) A-BC thanks the Spanish Ministry of Economy and Competitiveness for their FPI grant.

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