Determination of volatile fatty acids in digestate by solvent extraction with dimethyl carbonate and gas chromatography-mass spectrometry

Accepted Manuscript Determination of volatile fatty acids in digestate by solvent extraction with dimethyl carbonate and gas chromatography-mass spectrometry Michele Ghidotti, Daniele Fabbri, Cristian Torri, Sergio Piccinini PII:

S0003-2670(18)30848-1

DOI:

10.1016/j.aca.2018.06.082

Reference:

ACA 236098

To appear in:

Analytica Chimica Acta

Received Date: 18 January 2018 Revised Date:

24 June 2018

Accepted Date: 30 June 2018

Please cite this article as: M. Ghidotti, D. Fabbri, C. Torri, S. Piccinini, Determination of volatile fatty acids in digestate by solvent extraction with dimethyl carbonate and gas chromatography-mass spectrometry, Analytica Chimica Acta (2018), doi: 10.1016/j.aca.2018.06.082. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

OH O

800 OH

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DAI

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DMC extract GC-MS

KHSO4

aqueous fraction solid particles

Total VFA (mg/kg)

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OEI Validation

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ACCEPTED MANUSCRIPT Determination of volatile fatty acids in digestate by solvent extraction with dimethyl

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carbonate and gas chromatography-mass spectrometry

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Michele Ghidottia, Daniele Fabbria*, Cristian Torria, Sergio Piccininib

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a. Interdepartmental Centre for Industrial Research “Energy and Environment” and Department of Chemistry “Giacomo Ciamician”, University of Bologna, Campus of Ravenna, via S.Alberto 163, I-48123 Ravenna (Italy).

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b. CRPA Lab - Research Centre on Animal Production - Viale Timavo, 43/2, 42121 Reggio Emilia (Italy)

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(*) Corresponding author

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Declarations of interest: none

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Abstract

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Volatile fatty acids (VFAs) are among the most important parameters in process monitoring of

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anaerobic digestion plants for biogas production. The concentration of single VFA species is

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typically determined by direct injection of the acidified aqueous phase of digestate samples into

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GC-FID. Analysis of dimethyl carbonate extracts was investigated as an alternative method

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consisting of a simple and rapid in-vial procedure of acidification and solvent extraction of the

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sample, followed by centrifugation and GC-MS analysis. The principal figures of merit resulting

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from internal standard calibration were comparable to those proposed for the direct analysis of

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aqueous digestate, while the analysis of real samples did not provide statistically significant

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differences between the two methods according to parametric and non-parametric tests. Procedural

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aspects including sample amount and solid removal improved with dimethyl carbonate, while GC

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contamination was reduced. The method was applied to seventeen samples from fully operating

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anaerobic digesters fed with various feedstocks and enabled the individuation of high probability of

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system stress through the values of total VFA, propanoic acid, longer chained VFA concentrations

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and the ratio between acetic and propanoic acid concentrations. The use of dimethyl carbonate

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allowed the detection of alicyclic and aromatic acids that could represent new molecular markers in

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assessing the origin of feed and process conditions.

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ACCEPTED MANUSCRIPT Key words: short chain fatty acids; solvent extraction; gas chromatography; biomass; biomethane; methanogens.

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1. Introduction

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Volatile fatty acids (VFAs, also named as short chain fatty acids SCFAs) are key intermediates in

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anaerobic digestion for biogas production, in which complex microbial communities convert

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organic substrates into a mixture of CH4 and CO2 (biogas) utilized as a fuel. The multitude of

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biological reactions taking place in the reactor during the process are grouped into a sequence of

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four steps: hydrolysis, acidogenesis, acetogenesis and methanogenesis. VFAs are principally

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formed by Bacteria in the acidogenesis step and are consumed as substrates in acetogenesis where

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acetic acid is formed as principal product. Acetic acid and other small molecules are finally

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converted into the end products (CH4 and CO2) by Archaea. Due to their continuous formation and

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decomposition, VFAs normally occur at appreciable concentrations in the microbial broth of

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anaerobic reactors (digestate), but exceedingly high values (> 1000 - 4000 mg kg-1) can inhibit the

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activity of Archaea. Thus, VFAs are fundamental indicators of process imbalance that may cause a

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decrease in the yield of biogas. While total VFA concentration can be inferred by titration, the

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knowledge of the molecular composition can provide useful information for the evaluation of

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process stability and proper identification of impending digester failure [1,2]. VFAs encompass

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linear and branched saturated carboxylic acids from two to six carbon atoms (C2-C6). These acids

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are termed volatile because distillable at atmospheric pressure [3]. Higher homologues (> C7) are

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rarely encountered probably because of their low solubility in water, while formic acid (C1) is

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subjected to fast decomposition or outgassing [4]. High concentrations of acetic acid (> 800 mg L-1)

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and propanoic/acetic acid ratios (>1.4) are indicative of digester failure, but with low predictive

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power, while anomalous concentrations of isobutyric and/or isovaleric acids (> 5 mg L-1) are

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considered more suitable indicators of the progressive occurrence of system stress [5].

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The determination of individual VFAs requires their effective separation by means of gas and liquid

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chromatography, or capillary electrophoresis, that have been applied to a large variety of matrices

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ACCEPTED MANUSCRIPT [6]. Generally, gas chromatography with flame ionization detection (GC-FID) is the most utilized

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technique for the analysis of digestate due to its favorable linearity, sensitivity and cost [6,7]. The

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digestate sample is a complex matrix consisting of varying proportions of solids particles into an

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aqueous fraction, therefore, solid removal is mandatory prior to GC analysis. Centrifugation can

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provide high recovery as investigated on an anaerobically digested sludge samples spiked with

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VFAs [4], but filtration is also required according to the most utilized procedure for the analysis of

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VFAs in digester sludge [3]. The solution should be acidified, generally with phosphoric acid, to

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assure the predominance of the unionized form of carboxylic acids [6], to improve their

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vaporization when injected into GC and reduce the occurrence of peak tailing and ghosting [3,8].

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The acidified aqueous solution can be injected directly into the gas chromatograph equipped with a

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polar stationary phase (DAI, direct aqueous injection, not to be confused with on-column injection).

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DAI has the advantage to reduce the potential loss of analytes in enrichment steps. Despite of its

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simplicity, DAI is not immune to errors as highlighted in a comprehensive laboratory study on the

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determination of VFAs in aqueous samples [9]. This study identified calibration as a major source

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of variability. Therefore, a successive inter-laboratory exercise was focused on calibration and

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established acceptance criteria for VFA determination that included precision, linearity, limit of

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detection and quantification [7]. Comparative studies on real samples are scarce [4], but it is

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expected that matrix components could significantly affect the analytical performance. DAI can be

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detrimental to the GC system due to trapping of inorganic species [10] in the liner, hydrolytic

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degradation of coatings, and decrease in detector performance [11], factors that increase the

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frequency of maintenance [6]. Besides centrifugation, the standard protocol requires filtration for

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efficient solid elimination, while repeated blank analyses are recommended to ensure the

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cleanliness of the GC system [3]. Several procedures have been applied to overcome the problems

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inherent to DAI, such as the use of formic acid [12] and the use of a liner with wool plug, to prevent

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suspended particles entering into the GC system [13]. Several sample treatments have been

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proposed in alternative to DAI, that included liquid-liquid extraction, solid-phase extraction, solid-

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ACCEPTED MANUSCRIPT phase microextraction, simultaneous distillation-extraction, electrodialysis and derivatization [6,7]

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as well as dispersive liquid-liquid microextraction for longer chains fatty acids in complex matrices

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[14].

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Extraction of VFAs with an organic solvent (here indicated as Organic Extract Injection, OEI) is a

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procedure alternative to DAI and several solvents have been utilized including dimethyl ether,

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diethyl ether, methyl-tert-butyl ether, dichloromethane [6,9]. The main problem with OEI is the low

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recovery of the more water soluble VFAs, especially acetic acid, with non-polar solvents [15].

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Dimethyl carbonate (DMC) is more polar than alkyl ethers [16], and comparatively less toxic than

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chlorinated solvents [17]. DMC has been utilized for the analysis of VFAs [18], but the procedure

90

has not been rigorously validated. Mass spectrometry (MS) is a selective detector, thus well fitted

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for the reliable identification of target analytes and the individuation of potential interferents.

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Moreover, MS can provide structural information on other carboxylic acids that could be useful to

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understand the anaerobic process. Besides aliphatic VFAs, aromatic carboxylic acids, such as

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benzoic and phenylpropanoic acids were identified as intermediates in anaerobic digestion of corn

95

straw [19], while the accumulation of aromatic acids was observed in anaerobic degradation of

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kitchen waste with higher substrate loading [20].

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In this study, a procedure based on OEI with DMC (DMC-OEI) and GC-MS analysis was

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investigated for the determination of VFAs in digestate from different sources of operating biogas

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plants. The proposed protocol was compared with the conventional DAI method for a selected set of

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real samples. The final objective was to produce a detailed array of analytical data, useful to

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individuate the reliability of DMC-OEI in the analysis of VFAs in complex matrices. The potential

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of cyclic carboxylic acids as process parameters for monitoring anaerobic digestion was also

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assessed.

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2. Materials and methods

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2.1 Samples and reagents

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ACCEPTED MANUSCRIPT Digestate samples were collected from industrial scale anaerobic digestion plants and provided by

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CRPA Lab. Feedstock characteristics and digester type are listed in Table 1. The samples were

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stored at – 20 °C prior to analysis. Short-time storage of extracted samples containing VFAs at +

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4°C is an option for up to 7 days, while for longer periods storage at –20°C was suggested [4]. All

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solvents and reagents were purchased by Sigma-Aldrich and included: individual VFA standards

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(acetic, propanoic, 2-methyl propanoic, butanoic, 3-methyl butanoic, 2-ethyl butyric, purity ≥ 99,

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potassium bisulfate (KHSO4, ≥ 99.99%), phosphoric acid (85 wt. % in water), dimethyl carbonate

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(99%).

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2.2 VFA extraction with dimethyl carbonate (DMC-OEI).

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A given amount of digestate sample (100-300 mg) was directly weighed (± 0.1 mg) in a GC vial

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(volume 2 mL) and added sequentially with 0.1 mL of KHSO4 saturated solution, 0.1 mL of internal

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standard solution (2-ethyl butyric acid 0.1 mg mL-1 in deionized water). Then, 1 mL of DMC was

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added, and the closed vial was vigorously shaken. The mixture was centrifuged at 3800 rpm for 10

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min (ALC4232 centrifuge) to separate the solid and aqueous phase from the upper layer of DMC,

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ready to be sampled by the syringe of the GC autosampler. Multianalyte solutions were prepared by

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dissolving each pure VFA in deionized water followed by serial dilution with deionized water. The

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solutions were added with the internal standard and then extracted with DMC, according to the

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procedure outlined for digestate samples, to obtain a calibration curve over the concentration range

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of 0.14 µg mL-1 -1.4 mg mL-1 (12 points). For the quantification of pentanoic acid, calibration

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solutions were prepared from a standard VFA solution containing 0.1% of pentanoic acid purchased

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by Sigma-Aldrich.

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2.3 GC-MS analysis

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DMC solutions were injected into the split/splitless injector (splitless conditions, 250 °C) of an

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Agilent 7820A gas chromatograph. The syringe of the autosampler was programmed to take 1 µl of

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the solution to be injected at a fixed height from the bottom of the vial (10 mm), corresponding to

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the layer of DMC. Analytes were separated with a DB-FFAP polar column (Agilent, 30 m length,

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ACCEPTED MANUSCRIPT 0.25 mm i.d, 0.25 µm film thickness) with helium flow of 1 mL min-1. The starting GC oven

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temperature was set to 50 °C (5 min) and increased to 250 °C (10 °C min-1). Detection was made

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with a quadrupole mass spectrometer Agilent 5977E operating under electron ionization at 70 eV

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with full scan mode acquisition at 1 scan s-1 in the 29 - 450 m/z range. Identification was based on

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the retention time and mass spectra of the pure compounds and by library mass spectra matching

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(NIST). Quantification was made from the peak area integrated by extracting characteristic ions

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from the total ion current (Table 2).

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2.4 Recovery test

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Standard solutions were prepared by dissolving pure VFAs in DMC at two concentration levels

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(0.01 and 0.1 mg mL-1). Solutions with the same concentrations were prepared in acidified

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deionized water and extracted with DMC according to the procedure described in section 2.2. The

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extraction efficiency of the method was calculated by comparing the peak areas of VFAs from the

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biphasic solution to those obtained by the analysis of the solution in pure DMC. To the end of

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testing matrix effects, the method was applied to representative digestate samples (#7 of Table 1)

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spiked with standard VFAs solution in deionized water at two concentration levels (0.01 and 0.1 mg

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mL-1). The recovery of the method was calculated from the peak areas corrected by the contribution

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of VFAs originally present in the sample.

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2.5 Comparison with Direct Aqueous Injection (DAI)

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DAI of digestate samples was performed according to the procedure outlined in the Standard

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Methods for the examination of water and wastewater [3], but using MS instead of FID. Briefly,

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amounts of digestate samples (10-20 mL) were exactly weighted in centrifuge tubes, acidified with

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phosphoric acid (85% v/v), added with internal standard solution at 1 mg mL-1 in deionized water,

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centrifuged at 3800 rpm for 10 min and filtered with Buchner funnels and filter paper. The filtered

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solution was injected into GC-MS, under GC and MS conditions described in [3] and section 2.4,

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respectively. For DAI, the system was equipped with a polyethylene glycol guard column (1m, 0.25

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ACCEPTED MANUSCRIPT mm i.d.) and a wool packed split/splitless liner. The method was internally calibrated with aqueous

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VFA solutions in the concentration range of 1.75 – 350 mg L-1. Seven digestate samples were

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selected from Table 1 (# 1, 4, 6, 7, 10, 14, 17) and analysed with DAI for method comparison.

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The average recovery of VFAs of six calibration solutions from the vacuum filtrated solutions were

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compared to those from the analyses of non-filtrated solutions; no significant differences were

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observed indicating that filtration under vacuum did not cause loss of VFAs.

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2.6 Quantification and statistical analysis

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All calibration solutions and digestate samples were analyzed in triplicate. The signal of each VFA

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was acquired by the integration of characteristic ions extracted from total ion current

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chromatograms and peak areas were then normalized by the area of the internal standard. Weighted

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least squares linear regression was performed to correlate normalized areas of each VFA with the

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quantity of VFA divided by that of the internal standard. According to Raposo et al. the inverse of

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peak areas variance at each calibration point was used as weighting factor [7]. The analysis was

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performed with NCSS 11 Statistical software. The concentration of VFAs was expressed as mg kg-1

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of digestate. Bland-Altman, Deming, and Passing-Bablok statistics for method comparison were

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performed with NCSS11. Two digestate samples (#7 and #9 of Table 1) were analysed repeatedly

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(six times) along a period of two months. Procedural blanks (10 replicates) were performed to check

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for background contamination during method development and to calculate critical values (Yc) for

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VFA quantification. Instrument precision was measured by repeated injections (n=10) of a solution

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containing i.s. 0.1 mg mL-1 and measured as %RSD on the peak area (%RSDinst).

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3. Results and discussion

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The present study reports the use of DMC as a green, non-toxic solvent [17] for the extraction of

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VFAs from digestate samples. The extraction step was performed directly in-vial with only one

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millilitre of DMC per sample acidified with a saturated solution of KHSO4. Among the solvents

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that form an immiscible upper layer phase with aqueous solutions, DMC has a polarity similar to

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that of ethyl acetate [14], but the latter may produce acetic acid by acid-catalyzed hydrolysis. The

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ACCEPTED MANUSCRIPT higher polarity of DMC in comparison to dialkyl ethers should favour the extraction of the polar

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VFAs. KHSO4 was utilized in place of H3PO4 to combine the effect of acidification with that of

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salting out to favour the distribution of VFAs into the organic solvent. Typically, the pH of a

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saturated KHSO4 (about 500 g L-1 at 20 °C) aqueous solution is less than 2. The selected

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proportions of sample amount, KHSO4, internal standard and DMC were suitable for creating a

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biphasic system in the limited volume of a GC-vial, in which solid particles and the acidified

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aqueous fraction are well separated from the upper DMC layer sampled by the syringe of the

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autosampler. The performance of the method was initially tested on solutions of pure VFA

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compounds to obtain the main figures of merit which are described in the next section.

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3.1. Figures of merit

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DMC extracts of VFA calibration solutions prepared in deionized water were analysed by GC-MS

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to obtain internal standard normalized peak areas for each concentration level. Weighted least

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square regression analysis allowed to obtain better linearity of all standard compounds over the

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investigated range of concentrations compared to ordinary least squares, particularly on the position

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of the intercept, as already outlined in previous studies [21]. A multi-laboratory validation study

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compared two methods for VFA determination by GC-FID and HPLC. The study was performed on

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pure standard solutions and reported guidelines and acceptable values of parameters in VFA

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analysis [7]. The main figures of merit calculated for DMC-OEI of standard VFA solutions were

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reported in Table 2. The slope and intercept of linear models for each VFA are reported with

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relative standard error (SE). The selected concentration range was considered representative of real

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digestate samples. Acetic and propanoic acids are usually the most abundant VFAs and

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concentration up to 1 g L-1 can be indicative of system stress [5]. Therefore, the selected

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concentration range for these VFAs was between 1 mg L-1 and 1 g L-1. Given their appreciable

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concentration in digestate, lower levels were not investigated, in accordance with previous studies

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[7]. Contrarily, for isobutyric, butyric, isovaleric and valeric acids the selected calibration range did

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not include values higher than 100 mg L-1, due to their occurrence at relatively lower concentrations

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ACCEPTED MANUSCRIPT [5] . Instrument precision (%RSDinst) was measured by repeated injection (n = 10) of internal

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standard solution and resulted 5%. Among the linearity indices, coefficients of determination (R2),

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percent relative standard deviation on the sensitivity (%RSDsensitivity) and average relative errors of

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the estimated regression line (%REaver) were reported. The values of R2 for all acids were higher

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than 0.998 in agreement with the proposed guidelines [7]. %REaver is based on the distance between

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the actual concentration of the used calibration standards and the values derived from interpolation

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of the same points by the estimated calibration curves. For all VFAs average errors from the whole

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curve (%REaver) were lower than 6%, indicating good agreement with experimental data and the

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linear model. This parameter was considered helpful to appropriately evaluate the linearity of the

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calibration curve, with tolerance values between 15-20 % [22]. The precision calculated on the

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sensitivity of calibration curves (%RSDsensitivity) resulted lower than 9% for all the VFAs, in

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agreement with precisions of mass selective detectors [22]. The estimated limit of detection (LOD)

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and quantification (LOQ) for each VFA were calculated from the slope “a” and intercept “SDb” of

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the calibration curve (LOD = 3×SDb a-1, LOD = 10×SDb a-1). The analysis of ten procedural blanks

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confirmed that critical values for quantification (Yc) were below the estimated LOD. Yc and LOD

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are just estimates to check the absence of interference during VFA analysis, since the recurring

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concentrations in real samples do not require the detection of trace amounts. The figures of merit

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were considered adequate to the analysis of parameters derived from biological processes, that

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intrinsically present high variability, heterogeneity and concentrations in the range of µg L-1 – g L-1.

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The recovery of the method was calculated by assessing the extraction efficiency of DMC-OEI

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applied to standard VFA solutions in deionized water and a representative digestate sample (#7)

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spiked with standard VFA solutions. Two concentration levels were tested, 0.1 mg mL-1 and 0.01

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mg mL-1. Results from aqueous solutions (Table S1) indicated that the recovery was quantitative for

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all VFAs (from 97±8 to 106±4% at 0.1 mg mL-1; from 99±15 to 115±14% at 0.01 mg mL-1). This

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finding was tentatively attributed to the strong salting-out and effective buffering capacity of the

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saturated KHSO4 solution combined with the solvent power of DMC that favored the distribution of

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ACCEPTED MANUSCRIPT VFAs into the organic solvent. Quantitative recoveries of all the VFAs were observed also for the

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spiked digestate sample (Table S2, from 93±6 to 114±3% at 0.1 mg mL-1). However, yields were

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overestimated at the lowest concentration probably due to bias form the VFAs originally present in

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the real sample.

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The repeatability of DMC-OEI was tested with two digestate samples with total VFA

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concentrations indicative of steady state conditions and potentially associated with system stress (#7

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and 9 respectively). These samples were analysed six times over two months with DMC-OEI. As

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reported in Table 3, the precision of the measurements indicated by %RSD was lower than 15 % for

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all VFAs and total VFA concentrations, indicating good accordance of the results over the

244

investigated period.

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3.2. Determination of VFA in digestate samples with DMC-OEI

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The DMC-OEI method was applied to the analysis of VFAs in seventeen digestate samples from

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anaerobic digestion units fed with various animal- and plant-based feedstock or residues (Table 1).

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The digestates were sampled from different digester types including single reactors and

249

configurations that separate hydrolysis and acidogenesis (Table 1, primary reactor) from

250

acetogenesis and methanogenesis steps (Table 1, secondary reactor). A typical chromatogram

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acquired from the GC-MS analysis of digestate samples after DMC-OEI is reported in Figure 1.

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Total average VFA concentrations per kilogram of digestate ranged from 50 to 400 mg kg-1 (Table

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1). Only two samples (#9 and #11) presented values one order of magnitude higher than the

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previous ones, indicating the potential occurrence of system stress. When samples from both

255

primary and secondary digesters were available, primary digesters always showed higher total VFA

256

concentrations (Table 1) indicating the good separation of the hydrolysis/acidogenesis step from

257

that of methanogenesis. The most abundant VFAs in all digestate samples were acetic and

258

propanoic acid respectively, while isobutyric, butyric, isovaleric and valeric acids were only minor

259

constituents of the total VFA profiles. The precision of DMC-OEI measured as %RSD on total

260

VFA was variable according to the variability of the digestate samples and ranged from 2 to 19 %

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ACCEPTED MANUSCRIPT (calculated from Table 1). This variability can be considered acceptable on the fact that the

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parameters indicative of process stability are not clear-cut boundaries. For instance, in accordance

263

with the International Energy Agency, process stability is identified when total VFA are present

264

below 1 g L-1, while values exceeding 4 g L-1 are likely associated with instability with consequent

265

decrease in pH [23]. Between these two values, both stable and unstable processes are possible.

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Moreover, the stability depends on the buffering capacity of the digester so that stable conditions

267

could be possible also at higher VFA concentrations. The concentrations of single VFA species are

268

reported in Table 4 along with the molecular markers of system stability indicated by the IEA

269

guidelines. The RSD averaged to all the analyses was less than 12% for the most important VFAs,

270

therefore comparable to the RSD obtained from the calibration (section 3.1). All the investigated

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samples fulfil the requirement of total VFA < 1 g kg-1 [23], except # 9 and #11, where the values

272

fall into the range between both stable and unstable process occur. However, the concentration of

273

propanoic acid exceeds 1 g kg-1 in these samples and in guidelines these values are associated with

274

high probability of unstable process [23]. Consequently, the ratio between concentrations of acetic

275

and propanoic is less than the threshold below which high instability is expected. In accordance

276

with the previous parameters, also the content of longer chained VFA (butyric, isovaleric and

277

valeric acids) is higher than 50 mg kg-1, again indicating high probability of process instability [23].

278

All the aforementioned markers indicate that only two out of seventeen samples represented critical

279

conditions in anaerobic digesters, while the remaining ones are associated with stable processes.

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3.3. Cyclic carboxylic acids

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Among the peaks identified in the chromatograms of DMC-OEI, intense signals attributed to

282

alicyclic and aromatic carboxylic acids appeared. In some samples these peaks were even more

283

intense than those of VFAs and included mainly cyclohexanecarboxylic acid, benzoic acid,

284

phenylacetic and phenylpropanoic acids (Figure 1). Benzoic acids and alkylated derivatives are

285

typical products of the metabolism of alkylated benzenes in polluted sites under anaerobic

286

conditions [24]. Benzoic acid and phenylpropanoic acid were identified in anaerobic digestion of

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ACCEPTED MANUSCRIPT corn straw [19]. Recently, several studies indicated that aromatic compounds could be important

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intermediates during methanogenic degradation of lignocellulose, protein and fat rich waste, which

289

could originate from the degradation of lignin-carbohydrate complexes or aromatic amino acids

290

[20]. A previous study showed that benzoic, phenylpropanoic, and a small amount of phenylacetic

291

acids were produced as natural by-products in the anaerobic rhizosphere of rice field soil.

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Interestingly, cyclohexanecarboxylic acid was found to be a key intermediate of the degradation

293

pathway of benzoyl-CoA [25]. Thus, its formation could be attributed to the reduction of the

294

aromatic ring of benzoic acid in synthrophic association with hydrogen-producing bacteria, present

295

in the hydrolytic and acidogenic biomass, with the concomitant oxidation of two or three hydrogen

296

molecules [25]. The presence of this intermediate was also correlated with the degradation of p- and

297

m-cresols in olive mill wastewater [25]. The distribution of these molecular markers (area

298

normalised to sample weight) are reported in Figure 2, and their total abundance in Table 1.

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Notably, these species were mostly abundant in primary digesters with poultry litter (# 9 and 11)

300

that can be associated with system stress by the propanoic/acetic ratios. Secondary digesters (# 10

301

and 12) still presented high contributions of these species possibly indicating that their degradation

302

is slower even though the VFAs concentration were markedly reduced compared to the primary

303

digesters. Hecht and Griehl found that anaerobic degradation of kitchen waste also led to the

304

accumulation of aromatic acids, especially with higher substrate loading [18]. Phenylacetic acid,

305

phenylpropanoic

306

hydroxyphenylpropanoic acid were detected during anaerobic kitchen waste fermentation, and

307

phenylacetic acid could be a preferred substance indicator for showing imminent process failure

308

[20]. In this study, p-cresol, phenylpropanoic acid, phenol and benzoic acid were detected as

309

transient intermediates during anaerobic degradation of corn straw. More studies are needed to

310

highlight the potential of these acids as markers of source and process conditions.

311

3.4. Comparison of DMC-OEI vs DAI

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phenylalanine,

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indoleacetic

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acid,

hydroxyphenylacetic

acid

and

ACCEPTED MANUSCRIPT The most frequently reported method for the analysis of VFAs in digestate and wastewater samples

313

is DAI [3]. To the purpose of evaluating DMC-OEI on real cases, a set of seven digestate samples

314

was selected and analysed by both analytical procedures. The samples covered an increasing

315

concentration range that encompassed steady state conditions (# 1, 4, 6, 7, 10, 14, 17 of Table 1,

316

total VFAs from 74 to 355 mg kg-1, 205 mg kg-1 on average). The samples had different origin in

317

terms of feedstock composition and digester type and were extremely heterogeneous, with variable

318

contents of total solids, which included solid particles and soluble organic and inorganic

319

constituents. The time-consuming filtration step required in DAI [3] could be avoided by DMC-

320

OEI. Moreover, DMC-OEI assured the injection of less contaminated solutions in comparison to

321

DAI as indicated by the formation of a dark brown film in the GC liner utilised for DAI, not

322

observed with DMC-OEI.

323

In order to evaluate if the procedural improvements of DMC-OEI protocol simplified the analysis

324

without impairing data accuracy in comparison to DAI, the two methods were compared on a

325

quantitative basis utilizing different statistical approaches. When two methods are compared,

326

usually the concentration values calculated with both methods are plotted against each other, and

327

perfect agreement theoretically occurs when the points display along the line with equation y = x.

328

The use of ordinary least square regression in the interpolation of points in the graphs could be

329

inappropriate due to violation of two main assumptions: first, it is arbitrary to select which of the

330

variables is the dependent and which one is the independent one. Moreover, as the x-axis represents

331

experimental values, these are not fixed but subjected to the same error as the y-values [26]. Bland-

332

Altman, Deming and Passing-Bablok tests are alternative procedure for the comparison of

333

analytical methods. The results of method comparison are reported in Table 5. An example of the

334

application of the aforementioned tests to the total VFA concentrations calculated with DMC-OEI

335

and DAI are reported in Figure 3. Bland-Altman test is a useful screening tool based on a plot in

336

which the difference of concentrations between DAI and DMC-OEI on the sample set is reported on

337

the vertical axis, while average values represent the horizontal axis. Average distance of the two

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ACCEPTED MANUSCRIPT methods (bias) and the corresponding 95% confidence intervals are reported as horizontal lines to

339

analyse the dispersion of data points. Bland-Altman plot of total VFA concentrations (Figure 3A)

340

indicates that the methods differ by 53 mg L-1 (-43, +146). This value mainly reflected the higher

341

variability of acetic acid compared to other VFAs (Table 5). In a recent study, a simple titration

342

method for the estimation of total VFA levels in full scale anaerobic digestion plants was

343

developed. The method resulted in agreement with GC analysis with a difference of 873 mg L-1

344

[27]. In comparison, the extraction with DMC provided lower averaged differences. The observed

345

discrepancies between the methods on single and total VFA concentrations were considered

346

acceptable considering the complexity of the matrix and its heterogeneity. Large variations in VFAs

347

were observed in anaerobic digester plants sampled at different times, where acetic acid for

348

example varied between 15 mg L-1 and 2 g L-1. Despite this high variability, the indigenous

349

methanogenic communities were not inhibited and stable methane production continued throughout

350

the experiment, suggesting good buffering capacity of the investigated digester unit [28]. Since the

351

determination of VFAs is intentionally aimed at the investigation of parameters that can contribute

352

to the screening of system stability, the calculated difference between OEI and DAI was considered

353

acceptable and DMC-OEI method fit for purpose. Deming regression is a technique for fitting a

354

straight line to two-dimensional data where both variables, x and y, are measured with error. An

355

example of the weighted regression model for the total VFA concentrations is reported in Figure 3

356

B with 95% confidence intervals and the line y = x, while the results for single VFAs in Table 5.

357

The null hypothesis that the slope of the regression line is equal to one was not rejected (α > 0.05),

358

indicating that the two methods do not provide statistically significant difference values of VFA

359

concentration. Deming regression models for all VFAs are reported in Figure 4. Passing-Bablok is a

360

robust, nonparametric linear regression method that tests whether the slope and the intercept of the

361

developed model are one and zero respectively. Figure 3C represents the results for the model on

362

total VFA concentrations. The statistic indicated that the slope and intercept were not statistically

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ACCEPTED MANUSCRIPT different from 1 and 0 respectively (at α = 0.05). Moreover, a good accordance between Deming

364

and Passing-Bablok models is shown.

365

4. Conclusions

366

In this study, the performance of dimethyl carbonate was evaluated for the extraction of volatile

367

fatty acids (VFAs), the key molecular markers in anaerobic digestion, followed by their GC-MS

368

analysis. The procedure was rapid and simple and allowed the use of small amounts of sample (100

369

- 300 mg) in contrast with the Standard Methods for the Examination of Water and Wastewater,

370

whereby direct injection of aqueous extracts of digestate required higher amounts of sample

371

(typically 10 - 30 mL). Thus, the proposed method could be of interest for the analysis of VFAs in

372

micro-scale studies and other complex aqueous matrices (wastewater, sewage sludge, biological

373

fluids, food formulations, etc.). The use of the MS enabled the identification of other acids (cyclic

374

and aromatic) in digestate that could be considered in future studies as additional parameters of

375

process conditions. In the case of very complex matrices or small sample amounts the sensitivity of

376

the method could be further improved by operating in the SIM mode. The extraction, acidification

377

and centrifugation steps were performed directly in-vial, avoiding sample transfer between the steps

378

that could lead to cross contamination and loss of information. Filtering high volume of aqueous

379

digestate is a time-consuming pre-treatment step necessary in direct analysis. The extraction with

380

dimethyl carbonate bypassed the filtration of the sample leading to a faster preparative procedure.

381

In fact, the centrifugation step separated the particulate and aqueous fractions of digestate at the

382

bottom of the vial, while the overlying layer of dimethyl carbonate was readily sampled and

383

injected into GC-MS. Moreover, the use of dimethyl carbonate prevented the contamination of the

384

analytical apparatus that could occur when aqueous digestate is directly injected. Direct analysis

385

required the use of a pre-column and a wool-packed inlet liner to retain the inorganic fraction of the

386

sample and higher molecular weight components present in the sample. In fact, the liner presented

387

accumulation of brown thermally degraded material from the matrix. Contrarily, injection of

388

dimethyl carbonate led to improved cleanliness of the injection and separation systems; after sixty

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ACCEPTED MANUSCRIPT analyses of digestate matrices from different origin, no accumulation of particles or thermally

390

degraded products were observed in the liner. Seven digestate samples with increasing

391

concentrations of VFA were analysed both with the developed method and direct injection. The

392

analysis of the same set of samples resulted in similar precision, suggesting that the variability

393

related principally to the type of sample rather than the analytical procedure. Statistical comparison

394

of the two methods highlighted that no significant differences occurred in the analysis of single and

395

total VFAs. The observed differences were considered adequate to the purpose of investigating the

396

course of biogas process conditions that are highly variable with VFA concentrations that can span

397

between mg L-1 and g L-1.

398

Acknowledgments

399

The study was conducted within the project “GOBIOM” of the POR-FESR 2014-2020 Regione-

400

Emilia Romagna (Italy) ASSE 1 Ricerca e Innovazione, Azione 1.2.2. Mariangela Soldano and

401

Mirco Garuti from CRPA Lab are thanked for providing real samples and helpful information and

402

discussion.

403

References

404

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[12] J. Famme, Per; Knudsen, Direct gas chromatographic determination of short-chain (C2–C4) volatile fatty acids in aqueous solutions, Comp. Biochem. Physiol. Part B Comp. Biochem. 17

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[16] C. Reichardt, Solvatochromic Dyes as Solvent Polarity Indicators, Chem. Rev. 94 (1994) 2319–2358. doi:10.1021/cr00032a005.

[17] P. Tundo, M. Selva, The chemistry of dimethyl carbonate, Acc. Chem. Res. 35 (2002) 706–

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[20] C. Hecht, C. Griehl, Investigation of the accumulation of aromatic compounds during biogas

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production from kitchen waste, Bioresour. Technol. 100 (2009) 654–658.

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gas chromatographic procedure for the routine analysis of volatile fatty acids in wastewaters, 18

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substances degradation by monitoring cyclohexane carboxylic acid, Environ. Technol.

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(United Kingdom). 36 (2015) 1785–1794. doi:10.1080/09593330.2015.1012179.

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[26] P.J. Cornbleet, N. Gochman, Incorrect least squares regression coefficients in method-

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comparison analysis, Clin. Chem. 25 (1979) 432–438.

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http://clinchem.aaccjnls.org/content/25/3/432.long (accessed December 18, 2017).

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[27] H. Sun, J. Guo, S. Wu, F. Liu, R. Dong, Development and validation of a simplified titration method for monitoring volatile fatty acids in anaerobic digestion, Waste Manag. 67 (2017)

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43–50. doi:10.1016/j.wasman.2017.05.015. [28] I.H. Franke-Whittle, A. Walter, C. Ebner, H. Insam, Investigation into the effect of high

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concentrations of volatile fatty acids in anaerobic digestion on methanogenic communities,

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Waste Manag. 34 (2014) 2080–2089. doi:10.1016/j.wasman.2014.07.020.

487 488 489

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ACCEPTED MANUSCRIPT Table 1. Set of digestate samples from anaerobic digesters. Numbered samples are reported with

491

the type of feedstock and digester (primary, 1°, secondary, 2°, or single digester, S) and total solid

492

content. Total VFA concentrations in mg kg-1 and total normalized area of aromatic/alicyclic acids

493

(Area gdigestate-1) from DMC-OEI are reported with SD from triplicate analyses in brackets. Bold

494

type underlined samples (#) were used for methods comparison with DAI.

495 496 497 498 Sample n°

Feedstock

Digester type

#1 #2 #3 #4 #5 #6 #7

maize silage, cattle slurry cattle slurry, cattle manure, cereal silage cattle slurry, cattle manure, cereal silage cereal silage, ground grain, pig slurry cereal silage, ground grain, pig slurry cereal silage, ground grain, pig slurry maize silage, poultry litter, broiler litter, triticale maize silage, poultry litter, broiler litter, triticale poultry litter, olive pomace, cereal silage poultry litter, olive pomace, cereal silage poultry litter, olive pomace, cereal silage poultry litter, olive pomace, cereal silage cereal silage, ground grain slaugherhouse residues cattle slurry cattle and pig slurry cattle slurry and manure

2° 1° 2° 1° 2° 2° 1°

499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515

20

Total VFA (mg kg-1)

85 (3.2) 190 (15) 53 (4.9) 355 (60) 132 (26) 160 (12) 279 (34)

2°

68

170 (29)

1.4E+06 (4.9E+05)

1° 2° 1° 2° 1° S S 2° 2°

97 71 85 58 40 62 56 65

3456 (490) 346 (64) 3776 (301) 220 (35) 137 (11) 74 (7.2) 63 (3.3) 210 (28) 137 (2.5)

3.2E+08 (7.7E+07) 2.9E+08 (1.7E+08) 7.6E+07 (1.3E+08) 2.5E+08 (4.0E+07) 3.0E+05 (2.0E+05) 1.1E+06 (3.8E+05) 9.6E+05 (9.9E+04) 3.4E+06 (6.4E+05) 5.4E+06 (1.2E+06)

SC 68 103 79 81 85 88 82

Total aromatic/alicyclic acids (Area gdigestate-1) 5.1E+05 (1.2E+05) 3.4E+06 (1.2E+06) 3.4E+05 (1.1E+05) 7.3E+06 (2.4E+06) 1.8E+06 (4.7E+05) 2.2E+06 (2.3E+05) 6.8E+07 (3.0E+07)

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#8

Total solids (g kg-1)

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ACCEPTED MANUSCRIPT Table 2: Figures of merit of DMC-OEI calculated by the analysis of standard VFA solutions and

518

procedural blanks. Weighted least squares regression was used to test the linearity of experimental

519

data points. Mass to charge ratios (m/z) of base peak (bold), molecular ion (italic), quantitation ion

520

(underlined) and retention times of VFAs are reported.

Acetic

Propanoic

m/z

43, 45, 60

29, 45, 57, 74

2-methyl butanoic 43, 73, 88

11.95

13.09

13.45

1-1000 0.199 0.0014 0.00029 0.00017 0.999 7 5 0.88 2.5 8.3

1-1000 0.283 0.0020 -0.00050 0.00036 0.999 9 6 n.d. 3.8 13

0.1-100 0.735 0.0037 -0.00028 0.00007 0.999 9 6 n.d. 0.30 1.0

Retention time (min) -1

526 527 528 529 530 531 532

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Linear range (mg L ) Sensitivity (a) SE (a) Intercept (b) SE (b) R2 %RSD (a) %REaver Yc (µg) LOD (µg/ml) LOQ (µg/ml)

Butanoic

3-methyl butanoic

Pentanoic

60, 73, 88

60, 87, 102

60, 73, 102

14.21

14.69

15.54

0.5-100 0.944 0.0092 -0.00049 0.00021 0.998 8 6 0.067 0.68 2.3

0.5-100 1.054 0.0071 0.00081 0.00028 0.999 7 5 0.17 0.79 2.6

0.1-100 1.171 0.0104 0.00001 0.00016 0.999 6 4 n.d. 0.42 1.4

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533 534 535 536

21

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Table 3: repeatability of DMC-OEI, two digestate samples (# 7 and 9 of Table 1) were analyzed six

538

times over two months. Average values of single and total VFA concentrations were reported with

539

standard deviations in brackets and percent relative standard deviation (%RSD).

540 Acetic acid

Propanoic acid

#7

Average concentration (mg kg-1) %RSD Average concentration (mg kg-1) %RSD

228 (27)

#9

Butanoic acid

3-methyl butanoic acid

Total VFA

25 (1.4)

2-methyl propanoic acid 7.6 (0.50)

2.7 (0.31)

8.3 (0.77)

272 (29)

12 2131 (230)

6 1169 (82)

7 55 (4.6)

12 15 (1.6)

9 93 (7.2)

11 3463 (323)

11

7

8

8

9

542 543 544

549 550 551 552 553 554 555

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545 546

11

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Parameter

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Sample n°

556 557 558 559

22

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Table 4: quantitative analysis of principal VFAs in digestate samples with the DMC-OEI method.

561

Average concentrations from triplicate analyses are reported with SD in brackets. Parameters

562

indicative of system stress are also reported (ratio acetic/propanoic acid, longer chained VFA).

Acetic acid (mg kg-1)

Propanoic acid (mg kg-1)

Isobutyric acid (mg kg-1)

Butyric acid (mg kg-1)

Isovaleric acid (mg kg-1)

Pentanoic acid (mg kg-1)

#1

80 (3.1) 162 (13) 47 (3.7) 326 (58) 125 (25) 149 (12) 235 (33) 149 (27) 2117 (344) 302 (59) 2215 (218) 192 (33) 133 (11) 57 (6.5) 48 (3.4) 195 (26) 130 (2.4)

3.8 (0.082) 19 (1.6) 2.7 (0.27) 20 (2.1) 4.7 (0.38) 8.2 (0.21) 26 (1.1) 9.9 (1.0) 1171 (128) 22 (2.7) 1268 (62) 16 (1.7) 1.9 (0.11) 9.2 (0.50) 8.9 (0.24) 6.3 (1.0) 6.0 (0.046)

0.60 (0.062) 2.9 (0.19) 0.71 (0.13) 2.9 (0.12) 0.88 (0.090) 0.74 (0.074) 7.5 (0.45) 4.3 (0.39) 54 (6.7) 8.0 (0.95) 76 (6.0) 4.0 (0.30) 0.61 (0.054) 2.5 (0.15) 2.3 (0.083) 3.4 (0.20) 0.46 (0.031)

0.53 (0.0066) 1.8 (0.083) 0.59 (0.17) 3.3 (0.16) 0.42 (0.077) 0.36 (0.034) 2.6 (0.46) 1.9 (0.12) 16 (1.9) 6.2 (0.77) 17 (1.8) 3.6 (0.25) 0.33 (0.077) 1.0 (0.0041) 1.1 (0.063) 1.3 (0.19) 0.29 (0.038)

0.81 (0.0029) 4.3 (0.32) 1.7 (0.63) 2.7 (0.71) 1.0 (0.27) 0.96 (0.18) 8.4 (1.1) 4.7 (0.38) 95 (10) 8.3 (0.78) 196 (14) 4.5 (0.48) 0.88 (0.17) 4.2 (0.093) 2.3 (0.16) 4.2 (0.63) 0.50 (0.17)

n.d.

#7 #8 #9 #10 #11 #12 #13 #14 #15 #16 #17

Longer chained VFA (mg kg-1) 1.3

0.21 (0.0040) n.d.

8.5

6.3

18

2.3

0.12 (0.0053) 0.051 (0.0098) n.d.

16

6.1

26

1.5

18

1.3

n.d.

9.0

11

n.d.

15

6.6

3.9 (0.47) n.d.

1.8

114

14

15

4.7 (0.64) n.d.

1.7

218

12

8.1

0.10 (0.022) 0.10 (0.012) 0.13 (0.0087) n.d.

71

1.3

6.2

5.3

5.4

3.5

31

5.5

22

0.79

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#5

EP

#4

AC C

#3

Ratio acetic/ propanoic (-) 21

SC

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#2

564

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563

565 566 567 568 569 23

n.d.

ACCEPTED MANUSCRIPT Table 5: Results of statistical analysis for methods comparison for single and total VFAs (Bland-

571

Altman [1], Deming [2], and Passing-Bablok [3] statistics). [1]: average differences in VFA

572

concentrations (bias, mg kg-1) between DMC-OEI and DAI are reported with 95% confidence

573

intervals; [2]: slope (a) and intercept (b) of weighted Deming regression are reported with the

574

probability (p) of rejecting the null hypothesis (H0: a = 1) at α = 0.05; [3]: slope (a) and intercept (b)

575

of Passing-Bablok regression with 95% confidence limits are reported; *indicate a = 1 and b = 0 at

576

α = 0.05.

Weighted Deming regression [2] Slope Intercept p (a) (b)

VFA

bias (mg kg-1)

Acetic acid Propanoic acid Isobutyric acid Butyric acid Isovaleric acid Total VFA

52 1.2

95% confidence limit -36, 140 -3.1, 5.6

0.050

-1.6, 1.5

0.927

0.25

-0.99, 1.5

1.13

0.50

-2.1, 1.1

53

-41, 146

578

Passing-Bablok regression [3]

18.9 1.56

0.38 0.76

1.18* 0.949*

95% confidence limit 0.936, 1.44 0.848, 1.10

0.0743

0.52

0.962*

0.831, 1.19

0.0507*

-0.104

0.30

1.13*

0.986, 1.23

-0.0899*

0.878

-0.098

0.24

0.870*

0.709, 1.00

-0.0502*

1.17

20.3

0.41

1.15*

0.941, 1.37

35.0*

1.19 0.965

TE D

577

SC

Bland-Altman [1]

Slope (a)

M AN U

Test

RI PT

570

Intercept (b) 31.7* 1.82*

95% confidence limit -7.54, 65.8 -0.207, 2.53 -0.167, 0.210 -0.209, 0.012 -0.348, 0.218 -11.1, 69.5

FIGURE CAPTIONS

580 581 582

Figure 1: total ion current chromatogram obtained from DMC-OEI analysis of a digestate sample (#9 in Table 1) with peak assignments of C2-C6 straight chain and branched VFAs, alicyclic and aromatic carboxylic acids.

583 584

Figure 2: Normalized areas of alicyclic and aromatic carboxylic acids detected in digestate samples with the DMC-OEI method.

585 586 587

Figure 3: Example of the application of Bland-Altman (A), Deming (B) and Passing-Bablok (C) tests for the comparison of DMC-OEI and DAI with total VFA concentrations in digestate samples (# 1, 4, 6, 7, 10, 14, 17 in Table 1).

588 589 590 591

Figure 4: Deming regression of single and total VFA concentrations determined by organic solvent extraction with dimethyl carbonate (DMC-OEI) and direct aqueous injection (DAI). Weighted regression lines of average values are reported (red solid line) with 95% confidence intervals (red bands). The black dashed line represents the 45° y = x line.

AC C

EP

579

592 24

ACCEPTED MANUSCRIPT

O

HO

O

OH O OH

MCounts

i.s.

O

9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5

OH OH

O

O OH O

OH

OH O

O

OH

OH

O

RI PT

O

OH

O

O

SC

HO

HO

10

12

14

16

AC C

EP

TE D

8

M AN U

HO

18

20

22

O

24

minutes

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

100 50 0

100

200

300

400

Average total VFA (mg/kg)

400 200 0

500

-200

EP

-100 0

600

TE D

-50

B

y = 1.17*x + 20.3

0

100

200

300

OEI: average total VFA (mg/kg)

400

DAI: total VFA (mg/kg)

150

DAI: average total VFA (mg/kg)

800

A

AC C

Difference in total VFA (mg/kg)

200

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

800

C

y = 1.15*x + 35.0 600

400

200

0

0

100

200

300

400

OEI: total VFA (mg/kg)

500

ACCEPTED MANUSCRIPT

40

Acetic acid

y = 0.965*x + 1.56

y = 1.19*x + 18.9 600

30

400

20

200

10

Isobutyric acid y = 0.927*x + 0.0742

8 6

RI PT

4 2

0

0

-200 100

200

300

400

0

-2

-10 0

OEI: average concentration (mg/kg)

5

10

15

20

25

30

15 Isovaleric acid

Butyric acid

y = 0.878*x - 0.0981

y = 1.13*x - 0.104 10

10

5

5

2

4

6

8

OEI: average concentration (mg/kg)

OEI: average concentration (mg/kg)

15

0

SC

0

800

Total VFA y = 1.17*x + 20.3

600

400

200

0

0

-5

-5 2

4

6

8

0

2

4

6

8

OEI: average concentration (mg/kg)

TE D

OEI: average concentration (mg/kg)

EP

0

AC C

DAI: average concentration (mg/kg)

10

Propanoic acid

M AN U

DAI: average concentration (mg/kg)

800

10

0

-200

0

100

200

300

OEI: average concentration (mg/kg)

400

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

- Simple protocol based on dimethyl carbonate extraction for volatile fatty acid analysis; - Cleanliness of GC system improved in comparison with the direct aqueous injection; - Data accuracy comparable with the standard method; - Alicyclic and aromatic carboxylic acids concomitantly extracted as potential markers.