Naturally fermented soy sauce is one of the few globally valued food condiments. It is complex in its substrate, manufacturing processes, and chemical profile of salts and organic compounds, resulting from spontaneous, enzymatic and biochemical reactions. The overall chemical character of soy sauce has a few rivals relative to its chemical and bioactive complexity. Resulting from this complexity are unique sensory attributes contributing to the characteristic soy sauce flavor as well as potentiating other sensory sensations. Soy sauce is susceptible to deterioration after bottling during storage. This work examined soy sauces over an eight-month period using descriptive sensory methods and the discovery of metabolomic biomarkers with high resolution mass spectrometry, wherein samples were derivatized to enable volatility and identification of polar analytes. While several thousand metabolites were detected, only organic acids, amino acids, and various glycosylated metabolites were statistically defensible biomarkers of storage time. The relationships between sensory and metabolomic data were assessed using Kendall rank-based correlations to generate Kendall Tau correlation coefficients. A second approach filtered the data based on correlation significance and grouped molecules based on hierarchical clustering. Mass spectrometry analyses discovered several thousand unique analyte peaks with relevant changes denoted as significant relative to the fresh samples using volcano depictions of values versus changes in compound abundances. We present a metabolomic approach for the analysis of complex food systems capable of differentiating a quantifiable extrinsic variable, which is, in this case, storage time with a correlation coefficient of 0.99. We further demonstrate that changes in soy sauce resulting from storage are characterized by sensory decreases in fruity/grape and nutty/sesame aroma and increases in methional/potato aroma and astringent attributes with concomitant changes in the concentrations of several key biomarkers.

1. Introduction

Analytical technologies that enable effective food quality characterization, differentiation, and management are based on accurate and reliable data generation and have been increasing in type and performance, including emerging methodologies, such as high-resolution mass spectrometry metabolomic-based approaches [1, 2]. Different types of analytical platforms are often applied in combination with nontargeted analyses to maximize and broaden detection capabilities for improved sensitivity and molecule identification. Such approaches generate discriminatory patterns of complex chemical components from different samples through the creation and comparison of metabolic fingerprints, thus providing the capability to differentiate chemically complex biological samples [3, 4]. In fact, several studies have reported attempts to relate these chemical fingerprints to the discriminatory sensory qualities of food samples by examining these analytical data with various multivariate statistical designs [57].

In the case of exploratory studies for pursuing compounds in foods responsible for a particular sensory quality, sensory-guided techniques in conjunction with novel, advanced chromatographic and mass spectrometry analyses are applied. Such approaches can be useful for characterizing changes in sensory attributes with changes in chemical profiles resulting from independent experimental variables, such as product storage time [8]. However, such approaches include the risk of missing the more complicated collective or cumulative effects of biologically active substances that occur in authentic food systems. Yet, these exploratory approaches provide a critical foundation for further discoveries and can provide a more comprehensive understanding of the changing chemistries involved for the purposes of product differentiation. However, the elucidation of key, flavor-impact compound identities is not the universal goal of flavor assessments such as is the case with electronic nose technologies [9], where the compound-dependent signal can be used to differentiate treatment effects rather than elucidating the causative chemistries. Here, we present a similar approach using metabolomics to differentiate soy sauce (SS) samples exposed to an extended storage treatment.

Previous studies that characterize critical SS sensory attributes have been conducted, and some lexicons have been consequently developed by several research teams [10, 11]. These lexicons allow a common language for the evaluation of native SS sensory qualities, thus sharing new insights and applied discoveries among consumers, researchers, and manufacturers. The sensory attribute of freshness in SS is absent from existing SS lexicons, despite the fact that the loss of freshness has been anecdotally recognized as a key sensory attribute that determines SS quality as perceived by consumers, especially in markets that have high standards for SS sensory performance and value [12]. Furthermore, freshness as a key characteristic of foods has gained attention given its critical importance to consumers. However, it requires a complex and multisensory, cross-modal affective assessment [13].

As a condiment, SS is applied to a wide variety of foods ranging from common food to some of the most sophisticated meals crafted. After manufacturing and as a function of storage parameters, such as time, light exposure, and oxygen concentration, SS is anecdotally thought to lose its fresh character, manifested as a darkened color and altered sensory attributes, suggestive of spontaneous reactions that are especially detrimental, where SS is applied in fine culinary applications [12]. These adverse changes over storage time are typically expressed in the industry as a loss of freshness and are thought to be catalyzed when SS is exposed to oxygen, high temperatures, or excessive storage time. Chemical reactions generally associated with oxidative or Maillard reaction pathways have been suggested to contribute to these undesirable changes [12]; however, there is little information detailing discernable shifts in chemistry, and thus, there is no rationale to objectively mark its occurrence. In Japan, the trade association, called the Japan Soy Sauce Brewers Association (JSSB), proposed a standard “best before date” shelf life determination, in which freshness is a factor that must be sensorially determined. Yet, even in this JSSB method, there is no definitive sensory characterization for freshness aside from a general subjective loss of desirable character relative to a freshly prepared control anchor. This lack of definitive sensory and chemical change prevents the application of a means to prevent the loss of SS freshness. We further hypothesize that the native chemistries in flux over the course of a loss of freshness from storage can be elucidated through the comparison of differential metabolic fingerprints and multivariate analyses. Thus, the objectives for this work include the assessment of SS samples aged under conditions noted later as a means of elucidating sensory attribute changes with concomitant changes in chemical profiles. Study objectives were achieved based on changes in a limited set of metabolites, including organic acids, amino acids, and various glycosylated compounds, resulting from the aging process using a metabolomic approach.

2. Materials and Methods

2.1. Soy Sauce Samples

Traditionally brewed SS was directly obtained from the manufacturer (Kikkoman, Chiba, Japan) bottled in sealed, 1-liter plastic containers for storage treatments. Samples were stored in a dark incubator held at 30°C, and triplicate samples were randomly removed at two-month intervals up to and including the eight-month storage event. Upon removal from incubation, samples were stored at −80°C until analyses were completed.

2.2. Sensory Analyses

A protocol for sensory analysis of SS was filed and approved (North Carolina State University, Institutional Review Board) prior to initiation of study. A trained panel (n = 6 panelists, each with at least 100 h of experience in the descriptive analysis of foods and beverages) assessed SS attributes from the literature [10, 14] and novel attributes from preliminary studies using a 15-point universal intensity scale consistent with the SpectrumTM method. Based on this documented complexity and other recently published literature, eighteen aroma and flavor attributes were selected for the study (Table 1), from which eleven attributes generated responses in the study (Table 2). Each SS sample was evaluated by each panelist in triplicate. Paper ballots were used for data collection and were manually transferred into statistical software. SS samples were diluted at 1 : 1 with deionized water prior to the sensory evaluation. Training sessions and preliminary evaluations indicated that dilution at this level minimized fatigue with no effect () on perceived sensory attributes. Attribute intensities were scored on a 0- to 15-point universal intensity scale consistent with the Spectrum method; panelists were allowed to score beyond this range if warranted by a particular attribute intensity [19].

2.3. High-Resolution Gas Chromatography (HR-GC-MS)

A preliminary study revealed several hundred polar compounds in SS by HR-GC-MS to determine if the instrumental analyses were able to detect relative changes in compound concentrations and refine sample preparation methodologies and instrument run parameters. We further note that traditional volatile chemistries associated with aroma character, such as esters or aldehydes, were not discoverable using the sample extraction and derivatization methods employed. Yet, this methodology allowed a relatively simple, rapid approach that discovered of a host of compounds as noted later, and thus, we deemed this approach suitable for the intention of this study, which is the ability to differentiate sample aging treatments. In general, classes of compounds shifted by the aging treatment included organic acids, amino acids, and various glycosylated metabolites.

2.4. Sample Preparation for HR-GC-MS Analyses

SS samples were kept on ice along with all other laboratory reagents used. Aliquots of SS samples were diluted 10-fold with DI water. Performed in triplicate, 20 μL of diluted sample was pipetted into a 1.5 mL Eppendorf tube and 225 μL MeOH was added. The mixture was vortexed for 10 s. Next, 750 μL methyl tert-butyl ether was added, and the mixture was vortexed for 10 s. The solution was mixed using an orbital shaker for 6 min. To induce phase separation, 187.5 μL water was added, and the solution was vortexed again for 20 s. The sample was then centrifuged for 2 min at 12,000×g and 4°C. The upper phase in the 1.5 mL Eppendorf tube was discarded, and 250 μL of the lower (aqueous) phase was removed and placed in a separate 1.5 mL Eppendorf tube. To this, 250 μL ACN was added to precipitate protein. The mixture was vortexed for 15 s and centrifuged at 13,000×g for 5 min at 4°C. Then, 300 μL of the supernatant was aliquoted into glass autosampler vials. The mixture was dried in a vacuum concentrator. Once dry, samples were resuspended in 50 μL methoxyamine hydrochloride solution (20 mg/mL, pyridine solution), vortexed for 15 s, and heated at 37°C for 90 min. Then, 100 μL of N-methyl-N- trimethylsilyltrifluoroacetamide (MSTFA) was added and vortexed for 15 s, and the mixture was heated at 60°C for 60 min.

2.5. HR-GC-MS Analysis

Samples were analyzed using a GC-MS instrument (Trace 1310 GC, Thermo Scientific, Waltham, MA) coupled to a mass spectrometer (Q Exactive Orbitrap, Thermo Scientific, Waltham, MA). A temperature gradient ranging from 100°C to 320°C was employed spanning a total runtime of 25 min. Analytes were injected onto a 30 m × 0.25  mm ID × 1 μm thickness column (TraceGOLD TG-5SILMS, Thermo Scientific, Waltham, MA) using a 1 : 10 split at a temperature of 275°C and ionized using electron ionization (EI). The mass spectrometer was operated in full scan mode using a resolution of 30,000 [3].

2.6. HR-GC-MS Data Processing

Data processing was done using a software suite developed in-house that is available at https://github.com/coongroup. Following data acquisition, raw EI-GC/MS spectral data were deconvolved into “features” and then grouped into individual spectra containing only product ions stemming from a singular parent molecule. Feature groups from samples and background were compared and those found in both were removed from further analyses. Compound identifications for the metabolites analyzed were assigned by comparing deconvolved high-resolution spectra against unit-resolution reference spectra present in the NIST 12 MS/EI library and authentic standards run in-house. To calculate spectral similarity between experimental and reference spectra, a weighted dot product calculation was used. Metabolites lacking a confident identification were classified as “unknown.” Peak heights of specified quantified m/z ratios were used to represent metabolite abundance. The data set was further processed using a linear regression approach (non-log 2 transformed intensity values versus run order) to normalize for run order effects on signal.

2.7. Data Analysis

All MS measurements were integrated with sensory information and processed with data analysis and visualization software (see https://coonlabdatadev.com). values displayed in figures and tables were calculated using Student’s t-test comparing time points 2, 4, 6, and 8 months with 0 months. We performed covariant analysis in R using the “cor.test” function and method “Kendall”; with this method, we identified compounds from MS datasets correlating sensory descriptor scores (). From this subset of molecules which correlate with sensory terms, we performed hierarchical cluster analysis on the correlation matrix of molecule-sensory pairs using the R function “hclust” and k-means equal to 5. A heatmap of this matrix was generated using the R function “pheatmap” [2022]. Both chemical and sensory data were further analyzed for the purpose of generating predictive models of their correlative value using multiple, stepwise linear regression (JMP vs. 14, SAS Institute Inc., Cary, NC, 1989-2020).

3. Results and Discussion

Overall, sensory and HR-GC-MS analyses provided novel analyte discoveries resulting from the aging or storage of SS as described by the aforementioned conditions. Correspondingly, there were limited, but significant age-based sensory changes in the SS including the loss of fruity/grape and nutty/sesame aroma characteristics and the increase of methional/potato aroma. Changes in biomarker profiles varied across the aging variable yet intensified in number and quantity of compounds captured as described in detail later. We note that the analyses conducted were designed to discern the most notable changes in polar biomarker profiles discernable by the instrumental analyses as a function of the storage time variable. We further acknowledge that such an approach is not designed to or sufficient for establishing causal sensory changes; it is rather a means of discerning the influence of storage-based aging through specific biomarkers.

3.1. Sensory Profiles

Although eighteen attributes were initially considered by sensory panelists, eleven terms were selected for further consideration in that the other seven terms did not generate changes in sensory responses over the course of the study. Of the terms that generated responses, five were not affected by the treatment variable of storage time, leaving six that were affected. Two terms, namely, fruity/grape and nutty/sesame, displayed significant decreases in aroma intensity with increases in storage time. The term methional/potato increased with storage time, suggesting an increase in aroma resulting from sulfur-containing volatiles. Although the term caramel/sweet aromatic was influenced by storage time, the results were not consistent. The samples also displayed slight changes in the astringent and mouth burn sensations, yet the results were not consistent across storage time.

Changes in freshness in foods and beverages, such as spoilage in fluid milk, are complex and involve changes in various classes of compounds, which affect color, texture, aroma, and taste attributes created by spontaneous, enzymatic, and microbial activities. In isolation, none of these effects may be discernable by sensory assessment, yet collectively they can affect a notable sensory departure from the native state. A notable observation in this study is that newly manufactured or “fresh” SS is differentiated from aged SS by multiple distinguishable sensory attributes. Fresh SS was higher in caramel and fruity and nutty attributes and lower in methional notes. This latter aroma has been associated with deteriorative changes in other foods with complex compositional profiles [15, 21]. We further noted no changes in other SS attributes, such as umami or sweet tastes. The following is a regression model derived from sensory factors affected by age in months as a means of defining SS freshness loss:and we propose that this model infers that SS freshness is characterized from a sensory standpoint from the collective changes in the sensory attributes of caramel and fruity and nutty attributes and it is lower in methional notes as weighted by the correlation coefficients derived from regression analysis. Although the sensory character is complex, these four attributes were able to predict 79% of the changes manifested across the storage time assessed based on correlation analysis. A complementary model was created using instrumental data wherein compounds were first selected based on their relative strength of correlation to the storage variable and then assessed using a stepwise regression model. The final model for the prediction of SS freshness loss using instrumental data is shown as

Upon validation of the assessment using the aforementioned correlations, these five chemical variables were able to predict >99% of the variation in the storage variable.

3.2. HR-GC-MS Metabolomics

As anticipated, chromatograms were complex and yielded several thousand resolved analytes. While many analytes were tentatively identified, a significant number were not, namely, those with sugar moieties where the type and degree of ligand substitution were not differentiable through molecular weight or mass spectra database comparison (Table 3). A graphical depiction of the type and complexity of variation from the t = 0 control SS sample is presented in Figures 1(a)–1(d). To better understand which metabolites are associated with the critical sensory parameters, we performed a correlation analysis between metabolite abundance and sensory measurements for the 15 samples (0–8 mo). The Kendall Tau correlation coefficient was used to assess strength of association and was chosen to better account for the nonparametric nature of the sensory data. The resulting significant correlations are visualized using a heatmap (Figure 2(a)), where the strength of the correlation is indicated by heat color ranging from blue (indicating a strong negative correlation) to red color (strong positive correlation).

After applying hierarchical clustering and k-means (k = 5) clustering techniques, we were able to identify distinct patterns of associations between metabolites (Figure 2(a)) and sensory attributes. For example, in the yellow cluster, there are strong positive correlations between cluster member metabolites, like L-tryptophan, and sensory attributes, nutty/sesame and fruity grape. In contrast, the orange cluster metabolites, such as glyceric acid, show more negative correlations with nutty/sesame and positive correlations with astringent mouthfeel scores. Next, we explored how these cluster members (Figure 2(b)) changed over time, and we plotted the identified metabolite features abundance relative to the time zero months (Figures 2(c)–2(g)). The orange cluster members positively associated with astringent mouthfeel scores increased between 0 and 8 months, while the yellow cluster members associated with nutty/sesame decreased between 0 and 8 months. The green cluster members also decreased with time, and cluster members of blue and gray clusters showed less change with time. Lastly, we wanted to integrate these results to the model using metabolite features, sugar RT 13.6, sugar RT 19.4, sugar RT 19.5, arabinose, and L-tryptophan, to quantify “freshness.” Notably, the first three components of the model are members of the orange cluster, and the last two components of the model, arabinose and L-tryptophan, are members of the yellow cluster.

This work provides a depiction of modern metabolomic technologies applied to the subject of assessing storage-based aging of SS with time and sensory-based variables. Metabolite profiling such as the one applied in this study offers a promising technology to unravel complex, yet critical aroma-based attributes in foods such as SS. We further suggest that SS freshness, as a sensory attribute, is a complex term defined by the presence of specific aromatic attributes of fruity/grape and nutty/sesame and absence of methional/potato aroma. More quantitatively, it can be expressed as the models derived from the regression analyses mentioned earlier. Furthermore, we recognize that consumer perceptions of freshness and value of finished food products such as SS may vary based on prior experiences, familiarization, and exposure factors [23]. However, we suggest that a sensory basis for quality assessment provides a key means of discriminating important quality parameters such as those influenced by extended storage or product spoilage. We acknowledge that the changes in sensory attributes were not rationally associated with the changes noted in the chemical profiles, whereas changes in the fruity attribute did not correlate with compounds noted for fruity character, such as ethyl esters. We propose that these finer discoveries, at least on the volatile fraction, are the subject of future investigations such as those done in other works [23], wherein more comprehensive analytical examinations are conducted complemented by sensory validation using the addition of authentic standards to induce specific sensory attributes.

Yet, three novel contributions are served in this work. First, we have established that changes in sensory character are discernable in SS as a function of age or storage time. Second, the sensory character of SS freshness is indeed a multidimensional or meta-term attribute affected by changes in several key aromatic attributes as noted in other foods [24]. Third, a rapid, analytical assessment of SS compounds was developed that can predict or discern aging in SS across storage time with high precision using changes in polar metabolite profiles.

Data Availability

Data can be accessed through contacting author’s institution at https://www.minds.wisconsin.edu.

Conflicts of Interest

Author Coon is a consultant for Thermo Fisher Scientific. The remaining authors declare no conflicts of interest related to this work.


The authors acknowledge the funding and technical assistance for this work provided by the Kikkoman USA R&D Laboratory (Madison, WI). This work was supported in part by the National Institutes of Health (NIH) grant P41GM108538.