Data-driven flavoromics reveals postharvest quality deterioration and volatile compound dynamics in Stropharia rugosoannulata mushroom during cold storage

Data-driven flavoromics reveals postharvest quality deterioration and volatile compound dynamics in Stropharia rugosoannulata mushroom during cold storage | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Data-driven flavoromics reveals postharvest quality deterioration and volatile compound dynamics in Stropharia rugosoannulata mushroom during cold storage Wanchao Chen, Junying Zhu, Wen Li, Di Wu, Zhong Zhang, Peng Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9128298/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Fresh Stropharia rugosoannulata mushrooms are highly perishable, undergoing rapid postharvest quality and flavor degradation. However, how volatile organic compounds (VOCs) dynamically evolve and relate to macroscopic quality changes remains poorly characterized. Here, we employed a data‑driven flavoromics approach integrating HS‑SPME‑GC×GC‑TOF MS with multivariate analysis to investigate postharvest quality deterioration and volatile dynamics in S. rugosoannulata stored at 5 ℃ for 10 days. Quality assessment revealed progressive weight loss (1.22% at Day 10), biphasic texture changes (hardening then softening), and continuous browning, with browning index increasing 8.5‑fold. GC×GC‑TOF MS profiling detected nearly 10,000 volatile compounds, demonstrating a systematic succession from alcohol‑dominant (fresh) to ester‑enriched (mid‑storage) and finally to lipid oxidation‑dominated (late storage) profiles. Relative odor activity value (ROAV) analysis identified 11 key odorants driving sensory transitions, with dissipation of mushroom‑like 1‑octen‑3‑one and accumulation of fatty aldehydes marking the critical shift toward off‑flavor development. Integrative correlation networks revealed significant associations between specific volatiles and quality attributes, linking aldehyde accumulation with browning and softening processes. Multivariate modeling (PCA/PLS‑DA) delineated three distinct physiological stages during storage, and rigorous triple‑filter screening (VIP > 1, 95% CI > 1, FDR < 0.05) identified robust stage‑specific and continuous biomarkers. Notably, 7 continuously upregulated compounds were validated as progressive indicators of senescence, serving as molecular “ticking clocks” for freshness monitoring. This study provides the first comprehensive volatilome map of postharvest S. rugosoannulata and establishes a set of reliable volatile markers for real‑time quality assessment, offering a powerful framework for developing targeted preservation strategies and predicting shelf‑life in edible fungi during cold chain logistics. Stropharia rugosoannulata Flavoromics Postharvest storage GC×GC‑TOF MS Biomarkers Multivariate analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Stropharia rugosoannulata , commonly known as wine cap or garden giant mushroom, has been recognized by the Food and Agriculture Organization of the United Nations as a recommended edible mushroom for global consumption due to its exceptional nutritional and culinary properties [ 1 ]. This species has garnered increasing economic significance worldwide, with its cultivation expanding rapidly across Asia, Europe, and North America. The sensory qualities of S. rugosoannulata , particularly its characteristic earthy aroma, umami taste, and firm yet tender texture—are fundamental determinants of consumer acceptance and market competitiveness. The distinctive flavor profile of fresh S. rugosoannulata is primarily attributed to its volatile organic compounds (VOCs), with eight-carbon compounds such as 1-octen-3-ol (named as mushroom alcohol) serving as key aroma contributors, alongside various aldehydes, ketones, and alcohols that collectively orchestrate its appreciated organoleptic properties [ 2 , 3 ]. Furthermore, this mushroom is remarkably rich in flavor-active components including free amino acids, 5'-nucleotides, and soluble sugars, which synergistically contribute to its pronounced umami taste and overall flavor complexity. However, the postharvest storage of S. rugosoannulata presents significant challenges due to its inherently high metabolic activity and moisture content (80–90%), rendering it highly perishable and susceptible to rapid quality deterioration [ 4 , 5 ]. During storage, a cascade of interconnected physiological and biochemical changes occurs, including continuous respiration, transpiration, and senescence processes that manifest as visible quality degradation. Recent advances in understanding edible fungi postharvest biology have elucidated that quality deterioration encompasses four interrelated aspects: physical damage (water loss, temperature fluctuations, mechanical injury), physiological changes (ongoing respiration, texture softening, nutrient depletion), biochemical reactions (enzymatic browning, off-flavor development, degradation of bioactive compounds), and microbial spoilage (colonization by Pseudomonas, Enterobacter, and fungal pathogens) [ 4 ]. Studies specifically on S. rugosoannulata have demonstrated that during ambient storage, firmness declines progressively while browning index increases, with these changes closely correlated to oxidative damage indicators and the imbalance of reactive oxygen species metabolism[ 6 ]. The application of low-temperature storage combined with modified atmosphere packaging has been shown to maintain antioxidant enzyme activities and delay quality deterioration [ 6 , 7 ]. Concurrent with these physical and biochemical changes, profound alterations in volatile compound profiles drive flavor deterioration, the characteristic fresh mushroom aroma gradually diminishes while off-flavors emerge, with lipid oxidation pathways generating aldehydes and ketones associated with rancid and hay-like notes. Research on spoilage microorganisms affecting S. rugosoannulata has identified that Fusarium, Aspergillus, and Rhizopus species are primary decay-causing fungi, with optimal growth temperatures of 25 ~ 28 ℃ and inhibition at low temperatures (4 ~ 10 ℃), providing critical insights for storage optimization [ 8 , 9 ]. Despite these advances, the dynamic evolution of volatile compounds throughout the entire postharvest period and their quantitative relationships with instrumental quality parameters remain incompletely characterized, limiting the development of targeted preservation strategies. The advent of advanced analytical platforms and computational methods has revolutionized the study of food flavor systems. Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOF MS) represents the state-of-the-art in volatile analysis, offering substantially enhanced separation capacity, peak resolution, and sensitivity compared to conventional one-dimensional GC-MS systems [ 10 , 11 ]. This technological advancement enables the detection and identification of hundreds to thousands of volatile compounds in complex food matrices, providing an unprecedented view of the flavorome. Recent applications of GC×GC-TOF MS in food research have demonstrated its exceptional capability for comprehensive VOC profiling, with studies identifying up to 198 volatile compounds in grilled meat products and revealing subtle compositional differences between samples [ 12 ]. Flavoromics, an approach that comprehensively profiles volatile compounds using high-resolution instrumentation coupled with multivariate data analysis, enables the holistic characterization of flavor-active molecules and their dynamic changes during food processing and storage [ 13 , 14 ]. Despite the growing application of flavoromics in food science, a comprehensive understanding of the relationship between macroscopic quality deterioration (e.g., enzymatic browning) and microscopic volatile dynamics in S. rugosoannulata during cold storage is still lacking. To bridge this gap, this study applied a data-driven flavoromics approach to investigate the postharvest changes in S. rugosoannulata . The specific objectives were to: (1) evaluate the postharvest quality deterioration by monitoring surface color changes and the browning index; (2) characterize the dynamic evolution of VOCs during cold storage using HS-SPME-GC×GC-TOF MS; and (3) identify the key characteristic volatile markers associated with storage time utilizing ROAV and multivariate statistical analyses. The findings of this study will provide profound theoretical insights into the flavor deterioration mechanisms of S. rugosoannulata and offer data-driven guidance for developing targeted postharvest preservation strategies. 2. Materials and methods 2.1 Sample collection and storage conditions Fresh fruiting bodies of S. rugosoannulata (Variety: Huqiu No.5) were harvested at commercial maturity from Shanghai Maqiao Edible Fungi Planting Base during May 2025. Samples with similar maturity, and absence of mechanical damage or visible defects were selected for the experiment. Immediately after harvesting, the mushrooms were transported to the laboratory under refrigerated conditions (4 ℃) within 2 h. Upon arrival, the samples were randomly divided into six groups corresponding to storage time points: 0, 2, 4, 6, 8, and 10 days (Fig. 1 A). Each time point included 4 biological replicates, with approximately 150 g of mushrooms per replicate. All samples were stored at 5 ± 1 ℃ and 85 ± 5% RH in darkness to simulate ambient storage conditions, following established protocols for postharvest mushroom storage studies. At each designated time point, samples were randomly withdrawn for quality attribute measurements and volatile compound analysis. For volatile analysis, fresh samples were immediately frozen in liquid nitrogen and stored at -80 ℃ until further processing to minimize enzymatic and oxidative changes. 2.2 Chemicals and internal standard preparation Ethanol (99.8% purity) was purchased from Aladdin (Shanghai, China). n-Hexane (GR grade) was obtained from Yonghua (Shanghai, China). The internal standard, n-Hexyl-d13 Alcohol (98.5% purity), was supplied by C/D/N Isotopes INC (Quebec, Canada). A series of n-alkanes (C7–C30, 1000 mg/L) for retention index calculation was purchased from Sigma-Aldrich (St. Louis, MO, USA). Ultrapure water was prepared using a Milli-Q Direct-8 purification system (Millipore, Bedford, MA, USA). For internal standard solution preparation, a stock solution of n-Hexyl-d13 Alcohol (1 mg/L) was prepared in 50% ethanol aqueous solution and stored at 4 ℃ until use. Similarly, a stock solution of n-alkanes (1 mg/L) was prepared by serial dilution in n-hexane and stored at 4 ℃. 2.2 Quality attribute measurements 2.2.1 Weight loss Weight loss was determined gravimetrically by measuring the mass of each sample before storage and at each sampling time point. The weight loss percentage was calculated according to the following formula: $$Weightloss\left(\%\right)={100\times(W}_{0}-{W}_{t})/{W}_{0}$$ where W 0 represents the initial weight and W t represents the weight at each storage time point. 2.2.2 Texture analysis Texture properties of S. rugosoannulata fruiting bodies were measured using a texture analyzer (TA new plus, ISENSO, USA) equipped with a cylindrical P/0.5 probe. A texture profile analysis (TPA) test was performed following established methods for edible fungi with minor modifications [ 15 ]. Samples were taken from the central region of the mushroom cap to ensure uniformity. The specific settings were as follows: pre-test speed 1.0 mm/s, test speed 1.0 mm/s, post-test speed 1.0 mm/s, trigger force 5.0 g, compression strain 50%, and a time interval of 5 s between two compressions. From the force–time curves, hardness (maximum force during first compression, N) were calculated using the instrument's software. Each measurement was performed with 10 replicates per sample. 2.2.3 Color different measurement Surface color of S. rugosoannulata caps was measured using a chromameter (CS-580, Hangzhou Color Spectrum Technology Co., Ltd., China) calibrated with a standard white plate. The CIE L * a * b * color space was employed, where L * represents lightness (0 = black, 100 = white), a * represents red-green chromaticity (positive = red, negative = green), and b * represents yellow-blue chromaticity (positive = yellow, negative = blue) [ 16 ]. Measurements were taken at three equidistant points on the cap surface of each mushroom, and the mean values were recorded. Browning index (BI) was calculated according to the following equation to quantify the degree of enzymatic browning [ 17 ]: $$BI=\left[100\times\left(x-0.31\right)\right]/0.17$$ where \(x=({a}^{*}+1.75{L}^{*})/(5.645{L}^{*}+{a}^{*}-3.012{b}^{*})\) . 2.3 Volatile compound analysis by GC×GC-TOF MS 2.3.1 Sample preparation and headspace solid-phase microextraction (HS-SPME) Volatile compounds were extracted using headspace solid-phase microextraction (HS-SPME) following the methods reported by Li et. al.[ 18 ] with modifications. Frozen mushroom samples were ground into fine powder in liquid nitrogen. An aliquot of 2.0 g powder was accurately weighed into a 20 mL headspace vial, and 10 µL of the internal standard solution (n-Hexyl-d13 Alcohol, 1 mg/L) was added. The vial was immediately sealed with a PTFE-silicone septum. Prior to extraction, the SPME fiber coated with 50/30 µm DVB/CAR/PDMS (divinylbenzene/carboxen/polydimethylsiloxane, 1 cm length, Supelco, Bellefonte, PA, USA) was preconditioned in the GC injection port at 270 ℃ for 10 min. Samples were equilibrated at 80 ℃ for 10 min with continuous agitation. The preconditioned fiber was then exposed to the headspace of the sample vial for 25 min at 80 ℃ to adsorb volatile compounds. After extraction, the fiber was immediately inserted into the GC injection port for thermal desorption at 250 ℃ for 5 min in splitless mode. Following each injection, the fiber was reconditioned at 270 ℃ for 10 min to eliminate any carry-over effects. For retention index calculation, 10 µL of the n-alkane solution (1 mg/L) was transferred to a separate 20 mL headspace vial and analyzed under identical conditions. 2.3.2 GC×GC-TOF MS analysis Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOF MS) analysis was performed using a LECO Pegasus BT 4D system (LECO Corporation, St. Joseph, MI, USA), which consists of an Agilent 8890A gas chromatograph (Agilent Technologies, Palo Alto, CA, USA) equipped with a dual-stage cryogenic modulator and a high-resolution TOF mass spectrometer. This platform offers superior separation capacity, high sensitivity, and excellent reproducibility for complex sample analysis, with an acquisition rate of up to 500 full spectra per second. The first-dimensional column was a DB-Heavy Wax column (30 m × 250 µm i.d., 0.5 µm film thickness, Agilent Technologies, USA), and the second-dimensional column was an Rxi-5Sil MS column (2.0 m × 150 µm i.d., 0.15 µm film thickness, Restek Corporation, USA). High-purity helium (≥ 99.999%) was used as the carrier gas at a constant flow rate of 1.0 mL/min. The GC oven temperature program was as follows: initial temperature 50 ℃ held for 2 min; increased at 5 ℃/min to 230 ℃ and held for 5 min. The second-dimension oven temperature was maintained 5 ℃ higher than the main oven throughout the analysis. The modulator temperature was offset by 15 ℃ relative to the second-dimension column temperature, with a modulation period of 6.0 s. The GC injector temperature was maintained at 250 ℃. Mass spectrometric conditions were as follows: electron ionization (EI) mode at 70 eV; ion source temperature: 250 ℃; transfer line temperature: 250 ℃; detector voltage: 1960 V; mass acquisition range: m/z 35 ~ 550; acquisition rate: 200 spectra/s, which is consistent with the high acquisition rate capability of TOF MS for comprehensive two-dimensional separations. 2.3.3 Compound identification and quantification Data acquisition and processing were performed using ChromaTOF software (version 4.51, LECO Corporation, USA). Raw data were subjected to deconvolution, peak alignment, and compound identification. The deconvolution process enables the separation of co-eluting compounds and improves the accuracy of identification in complex chromatographic data. Compounds were identified by comparing their mass spectra with those in the NIST 20 and Wiley 9 mass spectral libraries, combined with retention index (RI) matching. RI values were calculated using the series of n-alkanes (C7 ~ C30) analyzed under identical chromatographic conditions. Semi-quantification was performed using the internal standard method following established protocols for volatile analysis in food matrices. 2.3.4 Odor activity value (OAV) calculation To evaluate the contribution of individual volatile compounds to the overall aroma profile, odor activity values (OAVs) were calculated as the ratio of the concentration of each compound to its odor threshold in water. The relative odor activity value (ROAV) of volatile compounds is a parameter used to determine key flavor compounds. Generally, the larger the ROAV, the greater the contribution of the substance to flavor [ 19 ]. Compounds with ROAV ≥ 1 were considered as aroma-active contributors to the mushroom's flavor profile. Define a component that contributes the most to the overall flavor of the sample, with a ROAV of 100. The component with the highest contribution is defined as the substance with the highest normalized quantification value/odor min threshold. The ROAV of other substances is calculated based on the ROAV of the component with the highest contribution, using the formula: $$ROAV\left(A\right)=100\times\left[RelativeConten\right(A)/T(A\left)\right]/\left[RelativeContent\right(stan)/T(stan\left)\right]$$ where Relative Content(A) is the normalized quantitative value of the test substance; T(A) is the odor min of the substance to be tested; Relative Content(stan) is the normalized quantitative value of substance with ROAV = 100; T(stan) is the odor min of substance with ROAV = 100. 2.4 Data analysis 2.4.1 Statistical analysis All experiments were performed with at least three biological replicates, and results were expressed as mean ± standard deviation (SD). Statistical analyses were conducted using Python 3.9. One-way analysis of variance (ANOVA) followed by Tukey's honestly significant difference (HSD) test was applied to evaluate significant differences among storage time points. Differences were considered statistically significant at P < 0.05. 2.4.2 Multivariate statistical analysis Metabolites detected in no more than two replicates within each storage stage (i.e., present in ≤ 50% of samples per group) were excluded from further analysis to reduce the influence of low-confidence and poorly reproducible features. To visualize the overall distribution patterns and clustering of samples based on volatile profiles, principal component analysis (PCA) was performed using MetaboAnalyst 6.0 ( https://www.metaboanalyst.ca/ ) with Normalization (None + Log transformation base 10 + Pareto scaling). Partial least squares discriminant analysis (PLS-DA) were conducted using the pandas, numpy, matplotlib, seaborn, scikit-learn, scipy, and statsmodels package of Python to maximize the separation among different storage time points and identify discriminant volatile compounds [ 20 ]. Model validation was performed using 200 permutation tests to prevent overfitting. The goodness-of-fit parameters (R²X, R²Y) and predictive ability parameter (Q²) were calculated. Variable importance in projection (VIP) scores was obtained from the PLS-DA model, and compounds with VIP > 1.0, 95% CI (confidence interval, Jackknife cross validation method) > 1.0 and FDR (false discovery rate, Benjamini-Hochberg method) adjusted P < 0.05 were considered as significant contributors to group discrimination. 2.4.3 Differential volatile compound screening Differential volatile compounds between storage time points were identified using a combination of statistical criteria: (1) fold change (FC) ≥ 2 or ≤ 0.5 (i.e., |log₂FC| ≥ 1); (2) P 1.0 from PLS-DA [ 9 ]. Volcano plots were generated to visualize the distribution of differential compounds using the pandas, numpy, scipy, and matplotlib package by Python 3.9. Venne plots were constructed using the matplotlib-venn package to display the expression patterns of differential compounds across the samples. 2.4.4 Correlation analysis Pearson correlation coefficients were calculated to evaluate the relationships between volatile compounds and quality attributes (weight loss, texture parameters, and color indices) using the WeiShengXin bioinformatics analysis platform ( https://www.bioinformatics.com.cn/ ). Correlation matrices were visualized as heatmaps, with significance levels indicated (∗ P < 0.05, ∗∗ P < 0.01, ∗∗∗ P < 0.001). 3. Results and Discussion 3.1 Dynamic changes in quality attributes during postharvest storage The evolution of physical quality attributes, including appearance, weight loss, texture (hardness), and surface color parameters, was systematically monitored in S. rugosoannulata fruiting bodies over a 10-day storage period under ambient conditions (Fig. 1 ). These attributes collectively serve as critical indicators of postharvest freshness, consumer acceptability, and overall marketability of edible fungi. Weight loss of S. rugosoannulata increased progressively throughout the 10-day storage period, with significant differences observed among all time points ( P < 0.05) (Fig. 1 B). After 2 days, cumulative weight loss was 0.33 ± 0.02%, which gradually increased to 0.85 ± 0.02% by Day 8, followed by a sharp rise to 1.22 ± 0.05% at Day 10, representing a 3.7-fold increase from Day 2. This acceleration during later storage stages reflects progressive cellular membrane deterioration and loss of compartmentalization integrity, facilitating unregulated water efflux [ 6 ]. The thin epidermal structure and absence of a protective cuticle in the fruiting bodies render them particularly susceptible to moisture loss, consistent with observations in other cultivated mushrooms [ 5 ]. Texture deterioration exhibited a distinct biphasic pattern (Fig. 1 C). Hardness increased significantly from 684.57 ± 24.04 g at Day 0 to 725.08 ± 25.47 g at Day 2 ( P < 0.05), representing a 5.9% increase. Subsequently, hardness declined progressively, reaching 563.73 ± 20.57 g at Day 10, a 17.7% decrease from initial values and 22.3% decrease from the Day 2 peak. No significant difference was observed between Day 8 and Day 10 values ( P > 0.05), indicating that softening reached a plateau during the final storage stage. The fruiting bodies experience an increase in hardness during the early stages of storage, followed by a decrease, possibly due to tissue fibrosis and lignification [ 9 , 21 ]. The subsequent progressive softening may be attributed to the enzymatic degradation of cell wall structural polysaccharides, such as chitin and glucans, by endogenous hydrolases, as reported in other mushroom species[ 22 – 24 ]. Color evolution during storage revealed progressive browning of S. rugosoannulata fruiting bodies (Fig. 1 D &E ). Lightness ( L * ) values showed no significant differences among days 0–8 ( P > 0.05), but decreased significantly from 51.08 ± 11.29 at Day 8 to 37.08 ± 8.52 at Day 10 ( P < 0.05), representing a 16.8% decrease from initial values. Redness ( a * ) values increased continuously throughout storage, with significant increases observed after Day 4 ( P < 0.05). Values rose from 7.92 ± 1.16 at Day 0 to 16.67 ± 3.03 at Day 10, representing a 2.1-fold increase. Days 4ཞ6 showed the most substantial increment (10.25 to 14.08). Yellowness ( b * ) values exhibited a dramatic increase from Day 0 to Day 8, rising from 2.00 ± 4.05 to 31.92 ± 11.69 ( P < 0.05), a 16-fold increase, followed by a modest decline to 24.50 ± 9.17 at Day 10. Day 8 showed significantly higher b * values compared to all earlier time points ( P < 0.05). The substantial increase in b * during mid-storage indicates accumulation of yellow-brown intermediate pigments, while the late-stage decline may reflect their polymerization into darker melanin complexes that shift the chromaticity toward redder tones [ 25 ]. BI integrates all three color parameters to provide a comprehensive metric of enzymatic browning, increased significantly throughout storage ( P < 0.05), from 17.11 ± 9.27 at Day 0 to 144.72 ± 64.43 at Day 10 (an 8.5-fold increase). The BI progression exhibited near-exponential characteristics, with particularly sharp increases during the 4 ~ 6 days interval (85% increase) and 8 ~ 10 days interval (17% increase). Days 8 and 10 showed significantly higher BI values compared to days 0 ~ 4 ( P < 0.05). This continuous BI elevation provides quantitative evidence of progressive enzymatic browning, consistent with polyphenol oxidase (PPO) and peroxidase (POD) activities oxidizing phenolic substrates following harvest-induced cellular disruption [ 26 ]. The temporal sequence of deterioration, browning initiation (Day 4) preceding accelerated softening (after Day 6) and weight loss acceleration (after Day 8), suggests that enzymatic oxidation may be among the earliest detectable quality deterioration events in stored S. rugosoannulata . This aligns with the mechanism where membrane disruption during early senescence enables PPO-phenolic contact before extensive cell wall degradation occurs [ 5 , 26 ]. The dramatic color deterioration after Day 8 ( L * decrease, a * increase, BI increase) signals the transition from acceptable to unacceptable visual quality for consumers, who typically associate browning with reduced freshness and potential nutritional degradation [ 27 , 28 ]. 3.2 Volatile compound profiling and dynamic changes during storage The volatile compounds of S. rugosoannulata at different storage stages were comprehensively analyzed using headspace solid-phase microextraction coupled with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (HS-SPME-GC×GC-TOF MS). Representative one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) total ion chromatograms (TIC) for samples at day 0 and day 10 are presented in Fig. 2 A,B &C , respectively. A total of 9,746 volatile compounds were identified across all samples, with the number of detected volatiles increasing progressively from 3,532 at day 0 to a maximum of 4,193 at day 10 (Fig. 2 D). This represents a 18.7% increase over the 10‑day storage period, suggesting active metabolic generation of volatiles during postharvest senescence, potentially driven by lipid oxidation, enzymatic reactions, and microbial metabolism [ 6 , 8 , 12 ]. The temporal dynamics of total volatile numbers showed three distinct phases: a rapid increase from Day 0 to Day 2 (3,532 to 3,934, + 11.4%), a slight decrease at Day 4 (3,681, 96.4% from Day 2), followed by a steady increase through Day 10 (4,193, + 13.9% from Day 4). This biphasic pattern may reflect initial burst of volatile production following harvest‑induced stress, temporary equilibration of metabolic pathways, and subsequent sustained generation as degradation processes intensify [ 26 ]. Meanwhile, Upset Plot display the number of flavor compounds specifically identified within a group and the number of flavor compounds collectively identified in different groups. As Fig. 2 D was shown, from Day0 to Day10, the overlap of volatile components exhibited a trend of first decreasing and then increasing, likely reflecting a dynamic adjustment process—characterized by rapid differentiation in the early stage and gradual convergence in the later stage. Specifically, from Day2 to Day4, the number of overlapping components began to decline, indicating a phase of dynamic change in the system with the emergence of numerous specific features. Between Day6 and Day10, the overlap remained relatively stable, with some time points showing a high degree of overlap (e.g., 770ཞ817), suggesting the presence of a stable set of responsive features during the later stage. The identified volatiles were classified into ten major chemical classes based on their structural characteristics: Organoheterocyclic compounds, hydrocarbons, benzenoids, ketones, esters, alcohols, lipids and lipid‑like molecules, heterocyclic compounds, ethers, and carboxylic acids ( Table S1 , Fig. 2 E). Alcohols dominated the early-stage profile, while esters peaked during mid‑storage, and lipid‑derived compounds accumulated progressively at later stages, reflecting the metabolic transition from fresh to senescent tissues. Alcohols were the predominant class at day 0, contributing 23.44% of total volatile content, but their relative abundance steadily declined to 16.22% by Day 10. This decrease, despite relatively stable compound numbers (248ཞ279), indicates that while alcohol diversity is maintained, their quantitative importance diminishes as other classes accumulate. The decline of 1‑octen‑3‑ol, the characteristic mushroom alcohol, likely contributes to the loss of fresh aroma during storage [ 5 ]. Esters exhibited the most dramatic fluctuations. Their relative content surged from 14.36% at Day 0 to 24.40% at Day 4, representing a 7 0% increase, before declining to 21.58% at Day 10. Compound numbers followed a similar pattern, rising from 197 to a peak of 293 at Day 6 (+ 48.7%). This suggests active ester biosynthesis during early to mid‑storage, potentially involving alcohol acyltransferases, with subsequent hydrolysis or metabolic conversion at later stages [ 26 ]. In summary, the volatile profile undergoes systematic succession during storage: from alcohol‑dominant (fresh) to ester‑enriched (mid‑storage) to lipid oxidation‑dominated (late storage), providing the chemical basis for progressive flavor deterioration. The relative content distribution ( Table S2 , Fig. 2 F) provides complementary insights into the quantitative importance of each class. Alcohols dominated the volatile profile at Day 0 (23.44% of total content), but their relative contribution progressively declined to 16.22% by Day 10, despite relatively stable compound numbers. This suggests that while alcohol diversity is maintained, their proportional abundance decreases as other classes accumulate. Esters showed the most striking content dynamics, increasing from 14.36% at Day 0 to 24.40% at Day 4, then declining to 21.58% at Day 10. This pattern mirrors the compound number trend and confirms active ester metabolism during mid‑storage. Ketones exhibited biphasic content patterns, with 16.42% at Day 0, declining to 8.82% at Day 4, then increasing to 18.48% at Day 8. This fluctuation may reflect differential production rates of various ketone families, with early decline of fresh‑related ketones and late accumulation of oxidation‑derived ketones [ 25 ]. Hydrocarbons maintained relatively stable content (7.61 ~ 10.42%) throughout storage, suggesting consistent contribution to the volatile profile. Organoheterocyclic compounds showed relatively stable content (12.68 ~ 17.06%), indicating their persistent importance in the overall volatile profile. 3.3 Evaluation of key odor-active compounds and dynamic sensory profiling based on ROAV To bridge the gap between chemical composition and human olfactory perception, the Relative Odor Activity Value (ROAV) was utilized to evaluate the actual sensory contribution of individual volatile organic compounds (VOCs). Based on the FlavorDB database, a total of 223 VOCs were successfully annotated with their odor thresholds and sensory descriptors ( Table S3 ). However, not all detected volatiles contribute equally to the overall flavor profile. Typically, compounds with an ROAV > 1 are considered key odor-active compounds that dictate the dominant aroma. Following this rigorous criterion, 11 critical odor-active compounds were screened out, encompassing specific aldehydes, ketones, furans, and acids. To visualize the dynamic sensory shifts during the postharvest storage of S. rugosoannulata , a hierarchical clustering heatmap (Fig. 3 A) was constructed based on the ROAVs of these 11 key odorants. The heatmap vividly illustrates a time-dependent transition in the flavor profile, with the compounds clustering into distinct temporal expression patterns corresponding to the deterioration stages. During the early storage period (Day 0–4), the flavor profile was dominated by characteristic “mushroom-like” and “fresh” notes. 1-Octen-3-one, a key contributor to the intrinsic aroma, exhibited high ROAVs initially but diminished significantly during later stages, serving as a primary indicator of freshness loss [ 29 ]. Notably, specific volatiles such as 2-Undecanone (“fresh, green”) and 2,3-Butanedione (“buttery”) reached their peak ROAVs on Day 4. In contrast, the mid-to-late phase (Day 6–10) was marked by the emergence of “fatty” and “rancid” off-flavors. A series of aliphatic aldehydes, including Heptanal, (E)-2-Octenal, and (E)-2-Dodecenal, exhibited elevated ROAVs during this period. The accumulation of these aldehydes is largely attributed to membrane lipid peroxidation mediated by the lipoxygenase (LOX) pathway, a process accelerated during fungal senescence [ 30 , 31 ]. Additionally, the late-stage appearance of 3-methyl-butanoic acid (“putrid/sweaty”) suggests advanced amino acid degradation, potentially linked to the metabolic activity of spoilage microorganisms [ 32 , 33 ]. Collectively, ROAV-based profiling reveals a distinct sensory transition: the dissipation of intrinsic “mushroom/green” notes driven by C8 ketones, followed by the dominance of “fatty/rancid” off-flavors derived from aldehydes and branched-chain acids. These shifts provide precise biomarkers for monitoring organoleptic quality deterioration. 3.4 Correlation network analysis between key flavorome dynamics and macroscopic quality deterioration To elucidate the cross-talk between flavorome dynamics and macroscopic quality deterioration, an integrative correlation network was constructed (Fig. 3 B). The pairwise Pearson heatmap revealed profound co-expression patterns among the 11 key odor-active compounds. Notably, a strong positive correlation ( \(r=0.96\) ) was observed between specific lipid oxidation products and amino acid derivatives, such as (E)-2-Dodecenal and 2-methyl-butanal. Conversely, early-stage intrinsic aroma compounds like 2-pentyl-furan exhibited significant negative correlations with late-stage off-flavor aldehydes (e.g., (E)-2-Octenal, \(r=-0.82\) ). These strong intra-metabolite correlations chemically corroborate the fundamental sensory shift from freshness to severe deterioration. Furthermore, the Mantel test visualized topological associations between these volatile modules and primary physical attributes. Although macroscopic decay is a highly complex process, several biologically meaningful association trends ( \(0.2\ler<0.4\) ) were identified. Specifically, the Browning index (BI) exhibited a distinct association with Heptanal. In edible fungi, both enzymatic browning and the generation of aliphatic aldehydes (like Heptanal) are tightly coupled oxidative stress responses, typically triggered by the loss of cellular compartmentalization and reactive oxygen species (ROS) accumulation [ 34 ]. Additionally, the decline in tissue hardness and increased weight loss showed moderate topological links with (E)-2-Dodecenal, 2-methyl-butanal, and 3-methyl-butanoic acid. This suggests that structural collapse and transpiration actively accelerate the enzymatic degradation of membrane lipids and intracellular amino acids into these specific off-flavors [ 35 , 36 ], providing a theoretical basis for using these VOCs to predict macroscopic shelf-life. 3.5 Multivariate statistical analysis of volatile compound dynamics and identification of key biomarkers 3.5.1 Unsupervised profiling and staging of volatile flavorome during storage To holistically understand the dynamic evolution of the volatile profile in S. rugosoannulata during cold storage, an unsupervised Principal Component Analysis (PCA) was initially performed on the GC×GC-TOF MS filtered dataset including 2,375 components. As shown in Fig. 4 A, the PCA score plot revealed a distinct temporal trajectory of the mushroom samples over the 10-day storage period. The first two principal components, PC1 and PC2, accounted for 14.7% and 11.2% of the total variance, respectively. Notably, the spatial distribution of the samples did not shift uniformly; instead, it clustered into three highly distinct physiological stages, indicating that postharvest flavor deterioration is a phased rather than a constant process. Samples from Day 0 and Day 2 clustered tightly in the upper-left quadrant, representing the fresh state (Stage I). A dramatic migration occurred at Day 4 and Day 6, forming a distinct cluster on the right side of the plot (Stage II), which likely corresponds to a transitional phase characterized by the initiation of rapid metabolic shifts and structural softening. Finally, samples from Day 8 and Day 10 aggregated in the lower-left quadrant (Stage III), marking the severe deterioration and senescence phase. This natural clustering aligns perfectly with the macroscopic quality degradation (hardness loss and browning) observed, justifying the categorization of the continuous storage period into three distinct stages for subsequent in-depth analysis. 3.5.2 Supervised discrimination and PLS-DA model validation To further maximize the separation among the three defined stages and to extract the volatile variables driving these sensory shifts, a supervised Partial Least Squares Discriminant Analysis (PLS-DA) was conducted. The PLS-DA 2D score plot (Fig. 4 B) demonstrated a complete spatial segregation among Stage I, Stage II, and Stage III without any overlap, confirming the profound and stage-specific alterations in the volatile metabolome. The reliability and predictive accuracy of the PLS-DA model were rigorously evaluated using cumulative goodness-of-fit metrics and a permutation test. The model exhibited excellent interpretability and predictive capability, yielding an \({R}^{2}Y\left(cum\right)\) of 0.9540 and a \({Q}^{2}\left(cum\right)\) of 0.8413 (Fig. 4 C). A \({Q}^{2}\) value substantially higher than 0.5 indicates that the model is highly robust for flavorome discrimination. Furthermore, the 200-iteration permutation test yielded regression lines with intercepts of \({R}^{2}=\left(\text{0.0,0.88}\right)\) and \({Q}^{2}=(0.0,-0.37)\) . The strictly negative intercept of \({Q}^{2}\) provides definitive statistical proof that the PLS-DA model is not overfitted, ensuring that the subsequent extraction of biomarker compounds is highly credible. 3.5.3 Identification of critical volatile markers responsible for flavor deterioration To identify the core volatile organic compounds (VOCs) driving postharvest flavor deterioration, a stringent “triple-filter” criterion was employed based on the PLS-DA model: Variable Importance in Projection (VIP) score > 1.0, the lower limit of the 95% Jackknife confidence interval (CI) > 1.0, and False Discovery Rate (FDR) adjusted P < 0.05. This conservative approach eliminates unstable variables and controls false positives, ensuring the screened markers are both statistically significant and biologically reproducible. Filtered result was illustrated in Table S4 . Figure 4 D presents the top 30 most discriminant VOCs ranked by their VIP scores, all of which exhibit relatively short error bars and are annotated with asterisks (*), indicating robust stability and high statistical significance across biological replicates. The most prominent discriminant markers were dominated by specific ketones, esters, and structurally complex hydrocarbons. For instance, long-chain ketones such as 6-Tridecanone (the highest VIP score) and 2-Heptadecanone were identified as major contributors to stage discrimination. Ketones in postharvest mushrooms are primarily generated through the enzymatic degradation and autoxidation of membrane polyunsaturated fatty acids (PUFAs) [ 37 ]. Their massive accumulation typically correlates with the onset of lipid peroxidation during senescence, contributing to the emergence of rancid or “off” notes that mask the fresh mushroom aroma [ 22 ]. Additionally, a significant proportion of the VIP features were esters, including Ethyl cyclohexanepropionate, Propanoic acid derivatives (ethyl esters), and Hexadecanoic acid propyl ester. While esters often impart fruity or sweet aromas, their rapid synthesis during late storage stages (Stage II and III) likely reflects an alteration in energy metabolism. The accumulation of these esters may result from the esterification of free fatty acids and alcohols under increasing cellular stress or mild anaerobic conditions internally as the mushroom tissues collapse [ 38 ]. Interestingly, several complex cyclic and aromatic compounds, such as Naphthalene derivatives (e.g., 1,2,3,4-tetrahydro-5-methyl-1-(1-methylethyl)-naphthalene) and 2-(4-Methoxyphenyl)ethanol, also ranked highly. In summary, the flavoromics analysis reveals that the sensory deterioration of S. rugosoannulata is not merely the dissipation of typical eight-carbon (C8) fresh aroma compounds, but is heavily driven by the active accumulation of lipid oxidation products (ketones/hydrocarbons) and stress-induced metabolites (esters). These Top 30 VIP features can serve as reliable, multivariate quantitative indicators for real-time quality monitoring and shelf-life prediction of S. rugosoannulata during commercial cold chain logistics. 3.6 Screening and characterization of stage-specific and continuous differential volatile markers 3.6.1 Global overview of volatile flux during phase transitions To quantify the magnitude of flavorome alterations during postharvest cold storage, univariate statistical analysis (Volcano plots) was employed to visualize the significantly upregulated and downregulated VOCs across the two critical phase transitions. As depicted in Fig. 5 A, the transition from Stage I to Stage II (early deterioration) triggered massive metabolic shifts, with 344 compounds significantly upregulated and 203 downregulated (|log₂FC| > 1.0, p < 0.05). Similarly, the transition from Stage II to Stage III (late senescence) resulted in 357 upregulated and 232 downregulated compounds (Fig. 5 B). Notably, in both transition phases, the number of upregulated VOCs substantially exceeded the downregulated ones. Biologically, this suggests that postharvest flavor deterioration in S. rugosoannulata is not merely the passive dissipation of intrinsic fresh aromas, but rather an active, senescence-driven biochemical process. The breakdown of cellular structures and macromolecules (e.g., lipids and proteins) leads to the massive de novo synthesis and accumulation of secondary volatile metabolites (off-flavors). 3.6.2 Stage-specific markers: From early esterification to severe lipid oxidation To robustly identify the most reliable biomarkers, a cross-validation strategy was applied using Venn diagrams to intersect the significant univariate features (up/down-regulated) with the rigorously screened multivariate PLS-DA features (VIP > 1, CI > 1, FDR < 0.05, Table S4 ). As highlighted by the yellow dashed circles in Fig. 5 C &D (Datasets illustrated in Table S5 ), stage-specific markers were successfully isolated. During this initial phase, several esters (e.g., n-Propyl acetate, Butanoic acid 3-methyl- propyl ester, and L- Leucine ethyl ester) and alkenes (e.g., 1-Decene) were significantly up-regulated. The early burst of esters is typically associated with the esterification of free fatty acids and amino acid catabolism under mild hypoxic stress or energy depletion as the mushroom continues to respire post-harvest. Conversely, several aldehydes (Nonanal, Dodecanal) and heterocyclic compounds (Furan, Pyrazine) were initially down-regulated in this phase, reflecting the disruption of the native flavor equilibrium and the rapid degradation of specific precursors responsible for the fresh mushroom profile. Conversely, as storage progressed to severe structural collapse (Stage II vs Stage III), the volatile profile underwent a dramatic secondary shift. Notably, straight-chain aldehydes that were initially suppressed, such as Nonanal, Undecanal, Dodecanal, and Tridecanal, experienced a massive resurgence and were identified as key up-regulated markers for late senescence. This “aldehyde burst” is a classic hallmark of severe lipid peroxidation, occurring when cellular compartmentalization is completely lost, allowing lipoxygenases (LOX) to violently attack membrane polyunsaturated fatty acids [ 39 , 40 ]. 3.6.3 Continuous biomarkers driving the entire deterioration continuum To identify the ultimate “time-temperature integrators” that continuously evolve throughout the entire shelf-life, we focused on the central intersection of the Venn diagrams (red dashed circles in Fig. 5 C &D ), representing compounds that strictly passed the PLS-DA screening and showed significant unidirectional changes across all stage comparisons (Stage I vs II, Stage II vs III, and Stage I vs III). Remarkably, only 7 compounds were identified as continuous, progressive up-regulated biomarkers: n-Caprylic acid isobutyl ester, 2-(4-Methoxyphenyl)ethanol, 1-hexyl-Cyclopentene, 2-Heptadecanone, 3-(4-Methoxyphenyl)propionic acid ethyl ester, Methoxyacetic acid hexyl ester, and Butanedioic acid diethyl ester. Among these, 2-Heptadecanone stands out as a critical continuous marker of lipolysis, while the accumulation of heavy esters and aromatic alcohols reflects continuous complex secondary metabolism driven by senescence and stress [ 41 ]. Concurrently, only 1 compound, 3-ethenyl-Pyridine, was identified as a continuous down-regulated progressive marker. These 8 core continuous biomarkers act as the molecular ticking clock, which provide the most reliable, time-independent chemical signatures for predicting the postharvest freshness and shelf-life of S. rugosoannulata . 4. Conclusion This study systematically elucidated the postharvest quality deterioration and volatile compound dynamics in S. rugosoannulata during cold storage using a data‑driven flavoromics approach. Comprehensive quality assessment revealed that storage induced progressive weight loss, biphasic texture changes (initial hardening followed by softening), and continuous browning, with enzymatic browning identified as an early and sensitive indicator of quality decline. HS‑SPME‑GC×GC‑TOF MS profiling enabled the detection of nearly 10,000 volatile compounds, demonstrating a systematic succession in the volatile profile from alcohol‑dominant (fresh) to ester‑enriched (mid‑storage) and finally to lipid oxidation‑dominated (late storage) patterns. ROAV analysis identified 11 key odor‑active compounds driving sensory transitions, with the dissipation of C8 mushroom‑like notes (e.g., 1‑octen‑3‑one) and the emergence of fatty/rancid aldehydes (e.g., heptanal, (E)‑2‑octenal) marking the critical shift from freshness to off‑flavor development. Integrative correlation networks revealed significant associations between volatile dynamics and macroscopic quality attributes, particularly linking aldehyde accumulation with browning and softening processes. Multivariate statistical modeling (PCA/PLS‑DA) delineated three distinct physiological stages during storage, and rigorous triple‑filter screening (VIP > 1, 95% CI > 1, FDR < 0.05) identified robust stage‑specific and continuous volatile biomarkers. Notably, seven continuously upregulated compounds (including 2‑heptadecanone and various esters) were validated as progressive indicators of senescence, serving as molecular “ticking clocks” for freshness monitoring. Collectively, these findings provide a comprehensive understanding of the flavor deterioration mechanisms in postharvest S. rugosoannulata and establish a set of reliable volatile markers for real‑time quality assessment. The integration of flavoromics with conventional quality metrics offers a powerful framework for developing targeted preservation strategies and predicting shelf‑life in edible fungi during cold chain logistics. Future studies should focus on validating these biomarkers across different cultivars and storage conditions, as well as exploring their biosynthetic pathways to enable precision postharvest management. Declarations Competing interests The authors declare no competing interests. Author Contribution Wanchao Chen: Methodology, Software, Visualization, Funding, and Writing-review & editing. Junying Zhu: Data curation, Formal analysis, and Software. Wen Li: Methodology, Supervision, and Validation. Di Wu: Conceptualization, and Validation. Zhong Zhang: Methodology, Conceptualization, and Software. Peng Liu: Methodology and Supervision. Yan Yang: Conceptualization, Writing-review & editing, Supervision, and Project administration. Acknowledgement This work was supported financially by Shanghai Agricultural Science and Technology Innovation Program (Grant No. T2024201), and the Food Nutrition and Health Research Center of Shanghai Academy of Agricultural Sciences (2025-001). Data Availability Included in the Supplementary Information. References Huang, L., et al. Nutritional, Bioactive, and Flavor Components of Giant Stropharia (Stropharia rugoso-annulata): A Review . Journal of Fungi, 2023. 9: 792. Wang, Y., et al. Difference in Volatile Aroma Components of Stropharia rugosoannulata under Two Cultivated Environments Investigated by SPME-GC-MS . Foods, 2023. 12: 2656. Zhu, R., et al., The flavors of edible mushrooms: A comprehensive review of volatile organic compounds and their analytical methods . 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Supplementary Files Supplementarymaterial.docx TableS3ROAVDataIntegrationTable.xlsx TableS4DatasetsfilteredbyVIPCIandFDRbasedonPLSDA.xlsx TableS5DatasetsforVennePlotofFigure4.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 16 Mar, 2026 Editor assigned by journal 16 Mar, 2026 Submission checks completed at journal 16 Mar, 2026 First submitted to journal 15 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9128298","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607270734,"identity":"c75bc116-9e58-4766-b636-56fb8cac6c54","order_by":0,"name":"Wanchao Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDACCShtwMDA+ADC5CFeC7MByVrYJIjSwj+7+dhjnpo78ubsh49V/qixs2fgP3uA4ecOPJbcOZZuzHPsmeHOnrS02zzHkhMbJPISGHvP4NZiIJFjJp3DdjjB4ECO2W0GNuYEBgkeA2bGNnxa8r9J5/wDajn/xqzwx796oMPOENKSwyad2wbUciPHjIG37TBjA0MOfi0SN9LMpP/2HTbccONZsjRv3/HENokcg4O9eLTwz0h+Jjnj22F5g/PJBz/++FZtz89/xvDBTzxaMAEbEB8gRcMoGAWjYBSMAkwAAD6+TO3yZ+OKAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Wanchao","middleName":"","lastName":"Chen","suffix":""},{"id":607270735,"identity":"a515320c-62df-42b5-ace0-6ace4702f0d0","order_by":1,"name":"Junying Zhu","email":"","orcid":"","institution":"Shanghai Agricultural Science and Technology Service Center","correspondingAuthor":false,"prefix":"","firstName":"Junying","middleName":"","lastName":"Zhu","suffix":""},{"id":607270736,"identity":"41490490-c88a-4ab9-ae2f-2f845d94ef01","order_by":2,"name":"Wen Li","email":"","orcid":"","institution":"Shanghai Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Li","suffix":""},{"id":607270738,"identity":"89604a10-d5c0-4c97-b8f6-cff46121035b","order_by":3,"name":"Di Wu","email":"","orcid":"","institution":"Shanghai Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Wu","suffix":""},{"id":607270740,"identity":"a91cdf2f-4ba3-4181-b0bb-a4fb6dd2f497","order_by":4,"name":"Zhong Zhang","email":"","orcid":"","institution":"Shanghai Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhong","middleName":"","lastName":"Zhang","suffix":""},{"id":607270741,"identity":"56d7f84e-b345-4a63-8bff-0e915185ecb8","order_by":5,"name":"Peng Liu","email":"","orcid":"","institution":"Shanghai Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Liu","suffix":""},{"id":607270743,"identity":"78aa1d04-646f-4f2f-b089-46f7f6dc56f9","order_by":6,"name":"Yan Yang","email":"","orcid":"","institution":"Shanghai Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2026-03-15 11:39:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9128298/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9128298/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104894226,"identity":"4a9c94ab-ff18-4ed7-8a88-c81995bda36b","added_by":"auto","created_at":"2026-03-18 11:26:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4225916,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in appearance, weight loss, hardness, color parameters, and browning index of postharvest \u003cem\u003eStropharia rugosoannulata\u003c/em\u003e during 10 days of storage\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9128298/v1/a41cdf499b39081b6e1c5981.png"},{"id":104894254,"identity":"36fc896f-0932-4133-af13-a86ddaaf5c41","added_by":"auto","created_at":"2026-03-18 11:27:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3470236,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative 1D (A), 2D (B) and 3D (C) total ion chromatogram of \u003cem\u003eS. rugosoannulata\u003c/em\u003e at Day 4; (D) Bar chart showing total number of volatile compounds identified at each storage time point and UpSet Plot shared and unique volatile compounds across six storage time points; (E) Stacked bar chart showing distribution of chemical classes at each time point; (F) Radar showing relative content distribution of chemical classes at each time point.\u003c/p\u003e","description":"","filename":"FIgure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9128298/v1/bf77c0f5ba80ef3889c64af7.png"},{"id":104894211,"identity":"8722126c-8ae3-499d-97b1-ecc4c51411c8","added_by":"auto","created_at":"2026-03-18 11:26:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2339510,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Hierarchical clustering heatmap of key odor-active compounds (ROAV \u0026gt; 1) illustrating the dynamic sensory transitions in \u003cem\u003eS. rugosoannulata\u003c/em\u003e during cold storage. (B) Integrative correlation network analysis between key odor-active compounds and macroscopic quality (weight loss rate, hardness, and browning index) attributes.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9128298/v1/5005c02de7fa6ebd3508a324.png"},{"id":104894216,"identity":"b8cedd11-39f7-47ec-8461-b7b88421718a","added_by":"auto","created_at":"2026-03-18 11:26:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2143092,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate flavoromics profiling and identification of key volatile biomarkers in \u003cem\u003eS. rugosoannulata\u003c/em\u003e during postharvest cold storage. (A) PCA score plot showing the natural clustering of samples into three distinct physiological stages (Stage I, II, and III) over a 10-day storage period. (B) PLS-DA score plot demonstrating clear separation among the three predefined deterioration stages. (C) Validation of the PLS-DA model through 200 permutation tests. The plot displays the regression lines for R\u003csup\u003e2\u003c/sup\u003e (green circles) and Q\u003csup\u003e2\u003c/sup\u003e (blue squares) along with cumulative goodness-of-fit parameters (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eX\u003c/em\u003e, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eY\u003c/em\u003e) and predictive ability (\u003cem\u003eQ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e). (D) The Top 30 critical differential volatile organic compounds ranked by VIP scores from the PLS-DA model. The red dashed line indicates the VIP threshold of 1.0. Error bars represent the 95% confidence intervals, and asterisks (*) denote statistical significance based on \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9128298/v1/85a0329477149aee8e0462de.png"},{"id":104894214,"identity":"3f667175-5938-4c37-9e84-f62904214b3b","added_by":"auto","created_at":"2026-03-18 11:26:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2624609,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate and multivariate integration revealing stage-specific and progressive volatile biomarkers of \u003cem\u003eS. rugosoannulata\u003c/em\u003e during storage. Volcano plots illustrating the global univariate changes of volatile compounds during the transition from Stage I to Stage II (A) and from Stage II to Stage III (B). Venn diagrams integrating the univariate differential compounds with the robust multivariate markers. Panel (C) displays the overlap of up-regulated compounds, while panel (D) displays down-regulated compounds.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9128298/v1/6c8186e2f633495e489e5f1b.png"},{"id":105034329,"identity":"4c700494-5535-454e-a301-c2556c24e22a","added_by":"auto","created_at":"2026-03-20 07:23:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16453299,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9128298/v1/56747fab-d528-499d-9409-60054edb34b2.pdf"},{"id":104894248,"identity":"bc7f58d6-41ec-42d6-9aeb-d527cbcd4fd4","added_by":"auto","created_at":"2026-03-18 11:27:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19556,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9128298/v1/59e32916bb325a960fa51c13.docx"},{"id":104894236,"identity":"e1413ab3-eda8-4e86-a44c-1f5c44458b91","added_by":"auto","created_at":"2026-03-18 11:26:52","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":38824,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3ROAVDataIntegrationTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9128298/v1/322bb92240e2ea9222c1da78.xlsx"},{"id":104894212,"identity":"ddce0aa3-3646-4ca4-ac56-97aef5f109ed","added_by":"auto","created_at":"2026-03-18 11:26:38","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":29556,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4DatasetsfilteredbyVIPCIandFDRbasedonPLSDA.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9128298/v1/309edb7df6d73350549ac999.xlsx"},{"id":104894259,"identity":"4562e3e2-e65d-415e-a69f-38e6aeb734d0","added_by":"auto","created_at":"2026-03-18 11:27:05","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":45632,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5DatasetsforVennePlotofFigure4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9128298/v1/6fbf5394b271a72e43e8760d.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Data-driven flavoromics reveals postharvest quality deterioration and volatile compound dynamics in Stropharia rugosoannulata mushroom during cold storage","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003eStropharia rugosoannulata\u003c/em\u003e, commonly known as wine cap or garden giant mushroom, has been recognized by the Food and Agriculture Organization of the United Nations as a recommended edible mushroom for global consumption due to its exceptional nutritional and culinary properties [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This species has garnered increasing economic significance worldwide, with its cultivation expanding rapidly across Asia, Europe, and North America. The sensory qualities of \u003cem\u003eS. rugosoannulata\u003c/em\u003e, particularly its characteristic earthy aroma, umami taste, and firm yet tender texture\u0026mdash;are fundamental determinants of consumer acceptance and market competitiveness. The distinctive flavor profile of fresh \u003cem\u003eS. rugosoannulata\u003c/em\u003e is primarily attributed to its volatile organic compounds (VOCs), with eight-carbon compounds such as 1-octen-3-ol (named as mushroom alcohol) serving as key aroma contributors, alongside various aldehydes, ketones, and alcohols that collectively orchestrate its appreciated organoleptic properties [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, this mushroom is remarkably rich in flavor-active components including free amino acids, 5'-nucleotides, and soluble sugars, which synergistically contribute to its pronounced umami taste and overall flavor complexity.\u003c/p\u003e \u003cp\u003eHowever, the postharvest storage of \u003cem\u003eS. rugosoannulata\u003c/em\u003e presents significant challenges due to its inherently high metabolic activity and moisture content (80\u0026ndash;90%), rendering it highly perishable and susceptible to rapid quality deterioration [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. During storage, a cascade of interconnected physiological and biochemical changes occurs, including continuous respiration, transpiration, and senescence processes that manifest as visible quality degradation. Recent advances in understanding edible fungi postharvest biology have elucidated that quality deterioration encompasses four interrelated aspects: physical damage (water loss, temperature fluctuations, mechanical injury), physiological changes (ongoing respiration, texture softening, nutrient depletion), biochemical reactions (enzymatic browning, off-flavor development, degradation of bioactive compounds), and microbial spoilage (colonization by Pseudomonas, Enterobacter, and fungal pathogens) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Studies specifically on \u003cem\u003eS. rugosoannulata\u003c/em\u003e have demonstrated that during ambient storage, firmness declines progressively while browning index increases, with these changes closely correlated to oxidative damage indicators and the imbalance of reactive oxygen species metabolism[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The application of low-temperature storage combined with modified atmosphere packaging has been shown to maintain antioxidant enzyme activities and delay quality deterioration [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Concurrent with these physical and biochemical changes, profound alterations in volatile compound profiles drive flavor deterioration, the characteristic fresh mushroom aroma gradually diminishes while off-flavors emerge, with lipid oxidation pathways generating aldehydes and ketones associated with rancid and hay-like notes. Research on spoilage microorganisms affecting \u003cem\u003eS. rugosoannulata\u003c/em\u003e has identified that Fusarium, Aspergillus, and Rhizopus species are primary decay-causing fungi, with optimal growth temperatures of 25\u0026thinsp;~\u0026thinsp;28 ℃ and inhibition at low temperatures (4\u0026thinsp;~\u0026thinsp;10 ℃), providing critical insights for storage optimization [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite these advances, the dynamic evolution of volatile compounds throughout the entire postharvest period and their quantitative relationships with instrumental quality parameters remain incompletely characterized, limiting the development of targeted preservation strategies.\u003c/p\u003e \u003cp\u003eThe advent of advanced analytical platforms and computational methods has revolutionized the study of food flavor systems. Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC\u0026times;GC-TOF MS) represents the state-of-the-art in volatile analysis, offering substantially enhanced separation capacity, peak resolution, and sensitivity compared to conventional one-dimensional GC-MS systems [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This technological advancement enables the detection and identification of hundreds to thousands of volatile compounds in complex food matrices, providing an unprecedented view of the flavorome. Recent applications of GC\u0026times;GC-TOF MS in food research have demonstrated its exceptional capability for comprehensive VOC profiling, with studies identifying up to 198 volatile compounds in grilled meat products and revealing subtle compositional differences between samples [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Flavoromics, an approach that comprehensively profiles volatile compounds using high-resolution instrumentation coupled with multivariate data analysis, enables the holistic characterization of flavor-active molecules and their dynamic changes during food processing and storage [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the growing application of flavoromics in food science, a comprehensive understanding of the relationship between macroscopic quality deterioration (e.g., enzymatic browning) and microscopic volatile dynamics in \u003cem\u003eS. rugosoannulata\u003c/em\u003e during cold storage is still lacking. To bridge this gap, this study applied a data-driven flavoromics approach to investigate the postharvest changes in \u003cem\u003eS. rugosoannulata\u003c/em\u003e. The specific objectives were to: (1) evaluate the postharvest quality deterioration by monitoring surface color changes and the browning index; (2) characterize the dynamic evolution of VOCs during cold storage using HS-SPME-GC\u0026times;GC-TOF MS; and (3) identify the key characteristic volatile markers associated with storage time utilizing ROAV and multivariate statistical analyses. The findings of this study will provide profound theoretical insights into the flavor deterioration mechanisms of \u003cem\u003eS. rugosoannulata\u003c/em\u003e and offer data-driven guidance for developing targeted postharvest preservation strategies.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample collection and storage conditions\u003c/h2\u003e \u003cp\u003eFresh fruiting bodies of \u003cem\u003eS. rugosoannulata\u003c/em\u003e (Variety: Huqiu No.5) were harvested at commercial maturity from Shanghai Maqiao Edible Fungi Planting Base during May 2025. Samples with similar maturity, and absence of mechanical damage or visible defects were selected for the experiment. Immediately after harvesting, the mushrooms were transported to the laboratory under refrigerated conditions (4 ℃) within 2 h.\u003c/p\u003e \u003cp\u003eUpon arrival, the samples were randomly divided into six groups corresponding to storage time points: 0, 2, 4, 6, 8, and 10 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Each time point included 4 biological replicates, with approximately 150 g of mushrooms per replicate. All samples were stored at 5\u0026thinsp;\u0026plusmn;\u0026thinsp;1 ℃ and 85\u0026thinsp;\u0026plusmn;\u0026thinsp;5% RH in darkness to simulate ambient storage conditions, following established protocols for postharvest mushroom storage studies. At each designated time point, samples were randomly withdrawn for quality attribute measurements and volatile compound analysis. For volatile analysis, fresh samples were immediately frozen in liquid nitrogen and stored at -80 ℃ until further processing to minimize enzymatic and oxidative changes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Chemicals and internal standard preparation\u003c/h2\u003e \u003cp\u003eEthanol (99.8% purity) was purchased from Aladdin (Shanghai, China). n-Hexane (GR grade) was obtained from Yonghua (Shanghai, China). The internal standard, n-Hexyl-d13 Alcohol (98.5% purity), was supplied by C/D/N Isotopes INC (Quebec, Canada). A series of n-alkanes (C7\u0026ndash;C30, 1000 mg/L) for retention index calculation was purchased from Sigma-Aldrich (St. Louis, MO, USA). Ultrapure water was prepared using a Milli-Q Direct-8 purification system (Millipore, Bedford, MA, USA).\u003c/p\u003e \u003cp\u003eFor internal standard solution preparation, a stock solution of n-Hexyl-d13 Alcohol (1 mg/L) was prepared in 50% ethanol aqueous solution and stored at 4 ℃ until use. Similarly, a stock solution of n-alkanes (1 mg/L) was prepared by serial dilution in n-hexane and stored at 4 ℃.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Quality attribute measurements\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Weight loss\u003c/h2\u003e \u003cp\u003eWeight loss was determined gravimetrically by measuring the mass of each sample before storage and at each sampling time point. The weight loss percentage was calculated according to the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$Weightloss\\left(\\%\\right)={100\\times(W}_{0}-{W}_{t})/{W}_{0}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e represents the initial weight and \u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e represents the weight at each storage time point.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Texture analysis\u003c/h2\u003e \u003cp\u003eTexture properties of \u003cem\u003eS. rugosoannulata\u003c/em\u003e fruiting bodies were measured using a texture analyzer (TA new plus, ISENSO, USA) equipped with a cylindrical P/0.5 probe. A texture profile analysis (TPA) test was performed following established methods for edible fungi with minor modifications [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Samples were taken from the central region of the mushroom cap to ensure uniformity. The specific settings were as follows: pre-test speed 1.0 mm/s, test speed 1.0 mm/s, post-test speed 1.0 mm/s, trigger force 5.0 g, compression strain 50%, and a time interval of 5 s between two compressions. From the force\u0026ndash;time curves, hardness (maximum force during first compression, N) were calculated using the instrument's software. Each measurement was performed with 10 replicates per sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Color different measurement\u003c/h2\u003e \u003cp\u003eSurface color of \u003cem\u003eS. rugosoannulata\u003c/em\u003e caps was measured using a chromameter (CS-580, Hangzhou Color Spectrum Technology Co., Ltd., China) calibrated with a standard white plate. The CIE \u003cem\u003eL\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ea\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eb\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e color space was employed, where \u003cem\u003eL\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e represents lightness (0\u0026thinsp;=\u0026thinsp;black, 100\u0026thinsp;=\u0026thinsp;white), \u003cem\u003ea\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e represents red-green chromaticity (positive\u0026thinsp;=\u0026thinsp;red, negative\u0026thinsp;=\u0026thinsp;green), and \u003cem\u003eb\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e represents yellow-blue chromaticity (positive\u0026thinsp;=\u0026thinsp;yellow, negative\u0026thinsp;=\u0026thinsp;blue) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Measurements were taken at three equidistant points on the cap surface of each mushroom, and the mean values were recorded. Browning index (BI) was calculated according to the following equation to quantify the degree of enzymatic browning [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$BI=\\left[100\\times\\left(x-0.31\\right)\\right]/0.17$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(x=({a}^{*}+1.75{L}^{*})/(5.645{L}^{*}+{a}^{*}-3.012{b}^{*})\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Volatile compound analysis by GC\u0026times;GC-TOF MS\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Sample preparation and headspace solid-phase microextraction (HS-SPME)\u003c/h2\u003e \u003cp\u003eVolatile compounds were extracted using headspace solid-phase microextraction (HS-SPME) following the methods reported by Li et. al.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] with modifications. Frozen mushroom samples were ground into fine powder in liquid nitrogen. An aliquot of 2.0 g powder was accurately weighed into a 20 mL headspace vial, and 10 \u0026micro;L of the internal standard solution (n-Hexyl-d13 Alcohol, 1 mg/L) was added. The vial was immediately sealed with a PTFE-silicone septum.\u003c/p\u003e \u003cp\u003ePrior to extraction, the SPME fiber coated with 50/30 \u0026micro;m DVB/CAR/PDMS (divinylbenzene/carboxen/polydimethylsiloxane, 1 cm length, Supelco, Bellefonte, PA, USA) was preconditioned in the GC injection port at 270 ℃ for 10 min. Samples were equilibrated at 80 ℃ for 10 min with continuous agitation. The preconditioned fiber was then exposed to the headspace of the sample vial for 25 min at 80 ℃ to adsorb volatile compounds. After extraction, the fiber was immediately inserted into the GC injection port for thermal desorption at 250 ℃ for 5 min in splitless mode. Following each injection, the fiber was reconditioned at 270 ℃ for 10 min to eliminate any carry-over effects. For retention index calculation, 10 \u0026micro;L of the n-alkane solution (1 mg/L) was transferred to a separate 20 mL headspace vial and analyzed under identical conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 GC\u0026times;GC-TOF MS analysis\u003c/h2\u003e \u003cp\u003eComprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC\u0026times;GC-TOF MS) analysis was performed using a LECO Pegasus BT 4D system (LECO Corporation, St. Joseph, MI, USA), which consists of an Agilent 8890A gas chromatograph (Agilent Technologies, Palo Alto, CA, USA) equipped with a dual-stage cryogenic modulator and a high-resolution TOF mass spectrometer. This platform offers superior separation capacity, high sensitivity, and excellent reproducibility for complex sample analysis, with an acquisition rate of up to 500 full spectra per second.\u003c/p\u003e \u003cp\u003eThe first-dimensional column was a DB-Heavy Wax column (30 m \u0026times; 250 \u0026micro;m i.d., 0.5 \u0026micro;m film thickness, Agilent Technologies, USA), and the second-dimensional column was an Rxi-5Sil MS column (2.0 m \u0026times; 150 \u0026micro;m i.d., 0.15 \u0026micro;m film thickness, Restek Corporation, USA). High-purity helium (\u0026ge;\u0026thinsp;99.999%) was used as the carrier gas at a constant flow rate of 1.0 mL/min.\u003c/p\u003e \u003cp\u003eThe GC oven temperature program was as follows: initial temperature 50 ℃ held for 2 min; increased at 5 ℃/min to 230 ℃ and held for 5 min. The second-dimension oven temperature was maintained 5 ℃ higher than the main oven throughout the analysis. The modulator temperature was offset by 15 ℃ relative to the second-dimension column temperature, with a modulation period of 6.0 s. The GC injector temperature was maintained at 250 ℃.\u003c/p\u003e \u003cp\u003eMass spectrometric conditions were as follows: electron ionization (EI) mode at 70 eV; ion source temperature: 250 ℃; transfer line temperature: 250 ℃; detector voltage: 1960 V; mass acquisition range: m/z 35\u0026thinsp;~\u0026thinsp;550; acquisition rate: 200 spectra/s, which is consistent with the high acquisition rate capability of TOF MS for comprehensive two-dimensional separations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Compound identification and quantification\u003c/h2\u003e \u003cp\u003eData acquisition and processing were performed using ChromaTOF software (version 4.51, LECO Corporation, USA). Raw data were subjected to deconvolution, peak alignment, and compound identification. The deconvolution process enables the separation of co-eluting compounds and improves the accuracy of identification in complex chromatographic data. Compounds were identified by comparing their mass spectra with those in the NIST 20 and Wiley 9 mass spectral libraries, combined with retention index (RI) matching. RI values were calculated using the series of n-alkanes (C7\u0026thinsp;~\u0026thinsp;C30) analyzed under identical chromatographic conditions. Semi-quantification was performed using the internal standard method following established protocols for volatile analysis in food matrices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Odor activity value (OAV) calculation\u003c/h2\u003e \u003cp\u003eTo evaluate the contribution of individual volatile compounds to the overall aroma profile, odor activity values (OAVs) were calculated as the ratio of the concentration of each compound to its odor threshold in water. The relative odor activity value (ROAV) of volatile compounds is a parameter used to determine key flavor compounds. Generally, the larger the ROAV, the greater the contribution of the substance to flavor [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Compounds with ROAV\u0026thinsp;\u0026ge;\u0026thinsp;1 were considered as aroma-active contributors to the mushroom's flavor profile.\u003c/p\u003e \u003cp\u003eDefine a component that contributes the most to the overall flavor of the sample, with a ROAV of 100. The component with the highest contribution is defined as the substance with the highest normalized quantification value/odor min threshold. The ROAV of other substances is calculated based on the ROAV of the component with the highest contribution, using the formula:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$ROAV\\left(A\\right)=100\\times\\left[RelativeConten\\right(A)/T(A\\left)\\right]/\\left[RelativeContent\\right(stan)/T(stan\\left)\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere Relative Content(A) is the normalized quantitative value of the test substance; T(A) is the odor min of the substance to be tested; Relative Content(stan) is the normalized quantitative value of substance with ROAV\u0026thinsp;=\u0026thinsp;100; T(stan) is the odor min of substance with ROAV\u0026thinsp;=\u0026thinsp;100.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll experiments were performed with at least three biological replicates, and results were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Statistical analyses were conducted using Python 3.9. One-way analysis of variance (ANOVA) followed by Tukey's honestly significant difference (HSD) test was applied to evaluate significant differences among storage time points. Differences were considered statistically significant at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Multivariate statistical analysis\u003c/h2\u003e \u003cp\u003eMetabolites detected in no more than two replicates within each storage stage (i.e., present in \u0026le;\u0026thinsp;50% of samples per group) were excluded from further analysis to reduce the influence of low-confidence and poorly reproducible features. To visualize the overall distribution patterns and clustering of samples based on volatile profiles, principal component analysis (PCA) was performed using MetaboAnalyst 6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with Normalization (None\u0026thinsp;+\u0026thinsp;Log transformation base 10\u0026thinsp;+\u0026thinsp;Pareto scaling). Partial least squares discriminant analysis (PLS-DA) were conducted using the pandas, numpy, matplotlib, seaborn, scikit-learn, scipy, and statsmodels package of Python to maximize the separation among different storage time points and identify discriminant volatile compounds [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Model validation was performed using 200 permutation tests to prevent overfitting. The goodness-of-fit parameters (R\u0026sup2;X, R\u0026sup2;Y) and predictive ability parameter (Q\u0026sup2;) were calculated. Variable importance in projection (VIP) scores was obtained from the PLS-DA model, and compounds with VIP\u0026thinsp;\u0026gt;\u0026thinsp;1.0, 95% CI (confidence interval, Jackknife cross validation method)\u0026thinsp;\u0026gt;\u0026thinsp;1.0 and FDR (false discovery rate, Benjamini-Hochberg method) adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered as significant contributors to group discrimination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Differential volatile compound screening\u003c/h2\u003e \u003cp\u003eDifferential volatile compounds between storage time points were identified using a combination of statistical criteria: (1) fold change (FC)\u0026thinsp;\u0026ge;\u0026thinsp;2 or \u0026le;\u0026thinsp;0.5 (i.e., |log₂FC| \u0026ge; 1); (2) \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 from Student's t-test or ANOVA; and (3) VIP\u0026thinsp;\u0026gt;\u0026thinsp;1.0 from PLS-DA [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Volcano plots were generated to visualize the distribution of differential compounds using the pandas, numpy, scipy, and matplotlib package by Python 3.9. Venne plots were constructed using the matplotlib-venn package to display the expression patterns of differential compounds across the samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4 Correlation analysis\u003c/h2\u003e \u003cp\u003ePearson correlation coefficients were calculated to evaluate the relationships between volatile compounds and quality attributes (weight loss, texture parameters, and color indices) using the WeiShengXin bioinformatics analysis platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.com.cn/\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Correlation matrices were visualized as heatmaps, with significance levels indicated (\u0026lowast; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026lowast;\u0026lowast; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u0026lowast;\u0026lowast;\u0026lowast; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Dynamic changes in quality attributes during postharvest storage\u003c/h2\u003e \u003cp\u003eThe evolution of physical quality attributes, including appearance, weight loss, texture (hardness), and surface color parameters, was systematically monitored in \u003cem\u003eS. rugosoannulata\u003c/em\u003e fruiting bodies over a 10-day storage period under ambient conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These attributes collectively serve as critical indicators of postharvest freshness, consumer acceptability, and overall marketability of edible fungi.\u003c/p\u003e \u003cp\u003eWeight loss of \u003cem\u003eS. rugosoannulata\u003c/em\u003e increased progressively throughout the 10-day storage period, with significant differences observed among all time points (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). After 2 days, cumulative weight loss was 0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02%, which gradually increased to 0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02% by Day 8, followed by a sharp rise to 1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05% at Day 10, representing a 3.7-fold increase from Day 2. This acceleration during later storage stages reflects progressive cellular membrane deterioration and loss of compartmentalization integrity, facilitating unregulated water efflux [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The thin epidermal structure and absence of a protective cuticle in the fruiting bodies render them particularly susceptible to moisture loss, consistent with observations in other cultivated mushrooms [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTexture deterioration exhibited a distinct biphasic pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Hardness increased significantly from 684.57\u0026thinsp;\u0026plusmn;\u0026thinsp;24.04 g at Day 0 to 725.08\u0026thinsp;\u0026plusmn;\u0026thinsp;25.47 g at Day 2 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), representing a 5.9% increase. Subsequently, hardness declined progressively, reaching 563.73\u0026thinsp;\u0026plusmn;\u0026thinsp;20.57 g at Day 10, a 17.7% decrease from initial values and 22.3% decrease from the Day 2 peak. No significant difference was observed between Day 8 and Day 10 values (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that softening reached a plateau during the final storage stage. The fruiting bodies experience an increase in hardness during the early stages of storage, followed by a decrease, possibly due to tissue fibrosis and lignification [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The subsequent progressive softening may be attributed to the enzymatic degradation of cell wall structural polysaccharides, such as chitin and glucans, by endogenous hydrolases, as reported in other mushroom species[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eColor evolution during storage revealed progressive browning of \u003cem\u003eS. rugosoannulata\u003c/em\u003e fruiting bodies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD\u003cb\u003e\u0026amp;E\u003c/b\u003e). Lightness (\u003cem\u003eL\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e) values showed no significant differences among days 0\u0026ndash;8 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), but decreased significantly from 51.08\u0026thinsp;\u0026plusmn;\u0026thinsp;11.29 at Day 8 to 37.08\u0026thinsp;\u0026plusmn;\u0026thinsp;8.52 at Day 10 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), representing a 16.8% decrease from initial values. Redness (\u003cem\u003ea\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e) values increased continuously throughout storage, with significant increases observed after Day 4 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Values rose from 7.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16 at Day 0 to 16.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03 at Day 10, representing a 2.1-fold increase. Days 4ཞ6 showed the most substantial increment (10.25 to 14.08). Yellowness (\u003cem\u003eb\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e)\u003c/sup\u003e values exhibited a dramatic increase from Day 0 to Day 8, rising from 2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05 to 31.92\u0026thinsp;\u0026plusmn;\u0026thinsp;11.69 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), a 16-fold increase, followed by a modest decline to 24.50\u0026thinsp;\u0026plusmn;\u0026thinsp;9.17 at Day 10. Day 8 showed significantly higher \u003cem\u003eb\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e values compared to all earlier time points (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The substantial increase in \u003cem\u003eb\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e during mid-storage indicates accumulation of yellow-brown intermediate pigments, while the late-stage decline may reflect their polymerization into darker melanin complexes that shift the chromaticity toward redder tones [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBI integrates all three color parameters to provide a comprehensive metric of enzymatic browning, increased significantly throughout storage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), from 17.11\u0026thinsp;\u0026plusmn;\u0026thinsp;9.27 at Day 0 to 144.72\u0026thinsp;\u0026plusmn;\u0026thinsp;64.43 at Day 10 (an 8.5-fold increase). The BI progression exhibited near-exponential characteristics, with particularly sharp increases during the 4\u0026thinsp;~\u0026thinsp;6 days interval (85% increase) and 8\u0026thinsp;~\u0026thinsp;10 days interval (17% increase). Days 8 and 10 showed significantly higher BI values compared to days 0\u0026thinsp;~\u0026thinsp;4 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This continuous BI elevation provides quantitative evidence of progressive enzymatic browning, consistent with polyphenol oxidase (PPO) and peroxidase (POD) activities oxidizing phenolic substrates following harvest-induced cellular disruption [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe temporal sequence of deterioration, browning initiation (Day 4) preceding accelerated softening (after Day 6) and weight loss acceleration (after Day 8), suggests that enzymatic oxidation may be among the earliest detectable quality deterioration events in stored \u003cem\u003eS. rugosoannulata\u003c/em\u003e. This aligns with the mechanism where membrane disruption during early senescence enables PPO-phenolic contact before extensive cell wall degradation occurs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The dramatic color deterioration after Day 8 (\u003cem\u003eL\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e decrease, \u003cem\u003ea\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e increase, BI increase) signals the transition from acceptable to unacceptable visual quality for consumers, who typically associate browning with reduced freshness and potential nutritional degradation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Volatile compound profiling and dynamic changes during storage\u003c/h2\u003e \u003cp\u003eThe volatile compounds of \u003cem\u003eS. rugosoannulata\u003c/em\u003e at different storage stages were comprehensively analyzed using headspace solid-phase microextraction coupled with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (HS-SPME-GC\u0026times;GC-TOF MS). Representative one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) total ion chromatograms (TIC) for samples at day 0 and day 10 are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA,B\u003cb\u003e\u0026amp;C\u003c/b\u003e, respectively. A total of 9,746 volatile compounds were identified across all samples, with the number of detected volatiles increasing progressively from 3,532 at day 0 to a maximum of 4,193 at day 10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). This represents a 18.7% increase over the 10‑day storage period, suggesting active metabolic generation of volatiles during postharvest senescence, potentially driven by lipid oxidation, enzymatic reactions, and microbial metabolism [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The temporal dynamics of total volatile numbers showed three distinct phases: a rapid increase from Day 0 to Day 2 (3,532 to 3,934, +\u0026thinsp;11.4%), a slight decrease at Day 4 (3,681, 96.4% from Day 2), followed by a steady increase through Day 10 (4,193, +\u0026thinsp;13.9% from Day 4). This biphasic pattern may reflect initial burst of volatile production following harvest‑induced stress, temporary equilibration of metabolic pathways, and subsequent sustained generation as degradation processes intensify [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Meanwhile, Upset Plot display the number of flavor compounds specifically identified within a group and the number of flavor compounds collectively identified in different groups. As Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD was shown, from Day0 to Day10, the overlap of volatile components exhibited a trend of first decreasing and then increasing, likely reflecting a dynamic adjustment process\u0026mdash;characterized by rapid differentiation in the early stage and gradual convergence in the later stage. Specifically, from Day2 to Day4, the number of overlapping components began to decline, indicating a phase of dynamic change in the system with the emergence of numerous specific features. Between Day6 and Day10, the overlap remained relatively stable, with some time points showing a high degree of overlap (e.g., 770ཞ817), suggesting the presence of a stable set of responsive features during the later stage.\u003c/p\u003e \u003cp\u003eThe identified volatiles were classified into ten major chemical classes based on their structural characteristics: Organoheterocyclic compounds, hydrocarbons, benzenoids, ketones, esters, alcohols, lipids and lipid‑like molecules, heterocyclic compounds, ethers, and carboxylic acids (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Alcohols dominated the early-stage profile, while esters peaked during mid‑storage, and lipid‑derived compounds accumulated progressively at later stages, reflecting the metabolic transition from fresh to senescent tissues. Alcohols were the predominant class at day 0, contributing 23.44% of total volatile content, but their relative abundance steadily declined to 16.22% by Day 10. This decrease, despite relatively stable compound numbers (248ཞ279), indicates that while alcohol diversity is maintained, their quantitative importance diminishes as other classes accumulate. The decline of 1‑octen‑3‑ol, the characteristic mushroom alcohol, likely contributes to the loss of fresh aroma during storage [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Esters exhibited the most dramatic fluctuations. Their relative content surged from 14.36% at Day 0 to 24.40% at Day 4, representing a 7 0% increase, before declining to 21.58% at Day 10. Compound numbers followed a similar pattern, rising from 197 to a peak of 293 at Day 6 (+\u0026thinsp;48.7%). This suggests active ester biosynthesis during early to mid‑storage, potentially involving alcohol acyltransferases, with subsequent hydrolysis or metabolic conversion at later stages [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In summary, the volatile profile undergoes systematic succession during storage: from alcohol‑dominant (fresh) to ester‑enriched (mid‑storage) to lipid oxidation‑dominated (late storage), providing the chemical basis for progressive flavor deterioration.\u003c/p\u003e \u003cp\u003eThe relative content distribution (\u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF) provides complementary insights into the quantitative importance of each class. Alcohols dominated the volatile profile at Day 0 (23.44% of total content), but their relative contribution progressively declined to 16.22% by Day 10, despite relatively stable compound numbers. This suggests that while alcohol diversity is maintained, their proportional abundance decreases as other classes accumulate. Esters showed the most striking content dynamics, increasing from 14.36% at Day 0 to 24.40% at Day 4, then declining to 21.58% at Day 10. This pattern mirrors the compound number trend and confirms active ester metabolism during mid‑storage. Ketones exhibited biphasic content patterns, with 16.42% at Day 0, declining to 8.82% at Day 4, then increasing to 18.48% at Day 8. This fluctuation may reflect differential production rates of various ketone families, with early decline of fresh‑related ketones and late accumulation of oxidation‑derived ketones [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Hydrocarbons maintained relatively stable content (7.61\u0026thinsp;~\u0026thinsp;10.42%) throughout storage, suggesting consistent contribution to the volatile profile. Organoheterocyclic compounds showed relatively stable content (12.68\u0026thinsp;~\u0026thinsp;17.06%), indicating their persistent importance in the overall volatile profile.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Evaluation of key odor-active compounds and dynamic sensory profiling based on ROAV\u003c/h2\u003e \u003cp\u003eTo bridge the gap between chemical composition and human olfactory perception, the Relative Odor Activity Value (ROAV) was utilized to evaluate the actual sensory contribution of individual volatile organic compounds (VOCs). Based on the FlavorDB database, a total of 223 VOCs were successfully annotated with their odor thresholds and sensory descriptors (\u003cb\u003eTable \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). However, not all detected volatiles contribute equally to the overall flavor profile. Typically, compounds with an ROAV\u0026thinsp;\u0026gt;\u0026thinsp;1 are considered key odor-active compounds that dictate the dominant aroma. Following this rigorous criterion, 11 critical odor-active compounds were screened out, encompassing specific aldehydes, ketones, furans, and acids. To visualize the dynamic sensory shifts during the postharvest storage of \u003cem\u003eS. rugosoannulata\u003c/em\u003e, a hierarchical clustering heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) was constructed based on the ROAVs of these 11 key odorants. The heatmap vividly illustrates a time-dependent transition in the flavor profile, with the compounds clustering into distinct temporal expression patterns corresponding to the deterioration stages.\u003c/p\u003e \u003cp\u003eDuring the early storage period (Day 0\u0026ndash;4), the flavor profile was dominated by characteristic \u0026ldquo;mushroom-like\u0026rdquo; and \u0026ldquo;fresh\u0026rdquo; notes. 1-Octen-3-one, a key contributor to the intrinsic aroma, exhibited high ROAVs initially but diminished significantly during later stages, serving as a primary indicator of freshness loss [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Notably, specific volatiles such as 2-Undecanone (\u0026ldquo;fresh, green\u0026rdquo;) and 2,3-Butanedione (\u0026ldquo;buttery\u0026rdquo;) reached their peak ROAVs on Day 4. In contrast, the mid-to-late phase (Day 6\u0026ndash;10) was marked by the emergence of \u0026ldquo;fatty\u0026rdquo; and \u0026ldquo;rancid\u0026rdquo; off-flavors. A series of aliphatic aldehydes, including Heptanal, (E)-2-Octenal, and (E)-2-Dodecenal, exhibited elevated ROAVs during this period. The accumulation of these aldehydes is largely attributed to membrane lipid peroxidation mediated by the lipoxygenase (LOX) pathway, a process accelerated during fungal senescence [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, the late-stage appearance of 3-methyl-butanoic acid (\u0026ldquo;putrid/sweaty\u0026rdquo;) suggests advanced amino acid degradation, potentially linked to the metabolic activity of spoilage microorganisms [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCollectively, ROAV-based profiling reveals a distinct sensory transition: the dissipation of intrinsic \u0026ldquo;mushroom/green\u0026rdquo; notes driven by C8 ketones, followed by the dominance of \u0026ldquo;fatty/rancid\u0026rdquo; off-flavors derived from aldehydes and branched-chain acids. These shifts provide precise biomarkers for monitoring organoleptic quality deterioration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation network analysis between key flavorome dynamics and macroscopic quality deterioration\u003c/h2\u003e \u003cp\u003eTo elucidate the cross-talk between flavorome dynamics and macroscopic quality deterioration, an integrative correlation network was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The pairwise Pearson heatmap revealed profound co-expression patterns among the 11 key odor-active compounds. Notably, a strong positive correlation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(r=0.96\\)\u003c/span\u003e\u003c/span\u003e) was observed between specific lipid oxidation products and amino acid derivatives, such as (E)-2-Dodecenal and 2-methyl-butanal. Conversely, early-stage intrinsic aroma compounds like 2-pentyl-furan exhibited significant negative correlations with late-stage off-flavor aldehydes (e.g., (E)-2-Octenal, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(r=-0.82\\)\u003c/span\u003e\u003c/span\u003e). These strong intra-metabolite correlations chemically corroborate the fundamental sensory shift from freshness to severe deterioration.\u003c/p\u003e \u003cp\u003eFurthermore, the Mantel test visualized topological associations between these volatile modules and primary physical attributes. Although macroscopic decay is a highly complex process, several biologically meaningful association trends (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(0.2\\ler\u0026lt;0.4\\)\u003c/span\u003e\u003c/span\u003e) were identified. Specifically, the Browning index (BI) exhibited a distinct association with Heptanal. In edible fungi, both enzymatic browning and the generation of aliphatic aldehydes (like Heptanal) are tightly coupled oxidative stress responses, typically triggered by the loss of cellular compartmentalization and reactive oxygen species (ROS) accumulation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Additionally, the decline in tissue hardness and increased weight loss showed moderate topological links with (E)-2-Dodecenal, 2-methyl-butanal, and 3-methyl-butanoic acid. This suggests that structural collapse and transpiration actively accelerate the enzymatic degradation of membrane lipids and intracellular amino acids into these specific off-flavors [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], providing a theoretical basis for using these VOCs to predict macroscopic shelf-life.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Multivariate statistical analysis of volatile compound dynamics and identification of key biomarkers\u003c/h2\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 Unsupervised profiling and staging of volatile flavorome during storage\u003c/h2\u003e \u003cp\u003eTo holistically understand the dynamic evolution of the volatile profile in \u003cem\u003eS. rugosoannulata\u003c/em\u003e during cold storage, an unsupervised Principal Component Analysis (PCA) was initially performed on the GC\u0026times;GC-TOF MS filtered dataset including 2,375 components. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, the PCA score plot revealed a distinct temporal trajectory of the mushroom samples over the 10-day storage period. The first two principal components, PC1 and PC2, accounted for 14.7% and 11.2% of the total variance, respectively.\u003c/p\u003e \u003cp\u003eNotably, the spatial distribution of the samples did not shift uniformly; instead, it clustered into three highly distinct physiological stages, indicating that postharvest flavor deterioration is a phased rather than a constant process. Samples from Day 0 and Day 2 clustered tightly in the upper-left quadrant, representing the fresh state (Stage I). A dramatic migration occurred at Day 4 and Day 6, forming a distinct cluster on the right side of the plot (Stage II), which likely corresponds to a transitional phase characterized by the initiation of rapid metabolic shifts and structural softening. Finally, samples from Day 8 and Day 10 aggregated in the lower-left quadrant (Stage III), marking the severe deterioration and senescence phase. This natural clustering aligns perfectly with the macroscopic quality degradation (hardness loss and browning) observed, justifying the categorization of the continuous storage period into three distinct stages for subsequent in-depth analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 Supervised discrimination and PLS-DA model validation\u003c/h2\u003e \u003cp\u003eTo further maximize the separation among the three defined stages and to extract the volatile variables driving these sensory shifts, a supervised Partial Least Squares Discriminant Analysis (PLS-DA) was conducted. The PLS-DA 2D score plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) demonstrated a complete spatial segregation among Stage I, Stage II, and Stage III without any overlap, confirming the profound and stage-specific alterations in the volatile metabolome.\u003c/p\u003e \u003cp\u003eThe reliability and predictive accuracy of the PLS-DA model were rigorously evaluated using cumulative goodness-of-fit metrics and a permutation test. The model exhibited excellent interpretability and predictive capability, yielding an \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}Y\\left(cum\\right)\\)\u003c/span\u003e\u003c/span\u003e of 0.9540 and a \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Q}^{2}\\left(cum\\right)\\)\u003c/span\u003e\u003c/span\u003e of 0.8413 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). A \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Q}^{2}\\)\u003c/span\u003e\u003c/span\u003e value substantially higher than 0.5 indicates that the model is highly robust for flavorome discrimination. Furthermore, the 200-iteration permutation test yielded regression lines with intercepts of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}=\\left(\\text{0.0,0.88}\\right)\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Q}^{2}=(0.0,-0.37)\\)\u003c/span\u003e\u003c/span\u003e. The strictly negative intercept of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Q}^{2}\\)\u003c/span\u003e\u003c/span\u003e provides definitive statistical proof that the PLS-DA model is not overfitted, ensuring that the subsequent extraction of biomarker compounds is highly credible.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3 Identification of critical volatile markers responsible for flavor deterioration\u003c/h2\u003e \u003cp\u003eTo identify the core volatile organic compounds (VOCs) driving postharvest flavor deterioration, a stringent \u0026ldquo;triple-filter\u0026rdquo; criterion was employed based on the PLS-DA model: Variable Importance in Projection (VIP) score\u0026thinsp;\u0026gt;\u0026thinsp;1.0, the lower limit of the 95% Jackknife confidence interval (CI)\u0026thinsp;\u0026gt;\u0026thinsp;1.0, and False Discovery Rate (FDR) adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. This conservative approach eliminates unstable variables and controls false positives, ensuring the screened markers are both statistically significant and biologically reproducible. Filtered result was illustrated in \u003cb\u003eTable \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD presents the top 30 most discriminant VOCs ranked by their VIP scores, all of which exhibit relatively short error bars and are annotated with asterisks (*), indicating robust stability and high statistical significance across biological replicates. The most prominent discriminant markers were dominated by specific ketones, esters, and structurally complex hydrocarbons. For instance, long-chain ketones such as 6-Tridecanone (the highest VIP score) and 2-Heptadecanone were identified as major contributors to stage discrimination. Ketones in postharvest mushrooms are primarily generated through the enzymatic degradation and autoxidation of membrane polyunsaturated fatty acids (PUFAs) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Their massive accumulation typically correlates with the onset of lipid peroxidation during senescence, contributing to the emergence of rancid or \u0026ldquo;off\u0026rdquo; notes that mask the fresh mushroom aroma [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, a significant proportion of the VIP features were esters, including Ethyl cyclohexanepropionate, Propanoic acid derivatives (ethyl esters), and Hexadecanoic acid propyl ester. While esters often impart fruity or sweet aromas, their rapid synthesis during late storage stages (Stage II and III) likely reflects an alteration in energy metabolism. The accumulation of these esters may result from the esterification of free fatty acids and alcohols under increasing cellular stress or mild anaerobic conditions internally as the mushroom tissues collapse [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Interestingly, several complex cyclic and aromatic compounds, such as Naphthalene derivatives (e.g., 1,2,3,4-tetrahydro-5-methyl-1-(1-methylethyl)-naphthalene) and 2-(4-Methoxyphenyl)ethanol, also ranked highly.\u003c/p\u003e \u003cp\u003eIn summary, the flavoromics analysis reveals that the sensory deterioration of \u003cem\u003eS. rugosoannulata\u003c/em\u003e is not merely the dissipation of typical eight-carbon (C8) fresh aroma compounds, but is heavily driven by the active accumulation of lipid oxidation products (ketones/hydrocarbons) and stress-induced metabolites (esters). These Top 30 VIP features can serve as reliable, multivariate quantitative indicators for real-time quality monitoring and shelf-life prediction of \u003cem\u003eS. rugosoannulata\u003c/em\u003e during commercial cold chain logistics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Screening and characterization of stage-specific and continuous differential volatile markers\u003c/h2\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1 Global overview of volatile flux during phase transitions\u003c/h2\u003e \u003cp\u003eTo quantify the magnitude of flavorome alterations during postharvest cold storage, univariate statistical analysis (Volcano plots) was employed to visualize the significantly upregulated and downregulated VOCs across the two critical phase transitions. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, the transition from Stage I to Stage II (early deterioration) triggered massive metabolic shifts, with 344 compounds significantly upregulated and 203 downregulated (|log₂FC| \u0026gt; 1.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, the transition from Stage II to Stage III (late senescence) resulted in 357 upregulated and 232 downregulated compounds (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eNotably, in both transition phases, the number of upregulated VOCs substantially exceeded the downregulated ones. Biologically, this suggests that postharvest flavor deterioration in \u003cem\u003eS. rugosoannulata\u003c/em\u003e is not merely the passive dissipation of intrinsic fresh aromas, but rather an active, senescence-driven biochemical process. The breakdown of cellular structures and macromolecules (e.g., lipids and proteins) leads to the massive de novo synthesis and accumulation of secondary volatile metabolites (off-flavors).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2 Stage-specific markers: From early esterification to severe lipid oxidation\u003c/h2\u003e \u003cp\u003eTo robustly identify the most reliable biomarkers, a cross-validation strategy was applied using Venn diagrams to intersect the significant univariate features (up/down-regulated) with the rigorously screened multivariate PLS-DA features (VIP\u0026thinsp;\u0026gt;\u0026thinsp;1, CI\u0026thinsp;\u0026gt;\u0026thinsp;1, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cb\u003eTable \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eAs highlighted by the yellow dashed circles in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u003cb\u003e\u0026amp;D\u003c/b\u003e (Datasets illustrated in \u003cb\u003eTable S5\u003c/b\u003e), stage-specific markers were successfully isolated. During this initial phase, several esters (e.g., n-Propyl acetate, Butanoic acid 3-methyl- propyl ester, and \u003cem\u003eL-\u003c/em\u003eLeucine ethyl ester) and alkenes (e.g., 1-Decene) were significantly up-regulated. The early burst of esters is typically associated with the esterification of free fatty acids and amino acid catabolism under mild hypoxic stress or energy depletion as the mushroom continues to respire post-harvest. Conversely, several aldehydes (Nonanal, Dodecanal) and heterocyclic compounds (Furan, Pyrazine) were initially down-regulated in this phase, reflecting the disruption of the native flavor equilibrium and the rapid degradation of specific precursors responsible for the fresh mushroom profile.\u003c/p\u003e \u003cp\u003eConversely, as storage progressed to severe structural collapse (Stage II vs Stage III), the volatile profile underwent a dramatic secondary shift. Notably, straight-chain aldehydes that were initially suppressed, such as Nonanal, Undecanal, Dodecanal, and Tridecanal, experienced a massive resurgence and were identified as key up-regulated markers for late senescence. This \u0026ldquo;aldehyde burst\u0026rdquo; is a classic hallmark of severe lipid peroxidation, occurring when cellular compartmentalization is completely lost, allowing lipoxygenases (LOX) to violently attack membrane polyunsaturated fatty acids [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e3.6.3 Continuous biomarkers driving the entire deterioration continuum\u003c/h2\u003e \u003cp\u003eTo identify the ultimate \u0026ldquo;time-temperature integrators\u0026rdquo; that continuously evolve throughout the entire shelf-life, we focused on the central intersection of the Venn diagrams (red dashed circles in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u003cb\u003e\u0026amp;D\u003c/b\u003e), representing compounds that strictly passed the PLS-DA screening and showed significant unidirectional changes across all stage comparisons (Stage I vs II, Stage II vs III, and Stage I vs III).\u003c/p\u003e \u003cp\u003eRemarkably, only 7 compounds were identified as continuous, progressive up-regulated biomarkers: n-Caprylic acid isobutyl ester, 2-(4-Methoxyphenyl)ethanol, 1-hexyl-Cyclopentene, 2-Heptadecanone, 3-(4-Methoxyphenyl)propionic acid ethyl ester, Methoxyacetic acid hexyl ester, and Butanedioic acid diethyl ester. Among these, 2-Heptadecanone stands out as a critical continuous marker of lipolysis, while the accumulation of heavy esters and aromatic alcohols reflects continuous complex secondary metabolism driven by senescence and stress [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Concurrently, only 1 compound, 3-ethenyl-Pyridine, was identified as a continuous down-regulated progressive marker. These 8 core continuous biomarkers act as the molecular ticking clock, which provide the most reliable, time-independent chemical signatures for predicting the postharvest freshness and shelf-life of \u003cem\u003eS. rugosoannulata\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study systematically elucidated the postharvest quality deterioration and volatile compound dynamics in \u003cem\u003eS. rugosoannulata\u003c/em\u003e during cold storage using a data‑driven flavoromics approach. Comprehensive quality assessment revealed that storage induced progressive weight loss, biphasic texture changes (initial hardening followed by softening), and continuous browning, with enzymatic browning identified as an early and sensitive indicator of quality decline. HS‑SPME‑GC\u0026times;GC‑TOF MS profiling enabled the detection of nearly 10,000 volatile compounds, demonstrating a systematic succession in the volatile profile from alcohol‑dominant (fresh) to ester‑enriched (mid‑storage) and finally to lipid oxidation‑dominated (late storage) patterns. ROAV analysis identified 11 key odor‑active compounds driving sensory transitions, with the dissipation of C8 mushroom‑like notes (e.g., 1‑octen‑3‑one) and the emergence of fatty/rancid aldehydes (e.g., heptanal, (E)‑2‑octenal) marking the critical shift from freshness to off‑flavor development. Integrative correlation networks revealed significant associations between volatile dynamics and macroscopic quality attributes, particularly linking aldehyde accumulation with browning and softening processes. Multivariate statistical modeling (PCA/PLS‑DA) delineated three distinct physiological stages during storage, and rigorous triple‑filter screening (VIP\u0026thinsp;\u0026gt;\u0026thinsp;1, 95% CI\u0026thinsp;\u0026gt;\u0026thinsp;1, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) identified robust stage‑specific and continuous volatile biomarkers. Notably, seven continuously upregulated compounds (including 2‑heptadecanone and various esters) were validated as progressive indicators of senescence, serving as molecular \u0026ldquo;ticking clocks\u0026rdquo; for freshness monitoring. Collectively, these findings provide a comprehensive understanding of the flavor deterioration mechanisms in postharvest \u003cem\u003eS. rugosoannulata\u003c/em\u003e and establish a set of reliable volatile markers for real‑time quality assessment. The integration of flavoromics with conventional quality metrics offers a powerful framework for developing targeted preservation strategies and predicting shelf‑life in edible fungi during cold chain logistics. Future studies should focus on validating these biomarkers across different cultivars and storage conditions, as well as exploring their biosynthetic pathways to enable precision postharvest management.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWanchao Chen: Methodology, Software, Visualization, Funding, and Writing-review \u0026amp; editing. Junying Zhu: Data curation, Formal analysis, and Software. Wen Li: Methodology, Supervision, and Validation. Di Wu: Conceptualization, and Validation. Zhong Zhang: Methodology, Conceptualization, and Software. Peng Liu: Methodology and Supervision. Yan Yang: Conceptualization, Writing-review \u0026amp; editing, Supervision, and Project administration.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported financially by Shanghai Agricultural Science and Technology Innovation Program (Grant No. T2024201), and the Food Nutrition and Health Research Center of Shanghai Academy of Agricultural Sciences (2025-001).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eIncluded in the Supplementary Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHuang, L., et al. \u003cem\u003eNutritional, Bioactive, and Flavor Components of Giant Stropharia (Stropharia rugoso-annulata): A Review\u003c/em\u003e. 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However, how volatile organic compounds (VOCs) dynamically evolve and relate to macroscopic quality changes remains poorly characterized. Here, we employed a data‑driven flavoromics approach integrating HS‑SPME‑GC\u0026times;GC‑TOF MS with multivariate analysis to investigate postharvest quality deterioration and volatile dynamics in \u003cem\u003eS. rugosoannulata\u003c/em\u003e stored at 5 ℃ for 10 days. Quality assessment revealed progressive weight loss (1.22% at Day 10), biphasic texture changes (hardening then softening), and continuous browning, with browning index increasing 8.5‑fold. GC\u0026times;GC‑TOF MS profiling detected nearly 10,000 volatile compounds, demonstrating a systematic succession from alcohol‑dominant (fresh) to ester‑enriched (mid‑storage) and finally to lipid oxidation‑dominated (late storage) profiles. Relative odor activity value (ROAV) analysis identified 11 key odorants driving sensory transitions, with dissipation of mushroom‑like 1‑octen‑3‑one and accumulation of fatty aldehydes marking the critical shift toward off‑flavor development. Integrative correlation networks revealed significant associations between specific volatiles and quality attributes, linking aldehyde accumulation with browning and softening processes. Multivariate modeling (PCA/PLS‑DA) delineated three distinct physiological stages during storage, and rigorous triple‑filter screening (VIP\u0026thinsp;\u0026gt;\u0026thinsp;1, 95% CI\u0026thinsp;\u0026gt;\u0026thinsp;1, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) identified robust stage‑specific and continuous biomarkers. Notably, 7 continuously upregulated compounds were validated as progressive indicators of senescence, serving as molecular \u0026ldquo;ticking clocks\u0026rdquo; for freshness monitoring. This study provides the first comprehensive volatilome map of postharvest \u003cem\u003eS. rugosoannulata\u003c/em\u003e and establishes a set of reliable volatile markers for real‑time quality assessment, offering a powerful framework for developing targeted preservation strategies and predicting shelf‑life in edible fungi during cold chain logistics.\u003c/p\u003e","manuscriptTitle":"Data-driven flavoromics reveals postharvest quality deterioration and volatile compound dynamics in Stropharia rugosoannulata mushroom during cold storage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 11:25:10","doi":"10.21203/rs.3.rs-9128298/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-17T02:52:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-16T14:17:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-16T14:17:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Agricultural Products Processing and Storage","date":"2026-03-15T11:28:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"agricultural-products-processing-and-storage","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Agricultural Products Processing and Storage](https://link.springer.com/journal/44462)","snPcode":"44462","submissionUrl":"https://submission.springernature.com/new-submission/44462/3","title":"Agricultural Products Processing and Storage","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a46456a0-8ee4-4f28-9813-ac3c393736d6","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T05:23:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 11:25:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9128298","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9128298","identity":"rs-9128298","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}