Volatile-Compound Fingerprinting by Headspace-Gas-Chromatography Ion-Mobility Spectrometry (HS-GC-IMS) as a Benchtop Alternative to 1H NMR Profiling for Assessment of the Authenticity of Honey

Multiple metabolite profiles uncover remarkable bioactive compounds and metabolic characteristics of noni fruit (Morinda citrifolia L.) at various stages of ripeness

This study aimed to investigate the changes in physicochemical properties, bioactive compounds, and metabolic characteristics of noni fruit at different ripeness levels. The results showed that there were significant differences in physicochemical properties. HPLC analysis was conducted, revealing succinic acid, scopoletin, deacetylasperulosidic acid, and asperulosidic acid were key bioactive compounds as the fruit ripened. Additionally, 4 differentbiomarkers (isocitric acid, 4,4-thiodiphenol, lobaric acid, and octocrylene), identified using 1HNMR and LC-IT-TOF-MS, were found to have a VIP value over 1. The results from HS-GC-IMS demonstrated noteworthy that 14 volatile compounds were identified as highly discriminative features during fruit ripening. Furthermore, correlation analysis showed that different ripeness had significant effects on bioactive components and functional activities, e.g., the inhibition rate of enzyme and E. coli of noni fruit with different ripeness exceeded 90% at the last stage. This study contributes new insights into the effective utilization of bioactive ingredients in noni fruit.

Gas chromatography ion mobility spectrometry (GC-IMS) dataset of honey samples with different botanical origins

Gas chromatography ion mobility spectrometry (GC-IMS) is a robust and sensitive benchtop technique commonly used for non-target screening of volatile organic compounds. It has been applied to authenticity analysis by generating characteristic "fingerprints" of food samples, well suited for chemometric data analysis. This dataset contains headspace GC-IMS spectra from 50 monofloral honey samples from three different botanical origins, 18 acacia honeys (Robinia pseudoacacia), 19 canola honeys (Brassica napus) and 18 honeydew honeys (forest flowers). Honeys were sourced from the beekeepers directly or obtained from governmental food inspectors from Baden-Wuerttemberg, Germany. Authenticity was confirmed by pollen analysis in the framework of the official control of foodstuffs. The data was acquired using a setup based on an Agilent 6890N gas chromatograph (Agilent Technologies, Palo Alto, CA) and an OEM Standalone IMS cell from G.A.S Sensorsysteme m. b. H. (Dortmund, Germany). All samples were recorded in duplicates and spectra are presented as raw data in the .mea file format. The dataset is available on Mendeley Data: https://data.mendeley.com/datasets/jxj2r45t2x.

Benchtop volatilomics supercharged: How machine learning based design of experiment helps optimizing untargeted GC-IMS gas phase metabolomics

Gas chromatography-ion mobility spectrometry (GC-IMS) plays a significant role in both targeted and non-targeted analyses. However, the non-linear behavior of IMS and its complex ion chemistry pose challenges for finding optimal experimental conditions using existing methodologies. To address these issues, integrating machine learning (ML) strategies offers a promising approach. In this study, we propose a hybrid strategy, combining design of experiment (DOE) and machine learning (ML) for optimizing GC-IMS conditions in non-targeted volatilomic/flavoromic analysis, with saffron volatiles as a case study. To begin, a rotatable circumscribed central composite design (CCD) is used to define five influential GC-IMS factors of sample amount, headspace temperature, incubation time, injection volume, and split ratio. Subsequently, two ML models are utilized: multiple linear regression (MLR) as a linear model and Bayesian regularized-artificial neural network (BR-ANN) as a nonlinear model. These models are employed to predict the response variables of total peak areas (PAs) and the number of detected peaks (PNs) in GC-IMS. The findings show that there is a direct correlation between the factors in GC-IMS and the PNs, suggesting that MLR is a suitable approach for building a model in this scenario. However, the PAs exhibit nonlinear behavior, suggesting that BR-ANN is better suitable to capture this complexity. Notably, Derringer's desirability function is utilized to integrate the PAs and PNs, and in this scenario, MLR demonstrates satisfactory performance in modeling the GC-IMS factors.

Characterization and comparison of flavors in fresh and aged fermented peppers: Impact of different varieties

The flavor profiles of fresh and aged fermented peppers obtained from four varieties were thoroughly compared in this study. A total of 385 volatile compounds in fermented pepper samples were detected by flavoromics (two-dimensional gas chromatography-time-of-flight mass spectrometry). As fermentation progressed, both the number and the total concentration of volatile compounds changed, with esters, alcohols, acids, terpenoids, sulfur compounds, and funans increasing, whereas hydrocarbons and benzenes decreased. In contrast to the fresh fermented peppers, the aged fermented samples exhibited lower values of pH, total sugars, and capsaicinoids but higher contents of organic acids and free amino acids. Furthermore, the specific differences and characteristic aroma substances among aged fermented peppers were unveiled by multivariate statistical analysis. Overall, 64 volatiles were screened as differential compounds. In addition, Huanggongjiao samples possessed the most abundant differential volatiles and compounds with odor activity values > 1, which were flavored with fruity, floral, and slightly phenolic odors. Correlation analysis demonstrated that the levels of 23 key aroma compounds (e.g., ethyl 2-methylbutyrate, 1-butanol, and ethyl valerate) showed a significantly positive correlation with Asp, Glu and 5 organic acids. By contrast, there is a negative association between the pH value and total sugar. Overall, aging contributed significantly to the flavor attributes of fermented peppers.

Authenticity assessment of ground black pepper by combining headspace gas-chromatography ion mobility spectrometry and machine learning

Currently, the authentication of ground black pepper is a major concern, creating a need for a rapid, highly sensitive and specific detection tool to prevent the introduction of adulterated batches into the food chain. To this aim, head space gas-chromatography ion mobility spectrometry (HS-GC-IMS), combined with machine learning, is tested in this initial, proof-of-concept study. A broad variety of authentic samples originating from eight countries and three continents were collected and spiked with a range of adulterants, both endogenous sub-products and an assortment of exogenous materials. The method is characterized by no sample preparation and requires 20 min for chromatographic separation and ion mobility data acquisition. After an explorative analysis of the data, those were submitted to two different machine learning algorithms (partial least squared discriminant analysis-PLS-DA and support vector machine-SVM). While the PLS-DA model did not provide fully satisfactory performances, the combination of HS-GC-IMS and SVM successfully classified the samples as authentic, exogenously-adulterated or endogenously-adulterated with an overall accuracy of 90 % and 96 % on withheld test set 1 and withheld test set 2, respectively (at a 95 % confidence level). Some limitations, expected to be mitigated by further research, were encountered in the correct classification of endogenously adulterated ground black pepper. Correct categorization of the ground black pepper samples was not adversely affected by the operator or the time span of data collection (the method development and model challenge were carried out by two operators over 6 months of the study, using ground black pepper harvested between 2015 and 2019). Therefore, HS-GC-IMS, coupled to an intelligent tool, is proposed to: (i) aid in industrial decision-making before utilization of a new batch of ground black pepper in the production chain; (ii) reduce the use of time-consuming conventional analyses and; (iii) increase the number of ground black pepper samples analyzed within an industrial quality control frame.

Making Every Single Puff Count-Simple and Sensitive E-Cigarette Aerosol Sampling for GCxIMS and GC-MS Analysis

The analysis of the aerosol from tobaccoless electronic cigarettes (e-cigarettes) is an important part of understanding their impact on human health, yet sampling aerosol from e-cigarettes is still considered a challenge. It lacks a standard method for research and quality control and there are a variety of methods. However, few are simple and inexpensive, and none have been suggested for the use with gas chromatography coupled ion mobility spectrometry (GCxIMS). This work presents and evaluates such a setup made from standard lab equipment to quickly collect a quantitative sample from the aerosol of a single puff (5 s totaling 125 mL). The aerosol condensates directly in the cooled headspace (HS) vial, which is analyzed in the HS-GCxIMS or mass spectrometer (HS-GC-MS). The combined use of GC-MS and GCxIMS allows the simple and sensitive identification of unknown substances in complex mixtures and the identification of degradation products in the aerosols. A calibration of 26 flavor compounds (0.2-20 µg/g) was created using single puffs of a spiked, flavorless commercial refill solution and 2-alkanones as internal standards. This sensitive but easily reproducible setup enables a wide range of further investigations, even for labs that were previously unable to afford it.

A geographical origin assessment of Italian hazelnuts: Gas chromatography-ion mobility spectrometry coupled with multivariate statistical analysis and data fusion approach

Hazelnut is a commodity that has gained interest in the food science community concerning its authenticity. The quality of the Italian hazelnuts is guaranteed by Protected Designation of Origin and Protected Geographical Indication certificates. However, due to their modest availability and the high price, fraudulent producers/suppliers blend, or even substitute, Italian hazelnuts with others from different countries, having a lower price, and often a lower quality. To contrast or prevent these illegal activities, the present work investigated the application of the Gas Chromatography-Ion mobility spectrometry (GC-IMS) technique on the hazelnut chain (fresh, roasted, and paste of hazelnuts). The raw data obtained were handled and elaborated using two different ways, software for statistical analysis, and a programming language. In both cases, Principal Component Analysis and Partial Least Squares-Discriminant Analysis models were exploited, to study how the Volatile Organic Profiles of Italian, Turkish, Georgian, and Azerbaijani products differ. A prediction set was extrapolated from the training set, for a preliminary models' evaluation, then an external validation set, containing blended samples, was analysed. Both approaches highlighted an interesting class separation and good model parameters (accuracy, precision, sensitivity, specificity, F1-score). Moreover, a data fusion approach with a complementary methodology, sensory analysis, was achieved, to estimate the performance enhancement of the statistical models, considering more discriminant variables and integrating at the same time further information correlated to quality aspects. GC-IMS could be a key player as a rapid, direct, cost-effective strategy to face authenticity issues regarding the hazelnut chain.

Application of GC-IMS coupled with chemometric analysis for the classification and authentication of geographical indication agricultural products and food

Geographical indications (GI) are used to protect the brand value of agricultural products, foodstuffs, and wine and promote the sustainable development of the agricultural and food industries. Despite the necessity for the traceability and recognition of GI product characteristics, no rapid, non-destructive approaches currently exist to identify, classify, and predict these properties. The application of gas chromatography-ion mobility spectrometry (GC-IMS) has increased exponentially due to instrument robustness and simplicity. This paper provided a detailed overview of recent GC-IMS applications in China for the quality evaluation of GI products and food, including agricultural products, as well as traditional Chinese food and liquor. The general workflow of GC-IMS coupled with chemometric analysis is presented, including sample collection, model construction and interpretation, and data acquisition, processing, and fusion. Several conclusions are drawn to increase partial least squares-discriminant analysis (PLS-DA) model precision, a chemometric technique frequently combined with GC-IMS.

Characteristics of changes in volatile organic compounds and microbial communities during the storage of pickles

The variations of volatile organic compounds (VOCs) and microbial communities of three pickles during storage at 4°C for one week were analyzed by headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), high-throughput sequencing, and Spearman correlation analysis. A total of 50 VOCs were identified from three pickles. During storage, most alcohols, aldehydes, ketones, and esters decreased, while acids increased, and sulfides, alkenes, and phenols were relatively equal. Firmicutes, Cyanobacteria, and Proteobacteria were the predominant bacterial phyla, and Weissella, Streptophyta, Leuconostoc, Bacillariophyta, and Lactobacillus were the predominant bacterial genera in three pickles. The bacterial diversity level significantly decreased during storage (P < 0.05). Spearman correlation coefficient indicated that Leuconostoc, Lactobacillus, and Weissella were highly correlated with the flavor of pickles, while Bacillariophyta and Streptophyta were highly correlated with the flavor formation of pickles during storage. These results could contribute to a better understanding of the impact of bacteria in flavor formation during pickle storage.

Headspace with Gas Chromatography-Mass Spectrometry for the Use of Volatile Organic Compound Profile in Botanical Origin Authentication of Honey

The botanical origin of honey determines its composition and hence properties and product quality. As a highly valued food product worldwide, assurance of the authenticity of honey is required to prevent potential fraud. In this work, the characterisation of Spanish honeys from 11 different botanical origins was carried out by headspace gas chromatography coupled with mass spectrometry (HS-GC-MS). A total of 27 volatile compounds were monitored, including aldehydes, alcohols, ketones, carboxylic acids, esters and monoterpenes. Samples were grouped into five categories of botanical origins: rosemary, orange blossom, albaida, thousand flower and "others" (the remaining origins studied, due to the limitation of samples available). Method validation was performed based on linearity and limits of detection and quantification, allowing the quantification of 21 compounds in the different honeys studied. Furthermore, an orthogonal partial least squares-discriminant analysis (OPLS-DA) chemometric model allowed the classification of honey into the five established categories, achieving a 100% and 91.67% classification and validation success rate, respectively. The application of the proposed methodology was tested by analysing 16 honey samples of unknown floral origin, classifying 4 as orange blossom, 4 as thousand flower and 8 as belonging to other botanical origins.

Comparison of the volatile organic compounds in Citrus reticulata 'Chachi' peel with different drying methods using E-nose, GC-IMS and HS-SPME-GC-MS

Introduction

Citrus reticulata 'Chachi' peel (CRCP), which is named "Guangchenpi" in China, is a geographical indication product with unique flavor properties. CRCP has been used for centuries as a traditional genuine herb because of its excellent therapeutic effects. In addition, owing to its unique odor and high nutrition, it is widely used in various food preparations. Volatile organic compounds (VOCs) are regarded as an important quality marker for CRCP and are highly susceptible to effects in the drying process due to their thermal instability.

Methods

In the current study, the main VOCs in CRCP were processed using different drying methods, including sun-drying, hot air drying, and vacuum-freeze drying. The VOCs were identified by the electronic nose (E-nose), gas chromatography-ion mobility spectrometry (GC-IMS), and headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS).

Results

The results showed that the CRCP dried by vacuum-freeze exhibited the highest VOCs contents and retained the richest compounds compared to those dried by other methods, which indicated that vacuum-freeze drying is the most suitable for CRCP production. Furthermore, the chemometrics analysis revealed that the primary differential metabolites of the samples generated using different drying methods were terpenes and esters.

Discussion

Overall, our study would help better understand the VOCs present in CRCP with different drying methods. The outcomes of the current study would guide the drying and processing of CRCP, which is beneficial for large-scale storage and industrial production of CRCP.

Effects of Different Probiotics on the Volatile Components of Fermented Coffee Were Analyzed Based on Headspace-Gas Chromatography-Ion Mobility Spectrometry

Headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was used to study the effects of four kinds of probiotics on the volatile components of fermented coffee. The fingerprints showed that 51 compounds were confirmed and quantified, including 13 esters, 11 aldehydes, 9 alcohols, 6 ketones, 3 furans, 5 terpenes (hydrocarbons), 2 organic acids, 1 pyrazine, and 1 sulfur-containing compound. After fermenting, the aroma of the green beans increases while that of the roasted beans decreases. After roasting, the total amount of aroma components in coffee beans increased by 4.48-5.49 times. The aroma differences between fermented and untreated roasted beans were more significant than those between fermented and untreated green beans. HS-GC-IMS can distinguish the difference in coffee aroma, and each probiotic has a unique influence on the coffee aroma. Using probiotics to ferment coffee can significantly improve the aroma of coffee and provide certain application prospects for improving the quality of commercial coffee beans.

Evaluation of IMS drift tube temperature on the peak shape of high boiling fragrance compounds towards allergen detection in complex cosmetic products and essential oils

Gas chromatography-ion mobility spectrometry (GC-IMS) has recently gained increasing attention for the analysis of volatile compounds due to its high sensitivity, selectivity, and robust design. Peak shape distortion, including peak tailing or broadening, are well known challenges in chromatographic analysis that result in peak asymmetry and decreased resolution. However, in IMS analysis peak tailing, which is independent on the column separation technique, have also been observed. As high boiling substances, such as monoterpenes, are mainly affected by enlarged peak tailing in GC-IMS, we propose that condensation or adsorption effects within the "cold" IMS cell, which is commonly operated at 45 °C-90 °C, are the root cause. To avoid condensation and to decrease peak tailing, we used a prototypic high temperature ion mobility spectrometry (HTIMS) in this work, which allows an increase of the IMS drift tube temperature up to 180 °C. This HTIMS was coupled to a GC column separation and used to analyse the peak shape of homologues series of ketones, alcohols, aldehydes, as well as high boiling fragrance compounds, such as monoterpenes and phenylpropanoids. While we were able to show that an increased IMS drift tube temperatures correlates well with improved peak shapes, the GC parameters of the HS-GC-HTIMS method, however, were found to have little effect on the peak shapes in IMS spectra. In particular monoterpenes, which display intense peak tailing at lower IMS drift tube temperatures, show significant improvement of the peak shape at higher IMS drift tube temperatures. This leads to the assumption that high boiling substances indeed undergo condensation effects within the IMS cell at low drift tube temperatures. For many separation tasks, such as the separation of the phenylpropanoids eugenol and isoeugenol, comparably low IMS temperatures of 120 °C are already sufficient to achieve a resolution above 1.5. However, the optimal drift tube temperature is dependent on the substance class. While the aspect ratio increases steadily for most monoterpenes, phenylpropanoids and aldehyde monomer peaks investigated, an optimal aspect ratio was found for ketones between 140 °C and 160 °C and alcohols between 120 °C and 140 °C. Lastly, the change of the reduced mobility K₀ with the increase of drift tube temperature was analysed. Compounds with similar chemical structure, such as the alcoholic monoterpenes citronellol and geraniol or the phenylpropanoids eugenol and isoeugenol show similar shifts of the K₀ value. Substances which differ in their chemical structure, such as the aldehyde monoterpenes citral and cinnamal have substantially different shifts of the K₀ value. With a future large substance database, the temperature dependant slope of the K₀ value of a substance could be used to identify the substance groups of unknown molecules. Furthermore, substances with the same drift time but different chemical composition could be separable through a change in drift tube temperature.

Hot Air Convective Drying of Ginger Slices: Drying Behaviour, Quality Characteristics, Optimisation of Parameters, and Volatile Fingerprints Analysis

Ginger is one of the most popular spices and medical herbs with its unique pungent flavour and taste. Although there has been much research into the drying methods of ginger, the effect of drying parameters in hot air convective drying on ginger quality needs to be explored in depth. This study investigated the differences in drying behaviour and quality characteristics of ginger with the variables of temperature, thickness, and loading density. The moisture states and diffusion pattern in the different stages during the drying process were analysed using low-field NMR techniques. The results of quality evaluation showed that the temperature greatly influenced the colour and gingerol content of dried ginger, and the thickness of a ginger slice greatly influenced the rehydration rate. Optimal drying conditions were determined by considering a combination of specific energy consumptions with quality retention based on the response surface methodology: a temperature of 66.41 °C, thickness of 2 mm, and loading density of 5 kg/m2. HS-GC-IMS combined with multivariate chemometrics was used to achieve the characterisation of flavour profiles and fingerprinting of dried ginger. The principal component analysis and correlation analysis revealed that the alterations in ginger quality were intimately related to moisture diffusion during drying.

Flavor components detection and discrimination of isomers in Huaguo tea using headspace-gas chromatography-ion mobility spectrometry and multivariate statistical analysis

Aroma components are one of the crucial factors in dynamic processes analysis, quality control, and origin traceability. Various categories of Huaguo Tea possessed different taste due to the generation of aroma. In this study, a comprehensive analysis of volatiles was conducted for five popular Huaguo Tea samples (Lemon Slices, Bitter Gourd Slices, Citri Reticulatae Pericarpium, Red Lycium Barbarum, and Black Lycium Barbarum) via gas chromatography-ion mobility spectrometry (GC-IMS) combining with multivariate statistical strategies. Comparison analysis was achieved with the properties of visually and intuitively by drawing of topography plots. A total of one hundred and eighty volatiles were distinguished. Aliphatic isomers were identified simultaneously by fingerprint spectra. Alcohols, aldehydes, esters, and ketones were the most abundant volatiles in Huaguo Tea samples. To characterize the Huaguo Tea precisely and establish an analysis model for their classification, multivariate statistical analysis was applied to distinguish different Huaguo Tea. Satisfied discrimination was obtained by principal component analysis (PCA) and orthogonal partial least squares discrimination analysis (OPLS-DA) based on the HS-GC-IMS results with the robustness parameter (R²Y) of 99.4%, and prediction ability parameter (Q²) of 98.6%, respectively. The results provide a theoretical basis for aroma discrimination, isomer identification, and categories analysis of Huaguo Tea.

A non-targeted metabolomic strategy for characterization of the botanical origin of honey samples using headspace gas chromatography-ion mobility spectrometry

In this work, characterization of the botanical origin of honey was carried out using headspace gas chromatography coupled to ion mobility spectrometry (HS-GC-IMS). The proposed methodology was applied for the analysis of 89 samples from ten different botanical origins. A total of 15 volatile compounds could be identified, namely, 3-methyl-1-butanol, heptanal, valeraldehyde, octanal, trans-2-hexenal, nonanal, hexanal, benzaldehyde, 2-heptanone, 2-butanone, 2-hexanone, 6-methyl-5-hepten-2-one, 2-pentanone, ethyl acetate and linalool. The analytical method was characterized in terms of limits of detection and quantification, and precision, in order to quantify the identified compounds. Compounds were quantified using the sum of the protonated monomer and proton-bound dimer and logarithmic regression (R² > 0.98), although the establishment of a concentration threshold that would allow creation of classification rules was not possible since there was variability within the group. Consequently, the establishment of chemometric models was necessary. A non-targeted strategy using 275 features is proposed. Orthogonal partial least squares-discriminant analysis (OPLS-DA) allowed the differentiation of five botanical origins: thousand flowers, rosemary, albaida, orange blossom, and "others" (rest of the investigated botanical origins, since a limited number of samples was available). A success validation rate of 100% allowed the classification of 14 honeys with unknown botanical origin.

Rapid authentication of Chaenomeles species by visual volatile components fingerprints based on headspace gas chromatography-ion mobility spectrometry combined with chemometric analysis

INTRODUCTION

Chaenomeles, including Chaenomeles speciosa (ZP), Chaenomeles sinensis (GP), Chaenomeles tibetica (XZ), and Chaenomeles japonica (RB), has been widely used as food in China for thousands of years. However, only ZP, was recorded to be the authentic medicinal Chaenomeles. Therefore, the rapid and accurate method for the authenticity identification of Chaenomeles species is urgently needed.

OBJECTIVE

To develop a method for rapid differentiation of Chaenomeles species.

METHODS

The visual volatile components fingerprints based on headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) combined with chemometric analysis, including principal component analysis (PCA), linear discriminant analysis (LDA) and partial least-squares discriminant analysis (PLS-DA), were utilised for the authentication of Chaenomeles species.

RESULTS

The visual volatile components fingerprints by the GC-IMS intuitively showed the distribution features of the volatile components for different Chaenomeles samples. The LDA and PLS-DA models successfully discriminated Chaenomeles species with original discrimination accuracy of 100%. Fifteen volatile compounds (VOCs) (peaks 9, 12, 13, 19, 23, 24, 35, 48, 57, 65, 67, 76, 79, 80, 83) were selected as the potential species-specific markers of Chaenomeles via variable importance of projection (VIP > 1.2) and one-way analysis of variance (P < 0.05).

CONCLUSIONS

This study showed that the visual volatile components fingerprints by HS-GC-IMS combined with chemometric analysis is a meaningful method in the Chaenomeles species authentication.

Differentiation of Monofloral Honey Using Volatile Organic Compounds by HS-GCxIMS

Honey is a natural product and can be described by its botanical origin, determined by the plants from which the bees collect nectar. It significantly influences the taste of honey and is often used as a quality criterion. Unfortunately, this opens up the possibility of food fraud. Currently, various methods are used to check the authenticity of monofloral honey. The laborious, manual melissopalynology is considered an essential tool in the verification process. In this work, the volatile organic compounds obtained from the headspace of honey are used to prove their authenticity. The headspace of 58 honey samples was analyzed using a commercial easy-to-use gas chromatography-coupled ion mobility spectrometer with a headspace sampler (HS-GCxIMS). The honey samples were successfully differentiated by their six different botanical origins using specific markers with principal component analysis in combination with linear discriminant analysis. In addition, 15 honey-typical compounds were identified using measurements of reference compounds. Taking a previously published strategy, retention times of marker compounds were correlated with GC-coupled mass spectrometry (GC-MS) measurements to assist in the identification process.

Compound synthesizing profiling based on quantitative HPLC fingerprints combined with antioxidant activity analysis for Zhizi Jinhua pills

BACKGROUND

Compound drug Zhizi Jinhua Pills (ZZJHP) is composed of 8 herbal medicines (HMs), and it is necessary to control the HMs to ensure its holistic quality.

PURPOSE

To establish a quality monitoring method for ZZJHP from precise control of multiple active ingredients to contour control of fingerprint, to calculate the contribution of HMs and predict the quality of compound drugs.

METHODS

In this study, HPLC method was established for content determination of 11 analytes and fingerprint assessment. In vivo and in vitro studies of antioxidant activity were performed, Orthogonal Partial Least Squares analysis was applied for spectrum-effect correlation between antioxidant activity and HPLC fingerprint. The compound synthesizing fingerprint (CSF) of ZZJHP was fitted with 8 HMs, and the contribution of the single herb to prescription was evaluated by Sub-quantified profiling method.

RESULTS

The content of 11 analytes and fingerprints of ZZJHP were measured simultaneously, 32 batches of samples were divided into 6 grades. In vivo and in vitro researches suggested significant antioxidant activity capacity of ZZJHP. Then, spectrum-effect relationship study showed that 24 of the 30 fingerprint peaks had antioxidant activity. By prescription and decomposition profiling, the qualitative and quantitative contributions of the 8 herbs were revealed in turn. The negative solution experiment proved that CSF could accurately predict the quality of composite drugs.

CONCLUSION

The intelligent prediction strategy could intervene at the source to realize rapid screening of HMs and prediction of the quality of preparations, which could provide guidance for the use of HMs and improve the quality of composite drugs.

Characterization of Volatile Compounds by HS-GC-IMS and Chemical Composition Analysis of Colored Highland Barley Roasted at Different Temperatures

Colored highland barley (CHB) is featured with its potential health-promoting benefits. CHB is frequently processed through roasting, which changes its volatile smells, color, and composition. The objective of this work was to establish the volatile fingerprints of CHB that had been roasted at different temperatures using E-nose and headspace-gas-chromatography-ion-mobility spectroscopy (HS-GC-IMS). The findings showed that roasting increased the relative contents of pyrazines, aldehydes, and ketones while decreasing the relative contents of alcohols, esters, and sulfides. Pyrazines were identified as the markers for volatile substances of the roasted CHB (RCHB). The outcomes of the principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that the volatiles could easily distinguish between raw CHB and RCHB instead of differentiating between CHB roasted at different temperatures. Additionally, after roasting, the color characteristics and CHB constituents underwent changes, and the effect of roasting temperature on these changes differed depending on the cultivar. Protein, free amino acids, and flavonoids appeared to primarily participate in the variations of volatile substances, and the free fluorescence intermediary compounds might involve changes in color parameters and aromas. These findings improved our knowledge of the volatiles in CHB that were roasted under various conditions.

Quantitative determination of acacia honey adulteration by terahertz-frequency dielectric properties as an alternative technique

The dielectric characteristics in the terahertz region contribute to a revealing insight into the material components and provide intermolecular information. The dielectric properties of adulterated honey, described as the real and imaginary parts of the complex dielectric constant (Re[ε] and Im[ε]), were obtained from 0.3 to 1.5 THz. The relationship between invert syrup proportions and complex dielectric constants at different frequencies implied the possibility of using the dielectric property as an indicator of honey authenticity. The selected effective dielectric variables of Re[ε] and Im[ε] and their combination were chosen by stability competitive adaptive reweighted sampling (SCARS) algorithm and then used to establish PLS models. The accuracy and uncertainty result revealed SCARS-PLS model based on the combination of Re[ε] and Im[ε] is the best model relatively. These findings indicated the potential utility of this rapid, non-destructive, and on-site method for authenticity verification.

Evaluation of Mutton Quality Characteristics of Dongxiang Tribute Sheep Based on Membership Function and Gas Chromatography and Ion Mobility Spectrometry

Dongxiang tribute sheep have a history of use in food dishes such as "Dongxiang Handgrip," which dates back hundreds of years and is a favorite halal food in northwestern China. However, little is known about the mutton quality characteristics of Dongxiang tribute sheep. Here, we measured the sensory characteristics, nutritional quality, and flavor substances to comprehensively evaluate the mutton quality characteristics of these sheep. The mutton qualities of Dongxiang tribute, Tibetan, Ujumqin, and Hu sheep were comprehensively evaluated by membership function. Subsequently, the volatile components in mutton samples from 30 Dongxiang tribute sheep were detected via gas chromatography and ion mobility spectrometry (GC-IMS), and their fingerprints were established. The result of meat quality revealed that the shear force, the contents of protein, essential amino acid (EAA), non-essential amino acid (NEAA), and n-6/n-3 ratio of Dongxiang tribute mutton were better than the other three breeds. Membership functions were calculated for 10 physical and chemical indexes of mutton quality, and the comprehensive membership function values of the four breeds in order of highest to lowest mutton quality were Tibetan sheep (0.76) > Dongxiang tribute sheep (0.49) > Hu sheep (0.46) > Ujumqin sheep (0.33). Thirty volatile compounds were identified via GC-IMS: seven alcohols, eight aldehydes, five ketones, two esters, two phenols, one ether, one furan, one acid, two hydrocarbons, and one pyrazine. Ketones, aldehydes, and alcohols were the main volatile compounds forming the flavor of Dongxiang tribute sheep mutton. The reliability of the results was validated by PCA (principal component analysis) and similarity analyses. Our results provide reference value for consumers of mutton in China.

Insights into the Aroma Profile of Sauce-Flavor Baijiu by GC-IMS Combined with Multivariate Statistical Analysis

Aroma is among the principal quality indicators for evaluating Baijiu. The aroma profiles of sauce-flavor Baijiu produced by 10 different manufacturers were determined by GC-IMS. The results showed that GC-IMS could effectively separate the volatile compounds in Baijiu, and a total of 80 consensus volatile compounds were rapidly detected from all samples, among which 29 volatile compounds were identified, including 5 alcohols, 14 esters, 2 acids, 2 ketones, 5 aldehydes, and 1 furan. According to the differences in aroma profile found by multivariate statistical analysis, these sauce-flavor Baijiu produced by 10 different manufacturers can be further divided into three types. The relative odor activity value of the identified volatile compounds indicated that seven volatile compounds contributed most to the aroma of sauce-flavor Baijiu in order of aroma contribution rate, and they were ethyl hexanoate, ethyl pentanoate, ethyl 2-methylbutanoate, ethyl octanoate (also known as octanoic acid ethyl ester), ethyl 3-methylbutanoate, ethyl butanoate, and ethyl isobutyrate. Correspondingly, the main aromas of these sauce-flavor Baijiu produced by 10 different manufacturers were sweet, fruity, alcoholic, etheral, cognac, rummy, and winey. On the one hand, this study proved that GC-IMS is well adapted to the detection of characteristic volatile aroma compounds and trace compounds in Baijiu, which is of positive significance for improving the aroma fingerprint and database of sauce-flavor Baijiu. On the other hand, it also enriched our knowledge of Baijiu and provided references for the evaluation and regulation of the flavor quality of sauce-flavor Baijiu.

Volatilomics-Based Microbiome Evaluation of Fermented Dairy by Prototypic Headspace-Gas Chromatography–High-Temperature Ion Mobility Spectrometry (HS-GC-HTIMS) and Non-Negative Matrix Factorization (NNMF)

Fermented foods, such as yogurt and kefir, contain a versatile spectrum of volatile organic compounds (VOCs), including ethanol, acetic acid, ethyl acetate, and diacetyl. To overcome the challenge of overlapping peaks regarding these key compounds, the drift tube temperature was raised in a prototypic high-temperature ion mobility spectrometer (HTIMS). This HS-GC-HTIMS was used for the volatilomic profiling of 33 traditional kefir, 13 commercial kefir, and 15 commercial yogurt samples. Pattern recognition techniques, including principal component analysis (PCA) and NNMF, in combination with non-targeted screening, revealed distinct differences between traditional and commercial kefir while showing strong similarities between commercial kefir and yogurt. Classification of fermented dairy samples into commercial yogurt, commercial kefir, traditional mild kefir, and traditional tangy kefir was also possible for both PCA- and NNMF-based models, obtaining cross-validation (CV) error rates of 0% for PCA-LDA, PCA-kNN (k = 5), and NNMF-kNN (k = 5) and 3.3% for PCA-SVM and NNMF-LDA. Through back projection of NNMF loadings, characteristic substances were identified, indicating a mild flavor composition of commercial samples, with high concentrations of buttery-flavored diacetyl. In contrast, traditional kefir showed a diverse VOC profile with high amounts of flavorful alcohols (including ethanol and methyl-1-butanol), esters (including ethyl acetate and 3-methylbutyl acetate), and aldehydes. For validation of the results and deeper understanding, qPCR sequencing was used to evaluate the microbial consortia, confirming the microbial associations between commercial kefir and commercial yogurt and reinforcing the differences between traditional and commercial kefir. The diverse flavor profile of traditional kefir primarily results from the yeast consortium, while commercial kefir and yogurt is primarily, but not exclusively, produced through bacterial fermentation. The flavor profile of fermented dairy products may be used to directly evaluate the microbial consortium using HS-GC-HTIMS analysis.

Adulteration detection of essence in sesame oil based on headspace gas chromatography-ion mobility spectrometry

Sesame oil is a traditional and delicious edible oil in China and Southeast Asia with a high price. However, sesame oil essence was often illegally added to cheaper edible oils to counterfeit sesame oil. In this study, a rapid and accurate headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) method was proposed to detect the counterfeit sesame oil where the other cheap oils were adulterated with essence. Combined with chemometric methods including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and random forest (RF), authentic and counterfeit sesame oils adulterated with sesame essence (0.5%, w/w) were easily separated into two groups. More importantly, 2-methylbutanoic acid, 2-furfurylthiol, methylpyrazine, methional, and 2,5-dimethylpyrazine were found to be markers of sesame essence, which were used to directly identify the sesame essence. The determination of volatile compounds based on HS-GC-IMS was proven to be an effective method for adulteration detection of essence in sesame oil.

Trapped Ion Mobility Incorporated in LC-HRMS Workflows as an Integral Analytical Platform of High Sensitivity: Targeted and Untargeted 4D-Metabolomics in Extra Virgin Olive Oil

Trapped ion mobility spectrometry (TIMS) is a promising technique for the separation of isomers based on their mobility. In the present work, TIMS coupled to liquid chromatography (LC) and high-resolution mass spectrometry (HRMS) was applied as a comprehensive analytical platform to address authenticity challenges, focusing on extra virgin olive oil (EVOO). Isomers detected in EVOO's phenolic fraction, classified into secoiridoids group, were successfully separated. Thanks to parallel accumulation serial fragmentation (PASEF) acquisition mode, high-quality spectra were obtained, facilitating identification. Moreover, a four-dimensional (4D) untargeted metabolomics approach was implemented to evaluate EVOO's global profile in cases of both variety and geographical origin discrimination. Potential authenticity markers, attributed to isomers, were successfully identified through the proposed workflow that incorporates ion mobility information along with LC-HRMS analytical evidence (i.e., mass accuracy, retention time, isotopic pattern, MS/MS fragmentation). Our study establishes LC-TIMS-HRMS in food authenticity and highlights mobility-enhanced metabolomics in four dimensions.

MIR spectroscopy versus MALDI-ToF-MS for authenticity control of honeys from different botanical origins based on soft independent modelling by class analogy (SIMCA) - A clash of techniques?

In this study, highly reproducible MIR spectroscopy and highly sensitive MALDI-ToF-MS data were directly compared for the metabolomic profiling of monofloral and multifloral honey samples from three different botanical origins canola, acacia, and honeydew. Subsequently, three different classification models were applied to the data of both techniques, PCA-LDA, PCA- kNN, and soft independent modelling by class analogy (SIMCA) as class modelling technique. All monofloral external test set samples were classified correctly by PCA-LDA and SIMCA with both data sets, while multifloral test set samples could only be identified as outliers by the SIMCA technique, which is a crucial aspect in the authenticity control of honey. The comparison of the two used analytical techniques resulted in better overall classification results for the monofloral external test set samples with the MIR spectroscopic data. Additionally, clearly more multifloral external samples were identified as outliers by MIR spectroscopy (91.3%) as compared to MALDI-ToF-MS (78.3%). The results indicate that the high reproducibility of the used MIR technique leads to a generally better ability of separating monofloral honeys and in particular, identifying multifloral honeys. This demonstrates that benchtop-based techniques may operate on an eye-level with high-end laboratory-based equipment, when paired with an optimal data analysis strategy.

Validation Study on the Simultaneous Quantitation of Multiple Wine Aroma Compounds with Static Headspace-Gas Chromatography-Ion Mobility Spectrometry

A new quantitative method based on static headspace-gas chromatography-ion mobility spectrometry (SHS-GC-IMS) is proposed, which enables the simultaneous quantitation of multiple aroma compounds in wine. The method was first evaluated for its stability and the necessity of using internal standards as a quality control measure. The two major hurdles in applying GC-IMS in quantitation studies, namely, nonlinearity and multiple ion species, were also investigated using the Boltzmann function and generalized additive model (GAM) as potential solutions. Metrics characterizing the model performance, including root mean squared error, bias, limit of detection, limit of quantitation, repeatability, reproducibility, and recovery, were investigated. Both nonlinear fitting methods, Boltzmann function and GAM, were able to return desirable analytical outcomes with an acceptable range of error. Potential pitfalls that would cause inaccurate quantitation, that is, effects of ethanol content and competitive ionization, were also discussed. The performance of the SHS-GC-IMS method was subsequently compared against that of a currently established method, namely, GC-MS, using commercial wine samples. These findings provide an initial validation of a GC-IMS-based quantitation method, as well as a starting point for further enhancing the analytical scope of GC-IMS.

The Aroma Fingerprints and Discrimination Analysis of Shiitake Mushrooms from Three Different Drying Conditions by GC-IMS, GC-MS and DSA

The aroma fingerprints and discrimination analysis of shiitake mushrooms under different drying conditions were performed by GC-IMS, GC-MS, and descriptive sensory analysis (DSA) with advanced chemometric methods. Three samples (A, B, and C) were treated with varied drying degree and rate. The sample A and C were at the same drying degree and the sample B and C were at the same drying rate. The GC-IMS volatile fingerprints, including the three-dimensional topographic map, topographic map, and gallery plot, showed that 29 compounds showed higher signal intensities in sample B. Moreover, 28 volatile compounds were identified by HS-SPME-GC-MS and only 8 compounds were ever detected by GC-IMS. The sample B not only had more varieties of volatile compounds, but also showed significant higher contents than sample A and C, especially C8 compounds (p < 0.05). Additionally, sample B showed the highest intensity in mushroom-like, chocolate-like, caramel, sweat, seasoning-like, and cooked potato-like odors by DSA. PCA, fingerprint similarity analysis (FSA) and PLSR further demonstrated that the sample B was different from sample A and C. These results revealed that samples with different drying degree were different and drying degree exerted more influence on the volatile flavor quality than the drying rate. This study will provide a foundation and establish a set of comprehensive and objective methods for further flavor analysis.

Discrimination and characterization of the volatile organic compounds in eight kinds of huajiao with geographical indication of China using electronic nose, HS-GC-IMS and HS-SPME-GC-MS

Huajiao (Zanthoxylum bungeanum maxim. and Zanthoxylum armatum DC.) is a highly prized spice in China due to its distinctive aroma and taste. The volatile organic compounds (VOCs) of eight kinds of red and green huajiao which varied according to geographical indication of P.R. China were evaluated by electronic nose (E-nose), headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). Results showed that red huajiao emitted more terpenes, esters, and fewer alcohols than green huajiao. Partial least squares-discriminant analysis based on GC-MS and GC-IMS data was revealed a good classifying tool for huajiao from different original habitats. Four and eight aroma substances were selected as the potential markers by the variable importance in projection (VIP) variable selection method, respectively. The results of the current study provide a useful basis in the huajiao aroma difference study. Additionally, a rapid huajiao aroma analysis method using GC-IMS was developed.

Full Workflows for the Analysis of Gas Chromatography-Ion Mobility Spectrometry in Foodomics: Application to the Analysis of Iberian Ham Aroma

Gas chromatography—ion mobility spectrometry (GC-IMS) allows the fast, reliable, and inexpensive chemical composition analysis of volatile mixtures. This sensing technology has been successfully employed in food science to determine food origin, freshness and preventing alimentary fraud. However, GC-IMS data is highly dimensional, complex, and suffers from strong non-linearities, baseline problems, misalignments, peak overlaps, long peak tails, etc., all of which must be corrected to properly extract the relevant features from samples. In this work, a pipeline for signal pre-processing, followed by four different approaches for feature extraction in GC-IMS data, is presented. More precisely, these approaches consist of extracting data features from: (1) the total area of the reactant ion peak chromatogram (RIC); (2) the full RIC response; (3) the unfolded sample matrix; and (4) the ion peak volumes. The resulting pipelines for data processing were applied to a dataset consisting of two different quality class Iberian ham samples, based on their feeding regime. The ability to infer chemical information from samples was tested by comparing the classification results obtained from partial least-squares discriminant analysis (PLS-DA) and the samples’ variable importance for projection (VIP) scores. The choice of a feature extraction strategy is a trade-off between the amount of chemical information that is preserved, and the computational effort required to generate the data models.

Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning

Due to its high sensitivity and resolving power, gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It is a robust and easy-to-handle technique, which has recently gained attention for non-targeted screening (NTS) approaches. In this article, the general working principles of GC-IMS are presented. Next, the workflow for NTS using GC-IMS is described, including data acquisition, data processing and model building, model interpretation and complementary data analysis. A detailed overview of recent studies for NTS using GC-IMS is included, including several examples which have demonstrated GC-IMS to be an effective technique for various classification and quantification tasks. Lastly, a comparison of targeted and non-targeted strategies using GC-IMS are provided, highlighting the potential of GC-IMS in combination with NTS.

Advanced techniques in edible oil authentication: A systematic review and critical analysis

Adulteration of edible substances is a potent contemporary food safety issue. Perhaps the overt concern derives from the fact that adulterants pose serious ill effects on human health. Edible oils are one of the most adulterated food products. Perpetrators are adopting ways and means that effectively masks the presence of the adulterants from human organoleptic limits and traditional oil adulteration detection techniques. This review embodies a detailed account of chemical, biosensors, chromatography, spectroscopy, differential scanning calorimetry, non-thermal plasma, dielectric spectroscopy research carried out in the area of falsification assessment of edible oils for the past three decades and a collection of patented oil adulteration detection techniques. The detection techniques reviewed have some advantages and certain limitations, chemical tests are simple; biosensors and nuclear magnetic resonance are rapid but have a low sensitivity; chromatography and spectroscopy are highly accurate with a deterring price tag; dielectric spectroscopy is rapid can be portable and has on-line compatibility; however, the results are susceptible to variation of electric current frequency and intrinsic factors (moisture, temperature, structural composition). This review paper can be useful for scientists or for knowledge seekers eager to be abreast with edible oil adulteration detection techniques.

HS-GC-IMS detection of volatile organic compounds in Acacia honey powders under vacuum belt drying at different temperatures

Honey is a commodity of great nutritional value, but deep-processed honey products are uncommon. Herein, we used vacuum belt dryer to dry Acacia honey at 60°C, 70°C, and 80°C, prepared it into powder, and analyzed its volatile compound differences. We established HS-GC-IMS method to detect the volatile organic compounds (VOCs) of these three Acacia honey powders (AHPs). In total, 77 peaks were detected, and 23 volatile compounds were identified, including eight aldehydes, six ketones, three furans, one alcohol, one phenol, one lactone, one ester, one acid, and one nitrile. Moreover, principal component analysis (PCA) and fingerprint similarity analysis based on the Euclidean distance distinguished the three heating temperature treatments. Clearly, it was concluded that there are significant differences in volatile substances at different tested temperatures, and when the AHP was incubated at 80°C, more volatile compounds were detected.

Identification of iron ore brands by multi-component analysis and chemometric tools

Identification of iron ore brand is one of the most important precautions against fraud in the international iron ore trade. However, the identification of iron ore brand can be sophisticated, due to fact that the role played by multi-component in iron ore brand identification was unclear. This study aims to establish an objective approach to identify iron ore brands based on their multi-component content. A total of 1469 batches of iron ore samples, covering 16 commonly consumed iron ore brands from 3 countries, were analyzed for multi-component content. It was investigated that 10 primary, minor, and trace chemical components varied significantly in contents according to different iron ore brands. This prospective relationship between the multi-component contents and the iron ore brand was then used to place 16 brands into 12 groups and 8 brands of them were correctly identified by a flowchart. Furthermore, chemometric tools such as linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and support vector machine (SVM) were applied to construct models to simultaneously discriminate 16 iron ore brands. Both the training and test results proved that LDA performed best in this circumstance. In the LDA method, MgO, Fe, SiO₂, and P are the feature components contributing the most to the identification of 16 brands of iron ore. Based on the findings, the multi-components are distinct variables to establish an internationally recognized model of iron ore brand identification.

Honey Phenolic Compound Profiling and Authenticity Assessment Using HRMS Targeted and Untargeted Metabolomics

Honey consumption is attributed to potentially advantageous effects on human health due to its antioxidant capacity as well as anti-inflammatory and antimicrobial activity, which are mainly related to phenolic compound content. Phenolic compounds are secondary metabolites of plants, and their content in honey is primarily affected by the botanical and geographical origin. In this study, a high-resolution mass spectrometry (HRMS) method was applied to determine the phenolic profile of various honey matrices and investigate authenticity markers. A fruitful sample set was collected, including honey from 10 different botanical sources (n = 51) originating from Greece and Poland. Generic liquid-liquid extraction using ethyl acetate as the extractant was used to apply targeted and non-targeted workflows simultaneously. The method was fully validated according to the Eurachem guidelines, and it demonstrated high accuracy, precision, and sensitivity resulting in the detection of 11 target analytes in the samples. Suspect screening identified 16 bioactive compounds in at least one sample, with abscisic acid isomers being the most abundant in arbutus honey. Importantly, 10 markers related to honey geographical origin were revealed through non-targeted screening and the application of advanced chemometric tools. In conclusion, authenticity markers and discrimination patterns were emerged using targeted and non-targeted workflows, indicating the impact of this study on food authenticity and metabolomic fields.

Honey authenticity: analytical techniques, state of the art and challenges

Honey is a high-value, globally consumed, food product featuring a high market price strictly related to its origin. Moreover, honey origin has to be clearly stated on the label, and quality schemes are prescribed based on its geographical and botanical origin. Therefore, to enhance food quality, it is of utmost importance to develop analytical methods able to accurately and precisely discriminate honey origin. In this study, an all-time scientometric evaluation of the field is provided for the first time using a structured keyword on the Scopus database. The bibliometric analysis pinpoints that the botanical origin discrimination was the most studied authenticity issue, and chromatographic methods were the most frequently used for its assessment. Based on these results, we comprehensively reviewed analytical techniques that have been used in honey authenticity studies. Analytical breakthroughs and bottlenecks on methodologies to assess honey quality parameters using separation, bioanalytical, spectroscopic, elemental and isotopic techniques are presented. Emphasis is given to authenticity markers, and the necessity to apply chemometric tools to reveal them. Altogether, honey authenticity is an ever-growing field, and more advances are expected that will further secure honey quality.

ICP-MS-based ionomics method for discriminating the geographical origin of honey of Apis cerana Fabricius

The identification of geographical origin is an important factor in evaluating the authenticity of honey. However, at present, there are few studies concerning the honey of Apis cerana Fabricius (A. cerana, Asiatic honeybee). To identify geographical origin, we used two common methods (multi-physicochemical parameters and phenolic chromatographic fingerprints) but achieved only poor identification. To compensate for this shortcoming, we established an ICP-MS-based ionomics method using 18 elements in 27 A. cerana honey samples from three different areas in Shaanxi Province, China. Multivariate analysis showed that significant differences in contents can be used to discriminate the geographical origin of A. cerana honey. The method was further validated using an independent test set of 11 samples with 90.91% accuracy, demonstrating its potential for the identification and prediction of the geographical origin of honey.

Flavor Changes of Tricholoma matsutake Singer under Different Processing Conditions by Using HS-GC-IMS

Headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was used to analyze the changes to volatile compounds in fried Tricholoma matsutake Singer under different heating temperatures and times. A total of 40 signals that corresponded to 24 compounds were identified through this approach. Differences in volatile compounds of T. matsutake samples were shown in topographic plots and fingerprints. The heating temperatures were more important than the heating times in affecting the volatile compounds. Moreover, changes to the main volatile compounds in T. matsutake under different processing conditions were based on the thermal decomposition and a series of chemical reactions of C8 compounds. Principal component analysis (PCA) results showed that samples under different processing conditions could be effectively distinguished. Hence, the combination of HS-GC-IMS and PCA can identify and classify the volatile compounds of T. matsutake quickly and sensitively. This study provides a new supplementary analytical method for the rapid identification of T. matsutake. The above results can provide a theoretical basis for the quality control and change mechanism of flavor in the processing of traditional edible fungi products.