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

Overview of industrial food fraud and authentication through chromatography technique and its impact on public health

Food fraud is widespread nowadays in the food products supply chain, from raw materials processing to the final product and during storage and transport. The most frequent fraud is practiced in staple food commodities like cereals. Their origin, variety, genotype, and bioactive compounds are altered to deceive consumers. Similarly, in various food sectors like beverage, baking, and confectionary, items like melamine, flour improver, and food colors are used in the market to temple consumers. To tackle food fraud and authentication, non-destructive techniques are being used. These techniques have limitations like lack of standardization, interference from multiple absorbing species, ambiguous results, and time-consuming to perform, depending on the type, size, and location of the system proved difficult to quantify the samples of adulteration. Chromatography has been introduced as an effective technique. It serves to safeguard public health due to its detection capabilities. Chromatography proved a crucial tool against fraudulent practices to preserve consumer trust.

Discrimination of coal geographical origins through HS-GC-IMS assisted with machine learning algorithms in larceny case

The process of globalization and industrialization has resulted in a rise in the theft of coal and other related products, thereby becoming a focal point for forensic science. This situation has engendered an escalated demand for effective detection and monitoring technologies. The precise identification of coal trace evidence presents a challenge with current methods, owing to its minute quantity, fine texture, and intricate composition. In this study, we integrated machine learning with the identification of volatiles to accurately differentiate coal geographical origins through the application of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). The topographic distribution of volatiles in coals was visually depicted to elucidate the subtle distinctions through spectra and fingerprint analysis. Additionally, four supervised machine learning algorithms were developed to quantitatively predict the geographical origins of natural coals utilizing the HS-GC-IMS dataset, and these were subsequently compared with unsupervised models. Remarkable volatile compounds were identified through the quantitative analysis and optimal Random Forest model, which offered a rapid readout and achieved an average accuracy of 100 % in coal identification. Our findings indicate that the integration of HS-GC-IMS and machine learning is anticipated to enhance the efficiency and accuracy of coal geographical traceability, thereby providing a foundation for litigation and trials.

Two flavors in adulterated sesame oil: discovery, confirmation, and content regularity study

Exploring and accurately detecting new adulteration markers in sesame oil is an important measure for sesame oil adulteration monitoring. In this study, two endogenous flavors sulfurol and γ-nonalactone which can be used as potential adulteration markers were first discovered in sesame oil and accurately quantified. First, the two endogenous flavors were discovered using gas chromatography-mass spectrometry (GC-MS), and their structures were confirmed by comparing the mass spectrograms with the NIST spectral library. Then the liquid chromatography-tandem mass spectrometry (LC-MS/MS) method using direct methanol extraction pretreatment and vanillin-D3 as an internal standard was developed for rapid quantitation and application. The method was successfully validated with recoveries ranging from 88.5% to 102.2% and relative standard deviations between 2.6% and 10.5% (n = 6). The combined method of GC-MS and LC-MS/MS was indicated to be efficient and highly sensitive for detection of sulfurol and γ-nonalactone in edible oil. Subsequently, 31 sesame oils from the market were detected, revealing that 31 samples contained the identified flavors within a relatively consistent range. However, the concentration of these flavor substances in one sample was abnormally high, indicating that there was a potential risk of adulteration. Therefore, the developed method shows good potential for quality evaluation and adulteration screening of sesame oil.

Characterization of Flavor Compounds in Chinese Indigenous Sheep Breeds Using Gas Chromatography-Ion Mobility Spectrometry and Chemometrics

This study analyzed the flavor compounds in the meat of four indigenous breeds of Chinese sheep through the use of gas chromatography-ion mobility spectrometry (GC-IMS). GC-IMS provided information on the characteristics and strength of 71 volatile flavor compounds (monomers and dimers), with aldehydes, alcohols and ketones being the most abundant in all types of sheep meat. The compounds with higher intensity peaks in the sheep meat were aldehydes (n-nonanal, octanal, heptanal, 3-methylbutanal, and hexanal), alcohols (1-octen-3-ol, hexanol, and pentanol), ketones (3-hydroxy-2-butanon, 2-butanone, and 2-propanone), esters (methyl benzoate), and thiazole (trimethylthiazole). The volatile flavor components in the meat of the different breeds of sheep obtained via GC-IMS were further differentiated using principal component analysis. In addition, orthogonal partial least squares discriminant analysis (OPLS-DA) and variable importance on projection (VIP) were used to determine the characteristic flavor compounds in the meats of different breeds of sheep, and 21 differentially volatile components were screened out based on having a VIP above 1. These results indicate that GC-IMS combined with multivariate analysis is a convenient and powerful method for characterizing and discriminating sheep meat.

Analysis and application of volatile metabolic profiles of Escherichia coli: a preliminary GC-IMS-based study

Nosocomial infections caused by Escherichia coli (E. coli) may pose serious risks to patients, and early identification of pathogenic bacteria and drug sensitivity results can improve patient prognosis. In this study, we clarified the composition and relative content of volatile organic compounds (VOCs) generated by E. coli in tryptic soy broth (TSB) using gas chromatography-ion mobility spectrometry (GC-IMS). We explored whether imipenem (IPM) could be utilized to differentiate between carbapenem-sensitive E. coli (CSEC) and carbapenem-resistant E. coli (CREC). The results revealed that 36 VOCs (alcohols, aldehydes, acids, esters, ketones, pyrazines, heterocyclic compounds, and unknown compounds) were detected using GC-IMS. Besides, the results indicated that changes in the relative content of VOCs as well as changes in the signal intensity of fingerprints were able to assess the growth state of bacteria during bacterial growth and help identify E. coli. Lastly, under selective pressure of IPM, volatile fingerprints of E. coli could be employed as a model to distinguish CSEC from CREC strains.

Volatilomics as a tool to ascertain food adulteration, authenticity, and origin

Over recent years, there has been an increase in the number of reported cases of food fraud incidents, whereas at the same time, consumers demand authentic products of high quality. The emerging volatilomics technology could be the key to the analysis and characterization of the quality of different foodstuffs. This field of omics has aroused the interest of scientists due to its noninvasive, rapid, and cost-profitable nature. This review aims to monitor the available scientific information on the use of volatilomics technology, correlate it to the relevant food categories, and demonstrate its importance in the food adulteration, authenticity, and origin areas. A comprehensive literature search was performed using various scientific search engines and "volatilomics," "volatiles," "food authenticity," "adulteration," "origin," "fingerprint," "chemometrics," and variations thereof as keywords, without chronological restriction. One hundred thirty-seven relevant publications were retrieved, covering 11 different food categories (meat and meat products, fruits and fruit products, honey, coffee, tea, herbal products, olive oil, dairy products, spices, cereals, and others), the majority of which focused on the food geographical origin. The findings show that volatilomics typically involves various methods responsible for the extraction and consequential identification of volatile compounds, whereas, with the aid of data analysis, it can handle large amounts of data, enabling the origin classification of samples or even the detection of adulteration practices. Nonetheless, a greater number of specific research studies are needed to unlock the full potential of volatilomics.

Investigation of different ingredients affected the flavor changes of Yu-Shiang shredded pork by using GC-IMS and GC-MS combined with E-nose and E-tongue

The objective of this study was to assess and compare the characteristics of Yu-Shiang Shredded Pork made with different ingredients by using physicochemical measurements and intelligent sensory analysis. The study revealed that there were 18 varied amino acids present, with the taste active values (TAVs) of Leu, Glu, Asp, Asn, and Ala all higher than 1.0. Intelligent sensory analysis showed that the samples lacking lettuce and fungus had similar aromas and flavors, while those lacking shredded pork and pickled chillies had distinct aromas and flavors. Moreover, VOCs (volatile organic compounds) were detected in five types of Yu-Shiang Shredded Pork, with 43, 42, 53, 36, and 50 identified in GC-MS (gas chromatography-mass spectrometry), respectively. Olefins (20.62 %-30.93 %) were the most abundant. GC-IMS (gas chromatography-ion mobility spectrometry) detected 68 volatiles flavor compounds, with esters having a significantly higher relative content than other compounds, indicating their significant role in the flavor formation process of Yu-Shiang Shredded Pork. Furthermore, the Orthogonal Partial Least Squares-discriminant analysis (OPLS-DA) model analysis identified 19 marker compounds that could differentiate the five types of Yu-Shiang Shredded Pork. These fundamental results lay the groundwork for future research on the connection between ingredients and the flavor characteristics of Yu-Shiang Shredded Pork.

Use of GC-IMS and Stoichiometry to Characterize Flavor Volatiles in Different Parts of Lueyang Black Chicken during Slaughtering and Cutting

Chilled and cut chicken is preferred by consumers for its safeness and readiness to cook. To evaluate the quality characteristics of various chilled chicken products, differences in volatile organic components (VOCs) of six different cut parts (breast, back, leg, heart, liver, and gizzard) of Lueyang black chicken were characterized through gas chromatography-ion mobility spectroscopy (GC-IMS) combined with stoichiometry. A total of 54 peaks in the signal of VOCs were detected by GC-IMS, and 43 VOCs were identified by qualitative analysis. There were 22 aldehydes (20.66-54.07%), 8 ketones (25.74-62.87%), 9 alcohols (4.17-14.69%), 1 ether (0.18-2.22%), 2 esters (0.43-1.54%), and 1 furan (0.13-0.52%), in which aldehydes, ketones, and alcohols were the main categories. Among the six cut parts, the relative content of aldehydes (54.07%) was the highest in the gizzard, and the relative content of ketones (62.87%) was the highest in the heart. Meanwhile, the relative content of alcohols (14.69%) was the highest in the liver. Based on a stable and reliable predictive model established by orthogonal partial least squares-discriminant analysis (OPLS-DA), 3-hydroxy-2-butanone (monomer and dimer), acetone, 2-butanone monomer, hexanal (monomer and dimer), isopentyl alcohol monomer, and n-hexanol monomer were picked out as characteristic VOCs based on variable importance in projection (VIP value > 1.0, p < 0.05). Principal component analysis (PCA) and the clustering heatmap indicated that the characteristic VOCs could effectively distinguish the six cut parts of Lueyang black chicken. The specific VOCs responsible for flavor differences among six different cut parts of Lueyang black chicken were hexanal (monomer and dimer) for the gizzard, 2-butanone monomer and hexanal dimer for the breast, hexanal monomer for the back, 3-hydroxy-2-butanone monomer for the leg, 3-hydroxy-2-butanone (monomer and dimer) for the heart, and acetone and isopentyl alcohol monomer for the liver. These findings could reveal references for quality assessment and development of chilled products related to different cut parts of Lueyang black chicken in the future.

Unravelling the metabolomic diversity of pigmented and non-pigmented traditional rice from Tamil Nadu, India

Rice metabolomics is widely used for biomarker research in the fields of pharmacology. As a consequence, characterization of the variations of the pigmented and non-pigmented traditional rice varieties of Tamil Nadu is crucial. These varieties possess fatty acids, sugars, terpenoids, plant sterols, phenols, carotenoids and other compounds that plays a major role in achieving sustainable development goal 2 (SDG 2). Gas-chromatography coupled with mass spectrometry was used to profile complete untargeted metabolomics of Kullkar (red colour) and Milagu Samba (white colour) for the first time and a total of 168 metabolites were identified. The metabolite profiles were subjected to data mining processes, including principal component analysis (PCA), Orthogonal Partial Least Square Discrimination Analysis (OPLS-DA) and Heat map analysis. OPLS-DA identified 144 differential metabolites between the 2 rice groups, variable importance in projection (VIP) ≥ 1 and fold change (FC) ≥ 2 or FC ≤ 0.5. Volcano plot (64 down regulated, 80 up regulated) was used to illustrate the differential metabolites. OPLS-DA predictive model showed good fit (R2X = 0.687) and predictability (Q2 = 0.977). The pathway enrichment analysis revealed the presence of three distinct pathways that were enriched. These findings serve as a foundation for further investigation into the function and nutritional significance of both pigmented and non-pigmented rice grains thereby can achieve the SDG 2.

Volatile aroma compounds of passion fruit seed Oils: HS-GC-IMS analysis and interpretation

The physicochemical properties, fatty acid composition and volatile aroma compounds of cold-pressed passion fruit seed oils were analyzed. The oils were rich in linoleic acid, oleic acid and volatile compounds. A total of 108 volatile compounds including 17 aldehydes, 23 alcohols, 21 esters, 19 ketones, 6 acids, 9 alkenes, 5 pyrazines and 8 others were identified using HS-GC-IMS. The significant differences of volatile compounds in the purple and yellow passion fruit seed oils were observed via the GalleryPlot graph and distinguished by principal component analysis. The results showed that acids, alcohols, esters and ketones were major aromatic compounds in purple passion fruit seed oils, which contribute to flavors such as flowery, fruity, creamy, yogurt. Whereas the contents of aldehydes, pyrazines, alkenes were higher in yellow passion fruit seed oils, which contributes to fatty and nutty odors. The findings filled in our understanding of volatilization characteristics in passion fruit seed oils.

Evaluation of Loquat Jam Quality at Different Cooking Times Based on Physicochemical Parameters, GC-IMS and Intelligent Senses

The study compared and analyzed the quality of loquat jam with different cooking times through physicochemical parameters, headspace-gas chromatography-ion migration spectroscopy (HS-GC-IMS) and intelligent senses. The results showed that with the prolongation of the cooking time, the color of loquat jam slowly deepened, the energy significantly increased, the adhesiveness, gumminess, hardness and chewiness enhanced, the free amino acid content increased from 22.40 to 65.18 mg/g. The organic acid content increased from 1.64 to 9.82 mg/g. Forty-seven volatile flavor compounds were identified in five types of loquat jam using HS-GC-IMS, among which the relative content of aldehydes was sharply higher than that of other chemical substances, playing an important role in the flavor formation of loquat jam. LJ0, LJ1 and LJ2 had higher aldehyde content, followed by LJ3 and LJ4 had the lowest aldehyde content. The orthogonal partial least squares-discriminant analysis (OPLS-DA) screened 15 marker compounds that could distinguish five types of loquat jam. The E-nose results showed a significant difference in olfactory sense between loquat jam cooked for 100 and 120 min. The E-tongue results corroborated the results of free amino acids (FAAs) and organic acids, indicating that the gustatory sense of loquat jam changed significantly when the cooking time reached 120 min. The results provided a basis for further research on the relationship between the cooking process and quality characteristics of loquat jam.

Design of an Artificial Intelligence of Things-Based Sesame Oil Evaluator for Quality Assessment Using Gas Sensors and Deep Learning Mechanisms

Traditional oil quality measurement is mostly based on chemical indicators such as acid value, peroxide value, and p-anisidine value. This process requires specialized knowledge and involves complex steps. Hence, this study designs and proposes a Sesame Oil Quality Assessment Service Platform, which is composed of an Intelligent Sesame Oil Evaluator (ISO Evaluator) and a Cloud Service Platform. Users can quickly assess the quality of sesame oil using this platform. The ISO Evaluator employs Artificial Intelligence of Things (AIoT) sensors to detect changes in volatile gases and the color of the oil during storage. It utilizes deep learning mechanisms, including Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) to determine and evaluate the quality of the sesame oil. Evaluation results demonstrate that the linear discriminant analysis (LDA) value is 95.13. The MQ2, MQ3, MQ4, MQ7, and MQ8 sensors have a positive correlation. The CNN combined with an ANN model achieves a Mean Absolute Percentage Error (MAPE) of 8.1820% for predicting oil quality, while the LSTM model predicts future variations in oil quality indicators with a MAPE of 0.44%. Finally, the designed Sesame Oil Quality Assessment Service Platform effectively addresses issues related to digitization, quality measurement, supply quality observation, and scalability.

Rapid and nondestructive identification of adulterate capsules by NIR spectroscopy combined with chemometrics

This study aims to develop a rapid and non-destructive method to identify counterfeit and substandard drugs, addressing the critical need for better quality control in drug production. According to the reasons for counterfeit products in actual production, the commonly used solid preparation excipients such as HPMC, MCC, Mg-St and Pregelatinized Starch, as well as three chemical drugs with similar efficacy to Guizhi-Fuling (GZFL) Capsule as adulterants, including Aspirin, Ibuprofen and Sinomenine Hydrochloride were selected and designed as adulteration samples with different levels of adulteration. NIR spectra were collected in a non-invasive mode and analyzed by one-class classification methods. The feasibility of using Near-infrared (NIR) spectroscopy as a detection method to qualitatively identify adulterated samples was explored at three packaging levels of powder, intact capsules and capsules in PVC. The differences between the samples were analyzed by NIR spectra comparison, cluster analysis and principal component analysis. The performance of SVM, OCPLS and DD-SIMCA models in dealing with the authentication of genuine and counterfeit products was established and compared. The results show that the spectra contain sample information and the adulterated samples could be discriminated correctly by established models. Moreover, applying appropriate spectral preprocessing methods can further improve the model's performance. In addition, a PLS regression model was developed to predict the adulteration levels of the three packing level samples, which yielded satisfactory results. This study highlights the potential of NIR spectroscopy combined with Chemometrics as a rapid and non-destructive testing analysis method to accurately identify counterfeit and substandard drugs, thereby ensuring drug quality.

A dedicated electronic nose combined with chemometric methods for detection of adulteration in sesame oil

Sesame oil (SO), one of the most popular and expensive edible oils, is prone to adulteration. In this study, the fatty acid profiles of pure sesame seed oil and samples adulterated with two less expensive edible oils (canola and sunflower) were analyzed using Gas Chromatography. A dedicated e-nose system was developed and tested on 15 mixtures of sesame-canola and sesame-sunflower samples. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Multi-Layered Perceptron (MLP) methods were utilized to identify adulteration through the evaluation of Volatile Organic Compound. Result of chromatography showed that most samples of sesame oil containing impurities at levels less than 30% were recognized incorrectly in the standard range of SO fatty acids. This is while the developed e-nose system was able to detect adulteration at much lower levels. According to the results, PCA and LDA methods can describe the data set variance with precision of 95.6% and 97%, respectively. The MLP model had better results compared to PCA and LDA, with high determination coefficient (R2 = 0.981) and low RMSE (0.0178). Results indicate that the e-nose system provided an effective non-destructive method to detect SO adulteration at levels as low as 5%, which GC was unable to detect.

Uncovering the differences in flavor volatiles of different colored foxtail millets based on gas chromatography-ion migration spectrometry and chemometrics

The differences of volatile organic compounds in commercially available foxtail millets with different colors (black, green, white and yellow) were assayed through gas chromatography-ion migration spectrometry (GC-IMS) to explore their volatile flavor characteristics. Fifty-five volatile components were found in various colored foxtail millets, including 25 kinds of aldehydes (accounting for 39.19-48.69%), 10 ketones (25.36-32.37%), 15 alcohols (20.19-24.11%), 2 ethers (2.29-2.45%), 2 furans (1.49-2.95%) and 1 ester (0.27-0.39%). Aldehydes, alcohols and ketones were the chief volatiles in different colored foxtail millet, followed by furans, esters and ethers. These identified volatile flavor components in various colored foxtail millets obtained by GC-IMS could be well distinguished by principal components and cluster analysis. Meanwhile, a stable prediction model was fitted via partial least squares-discriminant analysis (PLS-DA), in which 17 kinds of differentially volatile components were screened out based on variable importance in projection (VIP>1). These findings might provide certain information for understanding the flavor traits of colored foxtail millets in future.

Characterization and discrimination of flavor volatiles of different colored wheat grains after cooking based on GC-IMS and chemometrics

Changes in flavor volatiles of three colored wheat grains (black, green, and yellow) after cooking were detected via gas chromatography-ion migration spectrometry (GC-IMS) to explore corresponding volatile flavor traits. A total of 52 volatile chemicals were spotted among these cooked wheat grains, including 30 aldehydes (accounting for 73.86-83.78%), 11 ketones (9.53-16.98%), 3 alcohols (0.88-1.21%), 4 furans (4.82-7.44%), 2 esters (0.28-0.42%), and 2 pyrazines (0.18-0.32%). Aldehydes, ketones, and furans were the main volatile compounds in three different cooked wheat. For black-colored wheat, the relative contents of benzene acetaldehyde, benzaldehyde, 2-methyl butanal, and 3-methyl butanal were much higher (p < 0.05). For green-colored wheat, the relative contents of nonanal, 2-pentyl furan, (E)-hept-2-enal, 2-butanone, and acetone were significantly higher (p < 0.05). For yellow-colored wheat, the relative amounts of heptanal, hexanal, and pentanal were much higher (p < 0.05). The overall volatile substances of the three cooked wheat grains might be classified by GC-IMS data coupled with principal component analysis and heatmap clustering analysis. A reliable forecast set was established through orthogonal partial least squares-discriminant analysis (OPLS-DA), and 22 differential volatile compounds were screened out based on variable importance in projection (VIP) being higher than 1.0, as flavor markers for distinguishing the three cooked wheat grains. These results suggest that GC-IMS could be used for characterizing the flavor volatiles of different colored wheat, and the findings could contribute certain information for understand the aroma traits in different colored cooked wheat and related products in the future.

Identification of common buckwheat (Fagopyrum esculentum Moench) adulterated in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) flour based on near-infrared spectroscopy and chemometrics

Near-infrared spectroscopy (NIRS) presents great potential in the identification of food adulteration due to its advantages of nondestructive, simple, and easy to operate. In this paper, a method based on NIRS and chemometrics was proposed to predict the content of common buckwheat (Fagopyrum esculentum Moench) flour in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) flour. Partial least squares regression (PLSR) and support vector regression (SVR) models were used to analyze the spectrum data of adulterated samples and predict the adulteration level. Various preprocessing methods, parameter-optimization methods, and competitive adaptive reweighted sampling (CARS) wavelength-selection methods were used to optimize the model prediction accuracy. The results of PLSR and SVR modeling for predicting of Tartary buckwheat adulteration content were satisfactory, and the correlation coefficients of the optimum identification models were above 0.99. In conclusion, the combinations of NIRS and chemometrics indicated excellent predictive performance and applicability to analyze the adulteration of common buckwheat flour in Tartary buckwheat flour. This work provides a promising method to identify the adulteration of Tartary buckwheat flour and results obtained can give theoretical and data support for adulteration identification of agro-products.

Characterization of volatile organic compounds of different pigmented rice after puffing based on gas chromatography-ion migration spectrometry and chemometrics

The distinctness in volatile profiles of pigmented rice with various colors (black, green, purple, red, and yellow) after puffing were assayed through gas chromatography-ion migration spectrometry (GC-IMS) to explore their odor characteristics. Fifty-two volatile components were found in those puffed rice, including 27 kinds of aldehydes (accounting for 59.69-64.37 %), 9 ketones (25.55-29.73 %), 5 alcohols (2.45-5.29 %), 4 pyrazines (1.38-2.36 %), 3 ethers (0.81-1.27 %), 2 furans (0.95-1.39 %), 1 pyridine (1.0-1,16 %), and 1 pyrrole (0.59-0.71 %). Aldehydes and ketones were the two chief volatiles in different pigmented puffed rice. These identified volatile flavor components in various pigmented puffed rice obtained by GC-IMS might be well differentiated by principal component and cluster interpretation. Meanwhile, a stable prediction model was fitted via orthogonal partial least squares-discriminant analysis, and 19 differentially volatile components were screened out based on variable importance projection (VIP) above 1. These findings could add certain information for understanding the flavor profiles of pigmented puffed rice and related products.

Establishment and evaluation of multiple adulteration detection of camellia oil by mixture design

Multiple adulteration is a common trick to mask adulteration detection methods. In this study, the representative multiple adulterated camellia oils were prepared according to the mixture design. Then, these representative oils were employed to build two-class classification models and validate one-class classification model combined with fatty acid profiles. The cross-validation results indicated that the recursive SVM model possessed higher classification accuracy (97.9%) than PLS-DA. In OCPLS model, the optimal percentage of RO, SO, CO and SUO was 2.8%, 0%, 7.2%, 0% respectively in adulterated camellia oil, which is the most similar to the authentic camellia oils. Further validation showed that five adulterated oils with the optimal percentage could be correctly identified, indicating that the OCPLS model could identify multiple adulterated oils with these four cheaper oils. Moreover, this study serves as a reference for one class classification model evaluation and a solution for multiple adulteration detection of other foods.

Meat quality and flavor evaluation of Nanjing water boiled salted duck (NWSD) produced by different Muscovy duck (Cairina moschata) ingredients

Reports on meat quality and flavor evaluation of Nanjing water boiled salted duck (NWSD) produced by different Muscovy duck (Cairina moschata) ingredients are limited. To select a suitable Muscovy duck ingredient for the NWSD processing, six kinds of NWSD products were produced using female (65, 70, and 75 days) and male (75, 80, and 85 days) Muscovy duck ingredients. The meat quality, volatile organic compounds (VOCs), smell and taste were investigated by using colorimeter, texture analyzer, headspace-gas chromatography-ion mobility spectroscopy (HS-GC-IMS), electronic nose (E-nose), electronic tongue (E-tongue), etc. Results exhibited that 32 iconic VOCs were obtained by using partial least squares discrimination analysis (PLS-DA), principal component analysis (PCA), and variable importance projection (VIP) methods. 80-day-old male Muscovy duck showed moderate moisture and protein content, good meat texture and bright color, diverse iconic VOCs and clear differentiation, making it the preferred ingredient for NWSD processing.

Application of Maillard Reaction Products Derived Only from Enzymatically Hydrolyzed Sesame Meal to Enhance the Flavor and Oxidative Stability of Sesame Oil

The low-temperature roasting of sesame oil has become increasingly popular because of its nutritional benefits; however, the flavor is reduced. In order to improve the quality of sesame oil without exogenous addition, sesame meal was hydrolyzed and further used to prepare Maillard reaction products (MRPs) while protease hydrolysis (PH) and glucoamylase-protease hydrolysis (GPH) were used, and their respective Maillard products (PHM and GPHM) were added in the oils for reducing sugar and total sugar content determination, free amino acid determination, and color and descriptive sensory analysis, as well as electronic nose, SPME-GC-MS, odor activity value, and oxidative stability analyses. Results showed that the MRPs could be produced using the enzymatically hydrolyzed sesame meal without exogenous addition, and the oil flavor blended with GPHM (GPHM-SO) was significantly (p < 0.05) improved with the best sensory quality. The composition of pyrazines (119.35 μg/mL), furans (13.95 μg/mL), and sulfur substances (6.25 μg/mL) contributed positively to sensory properties in GPHM-SO, and 2,5-dimethylpyrazine, 2,6-dimethylpyrazine, and 2,3-dimethylpyrazine were characterized as the key flavor compounds with odor activity values of 7.01, 14.80, and 31.38, respectively. Furthermore, the oxidative stability of the oil was significantly improved with the addition of MRPs, and the shelf life of GPHM-SO was predicted to be extended by 1.9 times more than that of the crude oil based on the accelerated oxidation fitting analysis. In general, the MRPs derived only from sesame meal can enhance the flavor and oxidative stability of sesame oil and can be applied in the oil industry.

Characterization of Traditional Chinese Sesame Oil by Using Headspace Solid-Phase Microextraction/Gas Chromatography-Mass Spectrometry, Electronic Nose, Sensory Evaluation, and RapidOxy

Xiao Mo Xiang You (XMXY) is a traditional Chinese sesame oil variety that is obtained through a hot water flotation process. This unique process gives the oil a unique aroma, health benefits, and excellent product stability. Although XMXY is always the most expensive among all the sesame oil varieties, it is usually used as a flavoring in many traditional Chinese daily food products and is increasingly popular. In order to reveal the characteristics of the oil, the volatile components, sensory evaluation, and oxidation stability of five XMXY samples were, respectively, analyzed by using headspace solid-phase microextraction/gas chromatography−mass spectrometry, an electronic nose, sensory evaluation, and RapidOxy. Comparisons and multidimensional statistical analysis were also carried out to distinguish XMXY from roasted sesame oil (RSO) and cold-pressed sesame oil (CSO) samples. In total, 69 volatiles were identified from XMXY, RSO, and CSO samples. Some compounds possessed high odor activity value (OAV > 1) in XMXY, including heterocyclic compounds, phenols, and sulfur-containing compounds. Additionally, they were also the main volatile components that distinguish XMXY from RSO and CSO. Roasted and nutty aromas were the dominant aroma attributes of XMXY. XMXY had better flavor intensity and oxidation stability than the other two sesame oil samples. These results are very valuable for the quality control and product identification of traditional Chinese sesame oil.

Lipid oxidation in fragrant rapeseed oil: Impact of seed roasting on the generation of key volatile compounds

This work sought to identify the influence of roasting on lipid oxidation-derived volatile compounds in fragrant rapeseed oils (FROs) via gas chromatography–mass spectrometry and gas chromatography–ion mobility spectrometry. Seven volatiles could be regard as aroma-active compounds by application of odor activity value (OAV ≥ 1) calculation, and caused fatty-like, nutty-like, and green-like notes. After 60 min of roasting, the OAVs of hexanal, octanal, (E,E)-2,4-heptadienal, and nonanal in FROs were greater than 3. The same compounds, including hexanal, (E,E)-2,4-heptadienal, nonanal, 1-octanol, and nonanoic acid were also detected in the model systems of lipid oxidation. Notably, the values of p-anisidine, conjugated dienes, and conjugated trienes increased significantly (p < 0.05). Furthermore, correlation analysis showed that hexanal, (E,E)-2,4-heptadienal, and nonanal have a significant positive correlation with the oxidative degree of FROs (R = 0.70–0.94, p < 0.05). Thus, the three above-mentioned aldehydes could serve as important markers for FRO quality during roasting.

Discrimination and Characterization of the Volatile Organic Compounds in Schizonepetae Spica from Six Regions of China Using HS-GC-IMS and HS-SPME-GC-MS

Volatile organic compounds (VOCs) are the main chemical components of Schizonepetae Spica (SS), which have positive effects on the quality evaluation of SS. In this study, HS-SPME-GC-MS (headspace solid-phase microextraction-gas chromatography-mass spectrometry) and HS-GC-IMS (headspace-gas chromatography-ion mobility spectrometry) were performed to characterize the VOCs of SS from six different regions. A total of 82 VOCs were identified. In addition, this work compared the suitability of two instruments to distinguish SS from different habitats. The regional classification using orthogonal partial least squares discriminant analysis (OPLS-DA) shows that the HS-GC-IMS method can classify samples better than the HS-SPME-GC-MS. This study provided a reference method for identification of the SS from different origins.