Rapid detection of Aspergillus spp. infection levels on milled rice by headspace-gas chromatography ion-mobility spectrometry (HS-GC-IMS) and E-nose

Effects of sterilization intensity on the flavor profile of canned Antarctic krill (Euphausia superba): Moderate vs. excessive

Selecting the appropriate sterilization intensity is crucial for the canning of Antarctic krill (Euphausia superba). This study investigated the effects of different sterilization intensities on volatile organic compounds (VOCs) of canned krill. Using gas chromatography-ion mobility spectrometry (GC-IMS) and gas chromatography-mass spectrometry (GC-MS), which identified 45 and 36 VOCs, respectively. As the sterilization intensity was increased, the flavor profile became more stabilized; however, excessive sterilization led to the generation of off-flavor compounds. Eight key flavor markers were identified at different sterilization intensities. Cluster analysis could distinguish between samples obtained from low (F = 6, 9) and high (F = 12, 15) sterilization intensities. Odor Activity Value (OAV) analysis revealed that higher sterilization intensities led to the generation of fishy, fatty, and earthy notes. The findings suggest that sterilization at F = 9 can best maintain the desired flavor characteristics. Overall, this work provides valuable insights into the optimization of the canning process of Antarctic krill.

Comparative transcriptomic analysis and volatile compound characterization of Aspergillus tubingensis and Penicillium oxalicum during their infestation of Japonica rice

Volatile organic compounds (VOCs), a byproduct of mold metabolism, have garnered increasing interest because the VOCs can be used to detect food early contamination. So far, the use of VOCs as indicators of rice mildew, specifically caused by Aspergillus tubingensis and Penicillium oxalicum, and the mechanisms of their generation are not well investigated. This study examines the VOCs produced by these molds during paddy storage, utilizing headspace solid-phase micro-extraction gas chromatography-mass spectrometry (HS-SPME-GC-MS). We further elucidate the mechanisms underlying the formation of these VOCs through a comparative transcriptomic analysis. The VOCs characteristic to A. tubingensis and P. oxalicum, identified with a VIP value > 1 in the partial least squares discriminant analysis (PLS-DA) model, are primarily alkenes. Our transcriptome analysis uncovers key metabolic pathways in both molds, including energy metabolism and pathways related to volatile substance formation, and identifies differentially expressed genes associated with alkane and alcohol formation.

Gas chromatography-ion mobility spectrometry (GC-IMS) technique and its recent applications in grain research

Grains are the primary source of food for most people worldwide and constitute a major source of carbohydrates. Many novel technologies are being employed to ensure the safety and reliability of grain supply and production. Gas chromatography-ion mobility spectrometry (GC-IMS) can effectively separate and sensitively detect volatile organic compounds. It possesses advantages such as speed, convenience, high sensitivity, no pretreatment, and wide applicability. In recent years, many studies have shown that the application of GC-IMS technology for grain flavor analysis can play a crucial role in grains. This article elucidates the working principle of GC-IMS technology, reviews the application of GC-IMS in grains in the past 5 years. GC-IMS technology is mainly applied in four aspects in grains. In grain classification, it distinguishes varieties, quality, origin, production year, and processing methods based on the trace differences in volatile organic compounds, thereby fulfilling various grain classification requirements such as origin tracing, geographical indication product recognition, variety identification, production year identification, and detection of counterfeit and inferior grain samples. In optimizing the processing technology of grains and their products, it can improve food flavor, reduce undesirable flavors, and identify better processing parameters. In grain storage, it can determine the storage time, detect spoilage phenomena such as mold and discoloration during storage, eliminate pests affecting storage, and predict the vitality of seeds after storage. In aroma evaluation of grains and their processed products, it can assess the impact of new raw materials, new technologies, fermentation processes, and even oral processing on the quality of grain products. This article also summarizes the characteristics of GC-IMS technology, compiles typical grain flavor compounds, and provides prospects for the future application of GC-IMS. © 2024 Society of Chemical Industry.

Early warning of Aspergillus contamination in maize by gas chromatography-ion mobility spectrometry

Introduction

As one of the main grain crops in China, maize is highly susceptible to Aspergillus infection during processing, storage and transportation due to high moisture at harvest, which results in the loss of quality. The aim of this study is to explore the early warning marker molecules when Aspergillus infects maize kernels.

Methods

Firstly, strains MA and MB were isolated from moldy maize and identified by morphological characterization and 18S rRNA gene sequence analysis to be Aspergillus flavus (A. flavus) and Aspergillus niger (A. niger). Next, fresh maize was moldy by contaminated with strains MA and MB. The volatile organic compounds (VOCs) during the contamination process of two fungal strains were analyzed by gas chromatography-ion mobility spectrometry (GC-IMS). A total of 31 VOCs were detected in maize contaminated with strain MA, a total of 32 VOCs were detected in maize contaminated with strain MB, including confirmed monomers and dimers. Finally, heat maps and principal component analysis (PCA) showed that VOCs produced in different growth stages of Aspergillus had great differences. Combined with the results of GC-IMS, total fungal colony counts and fungal spores, it was concluded that the Aspergillus-contaminated maize was in the early stage of mold at 18 h.

Results

Therefore, the characteristic VOCs butan-2-one, ethyl acetate-D, Benzaldehyde, and pentan-2-one produced by maize at 18 h of storage can be used as early mildew biomarkers of Aspergillus infection in maize.

Discussion

This study provided effective marker molecules for the development of an early warning and monitoring system for the degree of maize mildew in granaries.

Real-time mildew detection and gradation in simulated containerized soybeans: Insights from GC-IMS analysis of mVOCs and VOCs

In the context of bulk grain container transportation, the complex logistics can lead to grain mildew and subsequent economic losses. Therefore, there is a pressing need to explore swift and real-time mildew detection technology. Our investigation, simulating actual transportation conditions, revealed that Aspergillus, Penicillium, and Rhizopus were the primary molds responsible for soybean mildew during container transportation. Utilizing gas chromatography-ion migration spectroscopy (GC-IMS), we analyzed the correlation between the mVOCs (microbial volatile organic compounds) produced by dominant mold and the VOCs emitted during soybean mildew. Principal Component Analysis (PCA) and clustering results demonstrated the distinctive identification of VOCs in soybeans with varying degrees of mildew. The mildew degree significantly influenced the content variation of VOCs. As the mildew degree increased, the concentrations of nonanal, octanal, etc. progressively decreased, contrasting with the rising levels of phenylacetaldehyde, 3-methyl-2-butenal, etc. Therefore, the combination of GC-IMS with chemometrics proves to be a viable method for identifying the mildew degree of soybeans. Therefore, this study underscores the importance of implementing effective mildew detection techniques in the challenging context of bulk grain container transportation.

Analysis of Volatile Aroma Components in Different Parts of Shiitake Mushroom (Lentinus edodes) Treated with Ultraviolet C Light-Emitting Diodes Based on Gas Chromatography-Ion Mobility Spectroscopy

Further assessment of ultraviolet C light-emitting diode (UVC-LED) irradiation for influencing shiitake mushrooms' (Lentinus edodes) volatile and sensory properties is needed. In this study, a comparison of UVC-LED irradiation treatment on the flavor profiles in various parts of shiitake mushrooms was conducted using gas chromatography-ion mobility spectrometry (GC-IMS) and sensory analysis. Sixty-three volatile compounds were identified in shiitake mushrooms. The fresh shiitake mushrooms were characterized by the highest values of raw mushroom odors. After UVC-LED treatment, the content of C8 alcohols decreased, especially that of 1-octen-3-ol, while the content of aldehydes increased, especially the content of nonanal and decanal. The score of fatty and green odors was enhanced. For fresh samples, the mushroom odors decreased and the mushroom-like odors weakened more sharply when treated in ethanol suspension than when treated with direct irradiation. The fruit odors were enhanced using direct UVC-LED irradiation for fresh mushroom samples and the onion flavor decreased. As for shiitake mushroom powder in ethanol suspension treated with UVC-LED, the sweaty and almond odor scores decreased and the vitamin D2 content in mushroom caps and stems reached 668.79 μg/g (dw) and 399.45 μg/g (dw), respectively. The results obtained from this study demonstrate that UVC-LED treatment produced rich-flavored, quality mushroom products.

Development an electronic nose to recognize pesticides in groundwater

Timely detection of Groundwater pollution is essential to protect human health, especially for pesticide pollution. To solve this issue, we proposed a novel solution to realize the prediction of pesticide in groundwater by using the electronic nose (e-nose). The main work of this paper was divided into three steps: 1) checking whether sample was polluted by pesticides, 2) further predicting the pesticide type, brand and pollution degree when the sample was polluted by pesticides, and 3) optimizing the sensor array. Random forest was used to complete the first step, which had the best accuracy and sensitivity of 100 %. Support vector machine was applied to complete the second step, and the accuracy reaching 98.08 %. As for the third step, recursive feature elimination was used to optimize the sensor array. After optimization, the number of sensors was reduced from 26 to 8. In addition, the e-nose developed in this paper was compared with a commercial e-nose. The results showed that the cost of the developed e-nose was much lower than that of the commercial e-nose despite its slightly weaker prediction performance. Thus, this e-nose can be employed to recognize the pesticides in groundwater, and even can be integrated into the while drilling technology to realize the in-situ detection of groundwater.

Technological tools for the measurement of sensory characteristics in food: A review

The use of technological tools, in the food industry, has allowed a quick and reliable identification and measurement of the sensory characteristics of food matrices is of great importance, since they emulate the functioning of the five senses (smell, taste, sight, touch, and hearing). Therefore, industry and academia have been conducting research focused on developing and using these instruments which is evidenced in various studies that have been reported in the scientific literature. In this review, several of these technological tools are documented, such as the e-nose, e-tongue, colorimeter, artificial vision systems, and instruments that allow texture measurement (texture analyzer, electromyography, others). These allow us to carry out processes of analysis, review, and evaluation of food to determine essential characteristics such as quality, composition, maturity, authenticity, and origin. The determination of these characteristics allows the standardization of food matrices, achieving the improvement of existing foods and encouraging the development of new products that satisfy the sensory experiences of the consumer, driving growth in the food sector. However, the tools discussed have some limitations such as acquisition cost, calibration and maintenance cost, and in some cases, they are designed to work with a specific food matrix.

Classification and characterization on sorghums based on HS-GC-IMS combined with OPLS-DA and GA-PLS

Headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) detected 206 and 186 samples of fresh and stored sorghums respectively with three major types in Baijiu industry. The fingerprints showed the differences of volatile compounds among fresh sorghum types by qualitative analysis and artificial recognition. Organic waxy sorghums had more contents of nonanal and 2-ethyl-1-hexanol but fewer ketones. The contents of acetoin in non-glutinous sorghums and organic non-glutinous sorghums were high. On the other hand, genetic algorithm-partial least squares (GA-PLS) selected 19 and 32 characteristic volatile compounds in fresh and stored sorghums. After centering and auto scaling to unit variance, the classification models with three major types of organic waxy sorghum, non-glutinous sorghum and organic non-glutinous sorghum were established based on orthogonal partial least squares-discriminant analysis (OPLS-DA). The goodness-of-fit (R2Y) and the goodness-of-prediction in cross-validation (Q2) in the model of fresh sorghum types all exceeded 0.9, in stored were over 0.8, the correct classification rates of external prediction were 95 % and 100 %, which revealed good performance and prediction. On this basis, the correct classification rates reached 87 % in organic waxy sorghums adulterated over 10 % ratio. GC-IMS combined with chemometrics is applicable in practical production for rapid identification of sorghum types and adulterations.

Effects of different lactic acid bacteria on the characteristic flavor profiles of Chinese rice wine

BACKGROUND

It has been well accepted that lactic acid bacteria (LAB) are the main bacterial genera present during the brewing of Chinese rice wine (CRW). LAB plays a decisive role in the flavor quality of CRW; however, its application in CRW has previously been overlooked. Therefore, effects of different LAB as co-fermenter on the flavor characteristics of CRW were investigated.

RESULTS

Co-fermentation of LAB increased the utilization rate of reducing sugar, concentration of lactic acid, amino acid nitrogen and total acidity, as well as the content of volatile flavor compounds. Different LAB doses had little effect on the flavor profiles of CRW, but the species of LAB greatly affected the flavor characteristic. The flavor of CRW co-fermented with Lactococcus lactis was characterized by long-chain fatty acid ethyl esters, while co-fermentation with Weissella confusa highlighted the ethyl esters of low molecular weight and short carbon chains in the resultant CRW. Alcohol compounds were dominant in the CRW co-fermented using Pediococcus pentosaceus.

CONCLUSION

The co-fermentation of LAB increased the number of volatile flavor compounds, especially esters. LAB exhibited great potential in the application of CRW industry to enrich the flavor characteristics and enhance the flavor diversity of the final product. © 2023 Society of Chemical Industry.

Rapid identification of adulteration in raw bovine milk with soymilk by electronic nose and headspace-gas chromatography ion-mobility spectrometry

The adulteration of soymilk (SM) into raw bovine milk (RM) to gain profit without declaration could cause a health risk. In this study, electronic nose (E-nose) and headspace-gas chromatography ion-mobility spectrometry (HS-GC-IMS) were applied to establish a rapid and effective method to identify adulteration in RM with SM. The obtained data from HS-GC-IMS and E-nose can distinguish the adulterated samples with SM by principal component analysis. Furthermore, a quantitative model of partial least squares was established. The detection limits of E-nose and HS-GC-IMS quantitative models were 1.53% and 1.43%, the root mean square errors of prediction were 0.7390 and 0.5621, the determination coefficients of prediction were 0.9940 and 0.9958, and the relative percentage difference were 10.02 and 13.27, respectively, indicating quantitative regression and good prediction performances of SM adulteration levels in RM were achieved. This research can provide scientific information on the rapid, non-destructive and effective adulteration detection for RM.

Development of an electronic nose to characterize water quality parameters and odor concentration of wastewater emitted from different phases in a wastewater treatment plant

For public health consideration, it is important to ensure the wastewater discharged from wastewater treatment plant is within the regulatory limits. This problem can be effectively solved by improving the accuracy and rapid characterization of water quality parameters and odor concentration of wastewater. In this paper, we proposed a novel solution to realize the precisive analysis of water quality parameters and odor concentration of wastewater by the electronic nose device. The main work of this paper was divided into three steps: 1) recognizing wastewater samples qualitatively from different sampling points, 2) analyzing the correlation between electronic nose response signals and water quality parameters and odor concentration, and 3) predicting the odor concentration and water quality parameters quantitatively. Combined with different feature extraction methods, support vector machine and linear discriminant analysis were applied as classifiers to recognize samples at different sampling points, which reported the best recognition rate of 98.83%. Partial least squares regression was applied to complete the second step, and R² was reaching 0.992. As for the third step, ridge regression was used to predict water quality parameters and odor concentration with the RMSE less than 0.9476. Thus, electronic noses can be applied to determine water quality parameters and odor concentrations in the effluent discharged from wastewater plants.

Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method

Accurate and rapid prediction of pesticides in groundwater is important to protect human health. Thus, an electronic nose was used to recognize pesticides in groundwater. However, the e-nose response signals for pesticides are different in groundwater samples from various regions, so a prediction model built on one region's samples might be ineffective when tested in another. Moreover, the establishment of a new prediction model requires a large number of sample data, which will cost too much resources and time. To resolve this issue, this study introduced the TrAdaBoost transfer learning method to recognize the pesticide in groundwater using the e-nose. The main work was divided into two steps: (1) qualitatively checking the pesticide type and (2) semi-quantitatively predicting the pesticide concentration. The support vector machine integrated with the TrAdaBoost was adopted to complete these two steps, and the recognition rate can be 19.3% and 22.2% higher than that of methods without transfer learning. These results demonstrated the potential of the TrAdaBoost based on support vector machine approaches in recognizing the pesticide in groundwater when there were few samples in the target domain.

E-Nose Technology for Mycotoxin Detection in Feed: Ready for a Real Context in Field Application or Still an Emerging Technology?

Mycotoxin risk in the feed supply chain poses a concern to animal and human health, economy, and international trade of agri-food commodities. Mycotoxin contamination in feed and food is unavoidable and unpredictable. Therefore, monitoring and control are the critical points. Effective and rapid methods for mycotoxin detection, at the levels set by the regulations, are needed for an efficient mycotoxin management. This review provides an overview of the use of the electronic nose (e-nose) as an effective tool for rapid mycotoxin detection and management of the mycotoxin risk at feed business level. E-nose has a high discrimination accuracy between non-contaminated and single-mycotoxin-contaminated grain. However, the predictive accuracy of e-nose is still limited and unsuitable for in-field application, where mycotoxin co-contamination occurs. Further research needs to be focused on the sensor materials, data analysis, pattern recognition systems, and a better understanding of the needs of the feed industry for a safety and quality management of the feed supply chain. A universal e-nose for mycotoxin detection is not realistic; a unique e-nose must be designed for each specific application. Robust and suitable e-nose method and advancements in signal processing algorithms must be validated for specific needs.

Potential volatile markers of brown rice infested by the rice weevil, Sitophilus oryzae (L.) (Coleoptera: Curculionidae)

The rice weevil, Sitophilus oryzae (L.) (Coleoptera: Curculionidae) could cause significant grain loss by feeding internally on seeds. In this study, we tried to analyze the volatile compounds in non-infested and S. oryzae-infested brown rice during different storage periods to identify potential markers in S. oryzae-infested brown rice and facilitate pest monitoring during brown rice storage. Headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) were used to identify the volatile compounds. On the basis of GC-MS and GC-IMS data, a reliable method to distinguish between non-infested and S. oryzae-infested brown rice was discovered using partial least squares-discriminant analysis (PLS-DA). 1-Octen-3-ol, 1-hexanol and 3-octanone were co-selected as potential markers because their variable importance in projection (VIP) was greater than 1 in both models. The current study's findings lay a foundation for further research on the brown rice infestation mechanism and safe storage monitoring.

Characterization of japonica rice aroma profiles during in vitro mastication by gas chromatography-ion mobility spectrometry (GC-IMS) and electronic nose technology

By simulating the aroma changes during in vitro mastication, we can better understand the aroma changes during rice eating, which is helpful in studying people’s sensory preferences. To investigate the rice aroma released during the in vitro mastication, the present study analyzed rice bolus’s odor fingerprints in vitro mastication using electronic nose and gas chromatography-ion mobility spectrometry (GC-IMS). The electronic nose analysis results showed significant differences in the flavor of japonica rice in vitro mastication. In addition, GC-IMS determined 30 volatile organic compounds (VOCs) during rice in vitro mastication. Among these compounds, the most important content was aldehydes, followed by ketones and alcohols. Although the concentration of various chemicals was relatively high in cooked rice, most compounds decreased after mastication. The concentration of propan-2-ol, ethanol, and methanol increased after mastication. Multivariate data analysis showed that isoamyl sovalerate, pentanal, hexanal, acetone, hexanal, and limonene were the main VOCs of japonica rice during in vitro mastication. GC-IMS and e-nose analyses are complementary and recommended for using the two techniques to achieve the VOCs’ rapid and comprehensive detection during in vitro mastication. Results from this study allowed us to understand rice flavor during oral processing.

Early Discrimination and Prediction of C. fimbriata-Infected Sweetpotatoes during the Asymptomatic Period Using Electronic Nose

Sweetpotato is prone to disease caused by C. fimbriata without obvious lesions on the surface in the early period of infection. Therefore, it is necessary to explore the possibility of developing an efficient early disease detection method for sweetpotatoes that can be used before symptoms are observed. In this study, sweetpotatoes were inoculated with C. fimbriata and stored for different lengths of time. The total colony count was detected every 8 h; HS-SPME/GC–MS and E-nose were used simultaneously to detect volatile compounds. The results indicated that the growth of C. fimbriata entered the exponential phase at 48 h, resulting in significant differences in concentrations of volatile compounds in infected sweetpotatoes at different times, especially toxic ipomeamarone in ketones. The contents of volatile compounds were related to the responses of the sensors. E-nose was combined with multiple chemometrics methods to discriminate and predict infected sweetpotatoes at 0 h, 48 h, 64 h, and 72 h. Among the methods used, linear discriminant analysis (LDA) had the best discriminant effect, with sensitivity, specificity, precision, and accuracy scores of 100%. E-nose combined with K-nearest neighbours (KNN) achieved the best predictions for ipomeamarone contents and total colony counts. This study illustrates that E-nose is a feasible and promising technology for the early detection of C. fimbriata infection in sweetpotatoes during the asymptomatic period.

Identification of Maize with Different Moldy Levels Based on Catalase Activity and Data Fusion of Hyperspectral Images

Maize is susceptible to mold infection during growth and storage due to its large embryo and high moisture content. Therefore, it is essential to distinguish the moldy sample from healthy groups to prevent the spread of mold and avoid huger economic losses. Catalase is a metabolite in the growth of microorganisms; hence, all maize samples were accurately divided into four moldy grades (health, mild, moderate, and severe levels) by determining their catalase activity. The visible and shortwave near-infrared (Vis-SWNIR) and longwave near-infrared (LWNIR) hyperspectral images were investigated to jointly identify the moldy levels of maize. Spectra and texture information of each maize sample were extracted and used to build the classification models of maize with different moldy levels in pixel-level fusion and feature-level fusion. The result showed that the feature-level fusion of spectral and texture within Vis-SWNIR and LWNIR regions achieved the best results, overall prediction accuracy reached 95.00% for each moldy level, all healthy maize was correctly classified, and none of the moldy samples were misclassified as healthy level. This study illustrated that two hyperspectral image systems, with complementary spectral ranges, combined with feature selection and data fusion strategies, could be used synergistically to improve the classification accuracy of maize with different moldy levels.

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes

Five potato varieties were studied using an electronic nose with nine MOS sensors. Parameters measured included carbohydrate content, sugar level, and the toughness of the potatoes. Routine tests were carried out while the signals for each potato were measured, simultaneously, using an electronic nose. The signals obtained indicated the concentration of various chemical components. In addition to support vector machines (SVMs that were used for the classification of the samples, chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models for sugar and carbohydrates. The predictive power of the regression models was characterized by a coefficient of determination (R²), a root-mean-square error of prediction (RMSEP), and offsets. PLSR was able to accurately model the relationship between the smells of different types of potatoes, sugar, and carbohydrates. The highest and lowest accuracy of models for predicting sugar and carbohydrates was related to Marfona potatoes and Sprite cultivar potatoes. In general, in all cultivars, the accuracy in predicting the amount of carbohydrates was somewhat better than the accuracy in predicting the amount of sugar. Moreover, the linear function had 100% accuracy for training and validation in the C-SVM method for classification of five potato groups. The electronic nose could be used as a fast and non-destructive method for detecting different potato varieties. Researchers in the food industry will find this method extremely useful in selecting the desired product and samples.

Characterization of the Flavor Profile of Bigeye Tuna Slices Treated by Cold Plasma Using E-Nose and GC-IMS

To avoid heat, treatment induces numerous physicochemical changes under severe conditions in the tuna, cold plasma (CP), as a non-thermal technology, possess objective potential on tuna processing. The effect of cold plasma on the volatile flavor compounds of bigeye tuna (Thunnus obesus) sashimi has been evaluated using electronic nose (E-nose) and gas chromatography-ion mobility spectrometry (GC-IMS). GC–IMS results revealed a total of 33 volatile compounds in tuna slices. The effect of CP treatment on tuna flavor was not significant, furthermore CP could protect volatile freshness compounds such as 1-hexanol. Principal component analysis (PCA) of the E-nose and GC–IMS results could effectively differentiate the effect of storage to tuna sashimi. There was a high correlation between the E-nose and GC–IMS results, providing a theoretical basis for establishing the flavor fingerprint of tuna sashimi.

Research advance in gas detection of volatile organic compounds released in rice quality deterioration process

Rice quality deterioration will cause grievous waste of stored grain and various food safety problems. Gas detection of volatile organic compounds (VOCs) produced by deterioration is a nondestructive detection method to judge rice quality and alleviate rice spoilage. This review discussed the research advance of VOCs detection in terms of nondestructive detection methods of rice quality deterioration, applications of VOCs in grain detection, inspection of characteristic gas produced during rice spoilage, rice deterioration prevention and control, and detection of VOCs released by rice mildew and insect attack. According to the main causes of rice quality deterioration and major sources of VOCs with off-odor generated during rice storage, deterioration can be divided into mold and insect infection. The results of literature manifested that researches mainly focused on the infection of Aspergillus in the mildew process and the attack of certain pests in recent years, thus the research scope was limited. In this paper, the gas detection methods combined with the chemometrics to qualitatively analyze the VOCs, as well as the correlation with the number of colonies and insects were further studied based on the common dominant strains during rice mildew, that is, Aspergillus and Penicillium fungi, and the common pests during storage, that is, Sitophilus oryzae and Rhyzopertha dominica. Furthermore, this paper pointed out that the quantitative determination of characteristic VOCs, the numeration relationship between VOCs and the degree of mildew and insect infestation, the further expansion of detection range, and the application of degraded rice should be the spotlight of future research.

Analysis of Volatile Compounds in Sea Bass (Lateolabrax japonicus) Resulting from Different Slaughter Methods Using Electronic-Nose (E-Nose) and Gas Chromatography-Ion Mobility Spectrometry

Sea bass (Lateolabrax japonicus) is known for its unique flavor and high nutritional value. In this study, the influence of slaughter methods on the volatile compounds (VOCs) in sea bass was investigated using electronic nose (E-nose) technology and gas chromatography-ion mobility spectrometry (GC-IMS). VOCs in raw and cooked sea bass resulting from different slaughter methods were effectively distinguished using both techniques. Aldehydes, ketones, and alcohols were associated with the basic flavor of sea bass, whereas esters, organic acids, and furans enriched the aroma. In raw sea bass, the fishy odor was the strongest in the HSD group (head shot control death), followed by that in the IFD (ice faint to death) and BDS (bloodletting to death) groups. The VOC content increased and stabilized after steaming, enhancing pleasant odors such as fatty and fruity aromas. In cooked sea bass, the content of diacetyl and ethanol was the highest in the EAD group (eugenol anesthesia to death), which may be a residue of eugenol, imparting a distinct irritating chemical odor. Furthermore, abundant (E)-2-octenal, 2-heptanone, benzaldehyde, and esters in the BDS group imparted a strong, pleasant aroma. The findings indicate that heart puncture and bloodletting is the preferred slaughter method to maintain sea bass quality, providing new insights into the volatile changes in sea bass induced by different slaughter methods.

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.

Recent trends in the chromatographic analysis of volatile flavor and fragrance compounds: Annual review 2020

The chromatographic analysis of volatile flavor and fragrance compounds is performed routinely in several industries and in many fields of scientific research. Typical applications include food-, environmental-, essential oil- and cosmetics analysis. Even though the analysis of flavors and fragrances have become increasingly standardized during the past decade, there are still a large variety of techniques that can be used for their extraction, chemical analysis, and sensory analysis. Moreover, there are certain less commonly used techniques that are now being used with increased frequency and that are showing the potential of being used as alternatives to the existing standard techniques. In this annual review, the techniques that were most commonly used in 2020 for the investigation of these volatile compounds are discussed. In addition, a number of emerging trends are discussed, notably the use of solvent assisted flavor evaporation (SAFE) for extraction, GC ion mobility spectrometry (IMS) for volatile compound analysis and electronic senses, that is, E-noses and E-tongues, for sensory analysis. Miscellaneous hyphenated techniques, advances in stationary phase chemistry and a number of interesting applications are also highlighted.

Food safety and quality assessment: comprehensive review and recent trends in the applications of ion mobility spectrometry (IMS)

Ion mobility spectrometry (IMS) is an analytical separation and diagnostic technique that is simple and sensitive and a rapid response and low-priced technique for detecting trace levels of chemical compounds in different matrices. Chemical agents and environmental contaminants are successfully detected by IMS and have been recently considered to employ in food safety. In addition, IMS uses stand-alone or coupled analytical diagnostic tools with chromatographic and spectroscopic methods. Scientific publications show that IMS has been applied 21% in the pharmaceutical industry, 9% in environmental studies and 13% in quality control and food safety. Nevertheless, applications of IMS in food safety and quality analysis have not been adequately explored. This review presents the IMS-related analysis and focuses on the application of IMS in food safety and quality. This review presents the important topics including detection of traces of chemicals, rate of food spoilage and freshness, food adulteration and authenticity as well as natural toxins, pesticides, herbicides, fungicides, veterinary, and growth promoter drug residues. Further, persistent organic pollutants (POPs), acrylamide, polycyclic aromatic hydrocarbon (PAH), biogenic amines, nitrosamine, furfural, phenolic compounds, heavy metals, food packaging materials, melamine, and food additives were also examined for the first time. Therefore, it is logical to predict that the application of the IMS technique in food safety, food quality, and contaminant analysis will be impressively increased in the future. HighlightsCurrent status of IMS for residues and contaminant detection in food safety.To assess all the detected contaminants in food safety, for the first time.Identified IMS-related parameters and chemical compounds in food safety control.

Targeted versus Nontargeted Green Strategies Based on Headspace-Gas Chromatography-Ion Mobility Spectrometry Combined with Chemometrics for Rapid Detection of Fungal Contamination on Wheat Kernels

Conventional methods for detecting fungal contamination are generally time-consuming and sample-destructive, making them impossible for large-scale nondestructive detection and real-time analysis. Therefore, the potential of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was examined for the rapid determination of fungal infection on wheat samples in a rapid and nondestructive manner. In addition, the validation experiment of detecting the percent A. flavus infection presented in simulated field samples was carried out. Because the dual separation of HS-GC-IMS could generate massive amounts of three-dimensional data, proper chemometric processing was required. In this study, two chemometric strategies including: (i) nontargeted spectral fingerprinting and (ii) targeted specific markers were introduced to evaluate the performances of classification and prediction models. Results showed that satisfying results for the differentiation of fungal species were obtained based on both strategies (>80%) by the genetic algorithm optimized support vector machine (GA-SVM), and better values were obtained based on the first strategy (100%). Likewise, the GA-SVM model based on the first strategy achieved the best prediction performances (R² = 0.979-0.998) of colony counts in fungal infected samples. The results of validation experiment showed that GA-SVM models based on the first strategy could still provide satisfactory classification (86.67%) and prediction (R² = 0.889) performances for percent A. flavus infection presented in simulated field samples at day 4. This study indicated the feasibility of HS-GC-IMS-based approaches for the early detection of fungal contamination in wheat kernels.