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

X-ray absorption spectroscopy combined with deep learning for auto and rapid illicit drug detection

Background: X-ray absorption spectroscopy (XAS) is a widely used substance analysis technique. It bases on the different absorption coefficients at different energy level to achieve material identification. Additionally, the combination of spectral technology and deep learning can achieve auto detection and high accuracy in material identification.Objectives: Current methods are difficult to identify drugs quickly and nondestructively. Therefore, we explore a novel approach utilizing XAS for the detection of prohibited drugs with common X-ray tube source and photon-counting (PC) detector.Method: To achieve automatic, rapid, and accurate detection of drugs. A CdTe detector and a common X-ray source were used to collect data, then dividing the data into training and testing sets. Finally, the improved transformer encoder model was used for classification. LSTM and ResU-net models are selected for comparation.Result: Fifty substances, which are isomers or compounds with similar molecular formulas of drugs, were selected for experiment substances. The results showed that the improved transformer model achieving 1.4 hours for training time and 96.73% for accuracy, which is better than the LSTM (2.6 hours and 65%) and ResU-net (1.5 hours and 92.7%).Conclusion: It can be concluded that the attention mechanism is more accurate for spectral material identification. XAS combined with deep learning can achieve efficient and accurate drug identification, offering promising application in clinical drug testing and drug enforcement.

Setting the Separation Factor α for Ketone Monomers and Dimers by the Use of Different Drift Gases

This study investigates the influence of different drift gases on ion mobility in ion mobility spectrometry (IMS) using ketones as model substances within a custom-built drift tube spectrometer. Different binary mixtures of nitrogen, helium, and argon were used as drift gases to investigate the influence of mobility on the monomers and dimers of the different ketones. Experimental results reveal shifts in ion drift times and separation factors (α) with varying gas compositions, in accordance with Blanc's Law. Furthermore, the study underscores the device-independent nature of α and the device-dependent resolution, emphasizing the importance of comparative analyses. Employing 2-hexanone and 2-decanone in the same sample but with different drift gases is used to show the impact of different drift gases. By changing the drift gas composition, total alignment of drift times and therefore no possible resolution or baseline resolution could be achieved. Through different experiments and analyses, this research provides insights into the interactions between gas composition and ion mobility, offering implications for diverse analytical applications from environmental monitoring to chemical detection.

Correlation between climatic environment and characteristic components of 14 kinds of huajiao by thermal analysis techniques, GC-MS and HS-IMS

Huajiao (Zanthoxylum bungeanum Maxim.) is extensively cultivated in various countries, including China, Korea, and India, owing to its adaptability to diverse environments. This study comprehensively analyzed the volatile substance composition of 14 varieties of red huajiao with distinct geographical origins. Thermal analysis methods, gas chromatography-mass spectrometry (GC-MS), and headspace-ion mobility chromatography (HS-IMS) were employed to evaluate the total volatile substance composition and content. The study revealed minor variations in water content, volatile matter content, and fractions among the geographically sourced huajiao samples. Utilizing correlation analysis based on GC-MS and orthogonal partial least squares discriminant analysis (OPLS-DA) with HS-IMS, a robust classification method for the 14 types of huajiao was developed. Applying the variable importance in the projection (VIP) method, seven distinctive components were identified as potential markers for distinguishing the geographical origins of red huajiao. By integrating climatic and topographical factors of the 14 huajiao varieties, the correlation analysis of GC-MS, and OPLS-DA classification outcomes from HS-IMS elucidated the influence of geo-environmental factors on huajiao components and contents. This research provides insights into the impact of diverse geographic environments on the constituents and characteristics of huajiao. It offers valuable guidance for selecting optimal cultivation locations to enhance huajiao quality, aiding consumers in making informed choices.

Analysis of the lipidomic profile of vegetable oils and animal fats and changes during aging by UPLC-Q-exactive orbitrap mass spectrometry

Vegetable oil and animal fat residues are common evidence in the cases of homicide, arson, theft, and other crimes. However, the lipid composition and content changes during aging on complex carriers remain unclear. Therefore, this study dynamically monitored the lipid composition and content changes during aging of 13 different types of vegetable oils and animal fats on five different carriers using the UPLC-Q-Exactive Orbitrap MS method. A total of 6 subclasses of 93 lipids including lysophosphatidylcholine (2 species), phosphatidylcholine (2 species), diglyceride (5 species), triglyceride (81 species), acylGlcCampesterol ester (2 species), and acylGlcSitosterol ester (1 species), were first identified in fresh vegetable oils and animal fats. By comparing the LC-MS/MS chromatograms of fresh vegetable oils and animal fats, it was found that there were significant differences between the chromatograms of vegetable oils and animal fats, but it was difficult to distinguish between the chromatograms of vegetable oils or animal fats. After aging at 60 °C for 200 days, there was a significant decrease in the content of diglyceride, triglyceride, acylGlcCampesterol ester, and acylGlcSitosterol ester, while the content of lysophosphatidylcholine and phosphatidylcholine initially increased and then decreased. Furthermore, statistical analysis of lipid differences between vegetable oils and animal fats was performed using cluster heat maps, volcanic maps, PCA, and OPLS-DA. On average, 33 significantly different lipids were screened (VIP > 1, p < 0.05), which could serve as potential biomarkers for distinguishing vegetable oils and animal fats. It was found that the potential biomarkers still existed during aging of vegetable oils and animal fats (100 and 200 days). This research provides important reference information for the identification of vegetable oil and animal fat residues in complex carriers at crime scenes.

Investigation of low-temperature partitioning with dispersive solid-phase extraction for quantification of pesticides in apples followed by electrospray-ionization mobility spectrometry: Comparison with conventional procedure

Ion mobility spectrometry (IMS) has a promising application prospect in food surveillance. However, due to the complexity of food matrix and trace levels of pesticide residues, the effective and rapid detection of pesticides by IMS has been a challenge, especially when using electrospray ionization (ESI) as an ion source. In this study, low-temperature partitioning with dispersive solid-phase extraction (LTP-dSPE) was explored and compared with conventional procedures. Both methods were validated for the quantification of eight pesticides in apples, obtaining a limit of detection (LOD) of 0.02-0.12 mg/kg for LTP-dSPE and 0.02-0.09 mg/kg for conventional solid-phase extraction (SPE), lower than those usually stipulated by government legislation in food matrices. For LTP-dSPE, the matrx effect (ME) ranged from -16.3 to -68.6 %, lower than that for the SPE method, ranging from -70.0 to -92.9 %. The results showed satisfactory efficiency and precision, with recovery values ranging from 67.9 to 115.4 % for LTP-dSPE and from 62.0 to 114.8 % for conventional SPE, with relative standard deviations below 13.0 %. Notably, the proposed LTP-dSPE/ESI-IMS has been shown to be more cost-effective, easier to use, more environment-friendly, more accessible, and, most importantly, less matrix effect than the conventional method, thereby being suitably applicable to a wide range of food safety applications.

The analysis of the migration of per and poly fluoroalkyl substances (PFAS) from food contact materials using ultrahigh performance liquid chromatography coupled to ion-mobility quadrupole time-of-flight mass spectrometry (UPLC- IMS-QTOF)

Per-poly fluoroalkyl substances (PFASs) are a group of synthetic fluorine compounds used in food packaging materials to repel water and fats. This study assessed the chemical migration of PFAS from different food contact materials, including cardboard, recycled cardboard, biopolymer, paper and Teflon trays, from various markets. Migration assays were conducted using Tenax® as a food simulant, which was optimized by subjecting it to three consecutive extractions with 3 mL of ethanol within an hour. The resulting extractions were combined and concentrated to 0.5 mL using a nitrogen stream. The analysis was performed using ultrahigh performance liquid chromatography (UPLC) coupled with ion-mobility (IMS) quadrupole-time-of-flight (QTOF) mass spectrometry, which provided a powerful and novel tool for identifying a library of targets containing collision cross section values (CCS) and increasing confidence in subsequent identifications. Eleven PFAS compounds belonging to the family of perfluorocarboxylic acid, perfluorosulfonic acid and perfluorooctanesulfonamidoacetic acid substances (PFCAs, PFSAs and FOSAAs) were found in packaging samples obtained from China, with migrant concentrations ranging 3.2 and 22.3 μg/kg. In contrast, no detectable levels of PFAS were observed in packaging samples obtained in Spain. All trays tested were deemed to be suitable for use as food contact materials due to the fact that their migrant values were lower than 0.025 mg/kg for PFOA and its salts, and lower than a maximum concentration of 1 mg/kg for PFOA-related compounds.

Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants

Ion mobility spectrometry (IMS) is a rapid gas-phase separation technique, which can distinguish ions on the basis of their size, shape, and charge. The IMS-derived collision cross section (CCS) can serve as additional identification evidence for the screening of environmental organic micropollutants (OMPs). In this work, we summarize the published experimental CCS values of environmental OMPs, introduce the current CCS prediction tools, summarize the use of IMS and CCS in the analysis of environmental OMPs, and finally discussed the benefits of IMS and CCS in environmental analysis. An up-to-date CCS compendium for environmental contaminants was produced by combining CCS databases and data sets of particular types of environmental OMPs, including pesticides, drugs, mycotoxins, steroids, plastic additives, per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs), as well as their well-known transformation products. A total of 9407 experimental CCS values from 4170 OMPs were retrieved from 23 publications, which contain both drift tube CCS in nitrogen (DTCCSN2) and traveling wave CCS in nitrogen (TWCCSN2). A selection of publicly accessible and in-house CCS prediction tools were also investigated; the chemical space covered by the training set and the quality of CCS measurements seem to be vital factors affecting the CCS prediction accuracy. Then, the applications of IMS and the derived CCS in the screening of various OMPs were summarized, and the benefits of IMS and CCS, including increased peak capacity, the elimination of interfering ions, the separation of isomers, and the reduction of false positives and false negatives, were discussed in detail. With the improvement of the resolving power of IMS and enhancements of experimental CCS databases, the practicability of IMS in the analysis of environmental OMPs will continue to improve.

Tris-Copper Nanozyme as a Novel Laccase Mimic for the Detection and Degradation of Phenolic Compounds

Phenolic compounds are one of the main organic pollutants in the environment that can seriously affect ecosystems, even at very low concentrations. Due to the resistance of phenolic compounds to microorganisms, conventional biological treatment methods face challenges in effectively addressing this pollution problem. In this study, a novel laccase mimic (Tris-Cu nanozyme) is prepared using a simple and rapid synthesis strategy based on the coordination of copper ions and amino groups in Tris(hydroxymethyl)aminomethane (Tris). It is found that the Tris-Cu nanozyme exhibits good catalytic activity against a variety of phenolic compounds, the Km, Vmax and Kcat are determined to be 0.18 mM, 15.62 μM·min-1 and 1.57 × 107 min-1 using 2,4-dichlorophenol (2,4-DP) as the substrate, respectively. Then, based on the laccase-like activity of the Tris-Cu nanozyme, a novel colorimetric method for 2,4-DP (the limit of detection (LOD) = 2.4 μM, S/N = 3) detection in the range of 10-400 μM was established, and its accuracy was verified by analyzing tap and lake water samples. In addition, the Tris-Cu nanozyme shows excellent removal abilities for six phenolic compounds in experiments. The removal percentages for 2,4-DP, 2-chlorophenol (2-CP), phenol, resorcinol, 2,6-dimethoxyphenol (2,6-DOP), and bisphenol A (BPA) are 100%, 100%, 100%, 100%, 87%, and 81% at 1 h, respectively. In the simulated effluent, the Tris-Cu nanozyme maintains its efficient catalytic activity towards 2,4-DP, with a degradation percentage of 76.36% at 7 min and a reaction rate constant (k0) of 0.2304 min-1. Therefore, this metal-organic complex shows promise for applications in the monitoring and degrading of environmental pollutants.

Food metabolomics: Latest hardware-developments for nontargeted food authenticity and food safety testing

The analytical requirements for food testing have increased significantly in recent years. On the one hand, because food fraud is becoming an ever-greater challenge worldwide, and on the other hand because food safety is often difficult to monitor due to the far-reaching trade chains. In addition, the expectations of consumers on the quality of food have increased, and they are demanding extensive information. Cutting-edge analytical methods are required to meet these demands. In this context, non-targeted metabolomics strategies using mass and nuclear magnetic resonance spectrometers (mass spectrometry [MS]) have proven to be very suitable. MS-based approaches are of particular importance as they provide a comparatively high analytical coverage of the metabolome. Accordingly, the efficiency to address even challenging issues is high. A variety of hardware developments, which are explained in this review, have contributed to these advances. In addition, the potential of future developments is highlighted, some of which are currently not yet commercially available or only used to a comparatively small extent but are expected to gain in importance in the coming years.

Identification of agricultural quarantine materials in passenger's luggage using ion mobility spectroscopy combined with a convolutional neural network

As economic globalization intensifies, the recent increase in agricultural products and travelers from abroad has led to an increase in the probability of invasive alien species. A major pathway for invasive alien species is agricultural quarantine materials (AQMs) in travelers' baggage. Thus, it is meaningful to develop efficient methods for early detection and prompt action against AQMs. In this study, a method based on the combination of odor detection of AQMs using ion mobility spectroscopy (IMS) and convolutional neural network (CNN) analysis for the identification of AQM species in luggage was developed. Two different ways were investigated to feed the IMS data of AQMs into the CNN, either as one-dimensional data (1D) (as a spectrum) or as two-dimensional data (2D) (as an IMS topographic map). The performances of CNN models were also compared to those of the commonly used classification algorithms: partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA). By doing gradient-weighted class activation mapping (Grad-CAM), the essential IMS feature regions from the CNN models to predict different AQM species were also identified. The results of this research demonstrated that the application of the CNN to the IMS data of AQMs yielded superior classification performance compared to PLS-DA and SIMCA. Especially, the CNN-2D model which utilized the IMS topographic map as input achieved the best classification accuracy both on the calibration and validation sets. In addition, the Grad-CAM method had an ability to detect critical discriminating spectral regions for different types of AQM samples, and could provide explanation for the CNNs' decision-making. Despite the inherent limitations of the present analytical protocol, the results showed that the method of IMS in combination with a CNN has great potential to be a complement for sniffer dogs and X-ray imaging techniques to detect AQMs.

CRISPR-Cas-based detection for food safety problems: Current status, challenges, and opportunities

Food safety is one of the biggest public issues occurring around the world. Microbiological, chemical, and physical hazards can lead to food safety issues, which may occur at all stages of the supply chain. In order to tackle food safety issues and safeguard consumer health, rapid, accurate, specific, and field-deployable detection methods meeting diverse requirements are one of the imperative measures for food safety assurance. CRISPR-Cas system, a newly emerging technology, has been successfully repurposed in biosensing and has demonstrated huge potential to establish conceptually novel detection methods with high sensitivity and specificity. This review focuses on CRISPR-Cas-based detection and its current status and huge potential specifically for food safety inspection. We firstly illustrate the pending problems in food safety and summarize the popular detection methods. We then describe the potential applications of CRISPR-Cas-based detection in food safety inspection. Finally, the challenges and futuristic opportunities are proposed and discussed. Generally speaking, the current food safety detection methods are still unsatisfactory in some ways such as being time-consuming, displaying unmet sensitivity and specificity standards, and there is a comparative paucity of multiplexed testing and POCT. Recent studies have shown that CRISPR-Cas-based biosensing is an innovative and fast-expanding technology, which could make up for the shortcomings of the existing methods or even replace them. To sum up, the implementation of CRISPR-Cas and the integration of CRISPR-Cas with other techniques is promising and desirable, which is expected to provide "customized" and "smart" detection methods for food safety inspection in the coming future.

Differential mobility spectrometry coupled to mass spectrometry (DMS-MS) for the classification of Spanish PDO paprika

Ion mobility spectrometry (IMS) has proved its huge potential in many research areas, especially when hyphenated with chromatographic techniques or mass spectrometry (MS). However, focusing on food analysis, and particularly in classification and authentication issues, very few applications have been reported. In this study, differential mobility spectrometry coupled to mass spectrometry (DMS-MS) is presented for the first time as an alternative and high-throughput technique for food classification and authentication purposes using a fingerprinting strategy. As a study case, 70 Spanish paprika samples (from La Vera, Murcia, and Mallorca) were analyzed by DMS-MS to address their classification -using partial least squares regression-discriminant analysis (PLS-DA)- and authentication -through soft independent modeling of class analogy (SIMCA). As a result, after external validation, complete sample classification according to their geographical origin and excellent La Vera and Mallorca sample authentication were reached.

Rapid screening of illegal additives in functional food using atmospheric pressure solids analysis probe coupled to a portable mass spectrometer

Pharmaceutical drugs like Sildenafil are illegally added to functional food such as nutritional supplements and herbal remedies to deliver drugs without a regular prescription to consumers. Rapid screening of illegal additives is desirable for the public security department. The seized samples are often large in number and unknown in composition; methods are needed for qualitative screening of unknown samples. Here, a new approach is presented based on atmospheric pressure solids analysis probe (ASAP) coupled with single-quadrupole mass spectrometer to rapidly screen 42 common illegal additives in six categories of functional food. The ASAP-MS method could be applied to solid or liquid sample analysis with a very simple pre-treatment and no LC chromatographic separation, using a home-built library; the identification of suspicious additives could be obtained rapidly. More importantly, the approach is sensitive enough for complex matrix samples like coffee samples. 21 batches of seized unknown samples were tested by the ASAP-MS, and the positive results were confirmed by LC-MS/MS(QQQ), indicating that the ASAP-MS method is effective and reliable. The ASAP-MS with home-built library is a promising method for rapid screening of illegal additives in functional food, which could be widely used in the grassroots police station that lack professional laboratory environment.

Eco-friendly control of licorice aqueous extract to increase quality and resistance to postharvest decay in apple and tangerine fruits

BACKGROUND

Postharvest diseases in fruits and vegetables are one of the major problems in storing them as a fresh agri-product. This study aimed to investigate the antifungal activity of licorice (Glycyrrhiza glabra) aqueous extract against the Penicillium expansum and the Penicillium digitatum in apple and tangerine fruits as well as their postharvest decay during storage time.

METHODS

The minimum inhibitory concentration (MIC) of the molds, and the decay inhibition percentage (%DI) with the P.expansum for apple and P.digitatum for tangerine after treatment with licorice aqueous extract were measured. Additionally, the lesion diameter, titratable acidity (TA), total soluble solids (TSS), pH, and organoleptic properties were determined.

RESULTS

The growth of molds was almost inhibited at the concentration of 62.5 mg/mL. The ability of licorice aqueous extract to significantly control and reduce the growth of P. expansum in apple by 60 and 20 % after 7 days and 21 days of storage time was proved, respectively. Furthermore, significant differences in pH and TSS (p < 0.05) were observed in apples. Also, the growth of P. digitatum in the tangerine reduced by 33.3 % after 7 days, while there was no significant difference between the control and treatment groups in pH and TSS for apples, and similarly, there was no significant difference in TA for tangerine samples.

CONCLUSIONS

Therefore, the licorice aqueous extract treatment could postpone the blue mold decay in apple fruits and green mold decay in tangerine without any significant effect on fruit quality characteristics. It can be considered as a new eco-friendly control in fruit preservation, while it did not result in any significant adverse effect on  the quality.