Data fusion of GC-IMS data and FT-MIR spectra for the authentication of olive oils and honeys—is it worth to go the extra mile?

Data fusion of HS-SPME-GCMS, NIRS, and fluorescence, using chemometrics, has the potential to explore the geographical origin of gentian rhizomes

Gentiana lutea rhizomes are known for their bitter tasting properties conferred by its unique biochemical content. They are currently of interest in phytotherapy, animal nutrition, food processing, cosmetic applications and agroecology. In this study, a NIRS, fluorescence and HS-SPME-GCMS dataset of 55 rhizomes from four different French mountains (Alpes, Jura, Massif Central and Pyrénées) was collected with the aim of assessing the variability of Gentiana lutea composition at different scales. The feasibility of data fusion strategies was demonstrated to be effective in distinguishing the geographical origin of Gentiana lutea roots over a wide area. The results suggest that data fusion methods have the potential to be more effective in the quality of separation of studied sites of Gentiana lutea roots than individual decisions obtained from individual analytical tools. However, to guarantee the geographical origin of Gentiana lutea roots within a single massif using these techniques, environmental factors must be considered.

Trends in authentication of edible oils using vibrational spectroscopic techniques

The authentication of edible oils has become increasingly important for ensuring product quality, safety, and compliance with regulatory standards. Some prevalent authenticity issues found in edible oils include blending expensive oils with cheaper substitutes or lower-grade oils, incorrect labeling regarding the oil's source or type, and falsely stating the oil's origin. Vibrational spectroscopy techniques, such as infrared (IR) and Raman spectroscopy, have emerged as effective tools for rapidly and non-destructively analyzing edible oils. This review paper offers a comprehensive overview of recent advancements in using vibrational spectroscopy for authenticating edible oils. The fundamental principles underlying vibrational spectroscopy are introduced and chemometric approaches that enhance the accuracy and reliability of edible oil authentication are summarized. Recent research trends highlighted in the review include authenticating newly introduced oils, identifying oils based on their specific origins, adopting handheld/portable spectrometers and hyperspectral imaging, and integrating modern data handling techniques into the use of vibrational spectroscopic techniques for edible oil authentication. Overall, this review provides insights into the current state-of-the-art techniques and prospects for utilizing vibrational spectroscopy in the authentication of edible oils, thereby facilitating quality control and consumer protection in the food industry.

Geographical discrimination of Paw San rice cultivated in different regions of Myanmar using near-infrared spectroscopy, headspace-gas chromatography-ion mobility spectrometry and chemometrics

Paw San rice, also known as "Myanmar pearl rice", is considered the highest quality rice in Myanmar. There are considerable differences in terms of the premium commercial value of Paw San rice, which is an incentive for fraud, e.g. adulteration with cheaper rice varieties or mislabelling its geographical origin. Shwe Bo District is one of the most popular rice growing areas in the Sagaing region of Myanmar which produces the most valued and highly priced Paw San rice (Shwe Bo Paw San). The verification of the geographical origin of Paw San rice is not readily undertaken in the rice supply chain because the existing analytical approaches are time-consuming and expensive. Therefore, there is a need for rapid, robust and cost-effective analytical techniques for monitoring the authenticity and geographical origin of Paw San rice. In this 4-year study, two rapid screening techniques, Fourier-transform near-infrared (FT-NIR) spectroscopy and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), coupled with chemometric modelling, were applied and compared for the regional differentiation of Paw San rice. In addition, low-level fusion of the FT-NIR and HS-GC-IMS data was performed and its effect on the discriminative power of the chemometric models was assessed. Extensive model validation, including the validation using independent samples from a different production year, was performed. Furthermore, the effect of the sample preparation technique (grinding versus no sample preparation) on the performance of the discriminative model, obtained with FT-NIR spectral data, was assessed. The study discusses the suitability of FT-NIR spectroscopy, HS-GC-IMS and the combination of both approaches for rapid determination of the geographical origin of Paw San rice. The results demonstrated the excellent potential of the FT-NIR spectroscopy as well as HS-GC-IMS for the differentiation of Paw San rice cultivated in two distinct geographical regions. The OPLS-DA model, built using FT-NIR data of rice from 3 production years, achieved 96.67% total correct classification rate of an independent dataset from the 4th production year. The DD-SIMCA model, built using FT-NIR data of ground rice, also demonstrated the highest performance: 94% sensitivity and 97% specificity. This study has demonstrated that FT-NIR spectroscopy can be used as an accessible, rapid and cost-effective screening tool to discriminate between Paw San rice cultivated in the Shwe Bo and Ayeyarwady regions of Myanmar.

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

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

Comparing the potential of IR-spectroscopic techniques to gas chromatography coupled to ion mobility spectrometry for classifying virgin olive oil categories

Virgin olive oil (OO) can be classified into three different categories: extra virgin, virgin and lampante. The official method for this classification, based on physicochemical analysis and sensory tasting, is considered useful and effective, although it is a costly and time-consuming process. The aim of this study was to assess the potential of some analytical techniques for classifying and predicting different OO categories to support official methods and to provide olive oil companies with a rapid tool to assess product quality. Thus, mid and near infrared spectroscopies (MIR and NIR) have been compared by using different instruments and with head-space gas chromatography coupled to an ion mobility spectrometer (HS-GC-IMS). High classification success rates in validation models were obtained using IR spectrometers (>70% and > 80% in average for ternary and binary classifications, respectively), although HS-GC-IMS showed greater classification potential (>85% and > 90%).

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

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

Data fusion and multivariate analysis for food authenticity analysis

A mid-level data fusion coupled with multivariate analysis approach is applied to dual-platform mass spectrometry data sets using Rapid Evaporative Ionization Mass Spectrometry and Inductively Coupled Plasma Mass Spectrometry to determine the correct classification of salmon origin and production methods. Salmon (n = 522) from five different regions and two production methods are used in the study. The method achieves a cross-validation classification accuracy of 100% and all test samples (n = 17) have their origins correctly determined, which is not possible with single-platform methods. Eighteen robust lipid markers and nine elemental markers are found, which provide robust evidence of the provenance of the salmon. Thus, we demonstrate that our mid-level data fusion - multivariate analysis strategy greatly improves the ability to correctly identify the geographical origin and production method of salmon, and this innovative approach can be applied to many other food authenticity applications.

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.

Quantitation of Flavor Compounds in Refill Solutions for Electronic Cigarettes Using HS-GCxIMS and Internal Standards

New regulations on the use of flavor compounds in tobaccoless electronic cigarettes require comprehensive analyses. Gas chromatography coupled ion mobility spectrometry is on the rise as an analytical technique for analyzing volatile organic compounds as it combines sensitivity, selectivity, and easy usage with a full-range screening. A current challenge is the quantitative GCxIMS-analysis. Non-linear calibration methods are predominantly used. This work presents a new calibration method using linearization and its corresponding fit based on the relation between the reactant and analyte ions from the chemical ionization. The analysis of e-liquids is used to compare the presented calibration with an established method based on a non-linear Boltzmann fit. Since e-liquids contain matrix compounds that have been shown to influence the analyte signals, the use of internal standards is introduced to reduce these effects in GCxIMS-analysis directly. Different matrix mixtures were evaluated in the matrix-matched calibration to improve the quantitation further. The system's detection and quantitation limits were determined using a separate linear calibration. A matrix-matched calibration series of 29 volatile compounds with 12 levels were used to determine the concentration of these substances in a spiked, flavorless e-liquid and a banana-flavored e-liquid, validating the quality of the different calibrations.

Stress-Induced Volatile Emissions and Signalling in Inter-Plant Communication

The sessile plant has developed mechanisms to survive the "rough and tumble" of its natural surroundings, aided by its evolved innate immune system. Precise perception and rapid response to stress stimuli confer a fitness edge to the plant against its competitors, guaranteeing greater chances of survival and productivity. Plants can "eavesdrop" on volatile chemical cues from their stressed neighbours and have adapted to use these airborne signals to prepare for impending danger without having to experience the actual stress themselves. The role of volatile organic compounds (VOCs) in plant-plant communication has gained significant attention over the past decade, particularly with regard to the potential of VOCs to prime non-stressed plants for more robust defence responses to future stress challenges. The ecological relevance of such interactions under various environmental stresses has been much debated, and there is a nascent understanding of the mechanisms involved. This review discusses the significance of VOC-mediated inter-plant interactions under both biotic and abiotic stresses and highlights the potential to manipulate outcomes in agricultural systems for sustainable crop protection via enhanced defence. The need to integrate physiological, biochemical, and molecular approaches in understanding the underlying mechanisms and signalling pathways involved in volatile signalling is emphasised.

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.

Geographical authentication of virgin olive oil by GC-MS sesquiterpene hydrocarbon fingerprint: Verifying EU and single country label-declaration

According to the last report from the European Union (EU) Food Fraud Network, olive oil tops the list of the most notified products. Current EU regulation states geographical origin as mandatory for virgin olive oils, even though an official analytical method is still lacking. Verifying the compliance of label-declared EU oils should be addressed with the highest priority level. Hence, the present work tackles this issue by developing a classification model (PLS-DA) based on the sesquiterpene hydrocarbon fingerprint of 400 samples obtained by HS-SPME-GC-MS to discriminate between EU and non-EU olive oils, obtaining an 89.6% of correct classification for the external validation (three iterations), with a sensitivity of 0.81 and a specificity of 0.95. Subsequently, multi-class discrimination models for EU and non-EU countries were developed and externally validated (with three different validation sets) with successful results (average of 92.2% of correct classification for EU and 96.0% for non-EU countries).

Data Fusion Approach to Simultaneously Evaluate the Degradation Process Caused by Ozone and Humidity on Modern Paint Materials

The knowledge of the atmospheric degradation reactions affecting the stability of modern materials is still of current interest. In fact, environmental parameters, such as relative humidity (RH), temperature, and pollutant agents, often fluctuate due to natural or anthropogenic climatic changes. This study focuses on evaluating analytical and statistical strategies to investigate the degradation processes of acrylic and styrene-acrylic paints after exposure to ozone (O3) and RH. A first comparison of FTIR and Py-GC/MS results allowed to obtain qualitative information on the degradation products and the influence of the pigments on the paints' stability. The combination of these results represents a significant potential for the use of data fusion methods. Specifically, the datasets obtained by FTIR and Py-GC/MS were combined using a low-level data fusion approach and subsequently processed by principal component analysis (PCA). It allowed to evaluate the different chemical impact of the variables for the characterization of unaged and aged samples, understanding which paint is more prone to ozone degradation, and which aging variables most compromise their stability. The advantage of this method consists in simultaneously evaluating all the FTIR and Py-GC/MS variables and describing common degradation patterns. From these combined results, specific information was obtained for further suitable conservation practices for modern and contemporary painted films.

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

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

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

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

Application of gas chromatography-ion mobility spectrometry in the analysis of food volatile components

Gas chromatography-ion mobility spectrometry (GC-IMS) is an emerging analytical technique that has the advantages of fast response, high sensitivity, simple operation, and low cost. The combination of the fast speed and resolution of GC with the high sensitivity of IMS makes GC-IMS play an important role in the detection of food volatile substances. This paper focuses on the basic principles and future development trend, and the comparative analysis of the functions, similarities and differences of GC-IMS, GC-MS and electronic nose in the detection of common volatile compounds. A comprehensive introduction to the main application of GC-IMS in food volatile components: fingerprint identification of sample differences and detection of characteristic compounds. On the basis of perfecting the spectral library, GC-IMS will have broad development prospects in food authentication, origin identification, process optimization and product classification, especially in the analysis and identification of trace volatile food flavor substances.

Characterisation of Flavour Attributes in Egg White Protein Using HS-GC-IMS Combined with E-Nose and E-Tongue: Effect of High-Voltage Cold Plasma Treatment Time

Egg white protein (EWP) is susceptible to denaturation and coagulation when exposed to high temperatures, adversely affecting its flavour, thereby influencing consumers' decisions. Here, we employ high-voltage cold plasma (HVCP) as a novel nonthermal technique to investigate its influence on the EWP's flavour attributes using E-nose, E-tongue, and headspace gas-chromatography-ion-mobilisation spectrometry (HS-GC-IMS) due to their rapidness and high sensitivity in identifying flavour fingerprints in foods. The EWP was investigated at 0, 60, 120, 180, 240, and 300 s of HVCP treatment time. The results revealed that HVCP significantly influences the odour and taste attributes of the EWP across all treatments, with a more significant influence at 60 and 120 s of HVCP treatment. Principal component analyses of the E-nose and E-tongue clearly distinguish the odour and taste sensors' responses. The HS-GC-IMS analysis identified 65 volatile compounds across the treatments. The volatile compounds' concentrations increased as the HVCP treatment time was increased from 0 to 300 s. The significant compounds contributing to EWP characterisation include heptanal, ethylbenzene, ethanol, acetic acid, nonanal, heptacosane, 5-octadecanal, decanal, p-xylene, and octanal. Thus, this study shows that HVCP could be utilised to modify and improve the EWP flavour attributes.

Untargeted Headspace-Gas Chromatography-Ion Mobility Spectrometry in Combination with Chemometrics for Detecting the Age of Chinese Liquor (Baijiu)

This paper proposes the combination of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and chemometrics as a method to detect the age of Chinese liquor (Baijiu). Headspace conditions were optimized through single-factor optimization experiments. The optimal sample preparation involved diluting Baijiu with saturated brine to 15% alcohol by volume. The sample was equilibrated at 70 °C for 30 min, and then analyzed with 200 μL of headspace gas. A total of 39 Baijiu samples from different vintages (1998–2019) were collected directly from pottery jars and analyzed using HS-GC-IMS. Partial least squares regression (PLSR) analysis was used to establish two discriminant models based on the 212 signal peaks and the 93 identified compounds. Although both models were valid, the model based on the 93 identified compounds discriminated the ages of the samples more accurately according to the goodness of fit value (R²) and the root mean square error of prediction (RMSEP), which were 0.9986 and 0.244, respectively. Nineteen compounds with variable importance for prediction (VIP) scores > 1, including 11 esters, 4 alcohols, and 4 aldehydes, played vital roles in the model established by the 93 identified compounds. Overall, we determined that HS-GC-IMS combined with PLSR could serve as a rapid and accurate method for detecting the age of Baijiu.

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.

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

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

Behavioral responses of Platycladus orientalis plant volatiles to Phloeosinus aubei by GC-MS and HS-GC-IMS for discrimination of different invasive severity

In recent years, the invasive cypress bark beetle (Phloeosinus aubei) has caused extensive damage to Platycladus orientalis plants in China, but its infestation is hard to monitor in the early stages. In this study, gas chromatography-mass spectrometry (GC-MS) was initially employed to investigate the volatile organic compound (VOC) emissions of P. aubei-infested P. orientalis saplings. The emissions of total sesquiterpenes were dominating (84-86% of total VOCs) and increased by 3.09-fold in P. aubei-damaged P. orientalis samples compared to undamaged samples, and the monoterpenes, aromatic compounds, and ketone emissions also had varying degrees of increase between 1.39-fold and 5.65-fold. Based on this variation, gas chromatography-ion mobility spectrometry (GC-IMS) was applied, as an untargeted analytical approach, to discriminate P. orientalis samples with different invasive severity. Two different features derived from GC-IMS data were adopted as the input information for classification and prediction models. Results showed that grid search support vector machine (GS-SVM) combined with multilinear principal component analysis (MPCA) based on spectral fingerprint achieved the best classification performances (> 88.98%), and partial least squares discriminant analysis (PLSR) method can accurately predict the pest numbers (R² > 0.9423 and RMSE < 0.9827). In a word, the VOC profiling-based approach had the potential for evaluating P. aubei invasive severity and pest management.

The use of analytical techniques coupled with chemometrics for tracing the geographical origin of oils: A systematic review (2013-2020)

The global market for imported, high-quality priced foods has grown dramatically in the last decade, as consumers become more conscious of food originating from around the world. Many countries require the origin label of food to protect consumers need about true characteristics and origin. Regulatory authorities are looking for an extended and updated list of the analytical techniques for verification of authentic oils and to support law implementation. This review aims to introduce the efforts made using various analytical tools in combination with the multivariate analysis for the verification of the geographical origin of oils. The popular analytical tools have been discussed, and scientometric assessment that underlines research trends in geographical authentication and preferred journals used for dissemination has been indicated. Overall, we believe this article will be a good guideline for food industries and food quality control authority to assist in the selection of appropriate methods to authenticate oils.

Process optimization of chili flavor beef tallow and analysis of its volatile compounds by GC-IMS

With chili and liquid beef tallow as the main raw materials, the processing conditions of chili flavor beef tallow were explored. Gas chromatograpy-ion mobility spectrometry (GC-IMS) was used to determine the volatile compounds in chili flavor beef tallow. The capsaicin and dihydrocapsaicin in chili flavor beef tallow were determined by high performance liquid chromatography (HPLC). The optimum technological conditions were determined, and the index of chromatic aberration, cholesterol was also determined. Based on GC-IMS analysis, 102 kinds of volatile compounds were detected, and the sample III (the ratio of solid–liquid was 1:5, the frying temperature was 120 °C, and the frying time was 15 min) performed better than other samples. The preparation of chili beef tallow improves its antioxidant activity and makes its aroma more intense and more in line with the taste of Chinese people, which provides a theoretical and practical basis for the development of spice beef tallow in the future.

Volatilomic Profiling of Citrus Juices by Dual-Detection HS-GC-MS-IMS and Machine Learning-An Alternative Authentication Approach

A prototype dual-detection headspace-gas chromatography-mass spectrometry-ion mobility spectrometry (HS-GC-MS-IMS) system was used for the analysis of the volatile profile of 47 Citrus juices including grapefruit, blood orange, and common sweet orange juices without requiring any sample pretreatment. Next to reduced measurement times, substance identification could be improved substantially in case of co-elution by considering the characteristic drift times and m/z ratios obtained by IMS and MS. To discriminate the volatile profiles of the different juice types, extensive data analysis was performed with both datasets, respectively. By principal component analysis (PCA), a distinct separation between grapefruit and orange juices was observed. While in the IMS data grapefruit juices not from fruit juice concentrate could be separated from grapefruit juices reconstituted from fruit juice concentrate, in the MS data, the blood orange juices could be differentiated from the orange juices. This observation leads to the assumption that the IMS and MS data contain different information about the composition of the volatile profile. Subsequently, linear discriminant analysis (LDA), support vector machines (SVM), and the k-nearest-neighbor (kNN) algorithm were applied to the PCA data as supervised classification methods. Best results were obtained by LDA after repeated cross-validation for both datasets, with an overall classification and prediction ability of 96.9 and 91.5% for the IMS data and 94.5 and 87.9% for the MS data, respectively, which confirms the results obtained by PCA. Additional data fusion could not generally improve the model prediction ability compared to the single data, but rather for certain juice classes. Consequently, depending on the juice class, the most suitable dataset should be considered for the prediction of the class membership. This volatilomic approach based on the dual detection by HS-GC-MS-IMS and machine learning tools represent a simple and promising alternative for future authenticity control of Citrus juices.

Recent applications of ion mobility spectrometry in natural product research

Ion mobility spectrometry (IMS) is a rapid separation technique capable of extracting complementary structural information to chromatography and mass spectrometry (MS). IMS, especially in combination with MS, has experienced inordinate growth in recent years as an analytical technique, and elicited intense interest in many research fields. In natural product analysis, IMS shows promise as an additional tool to enhance the performance of analytical methods used to identify promising drug candidates. Potential benefits of the incorporation of IMS into analytical workflows currently used in natural product analysis include the discrimination of structurally similar secondary metabolites, improving the quality of mass spectral data, and the use of mobility-derived collision cross-section (CCS) values as an additional identification criterion in targeted and untargeted analyses. This review aims to provide an overview of the application of IMS to natural product analysis over the last six years. Instrumental aspects and the fundamental background of IMS will be briefly covered, and recent applications of the technique for natural product analysis will be discussed to demonstrate the utility of the technique in this field.

Determination of the Geographical Origin of Walnuts (Juglans regia L.) Using Near-Infrared Spectroscopy and Chemometrics

The prices of walnuts vary according to their geographical origin and, therefore, offer a financial incentive for adulteration. A reliable analysis method is required to quickly detect possible misdeclarations and thus prevent food fraud. In this study, a method to distinguish between seven geographical origins of walnuts using Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics as a fast, versatile, and easy to handle analytical tool was developed. NIR spectra of 212 ground and afterwards freeze-dried walnut samples, harvested in three consecutive years (2017-2019), were collected. We optimized the data pre-processing by applying and evaluating 50,545 different pre-processing combinations, followed by linear discriminant analysis (LDA) which was confirmed by nested cross-validation. The results show that in the scope of our research minimal pre-processing led to the best results: By applying just multiplicative scatter correction (MSC) and median centering, a classification accuracy of 77.00% ± 1.60% was achieved. Consequently, this complex model can be used to answer economically relevant questions e.g., to distinguish between European and Chinese walnuts. Furthermore, the great influence of the applied pre-processing methods, e.g., the selected wavenumber range, on the achieved classification accuracy is shown which underlines the importance of optimization of the pre-processing strategy.

Multi-level data fusion strategies for modeling three-way electrophoresis capillary and fluorescence arrays enhancing geographical and grape variety classification of wines

Capillary electrophoresis with diode array detection (CE-DAD) and multidimensional fluorescence spectroscopy (EEM) second-order data were fused and chemometrically processed for geographical and grape variety classification of wines. Multi-levels data fusion strategies on three-way data were evaluated and compared revealing their advantages/disadvantages in the classification context. Straightforward approaches based on a series of data preprocessing and feature extraction steps were developed for each studied level. Partial least square discriminant analysis (PLS-DA) and its multi-way extension (NPLS-DA) were applied to CE-DAD, EEM and fused data matrices structured as two-way and three-way arrays, respectively. Classification results achieved on each model were evaluated through global indices such as average sensitivity non-error rate and average precision. Different degrees of improvement were observed comparing the fused matrix results with those obtained using a single one, clear benefits have been demonstrated when level of data fusion increases, achieving with the high-level strategy the best classification results.

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

Flash Gas Chromatography in Tandem with Chemometrics: A Rapid Screening Tool for Quality Grades of Virgin Olive Oils

This research aims to develop a classification model based on untargeted elaboration of volatile fraction fingerprints of virgin olive oils (n = 331) analyzed by flash gas chromatography to predict the commercial category of samples (extra virgin olive oil, EVOO; virgin olive oil, VOO; lampante olive oil, LOO). The raw data related to volatile profiles were considered as independent variables, while the quality grades provided by sensory assessment were defined as a reference parameter. This data matrix was elaborated using the linear technique partial least squares-discriminant analysis (PLS-DA), applying, in sequence, two sequential classification models with two categories (EVOO vs. no-EVOO followed by VOO vs. LOO and LOO vs. no-LOO followed by VOO vs. EVOO). The results from this large set of samples provide satisfactory percentages of correctly classified samples, ranging from 72% to 85%, in external validation. This confirms the reliability of this approach in rapid screening of quality grades and that it represents a valid solution for supporting sensory panels, increasing the efficiency of the controls, and also applicable to the industrial sector.

Sustainable Palm Oil-The Role of Screening and Advanced Analytical Techniques for Geographical Traceability and Authenticity Verification

Palm oil production from oil palm (Elaeis guineensis Jacq.) is vital for the economy of Malaysia. As of late, sustainable production of palm oil has been a key focus due to demand by consumer groups, and important progress has been made in establishing standards that promote good agricultural practices that minimize impact on the environment. In line with the industrial goal to build a traceable supply chain, several measures have been implemented to ensure that traceability can be monitored. Although the palm oil supply chain can be highly complex, and achieving full traceability is not an easy task, the industry has to be proactive in developing improved systems that support the existing methods, which rely on recorded information in the supply chain. The Malaysian Palm Oil Board (MPOB) as the custodian of the palm oil industry in Malaysia has taken the initiative to assess and develop technologies that can ensure authenticity and traceability of palm oil in the major supply chains from the point of harvesting all the way to key downstream applications. This review describes the underlying framework related to palm oil geographical traceability using various state-of-the-art analytical techniques, which are also being explored to address adulteration in the global palm oil supply chain.

An HS-GC-IMS Method for the Quality Classification of Virgin Olive Oils as Screening Support for the Panel Test

Sensory evaluation, carried out by panel tests, is essential for quality classification of virgin olive oils (VOOs), but is time consuming and costly when many samples need to be assessed; sensory evaluation could be assisted by the application of screening methods. Rapid instrumental methods based on the analysis of volatile molecules might be considered interesting to assist the panel test through fast pre-classification of samples with a known level of probability, thus increasing the efficiency of quality control. With this objective, a headspace gas chromatography-ion mobility spectrometer (HS-GC-IMS) was used to analyze 198 commercial VOOs (extra virgin, virgin and lampante) by a semi-targeted approach. Different partial least squares-discriminant analysis (PLS-DA) chemometric models were then built by data matrices composed of 15 volatile compounds, which were previously selected as markers: a first approach was proposed to classify samples according to their quality grade and a second based on the presence of sensory defects. The performance (intra-day and inter-day repeatability, linearity) of the method was evaluated. The average percentages of correctly classified samples obtained from the two models were satisfactory, namely 77% (prediction of the quality grades) and 64% (prediction of the presence of three defects) in external validation, thus demonstrating that this easy-to-use screening instrumental approach is promising to support the work carried out by panel tests.

Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000-4000 cm^-1) and Fourier transform mid-infrared (MIR: 4000-600 cm^-1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen's kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.