Untargeted volatile metabolomics using comprehensive two-dimensional gas chromatography-mass spectrometry – A solution for orange juice authentication

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

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

Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review

Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.

Solid-phase microextraction coupled to comprehensive multidimensional gas chromatography for food analysis

Solid-phase microextraction and comprehensive multidimensional gas chromatography represent two milestone innovations that occurred in the field of separation science in the 1990s. They have a common root in their introduction and have found a perfect coupling in their evolution and applications. This review will focus on food analysis, where the paradigm has changed significantly over time, moving from a targeted analysis, focusing on a limited number of analytes at the time, to a more holistic approach for assessing quality in a larger sense. Indeed, not only some major markers or contaminants are considered, but a large variety of compounds and their possible interaction, giving rise to the field of foodomics. In order to obtain such detailed information and to answer more sophisticated questions related to food quality and authenticity, the use of SPME-GC × GC-MS has become essential for the comprehensive analysis of volatile and semi-volatile analytes. This article provides a critical review of the various applications of SPME-GC × GC in food analysis, emphasizing the crucial role this coupling plays in this field. Additionally, this review dwells on the importance of appropriate data treatment to fully harness the results obtained to draw accurate and meaningful conclusions.

Metabolomics study on the main volatile components of Thai colored rice cultivars from different agricultural locations

This study investigated the main volatile components in ten Thai colored rice varieties cultivated in two agricultural locations of Thailand (Central and Northern region) using a static headspace GC-MS metabolomics approach. The results indicated that volatolomics could successfully differentiate between the geographical origins of the same rice variety grown in regions within the same country. The volatile profiles of the colored rice obtained from the two locations were clearly different, with three volatile compounds isolated as key aroma producers in each area. Primary volatile compounds upregulated in colored rice varieties grown in Northern Thailand included undecanoic acid, 10-methyl-methyl ester; methyl 8-methyl-nonanoate; and pyrimidine, 4-methyl. Hexadecanoic acid, methyl ester; methyl 9-cis,11-trans-octadecadienoate; and 10-octadecenoic acid methyl ester were upregulated in the rice samples grown in Central Thailand. The environmental factors that could affect colored rice aroma at the agricultural sites included temperature, downward surface shortwave radiation, and vapor pressure deficit.

Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography-A review

In recent years, comprehensive two-dimensional gas chromatography (GC × GC) has been gradually gaining prominence as a preferred method for the analysis of complex samples due to its higher peak capacity and resolution power compared to conventional gas chromatography (GC). Nonetheless, to fully benefit from the capabilities of GC × GC, a holistic approach to method development and data processing is essential for a successful and informative analysis. Method development enables the fine-tuning of the chromatographic separation, resulting in high-quality data. While generating such data is pivotal, it does not necessarily guarantee that meaningful information will be extracted from it. To this end, the first part of this manuscript reviews the importance of theoretical modeling in achieving good optimization of the separation conditions, ultimately improving the quality of the chromatographic separation. Multiple theoretical modeling approaches are discussed, with a special focus on thermodynamic-based modeling. The second part of this review highlights the importance of establishing robust data processing workflows, with a special emphasis on the use of advanced data processing tools such as, Machine Learning (ML) algorithms. Three widely used ML algorithms are discussed: Random Forest (RF), Support Vector Machine (SVM), and Partial Least Square-Discriminate Analysis (PLS-DA), highlighting their role in discovery-based analysis.

Determination of adulteration, geographical origins, and species of food by mass spectrometry

Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.

Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices

Fruit juices are one of the most widely consumed beverages worldwide, and their production is subject to strict regulations. Therefore, this study presents a methodology based on the use of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) in combination with machine-learning algorithms for the characterization juices of different raw material (orange, pineapple, or apple and grape). For this purpose, the ion mobility sum spectrum (IMSS) was used. First, an optimization of the most important conditions in generating the HS was carried out using a Box-Behnken design coupled with a response surface methodology. The following factors were studied: temperature, time, and sample volume. The optimum values were 46.3 °C, 5 min, and 750 µL, respectively. Once the conditions were optimized, 76 samples of the different types of juices were analyzed and the IMSS was combined with different machine-learning algorithms for its characterization. The exploratory analysis by hierarchical cluster analysis (HCA) and principal component analysis (PCA) revealed a clear tendency to group the samples according to the type of fruit juice and, to a lesser extent, the commercial brand. The combination of IMSS with supervised classification techniques reported an excellent result with 100% accuracy on the test set for support vector machines (SVM) and random forest (RF) models regarding the specific fruit used. Nevertheless, all the models have proven to be an effective alternative for characterizing and classifying the different types of juices.

Citrus juice off-flavor during different processing and storage: Review of odorants, formation pathways, and analytical techniques

As the most widespread juice produced and consumed globally, citrus juice (mandarin juice, orange juice, and grapefruit juice) is appreciated for its attractive and distinct aroma. While the decrease of characteristic aroma-active compounds and the formation of off-flavor compounds are easy to occur in processing and storage conditions. This review provides a comprehensive literature of recent research and discovery on citrus juice off-flavor, primarily focusing on off-flavor compounds induced during processing and storage (i.e., thermal, storage, light, oxygen, package, fruit maturity, diseases, centrifugal pretreatment, and debittering process), formation pathways (i.e., terpene acid-catalyzed hydration, caramelization reaction, Maillard reaction, Strecker degradation, and other oxidative degradation) of the off-flavor compounds, effective inhibitor pathway to off-flavor (i.e., electrical treatments, high pressure processing, microwave processing, ultrasound processing, and chemical treatment), as well as odor assessment techniques based on molecular sensory science. The possible precursors (terpenes, sulfur-containing amino acids, carbohydrates, carotenoids, vitamins, and phenolic acids) of citrus juice off-flavor are listed and are also proposed. This review intends to unravel the regularities of aroma variations and even off-flavor formation of citrus juice during processing and storage. Future aroma analysis techniques will evolve toward a colorimetric sensor array for odor visualization to obtain a "marker" of off-flavor in citrus juice.

Classification of sugarcane genotypes susceptible and resistant to the initial attack of sugarcane borer Diatraea saccharalis using epicuticular wax composition

Identifying compounds present in the sugarcane epicuticular wax and using these compounds to classify the genotypes susceptible and resistant to the initial attack of sugarcane borer (Diatraea saccharalis) was the aim of this study. A greenhouse experiment was performed in a factorial scheme with and without borer infestation using genotypes previously characterized as resistant or susceptible in field-based experiments. Sugarcane whorls of six-month-old plants were collected before (BI) and after (AI) 72 h of sugarcane borer infestation. The sugarcane epicuticular wax was extracted in both times, i.e., BI and AI and its chemical composition was assessed by gas chromatography coupled to mass spectrometry (GC-MS). Twenty-five compounds were identified for both BI and AI. Classification models were built using partial least squares for discriminant analysis (PLS-DA) and linear discriminant analysis (LDA). Variable selection methods were used to improve the classification models. Ordered predictors selection for discriminant analysis (OPSDA) selected compounds that correctly classified all the test samples before borer infestation (Error = 0.000), and exhibited the most suitable classification parameters for the test set after borer infestation (Error = 0.111). The C30 pentacyclic triterpene friedelin and a high alcohol/aldehyde ratio were associated with the classification of resistant genotypes. Our findings have applicability in developing a screening methodology for breeding programs interested in identifying genotypes resistant to the initial feeding of sugarcane borer.

Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data

Fruit juice production is one of the most important sectors in the beverage industry, and its adulteration by adding cheaper juices is very common. This study presents a methodology based on the combination of machine learning models and near-infrared spectroscopy for the detection and quantification of juice-to-juice adulteration. We evaluated 100% squeezed apple, pineapple, and orange juices, which were adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The spectroscopic data have been combined with different machine learning tools to develop predictive models for the control of the juice quality. The use of non-supervised techniques, specifically model-based clustering, revealed a grouping trend of the samples depending on the type of juice. The use of supervised techniques such as random forest and linear discriminant analysis models has allowed for the detection of the adulterated samples with an accuracy of 98% in the test set. In addition, a Boruta algorithm was applied which selected 89 variables as significant for adulterant quantification, and support vector regression achieved a regression coefficient of 0.989 and a root mean squared error of 1.683 in the test set. These results show the suitability of the machine learning tools combined with spectroscopic data as a screening method for the quality control of fruit juices. In addition, a prototype application has been developed to share the models with other users and facilitate the detection and quantification of adulteration in juices.

Comprehensive two-dimensional gas chromatography mass spectrometry-based untargeted metabolomics to clarify the dynamic variations in the volatile composition of Huangjiu of different ages

Aging plays an important role in the formation of aroma characteristics of Huangjiu, a traditional Chinese alcoholic beverage. Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-qMS)-based untargeted metabolomics combined with a multivariate analysis was used to investigate the dynamic variations in the aroma profile of Huangjiu during aging process and to establish the relationship between the changing volatile metabolite profiles and the age-dependent sensory attributes. A total of 144 volatile metabolites were identified by GC×GC-qMS and 63 were selected as critical metabolites based on variable importance in projection values and p-values. Based on the results of principal component analysis, orthogonal partial least-squares discriminant analysis, and hierarchical clustering analysis, the samples of six different ages were divided into three groups: 1Y and 3Y samples, 5Y and 8Y samples, and 10Y and 15Y samples. The partial least-squares analysis results further revealed the relationship between the aromas attributes and variations of these volatile compounds. The high esters, aldehydes, and lactones contents contributed to the high intensities of the sweet and ester aroma attributes of the aged Huangjiu, while the high alcohols and ethyl esters contents contributed to the alcoholic and fruity aroma attributes of the newly brewed Huangjiu. These results improve our understanding of the chemical nature of the aroma characteristics of aged Huangjiu. PRACTICAL APPLICATION: Huangjiu is often labeled with its age as a measure of quality, which influences consumers' choice. Dynamic variations in volatile compounds of Huangjiu during aging and its contribution to the aroma characteristics of Huangjiu were figured out, which will assist the industry to produce better quality aged Huangjiu for consumers.

Mapping Aspergillus niger Metabolite Biomarkers for In Situ and Early Evaluation of Table Grapes Contamination

The Aspergillus niger exometabolome was recently investigated using advanced gas chromatography in tandem with multivariate analysis, which allowed a metabolite biomarker pattern to be proposed. Microbial metabolomics patterns have gained enormous relevance, mainly due to the amount of information made available, which may be useful in countless processes. One of the great challenges in microbial metabolomics is related to applications in more complex systems of metabolomics information obtained from studies carried out in culture media, as complications may occur due to the dynamic nature of biological systems. Thus, the main objective of this research was to evaluate the applicability of the A. niger metabololite biomarkers pattern for in situ and early evaluation of table grapes contamination, used as study model. A. niger is a ubiquitous fungus responsible for food contamination, being reported as one of the main agents of the black mold disease, a serious post-harvest pathology of table grapes. This work included analysis from 1 day of growth time of pure A. niger cultures, A. niger cultures obtained from previously contaminated grapes, and finally, an in situ solid-phase microextraction (SPME) approach directly on previously contaminated table grapes. Supervised multivariate analysis was performed which revealed that after 1 day of inoculation it was possible to detect A. niger biomarkers, which can be extremely useful in making this type of method possible for the rapid detection of food contamination. The results obtained confirm the potential applicability of the pattern of A. niger biomarkers for early detection of the fungi (after 1 day of contamination), and may be further explored for access food susceptibility to fungi contamination, based on direct analysis of the food item.

Unique ion filter-A data reduction tool for chemometric analysis of raw comprehensive two-dimensional gas chromatography-mass spectrometry data

Comprehensive gas chromatography with time of flight mass spectrometry is a powerful tool in the analysis of complex samples. Chemometric analysis of raw chromatographic data is more useful in one- and two-dimensional separations relative to peak tables. The data volume from such experiments generally necessitates the use of data reduction tools. Such tools often sacrifice some of the multivariate information in the mass to charge ratio dimension. The unique ion filter reduces the over-redundancy in two-dimensional gas chromatography-mass spectrometry data by limiting the data to a few unique/pseudo-unique ions, sub-peaks/slices in the first dimension, and spectra in the second dimension. We explore the performance of this algorithm through careful inspection of two-dimensional gas chromatography-mass spectrometry data before and after application of the filter. A reduction (99%) in the number of variables in a two-dimensional gas chromatography-mass spectrometry chromatogram passed on to subsequent analysis was observed. Feature selection times for model optimization reduced from 229 (±13) to 6.8 (±0.5) min when the filter was applied. An estimate of two unique/pseudo-unique ions, one sub-peak in the first dimension and five spectra in the second dimension were considered to provide a true representation of each chromatogram and provided enough information to achieve 100% model prediction accuracy.

Investigation of the limit of discovery using tile-based Fisher ratio analysis with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry

Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) is followed by tile-based Fisher ratio (F-ratio) analysis to investigate the "limit of discovery" for low concentration levels of sulfur-containing compounds in JP8 jet fuel. A mixture of 14 sulfur-containing compounds was spiked at 30 ppm, 15 ppm, 3 ppm and 1.5 ppm into the neat fuel prior to GC×GC-TOFMS analysis with a "reversed" column format (aka polar first dimension (¹D) and non-polar second dimension (²D) column). Prior standard implementation of tile-based F-ratio analysis utilized an average F-ratio requiring a minimum of 3 mass channels (m/z) with the highest F-ratios. Herein, we explore the notion that use of the top F-ratio m/z for hitlist ranking is superior to the standard implementation for analytes near their limit-of-quantitation (LOQ), defined as an analyte concentration that produces a signal equal to ten times the standard deviation of the baseline noise (10σ_n). Hitlist ranking comparisons revealed that using only the top F-ratio m/z resulted in impressive improvements in discoverability for the low concentration comparisons. Specifically, for the 3 ppm versus neat hitlist, 1,4-oxathiane (LOQ = 2.5 ppm) improved from hit 114 via standard F-ratio analysis, to hit 25. For the 1.5 ppm versus neat hitlist, 2-propylthiophene (LOQ = 0.64 ppm) improved from hit 59 to 17, benzo[b]thiophene (LOQ = 1.1 ppm) from hit 98 to 28, and 2,5-dimethylthiophene (LOQ = 1.3 ppm) from hit 262 to 39. Additional hitlist ranking comparisons revealed the importance of proper tile size selection, as analyte discoverability deteriorated upon using either an inappropriately too small or too large of a tile.

Herbal Component Correlation and Matrix-based Resolution in Comprehensive two-dimensional Gas Chromatography - Mass Spectrometry data via Intelligent Clustering of Modulation Peaks

In order to facilitate correlation calculation and matrix-based resolution in comprehensive two-dimensional gas chromatography - mass spectrometry (GC × GC-MS) data-set, an intelligent clustering of modulation peaks (ICMP) algorithm was developed in this paper. ICMP is start with the second -dimension (²D) peak restriction, then conducting the peak shape restriction in the first dimension (¹D), finally end with the eigenvalues calculation against mass spectra in moving sub-windows. After this three-tier restriction, multi-component spectral correlative chromatography (MSCC) was applied in peak clustering result from a row-wise augmented "two-dimension (2D) slice" set. Then the component similarities and differences were distinguished rapidly/ accurately in chemical fingerprints from ChaiHu Shugan San and Cyperus rotundus. Faced with co-eluted phenomenon, matrix-based resolution was made in the representative sub-matrices that have been locked in ICMP procedure. From the example data shows that ICMP- multivariate curve resolution (MCR) can served as a good complement to (non) trilinear decomposition. To summarize, the GC × GC data-structure can be simplified to facilitate MSCC or MCR operation in fingerprints from herbal or biological samples.