Geographical discrimination and authentication of Chinese garlic based on multi-element, volatile and metabolomics profiling combined with chemometrics

Metabolomics applications for plant-based foods origin tracing, cultivars identification and processing: Feasibility and future aspects

Metabolomics, the systematic study of metabolites, is dedicated to a comprehensive analysis of all aspects of plant-based food research and plays a pivotal role in the nutritional composition and quality control of plant-based foods. The diverse chemical compositions of plant-based foods lead to variations in sensory characteristics and nutritional value. This review explores the application of the metabolomics method to plant-based food origin tracing, cultivar identification, and processing methods. It also addresses the challenges encountered and outlines future directions. Typically, when combined with other omics or techniques, synergistic and complementary information is uncovered, enhancing the classification and prediction capabilities of models. Future research should aim to evaluate all factors affecting food quality comprehensively, and this necessitates advanced research into influence mechanisms, metabolic pathways, and gene expression.

Rapid discrimination of geographical origin of garlic (Allium sativum L.): A metabolomic approach applied to paper spray mass spectrometry data

INTRODUCTION

Distinguishing and categorizing the origin of garlic are highly significant, considering its widespread use as a flavoring agent. With billions of dollars annually in global trade, garlic is frequently susceptible to fraudulent practices.

METHODOLOGY

Paper spray ionization mass spectrometry (PS-MS) was employed to quickly analyze garlic samples from distinct geographic origins: China and Brazil. The so-generated PS-MS data were treated with metabolomic multivariate approaches, and the garlic samples from these different geographic regions were easily discriminated.

RESULTS

Brazilian garlic was characterized to contain higher levels of amino acids, such as arginine, proline, and valine, and organosulfur compounds, such as allicin, alliin, and l-γ-glutamil-S-allyl-l-cysteine, compared to Chinese garlic. The PS-MS data were treated employing multivariate approaches, typically used in the metabolomics field, and this protocol was promptly able to discern among both types of samples.

CONCLUSION

Hence, this combined strategy holds promise not only as an effective tool for the authentication of the geographical origin of garlic but also as a powerful means for biomarker discovery.

Metabolic and elemental profiling as potential discriminating features among the black mahlab seeds (Monechma ciliatum) grown in three different regions

INTRODUCTION

Black mahlab (Monechma ciliatum) seed is a rich source of metabolites and minerals and is usually believed to have a similar composition between different areas of cultivation. Until now, no studies have assessed changes in black mahlab seeds (BMSs) to determine those constituents that help to discriminate them according to geographical origin.

OBJECTIVES

The present study attempted to compare the metabolomics and elemental profiles of BMSs of different geographical origins and identified the potential markers using ultrahigh-performance liquid chromatography quadrupole Orbitrap tandem mass spectrometry (UHPLC-Q-Orbitrap-MS2), and inductively coupled plasma mass spectrometry (ICP-MS) techniques and established the chemometric model to identify the potential markers and discriminate them according to cultivation sites.

MATERIAL AND METHODS

In this work, data from metabolites analysis by UHPLC-Q-Orbitrap-MS2 and multi-elemental data obtained from ICP-MS were combined with chemometrics for tracing the geographical origin of BMSs. Principal component analysis (PCA) was used to evaluate the overall grouping of samples. In contrast, partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed for authentication.

RESULTS

PLS-DA and OPLS-DA models were fully validated (R2Y and Q2 values > 0.5). Variable importance of various projections was applied to obtain valuable data about differential elements (seven markers were identified) and metabolites (23 markers were identified) with high discrimination potential. The outcomes presented in this study serve as an appropriate framework for developing novel discrimination approaches in food origin screening.

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

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

Gas chromatography-stable isotope ratio mass spectrometry prior solid phase microextraction and gas chromatography-tandem mass spectrometry: development and optimization of analytical methods to analyse garlic (Allium sativum L.) volatile fraction

Garlic (Allium sativum L.) is not only appreciated for its flavour and taste, but it is also recognized for various health properties. The European Commission, through the attribution of the Protected Designation of Origin (PDO) certification mark, has officially recognized some specific varieties of garlic. To protect not only the commercial value but also the reputation of this appreciated product, effective tools are therefore required. For the first time, a new compound specific isotope analysis method based on carbon stable isotopic ratio measurement of the three major volatile garlic compounds allyl alcohol (AA), diallyl disulphide (DD) and diallyl trisulphide (DT) through head-space solid phase microextraction (HS-SPME) followed by gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS) was developed. A within-day standard deviation (Srwithin-day) of 0.3 ‰, 0.4 ‰ and 0.2 ‰ for δ(13C) and a between-day standard deviation (Srbetween-day) of 0.8 ‰, 1.0 ‰ and 0.6 ‰ of AA, DT and DD was estimated. For the first time, the ranges of isotopic variability for the three volatile compounds of red garlic from two neighbouring Italian regions (Abruzzo and Lazio) were defined analysing 30 samples. The same dataset was also considered in analysing the percentage composition of the previously mentioned three volatile compounds through HS-SPME followed by gas chromatography-tandem mass spectrometry (GC-MS/MS). The two analytical approaches were combined in this explorative study, aiming to provide potential parameters to discriminate garlic samples based on their geographical origin.

Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information

The chemical composition and nutritional content of garlic are greatly impacted by its production location, leading to distinct flavor profiles and functional properties among garlic varieties from diverse origins. Consequently, these variations determine the preference and acceptance among diverse consumer groups. In this study, purple-skinned garlic samples were collected from five regions in China: Yunnan, Shandong, Henan, Anhui, and Jiangsu Provinces. Mid-infrared spectroscopy and ultraviolet spectroscopy were utilized to analyze the components of garlic cells. Three preprocessing methods, including Multiple Scattering Correction (MSC), Savitzky-Golay Smoothing (SG Smoothing), and Standard Normalized Variate (SNV), were applied to reduce the background noise of spectroscopy data. Following variable feature extraction by Genetic Algorithm (GA), a variety of machine learning algorithms, including XGboost, Support Vector Classification (SVC), Random Forest (RF), and Artificial Neural Network (ANN), were used according to the fusion of spectral data to obtain the best processing results. The results showed that the best-performing model for ultraviolet spectroscopy data was SNV-GA-ANN, with an accuracy of 99.73%. The best-performing model for mid-infrared spectroscopy data was SNV-GA-RF, with an accuracy of 97.34%. After the fusion of ultraviolet and mid-infrared spectroscopy data, the SNV-GA-SVC, SNV-GA-RF, SNV-GA-ANN, and SNV-GA-XGboost models achieved 100% accuracy in both training and test sets. Although there were some differences in the accuracy of the four models under different preprocessing methods, the fusion of ultraviolet and mid-infrared spectroscopy data yielded the best outcomes, with an accuracy of 100%. Overall, the combination of ultraviolet and mid-infrared spectroscopy data fusion and chemometrics established in this study provides a theoretical foundation for identifying the origin of garlic, as well as that of other agricultural products.

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.

Geographical differentiation of garlic based on HS-GC-IMS combined with multivariate statistical analysis

Garlic is famous for its unique flavor and health benefits. An effective means of authenticating garlic's origin is through the implementation of the Protected Geographical Indication (PGI) scheme. However, the prevalence of fraudulent behavior raises concerns regarding the reliability of this system. In this study, garlic samples from six distinct production areas (G1: Cangshan garlic, G2: Qixian garlic, G3: Dali single clove garlic, G4: Jinxiang garlic, G5: Yongnian garlic, and G6: Badong garlic) underwent analysis using HS-GC-IMS. A total of 26 VOCs were detected in the samples. The differences in VOCs among the different garlic samples were visually presented in a two-dimensional topographic map and fingerprint map. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed to demonstrate the capacity of the HS-GC-IMS method for effectively distinguishing garlic samples from different geographical sources. Further screening based on the p-value and VIP score threshold identified 12 different aroma substances, which can be utilized for the identification of garlic from different producing areas. The fusion of HS-GC-IMS with multivariate statistical analysis proved to be a rapid, intuitive, and efficient approach for identifying and categorizing garlic VOCs, offering a novel strategy for ascertaining garlic origin and ensuring quality control.

Future perspectives on aptamer for application in food authentication

Food fraudulence and food contamination are major concerns, particularly among consumers with specific dietary, cultural, lifestyle, and religious requirements. Current food authentication methods have several drawbacks and limitations, necessitating the development of a simpler, more sensitive, and rapid detection approach for food screening analysis, such as an aptamer-based biosensor system. Although the use of aptamer is growing in various fields, aptamer applications for food authentication are still lacking. In this review, we discuss the limitations of existing food authentication technologies and describe the applications of aptamer in food analyses. We also project several potential targets or marker molecules to be targeted in the SELEX process. Finally, this review highlights the drawbacks of current aptamer technologies and outlines the potential route of aptamer selection and applications for successful food authentication. This review provides an overview of the use of aptamer in food research and its potential application as a molecular reporter for rapid detection in food authentication process. Developing databases to store all biochemical profiles of food and applying machine learning algorithms against the biochemical profiles are urged to accelerate the identification of more reliable biomarker molecules as aptamer targets for food authentication.

An overview on multi-elemental profile integrated with chemometrics for food quality assessment: toward new challenges

Food products, especially those with high value-added, are commonly subjected to strict quality controls, which are of paramount importance, especially for attesting to some peculiar features related, for instance, to their geographical origin and/or the know-how of their producers. However, the sophistication of fraudulent practices requires a continuous update of analytical platforms. Different analytical techniques have become extremely appealing since the instrumental analysis tools evolution has substantially improved the capability to reveal and understand the complexity of food. In light of this, multi-elemental composition has been successful implemented solving a plethora of food authentication and traceability issues. In the last decades, it has existed an ever-increasing trend in analysis based on spectrometry analytical platforms in order to obtain a multi-elemental profile that combined with chemometrics have been noteworthy analytical methodologies able to solve these problems. This review provides an overview of published reports in the last decade (from 2011 to 2021) on food authentication and quality control from their multi-element composition in order to evaluate the state-of-the-art of this field and to identify the main characteristics of applied analytical techniques and chemometric data treatments that have permit achieve accurate discrimination/classification models, highlighting the strengths and the weaknesses of these methodologies.