Discrimination of conventional and organic rice using untargeted LC-MS-based metabolomics

Untargeted foodomics for authenticating the organic farming of water spinach (Ipomoea aquatica)

This study aimed to conduct a comprehensive analysis of the primary and secondary metabolites of water spinach (Ipomoea aquatica) using hydrophilic interaction liquid chromatography coupled with Orbitrap high-resolution mass spectrometry (HILIC-Orbitrap-HRMS). Certified samples from two cultivars, Green stem water spinach (G) and White stem water spinach (W) cultivated using organic and conventional farming methods, were collected from the Hong Kong market. Multivariate analysis was used to differentiate water spinach of different cultivars and farming methods. We identified 12 metabolites to distinguish between G and W, 26 metabolites to identify G from organic farming and 8 metabolites to identify W from organic farming. Then, two metabolites, isorhamnetin and jasmonic acid, have been proposed to serve as biomarkers for organic farming (in both G and W). Our foodomics findings provide useful tools for improving the crop performance of water spinach under abiotic/biotic stressesand authentication of organic produce.

Multi-omics in food safety and authenticity in terms of food components

One of the main goals of food science is to ensure the high quality and safety of food. The inspection technology for known hazards has matured, and the identification of unknown and potential food safety hazards, as well as the identification of their composition and origin, is a challenge faced by food safety. Food safety and authenticity require multi-omics methods to support the implementation of qualitative discrimination to precise quantitative analysis, from targeted screening to non-target detection, and from multi component to full component analysis to address these challenges. The present review aims to provide characterizations, advantages, the latest progress, and prospects of using omics (including genomics, proteomics, and metabonomics) in food safety and authenticity. Multi omics strategies used to detect and verify different standard biomarkers of food will contribute to understanding the basic relationship between raw materials, processing, foods, nutrition, food safety, and human health.

Metabolomic analysis for disclosing nutritional and therapeutic prospective of traditional rice cultivars of Cauvery deltaic region, India

Traditional rice is gaining popularity worldwide due to its high nutritional and pharmaceutical value, as well as its high resistance to abiotic and biotic stresses. This has attracted significant attention from breeders, nutritionists, and plant protection scientists in recent years. Hence, it is critical to investigate the grain metabolome to reveal germination and nutritional importance. This research aimed to explore non-targeted metabolites of five traditional rice varieties, viz., Chinnar, Chithiraikar, Karunguruvai, Kichili samba, and Thooyamalli, for their nutritional and therapeutic properties. Approximately 149 metabolites were identified using the National Institute of Standards and Technology (NIST) library and Human Metabolome Database (HMDB) and were grouped into 34 chemical classes. Major classes include fatty acids (31.1-56.3%), steroids and their derivatives (1.80-22.4%), dihydrofurans (8.98-11.6%), prenol lipids (0.66-4.44%), organooxygen compounds (0.12-6.45%), benzene and substituted derivatives (0.53-3.73%), glycerolipids (0.36-2.28%), and hydroxy acids and derivatives (0.03-2.70%). Significant variations in metabolite composition among the rice varieties were also observed through the combination of univariate and multivariate statistical analyses. Principal component analysis (PCA) reduced the dimensionality of 149 metabolites into five principle components (PCs), which explained 96% of the total variance. Two clusters were revealed by hierarchical cluster analysis, indicating the distinctiveness of the traditional varieties. Additionally, a partial least squares-discriminant analysis (PLS-DA) found 17 variables important in the projection (VIP) scores of metabolites. The findings of this study reveal the biochemical intricate and distinctive metabolomes of the traditional therapeutic rice varieties. This will serve as the foundation for future research on developing new rice varieties with traditional rice grain metabolisms to increase grain quality and production with various nutritional and therapeutic benefits.

Targeted lipidomics coupled with machine learning for authenticating the provenance of chicken eggs

Free-range eggs are ethically desirable but as with all high-value commercial products, the establishment of provenance can be problematic. Here, we compared a simple one-step isopropanol method to a two-step methyl-tert-butyl ether method for extracting lipid species in chicken egg yolks before liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. The isopropanol method extracted 937 lipid species from 20 major lipid subclasses with high reproducibility (CV < 30 %). Machine learning techniques could differentiate conventional cage, barn, and free-range eggs using an external test dataset with an accuracy of 0.94, 0.82, and 0.82, respectively. Lipid species that differentiated cage eggs were predominantly phosphocholines and phosphoethanolamines whilst the free-range egg lipidomes were dominated by acylglycerides with up to three fatty acids. The lipid profiles were found to be characteristic of the cage, barns, and free-range eggs. The lipidomic analysis together with the statistical modeling approach thus provides an efficient tool for verifying the provenance of conventional chicken eggs.

Untargeted metabolomics analysis based on LC-IM-QTOF-MS for discriminating geographical origin and vintage of Chinese red wine

Identifying wine geographical origin and vintage is vital due to the abundance of fraudulent activity associated with wine mislabeling of region and vintage. In this study, an untargeted metabolomic approach based on liquid chromatography/ion mobility quadrupole time-of-flight mass spectrometry (LC-IM-QTOF-MS) was used to discriminate wine geographical origin and vintage. Wines were well discriminated according to region and vintage with orthogonal partial least squares-discriminant analysis (OPLS-DA). The differential metabolites subsequently were screened by OPLS-DA with pairwise modeling. 42 and 48 compounds in positive and negative ionization modes were screened as differential metabolitesfor the discrimination of different wine regions, and 37 and 35 compounds were screened for wine vintage. Furthermore, new OPLS-DA models were performed using these compounds, and the external verification trial showed excellent practicality with an accuracy over 84.2%. This study indicated that LC-IM-QTOF-MS-based untargeted metabolomics was a feasible tool for wine geographical origin and vintage discrimination.

Geographical Origin Differentiation of Rice by LC–MS-Based Non-Targeted Metabolomics

Many factors, such as soil, climate, and water source in the planting area, can affect rice taste and quality. Adulterated rice is common in the market, which seriously damages the production and sales of high-quality rice. Traceability analysis of rice has become one of the important research fields of food safety management. In this study, LC–MS-based non-targeted metabolomics technology was used to trace four rice samples from Heilongjiang and Jiangsu Provinces, namely, Daohuaxiang (DH), Huaidao No. 5 (HD), Songjing (SJ), and Changlixiang (CL). Results showed that the discrimination accuracy of the partial least squares discriminant analysis (PLS-DA) model was as high as 100% with satisfactory prediction ability. A total of 328 differential metabolites were screened, indicating significant differences in rice metabolites from different origins. Pathway enrichment analysis was carried out on the four rice samples based on the KEGG database to determine the three metabolic pathways with the highest enrichment degree. The main biochemical metabolic pathways and signal transduction pathways involved in differential metabolites in rice were obtained. This study provides theoretical support for the geographical origins of rice and elucidates the change mechanism of rice metabolic pathways, which can shed light on improving rice quality control.

Mass spectrometry in food authentication and origin traceability

Food authentication and origin traceability are popular research topics, especially as concerns about food quality continue to increase. Mass spectrometry (MS) plays an indispensable role in food authentication and origin traceability. In this review, the applications of MS in food authentication and origin traceability by analyzing the main components and chemical fingerprints or profiles are summarized. In addition, the characteristic markers for food authentication are also reviewed, and the advantages and disadvantages of MS-based techniques for food authentication, as well as the current trends and challenges, are discussed. The fingerprinting and profiling methods, in combination with multivariate statistical analysis, are more suitable for the authentication of high-value foods, while characteristic marker-based methods are more suitable for adulteration detection. Several new techniques have been introduced to the field, such as proton transfer reaction mass spectrometry, ambient ionization mass spectrometry (AIMS), and ion mobility mass spectrometry, for the determination of food adulteration due to their fast and convenient analysis. As an important trend, the miniaturization of MS offers advantages, such as small and portable instrumentation and fast and nondestructive analysis. Moreover, many applications in food authentication are using AIMS, which can help food authentication in food inspection/field analysis. This review provides a reference and guide for food authentication and traceability based on MS.

Adverse Effects of Arsenic Uptake in Rice Metabolome and Lipidome Revealed by Untargeted Liquid Chromatography Coupled to Mass Spectrometry (LC-MS) and Regions of Interest Multivariate Curve Resolution

Rice crops are especially vulnerable to arsenic exposure compared to other cereal crops because flooding growing conditions facilitates its uptake. Besides, there are still many unknown questions about arsenic’s mode of action in rice. Here, we apply two untargeted approaches using liquid chromatography coupled to mass spectrometry (LC-MS) to unravel the effects on rice lipidome and metabolome in the early stages of growth. The exposure is evaluated through two different treatments, watering with arsenic-contaminated water and soil containing arsenic. The combination of regions of interest (ROI) and multivariate curve resolution (MCR) strategies in the ROIMCR data analyses workflow is proposed and complemented with other multivariate analyses such as partial least square discriminant analysis (PLS-DA) for the identification of potential markers of arsenic exposure and toxicity effects. The results of this study showed that rice metabolome (and lipidome) in root tissues seemed to be more affected by the watering and soil treatment. In contrast, aerial tissues alterations were accentuated by the arsenic dose, rather than with the watering and soil treatment itself. Up to a hundred lipids and 40 metabolites were significantly altered due to arsenic exposure. Major metabolic alterations were found in glycerophospholipids, glycerolipids, and amino acid-related pathways.

Elementomics combined with dd-SIMCA and K-NN to identify the geographical origin of rice samples from China, India, and Vietnam

The COVID-19 pandemic has impacted the food industry and consumers, with production gaps, shipping delays, and changes in supply and demand leading to an increased risk of food fraud. Rice has a high probability for adulteration by food fraudsters, being a staple commodity for more than half the global population, making the assessment of geographical origins of rice for authenticity important in terms of protecting businesses and consumers. In this study, we describe ICP-MS elemental profiling coupled with elementomic modelling to identify the geographical indications of Indian, Chinese, and Vietnamese rice. A PLS-DA model exhibited good discrimination (R² = 0.8393, Q² = 0.7673, accuracy = 1.0). Data-driven soft independent modelling of class analogy (dd-SIMCA) and K-nearest neighbours (K-NN) models have good sensitivity (98%) and specificity (100%).

Use of metabolomics in refining the effect of an organic food intervention on biomarkers of exposure to pesticides and biomarkers of oxidative damage in primary school children in Cyprus: A cluster-randomized cross-over trial

BACKGROUND

Exposure to pesticides has been associated with oxidative stress in animals and humans. Previously, we showed that an organic food intervention reduced pesticide exposure and oxidative damage (OD) biomarkers over time; however associated metabolic changes are not fully understood yet.

OBJECTIVES

We assessed perturbations of the urine metabolome in response to an organic food intervention for children and its association with pesticides biomarkers [3-phenoxybenzoic acid (3-PBA) and 6-chloronicotinic acid (6-CN)]. We also evaluated the molecular signatures of metabolites associated with biomarkers of OD (8-iso-PGF2a and 8-OHdG) and related biological pathways.

METHODS

We used data from the ORGANIKO LIFE + trial (NCT02998203), a cluster-randomized cross-over trial conducted among primary school children in Cyprus. Participants (n = 149) were asked to follow an organic food intervention for 40 days and their usual food habits for another 40 days, providing up to six first morning urine samples (>850 samples in total). Untargeted GC-MS metabolomics analysis was performed. Metabolites with RSD ≤ 20% and D-ratio ≤ 50% were retained for analysis. Associations were examined using mixed-effect regression models and corrected for false-discovery rate of 0.05. Pathway analysis followed.

RESULTS

Following strict quality checks, 156 features remained out of a total of 610. D-glucose was associated with the organic food intervention (β = -0.23, 95% CI: -0.37,-0.10), aminomalonic acid showed a time-dependent increase during the intervention period (β_int = 0.012; 95% CI:0.002, 0.022) and was associated with the two OD biomarkers (β = -0.27, 95% CI:-0.34,-0.20 for 8-iso-PGF2a and β = 0.19, 95% CI:0.11,0.28 for 8-OHdG) and uric acid with 8-OHdG (β = 0.19, 95% CI:0.11,0.26). Metabolites were involved in pathways such as the starch and sucrose metabolism and pentose and glucuronate interconversions.

DISCUSSION

This is the first metabolomics study providing evidence of differential expression of metabolites by an organic food intervention, corroborating the reduction in biomarkers of OD. Further mechanistic evidence is warranted to better understand the biological plausibility of an organic food treatment on children's health outcomes.

Bioactive Compounds, Sugars, and Sensory Attributes of Organic and Conventionally Produced Courgette (Cucurbita pepo)

Organic agriculture is considered one of the elements of sustainable food production and consumption, mainly due to its limited impact on the natural environment. At the same time, the quality features of organically produced foods, especially sensory attributes and health promoting values, are important factors determining consumers’ interest, and therefore play a key role in the organic sector’s development. The aim of this study was to investigate the sensory characteristics and concentrations of sugars and selected health-promoting bioactive compounds of organic courgette compared to conventionally grown courgette. In addition, untargeted metabolomic analysis of the courgette fruits was performed. The results of this study did not show a significant effect of the horticultural system (organic vs. conventional) on the concentrations of vitamin C, carotenoids, and chlorophylls in the courgette fruits. However, the fruits from the organic systems were significantly richer in sugars when compared to the conventionally cultivated ones (p = 0.038). Moreover, the organic fruits fertilized with manure contained significantly higher amounts of polyphenols, including gallic acid (p = 0.016), chlorogenic acid (p = 0.012), ferulic acid (p = 0.019), and quercetin-3-O-rutinoside (p = 0.020) compared to the conventional fruits. The untargeted analysis detected features significantly differentiating courgette fruits depending on the cultivar and horticultural system. Some significant differences in sensory values were also identified between fruits representing the two cultivars and coming from the horticultural systems compared in the study. Conventional courgettes were characterized by the most intensive peel color and aquosity, but at the same time were the least hard and firm compared to the fruits from the two organic systems. There was also a trend towards higher overall quality of the organically grown fruits. The presented study shows that the organic and conventional courgette fruits differ in a number of quality features which can influence consumers’ health and purchasing choices.

Using untargeted metabolomics to profile the changes in roselle (Hibiscus sabdariffa L.) anthocyanins during wine fermentation

In this study, an UHPLC-QE-MS approach in combination with multivariate statistical analyses was used to investigate the metabolic profiles, especially the anthocyanin profiles, during the fermentation of roselle wine. Overall, a large number of different metabolites (e.g., phenols, lipids, carbohydrates, amino acids and peptides, and others) were identified in the fermentation processes. Eight anthocyanin metabolites were identified in roselle wine for the first time, of which six were identified in the main fermentation process and two in the post-fermentation process. In addition, we identified several metabolic pathways during the fermentation process, and the metabolic pathways of anthocyanins in roselle wine are mainly related to flavonoid biosynthesis and to anthocyanin biosynthesis. These findings are expected to be useful for further studies on wine chemistry and yeast metabolism.

Cytogenetic toxicity from pesticide and trace element mixtures in soils used for conventional and organic crops of Allium cepa L

Pesticides and trace elements occur in complex mixtures in agroecosystems, affecting soil health and food security. Hence, it is necessary to determine their toxicity in field conditions and to develop monitoring approaches to assess conventional and organic agriculture. The aim of this research was to evaluate the associations between Allium cepa L. cytogenetic biomarkers and the realistic mixture of pesticides and trace elements found in soils of conventional, conversion, and organic crops in an intensive agricultural region in Colombia. Pesticide screening was conducted using GC-MS/MS and LC-MS/MS methods. Arsenic, cadmium, lead, and zinc were analyzed by ICP-MS; chromium, copper, nickel, and selenium by ICP-OES; and mercury by a direct analyzer. The meristematic cells in roots of Allium cepa L. were analyzed through microscopic observations to quantify cytogenetic effects. In conventional crops, 26 pesticides were detected in the soil samples, and those were below the limit of quantification in organic crops. The mean levels of As, Cd, Cr, Ni, Pb, and Se were also greater in soils of conventional crops compared to the organics. In addition, the biomarkers of cytotoxicity and genotoxicity appeared augmented in conventional samples, and those were correlated with pesticide and trace element concentrations, pollution indices, and hazard quotients. Subsequently, a discriminant function based on the mitotic index, chromosomal aberrations, and nuclear abnormalities was suitable to classify the samples by crop type. These results demonstrate the sensitivity of Allium cepa L. to the toxicity of complex mixtures in field crops and its potential as an in-situ approach for soil health monitoring in organic and conventional crop systems.

Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity

Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of data is recorded, which must be processed in a correspondingly complex manner. The evaluation of LC-MS based non-targeted data is not entirely trivial and a wide variety of strategies have been developed that can be used in this regard. In this paper, an overview of the mandatory steps regarding data acquisition is given first, followed by a presentation of the required preprocessing steps for data evaluation. Then some multivariate analysis methods are discussed, which have proven to be particularly suitable in this context in recent years. The publication closes with information on the identification of marker compounds.

Nutritional quality and health risks of wheat grains from organic and conventional cropping systems

The influence of cropping systems on nutrition and food safety is controversial. This study aimed to evaluate the effects of an organic cropping system (OCS) on wheat nutrition and food safety at the molecular level by using a comprehensive research method. Nutrient deviation in samples from an OCS and a conventional cropping system (CCS) were detected, and 58 biomarkers were selected through multivariate statistical analysis and were further qualitatively and quantitatively analyzed. The health risk of heavy metal(loid)s (HMs) for different populations was assessed based on the estimated average daily dose and recommended ingestion reference dose, which indicated that populations ingesting grains from OCSs had higher non-carcinogenic and carcinogenic risks. Additionally, HMs posed greater non-carcinogenic risks to children under five years old and greater carcinogenic risks to adults.This study highlights the need to consider the potential risk from HMs and nutritive ingredient differences in organic food.

Metabolomics Study of Subsurface Wastewater Infiltration System Under Fluctuation of Organic Load

Subsurface Wastewater Infiltration System (SWIS) is a sewage ecological treatment technology with low investment, energy consumption, and operating cost. SWIS soil contains a large variety of microorganisms. The metabolic process and production of microorganisms are an important basis for qualitatively describing the process of pollutant removal. In order to discover the microbial decontamination pathways in SWIS, the metabolic profiles of soil microorganisms in SWIS were analyzed by UPLC-MS. Partial least squares-discriminant analysis (PLS-DA)and principal component analysis (PCA) pattern recognition methods were used to classify the samples. According to the model's variable importance factor (VIP value), potential biomarkers were screened and biological information contained in the metabolites was also analyzed. The correlation between metabolites and environmental factors was explored by RDA analysis. In total, 230 differential metabolites with VIP value greater than 1.5 were screened out when the influent organic load fluctuated at 250 mg L^-1, 400 mg L^-1, and 500 mg L^-1. After identifying and screening, 35 differential metabolites were identified and used to further analyze the metabolic pathway. It turns out that microbial metabolites in SWIS were mainly glycosides, fatty acids, amino acids, pigments, diterpenoids, and some polymers under medium and high organic loading conditions. At low organic load, the microbial metabolites in SWIS were mainly ketones, alcohols, and esters.

Evaluation of Antifungal Phenolics from Helianthus tuberosus L. Leaves against Phytophthora capsici Leonian by Chemometric Analysis

Phytophthora capsici Leonian causes destructive economical losses in pepper production, and a promising source of natural fungicides- Helianthus tuberosus leaves was reported. The antifungal activities of different extracts and compounds from H. tuberosus leaves against the phytopathogen, P. capsici Leonian, were examined by chemometric analysis, including HPLC-MS/MS and multivariate data analyses. Principal component analysis and orthogonal partial least squares-discriminate analysis were applied to examine the four groups of H. tuberosus leaves samples, including crude extracts obtained by different methods, including refluxing, macerating, and refluxing under vacuum; four fractions, namely, petroleum ether (PE), chloroform (Chl), ethyl acetate (EA), and n-butanol (NB) fractions; the samples of three H. tuberosus cultivars; and the samples at three growth stages of cultivar Nan Yu. The phenolics contents were categorized based on 3,5-Dicaffeoylquinic acid (3,5-DiCQA), 1,5-Dicaffeoylquinic acid (1,5-DiCQA), 3-O-Caffeoylquinic acid (3-CQA), and 4,5-Dicaffeoylquinic acid (4,5-DiCQA), which were predominant in all the samples. Antifungal activity assay revealed that Chl and NB fractions were more active against P. capsici Leonian with lower IC50(half of maximal inhibitory concentration) values, whereas partial least squares-discriminate analysis suggested caffeoylquinic acid isomer(4-CQA), methyl-quercetin glycoside(MQG), and caffeic acid(CA) might be the main active components in H. tuberosus leaves against P. capsici Leonian. Furthermore, microscopic evaluation demonstrated structural deformities in P. capsici Leonian treated with Chl and NB fractions, indicating the antifungal effects of H. tuberosus leaves. These results imply that H. tuberosus leaves with a high concentration of phenolics might be a promising source of natural fungicides.

A study of the microbial metabolomics analysis of subsurface wastewater infiltration system

Microbial action in SWIS is one of the main ways to remove contaminants. Studying the metabolic processes and pathways of microorganisms is helpful to reveal the mechanism of pollutant removal in the “black box” process of SWIS. In this study, based on metabolomics and UPLC-MS, partial least squares (PLS-DA), principal component analysis (PCA) pattern recognition and cluster analysis were used to classify the microbial samples. According to the model's variable importance factor (VIP value) being greater than 1.5, a total of 53 potential biomarkers were screened out. There was a significant correlation between the microbial metabolites and soil profile. Most microbial metabolites were concentrated in the H2 layer (subsurface layer of SWIS), while there were relatively few in the H4 and H6 layers (middle and lower layers of SWIS); organic acids and alcohol metabolites mainly existed in the anoxic environment (H4 layer); antibiotics, growth hormones and pigments and other small molecule metabolites mainly existed under anaerobic conditions (H6 layer). The results of RDA analysis indicated that environmental factors had an effect on the microbial metabolites. With the variation of different height profiles, the metabolites were significantly affected by ORP and NO₃⁻, which were negatively correlated. The above conclusions indicated that metabolomics is a reliable, accurate and effective method to quantitatively characterize the stability of SWIS.

Microbial action in SWIS is one of the main ways to remove contaminants.