Multi-element and stable isotopes characterization of commercial avocado fruit (Persea americana Mill) with origin authentication purposes

Characterisation of avocado fruits from different Iberian regions: Integrating ion mobility in non-targeted LC-MS metabolomics

This study investigates the metabolic profile of Hass avocado fruits from eight Iberian regions using an advanced UHPLC-TimsTOF MS/MS analytical platform, with the hypothesis that distinct edaphoclimatic conditions give rise to region-specific metabolomic signatures. A comprehensive profiling of the methanolic extracts was performed to construct a metabolic library incorporating ion mobility descriptors. By applying unsupervised chemometrics-assisted non-targeted metabolomics, avocado fruits clustered according to geographical proximity, with the most significant metabolic differences observed between the northern and southern regions. Despite this general trend, each region exhibited distinct metabolic patterns, even between neighbouring areas. To further delineate the region-specific metabolic compositions, multiple two-class orthogonal partial least squares discriminant analysis (OPLS-DA) models were designed to identify the most influential variables in the projections, leading to the discovery of origin-specific biomarkers characteristic of avocados from each growing area. This research offers valuable information on how regional edaphoclimatic factors impact avocado quality and compositional diversity.

Geographical Origin Traceability of Navel Oranges Based on Near-Infrared Spectroscopy Combined with Deep Learning

The quality and price of navel oranges vary depending on their geographical origin, thus providing a financial incentive for origin fraud. To prevent this phenomenon, it is necessary to explore a fast, non-destructive, and precise method for tracing the origin of navel oranges. In this study, a total of 490 Newhall navel oranges were selected from five major production regions in China, and the diffuse reflectance near-infrared spectrum in 4000-10,000 cm-1 were non-invasively collected. We examined seven preprocessing techniques for the spectra, including Savitzky-Golay (SG) smoothing, first derivative (FD), multiplicative scattering correction (MSC), combinations of SG with MSC (SG+MSC), SG with FD (SG+FD), MSC with FD (MSC+FD), and three combined (SG+MSC+FD). A one-dimensional convolutional neural network (1DCNN) deep learning model for geographical origin tracing of navel orange was established, and five machine learning algorithms, i.e., partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), and back-propagation neural network (BPNN), were compared with 1DCNN. The results show that the 1DCNN model based on the SG+FD preprocessing method achieved the optimal performance for the testing set, with prediction accuracy, precision, recall, and F1-score of 97.92%, 98%, 97.95%, and 97.90%, respectively. Therefore, NIRS combined with deep learning has a significant research and application value in the rapid, nondestructive, and accurate geographical origin traceability of agricultural products.

Geographical origin traceability of kiwifruit products using stable isotope and multi-element analysis with multivariate modeling: Feature extraction, selection of model and variable, and discrimination

The mislabeling of kiwifruit origin frequently disturbs market competition and governmental supervision, significantly undermines brand reputation and consumer rights. In this work, a total of 370 kiwifruits from 8 different countries in global were collected, and 6 stable isotope ratios (SIRs), 10 mineral elements (MEs), and 16 rare earth elements (REEs) were determined for origin traceability study. One-way analysis of variance (ANOVA) showed that regional differences of 32 variables are at significant level (P value =0.00). Supervised methods, partial least squares-discriminant analysis (PLS-DA) and its derivative algorithm (OPLS-DA), linear discriminant analysis (LDA), enhanced identification performance and finally elevated the accuracies to 100 % for all kiwifruit origins. Lu, Tb, Eu, Ho, Pm, Y, δ34S, δ2H, δ15N, Mg, Se were main contributive variables for LDA modeling (AUC value >0.5). A blind test was conducted using 63 samples randomly selected from Chinese market. The predicted result indicated a significantly high probability of origin mislabeling of imported kiwifruit products, with percentages ranging from 30.0 % to 90.0 %. This study may provide technical supports for combating origin mislabeling conduct, and ensuring food authenticity of kiwifruit in global trade.

Geographical origin traceability of sweet cherry (Prunus avium (L.) Moench) in China using stable isotope and multi-element analysis with multivariate modeling

The deliberately origin mislabeling of sweet cherry causes significantly disruptions to market integrity and consumers' trust. In this study, 153 cherry samples from five provinces in China and the corresponding irrigation water and soil samples were collected. 5 stable isotope ratios (δ13C, δ15N, δ2H, δ18O, 87Sr/86Sr) and 8 multi-element contents (Na, Mg, P, K, Ca, Fe, Zn, Se) of cherry were determined by EA-IRMS and ICP-MS to study isotopic fractionation and elemental enrichment mechanisms for origin traceability. The results show the δ2H and δ18O of cherry exhibit a strong correlated with its irrigation water (r2 > 0.85), while δ15N, 87Sr/86Sr, Fe, Zn and Se contents are related to its cultivated soil (r2 > 0.75), and the δ13C is related to the local microclimate. ANOVA reveals that the regional differences of δ13C, δ2H, δ18O, 87Sr/86Sr as well as Na, Mg, Ca contents of cherry are significant (P < 0.05), and are important geographical indicators. Various multivariate modeling methods, HCA, PLS-DA, and LDA, were employed with the overall accuracy exceeding 90%. This strategy provides an effective mean to verify the label authenticity of cherry origin in Chinese market.

Authenticating the Geographical Origin of Jingbai Pear in Northern China by Multiple Stable Isotope and Elemental Analysis

The Jingbai pear is one of the best pear species in China with high quality and nutrition values which are closely linked to its geographical origin. With the purpose of discriminating the PGI Mentougou Jingbai pear from three other producing regions, the stable isotope ratios and elemental profiles of the pears (n = 52) and the corresponding soils and groundwater were determined using isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry (ICP-MS), respectively. The results revealed that δ15N, δ18OJ, and Li were significantly different (p < 0.05) in samples from different regions, which indicated their potential to be used in the geographical origin classification of the Jingbai pear. The nitrogen isotopic values of the pear pulp were positively correlated with the δ15N value and nitrogen content of the corresponding soils, whilst the B, Na, K, Cr, and Cd contents of the pear pulps were positively correlated with their corresponding soils. Orthogonal partial least squares discriminant analysis (OPLS-DA) was performed in combination with analysis of the stable isotopes and elemental profiles, making it possible to distinguish the cultivation regions from each other with a high prediction accuracy (a correct classification rate of 92.3%). The results of this study highlight the potential of stable isotope ratios and elemental profiles to trace the geographical origin of pears at a small spatial scale.

A Multi-Isotopic Chemometric Approach for Tracing Hazelnut Origins

High-value products, such as hazelnuts, are particularly vulnerable to fraud due to their price dependence on geographical origin. Guaranteeing hazelnuts' authenticity is essential for consumer trust and safety. Stable isotope analysis has become a reference method for origin authentication as it is reliable, robust, and easily transferable across laboratories. However, multiple isotopic markers coupled with chemometric techniques are often needed to authenticate food provenance accurately. In this study, we focused on assessing the potential of bulk δ18O, along with δ2H and δ13C of the main fatty acids, as hazelnut-origin authenticity markers. PLS-DA classification models were developed to differentiate samples (n = 207) according to their region of origin. This multi-isotopic approach provided promising external validation results, achieving a 94% global correct classification rate in discriminating hazelnuts from regions with distinct geographical and environmental conditions. This study lays the groundwork for further model development and evaluation across additional production areas and harvest years.

A new method to analysis synthetic acetic acid to vinegar: Hydrogen isotope ratio at the methyl site of acetic acid in vinegar by GC-IRMS

Acetic acid is the key organic substance used to verify the authenticity of vinegar. A new method for precisely determining acetic acid δDCH3 in vinegar via gas chromatography -pyrolytic-stable isotope ratio mass spectrometry (GC-P-IRMS) was established. The δDCH3 values were obtained via calibration with a series of standards. The optimised method demonstrated a repeatability standard deviation within 3 ‰. The standard deviation of accuracy of the new method compared with that of the SNIF-NMR method was within 2.6 ‰. The synthetic acetic acid δDCH3 values was -136.7 ‰ ± 29.6 ‰, and the δDCH3 value of acetic acid in vinegar was -414.9 ‰ ± 40.5 ‰, with significant isotopic distribution characteristics. This methodology serves as a supplementary method for measuring the δDCH3 value of acetic acid in vinegar. It has advantages over other methods in terms of time, sensitivity and operability. And provides a new idea for solving the problem of analyzing substances in the presence of exchangeable groups.

Towards Verifying the Imported Soybeans of China Using Stable Isotope and Elemental Analysis Coupled with Chemometrics

Verifying the geographical origin of soybeans (Glycine max [Linn.] Merr.) is a major challenge as there is little available information regarding non-parametric statistical origin approaches for Chinese domestic and imported soybeans. Commercially procured soybean samples from China (n = 33) and soybeans imported from Brazil (n = 90), the United States of America (n = 6), and Argentina (n = 27) were collected to characterize different producing origins using stable isotopes (δ2H, δ18O, δ15N, δ13C, and δ34S), non-metallic element content (% N, % C, and % S), and 23 mineral elements. Chemometric techniques such as principal component analysis (PCA), linear discriminant analysis (LDA), and BP-artificial neural network (BP-ANN) were applied to classify each origin profile. The feasibility of stable isotopes and elemental analysis combined with chemometrics as a discrimination tool to determine the geographical origin of soybeans was evaluated, and origin traceability models were developed. A PCA model indicated that origin discriminant separation was possible between the four soybean origins. Soybean mineral element content was found to be more indicative of origin than stable isotopes or non-metallic element contents. A comparison of two chemometric discriminant models, LDA and BP-ANN, showed both achieved an overall accuracy of 100% for testing and training sets when using a combined isotope and elemental approach. Our findings elucidate the importance of a combined approach in developing a reliable origin labeling method for domestic and imported soybeans in China.

Bulk and Compound-Specific Stable Isotope Analysis for the Authentication of Walnuts (Juglans regia) Origins

Walnuts are grown in various countries, and as product origin information is becoming more important to consumers, new techniques to differentiate walnut geographical authenticity are needed. We conducted bulk stable isotope analysis (BSIA) and compound-specific stable isotope analysis (CSIA) on walnuts grown in seven countries. The BSIA consisted of δ13Cbulk, δ15Nbulk, and δ34Sbulk, and CSIA covered δ2Hfatty acid, δ13Cfatty acid, δ13Camino acid, δ15Namino acid, and δ2Hamino acid. Analysis of variance (ANOVA) and linear discriminant analysis (LDA) were used for statistical analysis to compare samples from the USA and China. Parameters that yielded significant variations are δ2HC18:1n-9, δ13CC18:2n-6, δ13CC18:3n-3, δ13CGly, δ13CLeu, δ13CVal, δ2HGlu, δ2HIle, δ2HLeu, and δ2HThr. Our findings suggested that CSIA of fatty acids and amino acids can be useful to differentiate the geographical provenance of walnuts.

Assessing the RP-LC-MS-Based Metabolic Profile of Hass Avocados Marketed in Europe from Different Geographical Origins (Peru, Chile, and Spain) over the Whole Season

Spain dominates avocado production in Europe, with the Hass variety being the most prominent. Despite this, Spanish production satisfies less than 10% of the overall avocado demand in Europe. Consequently, the European avocado market heavily relies on imports from overseas, primarily sourced from Peru and Chile. Herein, a comprehensive characterization of the metabolic profile of Hass avocado fruits from Spain, Peru, and Chile, available in the European market throughout the year, was carried out. The determination of relevant substances was performed using high- and low-resolution RP-LC-MS. Remarkable quantitative differences regarding phenolic compounds, amino acids, and nucleosides were observed. Principal component analysis revealed a natural clustering of avocados according to geographical origin. Moreover, a specific metabolic pattern was established for each avocado-producing country using supervised partial least squares discriminant analysis. Spanish fruits exhibited high levels of coumaric acid malonyl-hexose II, coumaric acid hexose II, and ferulic acid hexose II, together with considerably low levels of pantothenic acid and uridine. Chilean avocado fruits presented high concentrations of abscisic acid, uridine, ferulic acid, succinic acid, and tryptophan. Fruits from Peru showed high concentrations of dihydroxybenzoic acid hexose, alongside very low levels of p-coumaric acid, ferulic acid, coumaric acid malonyl-hexose I, and ferulic acid hexose II.

A review of recent compound-specific isotope analysis studies applied to food authentication

Compound-specific stable isotope analysis (CSIA) of food products is a relatively new and novel technique used to authenticate food and detect adulteration. This paper provides a review of recent on-line and off-line CSIA applications of plant and animal origin foods, essential oils and plant extracts. Different food discrimination techniques, applications, scope, and recent studies are discussed. CSIA δ13C values are widely used to verify geographical origin, organic production, and adulteration. The δ15N values of individual amino acids and nitrate fertilizers have proven effective to authenticate organic foods, while δ2H and δ18O values are useful to link food products with local precipitation for geographical origin verification. Most CSIA techniques focus on fatty acids, amino acids, monosaccharides, disaccharides, organic acids, and volatile compounds enabling more selective and detailed origin and authentication information than bulk isotope analyses.. In conclusion, CSIA has a stronger analytical advantage for the authentication of food compared to bulk stable isotope analysis, especially for honey, beverages, essential oils, and processed foods.

Measuring trace element fingerprinting for cereal bar authentication based on type and principal ingredient

This paper introduces a method for determining the authenticity of commercial cereal bars based on trace element fingerprints. In this regard, 120 cereal bars were prepared using microwave-assisted acid digestion and the concentrations of Al, Ba, Bi, Cd, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Pb, Rb, Se, Sn, Sr, V, and Zn were later measured by ICP-MS. Results confirmed the suitability of the analyzed samples for human consumption. Multielemental data underwent autoscaling preprocessing for then applying PCA, CART, and LDA to input data set. LDA model accomplished the highest classification modeling performance with a success rate of 92%, making it the suitable model for reliable cereal bar prediction. The proposed method demonstrates the potential of trace element fingerprints in distinguishing cereal bar samples according to their type (conventional and gluten-free) and principal ingredient (fruit, yogurt, chocolate), thereby contributing to global efforts for food authentication.

Implementation of relevant fourth industrial revolution innovations across the supply chain of fruits and vegetables: A short update on Traceability 4.0

Food Traceability 4.0 refers to the application of fourth industrial revolution (or Industry 4.0) technologies to ensure food authenticity, safety, and high food quality. Growing interest in food traceability has led to the development of a wide range of chemical, biomolecular, isotopic, chromatographic, and spectroscopic methods with varied performance and success rates. This review will give an update on the application of Traceability 4.0 in the fruits and vegetables sector, focusing on relevant Industry 4.0 enablers, especially Artificial Intelligence, the Internet of Things, blockchain, and Big Data. The results show that the Traceability 4.0 has significant potential to improve quality and safety of many fruits and vegetables, enhance transparency, reduce the costs of food recalls, and decrease waste and loss. However, due to their high implementation costs and lack of adaptability to industrial environments, most of these advanced technologies have not yet gone beyond the laboratory scale. Therefore, further research is anticipated to overcome current limitations for large-scale applications.

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), a Useful Tool in Authenticity of Agricultural Products' and Foods' Origin

Fraudulent practices are the first and foremost concern of food industry, with significant consequences in economy and human's health. The increasing demand for food has led to food fraud by replacing, mixing, blending, and mislabeling products attempting to increase the profits of producers and companies. Consequently, there was the rise of a multidisciplinary field which encompasses a large number of analytical techniques aiming to trace and authenticate the origins of agricultural products, food and beverages. Among the analytical strategies have been developed for the authentication of geographical origin of foodstuff, Inductively Coupled Plasma Mass Spectrometry (ICP-MS) increasingly dominates the field as a robust, accurate, and highly sensitive technique for determining the inorganic elements in food substances. Inorganic elements are well known for evaluating the nutritional composition of food products while it has been shown that they are considered as possible tracers for authenticating the geographical origin. This is based on the fact that the inorganic component of identical food type originating from different territories varies due to the diversity of matrix composition. The present systematic literature review focusing on gathering the research has been done up-to-date on authenticating the geographical origin of agricultural products and foods by utilizing the ICP-MS technique. The first part of the article is a tutorial about food safety/control and the fundaments of ICP-MS technique, while in the second part the total research review is discussed.