Methods of Authentication of Food Grown in Organic and Conventional Systems Using Chemometrics and Data Mining Algorithms: a Review

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.

Application of machine learning approach on halal meat authentication principle, challenges, and prospects: A review

Meat is a source of essential amino acids that are necessary for human growth and development, meat can come from dead, alive, Halal, or non-Halal animal species which are intentionally or economically (adulteration) sold to consumers. Sharia has prohibited the consumption of pork by Muslims. Because of the activities of adulterators in recent times, consumers are aware of what they eat. In the past, several methods were employed for the authentication of Halal meat, but numerous drawbacks are attached to this method such as lack of flexibility, limited application, time,consumption and low level of accuracy and sensitivity. Machine Learning (ML) is the concept of learning through the development and application of algorithms from given data and making predictions or decisions without being explicitly programmed. The techniques compared with traditional methods in Halal meat authentication are fast, flexible, scaled, automated, less expensive, high accuracy and sensitivity. Some of the ML approaches used in Halal meat authentication have proven a high percentage of accuracy in meat authenticity while other approaches show no evidence of Halal meat authentication for now. The paper critically highlighted some of the principles, challenges, successes, and prospects of ML approaches in the authentication of Halal meat.

Classification of Vietnamese Cashew Nut (Anacardium occidentale L.) Products Using Statistical Algorithms Based on ICP/MS Data: A Study of Food Categorization

Fingerprinting techniques, which utilize the unique chemical and physical properties of food samples, have emerged as a promising approach for food authentication and traceability. Recent studies have demonstrated significant advancements in food authentication through the use of fingerprinting methods, such as multivariate statistical analysis techniques applied to trace elements and isotope ratios. However, further research is required to optimize these methods and ensure their validity and reliability in real-world applications. In this study, the inductively coupled plasma mass spectrometry (ICP-MS) analytical method was employed to determine the content of 21 elements in 300 cashew nut (Anacardium occidentale L.) samples from 5 brands. Multivariate statistical methods, such as principal components analysis (PCA), were employed to analyze the data obtained and establish the provenance of the cashew nuts. While cashew nuts are widely marketed in many countries, no universal method has been utilized to differentiate the origin of these nuts. Our study represents the initial step in identifying the geographical origin of commercial cashew nuts marketed in Vietnam. The analysis showed significant differences in the means of 21 of the 40 analyzed elements among the cashew nut samples from the 5 brands, including 7Li, 11B, 24Mg, 27Al, 44Ca, 48Ti, 51V, 52Cr, 55Mn, 57Fe, 60Ni, 63Cu, 66Zn, 93Nb, 98Mo, 111Cd, 115In, 121Sb, 138Ba, 208Pb, and 209Bi. The PCA analysis indicated that the cashew nut samples can be accurately classified according to their original locations. This research serves as a prerequisite for future studies involving the combination of elemental composition analysis with statistical classification methods for the accurate establishment of cashew nut provenance, which involves the identification of key markers for the original discrimination of cashew nuts.

Application of multiple stable isotopes to aid identification of the origin of regional and organic animal products in Hesse, Germany

There is an increasing global demand for regional and organic produce. However, the growth of these markets depends on consumers' trust. Thus, novel methods must be developed to aid the verification of the origin of produce. We built on our previous study to identify the geographical origin and production method of animal-derived food products. Thirty-samples of eggs, 99 of milk, 34 of beef, and 62 of pork were collected from different regions in central Germany and analysed for their stable isotopic composition. The analysis followed a single-variate authentification approach using five isotope signatures, δ18O, δ2H, δ13C, δ15N, and δ34S. The best-performing indicators for verification of the geographical origin were δ15N and δ34S for beef; δ18O, δ2H, and δ13C for milk, and δ2H and δ13C for pork. These tracers indicated statistically significant differences among regions with the exception of pork; the results recorded for eggs were inconclusive. It was possible to distinguish between production methods by means of δ15N and δ34S (beef); all five tracers (eggs), and δ13C, δ15N, and δ34S (milk). This study demonstrated how the analysis of stable isotopes can be employed to determine the geographic region of origin and production method of animal-derived products in Germany.

Assessment of rapid low-cost isotope (δ15 N, δ18 O) analyses of nitrate in fruit extracts by Ti(III) reduction to differentiate organic from conventional production

RATIONALE

The isotopic composition (δ¹⁵ N, δ¹⁸ O) of nitrate in fruits and vegetables differentiates organic from conventional food production practices. Organic systems do not use synthetic nitrate fertilizers high in ¹⁸ O and low in ¹⁵ N and thereby help reveal producers' fertilization claims. Isotope analyses of nitrate extracted from fruits and vegetables are done by bacterial reduction which is costly and by specialized laboratories. Rapid, low-cost methods are needed to promulgate nitrate isotope analyses of food products to support organic food product certification and to verify the authenticity of production claims.

METHODS

Fresh strawberry samples were obtained from certified organic and conventional growers in Andalucía, Spain. We applied a new, rapid, one-step Ti(III) reduction method to convert the nitrate from strawberry extracts to N₂ O gas for headspace isotope analyses using isotope-ratio mass spectrometry. Using the Ti(III) reduction method, 70 samples, controls and references were prepared and analyzed for NO₃ ^- , δ¹⁵ N and δ¹⁸ O per 48 h. We also analyzed extracts and solids for anions and cations and for bulk δ¹⁵ N for multivariate chemometric evaluation.

RESULTS

The Ti(III)-based isotope analyses of nitrate in strawberry extracts revealed clear differentiation between organic and conventional production with mean δ¹⁸ O and δ¹⁵ N values of +18.3 ± 1.2 ‰ and +17.6 ± 1.2 ‰ versus +28.2 ± 4.5 ‰ and +14.9 ± 3.0 ‰, respectively. The δ¹⁵ N of strawberry dry mass differed slightly (+3.0 ± 1.4 ‰ versus +4.0 ± 1.4 ‰) between organic and conventional samples, respectively. Chemometric analyses of nitrate isotopes and extract chemistry revealed that the δ¹⁸ O of nitrate along with δ¹⁵ N and Ca²⁺ fully differentiated organic from conventional strawberry production.

CONCLUSIONS

Our results show the Ti(III) reduction method provides a new low-cost and rapid analytical method to facilitate compound-specific δ¹⁵ N and δ¹⁸ O isotope analyses of nitrate in selected fruit types, and likely other food products, for the purposes of assessing nitrate fertilization practices of organic versus conventional production claims and to support authenticity investigations.

Organically vs. Conventionally Grown Vegetables: Multi-elemental Analysis and Nutritional Evaluation

Vegetables are important contributors to a healthy diet, and their adequate daily intake can help prevent some of the major illnesses. The aim of the study was to examine the content of the major and trace elements in selected organically grown (OG) and conventionally grown (CG) vegetables (cabbage, kohlrabi, Brussels sprout, beetroot, carrot, potato, and onion), taken from city green markets. Multi-elemental analysis was carried out by inductively coupled plasma method with optical emission spectrometry (ICP-OES). Nutritional quality evaluation in comparison to nutritional reference values was done. In studied vegetables, Al, Ca, K, Fe (with the exception of organic kohlrabi), Mg, Na, P, S, and Zn were quantified in all samples, whereas As, Cd, Co, Hg, Se, and V were below the limit of detection for these elements. Macroelements and trace elements were found at higher concentrations in OG and CG vegetables, respectively. Differences in concentrations of studied elements between the same vegetable species produced in two agricultural systems were significant, except for beetroot (p ≤ 0.05). Principal component analysis and hierarchical cluster analysis results showed that the botanical origin had higher influence on sample differentiation than the agronomic practice, which was in accordance with the results obtained by Mann-Whitney U test. Good quality of both OG and CG vegetables in respect of nutritionally beneficial elements was observed.

Determination of Metals in Walnut Oils by Means of an Optimized and Validated ICP-AES Method in Conventional and Organic Farming Type Samples

Agricultural products are indispensable for equilibrated diets since they discharge minerals and several bioactive constituents. Considering the increasing demand for organic products, research has been conducted over recent years to investigate whether organically grown food products are chemically different compared to those produced with conventional farming. In this work, a novel inductively coupled plasma atomic emission spectrometric method was developed and validated for the determination of nutrient and toxic elements in walnut oils produced with conventional and organic farming. The method presented good linearity (r2 > 0.9990) for each element at the selected emission line. The limits of detection and limits of quantification ranged between 0.09 μg g−1 to 2.43 μg g−1 and 0.28 μg g−1 to 8.1 μg g−1, respectively. Method accuracy and was assessed by analyzing the certified reference materials BCR 278-R and spiked walnut oil samples. The determined metals were quantified, and the results were analyzed by Student’s t-test to investigate the differences in the elemental profile of the walnut oils according to type of farming (conventional or organic).

A Rapid HPLC-UV Protocol Coupled to Chemometric Analysis for the Determination of the Major Phenolic Constituents and Tocopherol Content in Almonds and the Discrimination of the Geographical Origin

Reversed phase-high-pressure liquid chromatographic methodologies equipped with UV detector (RP-HPLC-UV) were developed for the determination of phenolic compounds and tocopherols in almonds. Nineteen samples of Texas almonds originating from USA and Greece were analyzed and 7 phenolic acids, 7 flavonoids, and tocopherols (-α, -β + γ) were determined. The analytical methodologies were validated and presented excellent linearity (r2 > 0.99), high recoveries over the range between 83.1 (syringic acid) to 95.5% (ferulic acid) for within-day assay (n = 6), and between 90.2 (diosmin) to 103.4% (rosmarinic acid) for between-day assay (n = 3 × 3), for phenolic compounds, and between 95.1 and 100.4% for within-day assay (n = 6), and between 93.2-96.2% for between-day assay (n = 3 × 3) for tocopherols. The analytes were further quantified, and the results were analyzed by principal component analysis (PCA), and agglomerative hierarchical clustering (AHC) to investigate potential differences between the bioactive content of almonds and the geographical origin. A decision tree (DT) was developed for the prediction of the geographical origin of almonds proposing a characteristic marker with a concentration threshold, proving to be a promising and reliable tool for the guarantee of the authenticity of the almonds.

Are the elemental fingerprints of organic and conventional food different? ED-XRF as screening technique

Research has been conducted the last years to assess whether organically grown food is chemically different from produce of conventional agriculture and which markers are appropriate to discriminate between them. Most articles focus on one single food commodity, produced under strict controlled organic farming conditions, leaving open the question whether the difference would be seen when applied to the same commodity under different growing conditions. In this work 118 organic and 151 conventional samples of commercially available paprika powder, cinnamon, coffee, tea, chocolate, rice, wheat flour, cane sugar, coconut water, honey and bovine milk were characterised for their elemental composition using energy dispersive X-ray fluorescence. Resulting profiles were analysed using univariate and multivariate statistical techniques. Organic samples of a given commodity clustered together and were separated from their conventional counterparts. Differences in the elemental composition of food, could be used to develop statistical models for verifying the agronomical production system.

Authentication of PDO paprika powder (Pimentón de la Vera) by multivariate analysis of the elemental fingerprint determined by ED-XRF. A feasibility study

Products with a Protected Denomination of Origin (PDO) are vulnerable to misdescription of their true geographical origin. In this work a method has been developed that allows the authentication of La Vera paprika powder (Pimentón de la Vera), a PDO product from the central-west Spanish region, Extremadura. The mass fractions of Br, Ca, Cr, Cl, Cu, Fe, K, Mn, Ni, P, Rb, S, Sr and Zn determined by energy dispersive X-ray fluorescence (ED-XRF) are used for classification purposes by multivariate analysis using Soft Independent Modelling of Class Analogy (SIMCA) (PCA-Class) and Partial Least Square-Discriminant Analysis (PLS-DA). Sixty-seven paprika samples purchased in supermarkets around Europe and on-line via the official web-site of Pimentón de La Vera, were used to build up the models for prediction purposes. The PCA-class model of La Vera paprika powder had a sensitivity of 82%, a specificity of 100% and an accuracy of 91%, whereas the PLS-DA model had a sensitivity of 100%, a specificity of 91% and an accuracy of 96%.

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Authentication of Pimentón de la Vera is achieved by ED-XRF elemental fingerprint.

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ED-XRF is fast and hardly requires any sample treatment.

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Hazardous reagents are not required and chemical waste is not generated.

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SIMCA and PLS-DA models are fit-for-the purpose of fighting food fraud.

A field trials-based authentication study of conventionally and organically grown Chinese yams using light stable isotopes and multi-elemental analysis combined with machine learning algorithms

In this study, stable isotopes and multi-element signatures combined with chemometrics were used to distinguish conventional and organic Chinese yams based on field trials. Four light stable isotopes δD, δ¹³C, δ¹⁵N, δ¹⁸O, and 20 elements (e.g. Li, Na, Mn) were determined, then evaluated using significance analysis and correlation analysis, and modeling of various chemometrics methods. Consequently, the RandomForest model showed the best performance with AUC value of 0.972 and predictive accuracy of 97.3%, and Mn, Cr, Se, Na, δD, As, and δ¹⁵N were screened as significant variables. Moreover, many chemical components and antioxidant activity of yam samples were determined spectrophotometrically. The results indicated that organic yams had advantages in secondary metabolites such as polyphenol, flavonoid and saponin; conversely, conventional samples had more primary metabolites like protein and amino acids. Above all, this work provides a beneficial case in the authentication and quality evaluation of conventional and organic yams.