Improved Discrimination for Brassica Vegetables Treated with Agricultural Fertilizers Using a Combined Chemometric Approach

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.

Multi-Element Analysis and Origin Discrimination of Panax notoginseng Based on Inductively Coupled Plasma Tandem Mass Spectrometry (ICP-MS/MS)

Panax notoginseng is an important functional health product, and has been used worldwide because of a wide range of pharmacological activities, of which the taproot is the main edible or medicinal part. However, the technologies for origin discrimination still need to be further studied. In this study, an ICP-MS/MS method for the accurate determination of 49 elements was established, whereby the instrumental detection limits (LODs) were between 0.0003 and 7.716 mg/kg, whereas the quantification limits (LOQs) were between 0.0011 and 25.7202 mg/kg, recovery of the method was in the range of 85.82% to 104.98%, and the relative standard deviations (RSDs) were lower than 10%. Based on the content of multi-element in P. notoginseng (total of 89 mixed samples), the discriminant models of origins and cultivation models were accurately determined by the neural networks (prediction accuracy was 0.9259 and area under ROC curve was 0.9750) and the support vector machine algorithm (both 1.0000), respectively. The discriminant models established in this study could be used to support transparency and traceability of supply chains of P. notoginseng and thus avoid the fraud of geographic identification.

Multivariate analysis for organic milk authentication

To differentiate organic milk (OM) from conventional milk (CM), an orthogonal projection to latent structure-discriminant analysis (OPLS-DA) model was constructed using δ¹³C, δ¹⁵N, δ¹⁸O, 51 elements and 35 fatty acids (FAs) as the variables. So far, most reported studies barely use three or more types of variables, but more variables could avoid one-sidedness and get stabler models. Our multivariate model combines geographical and nutritional parameters and displays better explanatory and predictive abilities (R²X = 0.647, R²Y = 0.962 and Q² = 0.821) than models based on fewer variables for differentiating OM and CM. In particular, δ¹⁵N, Se, δ¹³C, Eu, K and α-Linolenic acid (ALA) are found to be critical parameters for the discrimination of OM. These results show that the multivariate model based on stable isotopes, elements and FAs can be used to identify OM, and can potentially expand the global databases for quality and authenticity of milk.

Stable Isotope Effects of Biogas Slurry Applied as an Organic Fertilizer to Rice, Straw, and Soil

Biogas slurry (BS) is now increasingly used for organic rice production in China. However, the isotopic response and fractionation of different BS application rates to characterize organic rice cultivation have not yet been investigated. In this study, different fertilizer treatments were applied to rice paddy soil including urea, BS with five different application rates and a control with no fertilizer added. Multiproxy analyses (% C, % N, δ¹³C, δ¹⁵N, δ²H, and δ¹⁸O) of rice, rice straw, and soil were undertaken using elemental analyzer-isotope ratio mass spectrometry. Rice, straw, and soil showed only minor isotopic and elemental variations across all fertilizer treatments except for δ¹⁵N. δ¹⁵N values of rice and straw became more positive (+6.1 to +11.2‰ and +6.1 to +12.2‰, respectively) with increasing BS application rates and became more negative with urea fertilization (+2.8 and +3.0‰, respectively). The soil had more positive δ¹⁵N values after BS application but showed no significant change with different application rates. No obvious δ¹⁵N isotopic differences were found between the control soil and soils fertilized with urea. ¹⁵N fractionation was observed between rice, straw, and soil (Δ_rice-soil -2.0 to +4.3‰, Δ_straw-soil -1.9 to +5.3‰) and their isotopic values were strongly correlated to each other (r > 0.94, p < 0.01). Results showed that % C, % N, δ¹³C, δ²H, and δ¹⁸O in rice displayed only minor variations for different fertilizers. However, δ¹⁵N values increased in response to BS application, confirming that BS leaves an enriched ¹⁵N isotopic marker in soil, straw, and rice, indicating its organically cultivated status. Results from this study will enhance the stable isotope δ¹⁵N databank for assessing organic practices using different fertilizer sources.

Improved geographical origin discrimination for tea using ICP-MS and ICP-OES techniques in combination with chemometric approach

BACKGROUND

There is an urgent need to strengthen the testing and certification of geographically iconic foods, as well as to use discriminatory science and technology for their regulation and verification. Multi-element and stable isotope analyses were combined to provide a new chemometric approach for improving the discrimination tea samples from different geographical origins. Different stoichiometric methods [principal component analysis (PCA), hierarchical cluster analysis (HCA), partial least squares-discriminant analysis (PLS-DA), back propagation based artificial neural network (BP-ANN) and linear discriminant analysis (LDA)] were used to demonstrate this discrimination approach using Yongchuanxiuya tea samples in an experimental test.

RESULTS

Multi-element and stable isotope analyses of tea samples using inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry easily distinguished the geographical origins. However, the clustering ability of the two unsupervised learning methods (PCA and HCA) were worse compared to that of the three supervised learning methods (PLS-DA, BP-ANN and LDA). BP-ANN and LDA, with 100% recognition and prediction abilities, were found to be better than PLS-DA. ⁸⁶ Sr and ¹¹² Cd were the markers enabling the successful classification of tea samples according to their geographical origins. Under the validation by 'blind' dataset, the prediction accuracies of the BP-ANN and LDA methods were all greater than 90%. The LDA method showed the best performance, with an accuracy of 100%.

CONCLUSION

In summary, determination of mineral elements and stable isotopes using inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry techniques coupled with chemometric methods, especially the LDA method, is a good approach for improving the authentication of a diverse range of tea. The present study contributes toward generalizing the use of fingerprinting mineral elements and stable isotopes as a promising tool for testing the geographic roots of tea and food worldwide. © 2020 Society of Chemical Industry.

Long-Term Agricultural Effects on the Authentication Accuracy of Organic, Green, and Conventional Rice Using Isotopic and Elemental Chemometric Analyses

Organically farmed rice is believed to be healthier, safer, and eco-friendlier than its conventionally farmed counterparts and sells for a premium price in global markets. Deliberate mislabeling of organic rice has become a critical consumer concern in China and elsewhere, and there is an increased risk of buying fraudulent organic rice in the market place. In this study, stable isotopic and multielemental analysis combined with chemometrics was used to differentiate organically farmed rice from green and conventional rice in a 4-year experimental field trial from 2014 to 2017. A total of 108 rice samples and their associated soils were collected during the study from three farming (fertilization) systems to investigate whether there are long-term changes in the rice farming classification accuracy from climate effects. Stable carbon and nitrogen isotopic ratios (i.e., δ¹³C and δ¹⁵N) and 27 elemental contents (e.g., Na, K, Ca, Fe, and Zn) of rice and soil samples were determined and then evaluated using statistical analysis [i.e., one-way analysis of variance, multivariable correlation analysis, and modeling of partial least-squares discriminant analysis]. Although δ¹⁵N values can be an effective indicator for organic rice authentication during one crop rotation, both δ¹³C and δ¹⁵N values of rice were easily affected by rice cultivar and interannual soil fertilization and localized agroclimatic variations. These two isotopes were not able to separate organic rice from green and conventional rice accurately. Elemental contents of green and conventional rice (especially K and Ca) were found at higher levels due to the abundant application of synthetic fertilizers (e.g., KNO₃, KH₂PO₄, and CaHPO₄), unlike organically farmed rice, which primarily used animal manure and composts. Partial least-squares discriminant analysis modeling combined isotopic and elemental signatures to correctly differentiate organic rice from green and conventional counterparts, with an accuracy up to 100% over the 4-year study. Therefore, this multi-isotope and -element strategy proposes a more rigorous, alternative tool to combat fraudulent mislabeling of organic rice, increasing the trust of organically labeled rice products and supporting the integrity of the organic sector worldwide.

Differentiating Organically Farmed Rice from Conventional and Green Rice Harvested from an Experimental Field Trial Using Stable Isotopes and Multi-Element Chemometrics

Chemometric methods using stable isotopes and elemental fingerprinting were used to characterize organically grown rice from green and conventionally grown rice in experimental field trials in China. Carbon, nitrogen, hydrogen, and oxygen stable isotopes as well as 26 other elements were determined. Organic rice was found to be more depleted in ¹³C than green or conventionally grown rice because of the uptake of enriched ¹³C from carbon dioxide and methane respiring bacteria and more enriched in ¹⁵N because of the volatilization of the nitrogen from the urea and ammonium of the animal manures used to manufacture the organic composts. Chemometrics (principal-component analysis and linear-discriminant analysis) were used to separate the three farming methods and provided a promising scientific tool to authenticate the farming methods of different rice cultivars fertilized with animal manures, green composts, and synthetic fertilizers in China or elsewhere.