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

Assessing Seasonal Effects on Identification of Cultivation Methods of Short-Growth Cycle Brassica chinensis L. Using IRMS and NIRS

Seasonal (temporal) variations can influence the δ13C, δ2H, δ18O, and δ15N values and nutrient composition of organic (ORG), green (GRE), and conventional (CON) vegetables with a short growth cycle. Stable isotope ratio mass spectrometry (IRMS) and near-infrared spectroscopy (NIRS) combined with the partial least squares-discriminant analysis (PLS-DA) method were used to investigate seasonal effects on the identification of ORG, GRE, and CON Brassica chinensis L. samples (BCs). The results showed that δ15N values had significant differences among the three cultivation methods and that δ13C, δ2H, and δ18O values were significantly higher in winter and spring and lower in summer. The NIR spectra were relatively clustered across seasons. Neither IRMS-PLS-DA nor NIRS-PLS-DA could effectively identify all BC cultivation methods due to seasonal effects, while IRMS-NIRS-PLS-DA combined with Norris smoothing and derivative pretreatment had better predictive abilities, with an 89.80% accuracy for ORG and BCs, 88.89% for ORG and GRE BCs, and 75.00% for GRE and CON BCs. The IRMS-NIRS-PLS-DA provided an effective and robust method to identify BC cultivation methods, integrating multi-seasonal differences.

Effects of Water Isotope Composition on Stable Isotope Distribution and Fractionation of Rice and Plant Tissues

Rice origin authenticity is important for food safety and consumer confidence. The stable isotope composition of rice is believed to be closely related to its water source, which affects its origin characteristics. However, the influence of water availability on the distribution of rice stable isotopes (δ2H and δ18O) is not clear. In this study, three irrigation waters with different isotopic values were used to investigate isotopic water use effects of Indica and Japonica rice, using pot experiments. Under three different water isotope treatments, the δ2H values of Indica polished rice showed significant differences (-65.0 ± 2.3, -60.5 ± 0.8 and -55.8 ± 1.7‰, respectively, p < 0.05) compared to δ13C and δ15N, as did Japonica polished rice. The values of δ2H and δ18O of rice became more positive when applying more enriched (in 2H and 18O) water, and the enrichment effect was higher in rice than in the corresponding plant tissue. In addition, the δ2H and δ18O values of Indica rice leaves decreased at the heading stage, increased at the filling stage, and then decreased at the harvest stage. Japonica rice showed a similar trend. δ2H changes from stem to leaf were more negative, but δ18O changes were more positive, and δ2H and δ18O values from leaf to rice were more positive for both brown and polished rice. The results from this study will clarify different water isotopic composition effects on rice and provide useful information to improve rice origin authenticity using stable isotope-based methods.

Improved organic and pesticide-free rice (Oryza sativa L.) authentication based on multiple stable isotope ratio analysis and rice milling state

This study looked at the application of multiple bulk stable isotope ratio analysis to accurately authenticate organic rice and counteract organic fraud within the expanding global organic market. Variations of δ13C, δ15N, δ18O, and δ34S in organic, pesticide-free, and conventional rice were assessed across different milling states (brown, milled, and bran). Individual stable isotope ratio alone such as δ15N demonstrated limited capacity to correctly differentiate organic, pesticide-free, and conventional rice. A support vector machine model-incorporating δ13C, δ15N, δ18O, and δ34S in milled rice-yielded overall predictability (95%) in distinguishing organic, pesticide-free, and conventional rice, where δ18O emerged as the pivotal variable based on the feature weights in the SVM model. These findings suggest the potential of multi-isotope and advanced statistical approaches in combating organic fraud and ensuring authenticity in the food supply chain.

Do agronomic approaches aligned to regenerative agriculture improve the micronutrient concentrations of edible portions of crops? A scoping review of evidence

Regenerative Agriculture (RA) is used to describe nature-based agronomic approaches that aim to build soil health and crop resilience, minimize negative environmental outcomes, and improve farmer livelihoods. A benefit that is increasingly attributed to crops grown under RA practices is improved nutritional content. However, we do not know the extent to which RA influences crop nutritional quality and under what management approaches and context, can such effects be realized. A scoping review of recent literature (Web of Science, 2000-2021) was carried out to assess the evidence that RA approaches improve crop micronutrient quality. Papers included combinations of agronomic approaches that could be defined as Regenerative: "Organic Inputs" including composts and manures, cover crops, crop rotations, crop residues and biochars; "Reduced Tillage", "Intercropping", "Biostimulants" e.g. arbuscular mycorrhizal fungi; plant growth promoting bacteria, and "Irrigation", typically deficit-irrigation and alternate wetting and drying. The crop types reviewed were predetermined covering common sources of food and included: Tomato (Solanum lycopersicum L.), Wheat (Triticum aestivum L.), Rice (Oryza sativa L.), Maize (Zea mays L.), Pulses (Fabaceae), Alliums (Allium spp.), and "other" crop types (30 types). This scoping review supports a potential role for RA approaches in increasing the concentrations of micronutrients in the edible portions of several crop types under specific practices, although this was context specific. For example, rice grown under increased organic inputs showed significant increases in grain zinc (Zn) concentration in 15 out of 16 studies. The vitamin C concentration of tomato fruit increased in ~50% of studies when plants were grown under increased organic inputs, and in 76% of studies when plants were grown under deficit irrigation. Overall, the magnitude and reproducibility of the effects of RA practices on most crop nutritional profiles were difficult to assess due to the diversity of RA approaches, geographical conditions, and the limited number of studies for most crops in each of these categories. Future research with appropriate designs, improved on-farm surveillance and nutritional diagnostics are needed for better understanding the potential role of RA in improving the quality of food, human nutrition, and health.

Key secondary metabolite markers for Wuchang Daohuaxiang rice discrimination in China

This study aimed to comprehensively elucidate the vital secondary metabolites of Wuchang Daohuaxiang (DHX) rice through widely targeted metabolomics analysis. Among the secondary metabolites detected, a total of 30 differential ones were screened out and categorized into 4 different classes, including 6 alkaloids (20%), 15 flavonoids (50%), 6 phenolic acids (20%), and 3 terpenoids (10%) between DHX and control groups. Of these, compounds as zarzissine, fagomine, arbutin, p-Hydroxypheny-β-D-allopyranoside, pimaric acid, kaurenoic acid, and isopimaric acid were more abundant in DHX than control group, with the possibility in serve as key secondary metabolites of DHX rice. Furthermore, arbutin, trigonelline and 6'-O-Feruloyl-D-sucrose were optimized as potential biomarkers for DHX rice discrimination. This study would supply data support for DHX rice authenticity and quality improvement.

Green-Labelled Rice versus Conventional Rice: Perception and Emotion of Chinese Consumers Based on Review Mining

The COVID-19 pandemic increased public health awareness, changing consumers' sensitivity and beliefs about food health. Food anxiety and health scares turn consumers toward safe and healthy foods to strengthen their immunity, which makes green food more popular. However, it remains unclear how to understand the gap between consumer intention to purchase green food and their actual purchasing behaviour. Taking rice as an object of study, comparing differences in consumer perceptions and emotions towards green-labelled rice and conventional rice is beneficial for understanding the components and psychological characteristics of consumer perceptions of green food. Therefore, we used topic modelling and sentiment analysis to explore consumers' focus of attention, attitudinal preferences, and sentiment tendencies based on the review (n = 77,429) from JD.com. The findings revealed that (1) consumers' concerns about green-labelled rice are increasing rapidly, and most have a positive attitude; (2) consumers of green-labelled rice are more concerned about origin, aroma, and taste than conventional rice; (3) consumers of conventional rice are more concerned about the cost-performance ratio, while consumers of green-labelled rice are also price-sensitive; (4) green label mistrust and packaging breakage during logistics are the leading causes of negative emotions among consumers of green-labelled rice. This study provides a comparative analysis of consumer perceptions and emotions between the two types of rice, thus revealing the main influencing factors of the intention-behaviour gap and providing valuable consumer insights for the promotion of green consumption and the sustainable development of the green food industry.

Predicting isoscapes based on an environmental similarity model for the geographical origin of Chinese rice

The authentication of geographical origin of food is important using stable isotope analysis. However, the isotopic databank is still short of comprehensive. The isoscapes model based on environmental similarity is used for the first time to predict the geospatial distribution of δ¹³C, δ²H and δ¹⁸O in Chinese rice in 2017 and 2018. 794 rice samples in 2017 were used to build isoscapes model. Independent verification shows that the predicted isotope distribution from this new approach is of high accuracy, with a root mean square error (RMSE) of 0.51 ‰, 7.09 ‰ and 2.06 ‰ for δ¹³C, δ²H and δ¹⁸O values for 2017, respectively. Our results indicate that it is possible to predict the spatial distribution of stable isotopes in rice using an isoscapes model based on environmental similarity. This novel strategy can enrich and complement a stable isotope reference database for rice origin identification at regional scale.

Isotopes Don't Lie, differentiating organic from conventional banana (Musa AAA, Cavendish subgroup) fruits using C and N stable isotopes

With the dramatic increase of organic banana production worldwide, it is essential to be able to monitor compliance with organic specifications. While the detection of pesticide fraud is routinely controlled by detecting pesticide residues in organic bananas, the detection of fertilizer fraud is much more complex. We compared the δ¹³C and δ¹⁵N isotopic values of green bananas from organic and conventional farms at seven sites around the world. In our whole dataset, the δ¹⁵N values of banana fruits ranged between -1.25 and + 8.91‰. In all sites, δ¹⁵N values of organic banana were significantly higher than conventional fruits (mean value of + 5.24‰ and + 2.342‰, respectively). Conversely, the type of fertilization did not significantly alter δ¹³C values. Our results suggest that it is possible, upon arrival in importing countries, to differentiate bananas grown with synthetic fertilizer from those grown with organic fertilizer.

Improved accuracy of geographical origin identification of shiitake grown in sawdust medium: A compound-specific isotope model-based pilot study

In countries like South Korea and the USA, origin labeling of shiitake grown using imported Chinese-inoculated medium is an issue. Therefore, we evaluated the use of compound-specific isotope analysis (CSIA) for the accurate identification of the geographical origin of shiitake (Korean, Chinese-inoculated medium, and Chinese); Chinese-inoculated medium shiitake were cultivated in Korea using inoculated sawdust medium from China. The CSIA-discriminant model showed an overall accuracy of 100% in the geographical classification of the original set and 96.4% for the cross-validated set. Glutamate and aspartate δ¹⁵N values were the most important variables for differentiating shiitake based on their origins. Compared to that observed upon using the bulk stable isotope analysis, the CSIA model was associated with significantly improved predictability of origin identification. Our findings elucidate the importance of isotope signatures in developing a reliable origin labeling method for shiitake cultured on the sawdust medium for the global market.

Stable isotope and photosynthetic response of tea grown under different temperature and light conditions

The stable isotope and photosynthesis response of tea (Camellia sinensis) is determined under different light and temperature conditions. The results showed that isotopes of young tea leaves were more enriched with increasing light intensity (31 ~ 411 µmol m^-2∙s^-1). However, the value of δ¹³C and δ¹⁵N seemed depleted, while δ²H and δ¹⁸O became enriched as temperature increasing from 15 to 35 °C. Significant isotope differences were found in tea leaves harvested between early growth (0 ~ 10 days) and later growth (10 ~ 21 days) periods (p < 0.05). Pearson's correlation showed a negative correlation between isotopes (δ¹³C, δ¹⁵N and δ²H) and photosynthetic parameters (EVAP and CI) ranging from 0.497 to 0.872, under 25 °C/203 µmol m^-2∙s^-1. But δ¹⁸O had a weak correlation with all photosynthetic parameters under the same conditions. These distinctive correlations between isotopes and photosynthetic parameters provide new insights which could be used to predict tea isotope responses arising from subtle seasonal or climate change conditions.

Natural abundance analysis of the role played by 15 N as indicator for the certification of organic-system deriving food

BACKGROUND

The natural abundance of stable isotope ¹⁵ N (δ¹⁵ N) in production systems has emerged as an alternative to distinguish organic products from conventional ones. This study evaluated the use of δ¹⁵ N values recorded for nitrogen fertilizers, soil and plant tissue in order to set the differences between organic and conventional agricultural production systems applied to rice, potatoes, apple and banana crops.

RESULTS

Values of δ¹⁵ N recorded for N sources ranged from +5.58‰ to +18.27‰ and from -3.55‰ to +3.19‰ in organic and synthetic fertilizers, respectively. Values recorded for δ¹⁵ N in food from organic rice, potatoes and banana farms were higher than values recorded for δ¹⁵ N in conventional farms; the same was observed for values recorded for δ¹⁵ N in leaves from the four crops.

CONCLUSION

Results have allowed for differentiation between production systems due to values of δ¹⁵ N recorded in leaves of all crops and food, for rice, potatoes and banana trees. © 2021 Society of Chemical Industry.

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.

Classification of organic and ordinary kiwifruit by chemometrics analysis of elemental fingerprint and stable isotopic ratios

Elemental fingerprint, stable isotopic analysis, and chemometrics were combined to identify organic kiwifruit from ordinarily cultivated kiwifruit. Samples of organic (n1 = 78) and ordinary kiwifruit (n2 = 85) were collected from neighboring areas. For elemental fingerprint, the contents of 15 elements in fresh fruits, including Al, Cr, Mg, Pb, Zn, Ca, Cu, Mn, Se, Cd, Fe, Na, Sr, Co, and K, were determined by inductively coupled plasma optical emission spectrometry (ICP-OES). Three stable isotopes, including δ¹³ C, δ¹⁵ N, and δ¹⁸ O, were analyzed using an isotope-ratio mass spectrometer (IRMS). Different classification methods including soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLSDA), and least squares support vector machines (LS-SVM), were used to discriminate the organic and ordinary kiwifruits by fusion of elemental and stable isotopic. As a result, the sensitivity, specificity, and overall accuracy of SIMCA model were 0.885, 0.857, and 0.864, respectively. PLSDA and LS-SVM obtained 0.950 and 0.983 classification accuracy of organic and ordinary kiwifruits, respectively. It was demonstrated that elemental fingerprint and stable isotopic analysis would provide useful chemical information for the identification of organic fruits, and the capacity of these methods could be enhanced by chemometrics. PRACTICAL APPLICATION: The classification of kiwifruit usually relies on the label assigned by the merchant, which is prone to deceive consumers. This research has developed an accurate and effective classification method based on stable isotopes and mineral elements for the identification of ordinary kiwifruit and organic kiwifruit, providing a tool for the quality monitoring of organic food.

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.

Long-term isotopic model study for ecofriendly rice (Oryza sativa L.) authentication: Updating a case study in South Korea

To overcome the lack of consumer trust in ecofriendly products due to low reliability of ecofriendly certification and decreasing areas certified for growing ecofriendly agricultural products, alternative approaches for reliable certification are required. Isotopic-chemometric analysis has potential for determining organic authenticity, but previous studies have struggled to differentiate the authenticities of different rice types. The present study examined 5-year variations in δ¹³C and δ¹⁵N in ecofriendly and conventional rice sold at retail markets in South Korea, while assessing the feasibility of discriminant models for authentication of organic rice. Supporting vector machine analysis showed 4.4-14.6% better overall predictability of rice types than discriminant analysis and was effective in discriminating organic or conventional rice from pesticide-free rice, potentially enabling high-throughput screening to authenticate organic rice at marketplaces. Our findings provide reliable information for authenticating ecofriendly rice, with a potential to improve consumer safety and thus the confidence in organic products.

Preliminary Study on Tracing the Origin and Exploring the Relations between Growing Conditions and Isotopic and Elemental Fingerprints of Organic and Conventional Cavendish Bananas (Musa spp.)

The stable isotopic ratios and elemental compositions of 120 banana samples, Musa spp. (AAA Group, Cavendish Subgroup) cultivar Williams, collected from six countries (Colombia, Costa Rica, Dominica Republic, Ecuador, Panama, Peru), were determined by isotope ratio mass spectrometry and inductively coupled plasma mass spectrometry. Growing conditions like altitude, temperature, rainfall and production system (organic or conventional cultivation) were obtained from the sampling farms. Principal component analysis (PCA) revealed separation of the farms based on geographical origin and production system. The results showed a significant difference in the stable isotopic ratios (δ¹³C, δ¹⁵N, δ¹⁸O) and elemental compositions (Al, Ba, Cu, Fe, Mn, Mo, Ni, Rb) of the pulp and peel samples. Furthermore, δ¹⁵N was found to be a good marker for organically produced bananas. A correlation analysis was conducted to show the linkage of growing conditions and compositional attributes. The δ¹³C of pulp and peel were mainly negatively correlated with the rainfall, while δ¹⁸O was moderately positively (R values ~0.5) correlated with altitude and temperature. A moderate correlation was also found between temperature and elements such as Ba, Fe, Mn, Ni and Sr in the pulp and peel samples. The PCA results and correlation analysis suggested that the differences of banana compositions were combined effects of geographical factors and production systems. Ultimately, the findings contribute towards understanding the compositional differences of bananas due to different growing conditions and production systems linked to a defined origin; thereby offering a tool to support the traceability of commercial fruits.

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.