Mineral profile exploratory analysis for rice grains traceability
Introduction
Rice is one of the most consumed cereals in the world (FAO, 2018). Worldwide consumers are becoming more aware of food safety and intrinsic product characteristics, such as traceability, the guarantee of origin, and quality certificates (Arisseto-Bragotto, Feltes, & Block, 2017). Rice elemental composition is influenced by environmental and genetic factors (Kelly et al., 2002, Kokot and Phuong, 1999, Kumarathilaka et al., 2018, Tuli et al., 2010). Soil composition, crop management, season variability can influence elemental and isotopic content in rice grain. Therefore, it is necessary to go beyond chemistry and geochemistry of rice crop to understand how the elemental content is finally expressed.
In 2017, Brazil produced circa 12 million tons of rice. Rio Grande do Sul state was responsible for 60% of this production (SOSBAI, 2010), mostly by flooding irrigation (IBGE, 2017). South Region of Brazil has a complex rice production chain, which is formed by crop production industries, rice producers, storage and drying, processing, wholesaler, retailer, and consumer. The processing food companies receive grains of rice from several producers. Meaning the rice commercialized by one producer may be cultivated in different regions. Further, the rice from the same producer may sometimes present different elemental signatures, due to the characteristics of the soil where it has been grown. In a matter of production traceability, to know the origin of the grains allows the correct nutritional labeling, crop control and mitigation of potentially toxic elements, such as As, Cd, Pb, and other.
The mineral content and chemometric pattern recognition have been used as an important tool for the discrimination of the source or geographical origin of food (Cheajesadagul, Arnaudguilhem, Shiowatana, Siripinyanond, & Szpunar, 2013), as metal content is generally stable. Once the sampling was carried out, sample preparation is simple and, therefore, multielemental determinations can be obtained by using atomic spectrometric techniques (Paniz et al., 2018).
According to Callao and Ruisánchez (2018), the exploratory analysis provides information about the relationship between samples, variables, and/or both. Principal components analysis (PCA) is a well-known technique of exploratory analysis and generates new variables as a linear combination of the original variables, which associates maximum information from the original data.
In one hand, other countries such as Taiwan (Wang, Hsu, & Lu, 2011), Spain (Gonzálvez, Armenta, & Guardia, 2011), Thailand (Kukusamude & Kongsri, 2018) and Italy (Brandolini et al., 2006) traceability of rice is under development, and for some kinds of rice, it has already been adopted. On the other hand, few studies were developed in Brazil (Borges et al., 2015, Kato et al., 2018, Maione et al., 2016). In all these studies, PCA proved to be a suitable tool to identify geographical origin (Brandolini et al., 2006, Cheajesadagul et al., 2013, Chung et al., 2015), to discriminate organic and conventional grains (Borges et al., 2015, Brandolini et al., 2006), to find association maps of rice physiological disorders (Agrama & Yan, 2009).
In this scenario, the present study aimed to evaluate the main elemental content in 35 soils and 70 rice grains and husk samples from producers of the state of Rio Grande do Sul, Brazil. Inorganic and organic As content were also determined in the grains The potentially toxic elements (PTE) content of rice grains were compared with national and international regulations. Moreover, PCA was applied in order to i) evaluate how the rice elemental composition was affected by soil, ii) investigate the husk influence on rice discrimination and iii) establish which parameters could be used for rice geographical discrimination in a rice producing area of approximately 20 km2.
Section snippets
Hypothesis
Soil, rice grain, and husk mineral profile can provide a set of variables capable of rice geographical discrimination.
Rice sampling
Seventy rice samples were provided by 17 producers from 9 cities in the state of Rio Grande do Sul, in the southern Brazilian region, as presented in Table S1. Fig. 1 shows the cities included in this study. Itaqui is the most dislocated city from Pelotas producing area. All the other remaining cities are located in an approximated area of 20 km2 around Pelotas. Most samples came from Pelotas (n = 49), Arroio Grande (n = 30), and Santa Vitoria do Palmar (n = 28). The sample group included Puita
Results
The statistical summary of Astot, Asorg, Asinorg, major and trace elements measured in rice grains and husk is presented at Table S2. Table S2 presents values by cultivar (Puita and Irga), as well as per quadrant separation obtained in the PC. Mean, standard deviation and number of samples in each group is presented by variety (n = 2), by city (n = 9) and by producer (n = 17).
PTEs content in rice grains
The concentrations of As, Asinorg, Cd, and Pb in the husked grains of the present study were compared with the maximum levels of regulating agencies (ANVISA, 2013, Commission, 2016). All measured concentrations, except for Pb in 5 rice samples from a total of 140 (3.6% of samples), complied with reference levels (See Table S7). Therefore, the rice from this region poses no concern for human health, considering these analytes content.
Usually, the content of Cd and As is related to the soil redox
Conclusion
Considering the first 9 cities and 17 producers in the present work, three cities had enough data to allow rice traceability. PCA proved to be a useful tool to perform data reduction and to identify the main parameters that could explain rice variability among cities. Pelotas, Arroio Grande, Itaqui and Santa Vitoria do Palmar presented a remarkable degree of discrimination based on the elemental content in rice grain and husk. One particular producer from Pelotas was individually discriminated
Funding
This work was supported by Fundação de Amparo a Pesquisa do Estado de São Paulo (grants number 2014/05151-0, 2016-10060-9, 2017/20914-8 and 2018/21494-5) and by the Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil (grant number 444280/2014-6).
Availability of data and materials
All data supporting the conclusions of this article are provided as figures, tables, and supplementary tables and figures.
Authors’ contributions
Camila Neves Lange – analysis and manuscript writing; Lucilena Rebelo Monteiro – statistical analysis and manuscript writing; Bruna Moreira Freire – rice analysis and manuscript writing; Daniel Fernandez Franco – seeds sampling and manuscript writing; Rogério Oliveira de Sousa – soil sampling, analysis and manuscript writing; Cecília Sacramento dos Reis Ferreira – soil sampling, analysis and manuscript writing; Julio José Centeno da Silva – rice analysis and manuscript writing; Bruno Lemos
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Declaration of Competing Interest
The authors declare that they have no competing interests.
Acknowledgments
We are thankful to Fundação de Amparo a Pesquisa do Estado de São Paulo and to the Conselho Nacional de Desenvolvimento Científico e Tecnológico.
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