Identifying the Geographical Origin of Rice Using Elemental Profiling and Multi-Modeling Approach

Identification of geographical origin is of great importance for protecting the authenticity of valuable food products.

China is the world’s largest producer of rice, which is a vital staple food for almost half of the world’s population. Given the huge global demand for rice, varieties with higher value are a target for food fraudsters, who routinely adulterate or mislabel expensive food items for financial gain. The price of rice in China depends greatly on where the plants are grown, so producers use Geographical Indication (GI) to differentiate their products in the marketplace.

The elemental profiles of 90 rice samples from 5 different locations were collected directly from suppliers and determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Using a single helium cell gas mode for all analytes enabled the detection of 24 elements in rice sample digests. Data from 66 of the 90 rice samples was used to build prediction models for the characterization of the geographical origins of the remaining 24 samples. Agilent MPP statistical software was used to process the data set. PCA results showed that the elemental composition of rice was influenced by geographical origins. Prediction models using four different class prediction algorithms (partial least squares discriminant analysis, support vector machine, linear discriminant analysis, and soft independent modeling of class analogy) were built by following the simple steps in the MPP software.

The models were trained using the ICP-MS data and tested using ‘unknown’ samples. The study showed that it was possible to correctly predict the origins of all the 24 ‘unknown’ samples using multiple prediction models, rather than a single prediction model. The multiple-model approach could lead to more accurate prediction results and better understanding of the element profiles of samples.The methodology described in this study has the potential to characterize the geographical origin of rice, and other high value foodstuffs enabling routine authenticity analysis of foods.

Presenter: Shuofei Dong, Ph.D. (Senior Atomic Spectroscopy Application Development Scientist, Agilent Technologies (China) Co., Ltd.)

After receiving his Ph.D. degree in isotope geochemistry from Imperial College London, UK in 2012, he worked as a postdoctoral researcher in the Department of Earth and Atmospheric Sciences, Indiana University Bloomington, USA and at the Centre de Recherches Pétrographiques et Géochimiques (CRPG) of CNRS, France. His main research interest is about learning the biogeochemical cycle of metal elements, and the distribution and transmission in the environment. He joined Agilent in 2017 as a senior application development scientist, mainly focusing on developing new analytical methods for ICP-MS in the form of cooperative research. His projects include developing separation and detection methods of metal nanoparticles and microplastics in complex matrices; applying element fingerprint method for food authenticity studies; and using isotope ratios and REE patterns for tracing the sources of atmospheric particles.

Presenter: Jenny Nelson, PhD (Application Scientist, Agilent Technologies, Inc.)

Jenny Nelson received her Ph.D. in Analytical Chemistry from the University of Cincinnati in 2007, and her MBA from Saint Mary’s College of California in 2011. Currently, Jenny is an Application Chemist for the Life Science and Chemical Analysis team at Agilent Technologies, joining in 2012 (with a step away in 2019). Jenny is also an Adjunct Professor in the Department of Viticulture and Enology at University of California, Davis since 2013. Jenny has been very active with AOAC and ASTM over the past 8 years, serving on expert review panels, chairing committees, and volunteering to develop new methods needed by the industry.

Jenny has extensive experience in operating and method development for Inductively Coupled Plasma Mass Spectroscopy (ICP-MS) and Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). Jenny has broad knowledge and experience in different speciation analysis for many sample matrices using GC-ICPMS, LC-ICPMS. As well as vast experience with sp-ICP-MS, for many applications.