Описание: 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.
Лектори: Jenny Nelson, PhD, Application Scientist, Agilent Technologies, Inc
Shuofei Dong, Ph.D., Senior Atomic Spectroscopy Application Development Scientist, Agilent Technologies (China) Co., Ltd.
Продължителност: 39 минути
Дата: 18 яануари 2022, 22:30 ч. българско време