Fingerprinting of Volatile Organic Compounds for the Geographical Discrimination of Rice Samples from Northeast China

Abstract

Rice’s geographic origin and variety play a vital role in commercial rice trade and consumption. However, a method for rapidly discriminating the geographical origins of rice from a different region is still lacking. Therefore, the current study developed a volatile organic compound (VOC) based geographical discrimination method using headspace gas chromatography-mass spectrometry (HS-GC-MS) to discriminate rice samples from Heilongjiang, Jilin, and Liaoning provinces. The rice VOCs in Heilongjiang, Liaoning, and Jilin were analyzed by agglomerative hierarchical clustering (AHC), principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). The results show that the optimum parameters for headspace solid phase microextraction (HS-SPME) involved the extraction of 3.0 g of rice at 80 °C within 40 min. A total of 35 VOCs were identified from 30 rice varieties from Northeast China. The PLS-DA model exhibited good discrimination (R2 = 0.992, Q2 = 0.983, and Accuracy = 1.0) for rice samples from Heilongjiang, Liaoning, and Jilin. Moreover, K-nearest neighbors showed good specificity (100%) and accuracy (100%) in identifying the origin of samples. In conclusion, the present study established VOC fingerprinting as a highly efficient approach to identifying rice’s geographical origin. Our findings highlight the ability to discriminate rice from Heilongjiang, Liaoning, and Jilin provinces rapidly.

Construction of aroma association network of cooked rice based on gas chromatography–mass spectrometry (GC–MS) and sensory analysis

“…23 Studies have indicated that alkanes can be used as differential volatile compounds to distinguish rice from different geographical regions, but they contribute less to cooked rice aroma. 15,16 20,25,26 The content of hexanal in DHX was the highest, which was 877.72 ± 32.05 μg/kg, accounting for 54.71% of the total aldehydes, and had a strong green and grass aroma. Covering all the samples, (E)-2-heptenal (fresh, green), (E)-2-octenal (cucumber), (E)-2-decenal (citrus), and (E,E)-2,4-decadienal (citrus)…”

“…14 Many studies have reported the volatile compounds of rice and cooked rice, and found differential marker compounds through multivariate statistical analysis to distinguish the samples from different geographical origins. 15,16 However, current knowledge about the volatiles in cooked rice alone is insufficient to explain the aroma characteristics of cooked rice. 17,18 Most of these mathematical analysis research based on GC-MS identification results lack the combination with the actual food aroma sensory characteristics.…”

Identifying putative key metabolites from fingerprinting metabolomics for the authentication of rice origin: A case study of Sengcu rice

“…The identification of potential key biomarkers that can assist intergroup discrimination 22 still remains a challenging task in metabolic fingerprinting. 23 To date, numerous researches employing untargeted metabolomics combined with multivariate analysis such as PLS-DA have been performed for the determination of specific markers in rice based on their VIP scores larger than 1, 17,42,43 p values less than corrected significant thresholds, [44][45][46][47] or even manual identification based on the S-plot from orthogonal PLS-DA. 16 The application of sparse PLS-DA in this study has proved to be superior in terms of computational efficiency and interpretability compared with those conventional methods.…”

Geographical Origin Differentiation of Rice by LC–MS-Based Non-Targeted Metabolomics

“…Eight secondary metabolites are considered potential biomarkers for distinguishing organic rice and conventional rice based on LC–MS non-targeted metabolomics technology [ 7 ]. Non-targeted metabolomics based on headspace solid-phase microextraction, gas chromatography, and MS can also be used to identify japonica rice samples from different origins, and the OPLS-DA model presents excellent geographical discrimination ability [ 8 ].…”