Analysis of volatile compounds by GCMS reveals their rice cultivars

Due to the similarity in the grain and difference in the market value among many rice varieties, deliberate mislabeling and adulteration has become a serious problem. To check the authenticity, we aimed to discriminate rice varieties based on their volatile organic compounds (VOCs) composition by headspace solid phase microextraction (HS-SPME) coupled with gas chromatography mass spectrometry (GC-MS). The VOC profiles of Wuyoudao 4 from nine sites in Wuchang were compared to 11 rice cultivar from other regions. Multivariate analysis and unsupervised clustering showed an unambiguous distinction between Wuchang rice and non-Wuchang rice. Partial least squares discriminant analysis (PLS-DA) demonstrated a goodness of fit of 0.90 and a goodness of prediction of 0.85. The discriminating ability of volatile compounds is also supported by Random forest analysis. Our data revealed eight biomarkers including 2-acetyl-1-pyrroline (2-AP) that can be used for variation identification. Taken together, the current method can readily distinguish Wuchang rice from other varieties which it holds great potential in checking the authenticity of rice.

Dokha brand differentiation by elemental analysis measured by inductively coupled plasma-mass spectrometry

Dokha is a tobacco product commonly used in Middle Eastern and Northern African regions. It is available in three blends purportedly corresponding to the degree of "buzz" experienced by the user. The "buzz" has been linked in part to nicotine levels, which are higher than those found in cigarettes and is believed to be the reason dokha is abused as a "legal high." There have been reports of seizure activity from dokha users, and elevated concentrations of toxic metals have been measured in dokha tobacco. The purpose of this work was to determine whether we could use dokha's elemental content, measured by inductively coupled plasma-mass spectrometry, to link dokha back to its brand. This could aid investigators in identifying brands and/or distribution routes in the case of adverse effects resulting from dokha use. We measured the concentrations of Mg, K, Mn, Ni, Cu, Rb, Sr, and Ba in Medwakh, Nirvana, Scorpion, Enjoy, Kingdom, and Iconic dokha brands. Analysis of variance revealed statistical differences in concentrations of elements among groups. Discriminant function analysis (using leave-one-out classification) was 58.3% successful at differentiating brands. Enjoy dokha was the most, and Kingdom dokha the least, correctly classified among groups. Attempts to further link dokha blends back to light, medium, and heavy blends were less successful. These results indicate potential for using elemental content to discriminate among dokha brands. Our data may also help to understand the degree of additional processing and/or adulteration of dokha products available to users in the United States.

Geographical origin traceability of Sengcu rice using elemental markers and multivariate analysis

Multi-element analysis combined with chemometric method has been used to investigate the distinguish between Sengcu rice and other types of rice origins in Vietnam. In Sengcu rice, As, Ba Sr, Pb, Ca, Se were confirmed as the key elements for geographical traceability among three fields of Lao Cai, whereas Al, Ca, Fe, Mg, Ag, As were major factors to distinguish between Sengcu and other types of rice. Based on linear discriminant analysis and partial least squares-discriminant analysis model, overall correct identification rates distinguishing between Sengcu and other types of rice were approximately 100% in both training and validation test. Moreover, to distinguish geographical origin of Sengcu rice samples, these rates vary from 80% to 99%. These results suggest the presence of food adulteration illustrated in the latter.

Multi-Element Analysis and Origin Discrimination of Panax notoginseng Based on Inductively Coupled Plasma Tandem Mass Spectrometry (ICP-MS/MS)

Panax notoginseng is an important functional health product, and has been used worldwide because of a wide range of pharmacological activities, of which the taproot is the main edible or medicinal part. However, the technologies for origin discrimination still need to be further studied. In this study, an ICP-MS/MS method for the accurate determination of 49 elements was established, whereby the instrumental detection limits (LODs) were between 0.0003 and 7.716 mg/kg, whereas the quantification limits (LOQs) were between 0.0011 and 25.7202 mg/kg, recovery of the method was in the range of 85.82% to 104.98%, and the relative standard deviations (RSDs) were lower than 10%. Based on the content of multi-element in P. notoginseng (total of 89 mixed samples), the discriminant models of origins and cultivation models were accurately determined by the neural networks (prediction accuracy was 0.9259 and area under ROC curve was 0.9750) and the support vector machine algorithm (both 1.0000), respectively. The discriminant models established in this study could be used to support transparency and traceability of supply chains of P. notoginseng and thus avoid the fraud of geographic identification.

Elementomics combined with dd-SIMCA and K-NN to identify the geographical origin of rice samples from China, India, and Vietnam

The COVID-19 pandemic has impacted the food industry and consumers, with production gaps, shipping delays, and changes in supply and demand leading to an increased risk of food fraud. Rice has a high probability for adulteration by food fraudsters, being a staple commodity for more than half the global population, making the assessment of geographical origins of rice for authenticity important in terms of protecting businesses and consumers. In this study, we describe ICP-MS elemental profiling coupled with elementomic modelling to identify the geographical indications of Indian, Chinese, and Vietnamese rice. A PLS-DA model exhibited good discrimination (R² = 0.8393, Q² = 0.7673, accuracy = 1.0). Data-driven soft independent modelling of class analogy (dd-SIMCA) and K-nearest neighbours (K-NN) models have good sensitivity (98%) and specificity (100%).

Tracing the Geographical Origin of Thai Hom Mali Rice in Three Contiguous Provinces of Thailand Using Stable Isotopic and Elemental Markers Combined with Multivariate Analysis

Rice is a staple food for more than half of the world’s population. The discrimination of geographical origin of rice has emerged as an important issue to prevent mislabeling and adulteration problems and ensure food quality. Here, the discrimination of Thai Hom Mali rice (THMR), registered as a European Protected Geographical Indication (PGI), was demonstrated. Elemental compositions (Mn, Rb, Co, and Mo) and stable isotope (δ¹⁸O) in the rice were analyzed using inductively coupled plasma mass spectrometry (ICP-MS) and elemental analyzer isotope ratio mass spectrometry (EA-IRMS), respectively. The recoveries and precisions of all elements were greater than 98% and lower than 9%, respectively. The analytical precision (±standard deviation) was below ±0.2‰ for δ¹⁸O measurement. Mean of Mn, Rb, Co, Mo, and δ¹⁸O levels was 14.0 mg kg⁻¹, 5.39 mg kg⁻¹, 0.049 mg kg⁻¹, 0.47 mg kg⁻¹, and 25.22‰, respectively. Only five valuable markers combined with radar plots and multivariate analysis, linear discriminant analysis (LDA) could distinguish THMR cultivated from three contiguous provinces with correct classification and cross-validation of 96.4% and 92.9%, respectively. These results offer valuable insight for the sustainable management and regulation of improper labeling regarding geographical origin of rice in Thailand and other countries.

Multielement Principal Component Analysis and Origin Traceability of Rice Based on ICP-MS/MS

In this experiment, inductively coupled plasma tandem mass spectrometry (ICP-MS/MS) was used to determine the content of 30 elements in rice from six places of production and to explore the relationship between the multielement content in rice and the producing area. The contents of Ca, P, S, Zn, Cu, Fe, Mn, K, Mg, Na, Ge, Sb, Ba, Ti, V, Se, As, Sr, Mo, Ni, Co, Cr, Al, Li, Cs, Pb, Cd, B, In, and Sn in rice were determined by ICP-MS/MS in the SQ and MS/MS mode. By passing H2, O2, He, and NH3/He reaction gas into the ICP-MS/MS, respectively, the interference was eliminated by means of in situ mass spectrometry and mass transfer. The detection limit of each element was 0.0000662–0.144 mg/kg, and the limit of quantification was in the range of 0.000221–0.479 mg/kg, the linear correlation coefficient was greater or equal to 0.9987 (R2 ≥ 0.9987), and the detection results had low detection limit and great linear regression. Recovery of the method was in the range of 80.6% to 110.5% with spike levels of 0.10–100.00 mg/kg, and relative standard deviations were lower than 10%. For the multielement content of rice from different producing areas, the principal component factor analysis can get six principal component factors, 87.878% cumulative contribution rate, and the distribution of the principal component scores of each element and different producing areas. Based on the multielement content and cluster analysis, the samples were accurately divided into two major categories and six subcategories according to the places of production, which proved that there was a significant correlation between the multielement content in rice and the place of production, so that the place of rice origin can be traced.

Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice

Identification of geographical origin is of great importance for protecting the authenticity of valuable agri-food products with designated origins. In this study, a robust and accurate analytical method that could authenticate the geographical origin of Geographical Indication (GI) products was developed. The method was based on elemental profiling using inductively coupled plasma mass spectrometry (ICP-MS) in combination with machine learning techniques for model building and feature selection. The method successfully predicted and classified six varieties of Chinese GI rice. The elemental profiles of 131 rice samples were determined, and two machine learning algorithms were implemented, support vector machines (SVM) and random forest (RF), together with the feature selection algorithm Relief. Prediction accuracy of 100% was achieved by both Relief-SVM and Relief-RF models, using only four elements (Al, B, Rb, and Na). The methodology and knowledge from this study could be used to develop reliable methods for tracing geographical origins and controlling fraudulent labeling of diverse high-value agri-food products.

Metabolomic fingerprinting of volatile organic compounds for the geographical discrimination of rice samples from China, Vietnam and India

Rice is one of the most important cereals for human nutrition and is a basic staple food for half of the global population. The assessment of rice geographical origins in terms of its authenticity is of great interest to protect consumers from misleading information and fraud. In the present study, a head space gas chromatography mass spectrometry (HS-GC-MS) strategy for characterising volatile organic compounds (VOCs) profiles to distinguish rice samples from China, India and Vietnam is described. Partial Least Square Discriminant Analysis (PLS-DA) model exhibited a good discrimination (R² = 0.98182, Q² = 0.9722, and Accuracy = 1.0) for rice samples from China, India and Vietnam. Moreover, Data-Driven Soft Independent Modelling of Class Analogy (DD-SIMCA) and K-nearest neighbors shown good specificity 100% and accuracy 100% in identifying the origin of samples. The present study established VOC fingerprinting as a highly efficient approach to identify the geographical origin of rice.

Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy

Rice is the most consumed food worldwide, therefore its designation of origin (PDO) is very useful. Laser-induced breakdown spectroscopy (LIBS) is an interesting analytical technique for PDO certification, since it provides fast multielemental analysis requiring minimal sample treatment. In this work LIBS spectral data from rice analysis were evaluated for PDO certification of Argentine brown rice. Samples from two PDOs were analyzed by LIBS coupled to spark discharge. The selection of spectral data was accomplished by extreme gradient boosting (XGBoost), an algorithm currently used in machine learning, but rarely applied in chemical issues. Emission lines of C, Ca, Fe, Mg and Na were selected, and the best performance of classification were obtained using k-nearest neighbor (k-NN) algorithm. The developed method provided 84% of accuracy, 100% of sensitivity and 78% of specificity in classification of test samples. Furthermore, it is simple, clean and can be easily applied for rice certification.