Review of methods for the detection and quantification of adulteration of rice: Basmati as a case study

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

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.

DNA-Based Tools to Certify Authenticity of Rice Varieties—An Overview

Rice (Oryza sativa L.) is one of the most cultivated and consumed crops worldwide. It is mainly produced in Asia but, due to its large genetic pool, it has expanded to several ecosystems, latitudes and climatic conditions. Europe is a rice producing region, especially in the Mediterranean countries, that grow mostly typical japonica varieties. The European consumer interest in rice has increased over the last decades towards more exotic types, often more expensive (e.g., aromatic rice) and Europe is a net importer of this commodity. This has increased food fraud opportunities in the rice supply chain, which may deliver mixtures with lower quality rice, a problem that is now global. The development of tools to clearly identify undesirable mixtures thus became urgent. Among the various tools available, DNA-based markers are considered particularly reliable and stable for discrimination of rice varieties. This review covers aspects ranging from rice diversity and fraud issues to the DNA-based methods used to distinguish varieties and detect unwanted mixtures. Although not exhaustive, the review covers the diversity of strategies and ongoing improvements already tested, highlighting important advantages and disadvantages in terms of costs, reliability, labor-effort and potential scalability for routine fraud detection.

Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess commercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Furthermore, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies.

Optimization and Development of Ready to Eat Chocolate Coated Roasted Flaked Rice as Instant Breakfast Food

The present study aimed to optimize and develop ready-to-eat rice-based functional breakfast food using response surface methodology. The levels of ingredients viz. skim milk powder, guar gum, and ferrous sulfate were pre-optimized and remained constant, whereas jaggery and dark chocolate were taken as independent variables. The optimum levels of jaggery and dark chocolate for chocolate-coated roasted flaked rice (CCRFR) were 8.49 g and 25.43 g, respectively. The physical, pasting, textural, functional, morphological, optical, and sensory characteristics of CCRFR and uncoated roasted flaked rice (RFR) were also studied. CCRFR had significantly higher mineral (iron and calcium) and total polyphenolic contents. Furthermore, the dimensional, sensory, and functional properties were also improved. The changes in morphological structure were also observed between the CCRFR and uncoated product using scanning electron microscopy. The coating adds nutritional value to the roasted rice and renders it an essential functional RTE convenience gluten-free cereal breakfast item.

Feasibility study on the use of elemental profiles to authenticate aromatic rice: the case of Basmati and Thai rice

Among the thousands of existing rice varieties, aromatic rice has increasingly attracted consumer’s preference in recent years. Within aromatic rice, Basmati, cultivated in some regions in Pakistan and India, is highly demanded. Other aromatic rice, cultivated in specific regions, for instance in Thailand (commonly referred to as Jasmine Thai rice), are also highly appreciated by consumers. In this work, the elemental profiles of commercially available rice samples (17 Basmati, 11 Thai, and 7 Long Grain rice) were determined by energy-dispersive X-ray fluorescence (ED-XRF) spectroscopy. The mass fractions of P, Cl, S, K, Fe, Cu, and Zn were significantly different (95% confidence interval) between Basmati and Thai rice and between Thai and Long Grain rice; only Cl, S, and Zn were significantly different between Basmati and Long Grain rice. Multivariate evaluation of the results combining soft independent modelling by class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA) allowed the correct classification (true positives) of 94.1, 85.6, and 100% of the Basmati, Long Grain, and Thai rice, respectively. The specificity (true negatives) of Basmati, Long Grain, and Thai was 94.4, 82.1, and 100%, respectively.

Differences in physicochemical properties of commercial rice from urban markets in West Africa

Rice consumers in West Africa (WA) have an acquired preference for imported rice. Enhancing consumption of local rice requires matching the grain quality attributes of the imported benchmarks in addition to increasing productivity of local rice cultivars. Thus, there is a need to develop screening tools that will aid breeding programs select for high-yielding and stress-tolerant cultivars whose grain quality are at par with imported rice. Hence, this study evaluated various grain quality characteristics of 316 commercial milled rice samples from urban markets in three WA countries (Benin, Cameroon, and Ghana) and developed linear discriminant models (LDAs) to classify rice according to their origins and to predict the imported rice classification of local germplasm based on their grain quality attributes. More than half of the commercial rice samples that were collected originated from Thailand (60%); in contrast, only a small fraction was locally grown (2%). The commercial rice from different origins were distinguishable based on the quality attributes evaluated, contributing to the relatively high classification rates achieved by the fitted LDAs. These results indicate that multivariate models could be useful during varietal improvement as tools for screening for cultivars that can match the quality of imported rice.