## 1 Introduction  
Rice (_Oryza sativa_ L.) has increasingly become a hugely important staple food worldwide, and its consumption has surged as a result of population growth, changing food preferences, and urbanization [1]. Vietnam is among the top-five leading net exporters of rice and, while quantity is no longer the main problem, high quality is important to serve the high-end market segments. From 2015 to 2018, Vietnam's rice price increased at a fast pace, from 334 to 391 USD/ton, but then suddenly dropped to only 323 USD/ton in 1 year [2]. Not only the export market, but also the domestic market is facing the same problem: high volume but low value. Currently, it is estimated that more than 130 different types of rice brand exist on the market, and no brand accounts for more than 3% of the market. This means that Vietnam's rice market is considered as \"fragmented\", and only a few businesses have a clear vision for branding. The benefit race is more intense than ever, so fraud is one way that some producers choose to get an advantage in this game. This can be done easily when high-quality rice can be mixed with, or even replaced by, cheaper rice. Rice products are particularly vulnerable, and become a \"prime target\" of these frauds, as an additional processing step is included, and adulteration cannot easily be detected by visual examination alone. Moreover, with low awareness, limited knowledge about rice, and a loose tracing system, consumers and even some trading companies cannot distinguish or identify counterfeit products. Consequently, a reliable and highly reproducible technique that can effectively authenticate blended products with excellent sensitivity and accuracy is urgently needed. Techniques Used in Rice Authentication  
Given the importance rice has on a global level, in the literature it is possible to find several studies aimed at the analysis and characterization of this agro-food. In this regard, several analytical approaches have been tested. For instance, inductively coupled plasma atomic emission spectroscopy and isotopic data coupled with chemometrics have been used to classify rice according to the variety or quality [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. Nevertheless, due to the market value of this product, non-destructive approaches are generally preferred. Hence, imaging analysis has been proposed for the characterization and quality assessment of rice [3], as, for instance, has been done by [PERSON] et al. [14], where the images collected by a digital camera were elaborated and used to classify 30 different varieties of rice. In addition, spectroscopy represents a suitable non-destructive (or semi-destructive) solution. For instance, 1H-NMR has been used for discriminating rice according to its geographical origin [15], and lased-induced fluorescence to classify according to the variety [16], whereas terahertz spectroscopy imaging has been exploited to detect transgenic rice [17]. Despite the efficiency of traditional approaches, they present a common drawback: the preparation of samples can be time-consuming, as well as economically and environmentally disadvantageous. In this context, near infrared (NIR) spectroscopy has been widely used in the measurement of agricultural products due to its many advantages, such as being easy-to-use, non-destructive, fast and accurate, providing highly reproducible results, requiring minimum or, often, no sample preparation, and allowing the analysis of multiple constituents with a single measurement [18; 19; 20; 21]. Direct spectroscopic measurements have been widely applied for several foods and commodities, especially in the grain, cereal products, and oilseed processing industries [22; 23; 24; 25; 26; 27; 28; 29; 30]; additionally, they have been successfully applied for the classification of rice [24; 25; 26; 27; 28].