Detecting Thai Jasmine Rice Authenticity with the VideometerLab

Detecting Thai Jasmine Rice Authenticity with the VideometerLab

Each year, food fraud and adulteration cost the global food industry approximately 30 billion euro and, more importantly, cause health and safety issues to the deceived consumers (European Commission, 2018). With its growing tendency, the counterfeiting of food products has become more sophisticated, making it more difficult to detect with the traditional, manual methodologies.

Spectral imaging offers a solution to this problem, allowing for a non-destructive quality inspection. In particular, the VideometerLab instrument performs easy to use, rapid and documented analysis on food and crops, determining food adulteration with precision and in a matter of seconds.

In a recent article published for the Journal of Food Quality (Liu et al., 2021), researchers explained how the VideometerLab is an ideal tool for the determination of Thai Jasmine Rice authenticity – rice is one of the most adulterated products in the world (GRiSP, 2013).

Thai Jasmin Rice

With more than 500 million tons produced in 2020, rice (Oryza Sativa) is the third largest commodity worldwide (AMIS, 2021). For centuries, this grain has been domesticated by populations across the globe, who have learned to modify its morphology accordingly to their dietary needs. Today, more than 1.000 varieties of rice are cultivated in approximately 100 countries worldwide (GRiSP, 2013).

In this ever-growing rice market, with production and stock prices increasing by the year, the Asian region accounts for 92% of the crop’s cultivation. Thailand, in particular, stands out for its 20.0 million tons of rice produced each year (AMIS, 2021). The country is renowned for its Hom Mali 105, or Thai Jasmin rice, which has been awarded as the world’s best rice at the 2017 World Rice Conference.

Thai Jasmin rice is a particular rice variety because of its aromatic character, its long, slender grains, its peculiar colors (white and brown) and its texture when cooked (fluffy and moist). It is sold at premium prices, especially because of the volatile nature of its aroma, which vanishes rapidly in time, leading consumers to increasingly demand fresh harvests. However, because of the characteristics of the Thai arable land and the resources needed to grow high quality rice, authentic Thai Jasmine rice can only be harvested once a year (GRiSP, 2013), making it hard to meet the high demands.

To deceive and swindle the market for commercial profits, many rice producers have been mislabeling or adulterating Thai Jasmine rice. This has steered to an increased number of the premium rice variety counterfeits, misleading people across the world into deceptive consumption. Because of this, it is necessary to identify a system that allows for the fast and non-destructive inspection of Thai Jasmine Rice authenticity.

VideometerLab – Detecting Thai Jasmine Rice Authenticity

The VideometerLab, with its 20 wavelengths ranging from UV through the visual spectrum up to NIR, integrates spectral imaging with state-of-the-art image analysis statistics, machine learning and AI. The techniques used by the instrument to gather information with spectral imaging and machine learning allow for the visualization of chemical and physical features of the rice grain and, hence, for the authentication of its variety.

The authors of the paper (Liu et al., 2021) conducted two experiment utilizing the VideometerLab on Thai Jasmine Rice and on Jasmine Sticky, Simiao, Northeast Wuchang rice. These types of rice present great similarities and cannot be distinguished from one other by the naked eye solely. The experiment was executed utilizing 19 of the wavelengths provided by the instrument.

Rice Classification

The first experiment was performed to classify the different grains. 800 samples were analyzed, 600 of which were allocated to random calibration sets and 200 of which were allocated to random prediction sets. In total, there were 200 samples for each rice variety.

In this first experiment, the VideometerLab captured spectral images of each sample, which were later analyzed with in combination with chemometrics with an assessment of both spectral and morphological features.

The results showed that the Thai Jasmine Rice was discriminated by the other typologies at a rate of 100% in the calibration set, and at a rate of 92% in the prediction set.

Detection of Adulteration

In the second experiment, the VideometerLab captured spectral images of 220 samples of Thai Jasmine Rice, which had been adulterated with the three other rice varieties. The adulteration happened at different degrees, from 0% to 100% of foreign grains.

Again, the samples were analyzed with a combination of spectral imaging and chemometrics by assessing both spectral and morphological data.

The results showed a total level of accuracy of about 90.6% for the calibration set and 84.2% for the prediction set.


With their positive results, the two experiments (Liu et al., 2021) proved that by utilizing the VideometerLab multi-spectral imaging technology in combination with chemometrics, it is possible to assess the authenticity of Thai Jasmine Rice rapidly, precisely and non-destructively.

This methodology provides great potential in the identification of counterfeits for many other applications, including pharma, cosmetics, and textiles, safeguarding not only the market from fraudulent activities, but also the buyer from unconscious consumption of deceived goods.

To learn more about the VideometerLab and its applications for quality control, visit or contact us at


European Commission (2018), The EU Food Fraud Network and the System for Administrative Assistance – Annual Report

Liu, W. et al. (2021), Non-Destructive Detection of Authenticity of Thai Jasmine Rice Using Multispectral Imaging, Journal of Food Quality, vol. 2021

AMIS (2021), Commodity Market Monitor, N. 88

GRiSP (Global Rice Science Partnership). 2013. Rice almanac, 4th edition. Los Baños (Philippines): International Rice Research Institute. 283 p.

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