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

Vemireddy, Lakshminarayana R.; Satyavathi, Valluri V.; Siddiq, E. A.; Nagaraju, Javaregowda

doi:10.1007/s13197-014-1579-0

Cited by 22 publications

(27 citation statements)

References 96 publications

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“…Basmati rice is a long grain rice whose traditional varieties, grown in the Punjab provinces of Pakistan and India and the regions of Uttar Pradesh and Haryana of the same two countries, may reach prices five times higher than non-Basmati rice [3]. Basmati rice is susceptible to diseases, can only grow some periods of the year because it is photoperiod sensitive, and has tall culms [4]. To overcome these problems, many attempts were made to produce new Basmati varieties, some of them not successful in maintaining the appreciated organoleptic characteristics of the traditional Basmati [1,5].…”

Section: Introductionmentioning

confidence: 99%

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

2021

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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.

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Section: Introductionmentioning

confidence: 99%

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

2021

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“…Isotopic composition, mineral element content and chemical composition related to the climate, soil and temperature were used as biomarkers in rice tracing (Li et al, 2017;Vemireddy et al, 2015;Pramai et al, 2018). Various factors can be combined to accurately determine the origin and quality of rice.…”

Section: Introductionmentioning

confidence: 99%

Non-destructive Analysis of Volatile Compounds for Geographical Identification of Rice

Hu¹,

Ren²,

宋勇³

et al. 2020

Preprint

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Background In recent years, high-quality rice adulteration has become a serious problem. It is essential to prevent false origin labels and dishonest transactions. However, there is still a lack of rapid identification methods for discriminating rice from different sources. In this study, we developed a method to profile volatile organic compounds (VOCs) using headspace solid phase microextraction (HS-SPME) combined with gas chromatography mass spectrometry (GC-MS). In addition, the identification efficiency of the biomarkers was determined using several multivariate analysis methods. Results Based on the t-test, fold changes and volcano plots, eight typical biomarkers were used based their differential levels. Among them, 2-acetyl-1-pyrroline (2-AP) is the most important source of aroma in rice flavor. Unsupervised analyses, including principal component analysis (PCA) and Cluster analysis, demonstrated the potential for geographic classification of rice between Wuchang and other regions. In addition, partial least squares discriminant analysis (PLS-DA) yielded a goodness of fit of 0.900, a goodness of prediction of 0.853, and a probability of substitution test of 0.012. Random forest (RF) algorithm further strengthened the discriminating ability of volatile compounds. Conclusion In short, the current method can quickly distinguish rice from Wu Chang and other regions, and the research method can facilitate controlling the authenticity and quality of rice.

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“…In non‐DNA based methods, physico‐chemical schemes are employed, which are based on the quality trait ‘alkali spreading value’, although their accuracy is low . In DNA based methods, a combination of microsatellite markers and either capillary electrophoresis or a real‐time polymerase chain reaction has been widely employed; however, such methods are time‐consuming, destructive and involve complicated procedures . Therefore, a rapid, non‐destructive and precise method is needed to identify the authenticity of rice.…”

Section: Introductionmentioning

confidence: 99%

Rapid quantitative analysis of adulterated rice with partial least squares regression using hyperspectral imaging system

Guo

et al. 2019

J Sci Food Agric

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BACKGROUND: Rice adulteration in the food industry that infringes on the interests of consumers is considered very serious. To realize the rapid and precise quantitation of adulterated rice, a visible near infrared (VNIR) hyperspectral imaging system (380-1000 nm) was developed in the present study. A Savitsky-Golay first derivative (SG1) transform was utilized to eliminate the constant spectral baseline offset. Then, the adulterated levels of rice samples were quantified by partial least squares regression (PLSR). RESULTS:A SG1-PLSR model based on full-wavelength was attained with a coefficient of determination of prediction set (R P ) of 0.9909, root-mean-square error of prediction set (RMSE P ) of 0.0447 g kg −1 and residual predictive deviation (RPD P ) of 11.28. Furthermore, fifteen important wavelengths were selected based on the weighted regression coefficients (B W ) and a simplified model (PLSR-15) was established with R P of 0.9769, RMSE P of 0.0708 g kg −1 and RPD P of 3.49. Finally, two visualization maps produced by applying the optimal models (SG1-PLSR and PLSR-15) were used to visualize the adulterated levels of rice.CONCLUSION: These results demonstrate that VNIR hyperspectral imaging system is an effective tool for rapidly quantifying and visualizing the adulterated levels of rice.

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

Cited by 22 publications

References 96 publications

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

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

Non-destructive Analysis of Volatile Compounds for Geographical Identification of Rice

Rapid quantitative analysis of adulterated rice with partial least squares regression using hyperspectral imaging system

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