Identification of rice varieties and determination of their geographical origin in China using Raman spectroscopy

Geographical origin identification of Khao Dawk Mali 105 rice using combination of FT-NIR spectroscopy and machine learning algorithms

The mislabelled Khao Dawk Mali 105 rice coming from other geographical region outside the Thung Kula Rong Hai region is extremely profitable and difficult to detect; to prevent retail fraud (that adversely affects both the food industry and consumers), it is vital to identify geographical origin. Near infrared spectroscopy can be used to detect the specific content of organic moieties in agricultural and food products. The present study implemented the combinatorial method of FT-NIR spectroscopy with chemometrics to identify geographical origin of Khao Dawk Mali 105 rice. Rice samples were collected from 2 different region including the north and northeast of Thailand. NIR spectra data were collected in range of 12,500 - 4,000 cm-1 (800-2,500 nm). Five machine learning algorithms including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), C-support vector classification (C-SVC), backpropagation neural networks (BPNN), hybrid principal component analysis-neural network (PC-NN) and K-nearest neighbors (KNN) were employed to classify NIR data of rice samples with full wavelength and selected wavelength by Extremely Randomized Trees (Extra trees) algorithm. Based on the findings, geographical origin of rice could be specified quickly, cheaply, and reliably using combination of NIRS and machine learning. All models creating by full wavelength and selected wavelength exhibited accuracy between 65 and 100 % for identifying geographical region of rice. It was proven that NIR spectroscopy may be used for the quick and non-destructive identification of geographical origin of Khao Dawk Mali 105 rice.

Tracing sediment sources in a plain river network area by using optimized experimental design and reflectance spectroscopy

Soil erosion in a plain river network area with dense rivers, fertile land, and agricultural development is easily causes river siltation, agricultural non-point source pollution, and water eutrophication. Therefore, the negative impact of the sediment on the environment cannot be underestimated. Most traditional sediment fingerprint tracing studies have focused on mountain basins and lack a scheme suitable for plain river network sediment tracing. Here, a typical plain river network in the Taihu Basin was selected as the study area. The flow structure and characteristics were analysed, and a sampling scheme for the stream segment and a two-step model of sediment tracing in a plain river network were proposed to quantitatively distinguish the types of sediment sources. The results indicated that the traditional discriminant function analysis adequately distinguishes the contribution rate of basin soil and has a good validation accuracy (R2 = 0.96, root mean square error of calibration = 5.91 %), whereas Random Forest obtains better discrimination results by mining non-linear information in the soil spectra of different land types, with R2 values of 0.89, 0.83, and 0.80 for farmland, forest, and grassland, respectively. The average proportion of soil in the sediment in the watershed was 23 %, and the proportion of soil in the watershed increased from upstream to downstream. The sediment sources of the Caoqiao, Yincun, and Shaoxiang Rivers mainly came from grassland (44 %), forest (39 %), and farmland (42 %), respectively. Land-use distribution, water conservation facilities, and soil particle size were the main factors affecting these sources. Each river adopts measures to remove the corresponding pollutants, optimise water and soil conservation measures for riverbank green belts and forest, and regularly clean up silt in water conservancy ditches and rivers, which can reduce the pollution impact caused by sediment.

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice

Rice is a staple food for two-thirds of the world's population and is grown in over a hundred countries around the world. Due to its large scale, it is vulnerable to adulteration. In addition, the quality attribute of rice is an important factor affecting the circulation and price, which is also paid more and more attention. The combination of spectroscopy and chemometrics enables rapid detection of authenticity and quality attributes in rice. This article described the application of seven spectroscopic techniques combined with chemometrics to the rice industry. For a long time, near-infrared spectroscopy and linear chemometric methods (e.g., PLSR and PLS-DA) have been widely used in the rice industry. Although some studies have achieved good accuracy, with models in many studies having greater than 90% accuracy. However, higher accuracy and stability were more likely to be obtained using multiple spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. Future research should develop larger rice databases to include more rice varieties and larger amounts of rice depending on the type of rice, and then combine various spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. This article provided a reference for a more efficient and accurate determination of rice quality and authenticity.

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.

A review of conventional and rapid analytical techniques coupled with multivariate analysis for origin traceability of soybean

Soybean has developed a reputation as a superfood due to its nutrient profile, health benefits, and versatility. Since 1960, its demand has increased dramatically, going from a mere 17 MMT to almost 358 MMT in the production year 2021/22. These extremely high production rates have led to lower-than-expected product quality, adulteration, illegal trade, deforestation, and other concerns. This necessitates the development of an effective technology to confirm soybean's provenance. This is the first review that investigates current analytical techniques coupled with multivariate analysis for origin traceability of soybeans. The fundamentals of several analytical techniques are presented, assessed, compared, and discussed in terms of their operating specifics, advantages, and shortcomings. Additionally, significance of multivariate analysis in analyzing complex data has also been discussed.

Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy

Rice is an important source of nutrition and energy consumed around the world. Thus, quality inspection is crucial for protecting consumers and increasing the rice's value in the productive chain. Currently, methods for rice labeling depending on grain quality features are based on image and/or visual inspection. These methods have shown subjectivity and inefficiency for large-scale analyses. Laser-induced breakdown spectroscopy (LIBS) is an analytical technique showing attractive features due to how quick the analysis can be carried out and its capability of providing spectra that are true fingerprints of the sample's elemental composition. In this work, LIBS performance was evaluated for labeling rice according to grain quality features. The LIBS spectra of samples with their grain quality numerically described as Type 1, 2, and 3 were measured. Several spectral processing methods were evaluated when modeling a k-nearest neighbors (k-NN) classifier. Variable selection was also carried out by principal component analysis (PCA), and then the optimal k-value was selected. The best result was obtained by applying spectrum smoothing followed by normalization by using the first fifteen principal components (PCs) as input variables and k = 9. Under these conditions, the method showed excellent performance, achieving sample classification with 94% overall prediction accuracy. The sensitivities ranged from 90 to 100%, and specificities were in the range of 92-100%. The proposed method has remarkable characteristics, e.g., analytical speed and analysis guided by chemical responses; therefore, the method is not susceptible to subjectivity errors.

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.

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.

Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNets

Rice cultivation in cold regions of China is mainly distributed in Heilongjiang Province, where the growing season of rice is susceptible to low temperature and cold damage. Choosing and planting rice varieties with suitable GD according to the accumulated temperate zone is an important measure to prevent low temperature and cold damage. However, the traditional identification method of rice GD requires lots of field investigations, which are time consuming and susceptible to environmental interference. Therefore, an efficient, accurate, and intelligent identification method is urgently needed. In response to this problem, we took seven rice varieties suitable for three accumulated temperature zones in Heilongjiang Province as the research objects, and we carried out research on the identification of japonica rice GD based on Raman spectroscopy and capsule neural networks (CapsNets). The data preprocessing stage used a variety of methods (signal.filtfilt, difference, segmentation, and superposition) to process Raman spectral data to complete the fusion of local features and global features and data dimension transformation. A CapsNets containing three neuron layers (one convolutional layer and two capsule layers) and a dynamic routing protocol was constructed and implemented in Python. After training 160 epochs on the CapsNets, the model achieved 89% and 93% accuracy on the training and test datasets, respectively. The results showed that Raman spectroscopy combined with CapsNets can provide an efficient and accurate intelligent identification method for the classification and identification of rice GD in Heilongjiang Province.

Discrimination of the geographical origins of rice based on polycyclic aromatic hydrocarbons

Over the past few decades, several techniques have been applied to identify the geographical origins of rice products. In this study, the chemical characterization of polycyclic aromatic hydrocarbons (PAHs) was carefully conducted by analysing PAHs in rice samples collected from private sector planting areas located in Bali and Yogyakarta, Indonesia (i.e. ID; n = 20), west sides of Malaysia (i.e. MY; n = 20), Mandalay, Legend, Myingyan, Myanmar (i.e. MM; n = 20), northern parts of Lao PDR (i.e. LA; n = 20), central parts of Cambodia (i.e. KH; n = 20), northern parts of Vietnam (i.e. VN; n = 20), and Thailand (i.e. TH; n = 22). Percentage contributions show the exceedingly high abundance of 5-6 ring PAH congeners in rice samples collected from Indonesia, Malaysia, Thailand, Myanmar, Cambodia and Vietnam. Lao PDR rice samples were overwhelmed by 4-ring PAH congeners with the percentage contribution of 46% followed by 5-6 ring PAHs (33%) and 3-ring PAHs (21%). In addition, hierarchical cluster analysis and principal component analysis can successfully categorize some rice samples based on its geographical origins.

Raman Fingerprints of Rice Nutritional Quality: A Comparison between Japanese Koshihikari and Internationally Renowned Cultivars

Raman spectroscopy was applied to characterize at the molecular scale the nutritional quality of the Japanese Koshihikari rice cultivar in comparison with other renowned rice cultivars including Carnaroli from Italy, Calrose from the USA, Jasmine rice from Thailand, and Basmati from both India and Pakistan. For comparison, two glutinous (mochigome) cultivars were also investigated. Calibrated and validated Raman analytical algorithms allowed quantitative determinations of: (i) amylopectin and amylose concentrations, (ii) fractions of aromatic amino acids, and (iii) protein content and secondary structure. The Raman assessments non-destructively linked the molecular composition of grains to key nutritional parameters and revealed a complex intertwine of chemical properties. The Koshihikari cultivar was rich in proteins (but with low statistical relevance as compared to other investigated cultivars) and aromatic amino acids. However, it also induced a clearly higher glycemic impact as compared to long-grain cultivars from Asian countries. Complementary to genomics and wet-chemistry analyses, Raman spectroscopy makes non-destructively available factual and data-driven information on rice nutritional characteristics, thus providing customers, dietitian nutritionists, and producers with a solid science-consolidated platform.

Rapid Identification of Geographical Origin of Commercial Soybean Marketed in Vietnam by ICP-MS

Inductively coupled plasma mass spectrometry (ICP-MS) analytical method was used to determine the content of 40 elements in 38 soybean samples (Glycine Max) from 4 countries. Multivariate statistical methods, such as principal components analysis (PCA), were performed to analyze the obtained data to establish the provenance of the soybeans. Although soybean is widely marketed in many countries, no universal method is used to discriminate the origin of these cereals. Our study introduced the initial step to the identification of the geographical origin of commercial soybean marketed in Vietnam. The analysis pointed out that there are significant differences in the mean of 33 of the 40 analyzed elements among 4 countries' soybean samples, namely, ¹¹B, ²⁷Al, ⁴⁴Ca, ⁴⁵Sc, ⁴⁷Ti, ⁵⁵Mn, ⁵⁶Fe, ⁵⁹Co, ⁶⁰Ni, ⁶³Cu, ⁶⁶Zn, ⁶⁹Ga, ⁷⁵As, ⁷⁸Se, ⁸⁵Rb, ⁸⁸Sr, ⁸⁹Y, ⁹⁰Zr, ⁹³Nb, ⁹⁵Mo, ¹⁰³Rh, ¹³⁷Ba, ¹⁶³Dy, ¹⁶⁵Ho, ¹⁷⁵Lu, ¹⁷⁸Hf, ¹⁸¹Ta, ¹⁸²W, ¹⁸⁵Re, ¹⁹⁷Au, ²⁰²Hg, ²⁰⁵Tl, and ²⁰⁸Pb. The PCA analysis showed that the soybean samples can be classified correctly according to their original locations. This research can be used as a prerequisite for future studies of using the combination of elemental composition analysis with statistical classification methods for an accurate provenance establishment of soybean, which determined a variation of key markers for the original discrimination of soybean.

Evaluation of Nitrogen-Corrected Apparent Metabolizable Energy and Standardized Ileal Amino Acid Digestibility of Different Sources of Rice and Rice Milling Byproducts in Broilers

Simple Summary

Rice is the major cereal plant worldwide; the rice processing procedure has produced several rice byproducts that are not for human consumption but are usually used as a feed ingredient for broilers. However, due to the variation of geographic and processing methods, the quality of rice and rice byproducts is merely coincident. Thus, analysis of the chemical composition and evaluation of nutrition digestibility of rice and its byproducts in broilers and establishing the regression equation is vitally important in diet formulation and resource efficiency. Based on the above information, this study examined the differences in the chemical composition of rice, broken rice, and full-fat rice bran from the different major production areas of China, evaluated the nitrogen-corrected apparent metabolizable energy and standardized ileal amino acid digestibility in broilers by nitrogen-free diet method, established a regression equation based on partial correlation assay, and provided novel information in the diet formulation of rice, broken rice, and full-fat rice bran in broilers.

Abstract

Rice, broken rice (BR), and full-fat rice bran (FFRB) from six different origins were analyzed for their chemical composition, nitrogen-corrected apparent metabolized energy (AMEn), and standardized amino acid digestibility (SIAAD) in 14-day-old and 28-day-old Arbor Acres broilers. Results showed broilers fed with rice and BR had a similar AMEn regardless of the rice and BR having different CP, EE, NDF, ADF, and ash content. FFRB containing significantly different CP, EE, NDF, ADFm and starch presented variable AMEn (p < 0.05), suggesting that starch content in rice and its byproducts contributed most to the AMEn of broilers. The regression equation of AMEn = 14.312 − (0.198 × NDF) and AMEn = 6.491 + (0.103 × Starch) were feasible to integrally predict AMEn of broilers fed to rice and its byproducts. Moreover, 28-day-old broilers had higher SIAAD than 14-day-old ones. The SIAAD of rice were higher than BR and FFRB except for Met, Cys, Thr, and Tyr in 14-day-old broilers (p < 0.05), and the SIAAD of His, Asp, and Ser in BR were higher than FFRB (p < 0.05). In 28-day-old broilers, the SIAAD of Leu, Trp, Asp, Gly, and Pro of rice were still higher than BR and FFRB (p < 0.05), but BR and FFRB had no significant differences (p > 0.05). The regression equations to estimate the SIAAD of Thr, Lys, and Met were: Met = 81.46 + (0.578 × CP), Thr = 0.863 + (6.311 × CP), and Trp = 102.883 − (1.77 × CP), indicating that CP content in rice and its byproducts was likely a major factor for prediction of SIAAD.

Raman Molecular Fingerprints of Rice Nutritional Quality and the Concept of Raman Barcode

The nutritional quality of rice is contingent on a wide spectrum of biochemical characteristics, which essentially depend on rice genome, but are also greatly affected by growing/environmental conditions and aging during storage. The genetic basis and related identification of genes have widely been studied and rationally linked to accumulation of micronutrients in grains. However, genetic classifications cannot catch quality fluctuations arising from interannual, environmental, and storage conditions. Here, we propose a quantitative spectroscopic approach to analyze rice nutritional quality based on Raman spectroscopy, and disclose analytical algorithms for the determination of: (i) amylopectin and amylose concentrations, (ii) aromatic amino acids, (iii) protein content and structure, and (iv) chemical residues. The proposed Raman algorithms directly link to the molecular composition of grains and allow fast/non-destructive determination of key nutritional parameters with minimal sample preparation. Building upon spectroscopic information at the molecular level, we newly propose to represent the nutritional quality of labeled rice products with a barcode specially tailored on the Raman spectrum. The Raman barcode, which can be stored in databases promptly consultable with barcode scanners, could be linked to diet applications (apps) to enable a rapid, factual, and unequivocal product identification based on direct molecular screening.

Evaluation of nutrition components in Lanzhou lily bulb by confocal Raman microscopy

Lanzhou lily is a famous lily variety in China, which has many advantages different from other lily varieties. It is rich in nutrients and can be used as medicine or food. The present study is performed to evaluate the quality of Lanzhou lily by Raman spectroscopy. Here, Raman spectra of lily bulbs were collected by confocal Raman microscopy. Through study of a variety of samples, we found that Raman peaks of several important nutrients including starch, sucrose and amino acids were clearly observed from scales of lily bulb, while strong characteristic peaks of ferulic acid were observed at the epidermis of the same scale due to the stimulation of the external environment. We also compared lily bulbs with various sizes and shapes using an average Raman spectrum of selected area. Then, changes of nutrients were quantitively analyzed in different storage period. The results show that the nutrient components including starch, sucrose, amino acids and ferulic acid can be evaluated by Raman spectroscopy. Then the quality of Lanzhou lily can be evaluated by Raman spectroscopy. This is valuable for quality evaluation of lily using non-destructive methods.

Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination

A simple, fast, and efficient spark discharge-laser-induced breakdown spectroscopy (SD-LIBS) method was developed for determining rice botanic origin using predictive modeling based on support vector machine (SVM). Seventy-two samples from four rice varieties (Guri, Irga 424, Puitá, and Taim) were analyzed by SD-LIBS. Spectral lines of C, Ca, Fe, Mg, N and Na were selected as input variables for prediction model fitting. The SVM algorithm parameters were optimized using a central composite design (CCD) to find the better classification performance. The optimum model for discriminating rice samples according to their botanical variety was obtained using C = 5.25 and γ = 0.119. This model achieved 96.4% of correct predictions in test samples and showed sensitivities and specificities per class within the range of 92-100%. The developed method is robust and eco-friendly for rice botanic identification since its prediction results are consistent and reproducible and its application does not generate chemical waste.

Properties of South-Indian rice cultivars: physicochemical, functional, thermal and cooking characterisation

Physicochemical, functional, thermal, pasting and cooking properties of five Indian rice cultivars, ADT 36, 43, 39, IW PONNI and CR1009 were investigated. The starch, protein and fat contents varied from 1.321 to 2.489 mg/ml, 11.16-13.32% and 1.19-1.77% respectively, showing significant difference amongst the cultivars. ADT46 showed the highest amylose-amylopectin ratio. Water (103.55-132.48%) and oil (112.89-137.30%) absorption capacities also varied significantly. CR1009 showed highest swelling power at 60 °C, whereas IW PONNI exhibited the highest solubility (10.165%). The gel consistency of rice flours extended from 1.32 to 4.12 cm. The thermal properties of rice cultivars were found to be profoundly affected by amylopectin and showed correlation with amylose-amylopectin ratio. The pasting properties of rice flours also varied significantly, with peak viscosity and breakdown viscosity ranging between 2068.5-839 Cp and 1609.5-764.15 Cp respectively. The cooking time of the rice grains was found to be consistent with their shape and size. ADT43 and ADT46 showed the highest and least water uptake % on cooking. ADT46 showed the least cooking loss %, owing to the highest pasting viscosity. This study delivers the knowledge of the Indian rice cultivars, to be used for utilization of rice varieties for different products with relevance to the properties and enhance the post-harvest value chain improvement.

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.

Determination of Geographical Origin of Wuchang Rice with the Geographical Indicator by Multielement Analysis

The study aims to investigate whether the multielement analysis result can be used as a fingerprint to identify the geographical origin of Wuchang rice. The element contents of rice and soil samples from three regions in China (Wuchang, Qiqihar, and Jiamusi) were analyzed. The concentrations of 16 elements (Na, Mg, Al, K, Ca, V, Mn, Fe, Co, Cu, Zn, As, Rb, Sr, Cd, and Pb) in 194 rice samples and 112 soil samples from the harvest season in 2013 and 2014 were determined. The analysis of variance and linear discriminant analysis were performed to analyze the variation among regions and rice genotypes and classify the geographical origins of rice. Only the element of Cu showed significant differences among different genotypes. In the discriminant analysis, the overall correct identification rates of the rice samples obtained in 2013 and 2014 were, respectively, 96.6% and 89.6% and the overall correct identification rate for Wuchang rice reached 100%.