Elemental and isotopic profiling of Thai jasmine rice (Khao Dawk Mali 105) for discrimination of geographical origins in Thung Kula Rong Hai area, Thailand

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.

Combination of light stable isotopic and elemental signatures in Thai Hom Mali rice with chemometric analysis

This study aims to discriminate the geographical origin of Thai Hom Mali rice in order to protect consumers from mislabeling. Stable isotopic and elemental compositions (δ¹³C, δ¹⁵N, δ¹⁸O, As, Br, K, Mn, Rb, and Zn) of Thai Hom Mali rice cultivated inside and outside the Thung Kula Rong-Hai Plain were combined with chemometric analysis, linear discriminant analysis (LDA) and partial least squares-discriminant analysis (PLS-DA). The 9 variables combined with LDA can distinguish Thai Hom Mali rice cultivated inside and outside the Thung Kula Rong-Hai Plain with 98.2 % correct classification and 94.6 % cross-validation. The efficiency in using stable isotopic and elemental compositions combined with soft PLS-DA was achieved 100 % in discrimination of Thai Hom Mali rice cultivated inside and outside the Thung Kula Rong-Hai Plain. The variables δ¹⁵N, Br, K, and Rb were key parameters to discriminate the geographical origin of Thai Hom Mali rice.

Human-induced pollution and toxicity of river sediment by potentially toxic elements (PTEs) and accumulation in a paddy soil-rice system: A comprehensive watershed-scale assessment

This study aimed to assess pollution by potentially toxic elements (PTEs) in the Zarjoub and Goharroud river basins in northern Iran. Due to exposure to various types of pollution sources, these rivers are two of the most polluted rivers in Iran. They also play an important role in irrigation of paddy fields in the study area, increasing concerns about the contamination of rice grains by PTEs. Hence, we analyzed the concentrations of eight PTEs (i.e., As, Co, Cr, Cu, Mn, Ni, Pb, and Zn) at ten channel bed sediment sampling sites in each river, fifteen samples of paddy soils and fifteen co-located rice samples in the relevant watersheds. Results of the index-based assessment indicate moderate to heavy pollution and moderate toxicity for sediments in the Goharroud River, while both pollution and toxicity of the Zarjoub River sediment were characterized as moderate. Paddy soils in the watersheds were found to be moderate to heavily polluted by PTEs, but the values of the rice bioconcentration factor (RBCF) indicated intermediate absorption for Cu, Zn, and Mn, and weak and very weak absorption for Pb/Ni and As/Co/Cr, respectively. The concentration of Zn, Cu, Pb, and Cr was negatively correlated to the corresponding values of RBCF, highlighting the ability of rice grains to control bioaccumulation and regulate concentrations. Industrial/agricultural effluents, municipal wastewater, leachate of solid waste, traffic-related pollution, and weathering of parent materials were found to be responsible for pollution of the Zarjoub and Goharroud watersheds by PTEs. Mn, Cu, and Pb in rice grains might be responsible for non-carcinogenic diseases. Although weak absorption was observed for As and Cr in rice grains, the concentrations of these elements in rice grains indicate a high level of cancer risk if ingested. This study provides insights to control the pollution of sediment, paddy soils, and rice.

Dokha brand differentiation by elemental analysis measured by inductively coupled plasma-mass spectrometry

Dokha is a tobacco product commonly used in Middle Eastern and Northern African regions. It is available in three blends purportedly corresponding to the degree of "buzz" experienced by the user. The "buzz" has been linked in part to nicotine levels, which are higher than those found in cigarettes and is believed to be the reason dokha is abused as a "legal high." There have been reports of seizure activity from dokha users, and elevated concentrations of toxic metals have been measured in dokha tobacco. The purpose of this work was to determine whether we could use dokha's elemental content, measured by inductively coupled plasma-mass spectrometry, to link dokha back to its brand. This could aid investigators in identifying brands and/or distribution routes in the case of adverse effects resulting from dokha use. We measured the concentrations of Mg, K, Mn, Ni, Cu, Rb, Sr, and Ba in Medwakh, Nirvana, Scorpion, Enjoy, Kingdom, and Iconic dokha brands. Analysis of variance revealed statistical differences in concentrations of elements among groups. Discriminant function analysis (using leave-one-out classification) was 58.3% successful at differentiating brands. Enjoy dokha was the most, and Kingdom dokha the least, correctly classified among groups. Attempts to further link dokha blends back to light, medium, and heavy blends were less successful. These results indicate potential for using elemental content to discriminate among dokha brands. Our data may also help to understand the degree of additional processing and/or adulteration of dokha products available to users in the United States.

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.

Application of stable isotopic and mineral elemental fingerprints in identifying the geographical originof concentrated apple juice in China

Food traceability is an important component of food safety and quality. Currently, there is no authentic established technique to identify the origin of concentrated apple juice (CAJ) in China. In this study, the isotopes of δ¹³C, δ¹⁸O and the contents of 32 elements in CAJ from five production areas (BHB, NWR, SCH, LP and YRAR) were determined. The δ¹³C, δ¹⁸O and 28 elements were significantly different (P < 0.05: post-hoc Duncan's test) in the five production areas. PCA, PLS-DA and OPLS-DA were employed for regional classification of samples. The results show that ten key variables (Tl, Se, δ¹⁸O, B, Mg, Sr, Nd, Mo, As, and Na) are more relevant for discrimination of the samples. These findings contribute to understanding the variations of stable isotopic and element compositions in Chinese CAJ depending on geographic origins and offer valuable insight into the control of fraudulent labeling regarding the geographic origins of CAJ.

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.

Tracing the Geographical Origin of Thai Hom Mali Rice in Three Contiguous Provinces of Thailand Using Stable Isotopic and Elemental Markers Combined with Multivariate Analysis

Rice is a staple food for more than half of the world’s population. The discrimination of geographical origin of rice has emerged as an important issue to prevent mislabeling and adulteration problems and ensure food quality. Here, the discrimination of Thai Hom Mali rice (THMR), registered as a European Protected Geographical Indication (PGI), was demonstrated. Elemental compositions (Mn, Rb, Co, and Mo) and stable isotope (δ¹⁸O) in the rice were analyzed using inductively coupled plasma mass spectrometry (ICP-MS) and elemental analyzer isotope ratio mass spectrometry (EA-IRMS), respectively. The recoveries and precisions of all elements were greater than 98% and lower than 9%, respectively. The analytical precision (±standard deviation) was below ±0.2‰ for δ¹⁸O measurement. Mean of Mn, Rb, Co, Mo, and δ¹⁸O levels was 14.0 mg kg⁻¹, 5.39 mg kg⁻¹, 0.049 mg kg⁻¹, 0.47 mg kg⁻¹, and 25.22‰, respectively. Only five valuable markers combined with radar plots and multivariate analysis, linear discriminant analysis (LDA) could distinguish THMR cultivated from three contiguous provinces with correct classification and cross-validation of 96.4% and 92.9%, respectively. These results offer valuable insight for the sustainable management and regulation of improper labeling regarding geographical origin of rice in Thailand and other countries.

Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice

Identification of geographical origin is of great importance for protecting the authenticity of valuable agri-food products with designated origins. In this study, a robust and accurate analytical method that could authenticate the geographical origin of Geographical Indication (GI) products was developed. The method was based on elemental profiling using inductively coupled plasma mass spectrometry (ICP-MS) in combination with machine learning techniques for model building and feature selection. The method successfully predicted and classified six varieties of Chinese GI rice. The elemental profiles of 131 rice samples were determined, and two machine learning algorithms were implemented, support vector machines (SVM) and random forest (RF), together with the feature selection algorithm Relief. Prediction accuracy of 100% was achieved by both Relief-SVM and Relief-RF models, using only four elements (Al, B, Rb, and Na). The methodology and knowledge from this study could be used to develop reliable methods for tracing geographical origins and controlling fraudulent labeling of diverse high-value agri-food products.

Determination of geographical origin of concentrated apple juice through analysis of stable isotopic and mineral elemental fingerprints: preliminary results

BACKGROUND

With increasing attention being paid to food authenticity, the geographic origin of food has become a topic of interest for both consumers and producers. As far as we know, there are relatively few studies on the origin traceability of concentrated apple juice. The most commonly used methods of origin tracing research is by using stable isotopes and mineral elements technology, because these indicators are directly related to local geographical environment.

RESULTS

In this study, a discriminant model was established by determining the content of the stable isotopes (δ¹³ C, δ¹⁸ O) and 13 mineral elements (B, Mn, Co, Ni, Cu, Sr, V, Ba, Fe, Mg, Na, Ca and Cr) in concentrated apple juice. Linear discriminant analysis (LDA), principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed for regional classification of samples. After data conversion and correlation analysis, spatial and quantitative prediction models were established using multiple linear regressions. Finally, the experimental results showed that the eight key variables(δ ¹³ C, δ ¹⁸ O, B, Ca, Mg, Cu, Sr and Na) selected by the analysis can be used to further characterize the production area.

CONCLUSION

The results showed that the carbon and oxygen isotopes combined with certain mineral elements can be used to indicate the origin of concentrated apple juice. © 2020 Society of Chemical Industry.

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.

Traceability and Authentication of Manila Clams from North-Western Adriatic Lagoons Using C and N Stable Isotope Analysis

In the Adriatic lagoons of northern Italy, manila clam (Ruditapes philippinarum) farming provides important socio-economic returns and local clams should be registered with the Protected Designations of Origin scheme. Therefore, there is a need for the development of rapid, cost-effective tests to guarantee the origin of the product and to prevent potential fraud. In this work, an elemental analysis (EA) coupled with isotope ratio mass spectrometry (IRMS) was employed to identify the isotopic fingerprints of clams directly collected onsite in three Adriatic lagoons and bought at a local supermarket, where they exhibited certification. In particular, a multivariate analysis of C/N, δ¹³C and δ¹⁵N in manila clam tissues as well as δ¹³C in shells and Δ¹³C (calculated as δ¹³C_shell–δ¹³C_tissues) seems a promising approach for tracking the geographical origin of manila clams at the regional scale.

Food Fingerprinting: Using a Two-Tiered approach to Monitor and Mitigate Food Fraud in Rice

BACKGROUND

Rice is an important staple food that is consumed around the world. Like many foods, the price of rice varies considerably, from very inexpensive for a low-quality product to premium pricing for highly prized varieties from specific locations. Therefore, like other foods it is vulnerable to economically motivated adulteration through substitution or misrepresentation of inferior-quality rice for more expensive varieties.

OBJECTIVE

In this article we describe results of a research project focused on addressing potential food fraud issues related to rice supplies in China, India, Vietnam, and Ghana. Rice fraud manifests differently in each country; therefore, tailored solutions were required.

METHOD

Here we describe a two-tiered testing regime of rapid screening using portable Near Infrared technology supported by second tier testing using mass spectrometry-based analysis of suspicious samples.

RESULTS

Portable Near Infrared spectroscopy models and laboratory-based Gas Chromatography-Mass Spectrometry methods were developed to differentiate between: high-value Basmati rice varieties and their potential adulterants; six Geographic Indicated protected rice varieties from specific regions within China; various qualities of rice in Ghana and Vietnam; and locally produced and imported rice in Ghana. Furthermore, an Inductively Coupled Plasma-Mass Spectrometry method was developed to support the Chinese rice varieties methods as well as a Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry method for quality differentiation in Vietnam.

CONCLUSIONS/HIGHLIGHTS

This two-tier approach can provide a substantially increased level of testing through rapid screening outside of the laboratory with the reassurance of corroborating mass spectrometry-based laboratory analysis to support decision making.

Achieving Food Authenticity and Traceability Using an Analytical Method Focusing on Stable Isotope Analysis

High-value agricultural products are characterized by the geographical conditions of the production areas such as climatic and soil conditions. These products are protected by the geographical indication (GI) protection system, which has been introduced in more than 100 countries. Because GI products are expensive in the market, products are often mislabeled as GI. Thus, there is an urgent need for the development of analytical methods that enable the tracing of geographical origins of food materials. Stable isotope analysis is used to trace the geographical origin of food materials. In this study, we review the applications for tracing the geographical origin of agricultural products (especially rice, beef, and honey) focusing on an analytical method for analyzing stable isotopes (δD, δ13C, δ15N, δ18O, and δ34S).

Origin traceability of peanut kernels based on multi-element fingerprinting combined with multivariate data analysis

BACKGROUND

Multi-elements have been widely used to identify the geographical origins of various agricultural products. The objective of this study was to investigate the feasibility of identifying the geographical origins of peanut kernels at different regional scales by using the multi-element fingerprinting technique. The concentrations of 20 elements [boron (B), magnesium (Mg), phosphorus (P), potassium (K), calcium (Ca), etc.] were determined in 135 peanut samples from Jilin Province, Jiangsu Province, and Shandong Province of China. Data obtained were processed by one-way analysis of variance (ANOVA), principal components analysis (PCA), k nearest neighbors (k-NN), linear discriminant analysis (LDA), and support vector machine (SVM).

RESULTS

Peanut kernels from different regions had their own element fingerprints. The k-NN, LDA, and SVM were all suitable to predict peanut kernels according to their grown provinces with the total correct classification rates of 91.2%, 91.1%, and 91.1%, respectively. While SVM was the best to identify different grown cities of peanut kernels with the prediction accuracy of 91.3%, compared to 72.2% and 78.3% for k-NN and LDA, respectively.

CONCLUSION

It was an effective method to identify producing areas of peanut kernels at different regional scales using multi-element fingerprinting combined with SVM to enhance regional capabilities for quality assurance and control. © 2020 Society of Chemical Industry.

Heavy metals and probabilistic risk assessment via rice consumption in Thailand

In this study, heavy metals including Cr, Mn, Co, Ni, Cu, Zn, As, and Cd in 55 Thai local rice (4 varieties) were measured using ICP-MS. Health risks were estimated from various Thai population groups, classified according to ages and genders. The potential impact on Thai population who consumed Thai local rice contained heavy metals was assessed by means of probabilistic approach. The hazard quotient (HQ) for non-carcinogenic risks from heavy metal exposure was below the threshold limit of 1 for all rice varieties except Mn in Pka Am Pun rice and As in Pka Am Pun rice, Jek Chuey Sao Hai rice, and Leb Nok rice. Only the hazard index (HI) for consumption of Khaowong Kalasin sticky rice was below 1. The maximum cancer probabilities over the lifetime consumption of Thai local rice were in the range of 5 in 10,000 to 3 in 1000 chances in developing cancer.

Trust me? Consumer trust in expert information on food product labels

Food product labels can provide consumers with rich, specific, expert-certified product information. However, sources of label information differ. How do consumers then evaluate label trustworthiness of expert labels in comparison to other commonly used label types? We present results from a representative online survey (N = 10,000) of consumers in Japan, the USA, Germany, China and Thailand using professionally designed labels for four food types (milk, honey, oil, wine) and five different sources of food information (farmers, government/administration, producer associations, experts, and consumers). We tested label legibility through identification of the label information source and asked respondents to evaluate the trustworthiness of labels using a six-scale instrument ranging from overall label trust to purchase intent. Results show that label legibility varied between countries, with expert labels scoring lowest. Nevertheless, respondents correctly identifying all label information sources chose expert labels as the most or second-most trustworthy across all countries and food types, while consumer labels scored low. Demographic factors exhibited weak influence. Results suggest expert labels might play an important role as trusted sources of information in an increasingly complex global food system. Finally, we consider the implications of the study for a potential institutionalization of expert labels based on the Japanese context.

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.

Value Creation and Addition of Rice and Its Significance to Thai Culture

For Thai people, rice is not only regarded as a staple food but a sacred plant with a spirit, a life and a soul of its own. Many exquisite cultural traditions and customs associated with rice and Thai people are usually performed nationwide from sowing to harvesting such as the Royal Ploughing Ceremony, Morale Blessing Ceremony to Phosop. During the 2007-2016 period, while the planted area (72,217,000 rai), harvested area (68,332,000 rai), production (32,874,000 ton) and yield (480 kg/rai) varied slightly, the price (10,232 Baht/ton) and value (337,849 million Baht) at farm gate steadily decreased. To raise the farmers' income, rice strategy must be changed to create higher value from exports of rice-based products by using innovation instead of exporting a high volume of commodity-grade traded at a low price. Various kinds of value-added healthy processed rice-based foods are accessible in the markets as well as food supplements, food ingredients, cosmetics and medicinal products. In Thailand, rice is mainly consumed as normal cooked rice or glutinous rice and processed into flour, noodle, alcoholic drink, snacks, baked foods, desserts and beverages. Recently, organic rice, pigmented rice and low glycemic index rice with higher price and nutritive value have been available as an alternative choice for health conscious consumers and people with non-communicable disease. Innovative and functional products from that premium rice have been widely encouraged. This will help to support Thai rice farmers and help in strengthening and leading to sustainable Thai economy.

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%.