Geographic authentication of Asian rice (Oryza sativa L.) using multi-elemental and stable isotopic data combined with multivariate analysis

Bioavailability of selenium and the influence of trace elements in crops grown in selenium-rich areas

Trace elements in crops can affect the bioavailability of Se. To investigate the effect of trace element on Se bioavailability, trace element concentrations and Se bioavailability in crops from Se-rich area (Langao County, China) were analyzed using the physiologically-based extraction test (PBET) digestion model. Vegetables (rapes, radishes, and potatoes) had higher concentrations of total and bioavailable Se compared to grains (corn, rice, and sweet potatoes), suggesting they are more effective for Se supplementation. The bioavailability of Se was higher in the intestinal phase than in the gastric phase. Trace elements were positively correlated with the increased bioavailability of Se in corn, potato, and rape. Fe was a key element in increased bioavailability of Se in the gut model. Therefore, trace elements, particularly Fe, in crop can enhance Se bioavailability, suggesting their potential use in Se supplementation. The findings of this study provide valuable insights for dietary interventions to Se deficiencies.

Research on the regulation mechanism of drought tolerance in wheat

Wheat (Triticum aestivum L.) is one of the most important crops in arid and semi-arid areas of the world, and its sustainable and efficient production is essential for ensuring food security in China and globally. However, with the global climate change, wheat production is increasingly endangered by abiotic stress, and drought stress has become the main abiotic stress factor restricting wheat production efficiently. Therefore, investigating drought resistance genes and elucidating the mechanisms underlying drought resistance regulation is crucial for the genetic enhancement of drought resistance and the development of new drought-resistant wheat varieties. This paper reviews the majority of research conducted on wheat drought resistance over the past five years, focusing on aspects, such as transcriptional regulation, protein post-translational modifications, and other regulatory mechanisms related to drought resistance in wheat. Additionally, this paper discusses future directions for the genetic improvement of drought resistance and the breeding of new drought-resistant wheat varieties.

Can stable carbon isotope fingerprints be competent for geographic traceability of rice?

This study aimed to improve the traceability of rice-producing areas to address the increasing demand for accurate methods to confirm food quality and safety. Compound-specific δ13C of fatty acids, δ13C of starch and bulk of rice were measured. PCA, PLS-DA and VIP value analysis of the obtained data were performed to track the source of rice from the six regions. The PLS-DA model established with bulk δ13C, starch δ13C, and fatty acid δ13C, which clearly separated the rice from six regions. The VIP graph showed the value of starch, C18:0 and C18:2 δ13C values (VIP > 1) were important to distinguish the origin of rice. Also, according to loading plots the contribution of starch δ13C was the largest. The findings indicate that the introduction of starch δ13C improves the precision of rice traceability and provides an effective method for identifying rice origin.

United States Grown and Imported Rice on Sale in Las Vegas: Metal(loid)s Composition and Geographic Origin Discrimination

Concentrations of metal(loid)s, Ag, Al, As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Se, Sr, V and Zn, were determined in rice on sale in Las Vegas. The rice samples were grown in five different countries, the USA, Thailand, India, Pakistan, and Bangladesh. The elemental concentrations in rice grain were determined using inductively coupled plasma mass spectrometry (ICP-MS) following hot block-assisted digestion. The accuracy of the laboratory procedure was verified by the analysis of rice flour standard reference material (NIST SRM 1568b). The mean metal(loid) contents in rice of various geographic origins were 3.18-5.91 mg kg-1 for Al, 0.05-0.12 mg kg-1 for As, 3.64-41 μg kg-1 for Cd, 5.11-12 μg kg-1 for Co, 0.12-0.14 mg kg-1 for Cr, 1.5-1.91 mg kg-1 for Cu, 3.04-4.98 mg kg-1 for Fe, 4.2-10.4 mg kg-1 for Mn, 0.21-0.41 mg kg-1 for Ni, 0.02-0.07 mg kg-1 for Se, 0.68-0.88 mg kg-1 for Sr, 3.64-5.26 μg kg-1 for V, and 16.6-19.9 mg kg-1 for Zn. respectively. The mean concentration of As in US rice was significantly higher than in Indian, Pakistani, and Bangladeshi rice. On the other hand, it was found a significantly low mean level of Cd in US-grown rice. It was also found that the concentrations of metal(loid)s in black and brown rice on sale in Las Vegas were statistically similar, except for Mn and Se. The geographic origin traceability of rice grain involved the use of ICP-MS analysis coupled with chemometrics that allowed their differentiation based on the rice metal(loid) profile, thus confirming their origins. Data were processed by linear discriminant analysis, and US and Thai rice samples were cross-validated with higher accuracy (100%). This authentication quickly discriminates US rice from the other regions and adds verifiable food safety measures for consumers.

Identification of provenance of Basmati rice grown in different regions of Punjab through multivariate analysis

High-priced Basmati rice is vulnerable to deliberate mislabeling to increase profits. This type of fraud may lower consumers' confidence as inferior products can affect brand reputation. To address this problem, there is a need to devise a method that can efficiently distinguish Basmati rice grown in regions that are famous versus the regions that are not suitable for their production. Therefore, in this investigation, thirty-six samples of Basmati rice were collected from two zones of Punjab province (one known for Basmati rice) of Pakistan which is the major producer of Basmati rice. The elemental composition of rice samples was assessed using inductively coupled plasma-optical emission spectrometry and an organic elemental analyzer, whereas data on δ13C was acquired using isotopic ratio-mass spectrometry. Regional clustering of samples based on their respective cultivation zones was observed using multivariate data analysis techniques. Partial least squares-discriminant analysis was found to be effective in grouping rice samples from the different locations and identifying unknown samples belonging to these two regions. Further recommendations are presented to develop a better model for tracing the origin of unidentified rice samples.

Effects of Water Isotope Composition on Stable Isotope Distribution and Fractionation of Rice and Plant Tissues

Rice origin authenticity is important for food safety and consumer confidence. The stable isotope composition of rice is believed to be closely related to its water source, which affects its origin characteristics. However, the influence of water availability on the distribution of rice stable isotopes (δ2H and δ18O) is not clear. In this study, three irrigation waters with different isotopic values were used to investigate isotopic water use effects of Indica and Japonica rice, using pot experiments. Under three different water isotope treatments, the δ2H values of Indica polished rice showed significant differences (-65.0 ± 2.3, -60.5 ± 0.8 and -55.8 ± 1.7‰, respectively, p < 0.05) compared to δ13C and δ15N, as did Japonica polished rice. The values of δ2H and δ18O of rice became more positive when applying more enriched (in 2H and 18O) water, and the enrichment effect was higher in rice than in the corresponding plant tissue. In addition, the δ2H and δ18O values of Indica rice leaves decreased at the heading stage, increased at the filling stage, and then decreased at the harvest stage. Japonica rice showed a similar trend. δ2H changes from stem to leaf were more negative, but δ18O changes were more positive, and δ2H and δ18O values from leaf to rice were more positive for both brown and polished rice. The results from this study will clarify different water isotopic composition effects on rice and provide useful information to improve rice origin authenticity using stable isotope-based methods.

Metal(loid)s Profile of Four Traditional Ethiopian Teff Brands: Geographic Origin Discrimination

Among the most renowned Ethiopian food crops, teff (Eragrostis tef (Zucc.)Trotter) is the most nutritious and gluten-free cereal. Because of the increase in demand for teff, it is necessary to establish geographic origin authentication of traditional teff brands based on multi-element fingerprint. For this purpose, a total of 60 teff samples were analysed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Accuracy of the laboratory procedure was verified by the analysis of rice flour standard reference material (NIST SRM 1568b). In this context, four traditional teff brands (Ada'a, Ginchi, Gojam and Tulu Bolo) were analytically characterized using multi-element fingerprint and further treated statistically using linear discriminant analysis (LDA). Due to obvious extrinsic Fe, Al and V contamination, these elements were excluded from the discriminant model. Five elements (Cu, Mo, Se, Sr, and Zn) significantly contributed to discriminate the geographical origin of white teff. On the other hand, Mn, Mo, Se and Sr were used as discriminant variables for brown teff. LDA revealed 90 and 100% correct classifications for white and brown teff, respectively. Overall, multi-element fingerprint coupled with LDA can be considered a suitable tool for geographic origin discrimination of traditional teff brands.

Using stable carbon isotope ratio analysis to detect adulteration in red yeast rice dietary supplements

Red yeast rice (RYR) is marketed as a dietary supplement because it contains natural 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors (statins), including monacolin K. However, there is concern that some RYR supplements may be adulterated with the pharmaceutical drug lovastatin to enhance health claims. We have developed an optimized method to isolate monacolin K/lovastatin from complex RYR dietary supplement matrices to then test for adulteration in RYR supplements using stable carbon isotope (δ13C) analysis. Samples were initially screened for monacolin K/lovastatin using liquid chromatography with mass spectrometric detection (LC-MS). To ensure the extraction process did not affect the measured isotopic values (i.e., isotopic fractionation effects), neat lovastatin standards were spiked into two types of blank RYR matrices (powder and gel). The monacolin K/lovastatin peaks were detected using high performance liquid chromatography with ultraviolet detection (HPLC-UV) and isolated using fraction collection. Residual matrix components were removed from targeted fractions by solid phase extraction (SPE) using graphitized carbon black cartridges. The resulting isolates were then analyzed using elemental analyzer-isotope ratio mass spectrometry (EA-IRMS) to measure δ13C values. The δ13C values of the extracted lovastatin standards were compared to their respective neat lovastatin δ13C values and demonstrated negligible isotopic fractionation effects. Using this optimized clean up method and carbon isotope analysis, thirty-one samples were screened. Eight RYR dietary supplement samples had >0.8 mg/g of monacolin K/lovastatin, our minimum threshold for analyzing samples using this method. Four of these eight samples had δ13C values greater than -28.3‰, a previously proposed cutoff value for natural monacolin K, indicating likely adulteration. Additionally, five RYR powder samples were analyzed as part of a collaborative study using in-house methods from two laboratories and the data shows acceptable agreement in the δ13C values of monacolin K/lovastatin (differences ranging from ±0.02‰ to ±0.76‰). This optimized method represents a robust, reproducible procedure for detecting lovastatin adulteration in dietary supplements with minimal isotopic fractionation.

Cationic Conjugated Polymer Fluorescence Resonance Energy Transfer for DNA Methylation Assessment to Discriminate the Geographical Origins of Lonicerae japonicae flos

The flavor and taste of Lonicerae japonicae flos (LJF) products are heavily influenced by geographical origin. Tracing the geographical origin is an important aspect of LJF quality assessment. Here, DNA methylation analysis coupled with chemometrics revealed that, in 10 CpG islands upstream of genes in the chlorogenic acid and iridoid biosynthetic pathways, DNA methylation differences appear close association with LJF geographical origin. DNA methylation status in these CpG islands was determined using the cationic conjugated polymer fluorescence resonance energy transfer method. As a result, LJFs from 39 geographical origins were classified into four groups corresponding to Northern China, Central Plain of China, Southeast China, and Western China, according to cluster analysis and principal component analysis. Our findings contribute to an understanding of the modulation of LJF taste and can assist in understanding how DNA methylation in LJF varies with geographical origin.

Multi-stable isotope and multi-element origin traceability of rice from the main producing regions in Asia: A long-term investigation during 2017-2020

Stable isotope and multi-element analytical techniques with chemometrics were developed to trace the origin authenticity of rice in China market. In the long-term study from 2017 to 2020, a total of 115 batches of rice samples from 8 main producing areas of 7 Asian countries were determined 5 stable isotope ratios and 18 elemental contents. One-way analysis of variance (ANOVA) and various multivariate modeling methods were performed for the origin discrimination. Supervised multivariate modeling including partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) can realize more satisfactory identification of 8 rice origins than ANOVA comparison and unsupervised methods, their leave-one-out cross-validation accuracies approach 85.0 % and 90.9 %, respectively. δ²H, δ¹³C, Ba, Al, Mg, δ³⁴S, Pb and δ¹⁸O were screened as the most important variables for rice origin traceability (VIP > 1 or AUC > 0.5). This analytical strategy combining maybe promising to ensure the origin authenticity and combat illegal mislabeling in rice trade.

Towards a Standardized Approach for the Geographical Traceability of Plant Foods Using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Principal Component Analysis (PCA)

This paper presents a systematic literature review focused on the use of inductively coupled plasma mass spectrometry (ICP-MS) combined with PCA, a multivariate technique, for determining the geographical origin of plant foods. Recent studies selected and applied the ICP-MS analytical method and PCA in plant food geographical traceability. The collected results from many previous studies indicate that ICP-MS with PCA is a useful tool and is widely used for authenticating and certifying the geographic origin of plant food. The review encourages scientists and managers to discuss the possibility of introducing an international standard for plant food traceability using ICP-MS combined with PCA. The use of a standard method will reduce the time and cost of analysis and improve the efficiency of trade and circulation of goods. Furthermore, the main steps needed to establish the standard for this traceability method are reported, including the development of guidelines and quality control measures, which play a pivotal role in providing authentic product information through each stage of production, processing, and distribution for consumers and authority agencies. This might be the basis for establishing the standards for examination and controlling the quality of foods in the markets, ensuring safety for consumers.

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.

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.

Discrimination of Geographical Origin of Agricultural Products From Small-Scale Districts by Widely Targeted Metabolomics With a Case Study on Pinggu Peach

Geographical indications of agricultural products are characterized by high quality and regional attributes, while they are more likely to be counterfeited by similar products from nearby regions. Accurate discrimination of origin on small geographical scales is extremely important for geographical indications of agricultural products to avoid food fraud. In this study, a widely targeted metabolomics based on ultra-high-performance liquid chromatography-tandem mass spectrometry combined with multivariate statistical analysis was used to distinguish the geographical origin of Pinggu Peach of Beijing and its two surrounding areas in Heibei province (China). Orthogonal partial least squares-discriminant analysis (OPLS-DA) based on 159 identified metabolites showed significant separation from Pinggu and the other adjacent regions. The number of the most important discriminant variables (VIP value >1) was up to 62, which contributed to the differentiation model. The results demonstrated that the metabolic fingerprinting combined with OPLS-DA could be successfully implemented to differentiate the geographical origin of peach from small-scale origins, thus providing technical support to further ensure the authenticity of geographical indication products. The greenness of the developed method was assessed using the Analytical GREEnness Metric Approach and Software (ARGEE) tool. It was a relatively green analytical method with room for improvement.

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.

Mass spectrometry in food authentication and origin traceability

Food authentication and origin traceability are popular research topics, especially as concerns about food quality continue to increase. Mass spectrometry (MS) plays an indispensable role in food authentication and origin traceability. In this review, the applications of MS in food authentication and origin traceability by analyzing the main components and chemical fingerprints or profiles are summarized. In addition, the characteristic markers for food authentication are also reviewed, and the advantages and disadvantages of MS-based techniques for food authentication, as well as the current trends and challenges, are discussed. The fingerprinting and profiling methods, in combination with multivariate statistical analysis, are more suitable for the authentication of high-value foods, while characteristic marker-based methods are more suitable for adulteration detection. Several new techniques have been introduced to the field, such as proton transfer reaction mass spectrometry, ambient ionization mass spectrometry (AIMS), and ion mobility mass spectrometry, for the determination of food adulteration due to their fast and convenient analysis. As an important trend, the miniaturization of MS offers advantages, such as small and portable instrumentation and fast and nondestructive analysis. Moreover, many applications in food authentication are using AIMS, which can help food authentication in food inspection/field analysis. This review provides a reference and guide for food authentication and traceability based on MS.

An overview on multi-elemental profile integrated with chemometrics for food quality assessment: toward new challenges

Food products, especially those with high value-added, are commonly subjected to strict quality controls, which are of paramount importance, especially for attesting to some peculiar features related, for instance, to their geographical origin and/or the know-how of their producers. However, the sophistication of fraudulent practices requires a continuous update of analytical platforms. Different analytical techniques have become extremely appealing since the instrumental analysis tools evolution has substantially improved the capability to reveal and understand the complexity of food. In light of this, multi-elemental composition has been successful implemented solving a plethora of food authentication and traceability issues. In the last decades, it has existed an ever-increasing trend in analysis based on spectrometry analytical platforms in order to obtain a multi-elemental profile that combined with chemometrics have been noteworthy analytical methodologies able to solve these problems. This review provides an overview of published reports in the last decade (from 2011 to 2021) on food authentication and quality control from their multi-element composition in order to evaluate the state-of-the-art of this field and to identify the main characteristics of applied analytical techniques and chemometric data treatments that have permit achieve accurate discrimination/classification models, highlighting the strengths and the weaknesses of these methodologies.

Discrimination of Geographical Origins of Wolfberry (Lycium barbarum L.) Fruits Using Stable Isotopes, Earth Elements, Free Amino Acids, and Saccharides

To develop sophisticated approaches for distinguishing goji origins, 325 wolfberry fruit samples of a certain cultivar, plant age, drying method, and collection season were gathered from 26 producing areas across Northwest China in 2017 and 2018. We employed 49 indices, including stable isotopes, earth elements, soluble amino acids, and saccharides, to identify the regions of origin of these goji fruits. Analysis of variance (ANOVA) and heritability analysis were used to assess the effects of the environment (producing areas), cultivar, plant age, drying process, and collection season. Samples from the same place can be classified and partially discriminated using principal component analysis (PCA). We were able to distinguish fruits produced in Zhongning County from those produced in the other five producing provinces using orthogonal projection to latent structure-discriminant analysis (OPLS-DA). Calcium (Ca), manganese (Mn), ornithine (Orn), cystine (Cys-Cys), glutamate (Glu), phenylalanine (Phe), phosphoserine (Ps), serine (Ser), lysine (Lys), taurine (Tau), proline (Pro), and tyrosine (Tyr) indices were chosen using S-plots and heritability analysis, and their repeatability was established with samples collected in 2018. The indices selected in this study can distinguish goji berries produced in Zhongning County from fruits originating from five other Provinces with high repeatability, which was validated with various cultivars, drying methods, harvest seasons, and plant ages and with heritability analysis.

Improved accuracy of geographical origin identification of shiitake grown in sawdust medium: A compound-specific isotope model-based pilot study

In countries like South Korea and the USA, origin labeling of shiitake grown using imported Chinese-inoculated medium is an issue. Therefore, we evaluated the use of compound-specific isotope analysis (CSIA) for the accurate identification of the geographical origin of shiitake (Korean, Chinese-inoculated medium, and Chinese); Chinese-inoculated medium shiitake were cultivated in Korea using inoculated sawdust medium from China. The CSIA-discriminant model showed an overall accuracy of 100% in the geographical classification of the original set and 96.4% for the cross-validated set. Glutamate and aspartate δ¹⁵N values were the most important variables for differentiating shiitake based on their origins. Compared to that observed upon using the bulk stable isotope analysis, the CSIA model was associated with significantly improved predictability of origin identification. Our findings elucidate the importance of isotope signatures in developing a reliable origin labeling method for shiitake cultured on the sawdust medium for the global market.

Multivariate analysis for organic milk authentication

To differentiate organic milk (OM) from conventional milk (CM), an orthogonal projection to latent structure-discriminant analysis (OPLS-DA) model was constructed using δ¹³C, δ¹⁵N, δ¹⁸O, 51 elements and 35 fatty acids (FAs) as the variables. So far, most reported studies barely use three or more types of variables, but more variables could avoid one-sidedness and get stabler models. Our multivariate model combines geographical and nutritional parameters and displays better explanatory and predictive abilities (R²X = 0.647, R²Y = 0.962 and Q² = 0.821) than models based on fewer variables for differentiating OM and CM. In particular, δ¹⁵N, Se, δ¹³C, Eu, K and α-Linolenic acid (ALA) are found to be critical parameters for the discrimination of OM. These results show that the multivariate model based on stable isotopes, elements and FAs can be used to identify OM, and can potentially expand the global databases for quality and authenticity of milk.

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.

Geographical Differentiation of Hom Mali Rice Cultivated in Different Regions of Thailand Using FTIR-ATR and NIR Spectroscopy

Although Hom Mali rice is considered the highest quality rice in Thailand, it is susceptible to adulteration and substitution. There is a need for rapid, low-cost and efficient analytical techniques for monitoring the authenticity and geographical origin of Thai Hom Mali rice. In this study, two infrared spectroscopy techniques, Fourier-transform infrared spectroscopy with attenuated total reflection (FTIR-ATR) and near-infrared (NIR) spectroscopy, were applied and compared for the differentiation of Thai Hom Mali rice from two geographical regions over two production years. The Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) model, built using spectral data from the benchtop FTIR-ATR, achieved 96.97% and 100% correct classification of the test dataset for each of the production years, respectively. The OPLS-DA model, built using spectral data from the portable handheld NIR, achieved 84.85% and 86.96% correct classification of the test dataset for each of the production years, respectively. Direct NIR analysis of the polished rice grains (i.e., no sample preparation) was determined as reliable for analysis of ground rice samples. FTIR-ATR and NIR spectroscopic analysis both have significant potential as screening tools for the rapid detection of fraud issues related to the geographical origin of Thai Hom Mali rice.

Authentication and Chemometric Discrimination of Six Greek PDO Table Olive Varieties through Morphological Characteristics of Their Stones

Table olives, the number one consumed fermented food in Europe, are widely consumed as they contain many valuable ingredients for health. It is also a food which may be the subject of adulteration, as many different olive varieties with different geographical origin, exist all over the word. In the present study, the image analysis of stones of six main Greek protected designation of origin (PDO) table olive varieties was performed for the control of their authentication and discrimination, with cv. Prasines Chalkidikis, cv. Kalamata Olive, cv. Konservolia Stylidas, cv. Konservolia Amfissis, cv. Throuba Thassos and cv. Throuba Chios being the studied olive varieties. Orthogonal partial least square discriminant analysis (OPLS-DA) was used for discrimination and classification of the six Greek table olive varieties. With a 98.33% of varietal discrimination, the OPLS-DA model proved to be an efficient tool to authentify table olive varieties from their morphological characteristics.

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.

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.

Determining the geographical origin of milk by multivariate analysis based on stable isotope ratios, elements and fatty acids

To construct a reliable discrimination model for determining milk geographical origin, stable isotope ratios including δ¹³C, δ¹⁵N and δ¹⁸O, 51 elements and 35 fatty acids (FAs) in milk samples from Australia, New Zealand and Austria were detected and analyzed. It is found that all of the stable isotope ratios in the milk samples of Australia are the highest, followed by those of the samples from New Zealand and Austria. In addition, 14 elements and 8 FAs show different contents in the samples of different countries at the significance level of P < 0.05. Based on these results, a multivariate model with good robustness and predictive ability for authenticating milk origin (R²X = 0.693, Q² = 0.854) was successfully constructed. Element contents and stable isotope ratios are more reliable variables for milk origin discrimination and Rb, δ¹⁸O, Tl, Ba, Mo, Sr, δ¹⁵N, Cs, As, Eu, C20:4n6, Sc, C13:0, K, Ca and C16:1n7 are the critical markers in the multivariate model for verifying milk origin.

Are the elemental fingerprints of organic and conventional food different? ED-XRF as screening technique

Research has been conducted the last years to assess whether organically grown food is chemically different from produce of conventional agriculture and which markers are appropriate to discriminate between them. Most articles focus on one single food commodity, produced under strict controlled organic farming conditions, leaving open the question whether the difference would be seen when applied to the same commodity under different growing conditions. In this work 118 organic and 151 conventional samples of commercially available paprika powder, cinnamon, coffee, tea, chocolate, rice, wheat flour, cane sugar, coconut water, honey and bovine milk were characterised for their elemental composition using energy dispersive X-ray fluorescence. Resulting profiles were analysed using univariate and multivariate statistical techniques. Organic samples of a given commodity clustered together and were separated from their conventional counterparts. Differences in the elemental composition of food, could be used to develop statistical models for verifying the agronomical production system.

Spatial Fingerprinting: Horizontal Fusion of Multi-Dimensional Bio-Tracers as Solution to Global Food Provenance Problems

Building the capacity of efficiently determining the provenance of food products represents a crucial step towards the sustainability of the global food system. Despite species specific empirical examples of multi-tracer approaches to provenance, the precise benefit and efficacy of multi-tracers remains poorly understood. Here we show why, and when, data fusion of bio-tracers is an extremely powerful technique for geographical provenance discrimination. Specifically, we show using extensive simulations how, and under what conditions, geographical relationships between bio-tracers (e.g., spatial covariance) can act like a spatial fingerprint, in many naturally occurring applications likely allowing rapid identification with limited data. To highlight the theory, we outline several statistic methodologies, including artificial intelligence, and apply these methodologies as a proof of concept to a limited data set of 90 individuals of highly mobile Sockeye salmon that originate from 3 different areas. Using 17 measured bio-tracers, we demonstrate that increasing combined bio-tracers results in stronger discriminatory power. We argue such applications likely even work for such highly mobile and critical fisheries as tuna.

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.

Authentication of Rice (Oryza sativa L.) Using Near Infrared Spectroscopy Combined with Different Chemometric Classification Strategies

Rice is a staple food in Vietnam, and the concern about rice is much greater than that for other foods. Preventing fraud against this product has become increasingly important in order to protect producers and consumers from possible economic losses. The possible adulteration of this product is done by mixing, or even replacing, high-quality rice with cheaper rice. This highlights the need for analytical methodologies suitable for its authentication. Given this scenario, the present work aims at testing a rapid and non-destructive approach to detect adulterated rice samples. To fulfill this purpose, 200 rice samples (72 authentic and 128 adulterated samples) were analyzed by near infrared (NIR) spectroscopy coupled, with partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA). The two approaches provided different results; while PLS-DA analysis was a suitable approach for the purpose of the work, SIMCA was unable to solve the investigated problem. The PLS-DA approach provided satisfactory results in discriminating authentic and adulterated samples (both 5% and 10% counterfeits). Focusing on authentic and 10%-adulterated samples, the accuracy of the approach was even better (with a total classification rate of 82.6% and 82.4%, for authentic and adulterated samples, respectively).

Fatty Acids and Stable Isotope Ratios in Shiitake Mushrooms (Lentinula edodes) Indicate the Origin of the Cultivation Substrate Used: A Preliminary Case Study in Korea

Shiitake mushroom (Lentinula edodes) is commonly consumed worldwide and is cultivated in many farms in Korea using Chinese substrates owing to a lack of knowledge on how to prepare sawdust-based substrate blocks (bag cultivation). Consequently, issues related to the origin of the Korean or Chinese substrate used in shiitake mushrooms produced using bag cultivation have been reported. Here, we investigated differences in fatty acids (FAs) and stable isotope ratios (SIRs) in shiitake mushrooms cultivated using Korean and Chinese substrates under similar conditions (strain, temperature, humidity, etc.) and depending on the harvesting cycle. The total FA level decreased significantly by 5.49 mg∙g⁻¹ as the harvesting cycle increased (p < 0.0001); however, no differences were found in FAs between shiitake mushrooms cultivated using Korean and Chinese substrates. Linoleic acid was the most abundant FA, accounting for 77–81% of the total FAs during four harvesting cycles. Moreover, the SIRs differed significantly between the Korean and Chinese substrates, and the harvesting cycles resulted in smaller maximum differences in SIR values compared to those of the cultivation substrate origins. Our findings contribute to the identification of the geographical origin of shiitake mushrooms and may have potential applications in international shiitake-mushroom markets.