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

Constructing an origin discrimination model of japonica rice in Heilongjiang Province based on confocal microscopy Raman spectroscopy technology

An origin discrimination model for rice from five production regions in Heilongjiang Province was constructed based on the combination of confocal microscopy Raman spectroscopy and chemometrics. A total of 150 field rice samples were collected from the Fangzheng, Chahayang, Jiansanjiang, Xiangshui, and Wuchang production areas. The optimal sample processing conditions, instrument parameter settings, and spectrum acquisition techniques were identified by investigating the influencing factor. The Raman spectra of milled rice within the range of 100-3200 cm-1 were selected as the raw data, and the optimal preprocessing method combination consisting of normalization, Savitzky-Golay smoothing, and multivariate scatter correction was identified. Subsequently, the competitive adaptive reweighted sampling and discrete binary particle swarm optimization algorithms were employed to optimize the feature wavelength selection, resulting in the screening of 226 and 1899 feature wavelength variables, respectively. Using the full Raman spectrum data and feature wavelength data as inputs, partial least squares discriminant analysis, support vector machine and extreme learning machine origin discrimination models were constructed. The results indicated that the BPSO-GA-SVM model exhibited the best predictive ability, achieving a testing set accuracy of 86.67%.

Geographical origin traceability of kiwifruit products using stable isotope and multi-element analysis with multivariate modeling: Feature extraction, selection of model and variable, and discrimination

The mislabeling of kiwifruit origin frequently disturbs market competition and governmental supervision, significantly undermines brand reputation and consumer rights. In this work, a total of 370 kiwifruits from 8 different countries in global were collected, and 6 stable isotope ratios (SIRs), 10 mineral elements (MEs), and 16 rare earth elements (REEs) were determined for origin traceability study. One-way analysis of variance (ANOVA) showed that regional differences of 32 variables are at significant level (P value =0.00). Supervised methods, partial least squares-discriminant analysis (PLS-DA) and its derivative algorithm (OPLS-DA), linear discriminant analysis (LDA), enhanced identification performance and finally elevated the accuracies to 100 % for all kiwifruit origins. Lu, Tb, Eu, Ho, Pm, Y, δ34S, δ2H, δ15N, Mg, Se were main contributive variables for LDA modeling (AUC value >0.5). A blind test was conducted using 63 samples randomly selected from Chinese market. The predicted result indicated a significantly high probability of origin mislabeling of imported kiwifruit products, with percentages ranging from 30.0 % to 90.0 %. This study may provide technical supports for combating origin mislabeling conduct, and ensuring food authenticity of kiwifruit in global trade.

Integrated omic analysis of a new flavor yeast strain in fermented rice milk

Plant-based milk contains high nutritional value with enriched vitamins, minerals, and essential amino acids. This study aimed to enhance the biochemical and biological properties of rice milk through yeast fermentation, using the novel fermenting strain Saccharomyces cerevisiae RSO4, which has superb fermenting ability for an innovative functional beverage. An integrated omics approach identified specific genes that exhibited genetic variants related to various cellular processes, including flavor and aroma production (ARO10, ADH1-5, and SFA1), whereas the proteomic profiles of RSO4 identified key enzymes whose expression was upregulated during fermentation of cooked rice, including the enzymes in glycogen branching (Glc3), glycolysis (Eno1, Pgk1, and Tdh1/2), stress response (Hsp26 and Hsp70), amino acid metabolism, and cell wall integrity. Biochemical and metabolomic analyses of the fermented rice milk by the RSO4 strain using the two rice varieties, Homali (Jasmine) white rice or Riceberry colored rice, detected differentially increased levels of bioactive compounds, such as β-glucan, vitamins, di- and tripeptides, as well as pleasant flavors and aromas. The results of this study highlight the importance of selecting an appropriate fermenting yeast strain and rice variety to improve property of plant-based products as innovative functional foods.

Integrating different detection techniques and data analysis methods for comprehensive food authenticity verification

Traditional food testing methods, primarily confined to laboratory settings, are increasingly inadequate to detect covert food adulteration techniques. Hence, a crucial review of recent technological strides to combat food fraud is essential. This comprehensive analysis explores state-of-the-art technologies in food analysis, accentuating the pivotal role of sophisticated data processing methods and the amalgamation of diverse technologies in enhancing food authenticity testing. The paper assesses the merits and drawbacks of distinct data processing techniques and explores their potential synergies. The future of food authentication hinges on the integration of portable smart detection devices with mobile applications for real-time food analysis, including miniaturized spectrometers and portable sensors. This integration, coupled with advanced machine learning and deep learning for robust model construction, promises to achieve real-time, on-site food detection. Moreover, effective data processing, encompassing preprocessing, chemometrics, and regression analysis, remains indispensable for precise food authentication.

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.

Geographical discrimination of Paw San rice cultivated in different regions of Myanmar using near-infrared spectroscopy, headspace-gas chromatography-ion mobility spectrometry and chemometrics

Paw San rice, also known as "Myanmar pearl rice", is considered the highest quality rice in Myanmar. There are considerable differences in terms of the premium commercial value of Paw San rice, which is an incentive for fraud, e.g. adulteration with cheaper rice varieties or mislabelling its geographical origin. Shwe Bo District is one of the most popular rice growing areas in the Sagaing region of Myanmar which produces the most valued and highly priced Paw San rice (Shwe Bo Paw San). The verification of the geographical origin of Paw San rice is not readily undertaken in the rice supply chain because the existing analytical approaches are time-consuming and expensive. Therefore, there is a need for rapid, robust and cost-effective analytical techniques for monitoring the authenticity and geographical origin of Paw San rice. In this 4-year study, two rapid screening techniques, Fourier-transform near-infrared (FT-NIR) spectroscopy and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), coupled with chemometric modelling, were applied and compared for the regional differentiation of Paw San rice. In addition, low-level fusion of the FT-NIR and HS-GC-IMS data was performed and its effect on the discriminative power of the chemometric models was assessed. Extensive model validation, including the validation using independent samples from a different production year, was performed. Furthermore, the effect of the sample preparation technique (grinding versus no sample preparation) on the performance of the discriminative model, obtained with FT-NIR spectral data, was assessed. The study discusses the suitability of FT-NIR spectroscopy, HS-GC-IMS and the combination of both approaches for rapid determination of the geographical origin of Paw San rice. The results demonstrated the excellent potential of the FT-NIR spectroscopy as well as HS-GC-IMS for the differentiation of Paw San rice cultivated in two distinct geographical regions. The OPLS-DA model, built using FT-NIR data of rice from 3 production years, achieved 96.67% total correct classification rate of an independent dataset from the 4th production year. The DD-SIMCA model, built using FT-NIR data of ground rice, also demonstrated the highest performance: 94% sensitivity and 97% specificity. This study has demonstrated that FT-NIR spectroscopy can be used as an accessible, rapid and cost-effective screening tool to discriminate between Paw San rice cultivated in the Shwe Bo and Ayeyarwady regions of Myanmar.

Effects of sampling time and location on the geographical origin traceability of protected geographical indication (PGI) Hongyuan yak milk: Based on stable isotope ratios

Hongyuan yak milk is a protected geographical indication (PGI) product of rich nutritional value, which is popular among consumers. Stable isotope ratio analysis (SIRA) is an effective way to protect the authenticity of the geographical origin of PGI products, and it is crucial to study the factors affecting stable isotopes. Firstly, we proved that the SIRA could be used to identify the geographical origin of Hongyuan yak milk, and that the identification accuracy in combination with δ13C and δ18O was 100 %. Secondly, we analyzed the effect of sampling selection on the stable isotopes of Hongyuan yak milk in practical applications, which showed that sampling time influenced the δ13C, δ2H, and δ18O, while the sampling locations did not. There were interactions between the effect of sampling time and location on δ2H and δ18O. These results provide a reliable method for identifying PGI products and also provide new guidance on sampling models.

Towards generalizable food source identification: An explainable deep learning approach to rice authentication employing stable isotope and elemental marker analysis

In addressing the generalization issue faced by data-driven methods in food origin traceability, especially when encountering diverse input variable sets, such as elemental contents (C, N, S), stable isotopes (C, N, S, H and O) and 43 elements measured under varying laboratory conditions. We introduce an innovative, versatile deep learning-based framework incorporating explainable analysis, adept at determining feature importance through learned neuron weights. Our proposed framework, validated using three rice sample batches from four Asian countries, totaling 354 instances, exhibited exceptional identification accuracy of up to 97%, surpassing traditional reference methods like decision tree and support vector machine. The adaptable methodological system accommodates various combinations of traceability indicators, facilitating seamless replication and extensive applicability. This groundbreaking solution effectively tackles generalization challenges arising from disparate variable sets across distinct data batches, paving the way for enhanced food origin traceability in real-world applications.

Influence of Geographical Origin on Isotopic and Elemental Compositions of Pork Meat

Pigs are a primary source of meat, accounting for over 30% of global consumption. Consumers' preferences are determined by health considerations, paying more attention to foodstuffs quality, animal welfare, place of origin, and swine feeding regime, and being willing to pay a higher price for a product from a certain geographical region. In this study, the isotopic fingerprints (δ2H, δ18O, and δ13C) and 29 elements of loin pork meat samples were corroborated with chemometric methods to obtain the most important variables that could classify the samples' geographical origin. δ2H and δ18O values ranged from -71.0 to -21.2‱, and from -9.3 to -2.8‱, respectively. The contents of macro- and micro-essential elements are presented in the following order: K > Na > Mg > Ca > Zn > Fe > Cu > Cr. The LDA model assigned in the initial classification showed 91.4% separation of samples, while for the cross-validation procedure, a percentage of 90% was obtained. δ2H, K, Rb, and Pd were identified as the most representative parameters to differentiate the pork meat samples coming from Romania vs. those from abroad. The mean values of metal concentrations were used to estimate the potential health risks associated with the consumption of pork meat The results showed that none of the analyzed metals (As, Cd, Sn, Pb, Cu, and Zn) pose a carcinogenic risk.

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.

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), a Useful Tool in Authenticity of Agricultural Products' and Foods' Origin

Fraudulent practices are the first and foremost concern of food industry, with significant consequences in economy and human's health. The increasing demand for food has led to food fraud by replacing, mixing, blending, and mislabeling products attempting to increase the profits of producers and companies. Consequently, there was the rise of a multidisciplinary field which encompasses a large number of analytical techniques aiming to trace and authenticate the origins of agricultural products, food and beverages. Among the analytical strategies have been developed for the authentication of geographical origin of foodstuff, Inductively Coupled Plasma Mass Spectrometry (ICP-MS) increasingly dominates the field as a robust, accurate, and highly sensitive technique for determining the inorganic elements in food substances. Inorganic elements are well known for evaluating the nutritional composition of food products while it has been shown that they are considered as possible tracers for authenticating the geographical origin. This is based on the fact that the inorganic component of identical food type originating from different territories varies due to the diversity of matrix composition. The present systematic literature review focusing on gathering the research has been done up-to-date on authenticating the geographical origin of agricultural products and foods by utilizing the ICP-MS technique. The first part of the article is a tutorial about food safety/control and the fundaments of ICP-MS technique, while in the second part the total research review is discussed.

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.

Storage Changes Stable Isotope Composition of Cucumbers

Vegetable food stuff produced under controlled and identical conditions from one farm of identical “age” (batch) has a similar isotopic composition. This fact can be used to control the origin of vegetables. This question is of special relevance when food-contaminations have to be traced back to the producer, or certain production claims have to be controlled. However, as vegetables are harvested, brought to whole-sale merchants and to retail shops, where they remain until being bought by the consumer, one has to consider possible changes in isotopic composition during this transfer period, when comparing vegetables of questioned origin with reference samples taken directly from the field/producer. We investigated changes in the isotope composition of vegetables during storage by studying as an example cucumbers from one batch. We stored the cucumbers in a vegetable storage under controlled conditions and removed one sample every day and analyzed its isotopic composition. We found changes in the δ15N and δ18O isotope values over the investigated period of 21 days, with both parameters showing positive linear correlations, and maximum enrichments with time of more than 1.5‰ for δ15N and more than 2‰ for δ18O. However, within the interval the samples remained in a saleable condition the isotope variations remained more or less within the variability of the sample batch. Our study demonstrates that changes in the isotopic signature in vegetables might occur after harvest during storage and have to be taken into account when (commercial) samples collected in a market are investigated.