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

Multi-source data fusion for soybean origin traceability: Stable isotopes, elemental composition, & volatile organic compounds

Ensuring the sustainable sourcing of soybeans, as mandated by the European Union Deforestation Regulation (EUDR), requires high spatial resolution to trace soybeans back to their origin. Addressing this challenge necessitates integrating multiple analytical approaches, making data fusion a powerful solution. As global soybean demand nearly doubled over the past decade, the industry faces pressing issues like food fraud, deforestation, and climate change. This study evaluates four data fusion strategies-Low-level, Mid-Principal Component Analysis-Random Forest (PCA-RF), Mid-Uniform Manifold Approximation and Projection-Random Forest (UMAP-RF), and High-level fusion-using data from 60 soybean samples from six Brazilian states. Analytical techniques, including stable isotope analysis, elemental profiling, and volatile organic compound characterisation, were employed. High-level data fusion achieved 100 % classification accuracy for the test set, with Mid-UMAP-RF closely following at 99 %, demonstrating data fusion's role in improving traceability and ensuring sustainable agricultural practices.

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%.

Enhanced food authenticity control using machine learning-assisted elemental analysis

With the increasing attention being paid to the authenticity of food, efficient and accurate techniques that can solve relevant problems are crucial for improving public trust in food. This review explains two main aspects of food authenticity, namely food traceability and food quality control. More explicitly, they are the traceability of food origin and organic food, detection of food adulteration and heavy metals. It also points out the limitations of the commonly used morphology and organic compound detection methods, and highlights the advantages of combining the elements in food as detection indicators using machine learning technology to solve the problem of food authenticity. Taking elements as detection objects has the significant advantages of stability, machine learning technology can combine large data samples, ensuring both the accuracy and efficiency. In addition, the most suitable algorithm can be found by comparing their accuracy.

Insights into the Formation of Chlorinated Polycyclic Aromatic Hydrocarbons Related to Chlorine in Salt-Tolerant Rice: Profiles in Market Samples, Effects of Saline Cultivation, and Household Cooking

Halogenated polycyclic aromatic hydrocarbons (XPAHs) present potential risk owing to their greater toxicity than PAHs. This study aimed to explore their profiles in commercial salt-tolerant rice, effects of saline cultivation (0‰ and 3‰ saline conditions), and formation during home cooking. A validated SPE-GC-MS/MS method was used to analyze PAHs and XPAHs in 16 commercial salt-tolerant rice samples. The PAH24 and XPAH18 concentrations were 6.95-32.73 μg kg-1 and 0.013-0.593 μg kg-1, respectively. Chlorinated PAHs (ClPAHs) were significantly greater in salt-tolerant rice (0.14 μg kg-1) than in normal rice (0.048 μg kg-1). During cooking, a notable increase (210-1120%) in ClPAHs and a significant correlation (r = 0.70-0.81, p < 0.05) between newly formed ClPAHs and their parent PAHs were observed, suggesting cooking-induced chlorination of PAHs. Moreover, chlorine radical-induced chlorination of PAHs may be the primary mechanism involved. These findings highlight increased exposure to ClPAHs due to saline cultivation and cooking and provide new insight into ClPAH formation from household cooking.

Differentiating Pond-Intensive, Paddy-Ecologically, and Free-Range Cultured Crayfish (Procambarus clarkii) Using Stable Isotope and Multi-Element Analysis Coupled with Chemometrics

The farming pattern of crayfish significantly impacts their quality, safety, and nutrition. Typically, green and ecologically friendly products command higher economic value and market competitiveness. Consequently, intensive farming methods are frequently employed in an attempt to replace these environmentally friendly products, leading to potential instances of commercial fraud. In this study, stable isotope and multi-element analysis were utilized in conjunction with multivariate modeling to differentiate between pond-intensive, paddy-ecologically, and free-range cultured crayfish. The four stable isotope ratios of carbon, nitrogen, hydrogen, and oxygen (δ13C, δ15N, δ2H, δ18O) and 20 elements from 88 crayfish samples and their feeds were determined for variance analysis and correlation analysis. To identify and differentiate three different farming pattern crayfish, unsupervised methods such as hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used, as well as supervised multivariate modeling, specifically partial least squares discriminant analysis (PLS-DA). The HCA and PCA exhibited limited effectiveness in classifying the farming pattern of crayfish, whereas the PLS-DA demonstrated a more robust performance with a predictive accuracy of 90.8%. Additionally, variables such as δ13C, δ15N, δ2H, Mn, and Co exhibited relatively higher contributions in the PLS-DA model, with a variable influence on projection (VIP) greater than 1. This study is the first attempt to use stable isotope and multi-element analysis to distinguish crayfish under three farming patterns. It holds promising potential as an effective strategy for crayfish authentication.

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.

A high security coding and anti-counterfeiting method based on the nonlinear magnetization response of superparamagnetic nanomaterials

Traditional coding methods based on graphics and digital or magnetic labels have gradually decreased their anti-counterfeiting because of market popularity. This paper presents a new magnetic anti-counterfeiting coding method. This method uses a high-performance coding material, which, along with small changes to the material itself and the particle size of the superparamagnetic nanomaterials, results in a large difference in the nonlinear magnetization response. This method, which adopts 12-site coding and establishes a screening model by measuring the voltage amplitude of 12-site variables, can code different kinds of products, establishing long-term stable coding and decoding means. Through the anti-counterfeiting experiment of wine, the experiment results show that the authenticity of the coded products can be verified using the self-developed magnetic encoding and decoding system. The new coding technology can verify the anti-counterfeiting of 9000 products, with a single detection accuracy of 97% and a detection time of less than one minute. Moreover, this coding method completely depends on the production batch of the superparamagnetic nanomaterials, which is difficult to imitate, and it provides a new coding anti-counterfeiting technology for related industries with a wide range of potential applications.

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.

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.

Authentication of ST25 rice using temperature-perturbed Raman measurement with variable selection by Incremental Association Markov Blanket

A temperature-perturbed transmission Raman measurement was demonstrated for the discrimination of ST25 and non-ST25 rice samples. ST25 rice is a premium long-grain Vietnamese rice with the aroma of pandan leaves and the scent of early sticky rice. Raman spectra of rice samples were acquired with temperature perturbation ranging from 20 to 50 °C, and the variables (intensities of peaks) with greater discrimination were selected from the spectra using Incremental Association Markov Blanket (IAMB) for authentication. The combination of four, seven, and four variables selected from the spectra at 20, 30, and 50 °C, respectively, yielded the highest accuracy of 97.9%. The accuracies in the single-temperature measurements were lower, suggesting that the combination of mutually complementary spectral features acquired at these temperatures is synergetic to recognize the compositional differences between two sample groups, such as in the amylose/amylopectin ratio and the protein constituent.