Geographical Traceability Research Articles

Omics Technologies: A Strategy to Expedite the Geographical Traceability and Authenticity of Palm Oil

Omics Technologies: A Strategy to Expedite the Geographical Traceability and Authenticity of Palm Oil

Determining the geographical origins of goji berries using the Twin-Tower model for Multi-Feature

Gogi berry products, known for their medicinal value, are exclusively available in the Ningxia region of China. However, the nutritional components, active ingredients, and economic value of these berries vary considerably among different cultivation zones in Ningxia. Accurate geographical traceability is essential not only for fostering the sustainable growth of the goji berry industry but also to ensure consumer safety, safeguard regional brands, and prevent fraudulent labelling practices that can erode market confidence and disrupt economic stability. Consequently, identification of the precise geographical origin of goji berries in Ningxia has emerged as a focal point in the progression of the industry. In this study, the Twin-Tower Model (TTM) was employed in conjunction with hyperspectral spectral and image data to establish a classification model for determining the proximate geographical origins of goji berries in Ningxia, including the regions of Zhongning, Guyuan, Tongxin, and Huinong. The results show that the graph-based training method built on TTM improves the accuracy by 3.7% compared to traditional data fusion methods. Notably, it attained a flawless 100% recognition rate for the highest quality Zhongning goji berries. These findings highlight that the implementation of the TTM method for multitask learning signifies a novel direction for transforming the conventional data fusion paradigm. Furthermore, it provides an essential technological means for the robust development of the goji berry industry.

Geographical Traceability of Millet by Mid-Infrared Spectroscopy and Feature Extraction

To identify the geographical origin of millet accurately, 36 samples of Guangling millet, Qinzhouhuang millet, Liuseng millet, Qiananhuang millet, and 33 samples of Yuzhou millet were collected. Mid-infrared (mid-IR) spectra of all the samples were obtained. Denoising, standard normal variate (SNV), multiplicative scatter correction (MSC), and normalization were carried out to preprocess the data. Principal component analysis (PCA) was used to reduce the dimension of the data, combined with support vector machine (SVM), and the geographical origin of the five kinds of millet was identified. The recognition accuracy of the training set (99.2%) and the prediction set (98.3%) were highest when using the first 12 principal components, indicating that the established mid-IR spectroscopic identification model was feasible and effective. PCA, window analysis, hierarchical clustering analysis, and SVM were combined to extract the feature information of mid-IR spectra of millet from five producing areas. Five wavenumbers, 1026, 1053, 1685, 1715, and 1744 cm-1, were found to be with small correlation, and the recognition accuracy of the training set and the prediction set based on these five features were 95.8% and 100.0%, respectively. The feature extraction method established here could be used to improve the prediction efficiency of the identification model and provide data support for the analysis of differential components.

Geographic traceability of Gastrodia elata Blum based on combination of NIRS and Chemometrics

The content of the active ingredient in G. elata Bl. is affected by the soil and climate of different regions, so geographical traceability is essential to ensure its quality, commercial value. This study used a combination of NIRS and various chemometric methods to establish an effective geotraceability method for G. elata Bl.. Firstly, a traditional machine learning model was built based on the SF dataset NIRS, and a ResNet model was built based on NIRS generated 2DCOS images and 3DCOS images. Secondly, the model performance was validated using the ZT dataset. The results show that the 3DCOS-ResNet model performs the best with 100.00 % and 95.45 % test set and EV accuracy, respectively. This study provides a theoretical basis for regulators to quickly ensure the authenticity of G. elata Bl. sources. However, more data and in-depth studies are needed in the future to validate and improve the applicability of the model.

Pattern recognition of multiple metal ions using fluorescent Perylenyl nanoaggregates and application in food traceability

Metal ions (MIs) identification is essential for the safety assessment, traceability and authentication of food. Most current approaches for detecting MIs are difficult to reconcile the simplicity, sensitivity and stability simultaneously. In this work, we proposed a novel strategy for discriminating MIs based on fluorescent supramolecular nanoaggregates (SNAs). In the presence of MI, perylene diimide derivatives (PDI)-based SNAs could be formed through the multiple non-covalent interactions between them including electrostatic, coordination and π-π interactions. With the assistance of discriminant analysis (LDA), different MIs (Hg2+, Ag+, Cd2+, Fe3+, Cr3+, Fe2+, Zn2+, Cu2+ and Pb2+) were successfully identified at three different concentration levels. It featured good quantitative sensing abilities in buffer solutions and practical samples. Furthermore, a water-quality evaluation model was successfully constructed for the distinction of different sources of drinking water, and the fluorescence array sensor technology was applied for the first time to the geographical traceability of apples.

Discrimination of coal geographical origins through HS-GC-IMS assisted with machine learning algorithms in larceny case

The process of globalization and industrialization has resulted in a rise in the theft of coal and other related products, thereby becoming a focal point for forensic science. This situation has engendered an escalated demand for effective detection and monitoring technologies. The precise identification of coal trace evidence presents a challenge with current methods, owing to its minute quantity, fine texture, and intricate composition. In this study, we integrated machine learning with the identification of volatiles to accurately differentiate coal geographical origins through the application of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). The topographic distribution of volatiles in coals was visually depicted to elucidate the subtle distinctions through spectra and fingerprint analysis. Additionally, four supervised machine learning algorithms were developed to quantitatively predict the geographical origins of natural coals utilizing the HS-GC-IMS dataset, and these were subsequently compared with unsupervised models. Remarkable volatile compounds were identified through the quantitative analysis and optimal Random Forest model, which offered a rapid readout and achieved an average accuracy of 100 % in coal identification. Our findings indicate that the integration of HS-GC-IMS and machine learning is anticipated to enhance the efficiency and accuracy of coal geographical traceability, thereby providing a foundation for litigation and trials.

Fingerprinting alkaloids for traceability: Semi-untargeted UHPLC-MS/MS approach in raw lupins as a case study

Lupin seeds are recognized for their nutritional value and potential health benefits, but they contain also a significant amount of alkaloids, an anti-nutritive class of compounds, which vary significantly in composition within and between species due to environmental factors. In this study, we developed a predictive multi-experiment approach using ultra-high performance liquid chromatography coupled with triple quadrupole with linear ionic trap tandem mass spectrometry (UHPLC-QqQ-LIT-MS/MS) for comprehensive alkaloid profiling and geographical classification of Lupinus albus L. samples originating from four different Italian regions. Six targeted quinolizidine alkaloids were detected and 21 other alkaloids were putatively identified. Hierarchical Cluster Analysis (HCA) and partial least squares discriminant analysis (PLS-DA) were applied to explore the data structure and successfully classify samples according to their geographical origin. The data demonstrate the efficacy of the developed approach in providing valuable insights in alkaloid profiles of lupin seeds and their potential as markers for geographical traceability.

Rapid determination of geographical authenticity of Gastrodia elata f. glauca using Fourier transform infrared spectroscopy and deep learning

For quality evaluation of Gastrodia elata f. glauca (GEFG), it's crucial to develop an analytical method to determine the geographical origin. Herein, 371 GEFGs are collected from five provinces, focusing on analysis of dry matter content (DMC), origin identification, geographical indication (GI) production area discrimination by using a combination of Fourier transform infrared (FTIR) spectroscopy and deep learning, data driven version of soft independent modeling of class analogy (DD-SIMCA). A significant difference in DMC of GEFG between Yunnan and other origins, which may be related to precipitation, altitude, temperature, and soil. The residual neural network (ResNet) model based on synchronous two-dimensional correlation spectroscopy (2DCOS) images has stable performances, its accuracy is 100%. The DD-SIMCA model can differentiate GI production areas of GEFG, while for non-GI areas, the model specificity is 71.38%. This study provides a promising approach for GEFG geographical traceability and GI production area differentiation.

Geographical traceability of wolfberry pulp: Integrating stable isotopes, minerals, nutrients, and chemometric

The determination of the origin of wolfberry (Lycium barbarum) pulp in China remains a challenge despite its nutritional significance and popularity among consumers. To address this gap, 144 samples from four distinct regions in China were analyzed using nutrient elements, stable isotopes combined with multi-element assessments. Chemometric analyses successfully demonstrated the initial separation of wolfberry pulp samples. Notably, a nutrient element (Glucose) along with 12 mineral elements (Tb, Co, Fe, Cd, Pr, V, Mo, Gd, Al, Mg, As, Zn) emerged as pivotal factors for origin identification. Furthermore, employing a random forest algorithm resulted in the highest classification accuracy of 100 %, surpassing support vector machine (96.43 %) and K-nearest (71.43 %) methods. The study's findings underscore the efficacy of utilizing stable isotopes, mineral elements, and nutritional composition as effective markers for tracing the origin of wolfberry pulp. Moreover, this methodology offers promising insights into the potential identification of origins for other agricultural products.

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.

Geographical traceability of flavor compounds in Chinese mitten crab (Eriocheir sinensis): Implications for quality differentiation, authenticity assessment, and mechanism research

Geographical traceability plays a crucial role in ensuring quality assurance, brand establishment, and the sustainable development of the crab industry. In this study, we examined the possibility of using gas chromatography-ion mobility spectrometry with multivariate statistical authenticity analysis to identify the origin of crabs from five sites downstream of the Yangtze River. Significant variations were observed in the levels of alcoholic flavor compounds in the hepatopancreas and muscles of crabs from different geographical locations, and a support vector machine exhibited discriminant ability with 100% accuracy. These flavor variations exhibited significant correlations with the types and concentrations of elements within the crabs, as well as with free amino acids. This study offers a practical approach for determining the geographical traceability of Chinese mitten crabs and elucidates the role of elements in flavor modulation, thereby providing innovative strategies to enhance the efficiency of crab farming.

Simultaneous identification and differentiation of human fingernails and hair from different regions by Terahertz time-domain spectroscopy and chemometrics for forensic purpose

Hair and fingernails, emerging as crucial biological physical evidence, play a prominent role in geographical traceability studies within the forensic anthropology. This study explored the potential of terahertz spectroscopy and chemometrics techniques in identifying hair and fingernail samples from various geographical regions. A total of 196 hair and fingernail samples underwent scanning with terahertz time-domain spectroscopy. Different chemometric tools, including multilayer perceptron, support vector machine, and Fisher discriminant analysis, were employed for model building on fingernail dataset, hair dataset, and fusion data, respectively. The performance of models constructed based on different datasets was compared and evaluated using metrics such as accuracy, precision, recall, and F-score. Results revealed that the fusion data, employing the Fisher discriminant analysis (FDA) algorithm, exhibited the best recognition ability, achieving a 100 % accuracy rate and F-value. Terahertz spectroscopy combined with FDA algorithm emerged as an effective and convenient tool for differentiating hair and fingernail samples from diverse geographical regions. This innovative technique boasts environmental friendliness, rapid analyses, and minimal sample preparation requirements compared to traditional methods.

Improving the geographical traceability of tea in China based on stable isotope ratios

Improving the geographical traceability of tea in China based on stable isotope ratios

New metagenomic procedure for the investigation of the eukaryotes present in the digestive gland of Mytillus galloprovincialis

Mussels’ aquaculture is one of the most important cultures in Europe, with a high level of self-sufficiency. In Spain, the main cultivated species is M. galloprovincialis, whose production takes place primarily in Galicia. In that region, a Protected Designation of Origin certification exists to certify the high quality of the product and its sustainability. This species is also present in other regions making the control of its traceability complicated. In order to distinguish Galician mussels from the others, the eukaryotes present in its digestive gland could be a useful tool. However, to use them as biomarkers, having an adapted protocol to recover the diversity present in the digestive gland is essential. In this work, we have selected a new protocol to optimize the study of the diversity of eukaryotes associated with the digestive gland of M. galloprovincialis. This protocol is based on a combination of a pre-treatment and a commercial DNA extraction kit, the amplification of DNA with two newly designed primer sets to avoid the DNA from the mussel, and the use of next-generation sequencing. The results showed a significant reduction of mussel DNA amplification and enough DNA quality for the subsequent sequencing, increasing the number of sequences recovered from other eukaryotes. This new protocol offers then a good chance to determine the diversity of eukaryotes present in the digestive gland of M. galloprovincialis, and it can be used in future studies to examinate the seasonal variation of the present species and to evaluate its potential as geographical traceability tool.

Development of machine learning models using multi-source data for geographical traceability and content prediction of Eucommia ulmoides leaves

Rapid and scientific quality evaluation is a hot topic in the research of food and medicinal plants. With the increasing popularity of derivative products from Eucommia ulmoides leaves, quality and safety have attracted public attention. The present study utilized multi-source data and traditional machine learning to conduct geographical traceability and content prediction research on Eucommia ulmoides leaves. Explored the impact of different preprocessing methods and low-level data fusion strategy on the performance of classification and regression models. The classification analysis results indicated that the partial least squares discriminant analysis (PLS-DA) established by low-level fusion of two infrared spectroscopy techniques based on first derivative (FD) preprocessing was most suitable for geographical traceability of Eucommia ulmoides leaves, with an accuracy rate of up to 100 %. Through regression analysis, it was found that the preprocessing methods and data blocks applicable to the four chemical components were inconsistent. The optimal partial least squares regression (PLSR) model based on aucubin (AU), geniposidic acid (GPA), and chlorogenic acid (CA) had a residual predictive deviation (RPD) value higher than 2.0, achieving satisfactory predictive performance. However, the PLSR model based on quercetin (QU) had poor performance (RPD = 1.541) and needed further improvement. Overall, the present study proposed a strategy that can effectively evaluate the quality of Eucommia ulmoides leaves, while also providing new ideas for the quality evaluation of food and medicinal plants.

Casting light on the European anchovy: from biology to conservation and industry

This manuscript explores the role of European anchovies (Engraulis encrasicolus) in the central Mediterranean Region, shedding light on their ecological significance, conservation challenges, and sustainable utilization. The European anchovy is one of Europe’s most important fish resources in the Mediterranean basin, and it is considered a keystone species, playing a pivotal role in both ecological and socio-economic dimensions. However, in recent decades, European anchovy, together with Sardina pilchardus (commonly known as European sardine), has suffered a population decline for several reasons. Consequently, it is necessary to improve the management of anchovy fisheries by understanding the reproductive modes and characteristics, the influence of currents on the passive transport of eggs and larvae, the feeding habits, the environmental adaptability (e.g., salinity), and the distribution of ecotypes along the Italian coasts. Such information is fundamental for the management of fisheries, especially artisanal ones, and to control frauds, especially in protected, geographically referred, and traditional high-quality commercial products. Various aspects, ranging from their population dynamics to their industrial processing and the ecological implications of these activities were delved, highlighting the knowledge about anchovy populations and ecotypes and its importance in maintaining ecosystem balance and sustaining human communities. The ecological interaction of anchovies within the food web, as essential data in the conservation actions and management of these resources was emphasized. In addition, the metabolic and stomach contents diversity among anchovy populations and ecotypes was discussed, enhancing our understanding of their adaptability to varying environmental conditions. The manuscript then explores the traditional and industrial processing of anchovies, encompassing aspects ranging from fishing techniques (i.e., methods of capture) to their industrial significance, sustainability concerns, issues of fraud, and the establishment of geographical traceability. Finally, the opportunities for sustainable and biotechnological utilization of anchovy discards were also further explored, demonstrating the potential for waste reduction and resource optimization.

Retrieving complete plastid genomes of endangered Guibourtia timber using hybridization capture for forensic identification and phylogenetic analysis

The high economic value and increased demand for timber have led to illegal logging and overexploitation, threatening wild populations. In this context, there is an urgent need to develop effective and accurate forensic tools for identifying endangered Guibourtia timber species to protect forest ecosystem resources and regulate their trade. In this study, a hybridization capture method was developed and applied to explore the feasibility of retrieving complete plastid genomes from Guibourtia sapwood and heartwood specimens stored in a xylarium (wood collection). We then carried out forensic identification and phylogenetic analyses of Guibourtia within the subfamily Detarioideae. This study is the first to successfully retrieve high-quality plastid genomes from xylarium specimens, with 76.95–99.97% coverage. The enrichment efficiency, sequence depth, and coverage of plastid genomes from sapwood were 16.73 times, 70.47 times and 1.14 times higher, respectively, than those from heartwood. Although the DNA capture efficiency of heartwood was lower than that of sapwood, the hybridization capture method used in this study is still suitable for heartwood DNA analysis. Based on the complete plastid genome, we identified six endangered or commonly traded Guibourtia woods at the species level. This technique also has the potential for geographical traceability, especially for Guibourtia demeusei and Guibourtia ehie. Meanwhile, Bayesian phylogenetic analysis suggested that these six Guibourtia species diverged from closely related species within the subfamily Detarioideae ca. 18 Ma during the Miocene. The DNA reference database established based on the xylarium specimens provides admissible evidence for diversity conservation and evolutionary analyses of endangered Guibourtia species.

Geographical traceability of soybean: An electronic nose coupled with an effective deep learning method

The quality of soybeans is correlated with their geographical origin. It is a common phenomenon to replace low-quality soybeans from substandard origins with superior ones. This paper proposes the adaptive convolutional kernel channel attention network (AKCA-Net) combined with an electronic nose (e-nose) to achieve soybean quality traceability. First, the e-nose system is used to collect soybean gas information from different origins. Second, depending on the characteristics of the gas information, we propose the adaptive convolutional kernel channel attention (AKCA) module, which focuses on key gas channel features adaptively. Finally, the AKCA-Net is proposed, which is capable of modeling deep gas channel interdependency efficiently, realizing high-precision recognition of soybean quality. In comparative experiments with other attention mechanisms, AKCA-Net demonstrated superior performance, achieving an accuracy of 98.21%, precision of 98.57%, and recall of 98.60%. In conclusion, the combination of the AKCA-Net and e-nose provides an effective strategy for soybean quality traceability.

Combining indicators analysis and chemometrics to trace the geographical origin of crude oil

Geographic traceability is crucial to global oil trade security. This study discusses the possibility of using multivariate statistical methods combined with multi-indicator analysis to identify samples of crude oil imports from five major countries to China. The physicochemical properties and trace elements of crude oil were detected by Petroleum product standards and inductively coupled plasma atomic emission spectrometry (ICP-AES). Eight indexes (moisture, density, sulfur content, acid value, organochlorine, carbon residual, V, and Ni) were analyzed. Principal component analysis (PCA), hierarchical clustering analysis (HCA), Orthogonal projections to lateen structures-discriminant analysis (OPLS-DA), and other multivariate data analysis methods were used to determine the geographical origin of crude oil samples. Satisfying results have been obtained using PCA to reduce the dimensions of the indicators of crude oil from different origins. It allows the reduction of 8 variables to 3 principal components and accounts for 80.06% of the total variance. The HCA shows five clusters corresponding to five sources of crude oil. This will help to improve the utilization rate of crude oil with different characteristics, improve the quality of crude oil trade, and ensure the high quality of crude oil trade. For the sample set used for modeling, the model's accuracy was 97.19% after OPLS-DA optimization. These results show that the combination of multi-index analysis and stoichiometry is an effective tool for identifying crude oil origin, which fills the technical gap in the rapid identification of crude oil origin.

Geographical region traceability of wild topmouth culter (Culter alburnus) from Xingkai Lake based on muscle quality and aroma profiles

The wild topmouth culter (Culter alburnus) from Xingkai Lake (XKL) is highly regarded for its delicious taste and unique flavor. In this study, based on muscle quality and aroma analysis, we first differentiated the XKL population from three wild populations in Heilongjiang Province and one artificially cultured population (from Xingkai Lake). Compared with the other populations, the XKL population has a significantly higher crude protein content, essential amino acid content, delicious amino acid content, and n-3/n-6 PUFA ratio. Additionally, it exhibits superior hardness, elasticity, chewiness, recoverability, and viscosity. E-nose detection analysis revealed that W1S, W2S, and W3S were the potential sensors contributing the most to the differences among the five populations. HS–SPME–GC–MS and multivariate regression analysis showed that 21 volatile flavor compounds were identified as key markers for geographical identification of the Xingkai Lake region. These findings will provide guidance for the geographical traceability and identification of the XKL population.