Geographical Origin Identification Research Articles

Geographical Origin Identification of Panax notoginseng Using a Modified K-Nearest Neighbors Model with Near-infrared Spectroscopy

Geographical Origin Identification of <i>Panax notoginseng</i> Using a Modified K-Nearest Neighbors Model with Near-infrared Spectroscopy

Pollen Analysis of Elaeis guineensis-Dominated Honeys in Lower Casamance: Floral Diversity and Honey Typology

Background: A honey's pollen spectrum serves as an indicator for determining its place of origin in terms of vegetation resources. This data might be utilised to create pollen analytical standards, which would help certify honey. The melliferous plants are plant species on which the bee takes substances, including nectar, pollen and resin to feed and to develop its various products (honey, royal jelly...). Aim: The aim of this study was carried out to contribute to melliferous plants in Casamance (Senegal) : pollen spectrum of honeys with Elaeis guineensis dominant. Results: Pollen analysis of 14 samples of dominant Elaeis guineensis honey from Lower Casamance in the Ziguinchor region identified 25 plant taxa in 11 families foraging for bees. The species richness varied from 5 to 10 taxa per honey sample. The most representative families are Fabaceae with 7 species, followed by Malvaceae with 3 species, Combretaceae, Myrtaceae, Rubiaceae, Poaceae, Arecaceae, Lamiaceae with 2 species and Avicenniaceae, Méliaceae, Asteraceae with 1 species. Pterocarpus erinaceus is present in 11 spectra including 7 spectra where it is isolated and in 3 spectra where it is isolated. It is the only taxon that is a companion pollen in 1 of the spectra. The most common taxa associated with Elaeis are Detarium senegalense, Daniellia oliveri and Myrtaceae. Conclusion: The data reflects the floral situation of the place were particular honey was produced and the identification of geographical origin based on the presence of a combination of pollen types of that particular area.

Geographical origin identification of Dendrobium Officinale based on FT-NIR and ATR-FTIR spectroscopy

Geographical origin identification of Dendrobium Officinale based on FT-NIR and ATR-FTIR spectroscopy

Rapid Identification of Geographical Origin of Sliced Polygonati Rhizoma by Auto-focus Laser-induced Breakdown Spectroscopy Combined with Interpretable Machine Learning

Rapid Identification of Geographical Origin of Sliced Polygonati Rhizoma by Auto-focus Laser-induced Breakdown Spectroscopy Combined with Interpretable Machine Learning

Genetic structure of Saccharina japonica in Japan and evaluation of a potential mitochondrial region for identification of geographic origin

Genetic structure of Saccharina japonica in Japan and evaluation of a potential mitochondrial region for identification of geographic origin

Elemental fingerprinting based on ICP-MS/OES and machine learning: Sample digestion-free and standard-free strategy for geographical origin identification of Chinese liquor

The significant price disparity of Chinese baijiu originating from various regions has unfortunately partially given rise to the proliferation of counterfeit and substandard products, and this may cause disturbance in the market economy. Ensuring the traceability and differentiation of baijiu by its origin is therefore of paramount importance. In this study, the Baijiu samples were straightforward dilution without sample digestion, followed by the detection of elemental signal intensities using ICP-MS and ICP-OES without accurate quantification, to rapidly construct their multi-elemental fingerprints. Heatmap and HCA (hierarchical cluster analysis) were used for data visualization and tentative classification, respectively. PCA (principal component analysis) was implemented to corroborate the classification outcomes. Subsequently, the supervised OPLS-DA (orthogonal partial least squares discrimination analysis) was deployed to enhance the identification and predictive capabilities for the Baijiu samples. The unequivocal discrimination of the three distinct Baijiu origins with 100% classification accuracy. The validation values R2 and Q2 approached 1.0, indicating a great fit and prediction capability of the OPLS-DA model. The VIP plot evaluated the importance of elemental variables, revealing that the key contributions were primarily from elements K, Ca, Na, Mg, and Al. Collectively, these findings demonstrate the efficacy of chemometric techniques, when integrated with multi-elemental fingerprints based on their signal intensities only, in identifying the geographical origins of the Baijiu. This approach holds promise for applications in quality control and anti-counterfeiting measures in the baijiu industry.

Geographical origin identification of dendrobium officinale based on NNRW-stacking ensembles

Dendrobium officinale is a well-recognized functional food material. Considering its therapeutic effect and price vary among different geographical origins, this paper proposed an origin identification method based on Raman spectroscopy and NNRW (neural network with random weights)-stacking ensemble model. In a case study of dendrobium officinale samples from three different geographical origins, we compare both single estimators, i.e., KNN (k-nearest neighbors), MLP (multi-layer perceptron), DTC (decision tree classifier), and NNRW, and their stacking ensemble counterparts. The results showed that the NNRW-stacking ensemble has the best test accuracy (96.3%) and an impressive fitting speed (the fastest among all ensembles). In conclusion, the NNRW-stacking ensemble model combined with Raman spectroscopy can be a promising method for herb geographical original identification. The proposed model has demonstrated the speed advantage of NNRW (no need for gradient-based iterations) and the generalization power of stacking ensembles (reduce single-estimator bias).

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.

Elemental profile of Ipê species to support timber tracing in Amazon Rainforest

Accurate identification of geographical origin and species of wood is crucial for endorsing laws to combat illegal trade. Here, the multi-element profile of Ipê species of economic interest in Brazil was investigated by neutron activation analysis (NAA). Ba, Br, Ca, Ce, Co, Eu, Fe, K, La, Na, Rb, Sc, Sm, Sr and Zn were determined in the heartwood and sapwood tissues. The Random Forest algorithm identified Sr, Ba, Sm, La, Ce and Br as the most important elements for discriminating species. The analyses revealed significant differences between species and wood tissues, indicating that multi-element profile has great potential as a forensic tool. The establishment of comprehensive reference databases that cover large geographical areas and take into account the natural variability between species is fundamental to achieving this goal.

Lipid fingerprinting and geographical origin identification of large yellow croaker (Larimichthys crocea) using UHPLC-QTOF-MS

The Large Yellow Croaker (LYC) with rich lipids offers numerous health benefits, yet its lipid profile remains underexplored. Therefore, the lipids of LYC were comprehensively profiled and compared based lipidomics. Higher nutritional value of LYC was identified based on lipid and fatty acid assessment, particularly ovary and brain. A total of 963 lipid species belonging to 47 lipid subclasses were identified, exhibiting higher levels of glycerophospholipids (GPs) and sphingolipids (SPs) in the brain, as well as high levels of glycolipids (GLs) in the muscle. Furthermore, unique lipid subclasses were detected in the brain (Acylcarnitine (CAR), Alpha-hydroxy-N-stearoyl phytosphingosine (Cer-AP)) and ovary (N-arachidonoyl glycine (NAGly)). Notably, 8 lipid subclasses were selected as potential contributors for four tissues differentiation. Additionally, identification of LYC from various origins was achieved through lipidomics for the first time. LYC from Zhejiang and Fujian provinces could be distinguished by 100 lipid biomarkers. Most different lipids significantly negatively correlated with seawater pH and dissolved oxygen, but positively correlated with netting density, depth, environmental temperature and salinity. This comprehensive analysis provided valuable insights into LYC’s nutritional values and origin identified.

Chemometrics identification of geographical origin of garlic in Korea using elements concentrations data obtained by ICP-OES and ICP-MS

Discriminant analysis was performed according to the geographical origin of garlic using 60 elements. This study aimed to develop an analytical method for determining the geographical origin of garlic using inductively coupled plasma (ICP) analysis. Statistically significant differences were observed in 25 out of 60 elements. A total of 31 variable meanings in projection were selected from orthogonal partial least square discriminant analysis. The selected elements overlapped with those with significant differences, and those that did not were also included. The prediction of the selected element was confirmed using the area under the curve value of the receiver operating characteristic curve. A heatmap was also used to identify the geographic origin, and discriminant analysis of the geographic origin exhibited 100 % accuracy in standard discriminant analysis. This elemental analysis method exhibits promise as a food and agricultural product analysis tool, and can accurately determine the geographic origin of condiment vegetables other than garlic.

The combination of HSI and NMR techniques with deep learning for identification of geographical origin and GI markers of Lycium barbarum L.

Lycium barbarum L. (L. barbarum) is renowned worldwide for its nutritional and medicinal benefits. Rapid and accurate identification of L.barbarum's geographic origin is essential because its nutritional content, medicinal efficacy, and market price significantly vary by region. This study proposes an innovative method combining hyperspectral imaging (HSI), nuclear magnetic resonance (NMR), and an improved ResNet-34 deep learning model to accurately identify the geographical origin and geographical indication (GI) markers of L.barbarum. The deep learning model achieved a 95.63% accuracy, surpassed traditional methods by 6.26% and reduced runtime by 29.9% through SHapley Additive exPlanations (SHAP)-based feature selection. Pearson correlation analysis between GI markers and HSI characteristic wavelengths enhanced the interpretability of HSI data and further reduced runtime by 33.99%. This work lays the foundation for portable multispectral devices, offering a rapid, accurate, and cost-effective solution for quality assurance and market regulation of L.barbarum products.

Rapid and Non-Destructive Geographical Origin Identification of Chuanxiong Slices Using Near-Infrared Spectroscopy and Convolutional Neural Networks

Ligusticum Chuanxiong, a perennial herb of considerable medicinal value commonly known as Chuanxiong, holds pivotal importance in sliced form for ensuring quality and regulating markets through geographical origin identification. This study introduces an integrated approach utilizing Near-Infrared Spectroscopy (NIRS) and Convolutional Neural Networks (CNNs) to establish an efficient method for rapidly determining the geographical origin of Chuanxiong slices. A dataset comprising 300 samples from 6 distinct origins was analyzed using a 1D-CNN model. In this study, we initially established a traditional classification model. By utilizing the Spectrum Outlier feature in TQ-Analyst 9 software to exclude outliers, we have enhanced the performance of the model. After evaluating various spectral preprocessing techniques, we selected Savitzky–Golay filtering combined with Multiplicative Scatter Correction (S-G + MSC) to process the raw spectral data. This approach significantly improved the predictive accuracy of the model. After 2000 iterations of training, the CNN model achieved a prediction accuracy of 92.22%, marking a 12.09% improvement over traditional methods. The application of the Class Activation Mapping algorithm not only visualized the feature extraction process but also enhanced the traditional model’s classification accuracy by an additional 7.41% when integrated with features extracted from the CNN model. This research provides a powerful tool for the quality control of Chuanxiong slices and presents a novel perspective on the quality inspection of other agricultural products.

Nondestructive Identification of Wheat Seed Variety and Geographical Origin Using Near‐Infrared Hyperspectral Imagery and Deep Learning

ABSTRACTSeed purity assurance is an important aspect of maintaining the quality standards of wheat seeds. It relies significantly on quality parameters, like varietal classification and geographical origin identification. Hyperspectral imaging (HSI) has emerged as an advanced nondestructive technique to determine various quality parameters. In recent years, several studies have utilized HSI for varietal classification, although a limited number of varieties were considered. Additionally, no attention has been paid to determining the geographical origin of wheat seeds. To address these gaps, two separate experiments were performed for varietal classification and geographical origin identification. The seeds from 96 varieties grown across 5 different agricultural regions in India were collected. Hyperspectral images of wheat seeds were acquired in the wavelength ranging 900–1700 nm. The spectral reflectance values were obtained from the region of interest (ROI) corresponding to each seed. Subsequently, the deep learning models (convolutional neural networks [CNNs]) were established and compared with two conventional algorithms, including support vector machines (SVMs) and K‐nearest neighbors (KNNs). The experimental results indicated that the proposed CNN models outperformed the SVM and KNN models, achieving an overall accuracy of 94.88% and 99.02% for varietal classification and geographical origin identification, respectively. These results demonstrate that HSI combined with deep learning has the potential to accurately classify a large number of wheat varieties. Moreover, HSI can be used to precisely identify the geographical origins of wheat seeds. This study provides an accurate and nondestructive method that can assist in breeding, quality evaluation, and the development of high‐quality wheat seeds.

Attenuated total reflectance-flourier transformed infrared spectroscopy (ATR-FTIR) coupled with deep learning: A rapid method for geographical origin identification of sea cucumber Apostichopus japonicus

The sea cucumber Apostichopus japonicus is one of the China’s most prized seafoods, its geographical origin is a decisive factor for its economic value. To quickly and effectively identify the geographical origin of this species, ATR-FTIR spectroscopy coupled with deep learning methods was applied in the present study. Compared to conventional machine learning models (support vector machine, random forest and lightGBM), the proposed one-dimensional convolutional neural network (1D-CNN) model demonstrates significant advantages in terms of data automation and accuracy. The model can correctly classify sea cucumbers from different sea regions up to 90.7%, among which 100% identification of Apostichopus japonicus from the East China Sea can be realized. The results proved that ATR-FTIR spectroscopy combined with 1D-CNN could be used as a rapid and effective technique for tracing the geographical origin of Apostichopus japonicus.

Rapid discrimination of Alismatis Rhizoma and quantitative analysis of triterpenoids based on near-infrared spectroscopy

This study developed a rapid, accurate, objective and economic method to identify and evaluate the quality of Alismatis Rhizoma (AR) commodities. Traditionally, the identification of plant species and geographical origins of AR commodities mainly relied on experienced staff. However, the subjectivity and inaccuracy of human identification negatively impacted the trade of AR. Besides, liquid chromatographic methods such as ultra-high-performance liquid chromatography (UPLC) and high-performance liquid chromatography (HPLC), the major approach for the determination of triterpenoid contents in AR was time-consuming, expensive, and highly demanded in manoeuvre specialists. In this study, the combination of near-infrared (NIR) spectroscopy and chemometrics as the method was developed and utilised to address the two common issues of identifying the quality of AR commodities. Through the discriminant analysis (DA), the raw NIR spectroscopy data on 119 batches samples from two species and four origins in China were processed to the best pre-processed data. Subsequently, orthogonal partial least squares-discriminant analysis (OPLS-DA) and random forest (RF) as the major chemometrics were used to analyse the best pre-processed data. The accuracy rates by OPLS-DA and RF were respectively 100% and 97.2% for the two species of AR, and respectively100% and 94.4% for the four origins of AR. Meanwhile, a quantitative correction model was established to rapidly and economically predict the seven triterpenoid contents of AR through combining the partial least squares (PLS) method and NIR spectroscopy, and taking the triterpenoid contents measured by UPLC as the reference value, and carry out spectral pre-processing methods and band selection. The final quantitative model correlation coefficients of the seven triterpenoid contents of AR ranged from 0.9000 to 0.9999, indicating that prediction ability of this model had good stability and applicability.

Species classification and origin identification of Lonicerae japonicae flos and Lonicerae flos using hyperspectral imaging with support vector machine

Lonicerae japonicae flos (Jinyinhua, JYH) and Lonicerae flos (Shanyinhua, SYH) have high medical and economical value. Due to their similar appearance, the more expensive JYH is often adulterated with the cheaper SYH for economic gain. In this study, near-infrared hyperspectral imaging (HSI) was used to identify the geographical origins of JYH and SYH and differentiate JYH from SYH. Support vector classification (SVC) models using linear kernel function were established to achieve the research goals. For the identification of geographical origin, we explored the impact of different sample batches on classification performance. The overall testing sets classification accuracy of JYH and SYH was in the range of 60.10–85.59% and 63.35–91.67%, respectively. For species classification, the impact of sample geographical origins and sample batches on model performances was explored. The overall testing sets classification accuracy for distinguishing JYH and SYH was 98.46–100%. These results demonstrated the significant impact of sample sources on the performance of the models. Using SVC models, the important wavelengths contributing more to the classification were identified by recursive feature elimination (RFE). The results showed that HSI holds great potential for the identification of JYH and SYH, as well as their geographical origins. This technique can provide crucial technical support for the development and standardization of the Traditional Chinese Medicine industry.

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 Origin Identification of Red Chili Powder Using NIR Spectroscopy Combined with SIMCA and Machine Learning Algorithms

Geographical Origin Identification of Red Chili Powder Using NIR Spectroscopy Combined with SIMCA and Machine Learning Algorithms

Geographical origin identification of winter jujube (Ziziphus jujuba ‘Dongzao’) by using multi-element fingerprinting with chemometrics

Winter jujube (Ziziphus jujuba ‘Dongzao’) is greatly appreciated by consumers for its excellent quality, but brand infringement frequently occurs in the market. Here, we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer (ICP-MS). As a result, 16 elements (Mg, K, Mn, Cu, Zn, Mo, Ba, Be, As, Se, Cd, Sb, Ce, Er, Tl, and Pb) exhibited significant differences in samples from different producing areas. Supervised linear discriminant analysis (LDA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA) showed better performance in identifying the origin of samples than unsupervised principal component analysis (PCA). LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64% in the testing set, respectively. By using the multilayer perceptron (MLP) and C5.0, the prediction accuracy of the models could reach 96.36 and 91.06%, respectively. Based on the above four chemometric methods, Cd, Tl, Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube. Overall, this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics, and may also provide reference for establishing the origin traceability system of other fruits.