Geographical Origin Identification Research Articles

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.

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.

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.

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.

Geographical origin identification of Pujiang Kyoho grapes in China using stable isotope and elemental profiles

Grapes are an important seasonal fresh fruit in China, and their quality and nutrition is closely linked to their geographical origin. Stable isotopes and elemental profiles of grapes and corresponding soils from different regions and harvest years were determined using isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry (ICP-MS). Results showed that stable isotope ratios and most elemental contents of grapes were significantly correlated to distinct regions and different harvest years. Na, Li, V, Co, Ni and Pb contents of grapes and corresponding soils were positively correlated, while Mg, Cu, Rb and Sr were negatively correlated (p < 0.05). A supervised chemometric model (random forest) was used to discriminate PGI Pujiang grapes from four other grape producing regions with 100% accuracy. These findings confirm that stable isotopes and multi-elements combined with chemometric analysis can effectively improve the accuracy of grape origin identification and minimize fraudulent mislabeling.

Identification of geographical and botanical origin of Mediterranean honeys using UV-vis spectroscopy and multivariate statistical analysis

The present study aimed to differentiate Mediterranean honey samples with regards to their botanical and geographical origin, using UV-vis spectroscopy, combined with multivariate statistical analysis and chemometrics. A total of 151 commercial honey samples were collected from different countries (Greece, Malta, Spain, Tunisia, and Turkey), which were characterized as thyme, pine, or polyfloral. Discrimination of geographical and botanical origin was achieved successfully with Principal Component Analysis (PCA) together with Random Forest (RF), Partial Least Squares – Discriminant Analysis (PLS – DA), and Data Driven – Soft Independent Modelling of Class Analogies (DD-SIMCA) using the spectral range of 220–550 nm. DD-SIMCA achieved very high accuracy, sensitivity, and specificity on the discrimination of both the geographical and botanical origin of honey samples. The results support the good predictive capability of UV-vis spectroscopy combined with chemometrics for the determination of honey origin. This methodology could be utilized for the rapid and non-destructive assessment of Mediterranean honey origin.

Geographical Origin Traceability of Atractylodes macrocephala Koidz. Using Mass Spectrometry Data Fusion and Ensemble Learning

The use of data fusion based with multiple analytical techniques was investigated to provide the accurate geographical origin identification of Atractylodes macrocephala Koidz. (AMK). Inductively coupled plasma - mass spectrometry (ICP-MS), gas chromatography - mass spectrometry (GC-MS) and liquid chromatography - mass spectrometry (LC-MS) were used to characterize Hubei, Zhejiang, and Hunan production regions. After the implementation of data fusion, the ensemble learning method multi-forest joint network (MFJN) and classic machine learning methods were used to identify the AMK production regions. The MFJN based upon high-level data fusion distinguished AMK samples from different regions with the highest accuracy. The classification accurate rate of AMK in the prediction set was 95%, which was significantly better than the results obtained using twenty-five mineral element or nine bioactive component data sets. The results showed that mass spectrometry data fusion in combination with MFJN is suitable for the geographic origin determination of AMK and has potential to ensure this product’s fair trade.

Identification of geographical origin and adulteration of Northeast China soybeans by mid-infrared spectroscopy and spectra augmentation

Identification of geographical origin and adulteration of Northeast China soybeans by mid-infrared spectroscopy and spectra augmentation

Geographical origin identification of mandarin fruits by analyzing fingerprint signatures based on multielemental composition

Given rising traders and consumers concerns, the global food industry is increasingly demanding authentic and traceable products. Consequently, there is a heightened focus on verifying geographical authenticity as food quality assurance. In this work, we assessed pattern recognition approaches based on elemental predictors to discern the provenance of mandarin juices from three distinct citrus-producing zones located in the Northeast region of Argentina. A total of 202 samples originating from two cultivars were prepared through microwave-assisted acid digestion and analyzed by microwave plasma atomic emission spectroscopy (MP-AES). Later, we applied linear discriminant analysis (LDA), k-nearest neighbor (k-NN), support vector machine (SVM), and random forest (RF) to the element data obtained. SVM accomplished the best classification performance with a 95.1% success rate, for which it was selected for citrus samples authentication. The proposed method highlights the capability of mineral profiles in accurately identifying the genuine origin of mandarin juices. By implementing this model in the food supply chain, it can prevent mislabeling fraud, thereby contributing to consumer protection.

Fingerprinting black tea: When spectroscopy meets machine learning a novel workflow for geographical origin identification

Food fraud, along with many challenges to the integrity and sustainability, threatens the prosperity of businesses and society as a whole. Tea is the second most commonly consumed non-alcoholic beverage globally. Challenges to tea authenticity require the development of highly efficient and rapid solutions to improve supply chain transparency. This study has produced an innovative workflow for black tea geographical indications (GI) discrimination based on non-targeted spectroscopic fingerprinting techniques. A total of 360 samples originating from nine GI regions worldwide were analysed by Fourier Transform Infrared (FTIR) and Near Infrared spectroscopy. Machine learning algorithms (k-nearest neighbours and support vector machine models) applied to the test data greatly improved the GI identification achieving 100% accuracy using FTIR. This workflow will provide a low-cost and user-friendly solution for on-site and real-time determination of black tea geographical origin along supply chains.

Identification of geographical origin of semen ziziphi spinosae based on hyperspectral imaging combined with convolutional neural networks

The accurate identification of geographical origin is one of the main challenges in quality assessment of semen ziziphi spinosae. This paper proposed resnet50-based and shuffle-based convolutional neural network (CNN) models (CNN-resnet and CNN-shuffle for hyperspectral image input, as well as CNN-resnet1D and CNN-shuffle1D for spectra input) to discriminate semen ziziphi spinosae from five geographical origins. Specifically, minimum noise fraction combined with mask was adopted to remove background pixels. Savitzky-Golay smoothing, followed by standard normal variate, was applied to denoise in pixel-wise. Mahalanobis distances were calculated based on the average spectra to identify and exclude outliers. Partial least squares discriminant analysis and support vector machine were also adopted to compare with the four CNN networks, particular in model accuracy, computing time, and parameters. Overall, CNN-shuffle was the best, with accuracy (0.902) comparable to CNN-resnet and in less time (0.348 s) and with fewer parameters (5607). Feature wavelengths were selected based on the learned weight coefficients in self-defined layer of CNN-resnet1D and CNN-shuffle1D models. They were related to lipid and protein, of which contents were statistically analyzed. Both lipids and proteins were key parameters, and lipids played a greater role in the identification. Spinosin as a quality marker was measured via standard HPLC method, which was comparably discussed with the hyperspectral imaging (HSI). The potential application of HSI with CNN in quality control of semen ziziphi spinosae within the modern system of mass production was higher compared with HPLC.

Identification of geographical origins of Radix Paeoniae Alba using hyperspectral imaging with deep learning-based fusion approaches.

The Radix Paeoniae Alba (Baishao) is a traditional Chinese medicine (TCM) with numerous clinical and nutritional benefits. Rapid and accurate identification of the geographical origins of Baishao is crucial for planters, traders and consumers. Hyperspectral imaging (HSI) was used in this study to acquire spectral images of Baishao samples from its two sides. Convolutional neural network (CNN) and attention mechanism was used to distinguish the origins of Baishao using spectra extracted from one side. The data-level and feature-level deep fusion models were proposed using information from both sides of the samples. CNN models outperformed the conventional machine learning methods in classifying Baishao origins. The generalized Gradient-weighted Class Activation Mapping (Grad-CAM++) was utilized to visualize and identify important wavelengths that significantly contribute to model performance. The overall results illustrated that HSI combined with deep learning strategies was effective in identifying the geographical origins of Baishao, having good prospects of real-world applications.

Geographical origin identification of Hainan camellia oil based on fatty acid composition and near infrared spectroscopy combined with chemometrics

Ensuring the authenticity of the original production region is of utmost importance in safeguarding the reputation and ensuring the quality and safety of Hainan camellia oil, which possesses unique quality and commands a higher price than camellia oil from other main producing areas in China. This study explored the potential of fatty acid composition and near infrared (NIR) spectra for geographical traceability of Hainan camellia oil. The relative content of 16 fatty acids in camellia oil samples was determined using gas chromatography (GC), and the spectral information of the samples was obtained using NIR spectroscopy. The data were then analyzed using chemometrics methods, comparing the classification abilities of partial least squares discriminant analysis (PLS-DA), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms. The results demonstrated that the SVM model based on the fatty acid composition, the CNN model based on the NIR spectra, and the CNN model based on data fusion achieved prediction accuracies of 97.08%, 97.92%, and 98.75%, respectively, enabling high-precision identification of the geographical origin of Hainan camellia oil. This study reveals that the fatty acid composition and NIR spectra can serve as accurate tools for identifying the geographical origin of camellia oil.

Analysis of Volatile Compounds in Different Varieties of Plum Fruits Based on Headspace Solid-Phase Microextraction-Gas Chromatography-Mass Spectrometry Technique

To investigate the differences in the volatile compounds of plum fruit samples from different cultivars, the volatile compounds of the ‘Fengtang’ plum, ‘Kongxin’ plum, and ‘Cuihong’ plum fruits were analyzed using headspace solid-phase microextraction–gas chromatography–mass spectrometry (HS-SPME-GC-MS). The results demonstrated that a total of 938 volatile compounds were identified in three plum fruits, including 200 terpenoids, 171 esters, 116 heterocyclic compounds, 89 hydrocarbons, 82 ketones and alcohols, 63 aldehydes, 54 aromatic hydrocarbons, 21 amines, 18 acids, 17 phenols, 10 nitrogenous compounds, 7 sulfur compounds, and other compounds, 470 of which were common to all the cultivars. Moreover, 704, 691, and 704 volatile substances were detected, respectively, in the ‘Fengtang’ plum, ‘Kongxin’ plum, and ‘Cuihong’ plum, with terpenoids, esters, and heterocycles as the main compounds, accounting for 62.12~72.03% of the volatile compounds. The results of principal component analysis (PCA) and cluster analysis (CA) illustrated that the ‘Fengtang’ plum and ‘Cuihong’ plum were similar in terms of volatile compounds; the ‘Kongxin’ plum compounds were different from those in the other cultivars. Orthogonal partial least squares discriminant analysis was performed, revealing the typical volatile compounds that differed among the plum fruits of the different varieties; thus, the three plum fruits could be better distinguished. These results can provide a theoretical basis for the studies of plum fruit flavor, quality, and geographical origin identification.

Identification of geographical origins of soybean pastes using headspace gas chromatography-mass spectrometry by selecting sample-descriptive components with an Incremental Association Markov Blanket

The identification of geographical origins of soybean pastes using headspace gas chromatography-mass spectrometry was attempted in this study. Since soybean paste was odor-rich, 36 components were identified in the imported and domestic soybean samples. t-Test, variable importance in projection (VIP), and Incremental Association Markov Blanket (IAMB) were employed to select proper components that could effectively discriminate the two sample groups. The discrimination accuracies were below 87.3 % when all 36 components were fed for either LDA, k-NN, or SVM. When the five t-test-selected components or six VIP score-selected components were employed, the accuracies improved to 95.2–96.2 %. The IAMB selected three different components were 3-methylbutanal, 4-methylnonane, and 2,3-pentanedione, and the correlations among their peak areas were not significant. This suggests that these three components were independently relevant for the discrimination. The accuracy obtained using these three components was superior, 97.7 %, as undescriptive and/or redundant components for the discrimination were excluded.

Identification of geographical origin of winter jujube based on GC–MS coupled with machine-learning algorithms

Headspace solid-phase microextraction (HS-SPME) combined with gas chromatography–mass (GC–MS) is the most often used technique for the analysis of volatile organic compounds (VOCs). When coupled with machine-learning algorithms, the two provide the means to differentiate the volatile metabolome of foods from different origins. In this study, the volatile metabolites of winter jujube, a fruit protected by China and the European Union (EU) based on geographical indications (GI), was analyzed by HS-SPME–GC–MS. Extreme gradient boosting (XGBoost) was applied, for the first time, to evaluate differences in winter jujube grown in four distinct regions. Locational differences yielded 33 VOCs that differed by functionality and concentration with a recurrence probability of ≥ 60%. Zhanhua winter jujube contained the most abundant VOCs, with Yuncheng winter jujube the least. The 33 winter jujube VOCs were analyzed by random forest (RF), support vector machine (SVM), and XGBoost to identify the place of origin. The discrimination rates were about 85% using RF, about 93% using SVM, and about 98% with XGBoost. Compared to RF and SVM algorithms, XGBoost was more efficient, producing higher accuracy rates, and providing new opportunities for origin traceability.

μ-PESI-based MS profiling combined with untargeted metabolomics analysis for rapid identification of red wine geographical origin.

The commercial value of red wine is strongly linked to its geographical origin. Given the large global market, there is great demand for high-throughput screening methods to authenticate the geographical source of red wine. However, only limited techniques have been established up to now. Herein, a sensitive and robust method, namely probe electrospray ionization mass spectrometry (μ-PESI-MS), was established to achieve rapid analysis at approximately 1.2 min per sample without any pretreatment. A scotch near the needle tip provides a fixed micro-volume for each analysis to achieve satisfactory ion signal reproducibility (RSD < 26.7%). In combination with a machine learning algorithm, 16 characteristic ions were discovered from thousands of detected ions and were utilized for differentiating red wine origin. Among them, the relative abundances of two characteristic metabolites (trigonelline and proline) correlated with geographical conditions (sun exposure and water stress) were identified, providing the rationale for differentiation of the geographical origin. The proposed μ-PESI-MS-based method demonstrates a promising high-throughput determination capability in red wine traceability.