Discrimination Of Geographical Origin Research Articles

Identification of millet origin using terahertz spectroscopy combined with ensemble learning

It’s crucial for both producers and consumers to accurately trace the origin of millet, given the significant differences in price and taste that exist between millets from various origins. The traditional method of identifying the origin of millet is time-consuming, laborious, complex, and destructive. In this study, a new method for fast and non-destructive differentiation of millet origins is developed by combining terahertz time domain spectroscopy with ensemble learning. Firstly, three machine learning algorithms, namely support vector machine (SVM), random forest (RF), and kernel extreme learning machine (KELM), were used to build different discriminative models, and then the impact of six different preprocessing methods on the models’ classification performance was compared. It was observed that models employing Savitzky-Golay preprocessing exhibited pronounced superiority in accurately determining the millet’s geographical origins. Building upon these findings, the research introduces an innovative ensemble learning strategy, leveraging both topsis and stacking techniques, to harness the collective strengths of the three algorithms. The outcomes of this approach reveal its remarkable capacity to distinguish millets originating from five distinct locations without the necessity for any parameter fine-tuning. The accuracy, F1 score, and Kappa on the prediction set are all 100 %, which significantly outperforms the single model, traditional voting method, and stacking method. The culmination of this study suggests that the integration of terahertz time-domain spectroscopy and TOPSIS-Stacking ensemble learning emerges as a promising method for the swift and non-intrusive discrimination of millet geographical origins with remarkable precision.

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.

Predicting the geospatial distribution of Chinese rice nutrient element in regional scale for the geographical origin—A case study on the traceability of Japonica rice

AbstractEffective geographical origin discrimination of Chinese rice requires a large database of samples to ensure sufficient data for origin verification at a regional scale. In this study, environmental similarity was used to establish a spatial database of rice nutrient element, and then the validity of the database was verified using the back propagation artificial neural networks modeling (BPNN). The spatial distribution model of 14 rice nutrient element (Al, Ba, Ca, Cu, Cr, Fe, K, Mg, Mn, Mo, Na, Ni, Rb, and Zn) on regional scale was built using an environmental similarity method for the first time. Elemental concentrations of 692 samples were used to build a simulated geographical origin prediction model for northeastern (N‐E), middle to lower Yangtze River plain (Y‐R), southwestern (S‐W), and southeastern (S‐E) in China. The results indicated that the performance of the environmental similarity model for these four growing regions was S‐W > N‐E > S‐E > Y‐R based on the lowest ranking root mean square error (RMSE) for each region. For example, the RMSEs of Zn in S‐W, N‐E, S‐E, and Y‐R regions were 2.0, 2.4, 2.7, and 3.7 mg/kg, respectively. A case study on the traceability of Japonica rice was shown that Japonica rice could be discriminated with higher origin accuracy using a simulated database (91.8%) than by the actual database (87.0%) using the BPNN model. This indicates that a simulated rice element database could improve the accuracy of geographical origin discrimination for Chinese rice and potentially be applied to other large national‐scale crop datasets.

Elemental profile of wheat in the las vegas market: Geographic origin discrimination and probabilistic health risk assessment

This study investigates concentrations of toxic and potentially toxic elements (PTEs) in organic and conventional wheat flour and grains marketed in Las Vegas. Geographic origins of the samples were evaluated using Linear Discriminant Analysis (LDA). Monte Carlo Simulation technique was also employed to evaluate non-carcinogenic risk in four life stages. Concentrations of Al, As, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Se, Sr, and Zn were determined using inductively coupled plasma mass spectrometry (ICP-MS) following hot block-assisted digestion. Obtained results showed non-significant differences in contents of toxic and PTEs between conventional and organic wheat grains/flour. Using LDA, metal (loid)s were found to be indicative of geographical origin. The LDA produced a total correct classification rate of 95.8% and 100% for US and West Pacific Region samples, respectively. The results of the present study indicate that the estimated non-carcinogenic risk associated with toxic element intakes across the four life stages were far lower than the threshold value (Target Hazard Quotient (THQ) > 1). However, the probability of exceeding the threshold value for Mn is approximately 32% in children aged between 5 and 8 years. The findings of this study can aid in understanding dietary Mn exposure in children in Las Vegas.

Identifying the geographical origin and processing technology of Moyao () on the basis of near-infrared spectroscopy combined with chemometrics.

To evaluate the quality of Moyao (Myrrh) in the identification of the geographical origin and processing of the products. Raw Moyao (Myrrh) and two kinds of Moyao (Myrrh) processed with vinegar from three countries were identified using near-infrared (NIR) spectroscopy combined with chemometric techniques. Principal component analysis (PCA) was used to reduce the dimensionality of the data and visualize the clustering of samples from different categories. A classical chemometric algorithm (PLS-DA) and two machine learning algorithms [K-nearest neighbor (KNN) and support vector machine] were used to conduct a classification analysis of the near-infrared spectra of the Moyao (Myrrh) samples, and their discriminative performance was evaluated. Based on the accuracy, precision, recall rate, and F1 value in each model, the results showed that the classical chemometric algorithm and the machine learning algorithm obtained positive results. In all of the chemometric analyses, the NIR spectrum of Moyao (Myrrh) preprocessed by standard normal variation or Multivariate scattering correction combined with KNN achieved the highest accuracy in identifying the geographical origins, and the accuracy of identifying the processing technology established by the KNN method after first-order derivative pretreatment was the best. The best accuracy of geographical origin discrimination and processing technology discrimination were 0.9853 and 0.9706 respectively. NIR spectroscopy combined with chemometric technology can be an important tool for tracking the origin and processing technology of Moyao (Myrrh) and can also provide a reference for evaluations of its quality and the clinical use.

Rapid discrimination of geographical origin of garlic (Allium sativum L.): A metabolomic approach applied to paper spray mass spectrometry data.

Distinguishing and categorizing the origin of garlic are highly significant, considering its widespread use as a flavoring agent. With billions of dollars annually in global trade, garlic is frequently susceptible to fraudulent practices. Paper spray ionization mass spectrometry (PS-MS) was employed to quickly analyze garlic samples from distinct geographic origins: China and Brazil. The so-generated PS-MS data were treated with metabolomic multivariate approaches, and the garlic samples from these different geographic regions were easily discriminated. Brazilian garlic was characterized to contain higher levels of amino acids, such as arginine, proline, and valine, and organosulfur compounds, such as allicin, alliin, and l-γ-glutamil-S-allyl-l-cysteine, compared to Chinese garlic. The PS-MS data were treated employing multivariate approaches, typically used in the metabolomics field, and this protocol was promptly able to discern among both types of samples. Hence, this combined strategy holds promise not only as an effective tool for the authentication of the geographical origin of garlic but also as a powerful means for biomarker discovery.

Discrimination of geographical origin of Korean and Chinese red pepper paste via inductively coupled plasma atomic emission spectroscopy and mass spectrometry

BackgroundRed pepper paste is a common ingredient used in food in Korea. The discrimination of the geographical origin of agricultural products is important to protect the agricultural industry and customers from the misinformation regarding the product origin. Several studies have attempted to identify the geographical origin of red pepper based on its characteristic features using diverse methods, such as inorganic elemental analysis. However, similar studies on red pepper pastes have not been conducted thus far.ResultsIn, this study, we established methods based on inductively coupled plasma optical emission spectrometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS) for determining inorganic elements in red pepper pastes. The limit of detection (LOD) of ICP-AES was in the range of 0.006–0.531 mg∙kg−1 and the limit of quantification (LOQ) was 0.017–1.593 mg∙kg−1. In addition, LOD and LOQ ranges for ICP-MS were 0.001–1.553, and 0.002–5.176 μg∙kg−1, respectively. The concentrations of Ca, K, Mg, Na, P, S, Cr, Mn, Co, Ni, Cu, Ga, As, Sr, Zr, Mo, Pd, Cd, Sn, Sb, Ce, Pt, Pb, and U were high in the Korean red pepper paste. All the employed discrimination models could clearly distinguish between Korean and Chinese red pepper pastes. In particular, among the four different models, CDA showed the most accurate ability to discriminate the geological origin of Korean and Chinese red pepper paste compared to that achieved using the other models with 100% accuracy.ConclusionsBased on, the findings of this study, the use of ICP-AES and ICP-MS analyses for discriminating the inorganic elements in food products in combination with the aforementioned statistical analysis models could help the mitigation of issues associated with the misinformation of the geographical origin of agricultural products, aiding customer protection.Graphical

Geographical origin discrimination of lemon myrtle (Backhousia citriodora) leaf powder using near-infrared hyperspectral imaging

Lemon myrtle (LM), Backhousia citriodora, is a popular flavouring agent and herbal tea from Australia. To ensure traceability and consumer trust in global food supply chain, rapid and non-destructive tools are crucial. In this study, hyperspectral images were acquired from 91 L M samples sourced from three different origins (Malaysia, Queensland, and New South Wales (Australia)), within 950–2500 nm range. Classification models were developed using linear partial least squares-discriminant analysis (PLS-DA) with two approaches, pixel-based (trained by all spectral data points) and sample-based (trained by average spectrum). All models achieved classification accuracies above 96%. The sample-based PLS-DA model, trained by mean-centring transformed data, demonstrated the highest discriminatory performance. Both approaches show potential for LM origin classification, but the sample-based model is more suitable for automated and rapid industry applications due to its shorter calculation time. However, additional spectral data acquisition is necessary to improve the model and fully explore its capabilities and limitations.

Geographical origin discrimination of black beans using near-infrared (NIR) spectroscopy combined with fuzzy regularized complete linear discriminant analysis (FRCLDA)

Black beans from different geographical origins exhibit variations in terms of their nutritional and economic values. Therefore, ensuring traceability of the geographical origin of black beans is essential for both consumers and the product processing industries. The study designed a comprehensive black bean origin identification model using near-infrared (NIR) spectroscopy combined with discriminant analysis algorithms. To enhance the performance of the model, this study incorporated fuzzy logic into regularized complete linear discriminant analysis (RCLDA) and proposed the fuzzy regularized complete linear discriminant analysis (FRCLDA). This innovative approach aims to extract additional information from overlapping NIR spectra of black beans. NIR spectra of black beans from five locations were collected employing a portable near-infrared spectrometer. In the preprocessing stage, the Savitzky–Golay (SG) and mean centering (MC) were applied to process the raw spectra. Subsequently, complete linear discriminant analysis (CLDA), RCLDA, and FRCLDA were utilized to extract the information from the processed data. Finally, k-nearest neighbor (KNN), support vector machines (SVM), and extreme learning machine (ELM) were employed to classify the black bean samples based on the extracted features. The results indicated that KNN achieved the best classification performance. The FRCLDA-KNN model achieved the exceptional performance with a classification accuracy of 99.23% on the test set. Therefore, the system demonstrates satisfactory accuracy and performance in accurately identifying black beans from different geographical origins.

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.

Rapid discrimination of xiaokeng green tea geographical origin using surface-enhanced Raman spectroscopy coupled with chemometrics

Xiaokeng green tea (XKGT), named for its origin, brings high economic benefits to the region due to its superior quality, but it is susceptible to fraud. In this study, 131 roasted green teas from different origins in Anhui Province were used as samples to investigate the ability of surface-enhanced Raman spectroscopy (SERS) combined with chemometrics to accurately discriminate green tea origins in narrow regions. First, spherical Ag nanoparticles (AgNPs) were prepared as SERS substrates. A stratified 5-fold cross-validation method was used to divide the modelling samples. The radial basis function neural network (RBFNN), convolutional neural network (CNN), and random forest (RF) models were built, and four different preprocessing methods were compared. The results showed that the optimised RBFNN model with normalisation, as the preprocessing method, had an average prediction set accuracy of 97.69% in distinguishing samples from the Xiaokeng tea area from other tea areas. The RBFNN model was further used to differentiate tea samples from four different origins within Anhui Province, namely Xiaokeng Village (XK), Chizhou City except Xiaokeng Village (CZ), Lu'an City (L'A) and Huangshan City (HS), with an average accuracy of 91.85% for the prediction set. These findings point to the potential of combining SERS with chemometrics as an effective method for discriminating the geographic origins of XKGT in narrow regions.

A study of volatiles of young citrus fruits from four areas based on GC–MS and flash GC e-nose combined with multivariate algorithms

The present study has successfully established a scientific and precise approach for distinguishing the geographical origins of young citrus fruits (Qingpi) from four primary production regions in China, using gas chromatography-mass spectrometry (GC–MS) and flash gas chromatography electronic nose (flash GC e-nose) to analyze the volatile composition and odor characteristics. Through the application of chemometric analysis, a clear differentiation among Qingpi samples was established using GC–MS. Additionally, the application of flash GC e-nose facilitated the extraction of flavor information, which enabled the discrimination of geographical origins. Several flavor components were identified as significant factors for origin certification. Furthermore, two pattern recognition algorithms were employed to achieve high accuracy in regional identification. The results of this investigation demonstrate that the amalgamation of multivariate chemometrics and algorithms can proficiently discern the sources of those young citrus fruits. The findings of this research can provide a reference for the assessment of quality control in food and other agricultural commodities in the times ahead.

Elemental analysis of wild Eriocheir sinensis: Determining the geographic origin and human health risk assessment

Identification of composition characteristics of major and trace elements in wild mitten crabs (Eriocheir sinensis) from different water systems is important for protected geographical indication as well as food quality control and safety. In this study, inductively coupled plasma-mass spectrometry was employed to measure the contents of 23 elements in the muscle samples of wild Eriocheir sinensis from three water systems (Suifenhe, Nanliujiang, and Liaohe). The results of Kruskal–Wallis one-way ANOVA test comparisons revealed that most elements were significantly different in the samples collected from three water systems (P < 0.05). Geographic origin discrimination was achieved using principal component analysis in combination with hierarchical cluster analysis (HCA) and stepwise linear discriminant analysis (S-LDA). The HCA results showed the potential of eight characteristic elements for exploratory hierarchical clustering of the samples from three water systems; however, the clustering effect was unsatisfactory. The discrimination accuracy of S-LDA model for the samples from three water systems and that in cross-validation reached 100%. The health risk assessment further revealed that the normal level consumption of wild Eriocheir sinensis did not pose an appreciable health risk to consumers.

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.

Biomarker Development for Identifying Mud Loach (Misgurnus mizolepis) Origin Country Using Untargeted Metabolite Profiling.

Mud loach (Misgurnus mizolepis) has long been consumed in Korea. Recently, Chinese mud loaches were replaced with expensive Korean mud loaches, owing to taste and preference. Such issues occur in aquatic food distribution processes, leading to inferior food delivery. Previously, a study was conducted to confirm the origin of mud loaches using genetic analysis. However, untargeted metabolites profiling of mud loaches has not been reported. Untargeted metabolomics provides information on the overall metabolic profiling of a sample, allowing the identification of new metabolites. Here, we analyzed the metabolites of mud loaches of different geographical origins using liquid chromatography (LC)-quadrupole-time-of-flight mass spectrometry (MS). Orthogonal partial least squares discriminant analysis from LC/MS datasets showed a clear distinction between Korean and Chinese mud loaches, and univariate statistical analysis showed significantly different metabolites between them. N-acetylhistidine and anserine were selected as biomarkers for geographical origin discrimination using the receiver operating characteristic curve. N-acetylhistidine and anserine levels were significantly higher in Chinese than in Korean mud loaches. These results indicate that metabolic analysis can be used to discriminate between the geographical origins of mud loaches, curtailing the inadvertent substitution of mud loaches from different regions.

Determination of the geographical origin of chicken (breast and drumstick) using ICP-OES and ICP-MS: Chemometric analysis

This study aimed to develop a geographical origin discrimination analytical method for chicken breasts and drumsticks based on inductively coupled plasma (ICP). The sixty elements were set as variables, and the geographical origin discrimination analysis was conducted through chemometrics. In orthogonal partial least square discriminant analysis (OPLS-DA), twenty-three variable importance in projection (VIP) elements were selected in chicken breasts, and twenty-eight VIP elements were selected in drumsticks. The importance of the selected elements was displayed by the area under the curve (AUC) value of the receiver operating characteristic (ROC). Verification of OPLS-DA was performed through permutation test and good results were obtained. A heatmap was also used as a method for determining the geographical origin, and each top element discriminant classification was 100 % accurate, as determined through canonical discriminant analysis (CDA). This method shows potential as a food analysis tool and can accurately determine the geographic origin of chicken.

The discrimination of geographical origin of lotus root from major producing areas of China based on multi-elemental analysis combined with multivariate data analysis

Lotus root is a popular traditional Chinese food of great economic and nutritional significance, which has different qualities, characteristics, and commercial value from various regions due to the climate and growing environment. Therefore, it is essential and urgent to establish an accurate and effective method to trace the geographical origins of lotus roots. This study combined mineral element analysis and multivariate statistical analysis to discriminate the origins of 147 lotus root samples from different production regions of China. The concentrations of 15 elements were determined by inductively coupled plasma emission spectrometry and inductively coupled plasma mass spectrometry. In addition, chemometric techniques, including ANOVA, principal component analysis and stepwise linear discriminant analysis were utilized to identify the geographical origin of lotus roots. The results showed that the levels of the 15 elements were significantly different by ANOVA. The study indicates that stepwise linear discriminant analysis yielded an average discrimination rate of 99.3%.

Geographical origin classification of tobacco by stable isotope and multi-elemental analysis in combination with chemometric methods

Tobacco is an important economic crop widely grown around the world. Its quality and features are closely interrelated to its geographical origin. With a view to identify the geographical origin of tobacco leaves, stable isotope ratios (δ13C, δ15N and δ2H) and 33 element contents in 260 tobacco samples collected from six provinces in China were determined by stable isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry (ICP-MS), respectively, and analyzed with the aid of chemometric methods such as Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) and Random Forest (RF). Experimental results revealed that δ2H, Zn and Tl were significantly different in samples from different regions, which indicated their potential to be used in geographical origin classification of tobacco samples. Both OPLS-DA and RF models displayed satisfactory overall performance for the discrimination of geographical origins of tobacco samples from six cultivation areas. Particularly, the RF model built on the fused data of IRMS and ICP-MS had an exquisite sensitivity of 98.5% in comparison with the RF models on either the data set produced by IRMS or ICP-MS alone, whose sensitivity values were 92.3% and 95.4%, respectively. It was found that the top ten influential indicators for identifying the geographic origin of tobacco were δ2H, Cr, δ15N, 87/86Sr, Li, Ba, Sb, Pb, Tl, and Zn. This work may open up an avenue for geographical origin classification of tobacco.

Elemental compositions and stable isotope signatures for determining the geographical origin of salep orchids collected from different regions of Turkey

Salep is a highly valued natural food and medicinal commodity with strict regulations on its use and export. It is produced from tubers of orchids and consumed for its delectable and functional qualities in different parts of the world. This study aimed to identify and compare the elemental composition and isotope ratios of salep based on discrimination of geographical origins. The samples were collected from five different regions (North, Southwest, South, Southeast and East Anatolia) of Turkey. The 21 microelements (Li, Be, Ti, V, Cr, Mn, Fe, Ni, Co, Cu, Zn, Ga, Se, Rb, Sr, Mo, Sb, Cs, Ba, Tl and Bi) were determined by inductively coupled plasma mass spectrometry (ICP-MS), while the isotope ratios (δ13C, δ15N, δ18O and δ2H) were determined using isotope ratio mass spectrometry (IRMS). According to the ICP-MS results, Fe, Zn and Mn comprised around 77% of the elemental composition. North Anatolia samples had higher concentrations of Cr and Ni, while samples from Southeast and East Anatolia regions were enriched in Cs. Isotopic ratios varied significantly between samples and minimum and maximum ratios were determined as − 29.76 and − 26.63‰, 0 and 6.66‰, 28.09 and 37.31‰, − 144.86 and − 84.20‰ for the δ13C, δ15N, δ18O and δ2H, respectively. Collected isotopic data was also evaluated by multivariate statistical analyses to discriminate according to their geographical origins of salep tubers. According to the findings, the samples had a characteristic isotopic fingerprint and the isotope ratios can be important predictors for the discrimination of geographical origins. Results indicate the significant compositional variation between salep of different origins, which may be attributable to differences in environmental conditions such as climate and soil composition and intrinsic factors such as fungal associations of orchids.

Functional Compounds of Cold-Pressed Pomegranate Seed Oil: Fatty Acids and Phytosterols Profile as Quality Biomarkers for Origin Discrimination.

Cold-pressed pomegranate seed oil (PSO) is a product of the extraction of non-edible pomegranate seeds. Its unique chemical composition in terms of both polyunsaturated fatty acids, especially punicic acid (PA), and secondary metabolites, such as phytosterols, tocopherols and phenols, make it an interesting functional ingredient for food enrichment. It is not clear if the biomarkers profile of PSO depends to factors connected to the geographical origin of seeds. This work presents a statistical comparative analysis, concerning biomolecules composition and geographical origin of 32 commercial cold-pressed PSOs, performed by principal component analysis. The study discriminates between Turkish and Italian PSOs, on the base of the fatty acid profile and phytosterols, and not on the tocopherols and phenols. These results confirmed PA as the main characteristic biomarker of oil genuineness and, for the first time, disclosed a statistically relevant variability of phytosterols, which can be proposed as quality biomarkers for discrimination of geographical origins.