Authentication of the geographical origin of extra-virgin olive oil of the Arbequina cultivar by chromatographic fingerprinting and chemometrics

Enhancing quality evaluation in traditional Chinese medicine: Utilizing dual wavelength fusion fingerprint, electrochemical fingerprint, and DSC fingerprint

Developing a reliable and effective quality evaluation system for traditional Chinese medicine (TCM) is both challenging and crucial for its advancement. This study employs fingerprinting techniques to establish precise and comprehensive quality control for TCM, taking Xuezhikang capsules as an example and aiming to facilitate the internationalization of TCM. The "double wavelength absorption coefficient ratio fingerprint" and "Reliability theory" are developed to determine the fingerprint peak purity and fingerprint reliability respectively. Subsequently, the dual-wavelength fusion fingerprint was obtained to avoid the limitations of a single wavelength. In addition, an electrochemical fingerprint (ECFP) was obtained to assess the similarity of electroactive components in the sample, and the Differential Scanning Calorimetry quantized fingerprint (DSC QFP) was introduced for thermal analysis. Fingerprint-efficacy correlations between PL-EC* and dual-wavelength fusion fingerprint (DWFFP) provided valuable insights that there are 76.6 % of the fingerprint compounds exhibited electroactivity. Finally, samples were classified into grades 1∼3 by combining DWFFP, ECFP and DSC QFP through the mean method, meeting the evaluation standard (SL-M > 0.9, PL-M between 80 % and 120 %). This study provides valuable information for ensuring the quality of TCM products, which represents a significant step forward in enhancing the reliability and authenticity of TCM products.

Metabolomics: A suitable foodomics approach to the geographical origin traceability of Ethiopian Arabica specialty coffees

Coffee arabica, originated in Ethiopia, is considered a quality bean for its high sensory qualities, and has a special price in the world coffee market. The country is a pool of genetic diversity for Arabica coffee, and coffee from different regions has a distinct flavor profile. Their exceptional quality is attributed to their genetic diversity, favorable environmental conditions, and agroforestry-based production system. However, the country still needs to benefit from its single-origin product due to a lack of appropriate traceability information to register for its geographical indication. Certification of certain plants or plant-derived products emerged to inform consumers about their exceptional qualities due to their geographical origin and protect the product from fraud. The recently emerging foodomics approaches, namely proteomics, genomics, and metabolomics, are reported as suitable means of regional agri-food product authentication and traceability. Particularly, the metabolomics approach provides truthful information on product traceability. Despite efforts by some researchers to trace the geographical origin of Ethiopian Arabica coffees through stable isotope and phenolic compound profiling and elemental analysis, foodomics approaches are not used to trace the geographical origin of Arabica specialty coffees from various parts of the country. A metabolomics-based traceability system that demonstrates the connection between the exceptional attributes of Ethiopian Arabica specialty coffees and their geographic origin is recommended to maximize the benefit of single-origin coffees.

Combining different biomarkers to distinguish Chemlali virgin olive oils from different geographical areas of Tunisia

BACKGROUND

Despite their high potential, Tunisian virgin olive oils (VOOs) are mainly exported in bulk or blended with VOOs of other origin, hindering their international market placement. To face this situation, their valorization is needed by highlighting their unique features and by developing tools to guarantee their geographical authenticity. Compositional features of Chemlali VOOs produced in three Tunisian regions were assessed to identify suitable authenticity markers.

RESULTS

Quality indices ensured the quality of the VOOs studied. Volatile compounds, total phenols, fatty acid (FA) and chlorophylls are significantly influenced by the region of origin, which was justified by the differences found in soil and climatic conditions of the three geographical regions. To explore the capabilities of these markers for the geographical authentication of Tunisian Chemlali VOOs, classification models based on partial least squares-discriminant analysis (PLS-DA) were developed by grouping the minimum number of variables allowing the highest discrimination power, minimizing in this way the analytical procedure. The PLS-DA authentication model based on combining volatile compounds with FA or with total phenols achieved a correct classification of 95.7% of the VOOs according to their origin, as assessed by 10%-out cross-validation. Sidi Bouzid Chemlali VOOs achieved 100% of correct classification, while the misclassification between Sfax and Enfidha ones did not exceed 10%.

CONCLUSIONS

These results allowed to establish the most promising and affordable combination of markers for the geographical authentication of Tunisian Chemlali VOOs from distinct production regions and provide the basis to further develop authentication models based on wider datasets. © 2023 Society of Chemical Industry.

Identification of Chinese red wine origins based on Raman spectroscopy and deep learning

In this study, we combined Raman spectroscopy with deep learning for the first time to establish an accurate, simple, and fast method to identify the origin of red wines. We collected Raman spectra from 200 red wine samples of the Cabernet Sauvignon variety from four different origins with a portable Raman spectrometer. The red wine samples, made in 2021, were from the same producer in China. Differences were found by analyzing the Raman spectra of red wine samples. These differences are mainly caused by ethanol, carboxylic acids, and polyphenols. After further analysis, for different origins, the different performances of these substances on the Raman spectrum are related to the climate and geographical conditions of the origin. The Raman spectra were analyzed by principal component analysis (PCA). The data with PCA dimensionality reduction were imported into an artificial neural network (ANN), multifeature fusion convolutional neural network (MCNN), GoogLeNet, and residual neural network (ResNet) to establish red wine origin identification models. The classification results of the model prove that climate, geography, and other conditions can provide support for the classification of red wine origin. The experiments showed that all four models performed well, among which MCNN performed the best with 93.2% classification accuracy, and the area under the curve (AUC) was 0.987. This study provides a new means to classify the origin of red wine and opens up new ideas for identifying origins in the food field.

A classification and identification model of extra virgin olive oil adulterated with other edible oils based on pigment compositions and support vector machine

Adulteration identification of extra virgin olive oil (EVOO) is a vital issue in the olive oil industry. In this study, chromatographic fingerprint data of pigments combined with machine learning methodologies were successfully identified and classified EVOO, refined-pomace olive oil (R-POO), rapeseed oil (RO), soybean oil (SO), peanut oil (PO), sunflower oil (SFO), flaxseed oil (FO), corn oil (CO), extra virgin olive oil adulterated with rapeseed oil (EVOO-RO) and extra virgin olive oil adulterated with corn oil (EVOO-CO). Support vector machine (SVM) classification of EVOO, other edible oils, and EVOO adulteration identification achieved 100% accuracy for the training set sample and 94.44% accuracy for the test set sample. As a result, this SVM model could identify effectively the adulteration EVOO with the limit of 1% RO and 1% CO. Therefore, the excellent classification and predictive power of this model indicated pigments could be used as potential markers for identifying EVOO adulteration.

Comparative chemical characters of Pseudostellaria heterophylla from geographical origins of China

Objective

Pseudostellaria heterophylla has been paid more attention in recent years, mainly as a medicine food homology plant. The content determination of P. heterophylla is not specified in the Chinese Pharmacopoeia (version 2020). The environmental conditions in different production areas could exert an influence on the quality of P. heterophylla. The purpose of this study is to discriminate P. heterophylla collected from different geographical origins of China.

Methods

In this study, the content of polysaccharide in 28 batches of P. heterophylla was determined using phenol-sulfuric acid. HPLC fingerprints were established under optimised HPLC-PDA methods. Subsequently, the similarity analysis (SA) and the quantification of heterophyllin B were analyzed. The metabolites of P. heterophylla were identified and evaluated using UHPLC-Q Exactive HF orbitrap MS system. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), hierarchical cluster analysis (HCA) and orthogonal PLS-DA (OPLS-DA) were performed based on all peak areas.

Results

The polysaccharide content in Guizhou and Jiangsu was higher than that of other production areas, which varied significant from different origins. While the content of heterophyllin B in Anhui and Jiangsu was high. The correlation coefficients of HPLC fingerprints for 28 batches samples ranged from 0.877 to 0.990, and the characteristic map can be used to identify and evaluate the quality of P. heterophylla. The samples from Fujian, Guizhou, Jiangsu provinces can be relatively separated using multivariate statistical analysis including PCA, PLS-DA, HCA, OPLS-DA, indicating that their metabolic compositions were significantly different. Ultimately, a total of 15 metabolites which were filtrated by a VIP-value > 1 and a P-value < 0.05 associated with the separation of different origins were identified.

Conclusion

HPLC fingerprint was established to evaluate the quality and authenticity of P. heterophylla. The present work showed that the difference of geographic distributions had an influence on the internal chemical compositions. A sensitive and rapid untargeted metabolomics approach by UHPLC-Q Exactive HF orbitrap MS was utilized to evaluate P. heterophylla from different origins in China for the first time. Overall, this study provides insights to metabolomics of P. heterophylla and supplies important reference values for the development of functional foods.

Compound synthesizing profiling based on quantitative HPLC fingerprints combined with antioxidant activity analysis for Zhizi Jinhua pills

BACKGROUND

Compound drug Zhizi Jinhua Pills (ZZJHP) is composed of 8 herbal medicines (HMs), and it is necessary to control the HMs to ensure its holistic quality.

PURPOSE

To establish a quality monitoring method for ZZJHP from precise control of multiple active ingredients to contour control of fingerprint, to calculate the contribution of HMs and predict the quality of compound drugs.

METHODS

In this study, HPLC method was established for content determination of 11 analytes and fingerprint assessment. In vivo and in vitro studies of antioxidant activity were performed, Orthogonal Partial Least Squares analysis was applied for spectrum-effect correlation between antioxidant activity and HPLC fingerprint. The compound synthesizing fingerprint (CSF) of ZZJHP was fitted with 8 HMs, and the contribution of the single herb to prescription was evaluated by Sub-quantified profiling method.

RESULTS

The content of 11 analytes and fingerprints of ZZJHP were measured simultaneously, 32 batches of samples were divided into 6 grades. In vivo and in vitro researches suggested significant antioxidant activity capacity of ZZJHP. Then, spectrum-effect relationship study showed that 24 of the 30 fingerprint peaks had antioxidant activity. By prescription and decomposition profiling, the qualitative and quantitative contributions of the 8 herbs were revealed in turn. The negative solution experiment proved that CSF could accurately predict the quality of composite drugs.

CONCLUSION

The intelligent prediction strategy could intervene at the source to realize rapid screening of HMs and prediction of the quality of preparations, which could provide guidance for the use of HMs and improve the quality of composite drugs.

Geographical authentication of virgin olive oil by GC-MS sesquiterpene hydrocarbon fingerprint: Verifying EU and single country label-declaration

According to the last report from the European Union (EU) Food Fraud Network, olive oil tops the list of the most notified products. Current EU regulation states geographical origin as mandatory for virgin olive oils, even though an official analytical method is still lacking. Verifying the compliance of label-declared EU oils should be addressed with the highest priority level. Hence, the present work tackles this issue by developing a classification model (PLS-DA) based on the sesquiterpene hydrocarbon fingerprint of 400 samples obtained by HS-SPME-GC-MS to discriminate between EU and non-EU olive oils, obtaining an 89.6% of correct classification for the external validation (three iterations), with a sensitivity of 0.81 and a specificity of 0.95. Subsequently, multi-class discrimination models for EU and non-EU countries were developed and externally validated (with three different validation sets) with successful results (average of 92.2% of correct classification for EU and 96.0% for non-EU countries).

Quality assessment of Succus Bambusae oral liquids based on gas chromatography/mass spectrometry fingerprints and chemometrics

RATIONALE

Succus Bambusae is consumed as a kind of herbal medicine and natural beverage in China. However, the current quality standards for Succus Bambusae are low and lack safety indicators, which makes it difficult to effectively guarantee its quality. Therefore, it is of great significance to study the identification and quality control technology for the product.

METHODS

We have developed a set of qualitative and quantitative methods based on gas chromatography/mass spectrometry (GC/MS) for the analysis of volatile components in Succus Bambusae oral liquid (SBOL). Combining GC/MS fingerprint analysis and related chemometrics algorithms, with similarity evaluation, Hotelling T² and distance to Model X (DModX) as criteria, the quality consistency of different batches was evaluated, and SBOL samples from different manufacturers were differentiated.

RESULTS

Twenty-nine volatile components were preliminarily identified from 40 batches of SBOL samples from six manufacturers, and six Q-markers (Quality Markers) for the SBOLs were discussed and determined using GC/MS. The products from different manufacturers were distinguished using chemometrics.

CONCLUSIONS

The results showed that the quality of the SBOL samples from different batches and different manufacturers fluctuated greatly, which suggested that research into the raw materials and manufacturing techniques should be strengthened to improve the quality of SBOL and ensure its quality consistency.

Chromatographic Fingerprinting and Food Identity/Quality: Potentials and Challenges

Chromatograms are a valuable source of information about the chemical composition of the food being analyzed. Sometimes, this information is not explicit and appears in a hidden or not obvious way. Thus, the use of chemometric tools and data-mining methods to extract it is required. The fingerprint provided by a chromatogram offers the possibility to perform both identity and quality testing of foodstuffs. This perspective is aimed at providing an updated opinion of chromatographic fingerprinting methodology in the field of food authentication. Furthermore, the limitations, its absence in official analytical methods, and the future directions of this methodology are discussed.

Portable through Bottle SORS for the Authentication of Extra Virgin Olive Oil

The authenticity of olive oil has been a significant long-term challenge. Extra virgin olive oil (EVOO) is the most desirable of these products and commands a high price, thus unscrupulous individuals often alter its quality by adulteration with a lower grade oil. Most analytical methods employed for the detection of food adulteration require sample collection and transportation to a central laboratory for analysis. We explore the use of portable conventional Raman and spatially-offset Raman spectroscopy (SORS) technologies as non-destructive approaches to assess the adulteration status of EVOO quantitatively and for SORS directly through the original container, which means that after analysis the bottle is intact and the oil would still be fit for use. Three sample sets were generated, each with a different adulterant and varying levels of chemical similarity to EVOO. These included EVOO mixed with sunflower oil, pomace olive oil, or refined olive oil. Authentic EVOO samples were stretched/diluted from 0% to 100% with these adulterants and measured using two handheld Raman spectrometers (excitation at 785 or 1064 nm) and handheld SORS (830 nm). The PCA scores plots displayed clear trends which could be related to the level of adulteration for all three mixtures. Conventional Raman (at 785 or 1064 nm) and SORS (at 830 nm with a single spatial offset) conducted in sample vial mode resulted in prediction errors for the test set data ranging from 1.9–4.2% for sunflower oil, 6.5–10.7% for pomace olive oil and 8.0–12.8% for refined olive oil; with the limit of detection (LOD) typically being 3–12% of the adulterant. Container analysis using SORS produced very similar results: 1.4% for sunflower, 4.9% for pomace, and 10.1% for refined olive oil, with similar LODs ranging from 2–14%. It can be concluded that Raman spectroscopy, including through-container analysis using SORS, has significant potential as a rapid and accurate analytical method for the non-destructive detection of adulteration of extra virgin olive oil.

The use of analytical techniques coupled with chemometrics for tracing the geographical origin of oils: A systematic review (2013-2020)

The global market for imported, high-quality priced foods has grown dramatically in the last decade, as consumers become more conscious of food originating from around the world. Many countries require the origin label of food to protect consumers need about true characteristics and origin. Regulatory authorities are looking for an extended and updated list of the analytical techniques for verification of authentic oils and to support law implementation. This review aims to introduce the efforts made using various analytical tools in combination with the multivariate analysis for the verification of the geographical origin of oils. The popular analytical tools have been discussed, and scientometric assessment that underlines research trends in geographical authentication and preferred journals used for dissemination has been indicated. Overall, we believe this article will be a good guideline for food industries and food quality control authority to assist in the selection of appropriate methods to authenticate oils.

Preliminary Study on Tracing the Origin and Exploring the Relations between Growing Conditions and Isotopic and Elemental Fingerprints of Organic and Conventional Cavendish Bananas (Musa spp.)

The stable isotopic ratios and elemental compositions of 120 banana samples, Musa spp. (AAA Group, Cavendish Subgroup) cultivar Williams, collected from six countries (Colombia, Costa Rica, Dominica Republic, Ecuador, Panama, Peru), were determined by isotope ratio mass spectrometry and inductively coupled plasma mass spectrometry. Growing conditions like altitude, temperature, rainfall and production system (organic or conventional cultivation) were obtained from the sampling farms. Principal component analysis (PCA) revealed separation of the farms based on geographical origin and production system. The results showed a significant difference in the stable isotopic ratios (δ¹³C, δ¹⁵N, δ¹⁸O) and elemental compositions (Al, Ba, Cu, Fe, Mn, Mo, Ni, Rb) of the pulp and peel samples. Furthermore, δ¹⁵N was found to be a good marker for organically produced bananas. A correlation analysis was conducted to show the linkage of growing conditions and compositional attributes. The δ¹³C of pulp and peel were mainly negatively correlated with the rainfall, while δ¹⁸O was moderately positively (R values ~0.5) correlated with altitude and temperature. A moderate correlation was also found between temperature and elements such as Ba, Fe, Mn, Ni and Sr in the pulp and peel samples. The PCA results and correlation analysis suggested that the differences of banana compositions were combined effects of geographical factors and production systems. Ultimately, the findings contribute towards understanding the compositional differences of bananas due to different growing conditions and production systems linked to a defined origin; thereby offering a tool to support the traceability of commercial fruits.

Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

Olive oil is a basic element of the Mediterranean diet and a key product for the economies of the Mediterranean countries. Thus, there is an added incentive in the olive oil business for fraud through practices like adulteration and mislabeling. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) assisted by machine learning is used for the classification of 139 virgin olive oils in terms of their geographical origin. The LIBS spectra of these olive oil samples were used to train different machine learning algorithms, namely LDA, ERTC, RFC, XGBoost, and to assess their classification performance. In addition, the variable importance of the spectral features was calculated, for the identification of the most important ones for the classification performance and to reduce their number for the algorithmic training. The algorithmic training was evaluated and tested by means of classification reports, confusion matrices and by external validation procedure as well. The present results demonstrate that machine learning aided LIBS can be a powerful and efficient tool for the rapid authentication of the geographic origin of virgin olive oil.

Multiple-fingerprint analysis of Poria cocos polysaccharide by HPLC combined with chemometrics methods

In this study, the multiple fingerprints, which were integrated with HPGFC-ELSD (high performance gel filtration chromatography - evaporative light scattering detector) fingerprint, PMP-HPLC-DAD (1-phenyl-3-methyl-5-pyrazolone-high performance liquid chromatography - diode array detector) fingerprint of complete acid hydrolysates and HILIC-HPLC-ELSD (hydrophilic interaction - high performance liquid chromatography - evaporative light scattering detector) fingerprint of enzyme hydrolysates, were established to evaluate the quality of polysaccharides from Poria cocos (PCPs). The similarity evaluation showed that 16 batches of PCPs from different origins had high similarity in structural characteristics based on the multiple fingerprints. The chromatographic data of multiple fingerprints of PCPs were fused, processed and analyzed by chemometric methods including HCA (hierarchical cluster analysis), PCA (principal component analysis) and PLS-DA (partial least squares discriminant analysis). The 16 batches of PCPs were divided into 3 categories in PCA, indicating a certain relationship between the structural characteristics and the origins. PLS-DA analysis indicated that Man, Glc, Gal, Fuc, the components with m/z of 2.22 × 10⁴∼1.53 × 10⁴ Da and 3.46 × 10³∼2.69 × 10³ Da, oligosaccharides with DPs of 6 and 7, respectively, could be regarded as potential chemical markers for the classification of PCPs from different origins. According to the multiple fingerprints and chemometric analysis, the two commercial samples were proved to be adulterants.

State-of-the-Art of Analytical Techniques to Determine Food Fraud in Olive Oils

The benefits of the food industry compared to other sectors are much lower, which is why producers are tempted to commit fraud. Although it is a bad practice committed with a wide variety of foods, it is worth noting the case of olive oil because it is a product of great value and with a high percentage of fraud. It is for all these reasons that the authenticity of olive oil has become a major problem for producers, consumers, and legislators. To avoid such fraud, it is necessary to develop analytical techniques to detect them. In this review, we performed a complete analysis about the available instrumentation used in olive fraud which comprised spectroscopic and spectrometric methodology and analyte separation techniques such as liquid chromatography and gas chromatography. Additionally, other methodology including protein-based biomolecular techniques and analytical approaches like metabolomic, hhyperspectral imaging and chemometrics are discussed.

Development of Chemometric Models Based on a LC-qToF-MS Approach to Verify the Geographic Origin of Virgin Olive Oil

In the presented study a non-targeted approach using high-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (HPLC-ESI-qToF-MS) combined with chemometric techniques was used to build a statistical model to verify the geographic origin of virgin olive oils. The sample preparation by means of liquid/liquid extraction of polar compounds was optimized regarding the number of multiple extractions, application of ultrasonic treatment and temperature during concentration of the analytes. The presented workflow for data processing aimed to identify the most predictive features and was applied to a set of 95 olive oils from Spain, Italy, Portugal and Greece. Different strategies for data reduction and multivariate analysis were compared. Stepwise variable selection showed for both applied multivariate models—linear discriminant analysis (LDA) and logit regression (LR)—to be the most suitable variable selection strategy. The 10-fold cross validation of the LDA showed a classification rate of 83.1% for the test set. For the LR models the prediction accuracy of the test set was even higher with values of 90.4% (Portugal), 86.2% (Italy), 93.8% (Greece) and 88.3% (Spain). Moreover, the reduction of features allows an easier following up strategy for identification of the unknowns and defining marker substances.

A Non-Targeted High-Resolution Mass Spectrometry Study for Extra Virgin Olive Oil Adulteration with Soft Refined Oils: Preliminary Findings from Two Different Laboratories

This work presents a non-targeted high-resolution mass spectrometry inter-laboratory study for the detection of new chemical markers responsible of soft refined oils addition to extra virgin olive oils. Refined oils (soft deodorized and soft deacidified) were prepared on a laboratory scale starting from low-quality olive oils and analyzed together with a set of pure extra virgin olive oil (EVOO) samples and with mixtures of adulterated and pure EVOO at different percentages. The same analytical workflow was applied in two different laboratories equipped with two types of instrumentation (Q-Orbitrap and Q-TOF); a group of discriminant molecules was selected, and a tentative identification of compounds was also proposed. In summary, 12 molecules were identified as markers of this specific adulteration, and seven of them were selected as discriminative in both the laboratories, with a similar trend throughout the samples (i.e., propylene glycol 1 stearate). The results obtained in the two laboratories are comparable, concretely demonstrating the inter-laboratory repeatability of non-targeted studies. As a confirmation, the same markers were detected also in "in-house" mixtures and in suspect commercial deodorized mixtures, reinforcing the robustness of the results obtained and proving that, thanks to these molecules, mixtures containing at least 40% of adulterated oils can be detected.

Varietal and Geographical Discrimination of Greek Monovarietal Extra Virgin Olive Oils Based on Squalene, Tocopherol, and Fatty Acid Composition

Extra virgin olive oil (EVOO) is an important component of the Mediterranean diet and a highly priced product. Despite the strict legislation to protect it from fraudulent practices, there is an increasing demand to characterize EVOOs and evaluate their authenticity. For this purpose, 68 monovarietal EVOOs, originating from three regions of Greece (Peloponnese, Crete, and Lesvos) and two local cultivars (Koroneiki and Kolovi), were obtained during the harvesting period of 2018-2019. Fatty acids, squalene, and tocopherols were determined chromatographically according to official methods in order to study the effect of cultivar and geographical origin. Squalene and γ-tocopherol differed significantly amongst the cultivars tested. Koroneiki samples exhibited higher squalene content than Kolovi samples, whereas the opposite was observed for γ-tocopherol. The tocopherol level was highly geographical dependent, with EVOOs from Peloponnese displaying the highest concentration of α-tocopherol, whereas the content of γ-tocopherol was significantly higher in samples from Lesvos. Unsupervised and supervised multivariate analysis resulted in a satisfactory grouping of EVOOs according to cultivar. γ-Tocopherol, squalene, and the majority of fatty acids were the most discriminant variables, with γ-tocopherol, linoleic, linolenic, and gadoleic acid being present at higher levels in samples from the Kolovi cultivar. Koroneiki samples were characterized with higher levels of squalene, palmitic, palmitoleic, and arachidic acid.

Rapid Analytical Method to Characterize the Freshness of Olive Oils Using Fluorescence Spectroscopy and Chemometric Algorithms

One of the most important issues in the field of quality assurance of olive oils is the detection of the freshness of olive oil. In this study, 400 nm laser-induced fluorescence spectroscopy was used with supervised and unsupervised multivariate analysis methods to develop a rapid method able to discriminate between freshly produced olive oils and oil that has been stored for a period of time ranging from 12 to 24 months. The fluorescence spectral data were firstly processed by the PCA. This method shows strong discrimination of the three oil classes using the first three components which present 96% of the total variability of the initial data, and then supervised classification models were constructed using the discriminant partial least square regression PLS-DA, support vector machine SVM, and linear discriminant analysis LDA. These methods show a high capacity in the classification of the three classes of olive oil. The validation of these classification models by external samples shows a high capacity of classification of the samples in their class with an accuracy of 100%. This study demonstrated the feasibility of the fluorescence spectroscopy fingerprint (routine technique) for the classification of olive oils according to their freshness and storage time.

HPLC fingerprint analysis of Phyllanthus emblica ethanol extract and their antioxidant and anti-inflammatory properties

ETHNOPHARMACOLOGICAL RELEVANCE

Phyllanthus emblica L. (P. emblica) as a medical plant has been used to treat diseases in Asia. It is famous for a wide range of biological activities, especially for its antioxidant and anti-inflammatory activity. However, quality control underlying the bioactivity of P. emblica fruits remains to be studied.

MATERIALS AND METHODS

In this study, we evaluated the HPLC fingerprint and bioactivity of polyphenols extracted from P. emblica fruits grown in different habitats.

RESULTS

P. emblica fruits collected from 10 different habitats in Guangdong, Fujian, Yunnan, and Guangxi provinces in China were used to establish a simple and reliable HPLC fingerprint assay. Simultaneous quantification of three monophenols was also performed to determine assay quality and consistency. Additionally, chemical assessment of the different ethanolic extract (PEEE) from 10 P. emblica fruits demonstrated that they exhibited antioxidant activity by enhancing reducing power and total antioxidant capacity, scavenging hydroxyl radical and superoxide anion. PEEE protected RAW264.7 cells from oxidative damage by increasing glutathione content and total superoxide dismutase activity, suppressing MDA content. PEPE also alleviated lipopolysaccharide-induced inflammation in RAW 264.7 cells by decreasing release of pro-inflammatory mediators. Notably, the PEEE samples from Yunnan province showed the optimal antioxidant and anti-inflammatory effects among all the PEEE samples.

CONCLUSION

In conclusion, The PEEE HPLC fingerprint may help improve P. emblica quality control, and P. emblica with antioxidant and anti-inflammatory activities may be potentially applied in functional foods or in adjuvant therapy for medicinal development.

Geographical Origin Authentication of Agri-Food Products: Α Review

This study is a systematic literature review of geographical origin authentication by elemental analytical techniques. Authentication and certification of geographic origin of agri-food products is a useful tool toward the protection of the quality for products. The aim of this work was to map the current state of research in the area of agricultural products and food, identifying emerging fields to the geographical origin of products. The article is divided in three parts. The first part of the article deals with the analytical techniques applied in the food authentication. Special mention is made to elemental analysis and multiple isotope ratio. The second section focuses on statistically published data concerning published research for geographical origin authentication for the period 2015-2019. Specific results are presented inter alia: number of articles according to the type of product, articles according to the type of the analytical techniques, and others. The third part contains characteristic results from articles that were published in the period 2015-2019, on certification of geographical origin on specific agricultural products.

Quality consistency evaluation of commercial transfer factor injections by chromatographic fingerprint combined with multivariate statistical analysis

The current quality control methods relying mainly on chromogenic reaction can hardly ensure the quality and safety of the biochemical drug with complex chemical composition. Therefore, a chromatographic fingerprint method was developed for the quality evaluation of a multicomponent biochemical drug, transfer factor injection. High-performance liquid chromatography fingerprint was measured by using a C₁₈ column (250 × 4.6 mm, 5 µm) with a mobile phase composed of 0.1% trifluoroacetic acid-water and 0.085% trifluoroacetic acid-acetonitrile under gradient elution. The developed method was validated and was subsequently applied to 57 batches of commercial products which were sampled by National Drug Assessment Program. High-resolution mass spectrometry analysis was performed on characteristic peaks of fingerprints, and a series of amino acids, nucleosides, and deoxynucleosides were identified. In the fingerprint assessments, principal component analysis and Hotelling T² analysis yielded the best results. The results generally indicated that there was a significant difference among products of batch-to-batch or from different manufacturers. Abnormal samples and its discriminatory components were also explored. In summary, the established fingerprinting method with multivariate statistical analysis could offer an efficient, reliable, and practical approach for quality consistency evaluation of transfer factor injection, providing a reference for the quality control of other multicomponent biochemical drugs.