Identification Of Adulteration Research Articles

Rapid solid phase microextraction of DNA using mesoporous metal-organic framework coating for PCR-based identification of meat adulteration.

A rapid, convenient, low-cost, and selective DNA isolation method was developed for identifying meat adulteration. A mesoporous metal organic framework (Meso-UIO-66)-coated solid phase microextraction system was employed as an isolation device to simplify DNA isolation into three steps (lysis, washing, and elution). Meso-UIO-66 was utilized as the adsorbent because of its positively charged surface, high chemical stability, and mesoporous structure. Meso-UIO-66 was characterized by scanning electron microscopy, transmission electron microscopy, powder X-ray diffraction, Fourier transform infrared spectroscopy, ultraviolet‒visible spectroscopy, X-ray photoelectron spectroscopy, and nitrogen adsorption-desorption tests. Parameters that affected DNA isolation were optimized. This method can be used to isolate and purify DNA from meat in 60s, and the DNA concentration and purity are comparable to those of samples isolated with a commercial kit. Multiple DNA detection was achieved by coupling this method with the multiplex polymerase chain reaction technique, and the detection limit was lower than 1% (w/w).

Metamaterial-Based Sensor Integrating Microwave Dielectric and Near-Infrared Spectroscopy Techniques for Substance Evaluation

A novel era of “smart” sensors is emerging, in which they are being used in combination with artificial intelligence (AI) to achieve a better understanding of the world. Such sensors need to be ubiquitous. Accordingly, they should have miniaturized dimensions and low costs. However, there is a tradeoff between cost and accuracy of the sensors, which should be overcome with the use of new types of sensors and techniques. In this article, a metamaterial-based sensor that performs simultaneous microwave dielectric spectroscopy and near-infrared (NIR) spectroscopy measurements is presented. It uses a planar circular complementary split-ring resonator for dielectric measurement with an optical path, and NIR spectroscopy is performed in the same physical structure. A greater diversity of data can be achieved from the electromagnetic spectrum using the data from microwave dielectric spectroscopy and NIR spectroscopy; thus, it becomes possible to enhance the accuracy of substance identification with fewer samples. The proposed sensor was evaluated for the identification of olive oil adulteration and achieved an accuracy of 100% using only 14 training samples. To achieve this same accuracy, other techniques used for the detection of adulteration in olive oil need more than 50 training samples. Therefore, the proposed sensor needs 72% fewer samples for training classifier algorithms used in olive oil adulteration identification. This implies the reduction of computing requirements and wall clock time. The proposed sensor opens new avenues to perform material characterization and identification using a combination of microwave dielectric and NIR spectroscopies in the same structure.

State-of-the-Art Analytical Approaches for Illicit Drug Profiling in Forensic Investigations.

In forensic chemistry, when investigating seized illicit drugs, the profiling or chemical fingerprinting of drugs is considered fundamental. This involves the identification, quantitation and categorization of drug samples into groups, providing investigative leads such as a common or different origin of seized samples. Further goals of drug profiling include the elucidation of synthetic pathways, identification of adulterants and impurities, as well as identification of a drug’s geographic origin, specifically for plant-derived exhibits. The aim of this state-of-art-review is to present the traditional and advanced analytical approaches commonly followed by forensic chemists worldwide for illicit drug profiling. We discussed numerous methodologies for the physical and chemical profiling of organic and inorganic impurities found in illicit drug. Applications of powerful spectroscopic and chromatographic tools for illicit drug profiling including isotope-Ratio mass spectrometry (IRMS), gas chromatography–mass spectrometry (GC-MS), gas chromatography–isotope ratio mass spectrometry (GC-IRMS), ultra-high-performance liquid chromatography (UHPLC), thin layer chromatography (TLC), liquid chromatography–mass spectrometry (LC-MS) and inductively coupled plasma-mass spectrometry (ICP-MS) were discussed. Altogether, the techniques covered in this paper to profile seized illicit drugs could aid forensic chemists in selecting and applying a suitable method to extract valuable profiling data.

Molecular assay (Polymerase Chain Reaction based technology) for identification of cheating and adulteration of marketable Canned beef, Handmade sausage, Kofta and Hawawshi samples in Qalubia Governorate, Egypt

Molecular assay (Polymerase Chain Reaction based technology) for identification of cheating and adulteration of marketable Canned beef, Handmade sausage, Kofta and Hawawshi samples in Qalubia Governorate, Egypt

Multiple crRNAs-assisted CRISPR/Cas12a assay targeting cytochrome b gene for amplification-free detection of meat adulteration

Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas systems have been widely applied in nucleic acid analysis for the high specificity. Coupled with pre-amplification steps, the sensitivity of CRISPR-based detection is greatly improved. However, an extra pre-amplification step not only complicates the detection procedures but may also cause aerosol contaminations in the process of transferring amplified solution into CRISPR system. In this study, we demonstrate that combination of multiple crRNAs in CRISPR/Cas12a system can enhance the detection sensitivity. Based on it, we establish a multiple crRNAs enhanced CRISPR (meCRISPR) method and apply it to meat adulteration identification. Take cytochrome b (Cyt b) gene as a target, meCRISPR method can directly detect as low as 1.13 ng/μL extracted pork DNA and 5% (w/w) pork contamination in pork and beef meat mixtures. There is no cross-reaction with extracted chicken, beef, duck and fish DNA. meCRISPR reaction is incubated at an isothermal temperature, and the detection process can be completed in a designed portable apparatus with a heat block, a light emitting diode and filters. For the simplicity, specificity and sufficient sensitivity of meCRISPR method, it will have great prospects in species identification, food adulteration, and genetically modified food detection.

Rapid evaluation of milk acidity and identification of milk adulteration by Raman spectroscopy combined with chemometrics analysis

Milk can provide people with rich nutrition, and its freshness is widely concerned. Acidity, as an important indicator of freshness, can evaluate the freshness of milk. In this paper, a new method for rapid and non-destructive evaluation of milk acidity based on Raman spectroscopy and chemometrics is proposed. Two multivariate data analysis methods, partial least square regression (PLSR) and back propagation artificial neural network (BP-ANN), are used to build models. The determination coefficient (R2), root mean square error (RMSE) and the ratio of prediction to deviation (RPD) values for the best prediction model BP-ANN test sets are 0.9724, 0.7223 °T and 6.31, respectively. The results show that nonlinear model BP-ANN is more suitable for predicting the acidity of milk. Furthermore, the model can identify the illegal neutralization of the acidity of milk by adding Na2CO3. Thus, Raman spectroscopy with chemometrics is a fast and powerful tool to assess milk acidity. Finally, we conclude with an in-depth discussion and analysis of the model results to further validate that the acidity is predicted by establishing a link with the various components of milk such as protein and fat.

MALDI-TOF MS Protein Profiling Combined with Multivariate Analysis for Identification and Quantitation of Beef Adulteration

MALDI-TOF MS Protein Profiling Combined with Multivariate Analysis for Identification and Quantitation of Beef Adulteration

Blockchain-Enabled Supply Chain platform for Indian Dairy Industry: Safety and Traceability.

Conventional food supply chains are centralized in nature and possess challenges pertaining to a single point of failure, product irregularities, quality compromises, and loss of data. Numerous cases of food fraud, contamination, and adulteration are daily reported from multiple parts of India, suggesting the absolute need for an upgraded decentralized supply chain model. A country such as India, where its biggest strength is its demographic dividend, cannot afford to malnutrition a large population of its children by allowing them to consume contaminated and adulterated dairy products. In view of the gravity of the situation, we propose a blockchain-enabled supply chain platform for the dairy industry. With respect to the supply chain platform, the dairy products of choice include milk, cheese, and butter. Blockchain is one of the fastest growing technologies having widespread acceptance across multiple industry verticals. Blockchain possesses the power to transform traditional supply chains into decentralized, robust, transparent, tamper proof, and sustainable supply chains. The proposed supply chain platform goes beyond the aspect of food traceability and focuses on maintaining the nutritional values of dairy products, identification of adulteration and contamination in dairy products, the increasing economic viability of running a dairy farm, preventing counterfeit dairy products, and enhancing the revenue of the dairy company. The paper collates the mentioned functionalities into four distinct impact dimensions: social, economic, operations, and sustainability. The proposed blockchain-enabled dairy supply chain platform combines the use of smart contracts, quick response code (QR code) technology, and IoT and has the potential to redefine the dairy supply chains on socio-economic, operational, and sustainability parameters.

Identification of adulterants (sunflower oil and soybean oil) in grapeseed oil (Vitis vinifera L.) and chia oil (Salvia hispanica L.) by FT-MIR spectroscopy and chemometric

Grapeseed and chia oils are important for their fatty acids profile. Both oils are considered gourmet and due their high retail prices they are susceptible to adulteration. FT-MIR was used with chemometric analysis to identify and quantify the adulterants (sunflower and soybean oils) in grapeseed and chia oils. A ternary mixture system was designed for each oil using as adulterants sunflower and soybean oils (2-50%, w/w). Soft independent modelling by class analogy (SIMCA) discriminated adulterated samples and pure samples (100% of recognition and rejection). Partial least squares (PLS) showed the best calibration result (R2c: ≥ 0.95; SEC: 2.22-2.87) and validation (R2v: ≥ 0.99; SEP: 2.25-3.13). The models could be applied to identify and quantify two adulterants: sunflower and soybean oils in grapeseed and chia oils

Pharmacognostic evaluation of Artemisia maritima L. a highly medicinal specie of genus Artemisia

The light and scanning electron microscopic observations were carried out for anatomical features of leaf, pollens and powder.Microscopic studies provide useful information for identification and authentication of adulteration in A. maritima. Nutritional analysis of A. maritima revealed that life fundamental macromolecules such as carbohydrates (49.63 %) crude proteins (13.17 %) and crude fibers (21.06 %) were present in sufficient quantity while crude fats (4.11 %) reported in low quantity. The life essential elements such as Mg (9.472 ± 0.011), Ca (4.152 ± 0.135) and Fe (4.112 ± 0.002) were found in high concentration while heavy metals reported under the safety threshold of WHO. These observations favored A. maritima an alternative of food.Appreciable quantity of phenolics (17.64 ± 0.574) and flavonoids (7.67 ± 0.069) were found while qualitatively active phytochemicals were reported.The FTIR characterization of A. maritima crude powder revealed chromatogram in 3328.61 to 408.68 frequency range and 24 characteristic peaks on the basis of which different compounds of biological importance were classified. HPLC-UV technique quantifiedand identified six phenolic compounds morin,epigallocatechin gallate, catechin hydrate,ellagic acid, pyrogallol andrutin. Identification of compounds through GC–MS chromatogram revealed the presence of 46 compounds in methanolic fraction however 17 compounds of biological importance were selected.In-vitro biological evaluation of A. maritima for antioxidant, antimicrobial, antidiabetic (12.61 ± 0.113 %) and cytotoxic activities (LC50 = 20 μg/ml) suggested that methanolic fractions exhibited the highest activity as compared to chloroform and ethyl acetate fractions. The MIC values of 10 or 15 mg/ml were recorded for most of the fungal pathogens. Antibacterial activity revealed 3.75 mg/ml of MIC values against B. subtilis and 1.87 mg/ml against S. aureus, E. coli and P. aeruginosa. In-vivo biological evaluation revealed thatmaximum inhibition was observed for crude extract at 250 mg/kg body weight. The mechanism underlined in-vivo analgesic responses was carried out which revealed that naloxone (morphine and tramadol antagonist) showed no prominent effect while Glibenclamide pretreatment minutely modified the analgesic action. These observations clearly indicted the absence of opiod receptors and involvement of ATP sensitive potassium channels.

Droplet digital PCR for the identification of plant-derived adulterants in highly processed products

BackgroundThe high sensitivity of droplet digital PCR (ddPCR) contributes to its excellent performance in animal and microorganism identification, but the utilization of ddPCR is limited in plant adulterant identification of highly processed products for which effective methods are lacking. PurposeThis study investigated the feasibility of ddPCR in the identification of plant adulterants in Chinese patent medicine (CPM) as groundwork to develop ddPCR assays for other highly processed goods. MethodsThe original plant, processed and highly processed products of Mutong (Akebiae Caulis) and its two adulterants were used to analyze the specificity, sensitivity, and practical performance of the developed singleplex and triplex ddPCR assays. ResultsThe results revealed that the limit of detection (LOD) and limit of quantification (LOQ) for the selective ddPCR assays developed to identify Mutong and its adulterants were 0.00002 ng/μl and 0.00016 ng/μl, respectively, and that the regression equations representing the relationships between DNA concentration and target copy number all exhibited good linearity. Furthermore, the common adulterant of Mutong in three samples of Longdan Xiegan pills was successfully identified through ddPCR assays and confirmed by Sanger sequencing. ConclusionThis work comprehensively revealed the great ability of ddPCR technology in detecting plant adulterants in traditional Chinese medicine (TCM), providing a method for the quality control of highly processed plant products with complex components for commonly used goods.

Identification of Tartrazine adulteration and evaluating exposure to synthetic dyes of sunset yellow and Quinoline yellow through consumption of food products among children.

Excessive consumption of synthetic food dyes by children may raise concerns about their health. These dyes may aggravate the hyperactivity symptoms and exacerbate asthma in sensitive children. The purpose of this study was to determine the presence of sunset yellow and quinoline yellow dyes, as well as tartrazine in dairy-free fruit ice cream, freeze pop, jelly, and candy. Additionally, we evaluated the amount of two food dyes consumed by children. To do so, a total of 150 food samples, including 20 dairy-free fruit ice creams, 25 freeze pops, 57 jelly products, and 48 types of candy were randomly selected from stores in Shiraz, Iran. Then, using the high-performance liquid chromatography (HPLC) method and an ultraviolet (UV) detector, we measured the amounts of sunset yellow and quinoline yellow dyes and identified the use of tartrazine. Also, the per capita consumption (grams per day) of the mentioned foods was calculated using a checklist in two groups of male and female primary schoolchildren aged 6-9 years and 10-13 years in Shiraz, Iran. According to the results, 11 (7.33%) samples contained only tartrazine and 107 (71.33%) samples contained quinoline yellow and sunset yellow synthetic dyes. In addition, of 107 samples that used quinoline yellow and sunset yellow, 102 (95.33%) contained unauthorized tartrazine. Only seven (6.54%) samples contained exceedingly high concentrations of authorized quinoline yellow and sunset yellow synthetic dyes. However, the exposure assessment showed that the intake of quinoline yellow and sunset yellow was at average levels and the 95th percentile in both age groups was less than the associated acceptable daily intake (ADI). For synthetic dyes, the target hazard quotient (THQ) and hazard index (HI) were less than one, indicating that ingestion of these two dyes via food products does not pose a risk to children's overall health.

Identification of milk quality and adulteration by surface-enhanced infrared absorption spectroscopy coupled to artificial neural networks using citrate-capped silver nanoislands

Milk is one of the most important multicomponent superfoods owing to its rich macronutrient composition. It requires quality control at all the production stages from the farm to the finished products. A localized surface plasmon resonance optical sensor based on a citrate-capped silver nanoparticle (Cit-AgNP)–coated glass substrate was developed. The fabrication of such sensors involved a single-step synthesis of Cit-AgNPs followed by surface modification of glass slides to be coated with the nanoparticles. The scanning electron microscope micrographs demonstrated that the nanoparticles formed monolayer islands on glass slides. The developed surface-enhanced infrared absorption spectroscopy (SEIRA) sensor was coupled to artificial neural networking (ANN) for the qualitative differentiation between cow, camel, goat, buffalo, and infants’ formula powdered milk types. Moreover, it can be used for the quantitative determination of the main milk components such as fat, casein, urea, and lactose in each milk type. The qualitative results showed that the obtained FTIR spectra of cow and buffalo milk have high similarity, whereas camel milk resembled infant formula powdered milk. The most difference in FTIR characteristics was evidenced in the case of goat milk. The developed sensor adds several advantages over the traditional techniques of milk analysis using MilkoScan™ such as less generated waste, elimination of pre-treatment steps, minimal sample volume, low operation time, and on-site analysis.Graphical abstract

Identification of novel biomarkers in chilled and frozen chicken using metabolomics profiling and its application

Merchants used frozen chicken to pass it off as chilled chicken, resulting in Economically Motivated Adulteration incidents. Here in this work, firstly, we established OPLS-DA and OPLS-R models based on metabolomics to obtain differential metabolites in chilled and frozen chicken (with different storage times), the PLS-DA model based on above differential compounds could achieve accuracy of 91% (training) and 100% (testing) for the adulteration identification of uncooked chilled and frozen chicken. Secondly, cooking study was carried out to identify the discrepancy of the cooked chilled and frozen chicken. Higher nicotinamide, o-acetyl-l-carnitine, hypoxanthine, and IMP levels indicated better nutrition quality and more desirable flavor in chilled chicken nuggets, while higher bitter and sour peptides in frozen chicken nuggets indicated the loss of freshness.

Rapid Identification of Adulteration in Extra Virgin Olive Oil via Dynamic Headspace Sampling and High-Pressure Photoionization Time-of-Flight Mass Spectrometry.

High-pressure photoionization time-of-flight mass spectrometry (HPPI-TOFMS) combined with dynamic headspace sampling was developed for rapid identification of adulteration in extra virgin olive oil (EVOO). The volatile organic compound (VOC) fingerprints of EVOO, refined rapeseed oil (r-RO), peanut oil (PO), corn oil (CO), fragrant rapeseed oil (f-RO), and sunflower oil (SO) were obtained in just 1.5 min, which enabled satisfactory classification of different edible oils. 1,4-Bis(methylene)cyclohexane and dimethyl disulfide were unique VOCs in r-RO and f-RO, respectively, while 2,5-dimethylpyrazine and 2-methylpyrazine were distinctive VOCs in PO. Percentages as low as 3% r-RO, 1% PO, and 1% f-RO in r-RO-EVOO, PO-EVOO, and f-RO-EVOO mixtures, respectively, were successfully identified based on the characteristic VOCs. Linear regression equations of these VOCs were established and utilized for predicting the adulteration proportions. The good agreements between the actual adulteration proportions and the predicted ones demonstrated that HPPI-TOFMS was reliable for the quantification of EVOO adulteration.

Contemporary Developments and Emerging Trends in the Application of Spectroscopy Techniques: A Particular Reference to Coconut (Cocos nucifera L.).

The number of food frauds in coconut-based products is increasing due to higher consumer demands for these products. Rising health consciousness, public awareness and increased concerns about food safety and quality have made authorities and various other certifying agencies focus more on the authentication of coconut products. As the conventional techniques for determining the quality attributes of coconut are destructive and time-consuming, non-destructive testing methods which are accurate, rapid, and easy to perform with no detrimental sampling methods are currently gaining importance. Spectroscopic methods such as nuclear magnetic resonance (NMR), infrared (IR)spectroscopy, mid-infrared (MIR)spectroscopy, near-infrared (NIR) spectroscopy, ultraviolet-visible (UV-VIS) spectroscopy, fluorescence spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and Raman spectroscopy (RS) are gaining in importance for determining the oxidative stability of coconut oil, the adulteration of oils, and the detection of harmful additives, pathogens, and toxins in coconut products and are also employed in deducing the interactions in food constituents, and microbial contaminations. The objective of this review is to provide a comprehensive analysis on the various spectroscopic techniques along with different chemometric approaches for the successful authentication and quality determination of coconut products. The manuscript was prepared by analyzing and compiling the articles that were collected from various databases such as PubMed, Google Scholar, Scopus and ScienceDirect. The spectroscopic techniques in combination with chemometrics were shown to be successful in the authentication of coconut products. RS and NMR spectroscopy techniques proved their utility and accuracy in assessing the changes in coconut oil’s chemical and viscosity profile. FTIR spectroscopy was successfully utilized to analyze the oxidation levels and determine the authenticity of coconut oils. An FT-NIR-based analysis of various coconut samples confirmed the acceptable levels of accuracy in prediction. These non-destructive methods of spectroscopy offer a broad spectrum of applications in food processing industries to detect adulterants. Moreover, the combined chemometrics and spectroscopy detection method is a versatile and accurate measurement for adulterant identification.

Volatile fingerprints and biomarkers of Chinese fragrant and non-fragrant japonica rice before and after cooking obtained by untargeted GC/MS-based metabolomics

China has a rich resource of fragrant japonica rice that is gaining increasing consumers’ interests. In the present study, an untargeted gas chromatography-mass spectrometry-based metabolomics was employed to investigate the volatile profile of Chinese fragrant and non-fragrant japonica varieties. Partial least square discriminative analysis (PLS-DA) along with univariate analysis was taken to seek specific markers in raw and cooked Chinese japonica rice distinguishing fragrant and non-fragrant varieties. Volatile profile of raw Chinese japonica rice dominated by alkanes was completely different from that of cooked rice predominated by aldehydes. Thirteen and eleven volatile compounds were selected as the potential discriminative markers in raw and cooked Chinese japonica rice, respectively. Only 2-acetylpyrrole and decanal served as the specific marker in both raw and cooked Chinese japonica rice. Fragrant Chinese japonica rice varieties contained more 2-acetylpyrrole, (E)-2-octenal, nonanal, 1-tetradecene, decanal and (E)-2-decenal in raw rice. While fragrant Chinese japonica rice varieties contained more 2-acetyl-1-pyrroline, 2-acetylpyrrole, 1-hexanol, decanal and indole in cooked rice. The results provided an in-depth understanding of volatile variation in raw and cooked form of aromatic and non-aromatic cultivars and will benefit rice adulteration identification and marker-assisted flavor breeding in the future.

The fishmeal adulteration identification based on microscopic image and deep learning

The fishmeal adulteration identification based on microscopic image and deep learning was studied in this paper, which included the qualitative identification and component recognition of adulterated fishmeal. Mobilenetv2 was chosen as the qualitative identification model to distinguish between fishmeal samples and adulterated samples. And the accuracy, precision, recall, and F1-measure of the test set were adopted as the evaluation indicators. The YOLOv3-Mobilenetv2 was selected as the component recognition model to distinguish components in adulterated samples, in which the feature pyramid structure and multi-scale feature fusion strategy were applied to solve the multi-scale problem in object detection. And the mean average precision (mAP) was adopted as the precision evaluation index and the frame per second (FPS) as the speed evaluation index of the component recognition model. At the same time, the effects of image enhancement preprocessing and resolution on the identification of fishmeal adulteration were also considered in this paper. It was shown that Mobilenetv2 was more suitable for qualitative identification when using the local constrained mask transformed microscopic images with a resolution of 224 × 224 pixels. The average accuracy, precision, recall, and F1-measure of the model were 93.02%, 93.11%, 93.25%, and 92.69% respectively. The improved YOLOv3-Mobilenetv2 was more suitable for component recognition with a resolution of 608 × 608 pixels. The mAP reached 78.49%, and the FPS was 45.97 f·s−1. The model of fishmeal adulteration identification adopted in this paper had high accuracy, which could quickly identify adulterated fishmeal and recognize components, and provide a new method and reference for improving the objectivity of fishmeal adulteration identification.

Minor metabolites as chemical marker for the differentiation of cane, beet and coconut blossom sugar. From profiling towards identification of adulterations

The high similar look of different types of brown sugar in combination with high price discrepancies make these sugars a potential target of food fraud. We therefore analyzed the different metabolic profiles of browned beet sugar, unrefined cane sugar and coconut blossom sugar. The investigation was carried out by 1H NMR and supplementary ULPC-Q-TOF-MS analysis to confirm identified metabolites. In addition to the highly variable metabolic profiles, an unambiguous metabolite was identified for each sugar, which was not detectable in the other sugar types by NMR. Betaine was identified as a marker for browned beet sugar, trans-aconitic acid (TAA) for raw cane sugar and pyroglutamic acid as a unique marker for coconut blossom sugar. Based on the variable metabolic profile, we were also able to demonstrate the potential of a multivariate regression model to estimate the degree of adulteration of coconut blossom sugar. In the second part of our study, we additionally investigated the use of minor metabolites for the detection of added sugars in different foods. Using unrefined cane sugar as an example, we demonstrated the thermal and chemical stability of TAA as a marker metabolite. The results of this study show the high potential use of the different metabolic profiles for the detection of food fraud. In combination with multivariate data analysis even small amounts of admixtures can be detected in different types of sugar.

Compositional Analysis of Four Kinds of Citrus Fruits with an NMR-Based Method for Understanding Nutritional Value and Rational Utilization: From Pericarp to Juice.

Citrus is one of the most important economic crops and is widely distributed across the monsoon region. Citrus fruits are deeply loved by consumers because of their special color, fragrance and high nutritional value. However, their health benefits have not been fully understood, especially the pericarps of citrus fruits which have barely been utilized due to their unknown chemical composition. In the present study, the pericarp and juices of four typical varieties of citrus fruits (lemon, dekopon, sweet orange and pomelo) were analyzed by NMR spectroscopy combined with pattern recognition. A total of 62 components from the citrus juices and 87 components from the citrus pericarps were identified and quantified, respectively. The different varieties of the citrus fruits could be distinguished from the others, and the chemical markers in each citrus juice and pericarp were identified by a combination of univariate and multivariate statistical analyses. The nutritional analysis of citrus juices offers favorable diet recommendations for human consumption and data guidance for their potential medical use, and the nutritional analysis of citrus pericarps provides a data reference for the subsequent comprehensive utilization of citrus fruits. Our results not only provide an important reference for the potential nutritional and medical values of citrus fruits but also provide a feasible platform for the traceability analysis, adulteration identification and chemical composition analysis of other fruits.