Multivariate Chemometrics Research Articles

An Integrated Approach to Identify the Q-Markers of Banxia-Houpo Decoction Based on Nontargeted Multicomponent Profiling, Network Pharmacology, and Chemometrics.

The inherent complexity of traditional Chinese medicine (TCM) poses significant challenges in directly correlating quality evaluation with clinical efficacy. Banxia-Houpo Decoction (BHD), a classical TCM formula, has demonstrated efficacy in treating globus hystericus. However, the intricate composition of BHD, which contains both volatile and non-volatile active components, complicates efforts to ensure its consistent quality and clinical effectiveness. The aim of this study was to introduce an integrated approach that combines non-targeted multicomponent analysis, network pharmacology, and multivariate chemometrics to identify quality markers for the effective quality control of BHD. First, a nontargeted high-definition MSE method based on ultraperformance liquid chromatography-quadrupole time-of-flight-mass spectrometry (UHPLC-QTOF-MS) was developed for the comprehensive multi-component characterization of BHD. Next, the quality markers of nonvolatile compounds in BHD were identified through network pharmacology analysis. Subsequently, volatile organic compounds (VOCs) in BHD samples were analyzed via headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS). Finally, the orthogonal partial least squares discriminant analysis (OPLS-DA) model was applied to screen for potential markers. Based on in-house library-driven automated peak annotation and comparison with 25 reference compounds, 128 components were identified for the first time. Additionally, honokiol, magnolol, magnoflorine, 6-gingerol, rosmarinic acid, and adenosine were preliminarily identified as potential quality markers for BHD through network pharmacology analysis. By employing two complementary techniques, HS-SPME-GC-MS and HS-GC-IMS, a total of 145 volatile compounds was identified in the BHD samples. Four potential differential VOCs in the BHD samples were further identified based on the variable importance in projection (VIP ≥ 1.5) using HS-GC-IMS combined with chemometric analysis. In conclusion, this study not only contributes to establishing quality standards for BHD but also offers new insights into quality assessment and identification in the development of classical formulations enriched with volatile components.

A volatilomic approach using ion mobility and mass spectrometry combined with multivariate chemometrics for the assessment of lemon juice quality

A volatilomic approach using ion mobility and mass spectrometry combined with multivariate chemometrics for the assessment of lemon juice quality

Quantitative analysis of UF4 and ThF4 in Indian MSR fuel using laser induced breakdown spectroscopy

The Molten Salt Reactor (MSR) represents a significant addition to India's clean energy initiative through nuclear energy. Noteworthy for its inherent safety features and the utilization of mixed fluoride salts (U, Th, Li) as molten-liquid fuel, this reactor stands out as a unique advancement. This paper showcases the application of Laser-Induced Breakdown Spectroscopy (LIBS) to ensure the chemical composition of the fuel. The micro-destructive nature of the analysis, minimal sample preparation requirements and the potential for remote analysis position LIBS as a viable alternative to Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The study encompasses the quantification of UF4 and ThF4, demonstrating both univariate and multivariate chemometrics calibration approaches. Various normalization methods on the raw spectral data are explored, and the results are meticulously compared. Upon evaluating different analytical methods, the optimal accuracy and precision achieved were in the range of approximately 2.6% and 3.9% for UF4, and about 0.4% and 0.5% for ThF4, respectively. This underscores the effectiveness of LIBS in ensuring the accurate determination of chemical composition in the context of MSR fuel.

Establishment of chemical fingerprint and determination of main active ingredients in Centellae Herba by HPLC combined with multivariate chemometrics analysis

AbstractIn this paper, a simple and efficient high‐performance liquid chromatography method was established to analyze the chemical composition fingerprint of Centellae Herba (CHA) and determine five triterpenoid active components in CHA, namely, asiaticoside B (AB), madecassoside, asiaticoside, madecasic acid, and asiaticoic acid. The separation of the compounds was carried out on an XD‐C18 column (250 mm × 4.6 mm, 5 µm), the wavelength of the ultraviolet detector was set to 205 nm, and the mobile phase was composed of acetonitrile −0.05% phosphoric acid, 2 mmol/L β‐cyclodextrin aqueous solution, and gradient elution at the flow rate of 1.0 mL/min. A general chromatographic fingerprint consisting of 20 characteristic peaks of 18 batches of CHA samples was established, and the samples were classified and evaluated by similarity analysis, hierarchical cluster analysis, and principal component analysis combined with multivariate econometric analysis. In quantitative analysis, all calibration curves showed good linear regression in the test range (r > 0.999), the average recovery was 96.12%–104.63%, and the relative standard deviations of repeatability and stability were less than 2.00%. The results show that the method is accurate and effective and can be used for the comprehensive quality control of CHA.

Development of Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Coupled with Multivariate Classification Chemometric Model for Routine Screening of Paracetamol, Ibuprofen, and Aspirin Adulteration in Herbal Products

Objective: The objective of this study is to develop and validate a routine screening test for the determination of three common antipyretic-analgesic synthetic drugs (paracetamol, ibuprofen, and aspirin) adulteration in herbal products using Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) coupled with chemometric method. Methods: ATR-FTIR spectra of sixteen testing sets of herbal product samples for pain and fever indications were used for multivariate chemometrics model construction. Linear Discriminant Analysis (LDA) was selected as a method for model construction with IBM SPSS for statistical analysis. Model development employed feature selection, such as the stepwise method for variable selection. The model with a high %correct classification and cross-validation was selected and was then validated with an independent testing data set with an auto-prediction test, confusion matrix, and Receiver Operating Characteristic (ROC) curve. To validate the developed test for routine use, the result from ATR-FTIR method was compared with the standard HPLC and TLC analyses used for adulteration screening. Results: The selected model's overall %correct classification result was 97.7%, with a cross-validation of 93.8% rate in training set samples. External validation with an independent testing dataset gave an overall correct classification of 93.8%, with an area under the curve of ROC at 0.979. Comparative testing revealed that model performance was comparable with the HPLC and TLC methods, which routinely detect the presence of paracetamol, aspirin, and ibuprofen. The results of testing set samples classification were consistent with training set samples. Conclusion: Against the standard chromatographic methods, the multivariate chemometric model based on ATR-FTIR demonstrates comparable detection capability to determine adulteration of paracetamol, ibuprofen, and aspirin in herbal products.

Near-Infrared Spectroscopy and Chemometrics for Effective Online Quality Monitoring and Process Control during Pelagic Fishmeal and Oil Processing.

Near-infrared spectroscopy has become a common quality assessment tool for fishmeal products during the last two decades. However, to date it has not been used for active online quality monitoring during fishmeal processing. Our aim was to investigate whether NIR spectroscopy, in combination with multivariate chemometrics, could actively predict the changes in the main chemical quality parameters of pelagic fishmeal and oil during processing, with an emphasis on lipid quality changes. Results indicated that partial least square regression (PLSR) models from the NIR data effectively predicted proximate composition changes during processing (with coefficients of determination of an independent test set at RCV2 = 0.9938, RMSECV = 2.41 for water; RCV2 = 0.9773, RMSECV = 3.94 for lipids; and RCV2 = 0.9356, RMSECV = 5.58 for FFDM) and were successful in distinguishing between fatty acids according to their level of saturation (SFA (RCV2=0.9928, RMSECV=0.24), MUFA (RCV2=0.8291, RMSECV=1.49), PUFA (RCV2=0.8588, RMSECV=2.11)). This technique also allowed the prediction of phospholipids (PL RCV2=0.8617, RMSECV=0.11, and DHA (RCV2=0.8785, RMSECV=0.89) and EPA content RCV2=0.8689, RMSECV=0.62) throughout processing. NIR spectroscopy in combination with chemometrics is, thus, a powerful quality assessment tool that can be applied for active online quality monitoring and processing control during fishmeal and oil processing.

Ongoing Multivariate Chemometric Approaches in Bioactive Compounds and Functional Properties of Foods—A Systematic Review

In this review, papers published in the chemometrics field were selected in order to gather information and conduct a systematic review regarding food science and technology; more precisely, regarding the domain of bioactive compounds and the functional properties of foods. More than 50 papers covering different food samples, experimental techniques and chemometric techniques were selected and presented, focusing on the chemometric methods used and their outcomes. This study is one way to approach an overview of the current publications related to this subject matter. The application of the multivariate chemometrics approach to the study of bioactive compounds and the functional properties of foods can open up even more in coming years, since it is fast-growing and highly competitive research area.

Investigation of Rosa species by an optimized LC-QTOF-MS/MS method using targeted and non-targeted screening strategies combined with multivariate chemometrics.

Plants of the Rosa genus are renowned for their pronounced and pleasant aroma and colors. The aim of this work was to develop a novel liquid chromatographic triple quadrupole time-of-flight tandem mass spectrometric (LC-QTOF-MS/MS) method for the investigation of the bioactive fingerprint of petals of different genotypes belonging to Rosa damascena and Rosa centifolia species. Central composite design (CCD) of response surface methodology (RSM) was used for the optimization of the LC-QTOF-MS/MS method. The method was validated and target, suspect, and non-target screening workflows were applied. Statistical analysis and chemometric tools were utilized to explore the metabolic fingerprint of the Rosa species. RSM revealed that the optimal extraction parameters involved mixing 11 mg of sample with 1mL of MeOH:H2O (70:30, v/v). Target analysis confirmed the presence of 11 analytes, all of which demonstrated low limits of quantification (LOQs; as low as 0.048 ng mg-1) and sufficient recoveries (RE: 85%-107%). In total, 28 compounds were tentatively identified through suspect analysis. Non-target analysis enabled the generation of robust OPLS-DA and HCA models that classified the samples according to their species with 100% accuracy. A novel LC-QTOF-MS/MS method was developed and applied in the analysis of 47 R.centifolia and R.damascena flowers belonging to different genotypes.

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.

Nondestructive Metabolomic Fingerprinting: FTIR, NIR and Raman Spectroscopy in Food Screening

In recent years, there has been renewed interest in the maintenance of food quality and food safety on the basis of metabolomic fingerprinting using vibrational spectroscopy combined with multivariate chemometrics. Nontargeted spectroscopy techniques such as FTIR, NIR and Raman can provide fingerprint information for metabolomic constituents in agricultural products, natural products and foods in a high-throughput, cost-effective and rapid way. In the current review, we tried to explain the capabilities of FTIR, NIR and Raman spectroscopy techniques combined with multivariate analysis for metabolic fingerprinting and profiling. Previous contributions highlighted the considerable potential of these analytical techniques for the detection and quantification of key constituents, such as aromatic amino acids, peptides, aromatic acids, carotenoids, alcohols, terpenoids and flavonoids in the food matrices. Additionally, promising results were obtained for the identification and characterization of different microorganism species such as fungus, bacterial strains and yeasts using these techniques combined with supervised and unsupervised pattern recognition techniques. In conclusion, this review summarized the cutting-edge applications of FTIR, NIR and Raman spectroscopy techniques equipped with multivariate statistics for food analysis and foodomics in the context of metabolomic fingerprinting and profiling.

Application of multivariate chemometrics tools for spectrophotometric determination of naphazoline HCl, pheniramine maleate and three official impurities in their eye drops

This work is concerned with exploiting the power of chemometrics in the assay and purity determination of naphazoline HCl (NZ) and pheniramine maleate (PN) in their combined eye drops. Partial least squares (PLS) and artificial neural network (ANN) were the chosen models for that purpose where three selected official impurities, namely; NZ impurity B and PN impurities A and B, were successfully determined. The quantitative determinations of studied components were assessed by percentage recoveries, standard errors of prediction as well as root mean square errors of prediction. The developed models were constructed in the ranges of 5.0–13.0 μg mL−1 for NZ, 10.0–60.0 μg mL−1 for PN, 1.0–5.0 μg mL−1 for NZ impurity B and 2.0–14.0 μg mL−1 for two PN impurities. The proposed models could determine NZ and PN with respective detection limits of 0.447 and 1.750 μg mL−1 for PLS, and 0.494 and 2.093 μg mL−1 for ANN. The two established models were compared favorably with official methods where no significant difference observed.

Analysis and comparison of the quality and flavour of traditional and conventional dry sausages collected from northeast China

In this study, the physicochemical properties and flavour profile of traditional dry sausages (T-SH, T-DXAL, T-HG, T-MDJ, T-HRB) collected from various wet markets were compared with those of conventional dry sausages (C-QL, C-ND, C-YSD, C-YC, C-HRL) collected from various food companies in northeast China. Traditional dry sausages were characterised by a low moisture content, a low water activity, and a high shear force after a long fermentation time compared with conventional dry sausages. Electronic nose and electronic tongue signals combined with chemometrics methods were applied for a comprehensive qualitative analysis of the odour and taste of dry sausages. A total of 61 volatile compounds were identified using gas chromatography–mass spectrometry, and the multivariate chemometrics analysis confirmed the difference in volatile compounds between traditional and conventional samples. Moreover, the sensory evaluation revealed that conventional dry sausages lacked the characteristic fermented flavour of traditional dry sausages.

Complete chemical characterization of Crocus sativus via LC-HRMS: Does trimming affect the chemical content of saffron?

Saffron, a spice derived from Crocus sativus, which in Iran is subjected to different trimming, is known for its beneficial health effects and high market value. Authentication studies related to geographical origin and adulterants presence mainly exist in literature, however fraud due to trimming has not been reported. In the current research, chemical characterization of six saffron trims, namely Sargol, Negin, Pushal, Bunch, Style, and Powder, was accomplished through suspect and non-target screening employing LC-QToF-MS in both electrospray ionization modes. The samples were extracted using methanol:water (50:50,v:v) and 62 compounds were identified, including amino acids, vitamins, flavonoids, phenolics, carotenoids, cyclohexenones. A clear discrimination among the red trims (Pushal, Sargol and Negin), as well as between Style and Bunch using Multivariate Chemometrics techniques was achieved. Proline and isophorone were highlighted as authenticity markers. Finally, the effect of three harvesting year on the most contributing compounds for trimming discrimination has been evaluated.

Plasma glycoproteomics delivers high-specificity disease biomarkers by detecting site-specific glycosylation abnormalities

IntroductionThe human plasma glycoproteome holds enormous potential to identify personalized biomarkers for diagnostics. Glycoproteomics has matured into a technology for plasma N-glycoproteome analysis but further evolution towards clinical applications depends on the clinical validity and understanding of protein- and site-specific glycosylation changes in disease. ObjectivesHere, we exploited the uniqueness of a patient cohort of genetic defects in well-defined glycosylation pathways to assess the clinical applicability of plasma N-glycoproteomics. MethodsComparative glycoproteomics was performed of blood plasma from 40 controls and 74 patients with 13 different genetic diseases that impact the protein N-glycosylation pathway. Baseline glycosylation in healthy individuals was compared to reference glycome and intact transferrin protein mass spectrometry data. Use of glycoproteomics data for biomarker discovery and sample stratification was evaluated by multivariate chemometrics and supervised machine learning. Clinical relevance of site-specific glycosylation changes were evaluated in the context of genetic defects that lead to distinct accumulation or loss of specific glycans. Integrated analysis of site-specific glycoproteome changes in disease was performed using chord diagrams and correlated with intact transferrin protein mass spectrometry data. ResultsGlycoproteomics identified 191 unique glycoforms from 58 unique peptide sequences of 34 plasma glycoproteins that span over 3 magnitudes of abundance in plasma. Chemometrics identified high-specificity biomarker signatures for each of the individual genetic defects with better stratification performance than the current diagnostic standard method. Bioinformatic analyses revealed site-specific glycosylation differences that could be explained by underlying glycobiology and protein-intrinsic factors. ConclusionOur work illustrates the strong potential of plasma glycoproteomics to significantly increase specificity of glycoprotein biomarkers with direct insights in site-specific glycosylation changes to better understand the glycobiological mechanisms underlying human disease.

Integration of Chemometrics and Sensory Metabolomics to Validate Quality Factors of Aged Baijiu (Nianfen Baijiu) with Emphasis on Long-Chain Fatty Acid Ethyl Esters.

The storage process of Baijiu is an integral part of its production (the quality undergoes substantial changes during the aging process of Baijiu). As the storage time extends, the flavor compounds in Baijiu tend to undergo coordinated transformation, thereby enhancing the quality of Baijiu. Among them, long-chain fatty acid ethyl esters (LCFAEEs) were widely distributed in Baijiu and have been shown to have potential contributions to the quality of Baijiu. However, the current research on LCFAEEs in Baijiu predominantly focuses on the olfactory sensation aspect, while there is a lack of systematic investigation into their influence on taste and evaluation after drinking Baijiu during the aging process. In light of this, the present study investigates the distribution of LCFAEEs in Baijiu over different years. We have combined modern flavor sensory analysis with multivariate chemometrics to comprehensively and objectively explore the influence of LCFAEEs on Baijiu quality. The results demonstrate a significant positive correlation between the concentration of LCFAEEs and the fruity aroma (p < 0.05, r = 0.755) as well as the aged aroma (p < 0.05, r = 0.833) of Baijiu within a specific range; they can effectively reduce the off-flavors and spicy sensation of Baijiu. Furthermore, additional experiments utilizing a single variable suggest that LCFAEEs were crucial factors influencing the flavor of Baijiu, with Ethyl Palmitate (EP) being the most notable LCFAEE that merits further systematic investigation.

A volatilomics analytical protocol employing solid phase microextraction coupled to GC × GC-MS analysis and combined with multivariate chemometrics for the detection of pomegranate juice adulteration

In this work, a solid-phase microextraction (SPME) method combined with two-dimensional gas chromatography coupled to mass spectrometry (GC × GC-MS) was optimized and used to assess the authenticity of pomegranate juice to prevent fraudulent practices. A divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber was used for the extraction of the volatiles. The critical parameters that affect the extraction process, such as the sample volume, and the extraction time were studied. The optimized protocol involved the addition of 15 mL of juice in 50 mL vial and saturation with 30% w/v NaCl.The extraction was carried out within 45 min under 1000 rpm stirring and was applied in the analysis of real juice samples to assess authenticity and detect low levels of pomegranate juice adulteration with grape and apple juice down to 1%. Commercially available pomegranate juice samples were acquired (n1 = 6) and adulterated with 1% of apple juice (n2 = 6), 1% of grape juice (n3 = 6), and a mixture of 1% apple juice and 1% grape juice (n4 = 6). Authentic pomegranate juice samples and adulterated mixtures were analyzed by SPME-GC × GC-MS. The analysis resulted in the identification of 123 volatile compounds that were further processed with chemometric tools. Principal component analysis (PCA) was employed to visualize the clustering of the samples, and a two-way orthogonal partial least squares discriminant analysis (O2PLS-DA) chemometric model was developed and successfully classified the samples to authentic pomegranate juice or adulterated with an explained total variance of 87.4%. The O2PLS-DA prediction model revealed characteristic volatile markers that could be used to detect pomegranate juice fraud.

Expeditious and accurate detection of palm oil adulteration in virgin coconut oil by utilizing ATR-FTIR spectroscopy along with chemometrics and regression models

Virgin coconut oil (VCO), being one of the most treasured and healthy edible oil, is at great risk of blending with cheaper oils like palm oil (PO). Fourier transform infrared (FTIR) spectroscopy with an Attenuated total reflection (ATR) accessory was used together with multivariate chemometrics for the classification and evaluation of numerous concentrations of PO (0.5–30% v/v) in VCO. Linear Discriminant Analysis (LDA) showed a 100% correct classification for both the initial and cross-validation groups for all the subsets of PO blends. Principal components regression (PCR) and Partial least squares regression (PLS-R) calibration models were generated and compared for normal, 1st, and 2nd derivatives of the combined informational spectral domain (3010–2800 cm−1 & 1800–700 cm−1) and separate informational domains of the spectra 3010–2800 cm−1 and 1800–700 cm−1 to get the best-fitting models. Regression models for the 2nd derivatives of 1800–700 cm−1 informational spectral domain gave exquisite outcomes of prediction with immense accuracy and precision with the highest R2 of 0.998, and the root mean square error of prediction (RMSEP) of 0.375% v/v for PLS-R and R2 of 0.998 and RMSEP of 0.441% v/v for PCR respectively. The lowest detectable limit of PO in VCO was estimated as 0.5% v/v. Hence, it has been concluded that PO adulteration of up to 0.5% with VCO can be quickly determined by using ATR-FTIR spectroscopy fitted with multivariate chemometrics.

Swift and precise detection of argemone oil adulteration in virgin coconut oil by implementing ATR-FTIR spectroscopy integrated with multivariate chemometrics and regression modelling

Virgin coconut oil (VCO), being one of the most priced and healthy edible oil, is at great risk of mixing with cheaper oils like argemone oil (AO). Fourier transform infrared (FTIR) spectroscopy with an Attenuated total reflection (ATR) accessory was used along with multivariate chemometrics and regression modelling for the classification and evaluation of numerous concentrations of AO (0.5–30% v/v) in VCO. LDA showed a 100% correct classification for both the initial and cross-validation groups for all the subsets of AO blends. Principal components regression (PCR) and Partial least squares regression (PLS-R) calibration models were developed and compared for normal, 1st, and 2nd derivatives of the combined informational spectral domain (3010–2800 cm−1 & 1800–700 cm−1) and separate informational regions of the spectra 3010–2800 cm−1 and 1800–700 cm−1 to get the best-fitting models. PCR model for the 1st derivative and PLS-R model for the 2nd derivative spectra of the region of 1800–700 cm−1 gave exquisite outcomes of prediction with immense accuracy and precision with the highest R2 of 0.999, and the root mean square error of prediction (RMSEP) of 0.241% v/v for PCR and R2 of 0.999 and RMSEP of 0.229% v/v for PLS-R respectively. The lowest detectable limit of AO in VCO was estimated as 0.5% v/v.

Exploring the volatile metabolites of three Chorisia species: Comparative headspace GC–MS, multivariate chemometrics, chemotaxonomic significance, and anti-SARS-CoV-2 potential

Chorisia (syn. Ceiba) species are important ornamental, economic, and medicinal plants that are endowed with a diversity of secondary metabolites; however, their volatile organic compounds (VOCs) have been scarcely studied. Therefore, this work explores and compares the headspace floral volatiles of three common Chorisia species, namely Chorisia chodatii Hassl., Chorisia speciosa A. St.-Hil, and Chorisia insignis H.B.K. for the first time. A total of 112 VOCs of varied biosynthetic origins were identified at different qualitative and quantitative ratios, encompassing isoprenoids, fatty acid derivatives, phenylpropanoids, and others. Flowers of the investigated species showed perceptibly differentiated volatile profiles, with those emitted by C. insignis being dominated by non-oxygenated compounds (56.69 %), whereas oxygenated derivatives prevailed among the volatiles of C. chodatii (66.04 %) and C. speciosa (71.53 %). The variable importance in the projection (VIP) in the partial least-squares–discriminant (PLS-DA) analysis described 25 key compounds among the studied species, of which linalool was verified as the most important aroma compound based on VIP values and significance analysis, and it could represent the most typical VOC among these Chorisia species. Furthermore, molecular docking and dynamics analyses of both the major and the key VOCs displayed their moderate to promising binding interactions with four main proteins of SARS-CoV-2, including Mpro, PLpro, RdRp, and spike S1 subunit RBD. The current results collectively cast new light on the chemical diversity of the VOCs of Chorisia plants as well as their chemotaxonomic and biological relevance.

A study of the lipid profile of Coix seeds from four areas based on untargeted lipidomics combined with multivariate algorithms to enable tracing of their origin

The geographical traceability of food products is seen as a distinctive feature of the future of food which is increasingly becoming a concern for consumers. In this research, differences in the lipid composition of Coix seed samples from four major Chinese origins were investigated using non-targeted lipidomics. By multivariate statistical analysis, unsupervised PCA and OPLS-DA based differentiation between the four origins of Coix seed samples could be achieved. The OPLS-DA VIP > 1 screened 72 lipids out of 1211 lipids as potential markers to distinguish Coix seeds from different origins. In addition, the potential markers (SPH(d16:0), Cer(d18:2/20:0 + O) and PC(8:0e/8:0) were combined with statistical analysis algorithms to construct a discriminant function for rapid differentiation of Coix seed samples from different origins and a specific function for different origins with 100% discrimination accuracy. In general, a rapid and accurate method combining multivariate chemometrics and algorithms was developed based on untargeted lipidomics to determine the geographical origin of Coix seed samples, which can also be applied to other agricultural products.