Efficient Inhibition of Deep Conversion of Partial Oxidation Products in C-H Bonds' Functionalization Utilizing O2 via Relay Catalysis of Dual Metalloporphyrins on Surface of Hybrid Silica Possessing Capacity for Product Exclusion.

To inhibit the deep conversion of partial oxidation products (POX-products) in C-H bonds' functionalization utilizing O2, 5-(4-(chloromethyl)phenyl)-10,15,20-tris(perfluorophenyl)porphyrin cobalt(II) and 5-(4-(chloromethyl)phenyl)-10,15,20-tris(perfluorophenyl)porphyrin copper(II) were immobilized on the surface of hybrid silica to conduct relay catalysis on the surface. Fluorocarbons with low polarity and heterogeneous catalysis were devised to decrease the convenient accessibility of polar POX-products to catalytic centers on the lower polar surface. Relay catalysis between Co and Cu was designed to utilize the oxidation intermediates alkyl hydroperoxides to transform more C-H bonds. Systematic characterizations were conducted to investigate the structure of catalytic materials and confirm their successful syntheses. Applied to C-H bond oxidation, not only deep conversion of POX-products was inhibited but also substrate conversion and POX-product selectivity were improved simultaneously. For cyclohexane oxidation, conversion was improved from 3.87% to 5.27% with selectivity from 84.8% to 92.3%, which was mainly attributed to the relay catalysis on the surface excluding products. The effects of the catalytic materials, product exclusion, relay catalysis, kinetic study, substrate scope, and reaction mechanism were also investigated. To our knowledge, a practical and novel strategy was presented to inhibit the deep conversion of POX-products and to achieve efficient and accurate oxidative functionalization of hydrocarbons. Also, a valuable protocol was provided to avoid over-reaction in other chemical transformations requiring high selectivity.

An artificial intelligence platform for automated PFAS subgroup classification: A discovery tool for PFAS screening.

Since structural analyses and toxicity assessments have not been able to keep up with the discovery of unknown per- and polyfluoroalkyl substances (PFAS), there is an urgent need for effective categorization and grouping of PFAS. In this study, we presented PFAS-Atlas, an artificial intelligence-based platform containing a rule-based automatic classification system and a machine learning-based grouping model. Compared with previously developed classification software, the platform's classification system follows the latest Organization for Economic Co-operation and Development (OECD) definition of PFAS and reduces the number of uncategorized PFAS. In addition, the platform incorporates deep unsupervised learning models to visualize the chemical space of PFAS by clustering similar structures and linking related classes. Through real-world use cases, we demonstrate that PFAS-Atlas can rapidly screen for relationships between chemical structure and persistence, bioaccumulation, or toxicity data for PFAS. The platform can also guide the planning of the PFAS testing strategy by showing which PFAS classes urgently require further attention. Ultimately, the release of PFAS-Atlas will benefit both the PFAS research and regulation communities.

Double boron-oxygen-fused polycyclic aromatic hydrocarbons: skeletal editing and applications as organic optoelectronic materials.

An efficient one-pot strategy for the facile synthesis of double boron-oxygen-fused polycyclic aromatic hydrocarbons (dBO-PAHs) with high regioselectivity and efficient skeletal editing is developed. The boron-oxygen-fused rings exhibit low aromaticity, endowing the polycyclic aromatic hydrocarbons with high chemical and thermal stabilities. The incorporation of the boron-oxygen units enables the polycyclic aromatic hydrocarbons to show single-component, low-temperature ultralong afterglow of up to 20 s. Moreover, the boron-oxygen-fused polycyclic aromatic hydrocarbons can also serve as ideal n-type host materials for high-brightness and high-efficiency deep-blue OLEDs; compared to single host, devices using boron-oxygen-fused polycyclic aromatic hydrocarbons-based co-hosts exhibit dramatically brightness and efficiency enhancements with significantly reduced efficiency roll-offs; device 9 demonstrates a high color-purity (Commission International de l'Eclairage CIEy = 0.104), and also achieves a record-high external quantum efficiency (28.0%) among Pt(II)-based deep-blue OLEDs with Commission International de l'Eclairage CIEy < 0.20; device 10 achieves a maximum brightnessof 27219 cd/m2 with a peak external quantum efficiency of 27.8%, which representes the record-high maximum brightness among Pt(II)-based deep-blue OLEDs. This work demonstrates the great potential of the double boron-oxygen-fused polycyclic aromatic hydrocarbons as ultralong afterglow and n-type host materials in optoelectronic applications.

Efficient and Selective Oxygenation of Cycloalkanes and Alkyl Aromatics with Oxygen through Synergistic Catalysis of Bimetallic Active Centers in Two-Dimensional Metal-Organic Frameworks Based on Metalloporphyrins.

Confined catalytic realms and synergistic catalysis sites were constructed using bimetallic active centers in two-dimensional metal-organic frameworks (MOFs) to achieve highly selective oxygenation of cycloalkanes and alkyl aromatics with oxygen towards partly oxygenated products. Every necessary characterization was carried out for all the two-dimensional MOFs. The selective oxygenation of cycloalkanes and alkyl aromatics with oxygen was accomplished with exceptional catalytic performance using two-dimensional MOF Co-TCPPNi as a catalyst. Employing Co-TCPPNi as a catalyst, both the conversion and selectivity were improved for all the hydrocarbons investigated. Less disordered autoxidation at mild conditions, inhibited free-radical diffusion by confined catalytic realms, and synergistic C-H bond oxygenation catalyzed by second metal center Ni employing oxygenation intermediate R-OOH as oxidant were the factors for the satisfying result of Co-TCPPNi as a catalyst. When homogeneous metalloporphyrin T(4-COOCH3)PPCo was replaced by Co-TCPPNi, the conversion in cyclohexane oxygenation was enhanced from 4.4% to 5.6%, and the selectivity of partly oxygenated products increased from 85.4% to 92.9%. The synergistic catalytic mechanisms were studied using EPR research, and a catalysis model was obtained for the oxygenation of C-H bonds with O2. This research offered a novel and essential reference for both the efficient and selective oxygenation of C-H bonds and other key chemical reactions involving free radicals.

Predicting band gaps of MOFs on small data by deep transfer learning with data augmentation strategies.

Porphyrin-based MOFs combine the unique photophysical and electrochemical properties of metalloporphyrins with the catalytic efficiency of MOF materials, making them an important candidate for light energy harvesting and conversion. However, accurate prediction of the band gap of porphyrin-based MOFs is hampered by their complex structure-function relationships. Although machine learning (ML) has performed well in predicting the properties of MOFs with large training datasets, such ML applications become challenging when the training data size of the materials is small. In this study, we first constructed a dataset of 202 porphyrin-based MOFs using DFT computations and increased the training data size using two data augmentation strategies. After that, four state-of-the-art neural network models were pre-trained with the recognized open-source database QMOF and fine-tuned with our augmented self-curated datasets. The GCN models predicted the band gaps of the porphyrin-based materials with the lowest RMSE of 0.2767 eV and MAE of 0.1463 eV. In addition, the data augmentation strategy rotation and mirroring effectively decreased the RMSE by 38.51% and MAE by 50.05%. This study demonstrates that, when proper transfer learning and data augmentation strategies are applied, machine learning models can predict the properties of MOFs using small training data.

Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts.

Machine learning (ML) models have received increasing attention as a new approach for the virtual screening of organic materials. Although some ML models trained on large databases have achieved high prediction accuracy, the application of ML to certain types of organic materials is limited by the small amount of available data. On the other hand, metalloporphyrins and porphyrins (MpPs) have received increasing attention as potential photocatalysts, and recent studies have found that both HOMO/LUMO energy levels and energy gaps are important factors controlling the MpP photocatalysts. Since the training data of MpPs are insufficient and limited to porphyrin-based dyes, in this study, we proposed a deep transfer learning approach to rapidly predict the HOMO/LUMO energy levels and energy gaps of MpPs. To complement the open-source Porphyrin-based Dyes Database (PBDD), we curated a new database, the Metalloporphyrins and Porphyrins Database (MpPD), in which MpPs were specifically designed as potential photocatalysts and the HOMO/LUMO energies were calculated by advanced DFT functionals. We proposed PorphyBERT, a BERT-based regression model that was pre-trained with PBDD and fine-tuned with MpPD. The model performed satisfactorily in predicting HOMO and LUMO energies and energy gap with RMSEs of 0.0955, 0.0988, and 0.0787 eV and MAEs of 0.0774, 0.0824, and 0.0549 eV. Furthermore, due to its unique unsupervised pre-training phase, the model is not affected by the difference in computational functionals between pre-training and fine-tuning databases. Finally, we recommended 12 MpPs as potential photocatalysts for CO2 reduction with out-of-sample model predictions of energy gaps close to the values calculated by DFT.

Computational Exploration of Dirhodium Complex-Catalyzed Selective Intermolecular Amination of Tertiary vs. Benzylic C-H Bonds.

The mechanism and origins of site-selectivity of Rh2(S-tfpttl)4-catalyzed C(sp3)-H bond aminations were studied using density functional theory (DFT) calculations. The synergistic combination of the dirhodium complex Rh2(S-tfpttl)4 with tert-butylphenol sulfamate TBPhsNH2 composes a pocket that can access both tertiary and benzylic C-H bonds. The nonactivated tertiary C-H bond was selectively aminated in the presence of an electronically activated benzylic C-H bond. Both singlet and triplet energy surfaces were investigated in this study. The computational results suggest that the triplet stepwise pathway is more favorable than the singlet concerted pathway. In the hydrogen atom abstraction by Rh-nitrene species, which is the rate- and site-selectivity-determining step, there is an attractive π-π stacking interaction between the phenyl group of the substrate and the phthalimido group of the ligand in the tertiary C-H activation transition structure. By contrast, such attractive interaction is absent in the benzylic C-H amination transition structure. Therefore, the DFT computational results clearly demonstrate how the synergistic combination of the dirhodium complex with sulfamate overrides the intrinsic preference for benzylic C-H amination to achieve the amination of the nonactivated tertiary C-H bond.

Mechanistic Study of Chemoselectivity for Carbon Radical Hydroxylation versus Chlorination with FeIII (OH)(Cl) Complexes.

The FeIII (OH)(Cl) complex resembles the key intermediate proposed for the non-heme iron halogenases. Goldberg and co-workers reported that the FeIII (OH)(Cl) RC reacts with triphenylmethyl radical 1 to give an exclusive hydroxylation product. To understand the chemoselectivity of the reaction of RC with 1, density functional theory (DFT) calculations have been conducted. From RC, the competing pathways were identified as the OH-transfer, Cl-transfer, and isomerization pathways. The direct Cl-transfer is more favorable than direct OH-transfer by 2.8 kcal/mol. The hydrogen bonding interactions between the hydroxyl group and the pendent amine ligand impede the direct OH-transfer from RC. Compared with the direct Cl-transfer pathway, the isomerization pathways require lower barriers. In isomer RCiso2 , the equatorial hydroxyl group, which has smaller diabatic bond dissociation energy, prefers to transfer to form the hydroxylation product. In FeIII (Cl)2 RC2 and RC2iso , the equatorial chloride group also prefers to transfer to give the chlorination product.

A theoretical study of the ligand-controlled palladium-catalysed regiodivergent synthesis of dibenzosilepin derivatives.

Palladium-catalysed ligand-controlled 1,n-palladium migration of silicon-tethering substrates provides a regiodivergent synthesis strategy for constructing silicon-bridged π-conjugated compounds possessing a 6,6-fused or a 5,7-fused scaffold. Density functional theory (DFT) calculations were carried out to elucidate the detailed mechanism of this 1,n-palladium migration involving syn- or anti-carbopalladation. The computational results suggest that alkyne insertion is the regioselectivity-determining step. Upon catalysis without the BINAP ligand, the 1,2-insertion of an alkyne into the Pd-aryl bond leads to the formation of 6,6-fused benzophenanthrosiline, which is more favorable than the 2,1-insertion of alkyne by 4.2 kcal mol-1. The selective formation of 5,7-fused benzofluorenosilepins via the 2,1-alkyne insertion is facilitated by the BINAP ligand. The 1,2-alkyne insertion with the BINAP ligand is disfavoured due to the steric repulsion between the phenyl group of the substrate and the naphthalene group of the BINAP ligand. The 2,1-alkyne insertion with the BINAP ligand orients the ligand away from the phenyl group of the substrate, which can avoid steric repulsion.

A chemometric strategy to automatically screen selected ion monitoring ions for gas chromatography-mass spectrometry-based pseudotargeted metabolomics.

The pseudotargeted metabolomics based on gas chromatography-mass spectrometry (GC-MS) has the advantage of filtering out artifacts originating from sample treatment and accurately quantifying underlying compounds in the analyzed samples. However, this technique faces the problem of selecting high-quality selective ions for performing selected ion monitoring (SIM) on instruments. In this work, we proposed AntDAS-SIMOpt, an automatic untargeted strategy for SIM ion optimization that was accomplished on the basis of an experimental design combined with advanced chemometric algorithms. First, a group of diluted quality control samples was used to screen underlying compounds in samples automatically. Ions in each of the resolved mass spectrum were then evaluated by using the developed algorithms to identify the SIM ion. A Matlab graphical user interface (GUI) was designed to facilitate routine analysis, which can be obtained from http://www.pmdb.org.cn/antdassimopt. The performance of the developed strategy was comprehensively investigated by using standard and complex plant datasets. Results indicated that AntDAS-SIMOpt may be useful for GC-MS-based metabolomics.

Mechanistic Investigation of Palladium-Catalyzed meta-C-H Bond Activation of Arenes with a Carboxyl Directing Group.

The mechanism of Pd(II)-catalyzed meta-C-H bond olefination of arenes with a carboxyl directing group (DG)-containing template has been investigated with density functional theory. The reaction includes three major steps: C-H bond activation, alkene insertion, and ß-hydride elimination. The C-H activation step, which proceeds via a concerted metalation-deprotonation pathway, is found to be the rate- and regioselectivity-determining step. We proposed a mono-N-protected amino acid (MPAA)/DG-assisted C-H activation model, in which the carboxyl DG coordinates with the Pd center and delivers it to the meta-position of arene, and the bidentate dianionic MPAA acts as a base for deprotonation. There is a hydrogen bonding interaction between the carboxyl DG and the carboxylate group of MPAA. An alternative Pd(OAc)2-catalyzed mechanism without involvement of MPAA is also operative. The template is conformationally flexible, and multiple low-energy transition-state conformations contribute to the regioselectivity.

Microwave-assisted Natural Deep Eutectic Solvents Pretreatment Followed by Hydrodistillation Coupled with GC-MS for Analysis of Essential Oil from Turmeric (Curcuma longa L.).

In the past decade, natural deep eutectic solvents (NADESs) as green and sustainable extraction solvents with great potential for the efficient extraction of bioactive compounds from the plants are emerging. In this study, a microwave-assisted technology is used to prepare natural deep eutectic solvents. And natural deep eutectic solvents as pretreatment solvents coupled with microwave-assisted hydrodistillation (MAHD) for isolating essential oil (EO) derived from turmeric (Curcuma longa L.) is investigated. To improve the essential oil yield of turmeric (Curcuma longa L.) as a target, various factors affecting extraction efficiency including the type and amount of natural deep eutectic solvents, pretreatment time, pretreatment temperature and hydrodistillation (HD) time are discussed and optimized through central composite design (CCD) of the response surface methodology (RSM). The optimal conditions are as follows: natural deep eutectic solvent composed of choline chloride and oxalic acid (molar ratio with 1:1) as a pretreatment solvent, an amount of 60 g, a pretreatment time of 5 min, a pretreatment temperature of 84 ºC, a hydrodistillation time of 76 min. Under the optimum conditions, the highest essential oil yield of 0.85% is achieved. Additionally, the essential oil is analyzed by using gas chromatography-mass spectrometry (GC-MS), with a total of 49 compounds being identified. Through combining natural deep eutectic solvents with a microwave-assisted hydrodistillation technique, this work provides an eco-friendly extraction way of isolating essential oil, which boosts development in the monitoring other spice quality field.

Computational Studies on the Mechanism and Origin of the Different Regioselectivities of Manganese Porphyrin-Catalyzed C-H Bond Hydroxylation and Amidation of Equilenin Acetate.

The manganese porphyrin-catalyzed C-H bond hydroxylation and amidation of equilenin acetate developed by Breslow and his co-worker have been investigated with density functional theory (DFT) calculations. The hydroxylation of C(sp2)-H bond of equilenin acetate leading to the 6-hydroxylated product is more favorable than the hydroxylation of C(sp3)-H bond of equilenin acetate, leading to the 11ß-hydroxylation product. The computational results suggest that the C(sp2)-H bond hydroxylation of equilenin acetate undergoes an oxygen-atom-transfer mechanism, which is more favorable than the C(sp3)-H bond hydroxylation undergoing the hydrogen-atom-abstraction/oxygen-rebound (HAA/OR) mechanism by 1.6 kcal/mol. That is why, the 6-hydroxylated product is the major product and the 11ß-hydroxylated product is the minor product. In contrast, the 11ß-amidated product is the only observed product in manganese porphyrin-catalyzed amidation reaction. The benzylic amidation undergoes a hydrogen-atom-abstraction/nitrogen-rebound (HAA/NR) mechanism, in which hydrogen atom abstraction is followed by nitrogen rebound, leading to the 11ß-amidated product. The benzylic C(sp3)-H bond amidation at the C-11 position is more favorable than aromatic amidation at the C-6 position by 4.9 kcal/mol. Therefore, the DFT computational results are consistent with the experiments that manganese porphyrin-catalyzed C-H bond hydroxylation and amidation of equilenin acetate have different regioselectivities.

Rh(iii)-catalyzed, hydrazine-directed C-H functionalization with 1-alkynylcyclobutanols: a new strategy for 1H-indazoles.

Rh(iii)-catalyzed coupling of phenylhydrazines with 1-alkynylcyclobutanols was realized through a hydrazine-directed C-H functionalization pathway. This [4+1] annulation, based on the cleavage of a Csp-Csp triple bond in alkynylcyclobutanol, provides a new pathway to prepare diverse 1H-indazoles under mild reaction conditions.

Understanding the structures and aromaticity of heteroporphyrins with computations.

Heteroporphyrins are porphyrin derivatives with replacement of the pyrrolic NH moiety by other heteroatom-containing groups, such as PH, AsH, SiH2, O, S, etc. For all studied heteroporphyrins, the macrocycle structure is distorted due to the presence of large heteroatoms. The HOMO-LUMO gap of heteroporphyrins is generally decreased compared to regular porphyrins. Both nucleus independent chemical shifts values and visualized anisotropy of induced current density were computed to describe the aromaticity of heteroporphyrins. The plots of anisotropy of induced current density suggest that the ring current diverged into an outer and an inner pathway at each ring. The current mainly passes through the outer path at the pyrrolic rings with inner hydrogen and through the inner path at the pyrrolic rings without inner hydrogen. In both regular porphyrin and O-substituted heteroporphyrins, the aromatic pathway is mainly contributed by the 22π-electron aromatic route model. Heteroatoms such as PH, AsH, S, Se and Te have little contribution to the aromaticity of heteroporphyrins. In addition, the π conjugation is also interrupted at the CH2 and SiH2 moiety, and the ring current mainly passes through the outer path of the heteroporphyrins with CH2 and SiH2 replacing the pyrrolic NH moiety. Therefore the 18π-[18]annulene model is dominated in PH-, AsH-, S-, Se-, Te-, CH2- and SiH2-substituted heteroporphyrins. These computational studies shed new light on the aromatic characters of heteroporphyrins, and will facilitate the further development of various novel heteroporphyrins.

Mechanism and stereoselectivity of benzylic C-H hydroxylation by Ru-porphyrin: a computational study.

The mechanism and origin of the stereoselectivity of asymmetric benzylic C-H hydroxylation by Ru-porphyrin were elucidated with density functional theory calculations. The reaction proceeds via a hydrogen-atom abstraction/oxygen-rebound pathway, wherein a high-valent ruthenium-oxo species abstracts a hydrogen atom from ethylbenzene to generate a radical pair intermediate, followed by the oxygen-rebound process to form 1-phenylethanol. The hydrogen-atom abstraction step is the rate- and stereoselectivity-determining step. Based on the mechanistic model, the computed stereoselectivity is in agreement with the experimental observations. Analysis of the distortion/interaction model suggests that stereoselectivity is determined by both the distortion energy of the ethylbenzene and the interaction energy between the ethylbenzene and the chiral Ru-porphyrin. The steric repulsion between the phenyl group of ethylbenzene and the bulky substituent of Ru-porphyrin is the leading cause of chiral induction.

Computational Exploration of Chiral Iron Porphyrin-Catalyzed Asymmetric Hydroxylation of Ethylbenzene Where Stereoselectivity Arises from π-π Stacking Interaction.

The mechanism and origins of stereoselectivity of chiral iron porphyrin-catalyzed asymmetric hydroxylation of ethylbenzene were explored with density functional theory. The hydrogen atom abstraction is the rate- and stereoselectivity-determining step. In good agreement with experimental results, the formation of the (R)-1-phenylethanol product is found to be the most favorable pathway. The transition state of hydrogen atom abstraction which leads to the (S)-1-phenylethanol product is unfavorable by 1.7 kcal/mol compared to the corresponding transition state which leads to the (R)-1-phenylethanol product. Enantioselectivity arises from an attractive π-π stacking interaction between the phenyl group of ethylbenzene substrate and the naphthyl group of the porphyrin ligand.

ZnCdSe-CdTe quantum dots: A "turn-off" fluorescent probe for the detection of multiple adulterants in an herbal honey.

To enhance the power of untargeted detection, a "turn-off" fluorescent probe with double quantum dots (QDs) was developed and coupled with chemometrics for rapid detection of multiple adulterants in an herbal (Rhus chinensis Mill., RCM) honey. The double water-soluble ZnCdSe-CdTe QDs have two separate and strong fluorescent peaks, which can be quenched by honey and extraneous adulterants with varying degrees. Class models of pure RCM honey samples collected from 6 different producing areas (n = 122) were developed using one-class partial least squares (OCPLS). Four extraneous adulterants, including glucose syrup, sucrose syrup, fructose syrup, and glucose-fructose syrup were added to pure honey samples at the levels of 0.5% to 10% (w/w). As a result, the OCPLS model using the second-order derivative (D2) spectra could detect 1.0% (w/w) of different syrups in RCM honey, with a sensitivity of 0.949. The double water-soluble QDs, which can be adjusted for analysis of other water-soluble food samples, has largely extended the capability of traditional fluorescence and will provide a potentially more sensitive and specific analysis method for food frauds.

Characterization of Aroma-Active Components and Antioxidant Activity Analysis of E-jiao (Colla Corii Asini) from Different Geographical Origins.

E-jiao (Colla Corii Asini, CCA) has been widely used as a healthy food and Chinese medicine. Although authentic CCA is characterized by its typical sweet and neutral fragrance, its aroma components have been rarely investigated. This work investigated the aroma-active components and antioxidant activity of 19 CCAs from different geographical origins. CCA extracts obtained by simultaneous distillation and extraction were analyzed by gas chromatography-mass spectrometry (GC-MS), gas chromatography-olfactometry (GC-O) and sensory analysis. The antioxidant activity of CCAs was determined by ABTS and DPPH assays. A total of 65 volatile compounds were identified and quantified by GC-MS and 23 aroma-active compounds were identified by GC-O and aroma extract dilution analysis. The most powerful aroma-active compounds were identified based on the flavor dilution factor and their contents were compared among the 19 CCAs. Principal component analysis of the 23 aroma-active components showed 3 significant clusters. Canonical correlation analysis between antioxidant assays and the 23 aroma-active compounds indicates strong correlation (r = 0.9776, p = 0.0281). Analysis of aroma-active components shows potential for quality evaluation and discrimination of CCAs from different geographical origins.

Automatic untargeted metabolic profiling analysis coupled with Chemometrics for improving metabolite identification quality to enhance geographical origin discrimination capability.

Untargeted metabolic profiling analysis is employed to screen metabolites for specific purposes, such as geographical origin discrimination. However, the data analysis remains a challenging task. In this work, a new automatic untargeted metabolic profiling analysis coupled with a chemometric strategy was developed to improve the metabolite identification results and to enhance the geographical origin discrimination capability. Automatic untargeted metabolic profiling analysis with chemometrics (AuMPAC) was used to screen the total ion chromatographic (TIC) peaks that showed significant differences among the various geographical regions. Then, a chemometric peak resolution strategy is employed for the screened TIC peaks. The retrieved components were further analyzed using ANOVA, and those that showed significant differences were used to build a geographical origin discrimination model by using two-way encoding partial least squares. To demonstrate its performance, a geographical origin discrimination of flaxseed samples from six geographical regions in China was conducted, and 18 TIC peaks were screened. A total of 19 significant different metabolites were obtained after the peak resolution. The accuracy of the geographical origin discrimination was up to 98%. A comparison of the AuMPAC, AMDIS, and XCMS indicated that AuMPACobtained the best geographical origin discrimination results. In conclusion, AuMPAC provided another method for data analysis.