Geographical discrimination of red garlic (Allium sativum L.) produced in Italy by means of multivariate statistical analysis of ICP-OES data

Elemental Profiling and Safety Assessment of Four Spice Vegetables: Insights into Nutritional and Toxicological Implications

Spiced vegetables are consumed globally, appreciated for their distinctive aromas and flavor profiles. Additionally, their unique elemental composition is recognized as a crucial parameter. This study aimed to evaluate the safety and toxicological risk of four commonly consumed spice vegetables: garlic (Allium sativum), ginger (Zingiber officinale), green onion (Allium fistulosum), and chili pepper (Capsicum annuum). Using ICP-OES and ICP-MS, a total of 25 elements, including macro, trace, and toxic elements, were determined. The analytical methods were validated per AOAC guidelines. Results indicated that potassium was the most abundant element across all samples, with garlic showing significantly higher concentrations of phosphorus, sulfur, and potassium. Trace element analysis revealed zinc and manganese as the most prevalent, with ginger exhibiting elevated levels of manganese, barium, and gallium. While toxic elements were detected in all samples, their concentrations remained within safe limits for human consumption. The estimated daily intake (EDI) and hazard quotient (HQ) analysis, based on Korean dietary data, confirmed minimal health risks. Heatmap analysis and linear discriminant analysis plots demonstrated the potential to distinguish between the four spice vegetables based on element profiles. This study highlights the importance of monitoring elemental compositions in spice vegetables to ensure food safety and reduce health risks from toxic elements, offering crucial insights into their nutritional value and safety.

Geographical differentiation between South Korean and Chinese onions using stable isotope ratios and mineral content analysis

Geographical origin authentication of onions has become significant owing to origin labelling fraud in South Korea. Various analytical techniques based on stable isotope ratios, organic and inorganic constituents, or their combinations, can distinguish agricultural products geographically. However, studies on the geographical classification of South Korean and Chinese onions using stable isotopes and minerals remain scarce. This study aimed to discriminate geographically between South Korean and Chinese onions using stable isotope ratios (δ13C, δ15N, and δ34S) and mineral contents (K, Ca, Mg, Na, P, Fe, Zn, Mn, Cu, and Sr) combined with multivariate statistical analysis. Fifty-eight onion samples cultivated in South Korea and China were collected in 2023. The two stable isotope ratios (δ15N and δ34S) and six minerals (K, Ca, Na, Fe, Zn, and Sr) significantly differed between these onions. These variables were applied in orthogonal partial least squares discriminant analysis to classify the onion samples regionally. The predictive ability and goodness-of-fit parameters (R2X and R2Y) were 0.671, 0.383, and 0.677, respectively. K, Sr, δ34S, and Na served as potential markers contributing to the classification. Therefore, stable isotopes and mineral elements may serve as effective indicators for the geographical discrimination of South Korean and Chinese onion samples using multivariate analyses.

Comparative analysis of chemical elements and metabolites in diverse garlic varieties based on metabolomics and ionomics

As a plant classified under the "medicine food homology" concept, garlic offers various health benefits and comes in many different varieties. In this study, the metabolite composition of different garlic varieties were analyzed using LC-MS/MS quadrupole-Orbitrap mass spectrometry and ICP-MS. A total of 30 chemical elements and 1256 metabolites were identified. Significant differences in chemical elements and metabolomics profiles were observed among the five garlic groups (VIP > 1.5). Compared to WG, PG contained 5 unique compounds, HG had 15 unique compounds, SCG had 18 unique compounds, and SBG had 26 unique compounds. Furthermore, the results showed that WG had smaller differences with PG and HG, but significant differences with SBG and SCG. KEGG analysis revealed metabolic pathways associated with the formation of differential metabolites. These findings uncover the differences and mechanisms in the composition of various garlic varieties, providing a theoretical foundation for distinguishing the nutritional components of different garlic types.

Gas chromatography-stable isotope ratio mass spectrometry prior solid phase microextraction and gas chromatography-tandem mass spectrometry: development and optimization of analytical methods to analyse garlic (Allium sativum L.) volatile fraction

Garlic (Allium sativum L.) is not only appreciated for its flavour and taste, but it is also recognized for various health properties. The European Commission, through the attribution of the Protected Designation of Origin (PDO) certification mark, has officially recognized some specific varieties of garlic. To protect not only the commercial value but also the reputation of this appreciated product, effective tools are therefore required. For the first time, a new compound specific isotope analysis method based on carbon stable isotopic ratio measurement of the three major volatile garlic compounds allyl alcohol (AA), diallyl disulphide (DD) and diallyl trisulphide (DT) through head-space solid phase microextraction (HS-SPME) followed by gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS) was developed. A within-day standard deviation (Srwithin-day) of 0.3 ‰, 0.4 ‰ and 0.2 ‰ for δ(13C) and a between-day standard deviation (Srbetween-day) of 0.8 ‰, 1.0 ‰ and 0.6 ‰ of AA, DT and DD was estimated. For the first time, the ranges of isotopic variability for the three volatile compounds of red garlic from two neighbouring Italian regions (Abruzzo and Lazio) were defined analysing 30 samples. The same dataset was also considered in analysing the percentage composition of the previously mentioned three volatile compounds through HS-SPME followed by gas chromatography-tandem mass spectrometry (GC-MS/MS). The two analytical approaches were combined in this explorative study, aiming to provide potential parameters to discriminate garlic samples based on their geographical origin.

Nanosized quantum dots-wrapped metallic particles ensembles integrated into filter disc-based analytical device for garlic evaluation. Application to monitor fake pickled garlic in balsamic vinegar

Herein, an affordable and simple analytical device is presented to portable identify of garlic in 30 min; the evaluation needs no pre-treatment of sample. The analytical device fabrication was did employing a headspace-based nanosensor array using of inexpensive materials as commercial filter discs, quantum dots (QDs), and metallic nanoparticles (MNPs). The nanoarray is fabricated by the accumulation QDs on MNPs surface, that results in the production of ensembles of QDs/MNPs. The ensembles generate diverse colorimetric profiles as "fingerprints" regarding to each garlic sample. The volatile organosulfur compounds (OSCs) of garlic can prefer binding to the MNPs comparing with QDs. The color profiles can be displayed with a smartphone camera, which can be quantitatively distinguished by chemometrics approaches. The analytical device was used to assessment of fake pickled samples in balsamic vinegar. This device proves well potential for qualitative control of garlic.

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

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

Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information

The chemical composition and nutritional content of garlic are greatly impacted by its production location, leading to distinct flavor profiles and functional properties among garlic varieties from diverse origins. Consequently, these variations determine the preference and acceptance among diverse consumer groups. In this study, purple-skinned garlic samples were collected from five regions in China: Yunnan, Shandong, Henan, Anhui, and Jiangsu Provinces. Mid-infrared spectroscopy and ultraviolet spectroscopy were utilized to analyze the components of garlic cells. Three preprocessing methods, including Multiple Scattering Correction (MSC), Savitzky-Golay Smoothing (SG Smoothing), and Standard Normalized Variate (SNV), were applied to reduce the background noise of spectroscopy data. Following variable feature extraction by Genetic Algorithm (GA), a variety of machine learning algorithms, including XGboost, Support Vector Classification (SVC), Random Forest (RF), and Artificial Neural Network (ANN), were used according to the fusion of spectral data to obtain the best processing results. The results showed that the best-performing model for ultraviolet spectroscopy data was SNV-GA-ANN, with an accuracy of 99.73%. The best-performing model for mid-infrared spectroscopy data was SNV-GA-RF, with an accuracy of 97.34%. After the fusion of ultraviolet and mid-infrared spectroscopy data, the SNV-GA-SVC, SNV-GA-RF, SNV-GA-ANN, and SNV-GA-XGboost models achieved 100% accuracy in both training and test sets. Although there were some differences in the accuracy of the four models under different preprocessing methods, the fusion of ultraviolet and mid-infrared spectroscopy data yielded the best outcomes, with an accuracy of 100%. Overall, the combination of ultraviolet and mid-infrared spectroscopy data fusion and chemometrics established in this study provides a theoretical foundation for identifying the origin of garlic, as well as that of other agricultural products.

Geographical differentiation of garlic based on HS-GC-IMS combined with multivariate statistical analysis

Garlic is famous for its unique flavor and health benefits. An effective means of authenticating garlic's origin is through the implementation of the Protected Geographical Indication (PGI) scheme. However, the prevalence of fraudulent behavior raises concerns regarding the reliability of this system. In this study, garlic samples from six distinct production areas (G1: Cangshan garlic, G2: Qixian garlic, G3: Dali single clove garlic, G4: Jinxiang garlic, G5: Yongnian garlic, and G6: Badong garlic) underwent analysis using HS-GC-IMS. A total of 26 VOCs were detected in the samples. The differences in VOCs among the different garlic samples were visually presented in a two-dimensional topographic map and fingerprint map. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed to demonstrate the capacity of the HS-GC-IMS method for effectively distinguishing garlic samples from different geographical sources. Further screening based on the p-value and VIP score threshold identified 12 different aroma substances, which can be utilized for the identification of garlic from different producing areas. The fusion of HS-GC-IMS with multivariate statistical analysis proved to be a rapid, intuitive, and efficient approach for identifying and categorizing garlic VOCs, offering a novel strategy for ascertaining garlic origin and ensuring quality control.

Geographical origin discriminatory analysis of onions: Chemometrics methods applied to ICP-OES and ICP-MS analysis

Geographical origin is an important determinant of agricultural product quality and safety. Herein, inductively coupled plasma (ICP) analysis was applied to determine the inorganic elemental content of onions and identify their geographical origin (Korean or Chinese). Chemometric, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least square discriminant analysis (OPLS-DA) were applied to the ICP results. OPLS-DA distinguished each group, and 17 elements with variable importance in projection (VIP) values of ≥ 1 were selected. The receiver operating characteristic (ROC) curve had an area under the curve (AUC) of 1, indicating excellent discriminatory power. Differences in elemental content between groups were visually observed in a heatmap, and the country of origin was determined with 100% accuracy using canonical discriminant analysis (CDA). This method accurately distinguishes between Korean and Chinese onions and is expected to be beneficial for identifying agricultural products.

A comparative study of different methods for the determination of cadmium in various tissues of ramie (Boehmeria nivea L.)

Remediation of cadmium (Cd) pollution is one of the priorities of global environmental governance and accurate detection of Cd content is a key link in remediation of Cd pollution. This study aimed to compare three methods (inductively coupled plasma optical emission spectrometry (ICP-OES), inductively coupled plasma mass spectrometry (ICP-MS), and graphite furnace-atomic absorption spectrometry (GF-AAS)) for the determination of Cd with different tissues of various ramie varieties, and distinguish the advantage and disadvantage of each method. In total, 162 samples of ramie (Boehmeria nivea L.), which is an ideal plant for heavy metal remediation, were detected and the results showed that the three methods were all suitable for the de-termination of Cd content in ramie. ICP-OES and ICP-MS were simpler, faster, and more sensitive than GF-AAS. ICP-MS could be recommended for the determination of samples with various concentrations of Cd. ICP-OES could be used for measurement of samples with > 100 mg/kg Cd content, while GF-AAS was suitable for the detection of samples with very high (> 550 mg/kg) or very low (< 10 mg/kg) Cd content. Overall, considering the accuracy, stability, and the cost of measurement, ICP-MS was the most suitable method for determination of Cd content. This study provides significant reference information for the research in the field of Cd pollution remediation.

Quality Assessment and Classification of Codonopsis Radix Based on Fingerprints and Chemometrics

In China, Codonopsis Radix (CR) is frequently consumed both as food and medicine. Here, a comprehensive strategy based on fingerprinting and chemometric approaches was created to explore the influence of origins, storage time and kneading processing on the quality of CR. Firstly, high-performance liquid chromatography with diode array detection was used to obtain the fingerprints of 35 batches of CR from six different origins and 33 batches of CR from varying storage times or kneading procedures. Secondly, chemometric methods including similarity analysis (SA), principal component analysis (PCA), hierarchical clustering analysis (HCA), and two-way orthogonal partial least square with discriminant analysis (O2PLS-DA) were used to evaluate the differences of chemical components in CR so as to identify its source and reflect its quality. Moreover, 13 and 16 major compounds were identified as marker compounds for the discrimination of CR from different origins, storage time and kneading processing, respectively. Furthermore, the relative content of the marker components and the exact content of Lobetyolin were measured, indicating that the contents of these components vary significantly between various CR samples. Meanwhile, the chemical components of CR were identified using Mass spectrometry. According to the findings of our investigation, the quality of CR from Gansu was the best, followed by Shanxi and then Sichuan. The quality of CR from Chongqing and Guizhou was poor. At the same time, the quality of CR was the best when it was kneaded and stored for 0 years, indicating that the traditional kneading process of CR is of great significance. Conclusively, HPLC fingerprint in conjunction with chemical pattern recognition and component content determination can be employed to differentiate the raw materials of different CR samples. Additionally, it is also a reliable, comprehensive and prospective method for quality control and evaluation of CR.

A mid-infrared spectroscopy-random forest system for the origin tracing of Chinese geographical indication Aconiti Lateralis Radix Praeparata

Reliable origin certification methods are essential for the protection of high-value genuine medicinal material with designated origins and geographical indication (GI) products. Aconiti Lateralis Radix Praeparata (Fuzi), one well-known traditional Chinese medicine and geographical indication products have remarkable efficacy and wide clinical application, with high demand in domestic and international markets. The efficacy and price of Fuzi from different origins vary, and it is difficult for the general public to accurately identify them through traditional experience. The mass spectrometry detection technology based on the plant metabolomics is tedious and lengthy in test sample preparation, complicated in operation, long in detection time, and low in reproducibility. As a sophisticated, green, fast, and low-loss detection technique, infrared spectroscopy is integrated by machine learning to bring new ways for quality regulation and control of traditional Chinese medicines. An analytical method based on mid-infrared spectroscopy combined with a random forest algorithm was developed to verify the geographical origin of authentic herbs and/or GI products. The method successfully predicted and classified three varieties of Chinese GI Fuzi and four varieties of non-GI Fuzi. In this study, an environment-friendly traceability strategy with fast analysis, low sample loss and high precision was used to provide a new strategy for identifying the origin of Fuzi.

Characterization and Valorization of 'Sulmona Red Garlic' Peels and Small Bulbs

'Sulmona red garlic' is an Italian variety characterized by a red tunica surrounding a white bulb. Red tunicae and non-commercial small bulbs are food wastes that must be studied for their added value. Hydroalcoholic extracts, obtained by separated inner and outer tunicae and peeled bulbs of small commercial 'Sulmona red garlic' bulbs, harvested at two different years, were first characterized with respect to their color, polyphenolic content, and antiradical activity. Then, an untargeted metabolic profile by means of electrospray ionization Fourier transform ion cyclotron resonance (ESI FT-ICR) mass spectrometry led to a comparative evaluation of the chemical diversity of six different samples. The study was completed by biological tests aiming to evaluate the associated health potential. Data on monocytes/macrophages showed good biocompatibility and a promising cytoprotective effect under oxidative stress conditions of all the extracts. At a molecular level, all the garlic extracts were able to downregulate the hydrogen peroxide-induced cyclooxygenase-2 and inducible nitric oxide synthase expression through the modulation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-ĸB) and peroxynitrite intracellular amounts, at different extents depending on the extract, the cell type, and the concentration. On the whole, data highlight an associated health potential of the extracts of this waste plant material both in terms of cytoprotection and of anti-inflammatory activity.

Chemometric origin classification of Chinese garlic using sulfur-containing compounds, assisted by stable isotopes and bioelements

Geographical origin discrimination of agro-products is essential to guarantee food safety and fair trade. Garlic samples cultivated in six provinces or major production regions in China were characterized for stable isotopes (δ¹³C, δ²H, δ¹⁸O, δ¹⁵N, and δ³⁴S), bioelemental contents (% C, % N and % S), and sulfur-containing compounds (8 organosulfur components and 2 amino acids). Results showed that many of the 18 analyzed garlic variables had significant differences among production regions. Some sulfur-containing compounds found in garlic from different provinces had a strong correlation with sulfur isotopes, suggesting garlic sulfur isotopes were also affected by geographical origin. Two supervised pattern recognition models (PLS-DA and k-NN) were developed using stable isotopes, elemental contents, and sulfur-containing compounds, and had a discrimination accuracy of 93.4 % and 87.8 %, respectively. Chemometric classification models using multi-isotopes, elements and sulfur-containing compounds provides a useful method to authenticate Chinese garlic origins.

Intra-regional classification of Codonopsis Radix produced in Gansu province (China) by multi-elemental analysis and chemometric tools

Multi-elemental analysis is widely used to identify the geographical origins of plants. The purpose of this study was to explore the feasibility of combining chemometrics with multi-element analysis for classification of Codonopsis Radix from different producing regions of Gansu province (China). A total of 117 Codonopsis Radix samples from 7 counties of Gansu province were collected. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the determination of 28 elements (³⁹ K, ²⁴ Mg, ⁴⁴Ca, ²⁷Al, ¹³⁷Ba, ⁵⁷Fe, ²³Na, ⁸⁸Sr, ⁵⁵Mn, ⁶⁶Zn, ⁶⁵Cu, ⁸⁵Rb, ⁶¹Ni, ⁵³Cr, ⁵¹ V, ⁷Li, ²⁰⁸Pb, ⁵⁹Co, ⁷⁵As, ¹³³Cs, ⁷¹ Ga, ⁷⁷Se, ²⁰⁵Tl, ¹¹⁴Cd, ²³⁸U, ¹⁰⁷Ag, ⁴Be and ²⁰²Hg). Among macro elements, ³⁹ K showed the highest level, whereas ²³Na was found to have the lowest content value. Micro elements showed the concentrations order of: ⁸⁸Sr > ⁵⁵Mn > ⁶⁶Zn > ⁸⁵Rb > ⁶⁵Cu. Among trace elements, ⁵³Cr and ⁶¹Ni showed higher content and ⁴Be was not detected in all samples. Intra-regions differentiation was performed by principal component analysis (PCA), cluster analysis (CA) and supervised learning algorithms such as linear discriminant analysis (LDA), k-nearest neighbors (k-NN), support vector machines (SVM), and random forests (RF). Among them, the RF model performed the best with an accuracy rate of 78.79%. Multi-elemental analysis combined with RF was a reliable method to identify the origins of Codonopsis Radix in Gansu province.

An overview on multi-elemental profile integrated with chemometrics for food quality assessment: toward new challenges

Food products, especially those with high value-added, are commonly subjected to strict quality controls, which are of paramount importance, especially for attesting to some peculiar features related, for instance, to their geographical origin and/or the know-how of their producers. However, the sophistication of fraudulent practices requires a continuous update of analytical platforms. Different analytical techniques have become extremely appealing since the instrumental analysis tools evolution has substantially improved the capability to reveal and understand the complexity of food. In light of this, multi-elemental composition has been successful implemented solving a plethora of food authentication and traceability issues. In the last decades, it has existed an ever-increasing trend in analysis based on spectrometry analytical platforms in order to obtain a multi-elemental profile that combined with chemometrics have been noteworthy analytical methodologies able to solve these problems. This review provides an overview of published reports in the last decade (from 2011 to 2021) on food authentication and quality control from their multi-element composition in order to evaluate the state-of-the-art of this field and to identify the main characteristics of applied analytical techniques and chemometric data treatments that have permit achieve accurate discrimination/classification models, highlighting the strengths and the weaknesses of these methodologies.

Nitrogen modulates strontium uptake and toxicity in Hypericum perforatum plants

Strontium is an unavoidable element occurring in plants due to its abundance in the soil and similarity with calcium. To mimic natural conditions, impacts of additional inorganic (nitrate) or organic (urea and allantoin) nitrogen sources (1 mM of each N form in addition to 3.53 mM N in the basic cultivation solution) or N deficit on strontium-induced changes (100 µM Sr) in the widely used medicinal plant Hypericum perforatum L. were studied. Though various effects of Sr on primary (stimulation of amino acids but depression of most Krebs acids, ascorbic acid and thiols) and secondary metabolites (stimulation of phenols but no change of pseudo/hypericin) or mineral elements were observed (reduction of Ca amount in both shoots and roots), organic N forms often mitigated negative action of Sr or even combined stimulatory impact was observed. Organic N forms also elevated shoot accumulation of Sr while N deficit reduced it. Additional N forms, rather than Sr itself, modulated reactive oxygen species and nitric oxide formation in the root tissue. Germination experiment showed no toxicity of Sr to H. perforatum up to 1 mM Sr and even stimulated accumulation of amino acids and phenols, indicating similar ontogenetic-related responses.

Geographical identification of strawberries based on stable isotope ratio and multi-elemental analysis coupled with multivariate statistical analysis: A Slovenian case study

The geographical classification and authentication of strawberries were attempted using discriminant and class-modelling methods applied to stable isotopes of light elements and elemental composition. The work involved creating a database of 92 authentic Slovenian strawberry samples and 32 imported samples. All samples were harvested between 2018 and 2020. A good geographical classification of Slovenian and non-Slovenian strawberries was obtained despite different production years using discriminant approaches. However, for verifying compliance with a given specification (geographical indications), a class-modelling approach was used to build an unbiased verification model. Class models generated by data-driven soft independent modelling of class analogy (DD-SIMCA) had high sensitivity (96% to 97%) and good specificity (81% to 91%) on a yearly basis, while a more generalised model combining total yearly data gave a lower specificity (63%). Of the 33 commercially available samples (test samples) with declared Slovenian origin, 39% were from outside of Slovenia.

Multi-Elemental Composition Data Handled by Chemometrics for the Discrimination of High-Value Italian Pecorino Cheeses

The multi-elemental composition of three typical Italian Pecorino cheeses, Protected Designation of Origin (PDO) Pecorino Romano (PR), PDO Pecorino Sardo (PS) and Pecorino di Farindola (PF), was determined by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). The ICP-OES method here developed allowed the accurate and precise determination of eight major elements (Ba, Ca, Fe, K, Mg, Na, P, and Zn). The ICP-OES data acquired from 17 PR, 20 PS, and 16 PF samples were processed by unsupervised (Principal Component Analysis, PCA) and supervised (Partial Least Square-Discriminant Analysis, PLS-DA) multivariate methods. PCA revealed a relatively high variability of the multi-elemental composition within the samples of a given variety, and a fairly good separation of the Pecorino cheeses according to the geographical origin. Concerning the supervised classification, PLS-DA has allowed obtaining excellent results, both in calibration (in cross-validation) and in validation (on the external test set). In fact, the model led to a cross-validated total accuracy of 93.3% and a predictive accuracy of 91.3%, corresponding to 2 (over 23) misclassified test samples, indicating the adequacy of the model in discriminating Pecorino cheese in accordance with its origin.

Geographical Discrimination of Curcuma longa L. in Vietnam Based on LC-HRMS Metabolomics

Curcuma longa L. has been used as a food, cosmetic, traditional medicine, and natural dye for a long time in tropical and subtropical regions such as India, China, and Vietnam. Curcuminoids are considered the main bioactive compounds in this plant. This study focuses on metabolites profiling of the rhizome methanolic extract of C longa samples collected in 6 different provinces in Vietnam using liquid chromatography coupled with high-resolution mass spectrometry. The partial least-squares discriminant analysis model was then established to discriminate its metabolomes and identify the chemomarkers that help to distinguish C longa from 6 geographical locations. Consequently, collected samples were segregated into 3 main groups: northern (Lang Son, with typical content of 2 terpenoids), center (Nghe An), and southern highland (Lam Dong, with distinctive profile of 3 curcuminoids). The absolute curcuminoids’ amount was also measured based on the calibration curve of reference standards. The differential metabolites including curcumin, demethoxycurcumin, and bisdemethoxycurcumin were found with the highest range in samples from Lang Son, indicating the excellent quality of turmeric cultivated in this area.

Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice

Identification of geographical origin is of great importance for protecting the authenticity of valuable agri-food products with designated origins. In this study, a robust and accurate analytical method that could authenticate the geographical origin of Geographical Indication (GI) products was developed. The method was based on elemental profiling using inductively coupled plasma mass spectrometry (ICP-MS) in combination with machine learning techniques for model building and feature selection. The method successfully predicted and classified six varieties of Chinese GI rice. The elemental profiles of 131 rice samples were determined, and two machine learning algorithms were implemented, support vector machines (SVM) and random forest (RF), together with the feature selection algorithm Relief. Prediction accuracy of 100% was achieved by both Relief-SVM and Relief-RF models, using only four elements (Al, B, Rb, and Na). The methodology and knowledge from this study could be used to develop reliable methods for tracing geographical origins and controlling fraudulent labeling of diverse high-value agri-food products.

A fast and effective approach for the discrimination of garlic origin using wooden-tip electrospray ionization mass spectrometry and multivariate classification

This paper presents the combination of wooden-tip electrospray ionization mass spectrometry (WTESI-MS) and multivariate pattern recognition methods (principal component analysis, PCA and partial least squares discriminant analysis, PLS-DA) for the rapid and reliable discrimination, via chemical fingerprints, of garlic origin. A total of 312 garlic samples grown in different countries (Brazil, China, Argentina, Spain, and Chile) were studied. The methodology was based on a direct sampling approach, which relies on loading the sample by penetrating the garlic cloves with a pre-wetted wooden tip, followed by direct prompt analysis by WTESI-MS. Thus, no sample preparation is needed, which prevents the degradation of important metabolites and increases the analytical throughput. Parameters that affects the WTESI were optimized and the best performance in terms of signal stability and intensity was achieved using the positive ion mode. Most of the ions in WTESI mass spectra were assigned to amino acids, sugars, organosulfur compounds, and lipids. The discriminative model showed good performance (accuracy rates between 81.9% and 98.6%) and enabled identifying diagnostic ions for garlic samples from different origins. The differentiation and classification of garlic origin is of major importance as this food flavoring product is widely consumed, with worldwide trade representing billions of dollars every year, and is very often the subject of fraud.

Discrimination of turmeric from different origins in China by MRM-based curcuminoid profiling and multivariate analysis

In this research, a three-step strategy was utilized for discriminating turmeric samples from different provinces and regions in China. Firstly, MRM-based UPLC-MS/MS method for chemical profiling of curcuminoids in turmeric samples was established. Then, response surface methodology was applied for optimizing the extraction process of targeted curcuminoids. Finally, multivariate analysis was conducted for systematic characterization of 66 curcuminoids in turmeric. Principal component analysis (PCA) and orthogonal projection to latent structure-discriminant analysis (OPLS-DA) revealed that turmeric samples from Sichuan and other regions could be classified into two distinct groups. Turmeric samples from the same group had similar curcuminoids content distribution. 25 differential curcuminoids were discovered through OPLS-DA, among which most curcuminoids were more abundant in Sichuan. Furthermore, turmeric samples from different provinces could be clearly discriminated based on hierarchical cluster analysis (HCA) using the screened differential curcuminoids.

Origin traceability of peanut kernels based on multi-element fingerprinting combined with multivariate data analysis

BACKGROUND

Multi-elements have been widely used to identify the geographical origins of various agricultural products. The objective of this study was to investigate the feasibility of identifying the geographical origins of peanut kernels at different regional scales by using the multi-element fingerprinting technique. The concentrations of 20 elements [boron (B), magnesium (Mg), phosphorus (P), potassium (K), calcium (Ca), etc.] were determined in 135 peanut samples from Jilin Province, Jiangsu Province, and Shandong Province of China. Data obtained were processed by one-way analysis of variance (ANOVA), principal components analysis (PCA), k nearest neighbors (k-NN), linear discriminant analysis (LDA), and support vector machine (SVM).

RESULTS

Peanut kernels from different regions had their own element fingerprints. The k-NN, LDA, and SVM were all suitable to predict peanut kernels according to their grown provinces with the total correct classification rates of 91.2%, 91.1%, and 91.1%, respectively. While SVM was the best to identify different grown cities of peanut kernels with the prediction accuracy of 91.3%, compared to 72.2% and 78.3% for k-NN and LDA, respectively.

CONCLUSION

It was an effective method to identify producing areas of peanut kernels at different regional scales using multi-element fingerprinting combined with SVM to enhance regional capabilities for quality assurance and control. © 2020 Society of Chemical Industry.

Evaluation of Garlic Landraces from Foggia Province (Puglia Region; Italy)

Interest in local landraces has unfortunately decreased over, the last decades, in which they have been continuously subjected to a high genetic erosion in favour of new modern varieties. Within the Puglia region (S-E Italy), Foggia province was found to be the richest in vegetable landraces. In the present study, six garlic landraces collected from this area have been assessed for their chemical composition (minerals, organic acids, free sugars, volatile, and phenolic compounds) along with their main morpho-biometrical traits. A commercial genotype was also considered as a reference standard. The landraces show a large variability, but in general high morphological standards, high levels of cations and phenols, and low levels of volatile-(S)-compounds in comparison with the commercial genotype and the literature values. 'Aglio di Peschici' and 'Aglio Rosso di Monteleone di Puglia' are very rich in minerals and phenols (mainly ferulic acid and iso-rhamnetin). This increase in knowledge on the chemical properties of these garlic landraces could represent a tool for encouraging the consumption of a food product. At the same time, the consumption of these landraces would stimulate their cultivation and could highly contribute to protection against the risk of erosion of agro-biodiversity by their in situ/on-farm conservation.

Monitoring the authenticity of pu'er tea via chemometric analysis of multielements and stable isotopes

Mineral elements and stable isotopes combined with stoichiometric methods were used as a potential tool for first authenticating Chinese tea according to it's production year. A total of 86 mineral elements and stable isotope compositions were determined from the Xiangzhujing Pu'er tea in five different production years using ICP-MS and ICP-OES. On the basis of 78 statistically significant mineral elements and stable isotopes, HCA, PCA, PLS-DA, BP-ANN, and LDA were employed to build authentication models for predicting the Pu'er tea with different production years. The clustering results of the HCA and PCA were worse than that of PLS-DA, BP-ANN, and LDA. The PLS-DA model displayed a perfect model performance (R²X = 0.86, R²Y = 0.974, and Q² = 0.922). The authentication performance of LDA and BP-ANN revealed their 100% recognition sensitivity and prediction ability and was thus better than that of PLS-DA. Mn, ⁶⁸Zn, and ²⁰³Tl were the markers for enabling the successful authentication of Pu'er tea with different production years. This study contributes toward generalizing the use of mineral element and stable isotope fingerprinting combined with LDA and BP-ANN as a promising tool for authentication of tea worldwide.

Improved geographical origin discrimination for tea using ICP-MS and ICP-OES techniques in combination with chemometric approach

BACKGROUND

There is an urgent need to strengthen the testing and certification of geographically iconic foods, as well as to use discriminatory science and technology for their regulation and verification. Multi-element and stable isotope analyses were combined to provide a new chemometric approach for improving the discrimination tea samples from different geographical origins. Different stoichiometric methods [principal component analysis (PCA), hierarchical cluster analysis (HCA), partial least squares-discriminant analysis (PLS-DA), back propagation based artificial neural network (BP-ANN) and linear discriminant analysis (LDA)] were used to demonstrate this discrimination approach using Yongchuanxiuya tea samples in an experimental test.

RESULTS

Multi-element and stable isotope analyses of tea samples using inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry easily distinguished the geographical origins. However, the clustering ability of the two unsupervised learning methods (PCA and HCA) were worse compared to that of the three supervised learning methods (PLS-DA, BP-ANN and LDA). BP-ANN and LDA, with 100% recognition and prediction abilities, were found to be better than PLS-DA. ⁸⁶ Sr and ¹¹² Cd were the markers enabling the successful classification of tea samples according to their geographical origins. Under the validation by 'blind' dataset, the prediction accuracies of the BP-ANN and LDA methods were all greater than 90%. The LDA method showed the best performance, with an accuracy of 100%.

CONCLUSION

In summary, determination of mineral elements and stable isotopes using inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry techniques coupled with chemometric methods, especially the LDA method, is a good approach for improving the authentication of a diverse range of tea. The present study contributes toward generalizing the use of fingerprinting mineral elements and stable isotopes as a promising tool for testing the geographic roots of tea and food worldwide. © 2020 Society of Chemical Industry.

Geographical origin traceability of Keemun black tea based on its non-volatile composition combined with chemometrics

BACKGROUND

Non-volatile compounds play a key role in the quality and price of Keemun black tea (KBT). The non-volatile compounds in KBT samples from different producing areas normally vary greatly. The development of rapid methods for tracing the geographical origin of KBT is useful. In this study, we develop models for the discrimination of KBT's geographical origin based on non-volatile compounds.

RESULTS

Seventy-two KBT samples were collected from five towns in Anhui province to determine 13 KBT compounds by high-performance liquid chromatography (HPLC). Analysis of variance showed that the content of 13 compounds in KBT indicated significant differences (P < 0.05) among five towns. Three multivariate statistical models including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were built to discriminate origin. Principal component analysis effectively extracted three principal components, namely theaflavins, galloylated catechins, and simple catechins. The high sensitivity (64.5%-99.2%) was achieved of SIMCA model. To establish the discriminant functions, six variables (gallic acid, (+)-catechin, (-)-epigallocatechin gallate, theaflavin-3-gallate, theaflavin-3,3'-di-gallate, and total theaflavins) were chosen from 13 variables, and LDA was applied. This gave a satisfactory overall correct classification rate (94.4%) and cross-validation rate (88.9%) for KBT samples.

CONCLUSION

The results showed that HPLC analysis together with chemometrics is a reliable approach for tracing KBT and guaranteeing its authenticity. © 2019 Society of Chemical Industry.

Multistage Signals Based on Cyclic Chemiluminescence for Decoding Complex Samples

Identification of complex samples presents a difficult challenge for modern analytical techniques, and the differentiation among closely similar mixtures often remains indeterminate. In this article, we designed a simplified cyclic chemiluminescence (CCL) system that is able to measure multistage signals in a single sample injection. The system was used to investigate the CCL reactions of the binary, ternary, and multicomponent mixtures. Results showed that each mixture has a unique exponential decay equation (EDE) with a constant decay coefficient (k-value) to describe the change law of its multistage signals. Further studies found that different brands of liquor, beer, toner, and baby powder have different k-values, and the same brand of the commodities between different batches have the same k-values, which allows facile identification of these complex samples. We then used different catalysts to design digital codes of the k-value for further improving the identifying ability of CCL. Moreover, the multistage signals are like fingerprints and could be used for linear discriminate analysis, which provides another complementary approach for identification of complex samples. Finally, we demonstrated that CCL shows potential applications in certification and quality assurance according to the change of the k-values of the sample. This work demonstrates that excellent discrimination ability of CCL for the identification of complex samples and provides a promising technology for quality assurance.