Discriminating protected geographical indication Chinese Jinxiang garlic from other origins using stable isotopes and chemometrics

Traceability and authentication in agri-food production: A multivariate approach to the characterization ofthe Italian food excellence elephant garlic (Allium ampeloprasum L.), a vasoactive nutraceutical

A research platform for food authentication was set up by combining stable isotope ratio analysis, metabolomics by gas and liquid mass-spectrometry and NMR investigations, chemometric analyses for food excellences. This multi-analytical approach was tested on samples of elephant garlic (Allium ampeloprasum L.), a species belonging to the same genus of common garlic (Allium ampeloprasum L.), mainly produced in southern Tuscany-(Allium ampeloprasum). The isotopic composition allowed the product to be geographically characterized. Flavonoids, like (+)-catechin, cinnamic acids, quercetin glycosides were identified. The samples showed also a significant amount of dipeptides, sulphur-containing metabolites and glutathione, the latter of which could be considered a molecular marker of the analyzed elephant garlic. For nutraceutical profiling to reach quality labels, extracts were investigated in specific biological assays, displaying interesting vasorelaxant properties in rat aorta by mediating nitric oxide release from the endothelium and exhibited positive inotropic and negative chronotropic effects in rat perfused heart.

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.

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.

Tracing the geographical origin of tobacco at two spatial scales by stable isotope and element analyses with chemometrics

Tobacco is a widely cultivated cash crop, but it is often smuggled and sold illegally. Unfortunately, there is currently no way to verify the origin of tobacco in China. In an effort to address this issue, we conducted a study using stable isotopes and elements from 176 tobacco samples at both provincial and municipal scales. Our findings revealed significant differences in δ¹³C, K, Cs, and ^208/206Pb at the provincial-level, and Sr, Se, and Pb at the municipal level. We created a heat map at the municipal level, which showed a similar cluster classification to geographic grouping and provided an initial assessment of tobacco origins. Using OPLS-DA modeling, we achieved a 98.3% accuracy rate for the provincial scale and 97.6% for the municipal scale. It is worth noting that the importance of rankings of variables varied depending on the spatial scale of the evaluation. This study offers the first traceability fingerprint dataset of tobacco and has the potential to combat mislabeling and fraudulent conduct by identifying the geographical origin of tobacco.

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.

Proposing Two Local Modeling Approaches for Discriminating PGI Sunite Lamb from Other Origins Using Stable Isotopes and Machine Learning

For the protection of Protected Geographical Indication (PGI) Sunite lamb, PGI Sunite lamb samples and lamb samples from two other banners in the Inner Mongolia autonomous region were distinguished by stable isotopes (δ¹³C, δ¹⁵N, δ²H, and δ¹⁸O) and two local modeling approaches. In terms of the main characteristics and predictive performance, local modeling was better than global modeling. The accuracies of five local models (LDA, RF, SVM, BPNN, and KNN) obtained by the Adaptive Kennard–Stone algorithm were 91.30%, 95.65%, 91.30%, 100%, and 91.30%, respectively. The accuracies of the five local models obtained by an approach of PCA–Full distance based on DD–SIMCA were 91.30%, 91.30%, 91.30%, 100%, and 95.65%, respectively. The accuracies of the five global models were 91.30%, 91.30%, 91.30%, 100%, and 91.30%, respectively. Stable isotope ratio analysis combined with local modeling can be used as an effective indicator for protecting PGI Sunite lamb.

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.