Application and Preparation Progress of Stable Isotope Reference Materials in Traceability of Agricultural Products

Multilocus Distance-Regulated Sensor Array for Recognition of Polyphenols via Machine Learning and Indicator Displacement Assay

Developing new strategies to construct sensor arrays that can effectively distinguish multiple natural components with similar structures in mixtures is an exceptionally challenging task. Here, we propose a new multilocus distance-modulated indicator displacement assay (IDA) strategy for constructing a sensor array, incorporating machine learning optimization to identify polyphenols. An 8-element array, comprising two fluorophores and their six dynamic covalent complexes (C1-C6) formed by pairing two fluorophores with three distinct distance-regulated quenchers, has been constructed. Polyphenols with diverse spatial arrangements and combinatorial forms compete with the fluorophores by forming pseudocycles with quenchers within the complexes, leading to varying degrees of fluorescence recovery. The array accurately and effectively distinguished four tea polyphenols and 16 tea varieties, thereby demonstrating the broad applicability of the multilocus distance-modulated IDA array in detecting polyhydroxy foods and natural medicines.

Rapid characterization of isotopic purity of deuterium-labeled organic compounds and monitoring of hydrogen-deuterium exchange reaction using electrospray ionization-high-resolution mass spectrometry

RATIONALE

Deuterium-labeled organic compounds, reagents, and drugs are widely used in many scientific research fields. Isotopic purity as the feature parameter of deuterated compounds is of great importance. In this article, we used electrospray ionization with high-resolution mass spectrometry (ESI-HRMS) to study isotopic purity of deuterium-labeled organic compounds based on assigning and distinguishing the corresponding H/D (hydrogen-deuterium) isotopolog ions of deuterated compounds.

METHODS

We systematically considered the specific requirements of accuracy and resolution of ESI-HRMS when measuring isotopic purity and demonstrated some actual cases using ESI-HRMS and ultraperformance liquid chromatography (UPLC)-HRMS. Meanwhile, ESI-HRMS/MS of deuterated compounds was performed to obtain more information on deuterium-labeled position characterization.

RESULTS

Two isotopic purity calculation methods based on the relative abundance in ESI-HRMS and UPLC-HRMS of H/D isotopolog ions (D0 -Dn ) were compared, which gave consistent isotopic purity values and were in good agreement with the certified isotopic purity values. We further studied and monitored the H/D exchange reaction of ethyl 3-(4-bromophenyl)-3-oxopropanoate (EBPO) by evaluating the dynamic isotopic purity changes in EBPO-D2 in the H/D exchange reaction in situ.

CONCLUSION

The isotopic purity characterization methods using ESI-HRMS discussed in this article have some outstanding advantages: rapid, highly sensitive, very low sample consumption (even below nanogram level), and deuterated solvent-free. Thus, this low-impact analytical method requires less time and is cost effective and might have good application potential for in-situ isotopic purity analysis and for monitoring the H/D exchange reactions.

Simultaneously Verifying the Original Region of Green and Roasted Coffee Beans by Stable Isotopes and Elements Combined with Random Forest

Simultaneously verifying the original region of green and roasted coffee beans is very important for protecting legal interests of the stakeholder according to the chemical analyzing method. 131 green coffee bean samples are collected from six different original regions and pretreated with three degrees (green, middle, and dark roasted); five stable isotope ratios (δ13C, δ14N, δ18O, δ2H, and δ32S) and twelve elemental contents (Al, Cr, Ni, Zn, Ba, Cu, Na, Mn, Fe, Ca, K, and Mg) of green, middle, and dark roasted coffee bean samples (131×3) were analyzed. Fractionation of stable isotopes and variation of elemental contents were evaluated, only isotope hydrogen (2H) significantly fractionated, and elemental concentrations increased with a certain rate during the roasting process. One-way analysis of variance (ANOVA) was used to compare the stable isotope ratios and elemental concentrations of all coffee bean samples from six different original regions. Random forest (RF) was employed to build a discriminating model for simultaneously verifying the original regions of green and roasted coffee bean samples; this model provided 100% accuracy. Inclusion of this mathematical model for simultaneously verifying the original region of green and roasted coffee beans had powerful distinguishing capability and which will not be influenced by fractionation of hydrogen (2H) and variation of element contents during the roasted process.

Chemistry Combining Elemental Profile, Stable Isotopic Ratios, and Chemometrics for Fine Classification of a Chinese Herb Licorice (Glycyrrhiza uralensis Fisch.) from 37 Producing Area

A method based on elemental fingerprint, stable isotopic analysis and combined with chemometrics was proposed to trace the geographical origins of Licorice (Glycyrrhiza uralensis Fisch) from 37 producing areas. For elemental fingerprint, the levels of 15 elements, including Ca, Cu, Mg, Pb, Zn, Sr, Mn, Se, Cd, Fe, Na, Al, Cr, Co, and K, were analyzed by inductively coupled plasma atomic emission spectrometry (ICP-AES). Three stable isotopes, including δ ¹³C, δ ¹⁵N, and δ ¹⁸O, were measured using an isotope-ratio mass spectrometer (IRMS). For fine classification, three multiclass strategies, including the traditional one-versus-rest (OVR) and one-versus-one (OVO) strategies and a new ensemble strategy (ES), were combined with two binary classifiers, partial least squares discriminant analysis (PLSDA) and least squares support vector machines (LS-SVM). As a result, ES-PLSDA and ES-LS-SVM achieved 0.929 and 0.921 classification accuracy of GUF samples from the 37 origins. The results show that element fingerprint and stable isotope combined with chemometrics is an effective method for GUF traceability and provides a new idea for the geographical traceability of Chinese herbal medicine.

Multi-Mineral Element Profiles in Genuine and “Bathing” Cultured Chinese Mitten Crabs (Eriocheir sinensis) in Yangcheng Lake, China

The authentication of high-quality fishery products originating from specific geographical regions is urgently needed worldwide. Chinese mitten crabs (Eriocheir sinensis), originating from Yangcheng Lake, are prime counterfeiting targets for the same reasons. Foreign crabs that are cultured briefly in the lake, known as “bathed” crabs, are illegally marketed as natives, negatively impacting the product quality. To establish a method for distinguishing “bathed” and genuine crabs, in this paper we conduct a comparative investigation by an Agilent 7500ce ICP-MS on multi-mineral element profiling of the third pereiopod from genuine and one month deliberately “bathing” cultured crabs. The profiles of 11 elements were significantly different between the genuine and foreign crabs before and after bathing. The discriminant analysis reached 100% accuracy to separate the genuine and “bathed” crabs into different groups. Bathing culture was unable to converge element profiles between the genuine and foreign crabs. The biogeochemical profiles can be effective for distinguishing “bathed” crabs.

Determining the geographical origin of milk by multivariate analysis based on stable isotope ratios, elements and fatty acids

To construct a reliable discrimination model for determining milk geographical origin, stable isotope ratios including δ¹³C, δ¹⁵N and δ¹⁸O, 51 elements and 35 fatty acids (FAs) in milk samples from Australia, New Zealand and Austria were detected and analyzed. It is found that all of the stable isotope ratios in the milk samples of Australia are the highest, followed by those of the samples from New Zealand and Austria. In addition, 14 elements and 8 FAs show different contents in the samples of different countries at the significance level of P < 0.05. Based on these results, a multivariate model with good robustness and predictive ability for authenticating milk origin (R²X = 0.693, Q² = 0.854) was successfully constructed. Element contents and stable isotope ratios are more reliable variables for milk origin discrimination and Rb, δ¹⁸O, Tl, Ba, Mo, Sr, δ¹⁵N, Cs, As, Eu, C20:4n6, Sc, C13:0, K, Ca and C16:1n7 are the critical markers in the multivariate model for verifying milk origin.