Highly selective detection of epinephrine by a "turn-off" fluorescent sensor based on N-doped carbon quantum dots.

Epinephrine (EP) is a catecholamine hormone with a variety of physiological activities. Monitoring the concentration of EP in drugs, food, biological samples and cosmetics is of great significance for their quality control. Herein, a novel fluorescence sensing method was designed for the high-specificity detection of EP based on N-doped carbon quantum dots (N-CDs). The EP could interact with the fluorescent senor of N-CDs which emits blue fluorescence to produce concentration- dependent fluorescence quenching through the photo-induced electron transfer (PET). The established sensing method has good linearity in the range of 0.5-10 µM with the LOD of 0.15 µM. More importantly, it is highly selective because similar components with phenolic hydroxyl groups or primary amino groups, even norepinephrine (NEP), could not interfere with the detection. This method can provide a low-cost, rapid and simple new way for the detection of EP, and has a good application prospect in point-of-care assay and in situ test.

Discrimination of Free-Range and Caged Eggs by Chemometrics Analysis of the Elemental Profiles of Eggshell.

As one of the foods commonly eaten all over the world, eggs have attracted more and more attention for their quality and price. A method based on elemental profiles and chemometrics to discriminate between free-range and caged eggs was established. Free-range (n1 = 127) and caged (n2 = 122) eggs were collected from different producing areas in China. The content of 16 elements (Zn, Pb, Cd, Co, Ni, Fe, Mn, Cr, Mg, Cu, Se, Ca, Al, Sr, Na, and K) in the eggshell was determined using a inductively coupled plasma atomic emission spectrometer (ICP-AES). Outlier diagnosis is performed by robust Stahel-Donoho estimation (SDE) and the Kennard and Stone (K-S) algorithm for training and test set partitioning. Partial least squares discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) were used for classification of the two types of eggs. As a result, Cd, Mn, Mg, Se, and K make an important contribution to the classification of free-range and caged eggs. By combining column-wise and row-wise rescaling of the elemental data, the sensitivity, specificity, and accuracy were 91.9%, 91.1%, and 92.7% for PLS-DA, while the results of LS-SVM were 95.3%, 95.6%, and 95.1%, respectively. The result indicates that chemometrics analysis of the elemental profiles of eggshells could provide a useful and effective method to discriminate between free-range and caged eggs.

Determination of geographical origin for Atractylodes macrocephala Koidz by stable isotope and multielement analyses combined with chemometrics.

Atractylodes macrocephala Koidz (AMK) is an expensive edible Chinese herb with medicinal properties. Its economic value and medicinal properties are closely related to its geographical origin. In this study, a method based on stable isotopes and multiple elements combined with chemometrics was developed to identify the geographical origin of AMK. Five stable isotope ratios, including δ2 H, δ18 O, δ13 C, δ15 N, and δ34 S, and 41 elements in 281 AMK samples from 10 regions were analyzed. An analysis of variance of stable isotope ratios and elements revealed that the δ2 H, δ18 O, Mg, Ca, and rare-earth element concentrations in AMK from different geographical regions were significantly different. Orthogonal partial least squares discriminant analysis proved that Ca, K, Mg, and Na can be used for classifying (variable importance >1) and accurately identifying AMK from Panan, Xianfeng, and other areas with 100% discrimination accuracy. In addition, we achieved a good identification of protected geographic indication products of similar quality. This method realized the geographical discrimination of AMK from different producing areas and could potentially control the fair trade of AMK. PRACTICAL APPLICATION: The quality of AMK is highly dependent on its geographical origin. Confusion over the origin of AMK impacts consumer rights. This study developed an accurate and effective classification method based on stable isotopes and multiple elements to ascertain the geographical origin of AMK, thereby providing an effective method for determining its quality.

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.

An inner filter effect-based nitrogen-doped carbon dots-CoOOH nanoflakes fluorescence probe for detection of ascorbic acid by chemical redox modulation.

BACKGROUND: Ascorbic acid (AA) is an essential nutrient for humans, which must be obtained from vegetables, fruits, and other foods. The content of AA has become an important standard to evaluate the quality and nutritional value of food. The fluorescence sensing method based on nanomaterials is a good alternative for the rapid detection of AA. In this study, we developed an inner filter effect-based fluorescent probe that hybridized nitrogen-doped carbon dots (NCDs) with cobalt oxyhydroxide nanoflakes (CoOOH NFs). RESULTS: An optimal NCDs was successfully selected because it has a strong fluorescence at 430 nm and the most significant quenching phenomenon with CoOOH NFs due to the inner filter effect. When adding AA into the NCDs-CoOOH NFs probe solution, a specific redox reaction will occur between the enediol group of AA and the CoOOH NFs to interfere with the quenching ability of CoOOH NFs and recover the fluorescence of NCDs. The recovered fluorescence intensities demonstrated a linear relationship with the concentrations of AA. The assay based on the NCDs-CoOOH NFs probe allows AA to be tested in a wide range of 5-200 µmol L-1 with a detection limit of 2.31 nmol L-1 . Furthermore, to evaluate its practical application, the NCDs-CoOOH NFs fluorescence probe was utilized to analyze AA in vegetable, fruit, and serum matrixes with satisfactory results. CONCLUSION: An inner filter effect-based fluorescence probe for the rapid detection of AA was developed, and it has a good potential to be applied in both food and clinical testing. © 2022 Society of Chemical Industry.

Classification of organic and ordinary kiwifruit by chemometrics analysis of elemental fingerprint and stable isotopic ratios.

Elemental fingerprint, stable isotopic analysis, and chemometrics were combined to identify organic kiwifruit from ordinarily cultivated kiwifruit. Samples of organic (n1 = 78) and ordinary kiwifruit (n2 = 85) were collected from neighboring areas. For elemental fingerprint, the contents of 15 elements in fresh fruits, including Al, Cr, Mg, Pb, Zn, Ca, Cu, Mn, Se, Cd, Fe, Na, Sr, Co, and K, were determined by inductively coupled plasma optical emission spectrometry (ICP-OES). Three stable isotopes, including δ13 C, δ15 N, and δ18 O, were analyzed using an isotope-ratio mass spectrometer (IRMS). Different classification methods including soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLSDA), and least squares support vector machines (LS-SVM), were used to discriminate the organic and ordinary kiwifruits by fusion of elemental and stable isotopic. As a result, the sensitivity, specificity, and overall accuracy of SIMCA model were 0.885, 0.857, and 0.864, respectively. PLSDA and LS-SVM obtained 0.950 and 0.983 classification accuracy of organic and ordinary kiwifruits, respectively. It was demonstrated that elemental fingerprint and stable isotopic analysis would provide useful chemical information for the identification of organic fruits, and the capacity of these methods could be enhanced by chemometrics. PRACTICAL APPLICATION: The classification of kiwifruit usually relies on the label assigned by the merchant, which is prone to deceive consumers. This research has developed an accurate and effective classification method based on stable isotopes and mineral elements for the identification of ordinary kiwifruit and organic kiwifruit, providing a tool for the quality monitoring of organic food.

Determination of l-theanine in tea water using fluorescence-visualized paper-based sensors based on CdTe quantum dots/corn carbon dots and nano-porphyrin with chemometrics.

BACKGROUND: The quality of tea is influenced by numerous factors, especially l-theanine, which is one of the important markers used to evaluate the sweetness and freshness of tea. Sensitive, rapid, and accurate detection of l-theanine is therefore useful to identify the grade and quality of tea. RESULTS: A high-sensitivity, paper-based fluorescent sensor combined with chemometrics was established to detect l-theanine in tea water based on CdTe quantum dots / corn carbon dots and nano tetra pyridel-porphine zinc (ZnTPyP). To verify the reliability of this method, fluorescence spectra and fluorescence-visualized paper-based sensors were compared. The fluorescence spectrum method demonstrated a linear range of 1 to 10 000 nmol L-1 and a limit of detection (LOD) of 0.19 nmol L-1 . In the fluorescence-visualized paper-based sensors there was a linear range of 10-1000 nmol L-1 , and the LOD was 10 nmol L-1 . Partial least squares discriminant analysis (PLSDA) and partial least squares regression analysis (PLSR) were used successfully to determine l-theanine accurately in tea water with this approach. The accuracy of the PLSDA model was 100% both in the training set and the predicting set, and the correlation coefficient between the actual concentration and the predicted concentration was greater than 0.9997 in the PLSR model. CONCLUSION: This fluorescence-visualized paper-based sensor, combined with chemometrics, could be applied efficiently to the practical analysis of tea water samples, which provides a new idea to ensure the flavor and quality of tea. © 2020 Society of Chemical Industry.