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As a common fruit juice, grape juice is delicious and nutritious, making it very popular among consumers. However, some illegal manufacturers used shoddy products to lower costs and obtain high profits, which seriously threatens the health and interests of consumers. Hence, this paper proposed excitation-emission matrix (EEM) fluorescence spectroscopy combined with chemometric methods for the rapid identification and classification of commercial grape juices. Spectral characterization of different samples was achieved using the alternating trilinear decomposition (ATLD) algorithm, and chemically meaningful information was obtained and analyzed. Although both reconstituted and sweetened grape juices contain methyl anthranilate (MA) and 2'-aminoacetophenone (

A versatile triple cascade amplification strategy was developed for ultrasensitive simultaneous detection of multiple cancer biomarkers using single particle inductively coupled plasma mass spectrometry (spICP-MS). The triple cascade amplification strategy consisted of an enhanced RecJ

Herein, a novel liquid crystal microarray (LCM) film with optical regulation ability is first constructed by combining liquid crystals (LC) and the highly ordered microporous structure of inverse opal photonic crystals (IOPhCs). The LCM films are fabricated by infiltrating LC molecules into the LC polymer with the structure of IOPhCs, and their properties are very different from those without the LC. Interestingly, the optical property of LCM films can be controlled by changing the orientation of LC molecules, which varies with the interfacial force. In combination with polarization images, spectral reflection peak, circular dichroism spectra, potential difference, and fluorescence images of LCM films, the mechanism of this change is investigated. It is found that the exposed basic group of single-stranded DNA is the key to the change of the optical property of LC microarrays. Meanwhile, the optical signals of LC microarrays based on the PhCs provide a novel LC signal mode for an LC sensing system (microspectral signal mode), and it can be recorded by a fiber-optic spectrometer, which is a great improvement on LC sensing signals. Therefore, the LC microarray sensing signal can be used for accurate analysis of targets by the change of the reflection peak intensity of PhCs. When the LC molecules are induced by different aptamers, the LC microarray sensing interface can be further used for the determination of different targets, such as cocaine and Hg2+. The research on LCM films is of significant value for the development of LC sensing technology and also shows great application prospects in biochemical sensing fields.

Single-nanoparticle inductively coupled plasma mass spectrometry (SP-ICP-MS) has demonstrated unique advantages for the detection of biological samples. However, methods for enzyme activity detection based on SP-ICP-MS technology have been rarely explored. Here we report the development of a novel SP-ICP-MS assay for uracil-DNA glycosylase (UDG) activity detection based on its ability to specifically recognize and remove uracil to induce the cleavage of the DNA probe. Our design allows the generation of single gold nanoparticles correlated to the specific enzymatic reaction for a highly sensitive SP-ICP-MS measurement. The developed assay enables sensitive UDG activity detection with a detection limit of 0.0003 U/mL. The cell lysate analysis by the developed assay reveals its applicability for the detection of UDG activity in real samples. It is envisioned that our design may provide a new paradigm for developing the SP-ICP-MS assay for enzyme activity detection in biological samples.

Because of the low atomization and/or ionization efficiencies of many biological macromolecules, the application of mass spectrometry to the direct quantitative detection of low-abundance proteins and nucleic acids remains a significant challenge. Herein, we report mass spectrum tags (MS-tags) based upon gold nanoparticle (AuNP)-templated phosphatidylcholine phospholipid (DSPC) liposomes, which exhibit high and reliable signals via electrospray ionization (ESI). Using these MS-tags, we constructed a liposome signal amplification-based mass spectrometric (LSAMS) "digital" counting assay to enable ultrasensitive detection of target nucleic acids. The LSAMS system consists of liposomes modified with a gold nanoparticle core and surface-anchored photocleavable DNA. In the presence of target nucleic acids, the modified liposome and a magnetic bead simultaneously hybridize with the target nucleic acid. After magnetic separation and photolysis, the MS-tag is released and can be analyzed by ESI-MS. At very low target concentrations, one liposome particle corresponds to one target molecule; thus, the concentration of the target can be estimated by counting the number of liposomes. With this assay, hepatitis C (HCV) virus RNA was successfully analyzed in clinical samples.

Two-photon carbon-based nanoprobes hold great potential for biomedical applications as a result of their advantages of low fluorescence background, deep tissue imaging penetration and enhanced spatial resolution. However, the development of an activatable two-photon fluorescence carbon-based nanoprobe that simultaneously has the ability to target desired organs or cells is highly desired but remained a largely unsolved challenge. Herein, we developed boronate affinity BCNP@MnO2 nanocomposites, constructed by one step in situ growth of MnO2 nanosheets on the surface of aminophenylboronic acid-functionalized CNPs (BCNPs) via a redox reaction, which can feature efficient fluorescence energy transfer quenching to the BCNPs, allowing for tumor-specific affinity recognition and two-photon fluorescence activation imaging. By utilizing the inherent two-photon optical properties and sialic acid (SA) specific targeting ability of the BCNPs, good biocompatibility of the nanocomposites as well as highly sensitive and selective responses of MnO2 nanosheets towards GSH, the developed nanocomposites have demonstrated specific two-photon fluorescence activation imaging in target cancer cells and nude mouse tissues. Therefore, our proposed novel strategy could be used for monitoring GSH-triggered two-photon fluorescence activation events in SA-overexpressed cancer cells and has promising applications in both biological exploration and clinical diagnosis.

In this work, a simple and fast analytical method based on a self-weighted alternating trilinear decomposition (SWATLD) algorithm coupled with excitation-emission matrix (EEM) fluorescence was developed for the simultaneous determination of sulfamethoxazole (SMZ) and trimethoprim (TMP) illegally added to health products. With the second-order advantage, the proposed method obtained satisfactory results in the presence of peak overlap and unknown interferences. The analysis time for a single sample is only 0.8 minutes. The average spiked recoveries of SMZ and TMP in three health product spiked samples were in the range of 91.0-106.2% and 86.8-107.8%, respectively. The relative standard deviations (RSDs) were lower than 8.6%. In addition, verification parameters including sensitivity (SEN), selectivity (SEL), the limit of detection (LOD), the limit of quantification (LOQ), intra-day precision, and inter-day precision were calculated, and the results show that the proposed method is feasible. The quantitative results of the proposed method were further confirmed by the LC-MS/MS method, which proved that the proposed method was efficient and green for drug-abuse monitoring of SMZ and TMP in health products.

Investigating multiple miRNA expression patterns in living cells by DNA logic biocomputing is a valuable strategy for diagnosis and biomedical studies. The introduction of protein enzymes in DNA logic biocomputing circuits not only expands the toolbox of nucleic acid assembly techniques, but also further improves the specificity of recognizing and processing of DNA input. Herein, a polymerase-driven primer exchange reaction, acting as the sensing module, is introduced into the biocomputing system and transduces the multiple miRNAs sensing event into the intermediate triggers for activating the subsequent processing module, which further performs signal readout through DNAzyme catalytic substrate cleavage reaction. By using biomineralized zeolitic imidazolate framework-8 nanoparticles (ZIF-8 NPs) to deliver all the components of the biocomputing system, including polymerase and DNA probes, we realized polymerase-driven DNA biocomputing operations in living cells, including AND and OR gates. The results exhibited that biomineralized ZIF-8 NPs can protect the loaded cargoes against the external environment and deliver them efficiently to the cytoplasm. The polymerase-driven DNA biocomputing system based on multiple miRNAs sensing can be used for reliable cell identification and may provide a promising platform for more accurate diagnosis and programmable therapeutics.

Geographical origin has great influence on the quality of traditional Chinese medicine. This work reported an application of geographical origin traceability of Atractylodes macrocephala Koidz. based on chemometrics classification methods combined with data fusion of synchronous fluorescence spectroscopy and surface-enhanced Raman spectroscopy. The classification model was built by principal component analysis-linear discriminant analysis, partial least squares discriminant analysis and random forest. The cross-validation showed that the correct classification rate could achieve 91.0% for partial least squares discriminant analysis based on low-level data fusion and the classification model based on low-level data fusion could achieve accurate classification of 14 new Atractylodes macrocephala Koidz. samples. The results demonstrated that the synchronous fluorescence spectroscopy and surface-enhanced Raman spectroscopy complemented each other, and better classification results can be obtained based on data fusion strategy.

Camellia oil (CAO) is a premium edible vegetable oil with medical value and biological activity, but it is susceptible to adulteration. Therefore, the demand for intelligent analysis to decipher the category and proportion of adulterated oil in CAO was the main driver of this work. Excitation-emission matrix fluorescence (EEMF) spectra of 933 vegetable oil samples were characterized by a chemometric method to obtain chemically meaningful information. Authenticity identification models were constructed using four machine learning methods to realize the discrimination of oil species adulterated in CAO mixtures. Meanwhile, quantitative models were established aiming at the fraud of CAO proportion in blended oil. Results showed that the specially constructed CNN obtained the optimal performance when evaluating unseen real-world samples, with a classification accuracy of 95.8% and 92.2%, and mean-absolute quantitative errors between 2.6 and 6.7%. Therefore, EEMF fingerprints coupled with machine learning are expected to provide intelligent and accurate analysis for authenticity detection of CAO.

A simple but effective method for the detection of miRNAs was proposed by integrating exonuclease-III assisted target recycling amplification and repeated-fishing strategy. In the proposed method, exonuclease-III assisted target recycling amplification reaction is adopted to produce a large amount of DNA fragments with fluorescence group at its 5′ end in the presence of the target miRNA, which are then repeatedly fished out from the reaction mixture by a gold foil modified with a capture probe and transferred into a so-called ‘product tube’. The amount of the target miRNA can then be determined from the fluorescence measurement of the solution in the ‘product tube’. Application to the detection of miRNA-155 in samples of KH-2 and BRSA-2B cells revealed that the proposed method could achieve sensitive and accurate quantification of the target miRNA with a limit of detection of 36 fM and recovery rates in the range from 96.2% to 105%. Its simplicity, sensitivity and resistance to possible fluorescence interferences in complex biological samples make the proposed method a potentially competitive alternative for miRNAs detection in complex biological samples.

Luminescent nanoclusters (NCs) have attracted much attention because of their good photostability and low toxicity, however, the low quantum yield is still a deficiency, and many increasing efforts are being devoted to enhance the luminescence intensity of NCs. In this paper, a method of enhancing the fluorescent signal of gold-silver nanoclusters (AuAgNCs) by photonic crystals (PhCs) was proposed. The fluorescent intensity of AuAgNCs on PhCs can be enhanced 8.0-fold in comparison to the control sample without PhCs. Furthermore, a novel fluorescence sensor of AuAgNCs based on PhCs is used for the sensitive and selective detection of Hg2+ ion in the aqueous solution, the detection limit is 0.35 nM due to the PhCs enhancement effect for the fluorescence. This proposed method may not only develop a highly sensitive method for determination of Hg2+ ion, but also expand the application of AuAgNCs in ultra-trace analysis.

Here, we report a novel fluorescence method for the highly selective and sensitive detection of RNase H by combining the use of a dual-pyrene-labeled DNA/RNA duplex with supramolecular inclusion-enhanced fluorescence. Initially, the probe is in the “off” state due to the rigidness of the double-stranded duplex, which separates the two pyrene units. In the presence of RNase H, the RNA strand of the DNA/RNA duplex will be hydrolyzed, and the DNA strand transforms into a hairpin structure, bringing close the two pyrene units which in turn enter the hydrophobic cavity of a γ-cyclodextrin. As a result, the pyrene excimer emission is greatly enhanced, thereby realizing the detection of RNase H activity. Under optimal conditions, RNase H detection can be achieved in the range from 0.08 to 4 U/mL, with a detection limit of 0.02 U/mL.

In this paper, in order to solve signal instability of high-performance liquid chromatography-diode array detection (HPLC-DAD) data and maintain the second-order advantage, this work proposed piecewise direct standardization (PDS) assisted with second-order calibration methods to analyze two different complex HPLC-DAD data with signal instability, including simulated HPLC-DAD data and the data of pesticide residues in saffron. Accurate quantitative results of target analytes can be obtained by PDS combined with alternating trilinear decomposition algorithm (ATLD) and alternating trilinear decomposition assisted multivariate curve resolution algorithm (ATLD-MCR) in the presence of signal instability with time shifts and changes of peak shape. Quantitative results of the model after calibration transfer are better than those of the model established by calibration sets and prediction sets with signal instability using ATLD algorithm and ATLD-MCR algorithm under different situations. Meanwhile, t-test was used to judge whether there are significant differences between these quantitative results of models. The performances of MCR-ALS algorithm were compared with that of PDS-ATLD method and PDS-ATLD-MCR method and the proposed methods have greater potential in dealing with the case of signal instability with time shifts and changes of peak shape. In a word, this methodology can reduce the number of calibration samples for recalibration and modeling, improve the efficiency of experiment, conform to the principle of green chemistry, and obtain satisfactory quantitative results in the presence of signal instability with time shifts and changes of peak shape.

Quantitation of protoberberine alkaloids is an essential guarantee for efficacy control and medication safety of Coptidis Rhizoma (CR) related medicines. Traditional univariate chromatography faced challenges with co-elution, unknown interferences, and retention time shift when analyzing isomeric analytes in varying sample matrices. We presented a chemometrics-enhanced high-performance liquid chromatography-diode array detection (HPLC-DAD) strategy for simultaneous quantification of six protoberberine alkaloids and processed multi-channels chromatographic-spectral data with four second-order calibration algorithms. Chromatographic conditions were firstly optimized. Four groups of predicted samples were modeled individually with the designed calibration set. Mathematical resolutions were then obtained, and pseudo-univariate regression gave the quantitative concentration of each analyte. Four models were scored on fit, linearity, recovery, and robustness, where alternating trilinear decomposition assisted multivariate curve resolution (ATLD-MCR) exhibited an optimal and stable performance. Besides, the resolved spectra presented high consistency with the actual spectra (r≥0.9993). Limits of quantification (LOQ) fully met the pharmacopoeia stipulation and were 0.17, 0.60, 0.19, 0.74, 0.15, and 0.38 µg mL

Atractylodes macrocephala Koidz. (AM) is an important plant of traditional Chinese medicine (TCM), and its status can be comparable with ginseng in China. The efficacy and quality of AM are closely related to the place of origin. Hence, we proposed a simple and fast strategy to classify AM from different geographical origins by using multi-way fluorescence fingerprint combined with chemometric methods. AM samples with different dilution levels have different fluorescence characteristics, resulting from different content of fluorescence components and chemical microenvironment. Therefore, AM samples were diluted 5-fold, 10-fold, and 20-fold with 40% ethanol aqueous solution to obtain excitation-emission matrix data, and multi-way (three-way and four-way) data arrays were constructed. And then, the fluorescence fingerprints of AM samples were characterized by three-way and four-way parallel factor analysis (PARAFAC). In addition, four pattern recognition methods were used to classify AM from different provinces. The results show that the four-way data array can provide more abundant information than three-way data arrays, so it is more conducive to sample classification. According to the results obtained from the analysis of four-way data array, the correct classification rate (CCR) of the cross-validation and prediction set obtained by partial least squares-discrimination analysis (PLS-DA) were 90.5% and 100%, respectively. To sum up, the proposed method can be regarded as a powerful, feasible, convenient, reliable, and universal classification tool for the classification of AM samples from different provinces and can be used as a promising method to realize the geographical origin traceability of other TCMs.

Herein, a label free and sensitive miRNA detection method with enhanced practical applicability was developed based on the locked nucleic acid (LNA) assisted repeated fishing amplification strategy. The working mechanism of the proposed method is as follows: 1) a DNA probe (i.e, L-DNA) with LNA bases is immobilized onto the surface of a gold foil. The L-DNA hybridizes with the 3' terminus of the first strands of complementary deoxyribonucleic acid (cDNA) of the target miRNA in the test samples; 2) The protruding 5' terminus of the cDNA serves as a 'fishhook' to repeatedly fish the products of a hybridization chain reaction (HCR) out from a 'reaction tube'; 3) The HCR products can be unloaded from the gold foil into a 'product tube' through temperature-controlled dehybridization; 4) The concentration of the target miRNA is determined based on the fluorescence intensity generated by the addition of SYBR-Green I (SG) into the 'product tube'. The proposed platform was applied to the detection of miRNA-122 in cell lysate samples and obtained quantitative results with accuracy comparable to the quantitative reverse transcription PCR method (qRT-PCR). It is worth pointing out that the proposed platform achieved a limit of detection value of 2.9 fM for miRNA-122 by a simple but effective LNA-assisted repeated fishing amplification strategy instead of complicated enzyme-based amplification techniques. It is reasonable to expect that the proposed method provides a competitive alternative for designing practically applicable, cost-effective and label-free miRNA detection methods.

An intelligent chemometric second-order calibration method called alternating trilinear decomposition- assisted multivariate curve resolution combined with high-performance liquid chromatography-diode array detection was used for the simultaneous quantification of nine tyrosine kinase inhibitors in three complex biological systems. The method allows simultaneous quantification of the components in different biological matrices without the need for cumbersome pre-treatment steps, complex elution conditions, and complete peak separation. Even with the varying time shift, severe peak overlap, and various unknown interferences, the proposed method can extract pure chromatographic and spectroscopic information for each analyte, while providing accurate qualitative and quantitative results of nine common tyrosine kinase inhibitors in three different biological matrices. All the drugs were eluted in 7 min. The results showed that the nine drugs in each matrix showed good linearity (r > 0.984) in the calibration range with a root mean square error of calibration less than 0.9 μg/mL. The average spiked recoveries of the target analytes were all in the range of 83.4-110.0%, with standard deviations less than 9.0%. Finally, the classical method was used to validate the proposed method. In comparison to the traditional method, the proposed strategy is accuracy, simultaneous, and interference-free.

Without the estimation of the intermediate parameters, the direct position determination (DPD) method can achieve higher localization accuracy than conventional two-step methods. However, multipath environments are still a key problem, and complex high-dimensional matrix operations are required in most DPD methods. In this paper, a time-difference-of-arrival-based (TDOA-based) DPD method is proposed based on the subspace orthogonality in the cross-spectra between the different sensors. Firstly, the cross-spectrum between the segmented received signal and reference signal is calculated and eigenvalue decomposition is performed to obtain the subspaces. Then, the cost functions are constructed by using the orthogonality of subspace. Finally, the location of the radiation source is obtained by searching the superposition of these cost functions in the target area. Compared with other DPD methods, our proposed DPD method leads to better localization accuracy with less complexity. The superiority of this method is verified by both simulated and real measured data when compared to other TDOA and DPD algorithms.

In this work, a simple and effective strategy for the determination of 12 active compounds of Atractylodes macrocephala Koidz. (AM) was proposed by using high performance liquid chromatography-diode array detection (HPLC-DAD) combined with alternating trilinear decomposition (ATLD) algorithm. Utilizing the "second-order advantage", three common problems in HPLC could be resolved, namely baseline drifts, peak overlaps, and unknown interferences. 12 compounds were rapidly eluted within 12.5 min, and the average spiked recoveries were 80.8-109.9%. The figures of merit reflected the feasibility of the proposed method. Compared with the results of the traditional univariate calibration method based on HPLC-UV technique, the proposed strategy further verified the reliability and simplicity of the mathematical separation. On this basis, partial least squares-discriminant analysis (PLS-DA) was applied to discriminate 113 AM samples from different geographical origins, and variable importance in projection (VIP) was used to further screen the main differential components that affect the regional division of AM. A series of results show that the AM samples from the three regions have obviously different clustering trends. Overall, the strategy is expected to provide a scientific basis for the modern research of medicinal materials, and it is also conducive to the clinical use and market supervision of AM.

In this paper, a fast and efficient analytical strategy was proposed that chemometrics assisted with excitation-emission fluorescence matrices was used to quantify carbaryl (CAR) and thiabendazole (TBZ) in peach, soil and sewage. Even if there are serious overlapped peaks and unknown interferences in fluorescence analysis, the second-order calibration method based on alternating trilinear decomposition (ATLD) algorithm can be used to analyze CAR and TBZ in peach, soil and sewage. The recoveries of CAR and TBZ in peach are 110.4% and 99.7% and their standard deviations are lower than 2.1% and 0.3%, respectively. In addition, the accuracy of the method was assessed with figures of merit as well as intra-day and inter-day precision. The limit of detection, the limit of quantitation of CAR and TBZ in peach are 1.2 ng mL−1 and 0.3 ng mL−1, 3.5 ng mL−1 and 0.8 ng mL−1, respectively. And their root-mean-square error of prediction are 17.0 ng mL−1 and 5.0 ng mL−1 and there are high sensitivity and selectivity in this method. Meanwhile, the results obtained by ATLD algorithm were compared with those obtained by the self-weighted alternate trilinear decomposition algorithm (SWATLD) and the parallel factor analysis (PARAFAC) algorithm, and statistical methods such as the t-test, F-test and the elliptic joint confidence region were used to evaluate for analysis. There were no significant differences among these methods. At last, high performance liquid chromatography-fluorescence detector (HPLC-FLD) was used to evaluate the accuracy and reliability of the proposed method. These results are satisfactory and indicate that the proposed method can be used for accurate and rapid determination of pesticides in complex systems.

Camellia oil is a high quality oil mainly produced in southern China. It is common that unscrupulous merchants attempt to make huge profits by adulterating camellia oil with other cheaper or lower-quality vegetable oils. Therefore, this paper proposed excitation-emission matrix fluorescence spectroscopy combined with chemometric methods for the rapid identification and quantification of camellia oil adulteration with other cheaper vegetable oils. A five-component parallel factor analysis (PARAFAC) model roughly completed spectral characterization of oil samples, and obtained chemically meaningful information. Four advanced chemometrics methods were used for the classification of camellia oil and other vegetable oils (model 1) and the classification of camellia oil and adulterated camellia oil (models 2 and 3), respectively. Two-directional two-dimensional linear discriminant analysis ((2D)2LDA) was used for chemical data for the first time and showed huge potential. Furthermore, the developed N-PLS regression model used for the prediction of adulteration level in camellia oil showed satisfactory accuracy.

A new strategy for the determination of bisphenol A (BPA) and diphenyl carbonate (DPC) in polycarbonate plastics has been proposed, combing excitation-emission matrix fluorescence and second-order chemometrics methods. The studied compounds are two of the most frequently used chemical raw materials and intermediates, which is mainly used for bio-chemical products. The method is fast and sensitive as it can avoid tedious pretreatment steps and large amounts of organic solvents consumption. Chemometrics allowed accurate and precise quantification of two target analytes even in the presence of severe spectral overlap, unknown and background interferences, which benefit from “second-order advantages” provided by chemometrics multivariate calibration. Reasonable quantification results were obtained in real plastics samples, with limits of detection for two analytes were 0.04 and 1.18 × 103 ng mL−1 and limits of quantitation were 0.11 and 3.58 × 103 ng mL−1. In addition, the average recoveries for BPA and DPC were 99.35% and 83.50% with relative standard deviation

Fluorescent probes have been valuable tools for bioanalytical multiplex assays. However, as a common phenomenon in multiplex fluorescence assays, spectral overlap usually leads to difficulty in spectral analysis for multiple analytes. Although multiway calibrations have provided mathematic approaches for complex spectral analysis, it remains a grand challenge for these methods in practical applications because of the problems such as prior rank estimation. Herein, we report a novel second-order calibration algorithm of alternating residual trilinearization (ART) for the decomposition of complex spectra generated from multiplex fluorescence assays. By alternating iterative convergence to the spectral profiles of each component in convergence process, ART enables automatic rank estimation for second-order calibration, thus able to avoid the risk of chemical meaningless fitting of component spectra. Combined with fluorescence excitation-emission matrix (EEM) spectroscopy, the performance of ART has been demonstrated by a simulated example and an analytical experiment performed using molecular beacons (MBs) for the simultaneous assay of three breast cancer related DNA targets. The results revealed that the proposed algorithm is capable of automatic estimating the number of underlying components during its convergence process to produce acceptable performance in spectral profile resolution and concentration estimation. Compared with other existing iterative trilinear decomposition strategies such as parallel factor analysis (PARAFAC) requiring a prior rank estimation, the proposed ART therefore provides a robust second-order calibration strategy for complex spectral analysis in multiplex fluorescence assays.

Black carbon (BC) is important due to its complex influences on the environment and on climate in particular. However, reported BC data are largely dependent on measurement techniques due to the multitude of measurement principles. Here we focused on thermal-optical method which has been widely used to determine BC mass (as elemental carbon, EC). Several factors influencing EC measurement were investigated. Results from source samples representing vehicle engine emissions pointed to a continuum of EC components in thermal stability and provided direct observational evidence for the premature evolution of EC in inert atmosphere. It was also found that EC masses may be substantially underestimated for the vehicle exhaust samples if the adopted protocol requires an oxidizing atmosphere to define the split point between organic carbon (OC) and EC. Results from a field campaign conducted during winter in Beijing showed that the optical attenuation (ATN; i.e., the filter transmittance signal, I) was largely saturated for the samples with relatively high loadings, indicating their EC results were unreliable. Improved measurement of EC was achieved by extracting these heavily loaded filters using methanol, given that ATN was considerably reduced by the extraction and, moreover, saturation of ATN (or I) became not evident for the extracted samples. The methanol extraction also significantly reduced the transformation of OC to char-OC, by removing the majority (i.e., ~85%) of the deposited organic aerosols. Higher EC were measured for the extracted samples compared with the untreated ones, indicating that EC tends to be underestimated due to the charring-induced uncertainties. In addition, the methanol extraction largely reduced the inter-protocol discrepancy in the EC measurement results. Similar effects of methanol extraction have been observed during summer in Beijing, despite the seasonal variations of aerosol sources and compositions. This study indicates the potential benefits of methanol extraction for EC measurement.

Nitroxyl (HNO), as an electron reduced and protonated form of nitric oxide, is emerging as a potential diagnostic and therapeutic biomarker. It is still of great interest to develop probes of desirable properties to study its biological functions. Here we develop a near infrared fluorescence probe for detecting and visualizing exogenous and endogenous HNO in living cells. The probe is designed by coupling a HNO-responsive moiety, diphenylphosphinobenzoyl group, with a near infrared fluorophore with large of Stokes shift via an ester linker. The probe was initially nonfluorescent. HNO-catalyzed oxidation reaction generates an aza-ylide, which intramolecularly attacks the carbonyl carbon, liberating the initial fluorophore with activated fluorescence signals. The probe is proportional to the concentrations of HNO in the range of 2.0–80 μM with a limit of detection of 0.05 μM. Furthermore, the probe also exhibits high selectivity and fast response (reaching plateau within 600 s) towards HNO in vitro. Moreover, imaging studies reveal that the probe is capable of detecting exogenous HNO with dose-dependent fluorescence signals. Its ability to image endogenous HNO without or with induction is also demonstrated in living cells. This turn-on fluorescence probe provides a useful tool for studying HNO in living cells.

Recently, there has been growing interest in the decomposition of multilinear component models in the field of multi-way calibration. In this work, a novel quadrilinear decomposition method, i.e. four-way algorithm combination method (FACM), is proposed for four-way calibration. The FACM skillfully integrates the alternating quadrilinear decomposition (AQLD) algorithm with the four-way parallel factor analysis (FPARAFAC) algorithm and gives full play to their advantages. The performance of the FACM and the existing five algorithms are compared by using two simulated data sets. Moreover, a published four-way data array and a new four-way data array, which record a series of fluorescence spectra information of biomarkers in biological samples, are investigated by the proposed method respectively. The results demonstrate that the novel method can accurately and effectively extract the qualitative and quantitative information of analytes of interest even in the presence of unknown interferents and varying background. Besides, the FACM has attractive properties, such as very fast convergence, insensitive to initial values and excess number of components, and suitable for high noise level as well as severely collinear data. In addition, the proposed method can also be extended to combine other iterative algorithms, providing a feasible idea for the exploration and development of new algorithms.

DNA hydrogels are biocompatible and are suitable for many biomedical applications. However, to be useful imaging probes or drug carriers, the ordinary bulk size of DNA hydrogels must be overcome. Here we put forward a new strategy for fabricating a novel and simple protein-scaffolded DNA nanohydrogel, constructed through a direct DNA self-assembly using three types of streptavidin (SA)-based DNA tetrad for the activation of imaging and targeting therapy of cancer cells. The DNA nanohydrogels are easily prepared, and we show that by varying the initial concentration of DNA tetrad, it is possible to finely control their size within nanoscale range, which are favorable as carriers for intracellular imaging and transport. By further incorporating therapeutic agents and tumor-targeting MUC1 aptamer, these multifunctionalized SA-scaffolded DNA nanohydrogels (SDH) can specifically target cancer cells and selectively release the preloaded therapeutic agents via a structure switching when in an ATP-rich intracellular environment, leading to the activation of the fluorescence and efficient treatment of cancer cells. With the advantages of facile modular design and assembly, effective cellular uptake, and excellent biocompatibility, the method reported here has the potential for the development of new tunable DNA nanohydrogels with multiple synergistic functionalities for biological and biomedical applications.

Isothermal amplification techniques for nucleic acid detection have drawn increasing interest recently due to the simplicity and low-cost of instruments. We have developed a novel duplex-specific nuclease (DSN)-mediated target recycling amplification strategy for microRNA detection. The method utilizes the different adsorption ability of graphene oxide (GO) between single-stranded DNA and double-strand DNA to achieve fluorescence recovery. DSN was employed to mediate target recycling amplification, and desirable sensitivity for miR-21 detection was achieved. The developed method also demonstrated excellent selectivity and applicability for analysis of samples in complex biological media. The application of the developed method for analysis of miR-21 level in three different cell lines demonstrated its potential for real sample analysis.

Probe encapsulated by biologically localized embedding (PEBBLE) has emerged as a new type of sensing technique for complex systems. Generalized ratiometric PEBBLE nanosensors prepared by encapsulating an intensity-based probe and an inert reference dye inside the pores of stable matrix possess advantages of easy synthesis, immunity to interference, lower toxicity, and robustness to variations in probe loading. However, the selection of appropriate reference dyes used in generalized ratiometric PEBBLE nanosensors is a rather difficult task since they should satisfy some stringent requirements. In this contribution, the feasibility of using carbon dots (C-dots) as generic inert references in synthesizing PEBBLE nanosensors was first investigated in detail. And a dual-wavelength monitoring strategy and the quantitative fluorescence model for generalized ratiometric probes (QFMGRP) were adopted to solve the problems brought by the use of carbon dots as inert references. C-dots doped PEBBLE nanosensors (C-PEBBLE nanosensors) for the quantification of NO2- and free Ca2+ were synthesized by encapsulating C-dots and intensity based fluorescence probes (i.e., acriflavine for NO2-, and Rhod-2 for Ca2+, respectively) inside the pores of stable matrix. Experimental results showed that the combination of C-PEBBLEs, the QFMGRP model and the dual-wavelength monitoring strategy achieved accurate quantification of NO2- and the free Ca2+ in real-world samples. Their quantitative results were in good consistence with those determined by HPLC and atomic absorption spectrophotometer, respectively. The strategies proposed in this contribution have generic applicability in the synthesis of PEBBLE nanosensors and their quantitative applications.

Formaldehyde (FA), as a reactive carbonyl species, is endogenously generated in various biological processes. Abnormal levels of FA could lead to various cellular dysfunction and pathological conditions. Here, we develop a new activatable fluorescent probe for highly selective visualization of FA in living cells. Our probe (Naph-1) is designed using a naphthalene derivative as the fluorophore and hydrazone as a recognition site for FA. Naph-1 is essentially nonemissive. After reacting with FA, the amine moiety is converted into a Schiff base with electron-withdrawing ability and the fluorescence is simultaneously turned on due to synergetic intramolecular charge transfer and favoured excited state intramolecular proton transfer effects. Naph-1 exhibits a large Stokes shift upon reaction with FA. Furthermore, it possesses high selectivity and superior sensitivity toward FA with an estimated limit of detection of 0.35 μM. Moreover, Naph-1 is also successfully applied to image both endogenous and exogenous formaldehyde in living cells. These features demonstrate that Naph-1 holds great potential in the detection and imaging of formaldehyde in biological systems.

A rapid and sensitive analytical strategy combining high-performance liquid chromatography-diode array detection (HPLC-DAD) and chemometrics methods was developed for the determination of multi-class food additives in a variety of beverage samples. Different kinds of beverages, which contain diverse unknown interferences, can be directly injected into a chromatographic system after a simple dilution or pretreatment step, and the data were recorded in a short time under a simple gradient elution mode. Although peaks overlapped and changeable interferences existed in the dimension of the chromatographic and spectral in real beverages, all food additives were accurately resolved by using a second-order calibration method based on alternating trilinear decomposition (ATLD) algorithm, and accurate chromatographic profiles, spectral profiles, and concentration profiles were also obtained. Limits of detection for all additives were between 1.40 and 165.1 ng mL−1, while limits of quantitation ranged from 4.20 to 500.2 ng mL−1. The spiked recoveries were in the range of 87.3–103% (except amaranth) with RSDs less than 10.2%. Compared with the results obtained by classic HPLC method, our proposed method was more accurate. In all, the proposed method is fast, sensitive, and universal and could be used as a reliable tool to determine food additives and quality monitoring in different complex beverages.

Geographical origin is an important factor affecting the quality of traditional Chinese medicine. In this paper, the identification of geographical origin of Gastrodia elata was performed by using excitation-emission matrix fluorescence and chemometric methods. Firstly, excitation-emission matrix (EEM) fluorescence spectra of Gastrodia elata samples from different geographical origins were obtained. And then three chemometric methods, including multilinear partial least squares discriminant analysis (N-PLS-DA), unfold partial least squares discriminant analysis (U-PLS-DA), and k-nearest neighbor (kNN) method, were applied to build discriminant models. Finally, 45 Gastrodia elata samples could be differentiated from each other by these classification models according to their geographical origins. The results showed that all models obtained good classification results. Compared with the N-PLS-DA and U-PLS-DA, kNN got more accurate and reliable classification results and could identify Gastrodia elata samples from different geographical origins with 100% accuracy on the training and test set. Therefore, the proposed method was available for easily and quickly distinguishing the geographical origin of Gastrodia elata, which can be considered as a promising alternative method for determining the geographic origin of other traditional Chinese medicines.

DNA molecular probes have emerged as a powerful tool for RNA imaging. Hurdles in cell-specific delivery and other issues such as insufficient stability, limited sensitivity, or slow reaction kinetics, however, hinder the further application of DNA molecular probes in vivo. Herein, we report an aptamer-tethered DNA polymer for cell-specific transportation and amplified imaging of RNA in vivo via a DNA cascade reaction. DNA polymers are constructed through an initiator-triggered hybridization chain reaction using two functional DNA monomers. The prepared DNA polymers show low cytotoxicity and good stability against nuclease degradation and enable cell-specific transportation of DNA circuits via aptamer-receptor binding. Moreover, assembling the reactants of hairpins C1 and C2 on the DNA polymers accelerates the response kinetics and improves the sensitivity of the cascade reaction. We also show that the DNA polymers enable efficient imaging of microRNA-21 in live cells and in vivo via intravenous injection. The DNA polymers provide a valuable platform for targeted and amplified RNA imaging in vivo, which holds great implications for early clinical diagnosis and therapy.

Three intelligent chemometric multi-way calibration methods including alternating trilinear decomposition (ATLD), alternating trilinear decomposition assisted multivariate curve resolution (ATLD-MCR) and multivariate curve resolution-alternating least squares (MCR-ALS) combined with high performance liquid chromatography-diode array detection (HPLC-DAD) were used to quantify ten molecular targeted anti-tumor drugs in three complex biological matrices (plasma, urine and cell culture media matrices). All analytes can be successfully eluted in 6.5 min. In this experiment, various degrees of time shifts occurred in different samples. While slight time shifts exist in the chromatographic analysis, satisfactory results can be obtained by the three proposed methods. When the time shift was large (5.6 s), the average spiked recoveries obtained by ATLD analysis were in the range of 58.9%–116.5%, which was less than satisfactory. However, the average recoveries obtained by MCR-ALS and ATLD-MCR analysis were 89.8%–114.8% and 84.5%–106.1% respectively, and more satisfactory results were obtained. For further research, ATLD-MCR and MCR-ALS methods were compared, and the results were evaluated by statistical tests. Accuracies of concentrations obtained by them were considered to be no significant difference. In addition, compared with other methods currently published, the proposed chemometric methods combined with the HPLC-DAD can rapidly, simultaneously and accurately determine varieties of molecular targeted anti-tumor drugs in different complex biological matrices even in the presence of severe peak overlaps, severe time shifts, slight baseline drifts and different unknown background interferences.

We report a novel DNA-programmed plasmonic enzyme-linked immunosorbent assay (ELISA) for the ultrasensitive detection of protein biomarkers with the naked eye. The DNA-programmed plasmonic assay was based on two enzyme-free and isothermal nucleic acid amplification methods: hybridization chain reaction (HCR) and catalyzed hairpin assembly (CHA). In this study, a biotin-labeled DNA probe was utilized insteand of an enzyme-label probe in well-developed ELISA method. The biotin-labeled DNA probe was able to trigger the HCR and CHA processes, and the products could hybridize with DNA-modified gold nanoparticles (AuNPs) to induce the aggregation of the AuNPs and a color change in the solution. The developed method was able to detect as low as 1 pg mL-1 PSA target with the naked eye. Clinical serum samples demonstrated satisfactory results, indicating that the method is useful for early diagnostics and monitoring curative effects after a medical treatment. The developed method presents a simple and portable platform for ultrasensitive protein detection and has potential for point-of-care (POC) diagnostics in less developed areas.

The detection of nucleic acid is critical for clinic diagnostics. Single-nanoparticle inductively coupled plasma mass spectrometry (SP-ICPMS) has demonstrated unique advantages for nucleic acid detection. Here we report the development of a novel SP-ICPMS DNA assay based on a target-induced hybridization chain reaction to achieve controlled spherical nucleic acid assembly. The assembly process generated large gold nanoparticle aggregates, and the number of aggregates could be counted by SP-ICPMS, which was closely correlated to the concentration of the target DNA. This simple homogeneous assay could analyze DNA within the range of 5 fM to 10 pM with excellent selectivity and applicability for real sample analysis. It is envisioned that the developed approach might create a useful SP-ICPMS platform for biomolecule detection.

A novel soft strategy for combination and partition of mass spectra data recorded at different fragmentor voltages in full scan mode of a mass spectrometer was developed to generate abundant multi-way data. It is the first time that non-linear four-way and combined three-way LC-MS data have been obtained simultaneously in a single chromatographic run. This strategy ensures that each analyte can be ionized and detected at the most appropriate MS conditions (ionization modes, fragmentor voltages) and avoids a hard chromatographic segmentation in subsequent chemometric analysis. Two different experimental datasets were analyzed to validate the feasibility and applicability of this strategy. Some simple pretreatments were carried out before LC-MS analysis to prevent potential matrix effects. Proper chemometric tools were used to resolve three-way (partitioned data) and enhanced three-way LC-MS (combined data) data, respectively. The method was assessed by comparing the analytical results obtained from the same chemometric algorithm with both combined and partitioned datasets: (1) the combined data provided the best global overall resolution, higher sensitivity and more reliable results, (2) the partitioned data provided higher selectivity for some specific analytes. The results showed that the developed method could be a soft and ingenious tool to handle the unordered but information-rich raw LC-MS data. Moreover, the proposed strategy could take extra analytical advantages in terms of higher sensitivity and more reliable quantitative results when compared with LC-MS (with single fragmentor voltage) strategy and showed nearly the same capability in analytical quality as classic LC-MS/MS method.

A surface-enhanced Raman spectroscopy (SERS) based method was developed for the quantification of Cd2+ in rice. Gold nano-particles (AuNPs) modified with trimercaptotriazine served as a ratiometric SERS probe for the detection of Cd2+. A conical holed substrate was used to further enhance SERS signals, and hence to improve the sensitivity. A calibration model based on the spectral shape deformation quantitative theory was employed to mitigate the influence of variations in the number and distribution of "hot spots". The proposed SERS method was applied to quantitative analysis of Cd2+ in three types of rice, and achieved satisfactory quantitative results with accuracy comparable to that of the reference method-inductively coupled plasma mass spectrometry. The limit of detection of the proposed method was estimated to be 8 μg kg-1. The proposed SERS method has the potential to become a fast screening method for the detection of Cd2+ in rice.