Ultrasensitive visual detection of Hg2+ ions via the Tyndall effect of gold nanoparticles.

This work reports a new methodology for naked-eye nanosensing of Hg2+ where the Tyndall effect of gold nanoparticles (GNPs) acts as a light scattering signalling readout. Its utility is demonstrated with ultrasensitive detection of the target with a limit down to 0.13 nM (∼5461-fold sensitivity improvement over conventional GNP-based methods with surface plasmon resonance signalling).

Double quantum dots-nanoporphyrin fluorescence-visualized paper-based sensors for detecting organophosphorus pesticides.

A high-sensitivity fluorescence visualization paper-based sensor is developed and used to achieve specific detection and analysis of three organophosphorus pesticides (OPPs: dimethoate, dichlorvos, and demeton) in a "Turn-off-on" detection mode. The fluorescence visualization paper-based sensor is established through combining double quantum dots (QDs) with high-activity nanoporphyrins (QDs-nanoporphyrin), realizing double nanometer signal amplification and producing different color change responses to these three OPPs. In particular, this approach is based on Partial least squares discriminant analysis (PLSDA) for fingerprint spectrum analysis of three kinds of organophosphorus pesticides in complex matrix (apple and cabbage). Therefore, different concentrations of pesticide residues can be quickly identified at the same time with 100% accuracy for both training set and prediction set. In summary, this method has high selectivity and stability, and thus provides a new approach for the identification of organophosphorus residues in complex systems.

A comprehensive quality evaluation method by FT-NIR spectroscopy and chemometric: Fine classification and untargeted authentication against multiple frauds for Chinese Ganoderma lucidum.

The origins and authenticity against frauds are two essential aspects of food quality. In this work, a comprehensive quality evaluation method by FT-NIR spectroscopy and chemometrics were suggested to address the geographical origins and authentication of Chinese Ganoderma lucidum (GL). Classification for 25 groups of GL samples (7 common species from 15 producing areas) was performed using near-infrared spectroscopy and interval-combination One-Versus-One least squares support vector machine (IC-OVO-LS-SVM). Untargeted analysis of 4 adulterants of cheaper mushrooms was performed by one-class partial least squares (OCPLS) modeling for each of the 7 GL species. After outlier diagnosis and comparing the influences of different preprocessing methods and spectral intervals on classification, IC-OVO-LS-SVM with standard normal variate (SNV) spectra obtained a total classification accuracy of 0.9317, an average sensitivity and specificity of 0.9306 and 0.9971, respectively. With SNV or second-order derivative (D2) spectra, OCPLS could detect at least 2% or more doping levels of adulterants for 5 of the 7 GL species and 5% or more doping levels for the other 2 GL species. This study demonstrates the feasibility of using new chemometrics and NIR spectroscopy for fine classification of GL geographical origins and species as well as for untargeted analysis of multiple adulterants.

Using the Rubik's Cube to directly produce paper analytical devices for quantitative point-of-care aptamer-based assays.

In this article, we describe a facile method named as Rubik's Cube stamping (RCS) for equipment-free fabrication of microfluidic paper-based analytical devices (µPADs). RCS is inspired by the worldwide ubiquitous RC toy and requires no specialized electric equipment other than a classical six-faced RC that is assembled with home-made small iron components. It can pattern various rosin microstructures in paper simply by either using different functional faces of the modified RC or applying its internal pivot mechanism to adjust the components' patterning forms on one functional face. Such a versatile stamping method is quite simple and inexpensive, and thus holds potential for producing rosin-patterned µPADs by untrained users in resource-limited environments such as small laboratories and private clinics, or even at home and in the field. Moreover, a set of one-channel devices are fabricated to design a point-of-care aptamer-based assay with near sample-in-answer-out capability that integrates enzymatic reactions for robust yet efficient signal amplification and a personal glucometer for portable, user-friendly, rapid and quantitative readout. Its utility is well demonstrated with the sensitive and specific detection of adenosine as a model target in buffer samples and undiluted human urine within several minutes. With the advantages of low cost, simplicity, portability, rapidity, and aptamer variety, this general point-of-care assay system reported here may find broad applications including home healthcare, field-based environmental monitoring or food analysis and emergency situations.

Detection of unexpected frauds: Screening and quantification of maleic acid in cassava starch by Fourier transform near-infrared spectroscopy.

Fourier transform near-infrared (FT-NIR) spectroscopy and chemometrics were adopted for the rapid analysis of a toxic additive, maleic acid (MA), which has emerged as a new extraneous adulterant in cassava starch (CS). After developing an untargeted screening method for MA detection in CS using one-class partial least squares (OCPLS), multivariate calibration models were subsequently developed using least squares support vector machine (LS-SVM) to quantitatively analyze MA. As a result, the OCPLS model using the second-order derivative (D2) spectra detected 0.6%(w/w) adulterated MA in CS, with a sensitivity of 0.954 and specificity of 0.956. The root mean squared error of prediction (RMSEP) was 0.192(w/w, %) by using the standard normal variate (SNV) transformation LS-SVM. In conclusion, the potential of FT-NIR spectroscopy and chemometrics was demonstrated for application in rapid screening and quantitative analysis of MA in CS, which also implies that they have other promising applications for untargeted analysis.

"Turn-off" fluorescent sensor for highly sensitive and specific simultaneous recognition of 29 famous green teas based on quantum dots combined with chemometrics.

Fluorescent "turn-off" sensors based on water-soluble quantum dots (QDs) have drawn increasing attention owing to their unique properties such as high fluorescence quantum yields, chemical stability and low toxicity. In this work, a novel method based on the fluorescence "turn-off" model with water-soluble CdTe QDs as the fluorescent probes for differentiation of 29 different famous green teas is established. The fluorescence of the QDs can be quenched in different degrees in light of positions and intensities of the fluorescent peaks for the green teas. Subsequently, with aid of classic partial least square discriminant analysis (PLSDA), all the green teas can be discriminated with high sensitivity, specificity and a satisfactory recognition rate of 100% for training set and 98.3% for prediction set, respectively. Especially, the "turn-off" fluorescence PLSDA model based on second-order derivatives (2nd der) with reduced least complexity (LVs = 3) was the most effective one for modeling. Most importantly, we further demonstrated the established "turn-off" fluorescent sensor mode has several significant advantages and appealing properties over the conventional fluorescent method for large-class-number classification (LCNC) of green teas. This work is, to the best of our knowledge, the first report on the rapid and effective identification of so many kinds of famous green teas based on the "turn-off" model of QDs combined with chemometrics, which also implies other potential applications on complex LCNC classification system with weak fluorescence or even without fluorescence to achieve higher detective response and specificity.

Pharmacokinetic Analysis of Four Bioactive Iridoid and Secoiridoid Glycoside Components of Radix Gentianae Macrophyllae and Their Synergistic Excretion by HPLC-DAD Combined with Second-Order Calibration.

An HPLC-DAD method combined with second-order calibration based on the alternating trilinear decomposition (ATLD) algorithm with the aid of region selection was developed to simultaneously and quantitatively characterize the synergistic relationships and cumulative excretion of the four bioactive ingredients of Radix Gentianae Macrophyllae in vivo. Although the analytes spectra substantially overlapped with that of the biological matrix, the overlapping profiles between analytes and co-eluting interferences can be successfully separated and accurately quantified by the ATLD method on the basis of the strength of region selection. The proposed approach not only determined the content change but also revealed the synergistic relationships and the cumulative excretion in vivo of the four ingredients in urine and feces samples collected at different excretion time intervals. In addition, several statistical parameters were employed to evaluate the accuracy and precision of the method. Quantitative results were confirmed by HPLC-mass spectrometry. Satisfactory results indicated that the proposed approach can be utilized to investigate the pharmacokinetics of Radix Gentianae Macrophyllae excretion in vivo.