Near infrared (NIR) light utilization in a range of current technologies has gained huge significance due to its abundance in nature and nondestructive properties. NIR active lanthanide (Ln) doped upconversion nanomaterials synthesized in controlled shape, size, and surface functionality can be combined with various pertinent materials for extensive applications in diverse fields. Upconversion nanophosphors (UCNP) possess unique abilities, such as deep tissue penetration, enhanced photostability, low toxicity, sharp emission peaks, long anti-Stokes shift, etc., which have bestowed them with prodigious advantages over other conventional luminescent materials. As new generation fluorophores, UCNP have found a wide range of applications in various fields. In this Review, a comprehensive overview of lanthanide doped NIR active UCNP is provided by discussing the fundamental concepts including the different mechanisms proposed for explaining the upconversion processes, followed by the different strategies employed for the synthesis of these materials, and finally the technological applications of UCNP, mainly in the fields of bioimaging, drug delivery, sensing, and photocatalysis by highlighting the recent works in these areas. In addition, a brief note on the applications of UCNP in other fields is also provided along with the summary and future perspectives of these materials.

Lanthanide Doped Near Infrared Active Upconversion Nanophosphors: Fundamental Concepts, Synthesis Strategies, and Technological Applications.

In this study, we have design a new biosensor for E. coli, based on the fluorescence resonance energy transfer (FRET) between aptamer modified upconversion nanoparticles (UCNPs) as donors and layered tungsten disulfide (WS2) nanosheets as the effective acceptor. The aptamer modified-UCNPs can be fetched nearest to the WS2 surface, because of the potent van der Waals force between the nucleobases of aptamer and the basal plane of WS2. The spectral overlap between UCNPs fluorescence emission and WS2 absorption permits the occurrence of FRET, resulted in quenching of upconversion fluorescence. In the presence of E. coli, the specific aptamer preferentially bind to E. coli, causing changes in the conformation of aptamer, a phenomenon directs the dissociation of mixing probe away from the surface of WS2 nanosheets and therefore parts of the quenched fluorescence of UCNPs was retrieved. The fluorescence intensity increased with the raised concentration of E. coli. The aptasensor exhibited limitary over the concentration range 85 to 85 × 107 cfu mL−1 for E. coli and the detection limit (LOD) was 17 cfu mL−1. In addition, the proposed biosensing method comprehended huge advantages including the facile operation procedure, high sensitivity and selectivity, which confer a great potential for the quantification of E. coli in medical diagnostics, food and environmental analysis.

A highly sensitive upconversion nanoparticles-WS2 nanosheet sensing platform for Escherichia coli detection

Ultra-sensitive detection of malathion residues using FRET-based upconversion fluorescence sensor in food.

Fatty acid value is one of the important indicators to judge whether aging occurs during rice storage. This study innovatively proposes to use a homemade olfactory visualization sensor to realize the quantitative determination of fatty acid value during rice storage. First, based on GC–MS analysis results, 14 porphyrins and one pH indicator were preliminarily selected to prepare an olfactory visualization sensor array for obtaining odor information of rice samples of different storage periods. Then, the ant colony optimization combined with back propagation neural networks (ACO-BPNN) was used to optimize the color components of the preprocessed array difference image to obtain the best combination of color components. Finally, a back propagation neural networks (BPNN) model was established based on the optimized color component combination, and the number of hidden layer nodes of the network was optimized by using leave-one-out cross-validation (LOOCV). The results showed that the best BPNN model constructed on the color component numbers 41, 11 and 34, and the optimal topology of the network was 3-12-1. The correlation coefficient (RP) and root mean square error of prediction (RMSEP) of the best BPNN model were 0.9715 and 0.4310 mg/100 g, respectively. The overall results demonstrate that it is feasible to use the homemade olfactory visualization sensor to detect fatty acid values during rice storage. In addition, the ACO-BPNN can find the best color components combination, which can effectively improve the detection accuracy and stability of the BPNN model, and also reduce the cost of preparing the olfactory visualization sensor array.

Quantitative analysis of fatty acid value during rice storage based on olfactory visualization sensor technology

The identification of foodborne pathogenic bacteria types plays a crucial role in food safety and public health. In consideration of long culturing times, tedious operations and the desired specific recognition elements in conventional methods, the alternative fluorescent sensor arrays can offer a high-effective approach in bacterial identification by using multiple cross-reactive receptors. Herein, we achieve this goal by constructing an upconversion fluorescent sensor array based on anti-stokes luminogens featuring a series of functional lanthanide-doped upconversion nanoparticles (UCNPs) with phenylboronic acid, phosphate groups, or imidazole ionic liquid. The prevalent spotlight effect of microorganism and the electrostatic interaction between UCNPs and bacteria endow such sensor array an excellent discrimination property. Seven common foodborne pathogenic bacteria including two Gram-positive bacteria (Staphylococcus aureus and Listeria monocytogenes) and five Gram-negative bacteria (Escherichia coli, Salmonella, Cronobacter sakazakii, Shigella flexneri and Vibrio parahaemolyticus) are precisely identified with 100% accuracy via linear discriminant analysis (LDA). Furthermore, blends of bacteria have been identified accurately. Bacteria in real samples (tap water, milk and beef) have been effectively discriminated with 92.1% accuracy. Current fluorescence sensor array is a powerful tool for high-throughput bacteria identification, which overcomes the time-consuming bacteria culture and heavy dependence of specific recognition elements. The high efficiency of whole bacterial cell detection and the discrimination capability of life and death bacteria can brighten the application of fluorescence sensor array.

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Surface chemistry modified upconversion nanoparticles as fluorescent sensor array for discrimination of foodborne pathogenic bacteria.

Rare earth-doped upconversion nanoparticles (UCNPs) have promising potential in biodetection due to their peculiar frequency upconverting capabilities and high detection sensitivity. Here, we report a novel dual-color UCNP-based bacterium-sensing biosensor for simultaneously Escherichia coli and Staphylococcus aureus detection using UCNP as a fluorescence marker and conjugated with antibodies as the specific molecular recognition probe. Dual-color UCNPs were fabricated via varying lanthanide dopants to acquire the well-separated emission peaks. When E. coli and S. aureus were added into the reaction system, the recognition probes would capture the target bacteria through the specific binding of antibody. Then, the fluorescence intensities decreased (∆I = I
 o
 -I) were observed to increase linearly with the rising concentration of the E. coli (664 nm) and S. aureus (806 nm) from 47 to 47 × 106 cfu mL−1 (R
 2 = 0.98) and 64 to 64 × 106 cfu mL−1 (R
 2 = 0.97), respectively, resulting in the relatively low limit of 13 and 15 cfu mL−1 for E. coli and S. aureus. Furthermore, this UCNP-based bacterium-sensing biosensor could also be successfully applied to simultaneously detect E. coli and S. aureus in adulterated meat and milk samples.

Dual-Color Upconversion Nanoparticles (UCNPs)-Based Fluorescent Immunoassay Probes for Sensitive Sensing Foodborne Pathogens

The fermentation process plays an important role in black tea's quality control and determination. This article presented a portable artificial olfaction system for real-time monitoring of black tea. Herein, a colorimetric sensor array, which was fabricated by printing 12 chemical dyes, including 9 porphyrins/metalloporphyrins and 3 pH indicators on a C2 reverse silica-get flat plate, was utilized to develop the artificial olfaction system for data acquisition. With specific colorific fingerprint of volatile compounds from black tea, fermentation time can be achieved with efficient chemometrics. Additionally, a novel classification algorithm, which combined K-Nearest Neighbor and adaptive boosting, namely KNN-AdaBoost, was proposed with a comparison of two classical classification algorithms (e.g., KNN and BP-ANN). Results demonstrated that the optimum KNN-AdaBoost model was superior to others with discrimination rate of 100% in both the training and prediction set. The overall results sufficiently reveal that the colorimetric sensor has promising applications in real-time fermentation monitoring.

Practical applications
The fermentation process plays an important role in black tea's quality control and determination. However, traditional analytical chemical methods (i.e., mass spectrometry, liquid chromatography gas, and gas chromatography-mass spectrometry) can monitor the fermentation process and have some drawbacks, such as complicated operation, high costs of implementation, and lengthy analysis time. This research notably proposed a portable artificial olfaction system for real-time monitoring black tea; and examined the feasibility, accuracy and effectiveness of this sensor. The overall results obtained from this study sufficiently reveals that the colorimetric sensor has promising applications in real-time fermentation monitoring.

Monitoring black tea fermentation using a colorimetric sensor array‐based artificial olfaction system

The invention relates to a trace pesticide residue surface enhanced Raman spectrum detection method based on a nucleic acid aptamer, and belongs to the field of pesticide detection. The invention uses Raman active dye labeled gold nanoparticles as substrates, and uses the specific binding of pesticide and the aptamer binding to cause aggregation difference of reactive dye labeled gold nanoparticles in salt solution; a corresponding reactive dye SERS spectrum is obtained by a portable Raman spectrometer, so as to realize the quantitative detection of trace pesticide; and feasibility testing of the method is carried out in an actual tea sample. The method provided by the invention converts a conventional thinking of using the Raman characteristic peak of a target object to be detected as quantification determination in the traditional Raman technology into using the Raman characteristic peak of the reactive dye marker as the quantitative standard for target detection, so as to not only improve the detection sensitivity, simplify the preparation process of complex substrate, and provide convenience for research, inspection and supervision of trace pesticide residues in the fields of food industry.

Trace pesticide residue surface enhanced Raman spectrum detection method based on a nucleic acid aptamer

The invention discloses a slightly decayed fruit quick identification method based on smell spectral imaging technology, which includes the steps of (1) according to the smell characters of fruits, screening m porphyrin or metal porphyrin color-sensitive materials to produce a sensor array, determining a balance time responding to the smell of the fruits, and obtaining sensor arrays before and after a reaction; (2) by means of a hyperspectral imaging system, collecting a hyperspectral image of the sensor array, performing pivot element dimension reduction, and preferably selecting n characteristic wave lengths; (3) designing a multispectral system in a multiple wave filter sheet type, obtaining n characteristic wave length images, and calculating the difference value image before and after the equilibrium reaction; and (4) extracting m*n characteristic variables on m color-sensitive points from the difference value image, constructing a determination model through mode identification, and performing quick detection to decayed fruit. Compared with a wide frequency spectrum color image obtained through CCD or a scanning instrument in the prior art, the multispectral image allows the spectral difference of unique smell molecules after effect with the color-sensitive materials, thereby increasing precision and sensitivity of detection.

Slightly decayed fruit quick identification method based on smell spectral imaging technology

The invention discloses a quick identifying method for positive and negative food-borne pathogenic bacteria.The method comprises the steps that a graphite carbonization nitrogen/gold or graphite carbonization nitrogen/silver nanoparticle composite is adopted as enhancing reagent, special structure differences of cell walls of the positive and negative food-borne pathogenic bacteria are used, and by means of a surface enhanced raman spectrum and a chemometrics method, successful quick identification is conducted on the positive and negative food-borne pathogenic bacteria.According to the method, sensitivity is high, reliability is strong, the detecting speed is high, and cost is low; the method can be used for large-scale on-line detection of the positive and negative food-borne pathogenic bacteria and is suitable for the technical fields of food safety, environment monitoring and the like.

Zhang Bin

Papers

Dual-Color Upconversion Nanoparticles (UCNPs)-Based Fluorescent Immunoassay Probes for Sensitive Sensing Foodborne Pathogens

Monitoring black tea fermentation using a colorimetric sensor array‐based artificial olfaction system

Trace pesticide residue surface enhanced Raman spectrum detection method based on a nucleic acid aptamer

Slightly decayed fruit quick identification method based on smell spectral imaging technology

Quick identifying method for positive and negative food-borne pathogenic bacteria