Search

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.

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.

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.

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.

A facile MALDI-TOF mass spectrometric platform for quantitative analysis of protein biomarkers was developed based on magnetic ZnFe

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.

In real-time quantitative polymerase chain reaction (PCR), the standard curve between threshold cycle and logarithm of template concentration is currently the gold standard for template quantification. The efficacy of this approach is limited by the necessary assumption that all samples are amplified with the same efficiency. To overcome this limitation, a new method has been proposed in this contribution for quantitative PCR with internal standard. Unlike existing methods based upon analysis of amplification profile position, the new method tries to determine the initial quantity of the target template in a sample from the fluorescence spectrum measured at a certain point during its PCR reaction. There is no unrealistic prerequisite (e.g., constant amplification efficiency) for the successful application of the new method. The performance of the new method was evaluated by the quantification of KRAS gene in HepG2 samples. Quantitative results with recovery rates in the range of 91.2-118% were achieved by the new method. It is reasonable to expect that the new method would have a place in real-time quantitative PCR, thanks to its features of no unrealistic prerequisite, sound theoretical basis, good performance, and implementation simplicity.

Metabonomics has been widely used in disease diagnosis and clinically practical methods often require the detection of multi-class bio-samples. In this work, multi-class classification methods were investigated to simultaneously discriminate among 6 inherited metabolic diseases (IMDs) and the normal instances using gas chromatography-mass spectrometry (GC-MS) of urine samples. Two common multi-class classification strategies, one-against-all (OAA) and one-against-one (OAO) were compared and enhanced using a novel ensemble classification strategy (ECS), which developed a set of sequential sub-classifiers by fusion of OAA and OAO and made the final classification decisions using softmax function. GC-MS data of 240 instances of 6 IMDs and healthy controls were classified by different strategies based on orthogonal partial least squares discriminant analysis (OPLS-DA) and particle swarm optimization (PSO) algorithm was performed for feature selection. By OAA and OAO, the classification accuracies were 70.00% and 82.86%, respectively. Using the two methods based on ECS, the total classification accuracies were 0.9143 and 0.9429. The newly proposed ECS will provide a useful multi-class classification tool for simultaneous detection of clinically similar IMDs and promote practical and reliable diagnosis of IMDs using metabonomics data.

5'-Polynucleotide kinase (PNK) is a crucial enzyme that catalyzes the phosphorylation of nucleic acid with 5'-OH termini and this phosphorylation reaction has been involved in many important cellular activities. The evaluation of PNK activity has received an increasing attention due to the significance of PNK. Here, the polydopamine nanospheres (PDANS) could adsorb single-stranded DNA (ssDNA) through π-π stacking or hydrogen bonding between nucleobases and aromatic groups of PDANS, while the interaction between double-stranded DNA (dsDNA) with PDANS was weakened due to the changed conformation. Hence, a novel DNA/PDANS platform was constructed for the sensitive and selective determination of T4 PNK activity based on the preferential binding properties of PDANS for ssDNA over dsDNA and the excellent fluorescence quenching property of PDANS. The dye-labeled dsDNA was phosphorylated by T4 PNK and then digested by λ exonuclease, yielding dye-labeled ssDNA, which would be adsorbed on the surface of the PDANS and the fluorescence was greatly quenched by PDANS. Because of the preferential binding properties of PDANS for ssDNA over dsDNA and the high quenching property of PDANS, the developed DNA/PDANS platform exhibited good analytical performance for T4 PNK sensing in complex biological matrix and applied to screening inhibitors. The proposed DNA/PDANS based platform is promising in developing high-throughput assays for drug screening and clinical diagnostics.

The aim of the present work was to develop a rapid and interference-free method based on liquid chromatography-mass spectrometry (LC-MS) for the simultaneous determination of nine B-group vitamins in various energy drinks. A smart and green strategy that modeled the three-way data array of LC-MS with second-order calibration methods based on alternating trilinear decomposition (ATLD) and alternating penalty trilinear decomposition (APTLD) algorithms was developed. By virtue of "mathematical separation" and "second-order advantage", the proposed strategy successfully solved the co-eluted peaks and unknown interferents in LC-MS analysis with the elution time less than 4.5min and simple sample preparation. Satisfactory quantitative results were obtained by the ATLD-LC-MS and APTLD-LC-MS methods for the spiked recovery assays, with the average spiked recoveries ranging from 87.2-113.9% to 92.0-111.7%, respectively. These results acquired from the proposed methods were confirmed by the LC-MS/MS method, which shows a quite good consistency with each other. All these results demonstrated that the developed chemometrics-assisted LC-MS strategy had advantages of being rapid, green, accurate and low-cost, and it could be an attractive alternative for the determination of multiple vitamins in complex food matrices, which required no laborious sample preparation, tedious condition optimization or more sophisticated instrumentations.

When using circular dichroism (CD) spectroscopy for quantitative analysis, the samples to be analyzed must be free of light-absorbing interferences. However, in real-world samples, the presence of background absorbers is practically unavoidable. The difference in the matrices between the real-world samples to be analyzed and the standard samples (on which either univariate or multivariate calibration model was built) would result in systematic errors in the quantification results of CD spectroscopy. In this contribution, a novel calibration strategy for quantitative CD spectroscopic analysis was proposed. The main idea of the proposed calibration strategy is to project the CD spectra of both the standard samples and the real-world sample to be analyzed onto a projection space orthogonal to the space spanned by the background CD spectrum of the real-world sample and then build a multivariate calibration model on the transformed CD spectra of the standard samples. The performance of the proposed calibration strategy was tested and compared with conventional univariate and multivariate calibration methods in the quantification of Pb2+ in cosmetic samples using CD spectroscopy in combination with a G-quadruplex DNAzyme (e.g. PS2.M). Experiments results revealed that the proposed calibration strategy could mitigate the influence of the difference in the matrices between the standard samples and cosmetic samples and realized quantitative analysis of Pb2+ in cosmetic samples, with precision and accuracy comparable to atomic absorption spectroscopy. The proposed calibration strategy has the features of simplicity and effectiveness, its combination with CD spectroscopic probes can realize accurate and precise quantification of analytes in complex samples using CD spectroscopy.

MicroRNAs (miRNAs) play a significant role in diverse biological processes. The abnormal expression of miRNAs is related to the development of cancers and various diseases. It is of great importance to sensitively and accurately detect miRNAs for early disease diagnosis and treatment. Here, a new fluorescence strategy was initially proposed for the enzyme-free sensing of let-7a by combining the strand displacement reaction (SDR) with the hybridization chain reaction (HCR). The sensor was successfully applied to the detection of the let-7a gene with a wide linear range from 25 pM to 250 nM and a limit of detection (LOD) of 9.01 pM. The fluorescence intensity has a good linear relationship with the logarithm of the target concentration. In addition, the biosensor allowed for the highly sensitive detection of the target genes even in complex human serum samples. With simple operation yet improved detection capability for let-7a, the developed fluorescent biosensor thus shows great potential for early clinical diagnosis as well as biological research.

Biosensors based on various spectroscopic techniques discriminate the target microRNA (miRNA) from non-target ones with single nucleotide polymorphisms (SNPs) according to the differences in signal intensities which can be caused by other factors besides SNPs. As a result, they are liable to produce false positive results. Herein, we report an attempt to develop a false-positive resistance, sensitive and reliable mass spectrometric platform for miRNA detection. In the proposed platform, the qualitative and quantitative information of the target miRNA was obtained through analyzing mass spectral responses of the multiply charged ions of the residual fragments of the probe DNA produced during exonuclease III assisted signal amplification reaction using an advanced data analysis method. The proposed platform could achieve sensitive and accurate quantitative results for the target miRNA (e.g., miRNA-141) in complex medium with a detection limit of about 1 pM, and unambiguously identify non-target miRNAs with SNPs based on the length distribution patterns of residual fragments of probe DNA. The findings obtained in this study might open an avenue for the design of new miRNA detection methods based on mass spectrometry in combination with various nuclease assisted signal amplification strategies.

A turn-on fluorescent probe (Naphsulf-O) for hydrazine was developed by protecting the hydroxy group of the fluorophore 6-acetyl-2-hydroxynaphthalene via O-4-nitrobenzenesulfonylation, where 4-nitrobenzene was used as a fluorescence quenching moiety as well as an electrophile. Upon nucleophilic aromatic substitution (NAS) reaction of hydrazine toward the probe, the protecting group was removed and fluorophore was released. The probe exhibits a large Stokes shift, excellent selectivity and high sensitivity for hydrazine detection in aqueous solution with a detection limit of 0.716 ppb (22nM), which is of great importance in both environmental and biological system. Furthermore, it was successfully applied to imaging of hydrazine in living cells.

Alkaline phosphatase (ALP) is an important biomarker for clinical diagnosis. Abnormal levels of ALP are closely related to many diseases. In this contribution, a ratiometric fluorescent sensing method based on the competition between two oxidation-reduction reactions related to MnO

Fluoride ions (F−), as a strongest electron withdrawing anion of the smallest size, have played an indispensable role in biological systems. Here a mitochondria-targeted near-infrared (NIR) fluorescent probe (Mito-FP) is developed for selective detection of F− in living cells. Mito-FP is designed using a NIR hemicyanine as a fluorophore and tert-butyldimethylsilyl (TBDMS) as a recognition site which is linked to the fluorophore via a 4-hydroxylbenzyl alcohol self-immolative linker. Mito-FP is essentially nonfluorescent. F− can specifically cleave the Si–O bond which triggers the elimination of the quinone methide intermediate and generates the initial NIR hemicyanine fluorophore with distinct intramolecular charge transfer and activated fluorescence signal. The Mito-FP probe is responsive to the concentrations of F− in the range of 10–120 μM with an estimated limit of detection of 3.2 μM. Mito-FP also exhibits high selectivity toward F− with good stability in the physiological pH conditions. Mito-FP is successfully introduced to detect and image F− in HeLa cells. Moreover, Mito-FP is also demonstrated to exhibit excellent mitochondrial-targeting ability. This NIR fluorescence probe could provide a useful tool for studying F− in mitochondria.

Chemometrics multivariate calibration coupled with high performance liquid chromatography-diode array detection (HPLC-DAD) analytical strategy was applied for fast and sensitive quantification of the eight small molecules (uric acid, creatinine, tyrosine, homovanillic acid, hippuric acid, indole-3-acetic acid, tryptophan and 2-methylhippuric acid) in human urine. The objective of this work was to get the successful resolution of the complex matrix with minimum experimental time in the presence of highly overlapping peaks, of distortions in the time and baseline aspects among chromatograms, and of the presence of unknown and background interferences. All the analysis were based on a short C18 column with the chromatographic system operating in isocratic mode and all analytes can be successfully quantified within 6 min. The second-order HPLC-DAD data acquired were handled intelligently by two typical chemometrics tools including alternating trilinear decomposition (ATLD) and multivariate curve resolution-alternating least squares (MCR-ALS). Reasonable resolution and satisfactory quantification results were obtained regardless of the complex matrix interferences from the urine samples and the second-order advantage was fully exploited. With the validation by classic HPLC method, the proposed strategy could take extra advantages such as increased selectivity and sensitivity, shorter analysis time, undemanding elution conditions and sufficiency of lower limit of quantification benefit from multivariate calibration. The method was shown as a promising means for fast and sensitive determination of small molecules in human urine and also for fast diagnosis or surveillance in related diseases.

The simultaneous quantitative analysis of intracellular metabolic coenzymes flavin adenine dinucleotide (FAD) and flavin mononucleotide (FMN) is of interest because they participate in many electron-transfer reactions of metabolism. But, the simultaneous quantitative analysis of FAD and FMN is hard to be achieved by traditional analytical methods. This paper proposes a novel strategy of intrinsic fluorescence coupled with four-way calibration method for simultaneous quantitative analysis of intracellular metabolic coenzymes FAD and FMN. Through mathematical separation, this proposed analytical method efficiently achieved the simultaneous quantitative analysis of metabolic coenzymes FAD and FMN in the cell, despite the fact that uncalibrated spectral interferents coexist in the system. The predicted concentrations of FAD and FMN in the cell are 217.0 ± 6.9 and 155.0 ± 1.7 pmol/106 cells respectively, which were validated by the approved liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. This analytical method with second-order advantage simply requires the cell solution to be diluted by a buffer, it could introduce an interesting analytical strategy for multianalyte direct quantitative analysis in complex biological systems. In addition, we explore the third-order advantage of four-way calibration by a comparative study based on this real fluorescence data. The comparisons indicate that a four-way calibration method can provide higher sensitivity and more resolving power than a three-way calibration method.

A facile MALDI-TOF mass spectrometric platform for quantitative analysis of protein biomarkers was developed based on magnetic ZnFe2O4 nanoparticles and mass tagging signal amplification. In this platform, magnetic ZnFe2O4 nanoparticles functionalized with an aptamer of the biomarker of interest was used to magnetically separate silica nanoparticles modified with another aptamer of the target biomarker and a barcoding peptide from solution phase in the presence of the biomarker of interest. After the silica nanoparticles were dissolved by KHF2, the released barcoding peptide was detected by MALDI-TOF mass spectrometry with magnetic ZnFe2O4 nanoparticles used as assisting matrix of laser desorption ionization. Since the mass spectral intensity of the barcoding peptide is directly related to the concentration of the target biomarker, the proposed platform can be applied to the quantification of the target biomarker in complex biological samples. The effectiveness of the proposed platform was tested on the detection of carcinoembryonic antigen (CEA) in serum. Experimental results revealed that the proposed platform could achieve quite reliable quantitative results for CEA in human serum samples with accuracy comparable to a commercial CEA ELISA Kit. Its limit of detection and limit of quantification for CEA were estimated to be 0.6 × 10−3 and 1.8 × 10−3 ng/mL, respectively, considerably lower than the corresponding values reported in literature. Due to its features of simplicity in design, extremely low background signal, high sensitivity and selectivity, the proposed method can be further developed to be a competitive alternative for the quantification of CEA and other protein biomarkers as well.

Biomarker selection has played an increasingly important part in modern medicine with advances of omics techniques. Kohonen self-organizing map is a well-established variable reduction algorithm in identifying significant biomarkers based on variable clustering. However, high dimensionality but small sample size of omics data makes self-organizing map-based model problematic in terms of selection stability and reproducibility. A novel feature screening system is presented in this study by coupling bootstrap with synergy self-organizing map-based orthogonal partial least squares discriminant analysis for stable and biologically meaningful metabolic biomarker selection. In the proposed feature screening system, particle swarm optimization algorithm is utilized to configure synergy self-organizing map-based orthogonal partial least squares discriminant analysis to perform the combination of clusters in a heuristic learning manner, enabling flexible selection of more informative features cost-effectively. Based on the paradigm of ensemble feature selection, bootstrap is adopted to explore significant variables consistently identified across multiple feature selectors rather than a single one. The feasibility of the novel feature screening system is evaluated by two most common inherited metabolic diseases, methylmalonic academia and propionic academia, using urinary metabolomics data. With the desirable classification performance, the proposed feature screening system outperforms simpler techniques in the identification of more features closely correlated with the metabolic mechanisms and the stability of selected candidate biomarkers against sample variations. Besides, the novel feature screening system greatly degrades the sensitivity of identified candidate biomarkers to the network size of self-organizing map, benefiting the identification of a suitable and stable final candidate biomarker list.

Alkaline phosphatase (ALP) is an important biomarker for clinical diagnosis. Abnormal levels of ALP are closely related to many diseases. In this contribution, a ratiometric fluorescent sensing method based on the competition between two oxidation-reduction reactions related to MnO2 nanosheets was developed for ALP detection. Moreover, an advanced model was derived for the quantitative analysis of the fluorescence measurements obtained by the proposed ratiometric fluorescent sensing method. With the aid of the advanced model, the proposed method achieved satisfactory quantitative results for ALP in real-world serum samples, with accuracy comparable to the corresponding results obtained by an automatic biochemical analyzer. Its recovery rates for the spiked serum samples were in the range of 98.4–115.0%, which were quite satisfactory considering the complexity of the matrices of the samples. The limit of detection and limit of quantification were estimated to be 0.09 and 0.30 U L−1, respectively. Therefore, it is reasonable to expect that the proposed ratiometric fluorescent sensing method can be further developed to be a competitive alternative for ALP detection.

Detection of specific biomarkers in cell membranes is critical for cell biology and disease theranostics. Here we develop a versatile terminal protection assay strategy for wash-free quantification and imaging of cell surface proteins using small molecule-linked DNA with programmable signal sequences. DNA probes are designed to link to a small molecule ligand at 3' end for specific recognition of the cell surface protein and a programmable signal sequence at 5' terminal for delivering detectable responses. Binding of the small molecule ligand to target protein enables protection of the DNA probes from exonuclease I mediated degradation, leaving the surface-binding probes intact while the non-binding probes degraded. This strategy thus allows wash-free detection of the cell surface protein via the selectively protected signal sequence. By programming the signal sequences as peroxidase-like DNAzyme, quantitative polymerase chain reaction (qPCR) targeting DNA and Ag nanoclusters (AgNCs) forming DNA template based on our new finding that the exonuclease I is able to quench the fluorescence of AgNCs, we can develop this strategy into a versatile platform for colorimetric detection, qPCR quantification and fluorescence imaging of the cell surface protein. This platform is demonstrated using a folate-linked DNA probe for folate receptor detection on tumor cell surface. The results revealed that this strategy enables highly selective and sensitive detection of the tumor cells as well as quantification and localization of the membrane protein on the cells, implying its potential in membrane protein based biomedical and clinical applications.

A ratiometric surface-enhanced Raman scattering (SERS) aptasensor based on internal standard (IS) methods was proposed for the ultrasensitive and reproducible quantitative detection of silver ion (Ag+) with Au@Ag core-shell nanoparticle (Au@Ag NP) substrate. In principle, the thiolated 5′-Rox C-containing labeled aptamer probe (Rox-aptamer) is firstly immobilized on the SERS substrate surface and then hybridizes with the complementary DNA (cDNA) to form a rigid double-stranded DNA (dsDNA), in which the Rox Raman labels is used to produce the Raman signal. Furthermore, the pyridine is employed as an IS element to provide the ratiometric determination of target. In the presence of Ag+, the Rox-aptamer is turned into the cytosine (C)-Ag+-C mediated hairpin structure, which remarkably reduces the distance between the Rox labels and the Au@Ag NP surface responsible for a measurable ‘turn-on’ signal change of Rox. This IS-based ratiometric SERS aptasensor exhibits a limit of detection of 50 pM for Ag+ with a linear detection range from 0.1 to 100 nM and the shortcoming of irreproducibility of SERS signal could be overcome. The proposed method provides a simple, robust, and rapid approach for the sensitive and reproducible quantitative detection of Ag+, and it could also be used for the detection of other metal ions which exhibits specific interactions with natural or synthetic bases.

The study of spatial distribution characteristics of the adsorbed compounds for absorbent materials has significant importance in understanding the behaviors of aerosols while they migrating in the absorbent materials. Herein, for the first time, desorption corona beam ionization–mass spectrometry (DCBI–MS) has proposed for direct in-situ analysis of adsorbed aerosol for absorbent materials. DCBI is a novel atmospheric pressure chemical ionization (APCI)-related technique developed by our group in recent years. It can facilitate accurately localizing sampling by forming a visible thin corona beam and avoid the risk of sample contamination and matrix interference compared with other similar techniques. The advantages of DCBI–MS allow rapid screening of the spatial distribution characteristics of the adsorbed compounds for absorbent materials. The distribution characteristic of phenol in cigarette filter tip filled with cellulose acetate fiber was studied as a model case for demonstrating the feasibility of the developed method. As a comparison, conventional HPLC was also used for the study of the distribution characteristic of phenol. The results revealed DCBI–MS had highly improved assay simplicity in spatial distribution characteristic analysis of phenol for the acetate fiber tip, therefore, exhibiting a great potential for convenient, rapid and cost-efficient analysis of the spatial distribution characteristic investigation of adsorbed compounds for adsorbent materials.

A novel intrinsic fluorescence method for the direct determination of l-phenylalanine, l-tyrosine, and l-tryptophan in human plasma is presented. By using fluorescence excitation-emission-pH-sample data array in combination with four-way calibration method based on the quadrilinear component model, the proposed approach successfully achieved quantitative analysis of the aromatic amino acids in human plasma, even in the presence of an unknown, uncalibrated serious interferent. It needs little preparation, uses the "mathematical separation" instead of "analytical separation", what makes it fast and environmentally friendly. Satisfactory results have been achieved for calibration set, validation set, and prediction set. The ranges for phenylalanine, tyrosine, and tryptophan are 2.0 × 10(3)-20.0 × 10(3), 50.0-500.0, and 20.0-200.0 ng mL(-1) respectively. Average spike recoveries (mean ± standard deviation) are 93.3 ± 7.7%, 104.3 ± 6.6%, and 99.5 ± 9.0% respectively. The real concentrations in human plasma are 10.2 ± 0.3, 6.6 ± 0.1, and 5.3 ± 0.1 μg mL(-1) respectively, which are consistent with the results obtained by LC-MS/MS method and reference values. In addition, we explored the third-order advantages through the real four-way array; it has shown that higher resolving power is one of the main advantages of higher-order tensor calibration method. These results demonstrated that the proposed method is sensitive, accurate, and efficient for direct quantitative analysis of aromatic amino acids in human plasma.

DNA sequences with guanine repeats can be induced to form G-quartets that adopt G-quadruplex structures in the presence of thioflavin T (ThT). ThT plays a dual role of inducing DNA sequences to fold into quadruplex structures and of sensing the change by its remarkable fluorescence enhancement. ThT binding to the DNA sequences with guanine repeats showed highly specific fluorescence enhancement compared with single/double-stranded DNA. In this work, we have utilized the conformational switch from G-quadruplex complex induced by fluorogenic dye ThT to Hg(2+) mediated T-Hg-T double-stranded DNA formation, thereby pioneering a facile approach to detect Hg(2+) with fluorescence spectrometry. Through this approach, Hg(2+) in aqueous solutions can be detected at 5 nM with fluorescence spectrometry in a facile way, with high selectivity against other metal ions. These results indicate the introduced label-free method for fluorescence spectrometric Hg(2+) detection is simple, quantitative, sensitive, and highly selective.

A new and facile strategy using double-stranded DNA-copper nanoparticles (dsDNA-Cu NPs) as fluorescence reporters for the highly sensitive and selective detection of l-histidine was demonstrated. In the dsDNA-Cu NPs-based sensing system, the fluorescence was quenched considerably upon the addition of l-histidine. Under the optimized experimental conditions, the probe exhibits excellent performance (e.g., a satisfactory detection limit of 5 μM and high specificity). Our in situ method requires no covalent attachment of DNA to a fluorophore, which could significantly reduce the cost and simplify the procedure for l-histidine detection. Moreover, the proposed sensing system could be applicable for the detection of target biomolecule in complex biological samples. These striking properties make it an attractive platform for the direct detection of l-histidine.

Rapid, sensitive and quantitative assays for peptide hydrolysis enzymes are of paramount importance for drug development and in the diagnosis of disease. Here, we proposed a novel biosensor for sensitive and selective active screening of peptidases. This strategy relies on the proteolysis-mediated protection of gold nanoparticles (AuNPs) that were decorated with biotin-labeled substrate peptides and can be aggregated by streptavidin. Enzyme-mediated protection of AuNPs offers this strategy high specificity, and the use of AuNPs additionally allows a visual and homogeneous assay format, thus permitting improved simplicity and throughput of the assays. As a model case, desirable selectivity and sensitivity in peptidase assay were achieved in the active assays of pancreatic elastase with a wide linear response range from 0.005 to 0.10 U/mL and a detection limit of 0.003 U/mL. The results indicated that this strategy can offer a simple, robust and convenient platform for visualized peptidase activity analysis and related biochemical studies with high sensitivity and selectivity.

GC-MS urinary metabolomic analysis coupled with chemometrics is used to detect inborn errors of metabolism (IEMs), which are genetic disorders causing severe mental and physical debility and even sudden infant death. Orthogonal partial least squares discriminant analysis (OPLS-DA) is an efficient multivariate statistical method that conducts data analysis of metabolite profiling. However, performance degradation is often observed for OPLS-DA due to increasing size and complexity of metabolomic datasets. In this study, hybrid particle swarm optimization (HPSO) is employed to modify OPLS-DA by simultaneously selecting the optimal variable subset, associated weights and the appropriate number of orthogonal components, constructing a new algorithm called HPSO-OPLSDA. Investigating two IEMs, methylmalonic acidemia (MMA) and isovaleric acidemia (IVA), results suggest that HPSO-OPLSDA can significantly outperform OPLS-DA in terms of the discrimination between disease samples and healthy controls. Moreover, main discriminative metabolites are identified by HPSO-OPLSDA to aid the clinical diagnosis of IEMs, including methylmalonic-2, methylcitric-4(1) and 3-OH-propionic-2 for MMA and isovalerylglycine-1 for IVA.

A new paper-based biosensing approach has been developed for sensitive and rapid detection of acetylcholinesterase (AChE) inhibitors. The biosensing zone of the paper strip is constructed with an inkjet printing method, and the biomolecule AChE is immobilized into two layers of biocompatible sol-gel-derived silica ink with a "sandwich" form. Indoxyl acetate (IDA) is used as a chromogenic substrate, which is colorless and can be catalytically hydrolyzed into blue-colored indigo dipolymer. When the enzymatic activity of AChE is inhibited after incubation with organophosphate pesticides (OPs), there is a decreased hydrolysis of IDA accompanying with a drop in color intensity. Paraoxon and trichlorfon are used as the representative OPs in the assay. Due to the low solubility and high molar absorption coefficient of the IDA dipolymer product, the paper-based strip can form a neat blue sensing zone and shows obviously improved sensitivity with a limit of detection (LOD) of 0.01ngmL-1 paraoxon and 0.04ngmL-1 trichlorfon (S/N=3) and the LODs for visual detection are 0.03ngmL-1 for paraoxon and 0.1ngmL-1 for trichlorfon comparing with the previously reported colorimetric methods. The concentrations of paraoxon in apple juice samples are also detected, and the results are in accord well with these results from high-performance liquid chromatography, showing great potential for on-site detection of OPs in practical application. The developed assay can be used to qualitatively and semiquantitatively estimate with naked eyes and quantitatively assess OPs through image analysis.

A novel strategy that combines the second-order calibration method based on the trilinear decomposition algorithms with high performance liquid chromatography with diode array detector (HPLC-DAD) was developed to mathematically separate the overlapped peaks and to quantify quinolones in honey samples. The HPLC-DAD data were obtained within a short time in isocratic mode. The developed method could be applied to determine 12 quinolones at the same time even in the presence of uncalibrated interfering components in complex background. To access the performance of the proposed strategy for the determination of quinolones in honey samples, the figures of merit were employed. The limits of quantitation for all analytes were within the range 1.2–56.7 μg kg−1. The work presented in this paper illustrated the suitability and interesting potential of combining second-order calibration method with second-order analytical instrument for multi-residue analysis in honey samples.

A highly sensitive electrochemical immunosensor based on combination of chitosan (CHIT) and coral-shaped AuNPs (C-AuNPs) to form an immobilization matrix has been developed using human IgG as a model analyte. The inorganic–organic hybrid film with abundant adsorbing sites and large surface area can reserve the biocompatibility of the biomaterials which greatly increase loading amounts of assembling, thus, significantly improves the performance of biosensing. The morphology is studied by scanning electron microscopy (SEM). Under the optimized experimental conditions, the immunosensor exhibits excellent performance (e.g., a detection limit of 5 pmol L−1, a linear dynamic range of 3 orders of magnitude, high specificity). This possibly makes it an attractive platform for the direct immunoassay of human IgG or other biomolecules.

As a greedy search algorithm, classification and regression tree (CART) is easily relapsing into overfitting while modeling microarray gene expression data. A straightforward solution is to filter irrelevant genes via identifying significant ones. Considering some significant genes with multi-modal expression patterns exhibiting systematic difference in within-class samples are difficult to be identified by existing methods, a strategy that unimodal transform of variables selected by interval segmentation purity (UTISP) for CART modeling is proposed. First, significant genes exhibiting varied expression patterns can be properly identified by a variable selection method based on interval segmentation purity. Then, unimodal transform is implemented to offer unimodal featured variables for CART modeling via feature extraction. Because significant genes with complex expression patterns can be properly identified and unimodal feature extracted in advance, this developed strategy potentially improves the performance of CART in combating overfitting or underfitting while modeling microarray data. The developed strategy is demonstrated using two microarray data sets. The results reveal that UTISP-based CART provides superior performance to k-nearest neighbors or CARTs coupled with other gene identifying strategies, indicating UTISP-based CART holds great promise for microarray data analysis.

The paper focuses on solving a common and important problem of NIR quantitative analysis in multi-component systems: how to significantly reduce the size of the calibration set while not impairing the predictive precision. To cope with the problem orthogonal discrete wavelet packet transform (WPT), the least correlation design and correlation coefficient test (r-test) have been combined together. As three examples, a two-component carbon tetrachloride system with 21 calibration samples, a two-component aqueous system with 21 calibration samples, and a two-component aqueous system with 41 calibration samples have been treated with the proposed strategy, respectively. In comparison with some previous methods based on much more calibration samples, the results out of the strategy showed that the predictive ability was not obviously decreased for the first system while being clearly strengthened for the second one, and the predictive precision out of the third one was even satisfactory enough for most cases of quantitative analysis. In addition, all important factors and parameters related to our strategy are discussed in detail.

Multivariate spectral analysis has been widely applied in chemistry and other fields. Spectral data consisting of measurements at hundreds and even thousands of analytical channels can now be obtained in a few seconds. It is widely accepted that before a multivariate regression model is built, a well-performed variable selection can be helpful to improve the predictive ability of the model. In this paper, the concept of traditional wavelength variable selection has been extended and the idea of variable weighting is incorporated into least-squares support vector machine (LS-SVM). A recently proposed global optimization method, particle swarm optimization (PSO) algorithm is used to search for the weights of variables and the hyper-parameters involved in LS-SVM optimizing the training of a calibration set and the prediction of an independent validation set. All the computation process of this method is automatic. Two real data sets are investigated and the results are compared those of PLS, uninformative variable elimination-PLS (UVE-PLS) and LS-SVM models to demonstrate the advantages of the proposed method.

Through randomly arranging samples of a calibration set, treating their NIR spectra with orthogonal discrete wavelet transform, and selecting suitable variables in terms of correlation coefficient test (r-test), it is possible to extract features of each component in a multi-component system respectively and partial least squares (PLS) models based on these features are capable of predicting the concentration of every component. What is perhaps more important, with the proposed strategy, the predictive ability of the model is at least not impaired while the size of the calibration set can be obviously reduced. Therefore, it provides a more economical, rapid, as well as convenient approach of NIR quantitative analysis for multi-component system. In addition, all important factors and parameters related to the proposed strategy are discussed in detail.

A highly sensitive chemiluminescence immunosensor for the detection of prostate-specific antigen (PSA) was developed based on a novel amplification procedure with the application of enzyme encapsulated liposome. Horseradish peroxidase (HRP) encapsulated and antibody-modified liposome acts as the carrier of a large number of markers and specific recognition label for the amplified detection of PSA. In the detection of PSA, the analyte was first bound to the specific capture antibody immobilized on the microwell plates, and then sandwiched by the antibody-modified liposomes encapsulating HRP. The encapsulated markers, HRP molecules were released by the lysis of the specifically bound liposomes in the microwell with Triton X-100 solution. Then, the analyte PSA could be determined via the chemiluminescence signal of HRP-catalyzed luminol/peroxide/enhancer system. The “sandwich-type” immunoassay provides the amplification route for the PSA detection in ultratrace levels. The CL emission intensity exhibits dynamic correlation to PSA concentration in the range from 0.74 pg/ml to 0.74 μg/ml with readily achievable detection limit of 0.7 pg/ml.

This paper has demonstrated the study on the adsorption kinetics of orthoxylene on silica gel with a novel experimental methodology. In the method, there was a differential adsorption bed (DAB) where the solid adsorbent always contacted with the same bulk concentration of the adsorbate vapor, and the DAB was monitored with near-infrared diffuse reflectance spectroscopy (NIRDRS) continuously as well as non-invasively. Local partial least squares (PLS) algorithm was suggested to replace normal global PLS method in multivariate calibration models for processing NIRDRS data, because the concentration of the adsorbate on the adsorbent varied greatly as the adsorption process was going on. In this way, we, conveniently as well as promptly, obtained instantaneous adsorption rates of several orthoxylene/silica gel adsorption processes under different conditions like partial pressure of orthoxylene vapor and velocity of gas, and discovered that the adsorption process was physical adsorption, and mainly controlled by external diffusion.

A novel electrochemical immunosensor using functionalized silica nanoparticles (Si NPs) as protein tracer has been developed for the detection of prostate specific antigen (PSA) in human serum. The immunosensor was carried out based on a heterogeneous sandwich procedure. The PSA capture antibody was immobilized on the gold electrode via glutaraldehyde crosslink. After reaction with the antigen in human serum, Si NPs colabeled with detection antibody and alkaline phosphatase (ALP) was sandwiched to form the immunocomplex on the gold electrode. ALP carried by Si NPs convert nonelectroactive substrate into the reducing agent and the latter, in turn, reduce metal ions to form electroactive metallic product on the electrode. Linear sweep voltammetry (LSV) was used to quantify the amount of the deposited silver and give the analytical signal for PSA. The parameters including the concentration of the ALP used to functionalize the Si NPs and the enzyme catalytic reaction time have been studied in detail and optimized. Under the optimum conditions of immunoreaction and electrochemical detection, the electrochemical immunosensor was able to realize a reliable determination of PSA in the range of 1-35 ng/mL with a detection limit of 0.76 ng/mL. For six human serum samples, the results performed with the electrochemical immunosensor were in good agreement with those obtained by chemiluminescent microparticle immunoassay (CMIA), indicating that the electrochemical immunosensor could satisfy the need of practical sample detection.

A novel immunoassay based on surface-enhanced Raman scattering (SERS) has been developed. The method exploits the SERS-derived signal from reporter molecules (crystal violet, CV) encapsulated in antibody-modified liposome particles. The antigen is firstly captured by the primary antibody immobilized in microwell plates and then sandwiched by secondary antibody-modified liposome. The CV molecules are released from the liposome and transferred to specially designed substrate of gold nanosphere arrays with sub-10-nm gaps. The concentration of the antigen is indirectly read out by the SERS intensity of the CVs. The substrate used could substantially improve the sensitivity and reproducibility of SERS measurement. The SERS intensity responses are linearly correlated to logarithm of antigen concentration in the range of 1.0 × 10−8 to 1.0 × 10−4 g mL−1 with a detection limit of 8 ng mL−1. To our knowledge, this is the first report describing liposome-mediated enhancement of the sensitivity in immunoassay based on surface-enhanced Raman scattering. Experimental results show that the proposed method illustrates a potential prospect of applications in immunoassay.

A novel method was proposed to determine simultaneously carbaryl and its degradation product 1-naphthol in river and tap water in this paper. The parallel factor analysis (PARAFAC) algorithm was adopted to analyze the excitation-emission matrix (EEM) fluorescence data. The second-order advantage of the PARAFAC-based second-order calibration algorithm was exploited, which make it possible that calibration can be performed even in the presence of unknown interferences. Good recoveries were obtained although the excitation and emission spectral profiles of the analytes were overlapped with background in the river water. It was also applied to investigate the hydrolysis kinetics of carbaryl in river water and tap water. The rate equation, the rate constant and the half life were calculated.

We demonstrate herein a novel electrochemical protocol for quantification of human IgG based on the precipitation of copper on gold nanoparticle tags and a subsequent electrochemical stripping detection of the dissolved copper. The immunoassay was conducted by following the typical procedure for sandwich-type immunoreaction. Goat anti-human IgG was immobilized on the wells of microtiter plates. The human IgG analyte was first captured by the primary antibody and then sandwiched by secondary antibody labeled with gold nanoparticles. The copper enhancer solution was then added to deposite copper on the gold nanoparticle tags. After dissolved with HNO(3), the released copper ions were then quantified by ASV. The detection limit is 0.5ng/mL by 3sigma-rule. In order to investigate the feasibility of the newly developed technique to be applied for clinical analysis, several standard human IgG serum specimens were also examined by the method. To our knowledge, the copper enhancing procedure is the first time to be developed for immunoassay. The new strategy of using copper-enhanced gold nanoparticle tags for electrochemical stripping detection holds great promise for immunoassay and DNA detection.

This paper presents a new method for the determination of Sudan dyes contained in hot chilli samples. The method employs second-order calibration algorithms to handle the recorded data. The second-order calibration algorithms are based on the popular parallel factor analysis (PARAFAC), alternating trilinear decomposition (ATLD) and self-weighted alternating trilinear decomposition (SWATLD), respectively. These chemometric methodologies have the second-order advantage, which is the ability to get accurate concentration estimates of interested analytes even in the presence of uncalibrated interfering components. The results on a set of spiked chilli test shows that low contents of Sudan I and Sudan II in complex chilli mixtures can be accurately determined using the new method. The sample preparation was based on solvent extraction, and internal standard was not required. Quantification was carried out with simple mobile phase.

An electrochemical immunoassay technique has been developed based on the sensitive detection of the enzyme-generated product with a bi-electrode signal transduction system. The system uses two separate electrodes, an immunoelectrode and a detection electrode to form a galvanic cell to implement the redox reactions on two different electrodes, that is the enzyme-generated reductant in the anode region is electrochemically oxidized by an oxidant (silver ions) in the cathode apartment. Based on a sandwich procedure, after immunoelectrode with antibody immobilized on its surface bound with the corresponding antigen and alkaline phosphatase conjugated antibody successively, the immunoelectrode was placed in enzyme reaction solution and wired to the detection electrode which was immerged into a silver deposition solution. These two solutions are connected with a salt bridge. Thus a bi-electrode signal transduction system device is constructed in which the immunoelectrode acts as anode and the detection electrode serves as cathode. The enzyme bound on the anode surface initiates the hydrolysis of ascorbic acid 2-phosphate to produce ascorbic acid in the anode region. The ascorbic acid produced in the anodic apartment is electrochemically oxidized by silver ions coupled with the deposition of silver metal on the cathode. Via a period of 30 min deposition, silver will deposited on the detection electrode in an amount corresponding to the quantity of ascorbic acid produced, leading to a great enhancement in the electrochemical stripping signal due to the accumulation of metallic silver by enzyme-generated product. Compared with the method using chemical deposition of silver, the electrochemical deposition of silver on a separate detection electrode apartment avoids the possible influence of silver deposition on the enzyme activity.

A new amperometric biosensor for glucose was developed based on adsorption of glucose oxidase (GOx) at the gold and platinum nanoparticles-modified carbon nanotube (CNT) electrode. CNTs were covalently immobilized on gold electrode via carbodiimide chemistry by forming amide linkages between carboxylic acid groups on the CNTs and amine residues of cysteamine self-assembled monolayer (SAM). The fabricated GOx/Au(nano)/Pt(nano)/CNT electrode was covered with a thin layer of Nafion to avoid the loss of GOx in determination and to improve the anti-interferent ability. The immobilization of CNTs on the gold electrode was characterized by quartz crystal microbalance technique. The morphologies of the CNT/gold and Pt(nano)/CNT/gold electrodes have been investigated by scanning electron microscopy (SEM), and the electrochemical performance of the gold, CNT/gold, Pt(nano)/gold and Pt(nano)/CNT/gold electrodes has also been studied by amperometric method. In addition, effects of electrodeposition time of Pt nanoparticles, pH value, applied potential and electroactive interferents on the amperometric response of the sensor were discussed. The enzyme electrode exhibited excellent electrocatalytic activity and rapid response for glucose in the absence of a mediator. The linear range was from 0.5 to 17.5mM with correction coefficient of 0.996. The biosensor had good reproducibility and stability for the determination of glucose.

A novel immunoassay strategy based on combination of chitosan (CHIT) and a gold nanoparticle (GNP) label has been developed. The susceptibility of CHIT to further chemical modifications due to the abundant amino groups is explored in order to covalently immobilize antibody (Ab) onto the (3-aminopropyl) triethoxysilane derivatized glass slide by cross-linking with glutaraldehyde (GA). After incubating in antigen (Ag) solution, the obtained substrate is immersed in GNP labeled antibody solution for signal generation. The two steps were repeated alternatively for three times, forming multilayer of gold nanoparticles via antigen–antibody specific reaction. Ultraviolet-visible (UV–vis) absorption spectrum is recorded to obtain quantitative information about the specific antigen. The presented immunoassay strategy is applied for determination of human serum albumin (HSA) as a model analyte. The immunoassay of HSA is specific. Compared to previous correlative work, the proposed immunosensing strategy shows some advantages, such as improved sensitivity as much more gold nanoparticles can be coupled to the functionalized surface making use of the abundant amino groups of CHIT. Moreover, a significantly extended linear detection range of 8.0–512.0 μg/mL is gained under the optimized experimental conditions. In particular, the presented biosensing method shows low cost and simplicity, and only a conventional UV–vis detector is involved.

In ordinary multivariate calibration methods, when the calibration set is determined to build the model describing the relationship between the dependent variables and the predictor variables, each sample in the calibration set makes the same contribution to the model, where the difference of representativeness between the samples is ignored. In this paper, by introducing the concept of weighted sampling into partial least squares (PLS), a new multivariate regression method, optimized sample-weighted PLS (OSWPLS) is proposed. OSWPLS differs from PLS in that it builds a new calibration set, where each sample in the original calibration set is weighted differently to account for its representativeness to improve the prediction ability of the algorithm. A recently suggested global optimization algorithm, particle swarm optimization (PSO) algorithm is used to search for the best sample weights to optimize the calibration of the original training set and the prediction of an independent validation set. The proposed method is applied to two real data sets and compared with the results of PLS, the most significant improvement is obtained for the meat data, where the root mean squared error of prediction (RMSEP) is reduced from 3.03 to 2.35. For the fuel data, OSWPLS can also perform slightly better or no worse than PLS for the prediction of the four analytes. The stability and efficiency of OSWPLS is also studied, the results demonstrate that the proposed method can obtain desirable results within moderate PSO cycles.

In the present study a new version of nonlinear partial least-square method based on artificial neural network transformation (ANN-NLPLS) has been proposed. This algorithm firstly transforms the training descriptors into the hidden layer outputs using the universal nonlinear mapping carried by an artificial neural network, and then utilizes PLS to relate the outputs of the hidden layer to the bioactivities. The weights between the input and hidden layers are optimized by a particle swarm optimization (PSO) method using the criterion of minimized model error via PLS modeling. An F-statistic is introduced to determine automatically the number of PLS components during the weight optimization. The performance is assessed using a simulated data set and two quantitative structure-activity relation (QSAR) data sets. Results of these three data sets demonstrate that ANN-NLPLS offers enhanced capacity in modeling nonlinearity while circumventing the overfitting frequently involved in nonlinear modeling.

Estimating an appropriate chemical rank of a three-way data array is very important to second-order calibration. In this paper, a simple linear transform incorporating Monte Carlo simulation approach (LTMC) to estimate the chemical rank of a three-way data array was suggested. The new method determines the chemical rank through performing a simple linear transform procedure on the original cube matrix to produce two subspaces by singular value decomposition. One of two subspaces is derived from the original three-way data array itself and the other is derived from a new three-way data array produced by the linear transformation of the original one. Projection technique incorporating the Monte Carlo approach acts as distinguishing criterion to choose the appropriate component number of the system. Simulated three-way trilinear data arrays with different noise types (homoscedastic and heteroscedastic), various noise level as well as high collinearity are used to illustrate the feasibility of the new method. The results have shown that the new method could yield accurate results with different conditions appended. The feasibility of the new method is also confirmed by two real arrays, HPLC-DAD data and excitation-emission fluorescent data. All the results are compared with the other three factor-determining methods: factor indicator function (IND), core consistency diagnostic (CORCONDIA) and two-mode subspace comparison (TMSC) approach. It shows that the newly proposed algorithm can objectively and quickly determine the chemical rank to fit the trilinear model.