New discrimination method combining hit quality index based spectral matching and voting

A new discrimination method, called hit quality index (HQI)-voting, that uses the HQI for discriminant analysis has been developed. HQI indicates the degree of spectral matching between two spectra as known. In this method, a library sample yielding the highest HQI value for an unknown sample was initially searched and a group containing this sample was chosen as the group for the unknown sample. When overall spectral features of two groups are quite close to each other, many library samples with similar HQI values could be available for an unknown sample. In this situation, the simultaneous consideration of multiple votes (several library samples with close HQI values) for final decision would be more robust. In order to evaluate the discrimination performance of HQI-voting, three different near-infrared (NIR) spectroscopic datasets composed of two sample groups were used: (1) domestic and imported sesame samples, (2) domestic and imported Angelica gigas samples, and (3) diesel and light gas oil (LGO) samples. For the purpose of comparison, principal component analysis–linear discriminant analysis (PCA–LDA), partial least squares–discriminant analysis (PLS–DA) as well as k-nearest neighbor (k-NN) were also performed using the same datasets and the resulting accuracies were compared. The discrimination performances improved with the use of HQI-voting in comparison with those resulted from PCA–LDA and PLS–DA. The overall results support that HQI-voting is a comparable discrimination method to that of existing factor-based multivariate methods.     

Using near-infrared overtone regions to determine biodiesel content and adulteration of diesel/biodiesel blends with vegetable oils

This work evaluates the use of near-infrared (NIR) overtone regions to determine biodiesel content, as well potential adulteration with vegetable oil, in diesel/biodiesel blends. For this purpose, NIR spectra (12,000–6300 cm⁻¹) were obtained using three different optical path lengths: 10 mm, 20 mm and 50 mm. Two strategies of regression with variable selection were evaluated: partial least squares (PLS) with significant regression coefficients selected by Jack-Knife algorithm (PLS/JK) and multiple linear regression (MLR) with wavenumber selection by successive projections algorithm (MLR/SPA). For comparison, the results obtained by using PLS full-spectrum models are also presented. In addition, the performance of models using NIR (1.0 mm optical path length, 9000–4000 cm⁻¹) and MIR (UATR – universal attenuated total reflectance, 4000–650 cm⁻¹) spectral regions was also investigated. The results demonstrated the potential of overtone regions with MLR/SPA regression strategy to determine biodiesel content in diesel/biodiesel blends, considering the possible presence of raw oil as a contaminant. This strategy is simple, fast and uses a fewer number of spectral variables. Considering this, the overtone regions can be useful to develop low cost instruments for quality control of diesel/biodiesel blends, considering the lower cost of optical components for this spectral region.     

Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis

The goal of this study was to explore the potential of near-infrared (NIR) hyperspectral imaging in combination with multivariate analysis for the prediction of some quality attributes of lamb meat. In this study, samples from three different muscles (semitendinosus (ST), semimembranosus (SM), longissimus dorsi (LD)) originated from Texel, Suffolk, Scottish Blackface and Charollais breeds were collected and used for image acquisition and quality measurements. Hyperspectral images were acquired using a pushbroom NIR hyperspectral imaging system in the spectral range of 900–1700 nm. A partial least-squares (PLS) regression, as a multivariate calibration method, was used to correlate the NIR reflectance spectra with quality values of the tested muscles. The models performed well for predicting pH, colour and drip loss with the coefficient of determination (R²) of 0.65, 0.91 and 0.77, respectively. Image processing algorithm was also developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of quality parameter in the imaged lamb samples. In addition, textural analysis based on gray level co-occurrence matrix (GLCM) was also conducted to determine the correlation between textural features and drip loss. The results clearly indicated that NIR hyperspectral imaging technique has the potential as a fast and non-invasive method for predicting quality attributes of lamb meat.     

A novel aptamer functionalized CuInS2 quantum dots probe for daunorubicin sensing and near infrared imaging of prostate cancer cells

In this paper, a novel daunorubicin (DNR)-loaded MUC1 aptamer-near infrared (NIR) CuInS₂ quantum dot (DNR–MUC1–QDs) conjugates were developed, which can be used as a targeted cancer imaging and sensing system. After the NIR CuInS₂ QDs conjugated with the MUC1 aptamer–(CGA)₇, DNR can intercalate into the double-stranded CG sequence of the MUC1–QDs. The incorporation of multiple CG sequences within the stem of the aptamers may further increase the loading efficiency of DNR on these conjugates. DNR–MUC1–QDs can be used to target prostate cancer cells. We evaluated the capacity of MUC1–CuInS₂ QDs for delivering DNR to cancer cells in vitro, and its binding affinity to MUC1-positive and MUC1-negative cells. This novel aptamer functionalized QDs bio-nano-system can not only deliver DNR to the targeted prostate cancer cells, but also can sense DNR by the change of photoluminescence intensity of CuInS₂ QDs, which concurrently images the cancer cells. The quenched fluorescence intensity of MUC1–QDs was proportional to the concentration of DNR in the concentration ranges of 33–88 nmol L⁻¹. The detection limit (LOD) for DNR was 19 nmol L⁻¹. We demonstrate the specificity and sensitivity of this DNR–MUC1–QDs probe as a cancer cell imaging, therapy and sensing system in vitro.     

Rapid tool for assessment of C13 norisoprenoids in wines

Two novel methodologies for quantification of C₁₃ norisoprenoids in wines were developed. The first methodology, method A (reference method) was based on the headspace solid-phase microextraction combined with gas chromatography–quadrupole mass spectrometry operating in selected ion monitoring mode (HS-SPME–GC–qMS–SIM). This methodology allowed to select the GC conditions for an adequate chromatographic resolution of wine components. The second methodology, method B (rapid method) was based on the HS-SPME–GC–qMS–SIM, using GC conditions that allowed to obtain a C₁₃ norisoprenoid volatile signature. In the later, the GC capillary column of 30 m at 220 °C was used acting as a transfer line of the components sorbed by the SPME coating fibre to the mass spectrometer, which acts as a sensor for m/z fragments 142 and 192. It does not require any pre-treatment of the sample, and the C₁₃ norisoprenoid composition of the wine was evaluated based on the chromatographic profile and specific m/z fragments, without complete chromatographic separation of its components. For quantification purposes, external calibration curves were constructed with β-ionone chemical standard. Calibration curves with regression coefficient (r²) of 0.9940 and 0.9968, RSD of 1.08% and 12.51%, and detection limits of 1.10 and 1.57 μg L⁻¹ were obtained for methods A and B, respectively. These methodologies were applied to seventeen white and red table wines. Two vitispirane isomers (158–1529 μg L⁻¹) and 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN) (6.42–39.45 μg L⁻¹) were quantified. The data obtained for vitispirane isomers and TDN using the two methods were highly correlated (r² of 0.9756 and 0.9630, respectively). Associated to the fast and robust character of the proposed rapid method B and considering the extraction time, it is important to focus its selectivity and potential applicability if specific m/z fragments would be established for new analytes.     

近红外光谱分析模型传递简易方法研究

本文在不同时间安装的多台同型号近红外光谱仪上建立推进剂校正模型时,由于推进剂样品数量少且难于保存,新到仪器在建模时常遇到代表性样品数量严重不足.为此,提出将2台波长一致性好的近红外光谱仪器上采集的光谱组成一个混合校正样品光谱集,使用偏最小二乘法(PLS)建立模型的方法.结果表明,在用户缺少专业模型传递软件情况下,该方法...     

Development, validation and application of Micellar Electrokinetic Capillary Chromatography method for routine analysis of catechins, quercetin and thymol in natural samples

Natural matrices such as several vegetables, fruits or beverages are the origin of different natural antioxidants with great interest to be used instead of synthetic antioxidants in several applications, as a result of their health-promoting and disease-preventing properties. In this work, a capillary electrophoresis method capable of separating and quantifying some of these antioxidants (seven catechins with the same or similar molecular weight and relation q/z) is showed. Short analysis time and precision parameters equivalent to those obtained by liquid chromatography were obtained. This method was developed using the micellar electrokinetic chromatography (MEKC) mode with ultraviolet–visible detection. Quality parameters were established, obtaining low instrumental detection and quantification limits (0.6–2.0 mg L^{− 1} and 2.0–6.5 mg L^{− 1}, respectively), good precision (relative standard deviation in the intermediate repeatability lower than 7% for every compound) and short analysis time. The developed method also showed good performance for the determination of two more natural antioxidants, quercetin and thymol. The applicability of the method to the analysis of catechins in several natural samples was tested.     

The transfer between instruments of a reflectance near-infrared assay for paracetamol in intact tablets

This study compares several correction methods to facilitate the transfer of a validated near-infrared (NIR) assay for paracetamol in intact tablets between two reflectance NIR instruments of the same type. Transfer was defined as the ability to accurately predict the true assay value of a sample measured on a NIR system using an assay developed on a different system, and was assessed using a comprehensive set of statistical tests. Direct electronic transfer of the calibration models, representing the NIR assay, was not possible as a result of a definite residual spectrum between instruments. The use of a correction method based on the standardisation of the material used to record the reference spectrum also proved ineffective. Two methods investigated did succeed, the first employed a response surface calculated between the reflectance values of a set of six certified photometric standards measured on both instruments, with all full range partial least square (PLS) regression models subsequently transferred. The next was correction of the spectra from the second instrument utilising the residual spectrum between the mean sample of the validation set measured on both instruments. Through this approach all PLS regression models and also a single multiple linear regression (MLR) model were transferred. As an outcome of this study guidelines are suggested for the transfer of NIR assays along with the criteria deemed necessary to conclusively prove transfer and justify any correction method utilised. The significant criteria were determined to be the paired t-test with both the UV reference assay data and the original NIR assay data, and comparison of the coefficient of multiple determinations.     

Detection of banned meat and bone meal in feedstuffs by near-infrared microscopic analysis of the dense sediment fraction

In this paper we present an alternative method for detection of meat and bone meal (MBM) in feedstuffs by near-infrared microscopic (NIRM) analysis of the particles in the sediment fraction (dense fraction (d >1.62) from dichloroethylene) of compound feeds. To apply this method the particles of the sediment fraction are spread on a sample holder and presented to the NIR microscope. By using the pointer of the microscope the infrared beam is focussed on each particle and the NIR spectrum (1112–2500 nm) is collected. This method can be used to detect the presence of MBM at concentrations as low as 0.05% mass fraction. When results from the NIRM method were compared with the classical microscopic method, a coefficient of determination (R²) of 0.87 was obtained. The results of this study demonstrated that this method could be proposed as a complementary tool for the detection of banned MBM in feedstuffs by reinforcement of the monitoring of feeds.     

Method development for liquid chromatographic/triple quadrupole mass spectrometric analysis of trace level perfluorocarboxylic acids in articles of commerce

An analytical method to identify and quantify trace levels of C5–C12 perfluorocarboxylic acids (PFCAs) in articles of commerce (AOCs) was developed and rigorously validated. Solid samples were extracted in methanol, and liquid samples were diluted with a solvent consisting of 60:40 (v/v) methanol and 2 mM ammonium acetate (NH₄Ac) aqueous solution. In both cases, the samples were spiked with an isotopically labeled recovery check standard. The samples were concentrated in a nitrogen atmosphere (solid samples only), filtered, and then analyzed by HPLC coupled with a tandem mass spectrometer. Method evaluation included selection of the extraction solvent and the sample preparation solvent used to facilitate sample injection into the analytical system, method comparison for extraction and sample concentration, determination of extraction efficiency, instrument and method detection limits, and determination of potential sample loss during filtration and sample storage. Results of consecutive extractions demonstrated that a single extraction step accounts for 70–100% of the “total” PFCAs in the AOCs with the exception of cookware. The instrument's detection limit was ≤0.05 ng/mL, and the method detection limit were 1.0–3.9 ng/g for solid AOCs and 1.1–6.8 ng/g for liquid AOCs. The method has been used to determine the PFCA content in a wide range of AOCs containing or treated with fluoropolymers and fluorotelomers.     

Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration

This study attempted the feasibility to use near infrared (NIR) spectroscopy as a rapid analysis method to qualitative and quantitative assessment of the tea quality. NIR spectroscopy with soft independent modeling of class analogy (SIMCA) method was proposed to identify rapidly tea varieties in this paper. In the experiment, four tea varieties from Longjing, Biluochun, Qihong and Tieguanyin were studied. The better results were achieved following as: the identification rate equals to 90% only for Longjing in training set; 80% only for Biluochun in test set; while, the remaining equal to 100%. A partial least squares (PLS) algorithm is used to predict the content of caffeine and total polyphenols in tea. The models are calibrated by cross-validation and the best number of PLS factors was achieved according to the lowest root mean square error of cross-validation (RMSECV). The correlation coefficients and the root mean square error of prediction (RMSEP) in the test set were used as the evaluation parameters for the models as follows: R = 0.9688, RMSEP = 0.0836% for the caffeine; R = 0.9299, RMSEP = 1.1138% for total polyphenols. The overall results demonstrate that NIR spectroscopy with multivariate calibration could be successfully applied as a rapid method not only to identify the tea varieties but also to determine simultaneously some chemical compositions contents in tea.     

Amorphous carbon nanoparticle used as novel resonance energy transfer acceptor for chemiluminescent immunoassay of transferrin

Amorphous carbon nanoparticles (ACNPs) showing highly efficient quenching of chemiluminescence (CL) were prepared from candle soot with a very simple protocol. The prepared ACNP was employed as the novel energy acceptor for a chemiluminescence resonance energy transfer (CRET)-based immunoassay. In this work, ACNP was linked with transferrin (TRF), and horseradish peroxidase (HRP) was conjugated to TRF antibody (HRP–anti-TRF). The immunoreaction rendered the distance between the ACNP acceptor and the HRP-catalyzed CL emitter to be short enough for CRET occurring. In the presence of TRF, this antigen competed with ACNP–TRF for HRP–anti-TRF, thus led to the decreased occurrence of CRET. A linear range of 20–400 ng mL⁻¹ and a limit of detection of 20 ng mL⁻¹ were obtained in this immunoassay. The proposed method was successfully applied for detection of TRF levels in human sera, and the results were in good agreement with ELISA method. Moreover, the ACNPs show higher energy transfer efficiency than other conventional nano-scaled energy acceptors such as graphene oxide in CRET assay. It is anticipated that this approach can be developed for determination of other analytes with low cost, simple manipulation and high specificity.     

A new interface for coupling solid phase microextraction with liquid chromatography

A modified Rheodyne 7520 microsample injector was used as a new solid phase microextraction (SPME)–liquid chromatography (LC) interface. The modification was focused on the construction of a new sample rotor, which was built by gluing two sample rotors together. The new sample rotor was further reinforced with 3 pieces of stainless steel tubing. The enlarged central flow passage in the new sample rotor was used as a desorption chamber. SPME fiber desorption occurred in static mode. But all desorption solvent in the desorption chamber was injected into LC system with the interface. The analytical performance of the interface was evaluated by SPME–LC analysis of PAHs in water. At least 90% polycyclic aromatic hydrocarbons (PAHs) were desorbed from a polyacrylonitrile (PAN)/C18 bonded fuse silica fiber in 30 s. And injection was completed in 20 s. About 10–20% total carryovers were found on the fiber and in the interface. The carryover in the interface was eliminated by flushing the desorption chamber with acetonitrile at 1 mL min⁻¹ for 2 min. The repeatability of the method was from 2% to 8%. The limit of detection (LOD) was in the mid pg mL⁻¹ range. The linear ranges were from 0.1 to 100 ng mL⁻¹. The new SPME–LC interface was reliable for coupling SPME with LC for both qualitative and quantitative analysis.     

Combining linear polarization spectroscopy and the Representative Layer Theory to measure the Beer–Lambert law absorbance of highly scattering materials

Visible and Near Infrared (Vis–NIR) Spectroscopy is a powerful non destructive analytical method used to analyze major compounds in bulk materials and products and requiring no sample preparation. It is widely used in routine analysis and also in-line in industries, in-vivo with biomedical applications or in-field for agricultural and environmental applications. However, highly scattering samples subvert Beer–Lambert law's linear relationship between spectral absorbance and the concentrations. Instead of spectral pre-processing, which is commonly used by Vis–NIR spectroscopists to mitigate the scattering effect, we put forward an optical method, based on Polarized Light Spectroscopy to improve the absorbance signal measurement on highly scattering samples. This method selects part of the signal which is less impacted by scattering. The resulted signal is combined in the Absorption/Remission function defined in Dahm's Representative Layer Theory to compute an absorbance signal fulfilling Beer–Lambert's law, i.e. being linearly related to concentration of the chemicals composing the sample. The underpinning theories have been experimentally evaluated on scattering samples in liquid form and in powdered form. The method produced more accurate spectra and the Pearson's coefficient assessing the linearity between the absorbance spectra and the concentration of the added dye improved from 0.94 to 0.99 for liquid samples and 0.84–0.97 for powdered samples.     

Fabric phase sorptive extraction: A new sorptive microextraction technique for the determination of non-steroidal anti-inflammatory drugs from environmental water samples

Fabric phase sorptive extraction (FPSE) is a new, yet very promising member of the sorbent-based sorptive microextraction family. It has simultaneously improved both the extraction sensitivity and the speed of the extraction by incorporating high volume of sol–gel hybrid inorganic–organic sorbents into permeable fabric substrates. The advantages of FPSE have been investigated for the determination of four non-steroidal anti-inflammatory drugs, ibuprofen, naproxen, ketoprofen and diclofenac, in environmental water samples in combination with gas chromatography–mass spectrometry. Initially, the significance of several parameters affecting FPSE: sorbent chemistry, matrix pH and ionic strength were investigated using a mixed level factorial design (3¹ × 2²). Then, other important parameters e.g., sample volume, extraction kinetics, desorption time and volume were also carefully studied and optimized. Due to the high sorbent loading on the FPSE substrate in the form of ultra-thin coating and the open geometry of the microextraction device, higher mass transfer of the target analytes occurs at a faster rate, leading to high enrichment factors in a relatively short period of time (equilibrium times: 45–100 min). Under optimal operational conditions, the limits of detection (S/N = 3) were found to be in the range of 0.8 ng L⁻¹ to 5 ng L⁻¹. The enrichment factors ranged from 162 to 418 with absolute extraction efficiencies varied from 27 to 70%, and a good trueness (82–116% relative recoveries) indicating that the proposed method can be readily deployed to routine environmental pollution monitoring. The proposed method was successfully applied to the analysis of target analytes in two influent and effluent samples from a wastewater treatment plant and two river water samples in Spain.     

A practical approach for near infrared spectral quantitative analysis of complex samples using partial least squares modeling

The number of latent variables (LVs) or the factor number is a key parameter in PLS modeling to obtain a correct prediction. Although lots of work have been done on this issue, it is still a difficult task to determine a suitable LV number in practical uses. A method named independent factor diagnostics (IFD) is proposed for investigation of the contribution of each LV to the predicted results on the basis of discussion about the determination of LV number in PLS modeling for near infrared (NIR) spectra of complex samples. The NIR spectra of three data sets of complex samples, including a public data set and two tobacco lamina ones, are investigated. It is shown that several high order LVs constitute main contributions to the predicted results, albeit the contribution of the low order LVs should not be neglected in the PLS models. Therefore, in practical uses of PLS for analysis of complex samples, it may be better to use a slightly large LV number for NIR spectral analysis of complex samples. Supported by the National Natural Science Foundation of China (Grant Nos. 20775036 & 20835002)     

Preparation of liposomes loaded with quantum dots, fluorescence resonance energy transfer studies, and near-infrared in-vivo imaging of mouse tissue

We report on a simple, fast and convenient method to engineer lipid vesicles loaded with quantum dots (QDs) by incorporating QDs into a vesicle-type of lipid bilayer using a phase transfer reagent. Hydrophilic CdTe QDs and near-infrared (NIR) QDs of type CdHgTe were incorporated into liposomes by transferring the QDs from an aqueous solution into chloroform by addition of a surfactant. The QD-loaded liposomes display bright fluorescence, and the incorporation of the QDs into the lipid bilayer leads to enhanced storage stability and reduced sensitivity to UV irradiation. The liposomes containing the QD were applied to label living cells and to image mouse tissue in-vivo using a confocal laser scanning microscope, while NIR images of mouse tissue were acquired with an NIR fluorescence imaging system. We also report on the fluorescence resonance energy transfer (FRET) that occurs between the CdTe QDs (the donor) and the CdHgTe QDs (the acceptor), both contained in liposomes. Based on these data, this NIR FRET system shows promise as a tool that may be used to study the release of drug-loaded liposomes and their in vivo distribution.

Preparation of magnetite/poly(styrene-divinylbenzene) nanoparticles for selective enrichment-determination of fenitrothion in environmental and biological samples

In the present study, a cross-linked nano-sized spherical magnetic poly(styrene-divinylbenzene) is synthesized and used as an adsorbent for enrichment-determination of fenitrothion. A miniemulsion polymerization procedure was used to prepare the adsorbent. The magnetic adsorbent was characterized by FT-IR, SEM and TEM. The prepared magnetic adsorbent nanoparticles were mixed with magnetite nanoparticles for faster and more efficient magnetic precipitation. The reduced fenitrothion was coupled with 3-methyl-2-benzothiazolinone hydrazone and then the blue colored complex was extracted. The blue derivative of fenitrothion was eluted by a 1 mL aliquot of 1-propanol prior to spectrophotometry at 571 nm. Beer's law was obeyed in the range of 2–230 ng mL⁻¹ of fenitrothion with relative standard deviation and recovery in the ranges of 0.9–5.1% and 97.2–100.0%, respectively. Selectivity of the method was evaluated, and the method was successfully applied to the determination of fenitrothion in various water, soil, urine and human plasma samples.     

An adaptive strategy for selecting representative calibration samples in the continuous wavelet domain for near-infrared spectral analysis

Sample selection is often used to improve the cost-effectiveness of near-infrared (NIR) spectral analysis. When raw NIR spectra are used, however, it is not easy to select appropriate samples, because of background interference and noise. In this paper, a novel adaptive strategy based on selection of representative NIR spectra in the continuous wavelet transform (CWT) domain is described. After pretreatment with the CWT, an extension of the Kennard–Stone (EKS) algorithm was used to adaptively select the most representative NIR spectra, which were then submitted to expensive chemical measurement and multivariate calibration. With the samples selected, a PLS model was finally built for prediction. It is of great interest to find that selection of representative samples in the CWT domain, rather than raw spectra, not only effectively eliminates background interference and noise but also further reduces the number of samples required for a good calibration, resulting in a high-quality regression model that is similar to the model obtained by use of all the samples. The results indicate that the proposed method can effectively enhance the cost-effectiveness of NIR spectral analysis. The strategy proposed here can also be applied to different analytical data for multivariate calibration.     

Dispersive liquid–liquid microextraction with little solvent consumption combined with gas chromatography–mass spectrometry for the pretreatment of organochlorine pesticides in aqueous samples

Dispersive liquid–liquid microextraction with little solvent consumption (DLLME-LSC), a novel dispersive liquid–liquid microextraction (DLLME) technique with few solvent requirements (13 μL of a binary mixture of disperser solvent and extraction solvent in the ratio of 6:4) and short extraction time (90 s), has been developed for extraction of organochlorine pesticides (OCPs) from water samples prior to gas chromatography/mass spectrometry analysis. In DLLME-LSC, much less volume of organic solvent is used as compared to DLLME. The new technique is less harmful to environment and yields a higher enrichment factor (1885–2648-fold in this study). Fine organic droplets were formed in the sample solution by manually shaking the test tube containing the mixture of sample solution and extraction solvent. The large surface area of the organic solvent droplets increases the rate of mass transfer from the water sample to the extractant and produces efficient extraction in a short period of time. DLLME-LSC shows good repeatability (RSD: 4.1–9.7% for reservoir water; 5.6–8.9% for river water) and high sensitivity (limits of detection: 0.8–2.5 ng/L for reservoir water; 0.4–1.3 ng/L for river water). The method can be used on various water samples (river water, tap water, sea water and reservoir water). It can be used for routine work for the investigation of OCPs.