A pH-responsive dual-emission composite for fast detection of BAs and visual monitoring seafood freshness with large luminescence color difference

Sensing biogenic amine (BAs) content is very important for assessing food freshness. To address the limitations such as small color difference values (ΔE) and complex preparation of probes for visualizing the freshness of seafood, a pH-responsive ratiometric fluorescent probe (EnEB) was prepared by Eu(NO3)3, trimeric acid (BTC), and hydrochloric acid norepinephrine (Enr). EnEB emitted blue (446 nm) and red fluorescence (616 nm) originating from Enr and Eu3+, respectively, and exhibiting a fluorescence wavelength difference up to 170 nm. The ratiometric fluorescent signals of EnEB showed a linear correlation with pH in the range of 5.5-8.0. Thus, EnEB can rapidly and precisely detect BAs, such as histamine, tyramine, and spermine, with detection limits and response times of 1.14 μmol/L (3 s), 1.04 μmol/L (8 s), and 0.41 μmol/L (2 s), respectively. Furthermore, an EnEB aerogel was prepared by loading EnEB in a matrix formed by polyvinyl alcohol (PVA) and agarose (AG). EnEB aerogel exhibited excellent acid-base gas-sensing properties. The fluorescence color of EnEB aerogel can change significantly with the deterioration of seafood. When seafood changed from fresh to decayed, the ΔE value of EnEB aerogel was as high as 80.9. Importantly, the results of seafood freshness by naked eye using EnEB aerogel was consistent well with the TVB-N content and the freshness standard stipulated by national food standard, indicating EnEB aerogel can accurately visually and real-time monitor seafood freshness. Furthermore, the strategy for sensing food freshness based on EnEB aerogel also offered multiple color variations to indicate fine freshness levels of seafood. This work provided a convenient, efficient, and accurate approach to assessing the freshness of seafood. Additionally, EnEB also has promising applications in security and anti-counterfeiting.

A New Application of PARAFAC Model to UPLC Dataset for the Quantitative Resolution of a Tri-Component Drug Mixture

In the presented work, a three-way analysis of ultra-performance liquid chromatography-photodiode array (UPLC-PDA) dataset was performed by parallel factor analysis (PARAFAC) for quantitatively resolving a ternary mixture containing paracetamol and methocarbamol with indapamide selected as an internal standard in their co-eluted chromatographic conditions. Paracetamol and methocarbamol were quantified in the working range between 3-24 and 5-50 μg/mL by applying PARAFAC decomposition to UPLC-PDA data array obtained under unresolved chromatographic peak conditions. To compare the experimental results provided by co-eluted UPLC-PARAFAC method, an ordinary UPLC method was developed ensuring proper separation of the peaks. The performance of both PARAFAC and ordinary UPLC methods were assessed by quantifying independent test samples, intra- and inter-day samples and spiked samples of pharmaceutical preparations. Then, both methods were applied for quantitative estimation of the related drugs in a commercial pharmaceutical preparation. In this study, PARAFAC method was proved to be a very powerful alternative for the quality control of pharmaceutical preparations containing paracetamol and methocarbamol even in their co-eluted chromatograms with high precision and accuracy in a short chromatographic runtime of 1.2 min.

A chemometric-assisted method for the simultaneous determination of malachite green and crystal violet in water based on absorbance-pH data generated by a homemade pH gradient apparatus

An attractive method of generating second-order data was developed by a dropping technique to generate pH gradient simultaneously coupled with diode-array spectrophotometer scanning. A homemade apparatus designed for the pH gradient. The method and the homemade apparatus were used to simultaneously determine malachite green (MG) and crystal violet (CV) in water samples. The absorbance-pH second-order data of MG or CV were obtained from the spectra of MG or CV in a series of pH values of HCl-KCl solution. The second-order data of mixtures containing MG and CV that coexisted with interferents were analyzed using multidimensional partial least-squares with residual bilinearization. The method and homemade apparatus were used to simultaneously determine MG and CV in fish farming water samples and in river ones with satisfactory results. The presented method and the homemade apparatus could serve as an alternative tool to handle some analysis problems.

Chemometrics-assisted cyclodextrin-enhanced excitation-emission fluorescence spectroscopy for the simultaneous green determination of bisphenol A and nonylphenol in plastics

The aim of this work was to quantify two relevant priority chemicals, bisphenol A (BPA) and 4-nonylphenol (NP), coupling the sensitivity of fluorescence in organized media and the selectivity of multivariate calibration, measuring excitation-emission fluorescence matrices in an aqueous methyl-β-cyclodextrin solution. The studied priority pollutants are two of the most frequently found xenoestrogens in the environment, and are therefore of public health concern.The data were successfully processed by applying unfolded partial least-squares coupled to residual bilinearization (U-PLS/RBL), which provided the required selectivity for overcoming the severe spectral overlapping among the analyte spectra and also those for the interferents present in real samples. A rigorous International Union of Pure and Applied Chemistry (IUPAC)-consistent approach was applied for the calculation of the limits of detection. Values in the ranges of 1-2 and 4-14 ng mL(-1) were obtained in validation samples for BPA and NP, respectively. On the other hand, low relative prediction errors between 3% and 8% were achieved. The proposed method was successfully applied to the determination of BPA and NP in different plastics. In positive samples, after an easy treatment with a small volume of ethanol at 35°C, concentrations were found to range from 26 to 199 ng g(-1) for BPA, and from 95 to 30,000 ng g(-1) for NP.

Analysis of variation matrix array by bilinear least squares-residual bilinearization (BLLS-RBL) for resolving and quantifying of foodstuff dyes in a candy sample

Bilinear least square (BLLS) method is one of the most suitable algorithms for second-order calibration. Original BLLS method is not applicable to the second order pH-spectral data when an analyte has more than one spectroscopically active species. Bilinear least square-residual bilinearization (BLLS-RBL) was developed to achieve the second order advantage for analysis of complex mixtures. Although the modified method is useful, the pure profiles cannot be obtained and only the linear combination will be obtained. Moreover, for prediction of analyte in an unknown sample, the original algorithm of RBL may diverge; instead of converging to the desired analyte concentrations. Therefore, Gauss Newton-RLB algorithm should be used, which is not as simple as original protocol. Also, the analyte concentration can be predicted on the basis of each of the equilibrating species of the component of interest that are not exactly the same. The aim of the present work is to tackle the non-uniqueness problem in the second order calibration of monoprotic acid mixtures and divergence of RBL. Each pH-absorbance matrix was pretreated by subtraction of the first spectrum from other spectra in the data set to produce full rank array that is called variation matrix. Then variation matrices were analyzed uniquely by original BLLS-RBL that is more parsimonious than its modified counterpart. The proposed method was performed on the simulated as well as the analysis of real data. Sunset yellow and Carmosine as monoprotic acids were determined in candy sample in the presence of unknown interference by this method.

Higher order partial least squares (HOPLS): a generalized multilinear regression method

A new generalized multilinear regression model, termed the higher order partial least squares (HOPLS), is introduced with the aim to predict a tensor (multiway array) Y from a tensor X through projecting the data onto the latent space and performing regression on the corresponding latent variables. HOPLS differs substantially from other regression models in that it explains the data by a sum of orthogonal Tucker tensors, while the number of orthogonal loadings serves as a parameter to control model complexity and prevent overfitting. The low-dimensional latent space is optimized sequentially via a deflation operation, yielding the best joint subspace approximation for both X and Y. Instead of decomposing X and Y individually, higher order singular value decomposition on a newly defined generalized cross-covariance tensor is employed to optimize the orthogonal loadings. A systematic comparison on both synthetic data and real-world decoding of 3D movement trajectories from electrocorticogram signals demonstrate the advantages of HOPLS over the existing methods in terms of better predictive ability, suitability to handle small sample sizes, and robustness to noise.