Multiplex Sensing of Complex Mixtures by Machine Vision Analysis of TLC-SERS Images.

Thin layer chromatography in tandem with surface-enhanced Raman scattering (TLC-SERS) has demonstrated tremendous potentials as a new analytical chemistry tool to detect a wide range of substances from real-world samples. However, it still faces significant challenges of multiplex sensing from complex mixtures due to the imperfect separation by TLC and the resulting interference of SERS detection. In this article, we propose a multiplex sensing method of complex mixtures by machine vision analysis of the scanning image of the TLC-SERS results. Briefly, various pure substances in solution and the complex mixture solution are separated by TLC followed by one-dimensional SERS scanning of the entire TLC plate, which generates TLC-SERS images of all target substances along the chromatography path. After that, a machine vision method is employed to extract the template images from the TLC-SERS images of pure substance solutions. Finally, we apply a feature point matching strategy based on the Winner-take-all principle, which matches the template image of each pure substance with the mixture image to confirm the existence and derive the position of each target substance in the TLC plate, respectively. Our experimental results based on the mixture solution of five different substances show that the proposed machine vision analysis is highly selective, sensitive and does not require artificial analysis of the SERS spectra. Therefore, we envision that the proposed machine vision analysis of the TLC-SERS imaging is an objective, accurate, and efficient method for multiplex sensing of trace level of target substances from complex mixtures.

Sub-Part-Per-Billion Level Sensing of Fentanyl Residues from Wastewater Using Portable Surface-Enhanced Raman Scattering Sensing.

Detection of illicit drug residues from wastewater provides a new route toward community-level assessment of drug abuse that is critical to public health. However, traditional chemistry analytical tools such as high-performance liquid chromatography in tandem with mass spectrometry (HPLC-MS) cannot meet the large-scale testing requirement in terms of cost, promptness, and convenience of use. In this article, we demonstrated ultra-sensitive and portable surface-enhanced Raman scattering sensing (SERS) of fentanyl, a synthetic opioid, from sewage water and achieved quantitative analysis through principal component analysis and partial least-squares regression. The SERS substrates adopted in this application were synthesized by in situ growth of silver nanoparticles on diatomaceous earth films, which show ultra-high sensitivity down to 10 parts per trillion in artificially contaminated tap water in the lab using a commercial portable Raman spectrometer. Based on training data from artificially contaminated tap water, we predicted the fentanyl concentration in the sewage water from a wastewater treatment plant to be 0.8 parts per billion (ppb). As a comparison, the HPLC-MS confirmed the fentanyl concentration was below 1 ppb but failed to provide a specific value of the concentration since the concentration was too low. In addition, we further proved the validity of our SERS sensing technique by comparing SERS results from multiple sewage water treatment plants, and the results are consistent with the public health data from our local health authority. Such SERS sensing technique with ultra-high sensitivity down to sub-ppb level proved its feasibility for point-of-care detection of illicit drugs from sewage water, which is crucial to assess public health.

Wavelet-Based Kalman Smoothing Method for Uncertain Parameters Processing: Applications in Oil Well-Testing Data Denoising and Prediction.

The low-distortion processing of well-testing geological parameters is a key way to provide decision-making support for oil and gas field development. However, the classical processing methods face many problems, such as the stochastic nature of the data, the randomness of initial parameters, poor denoising ability, and the lack of data compression and prediction mechanisms. These problems result in poor real-time predictability of oil operation status and difficulty in offline interpreting the played back data. Given these, we propose a wavelet-based Kalman smoothing method for processing uncertain oil well-testing data. First, we use correlation and reconstruction errors as analysis indicators and determine the optimal combination of decomposition scale and vanishing moments suitable for wavelet analysis of oil data. Second, we build a ground pressure measuring platform and use the pressure gauge equipped with the optimal combination parameters to complete the downhole online wavelet decomposition, filtering, Kalman prediction, and data storage. After the storage data are played back, the optimal Kalman parameters obtained by particle swarm optimization are used to complete the data smoothing for each sample. The experiments compare the signal-to-noise ratio and the root mean square error before and after using different classical processing models. In addition, robustness analysis is added. The proposed method, on the one hand, has the features of decorrelation and compressing data, which provide technical support for real-time uploading of downhole data; on the other hand, it can perform minimal variance unbiased estimates of the data, filter out the interference and noise, reduce the reconstruction error, and make the data have a high resolution and strong robustness.

Improving the analysis accuracy of components in blood by SSP-MCSD and multi-mode spectral data fusion.

In recent years, spectral quantitative analysis for blood components has been a research hotspot in biomedical engineering. But researches have been limited to the application of high-sensitivity spectroscopy instruments and the complexity of blood components-the overlapping of absorption curves for many components is severe. This has led to the difficulty in achieving satisfactory results when using spectroscopy to quantify components in blood. In order to enhance the model robustness and improve the model performance, this paper proposed a sample set partitioning strategy based on multi-component spatial distance (SSP-MCSD). Different from the other sample set partitioning strategies, which only consider the uniformity of the concentration distribution of the target component, this strategy also concerns to the concentration distribution of non-target components. The concentration of the target component and non-target components are used to construct a multi-dimensional space, and the Euclidean Distance of sample points in this space is used as the criterion to partition the sample set. At the same time, the spectra collected in multi-modes are fused for increasing the amount of information. So as to enhance the model robustness and to improve the analysis accuracy of the target components. In order to verify the effectiveness of this strategy, the serum of 101 volunteers was analyzed. Taking total protein in serum as the non-target component, the regression model for bilirubin concentration was established by transmission spectra, fluorescence spectra, and the joint spectra after fusion of the above two spectra, respectively. The experimental results showed that the prediction accuracy of the model established by SSP-MCSD combined with multi-mode spectral fusion is obviously higher than that of other methods. It can effectively improve the analysis accuracy of blood components.

Accuracy improvement of quantitative analysis in VIS-NIR spectroscopy using the GKF-WTEF algorithm.

The extraction of effective information in visible-near-infrared (VIS-NIR) spectroscopy is crucial and difficult for spectral analysis. In this research, an algorithm of wavelet feature extraction based on the Gaussian kernel function (GKF-WTEF) was developed to suppress the influence of external interference on VIS-NIR spectroscopy and improve the accuracy of quantitative analysis. This algorithm takes the root-mean-square error of the prediction set (RMSEP) of the model, which is established by partial least-squares regression, as the optimization criteria. First, the optimal type of wavelet function, the decomposition level, and the Gauss kernel function central frequency band are determined according to the RMSEP. Second, the Gauss kernel function bandwidth is determined by Newton's method. Then, the Hadamard product of the Gaussian kernel function and the wavelet coefficient is obtained. Finally, the wavelet coefficients after the Hadamard product can be reconstructed to obtain the spectral data after feature extraction. In order to verify the effectiveness of this algorithm, the difference in the optical parameters of the polyvinyl chloride material container was used as an external interference source. And the spectrum of Intra-lipid and India-ink mixed solution with different concentrations was collected therein. The volume fraction of India-ink in complex mixed solution was quantitatively analyzed by using the RMSEP and the average relative error of the prediction set as the evaluation criteria. The research results demonstrated that the Gaussian-wavelet transform feature extraction algorithm is an effective pretreatment method, it can satisfactorily suppress the influence of external interference on the spectrum, and it can improve the analytical accuracy of VIS-NIR spectroscopy.

Improving the nondestructive analysis accuracy of liquids in a flexible container based on the multi-pathlength spectrum method.

In order to achieve nondestructive analysis of liquid samples, the spectral analysis of solution in a flexible container is proposed in this paper. However, the difference in the flexible containers reduces the accuracy of the spectral analysis. By analyzing the relationship between the condition number of coefficient matrix (CNCM) and the solution accuracy, a method of reducing CNCM by multi-pathlength spectrum is proposed to reduce the error caused by the differences in flexible containers. To verify the feasibility of the method, different concentrations of samples were prepared with intralipids and India-ink, and polyvinyl chloride sample tubes were used as the flexible container. The experimental results show that the proposed strategy can effectively reduce the error caused by the differences in flexible containers.

Principal frequency component analysis based on modulate chopper technique used in diffuse reflectance spectroscopy measurement.

Diffuse reflectance spectroscopy (DRS) is significantly affected from the interference of the ambient light and dark current of the instrument. Optical choppers, together with lock-in/synchronous amplification, can overcome these interferences. However, in spectral measurement, the sampling rate of the spectrometer is different from the Δ-pulse sampling, which is not high enough because of the integration time. In addition, the energy distribution is not perfectly concentrated as expected in modulate chopper technology. Therefore, in this study, based on the modulate chopper technique, we proposed a principal frequency component analysis (PFCA) method for DRS. This technique not only effectively eliminated the interference and dark current of the instrument but also improved the measurement precision using the energy of different frequencies. First, experiments were designed to successfully verify the function of optical choppers, eliminating the interference of the ambient light. Second, a set of 64 mixture solutions was designed and measured by DRS using the PFCA method to prove the feasibility of the proposed method. The solution was mixed with intralipid-20% suspension, India ink, and rhodamine B. These samples were analyzed by DRS under different conditions: no-chopper with overlapping and averaging, chopper demodulated by Fourier transform, and chopper demodulated by PFCA. The partial least square regression analysis was implemented to predict the concentration. Compared to the result of three methods, DRS equipped with chopper using the PFCA method showed the best results. The results of this study showed that the PFCA method not only satisfactorily eliminated the interference signals but also extracted useful information as much as possible, improving the analysis accuracy.

Differential expansion and evolution of the exon family encoding the Serpin-1 reactive centre loop has resulted in divergent serpin repertoires among the Lepidoptera.

Serpins are a unique class of serine protease inhibitors that are becoming increasingly recognized as important regulators of insect defense mechanisms and developmental processes. Previously, we identified three Mamestra configurata serpins that were similar in structure to those encoded by the Manduca sexta Serpin-1 gene. To gain insight into the evolution and function of serpins in lepidopterans, we developed a bacterial artificial chromosome library and sequenced the entire M. configurata gene. The Serpin-1 gene was 28 kbp and had the capacity to encode nine serpin isoforms via alternate splicing of exons encoding variant reactive center loops onto a common scaffold. The relative abundance of each isoform was estimated by expressed sequence tag analysis and their expression patterns examined in various developmental stages and larval tissues. The organization of the M. configurata Serpin-1 gene was very similar to that of M. sexta Serpin-1; however, only the Ms Serpin-1Z (1 of 12) and the Mc Serpin-1a isoforms exhibited a high degree of similarity. Orthologs similar to this variant were also found in other lepidopterans, namely Bombyx mori and Plutella xylostella, suggesting that they are involved in a conserved biochemical process and likely represent the ancestral serpin variant. Expansion of the exon family encoding the Serpin-1 reactive centre loop region appears to be a product of recent duplication events that has given rise to different serpin repertoires in related insect taxa.

Characterization of the anti-fungal activity of a Bacillus spp. associated with sclerotia from Sclerotinia sclerotiorum.

Sclerotinia sclerotiorum fruiting bodies (sclerotia) were found to harbour bacteria that possess anti-fungal activity. Among 1,140 bacterial isolates collected, 32 were found to inhibit the growth of four common fungal pathogens of canola, S. sclerotiorum, Rhizoctonia solani, Alternaria brassicae and Leptosphaeria maculans. One of these broad-spectrum isolates, LEV-006, was found to be closely related to Bacillus subtilis based on 16S rRNA analysis. The anti-fungal activities were purified and found to be associated with a low molecular weight peptide complex consisting mostly of the cyclic lipopeptide fengycin A and B, as revealed by matrix-assisted laser desorption/ionization time-of-flight and post-source decay analysis, as well as two proteins of 20 and 55 kDa. Peptide mass fingerprinting revealed that the 55-kDa protein was similar to vegetative catalase 1; however, when the enzyme was expressed in Escherichia coli, it exhibited catalase but not anti-fungal activity. The sequences of several peptides from the 20-kDa protein were obtained and indicated that it was a unique anti-fungal protein.

Granary trial of protein-enriched pea flour for the control of three stored-product insects in barley.

A granary trial was conducted to evaluate the efficacy of protein-enriched pea flour against three common stored-grain insects, Sitophilus oryzae (L.), Tribolium castaneum (Herbst), and Cryptolestes ferrugineus (Stephens). Six 30-t farm granaries were filled with approximately 11 t of barley. The barley was either not treated, treated with protein-enriched pea flour at 0.1% throughout the entire grain mass, or treated at 0.5% throughout the top half of the grain mass. Adult insects were released in screened boxes (two insects per kilogram barley for S. oryzae and T. castaneum 1.4 insects per kilogram barley for C. ferrugineus). Barley was sampled four times during the 70-d trial. The number and mortality of adults and emerged adults in the samples were noted. Four kinds of traps, flight, surface-pitfall, probe-pitfall, and sticky-bar, were placed at different locations in the granaries to estimate the movement of insects. The 0.1% protein-enriched pea flour treatment reduced adult numbers of S. oryzae by 93%, T. castaneum by 66%, and C. ferrugineus by 58%, and reduced the emerged adults by 87, 77, and 77%, respectively. Treating the top half of the barley with 0.5% protein-enriched pea flour had similar effects as treating the entire grain mass with 0.1% pea-protein flour. However, the top-half treatment failed to prevent insects from penetrating into the untreated lower layer. Differences between traps are discussed.