Key defatting tissue pretreatment protocol for enhanced MALDI MS Imaging of peptide biomarkers visualization in the castor beans and their attribution applications

IntroductionCastor bean or ricin-induced intoxication or terror events have threatened public security and social safety. Potential resources or materials include beans, raw extraction products, crude toxins, and purified ricin. The traceability of the origins of castor beans is thus essential for forensic and anti-terror investigations. As a new imaging technique with label-free, rapid, and high throughput features, matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) has been gradually stressed in plant research. However, sample preparation approaches for plant tissues still face severe challenges, especially for some lipid-rich, water-rich, or fragile tissues. Proper tissue washing procedures would be pivotal, but little information is known until now.MethodsFor castor beans containing plenty of lipids that were fragile when handled, we developed a comprehensive tissue pretreatment protocol. Eight washing procedures aimed at removing lipids were discussed in detail. We then constructed a robust MALDI-MSI method to enhance the detection sensitivity of RCBs in castor beans.Results and DiscussionA modified six-step washing procedure was chosen as the most critical parameter regarding the MSI visualization of peptides. The method was further applied to visualize and quantify the defense peptides, Ricinus communis biomarkers (RCBs) in castor bean tissue sections from nine different geographic sources from China, Pakistan, and Ethiopia. Multivariate statistical models, including deep learning network, revealed a valuable classification clue concerning nationality and altitude

BDWRPN : Belief divergence weighted risk priority number for failure modes ranking and its application

Ranking failure modes in complex system is important for analysis, diagnosis and prevention of risks in practical engineering. Failure mode and effects analysis (FMEA) theory provides the risk priority number (RPN) for ranking of failure modes. However, researches find that some shortcomings exist in the classical RPN methods of FMEA. For example, the uncertainty among FMEA experts is not modeled properly. To address this issue, we propose the belief divergence weighted risk priority number (BDWRPN) for failure modes ranking in FMEA in the framework of Dempster-Shafer evidence theory (DST). The belief divergence measure in DST is adopted to measure the uncertainty judgements from FMEA experts. The Deng entropy in DST is designed to measure the relative importance among FMEA experts. The Dempster’s combination rule in DST is adopted for fusion of assessments on each risk factor coming from different FMEA experts. A case study of failure modes ranking for a sheet steel production process is designed to verify the effectiveness of the proposed method

An improved risk priority number model for FMEA based on belief measure

Decision making under uncertainty in risk analysis is a key issue in practical engineering. The risk priority number (RPN) model in failure mode and effects analysis (FMEA) is a widely used tool for ranking of risk items. However, there are limitations in the traditional RPN model. For example, it cannot represent the subjective or inaccurate judgements coming from FMEA experts. In addition, the uncertainty in the assessments of experts in FMEA item is not modelled and transformed to the RPN values. In this paper, a new risk priority number model based on belief Jensen-Shannon divergence measure and Deng entropy in Dempster-Shafer evidence theory is proposed. In the proposed method, the belief Jensen-Shannon divergence measure is adopted to effectively deal with the fuzziness and abnormal adjustment coming from all the FMEA experts. In addition, Deng entropy is used to quantify the uncertain degree of each expert and the result is modelled as a relative importance degree of expert. Dempster’s combination rule is used to fuse experts’ assessments of different failure modes to generate the integrated values of each risk factor. The rationality, superiority and effectiveness of the proposed RPN model are verified based on a case study of a production process in steel industry

Variational Mode Decomposition Weighted Multiscale Support Vector Regression for Spectral Determination of Rapeseed Oil and Rhizoma Alpiniae Offcinarum Adulterants

The accurate prediction of the model is essential for food and herb analysis. In order to exploit the abundance of information embedded in the frequency and time domains, a weighted multiscale support vector regression (SVR) method based on variational mode decomposition (VMD), namely VMD-WMSVR, was proposed for the ultraviolet-visible (UV-Vis) spectral determination of rapeseed oil adulterants and near-infrared (NIR) spectral quantification of rhizoma alpiniae offcinarum adulterants. In this method, each spectrum is decomposed into K discrete mode components by VMD first. The mode matrix Uk is recombined from the decomposed components, and then, the SVR is used to build sub-models between each Uk and target value. The final prediction is obtained by integrating the predictions of the sub-models by weighted average. The performance of the proposed method was tested with two spectral datasets of adulterated vegetable oils and herbs. Compared with the results from partial least squares (PLS) and SVR, VMD-WMSVR shows potential in model accuracy