Near-Infrared Spectroscopy Coupled with Kernel Partial Least Squares-Discriminant Analysis for Rapid Screening Water Containing Malathion

Near-infrared spectroscopy coupled with kernel partial least squares-discriminant analysis was used to rapidly screen water containing malathion. In the wavenumber of 4348 cm-1 to 9091 cm-1, the overall correct classification rate of kernel partial least squares-discriminant analysis was 100% for training set, and 100% for test set, with the lowest concentration detected malathion residues in water being 1 μg·ml-1. Kernel partial least squares-discriminant analysis was able to have a good performance in classifying data in nonlinear systems. It was inferred that Near-infrared spectroscopy coupled with the kernel partial least squares-discriminant analysis had a potential in rapid screening other pesticide residues in water.

Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Identification of Soy Sauce Adulteration

The identification of soy sauce adulteration can avoid fraud, and protect the rights and interests of producers and consumers. Based on two measurement models (1 mm, 10 mm), the visible and near-infrared (Vis-NIR) spectroscopy combined with standard normal variate-partial least squares-discriminant analysis (SNV-PLS-DA) was used to establish the discriminant analysis models for adulterated and brewed soy sauces. Chubang soy sauce was selected as an identification brand (negative, 70). The adulteration samples (positive, 72) were prepared by mixing Chubang soy sauce and blended soy sauce with different adulteration rates. Among them, the “blended soy sauce” sample was concocted of salt water (NaCl), monosodium glutamate (C5H10NNaO5) and caramel color (C6H8O3). The rigorous calibration-prediction-validation sample design was adopted. For the case of 1 mm, five waveband models (visible, short-NIR, long-NIR, whole NIR and whole scanning regions) were established respectively;in the case of 10 mm, three waveband models (visible, short-NIR and visible-short-NIR regions) for unsaturated absorption were also established respectively. In independent validation, the models of all wavebands in the cases of 1 mm and 10 mm have achieved good discrimination effects. For the case of 1 mm, the visible model achieved the optimal validation effect, the validation recognition-accuracy rate (RARV) was 99.6%;while in the case of 10 mm, both the visible and visible-short-NIR models achieved the optimal validation effect (RARV = 100%). The detection method does not require reagents and is fast and simple, which is easy to promote the application. The results can provide valuable reference for designing small dedicated spectrometers with different measurement modals and different spectral regions.

Targeted metabolomics study of fatty-acid metabolism in lean metabolic-associated fatty liver disease patients

BACKGROUND The annual incidence of metabolic-associated fatty liver disease(MAFLD)in China has been increasing and is often overlooked owing to its insidious charac-teristics.Approximately 50%of the patients have a normal weight or are not obese.They are said to have lean-type MAFLD,and few studies of such patients are available.Because MAFLD is associated with abnormal lipid metabolism,lipid-targeted metabolomics was used in this study to provide experimental evidence for early diagnosis and pathogenesis.MAFLD and analyze metabolic pathways.UPLC-Q-Orbitrap/MS content determination was used to determine serum palmitic acid(PA),oleic acid(OA),linoleic acid(LA),and arachidonic acid(AA)levels in lean-type MAFLD patients.RESULTS Urea nitrogen and uric acid levels were higher in lean-type MAFLD patients than in healthy individuals(P<0.05).Alanine transaminase and cholinesterase levels were higher in lean-type MAFLD patients than in healthy indi-viduals(P<0.01).The expression of high-density lipoprotein and apolipoprotein A-1 were lower in lean-type MAFLD patients than in healthy individuals(P<0.05)and the expression of triglycerides and fasting blood glucose were increased(P<0.01).A total of 65 biomarkers that affected the synthesis and metabolism of fatty acids were found with P<0.05 and variable importance in projection>1.The levels of PA,OA,LA,and AA were significantly increased compared with healthy individuals.CONCLUSION The metabolic profiles of lean-type MAFLD patients and healthy participants differed significantly,yielding 65 identified biomarkers.PA,OA,LA,and AA exhibited the most significant changes,offering valuable clinical guidance for prevention and treatment of lean-type MAFLD.

Rock and Soil Classification Using PLS-DA and SVM Combined with a Laser-Induced Breakdown Spectroscopy Library

Laser-induced breakdown spectroscopy （LIBS） has become a powerful technology in geological applications. The correct identification of rocks and soils is critical to many geological projects. In this study, LIBS database software with a user-friendly and intuitive interface is developed based on Windows, consisting of a database module and a sample identification module. The database module includes a basic database containing LIBS persistent lines for elements and a dedicated geological database containing LIBS emission lines for several rock and soil reference standards. The module allows easy use of the data. A sample identification module based on partial least squares discriminant analysis （PLS-DA） or support vector machine （SVM） algorithms enables users to classify groups of unknown spectra. The developed system was used to classify rock and soil data sets in a dedicated database and the results demonstrate that the system is capable of fast and accurate classification of rocks and soils, and is thus useful for the detection of geological materials.

Discrimination of Acori Tatarinowii Rhizoma from two habitats based on GC-MS fingerprinting and LASSO-PLS-DA

This study is intended to explore the chemical differences of Acori Tatarinowii Rhizoma （ATR） samples collected from two habitats, Sichuan and Anhui provinces, China. Gas chromatography-mass spectrometry （GC-MS） was applied to establishing the quantitative chemical fingerprints of ATRs. A total of 104 volatile compounds were identified and quantified with the information of mass spectra and retention index （RI）. Furthermore, least absolute shrinkage and selection operator （LASSO）, a sparse regularization method, combined with subsampling was employed to improve the classification ability of partial least squares-discriminant analysis （PLS-DA）. After variable selection by LASSO, three chemical markers,β-elemene, α-selinene and α-asarone, were identified for the discrimination of ATRs from two habitats, and the total classification correct rate was increased from 82.76% to 96.55%. The proposed LASSO-PLS-DA method can serve as an efficient strategy for screening marked chemical components and geo-herbalism research of traditional Chinese medicines.

Learning models for colorectal cancer signature reconstruction and classification in patients with chronic inflammatory bowel disease

BACKGROUND In their everyday life,clinicians face an overabundance of biological indicators potentially helpful during a disease therapy.In this context,to be able to reliably identify a reduced number of those markers showing the ability of optimising the classification of treatment outcomes becomes a factor of vital importance to medical prognosis.In this work,we focus our interest in inflammatory bowel disease(IBD),a long-life threaten with a continuous increasing prevalence worldwide.In particular,IBD can be described as a set of autoimmune conditions affecting the gastrointestinal tract whose two main types are Crohn’s disease and ulcerative colitis.AIM To identify the minimal signature of microRNA(miRNA)associated with colorectal cancer(CRC)in patients with one chronic IBD.METHODS We provide a framework of well-established statistical and computational learning methods wisely adapted to reconstructing a CRC network leveraged to stratify these patients.RESULTS Our strategy resulted in an adjusted signature of 5 miRNAs out of approximately 2600 in Crohn’s Disease(resp.8 in Ulcerative Colitis)with a percentage of success in patient classification of 82%(resp.81%).CONCLUSION Importantly,these two signatures optimally balance the proportion between the number of significant miRNAs and their percentage of success in patients’stratification.

Antipyretic Effect of Herba Ephedrae-Ramulus Cinnamomi Herb Pair on Yeast-Induced Pyrexia Rats: A Metabolomics Study

Objective： To investigate the antipyretic mechanism of Herba Ephedrae （Eph）-Ramulus Cinnamomi （RC） herb pair on yeast-induced pyrexia in rats. Methods： Totally 30 qualified male SD rats were randomly assigned to the normal control （NC） group, the pyrexia model （model） group, the Eph, RC and Eph-RC treatment groups by a random digital table, 6 rats in each group. Each rat received a 20% aqueous suspension of yeast （10 mL/kg） except the NC group. The 3 treatment groups were administered 8.1, 5.4 and 13.5 g/kg Eph, RC and Eph-RC respectively at 5 and 12 h after yeast injection, the NC group and the model groups were administered equal volume of distilled water. Rectal temperatures were measured at 0, 6, 8, 10, 12, 15, 18, 24 and 30 h and urine was collected prior to yeast injection and at 6, 10, 18, 24, 30, and 36 h after yeast injection. Then urine metabolomic profiling by gas chromatography tandem mass spectrometry, coupled with multivariate statistical analysis and pattern recognition techniques were used to explore the antipyretic effects of Eph-RC. Partial least squares discriminate analysis was used to analyze the metabolomics dataset including classification and regression in metabolomics plot profiling. Results： Compared with the NC group, rectal temperatures were significantly higher in the model group （P〈0.01）, while 3 treatment groups decreased significantly compared with the model group （P〈0.05 or P〈0.01）. Rectal temperatures of Eph-RC-treated rats started to go down at 6 h, and markedly decreased at 8, 12, 15, 18 and 24 h （P〈0.05 or P〈0.01）, while those of the Eph and RC groups had decreased firstly at 8 h and were markedly lower at 12 h （P〈0.05 or P〈0.01）. Seventeen potential biomarkers related to pyrexia were confirmed and identified, including pyruvic acid, L-phenylalanine, L-tyrosine, phenylacetic acid, hippuric acid, succinic acid, citrate and so on. Eight potential alterations of metabolic pathways including phenylalanine metabolism, citrate cycle, tryptophan metabolism, biosynthesis of valine, leucine and isoleucine, were identified in relation to the antipyretic effects of Eph-RC using MetPA software. Conclusion： The antipyretic effect of Eph-RC herb pair on yeast-induced pyrexia in rats involved correction of perturbed amino acid, fatty acid, and carbohydrate metabolism according to the metabolic pathway analysis with MetPA.

Detection of explosives with laser-induced breakdown spectroscopy

Our recent work on the detection of explosives by laser-induced breakdown spectroscopy （LIBS） is reviewed in this paper. We have studied the physical mechanism of laser-induced plasma of an organic explosive, TNT. The LIBS spectra of TNT under single-photon excitation are simulated using MATLAB. The variations of the atomic emission lines intensities of carbon, hydrogen, oxygen, and nitrogen versus the plasma temperature are simulated too. We also investigate the time-resolved LIBS spectra of a common inorganic explosive, black powder, in two kinds of surrounding atmospheres, air and argon, and find that the maximum value of the O atomic emission line SBR of black powder occurs at a gate delay of 596 ns. Another focus of our work is on using chemometic methods such as principle component analysis （PCA） and partial least squares discriminant analysis （PLS-DA） to distinguish the organic explosives from organic materials such as plastics. A PLS-DA model for classification is built. TNT and seven types of plastics are chosen as samples to test the model. The experimental results demonstrate that LIBS coupled with the chemometric techniques has the capacity to discriminate organic explosive from plastics.

Metabolic Profiling of Human Colorectal Cancer Using High Resolution 1H Nuclear Magnetic Resonance Spectroscopy

Colorectal cancer （CRC） is the third commonest malignancy cancer worldwide. Clear understandings of global metabolic profiling of the normal mucosa and cancer tissues are vitally important to aid optimizing the clinical management strategy and understanding CRC biology. We studied metabolic characteristics of 20 CRC and 20 distant normal mucosa tissues extracts from 20 patients using high resolution ^1H NMR spectroscopy in conjunction with multivariate analyses, such as principal component analysis （PCA）. Compared with distant normal mucosa tissues, lactate, taurine, ornithine and polyamine were present at significantly higher levels in CRC tissue extracts whereas myo-inositol was present at significantly lower level. Two metabolites ratios such as myo-inositolltaurine and myo-inositol/（ornithine＋polyamine） appear to be the most valuable biomarkers for the differentiation CRC from normal mucosa tissues. Our data suggested that HR ~H NMR spectroscopy combined with multivariate analy- ses is a potentially useful technology for detecting malignant changes in the normal mucosa tissues, the technique may be further exploited for future CRC biomarker research or identification of targets for therapeutic manipulations.