Discriminant Partial Least Squares Research Articles

Epidemic of Wheat Stripe Rust Detected by Hyperspectral Remote Sensing and Its Potential Correlation with Soil Nitrogen during Latent Period.

Climate change affects crops development, pathogens survival rates and pathogenicity, leading to more severe disease epidemics. There are few reports on early, simple, large-scale quantitative detection technology for wheat diseases against climate change. A new technique for detecting wheat stripe rust (WSR) during the latent period based on hyperspectral technology is proposed. Canopy hyperspectral data of WSR was obtained; meanwhile, duplex PCR was used to measure the content of Puccinia striiformis f.sp. tritici (Pst) in the same canopy section. The content of Pst corresponded to its spectrum as the classification label of the model, which is established by discriminant partial least squares (DPLS) and support vector machine (SVM) algorithm. In the spectral region of 325–1075 nm, the model’s average recognition accuracy was between 75% and 80%. In the sub-band of 325–1075 nm, the average recognition accuracy of the DPLS was 80% within the 325–474 nm. The average recognition accuracy of the SVM was 83% within the 475–624 nm. Correlation analysis showed that the disease index of WSR was positively correlated with soil nitrogen nutrition, indicating that the soil nitrogen nutrition would affect the severity of WSR during the latent period.

Evaluation of cut alfalfa moisture content and operative conditions by hyperspectral imaging combined with chemometric tools: In-field application

High quality of hay or silage can be obtained by monitoring the moisture level at mowing or harvest time to reduce the losses of yield and nutrients and preserve feeds in the long term. Mechanical conditioning is one of the most common ways to increase the rate of water loss from forage during drying. Near Infrared (NIR) spectroscopy is a good alternative to the classical gravimetric method to timely provide information on forage moisture content. The aim of this work was to evaluate the potential of in-field Vis/NIR hyperspectral imaging combined with chemometric tools to monitor alfalfa parameters after conditioning. Partial LeastSquares Discriminant Analysis (PLSDA) models were developed to discriminate samples according to several operative conditions (level of conditioning, field type, time after conditioning, and time of day) and yielded good discrimination power (mean sensitivity = 87%, mean probability = 89%). Moisture content was estimated by Partial LeastSquares (PLS) models obtaining determination coefficient (R 2 ) = 0.86 and Root Mean Square Error (RMSE) = 2.00% (external validation). The number of spectral bands was reduced by using the Variable Importance in Projection (VIP) method, passing from 272 to 74 bands. Further PLS models were built considering the reduced variable numbers and achieved comparable results to those obtained with the full spectra, demonstrating that reduction of the number of variables did not affect the goodness of the models. Finally, the best PLS model was applied to each pixel of the hyperspectral images to obtain false colour images. Pixels having similar colours were characterized by comparable moisture content. The models developed may be useful to determine moisture content of alfalfa remotely and in real-time during mowing or harvest procedures. • Quality of alfalfa after cutting was evaluated. • In-field Vis/NIR hyperspectral imaging was used. • Regression models to predict moisture content were developed. • The samples were discriminated according to several operative parameters. • The models could be used remotely or in real-time during mowing or harvest procedures.

A Multi-Omics Analysis Reveals Anti-Osteoporosis Mechanism of Four Components from Crude and Salt-Processed Achyranthes bidentata Blume in Ovariectomized Rats.

The root of Achyranthes bidentata Blume (AB) is a well-known traditional Chinese medicine for treating osteoporosis. Plenty of studies focused on the pharmacological mechanism of the whole extract; however, the contribution of different components to the anti-osteoporosis effect remains unknown. The aim of this study is to explore the anti-osteoporosis mechanism of different components of crude and salt-processed AB under the guidance of network pharmacology, metabolomics, and microbiomics. First, network pharmacology analysis was applied to constructing the compound-target-disease network of AB to provide a holistic view. Second, the anti-osteoporosis effects of the four components were evaluated in female Wistar rats. The subjects were divided into a normal group, a model group, a 17α-estradiol (E2)-treated group, a polysaccharide-component-treated groups, and a polysaccharide-knockout-component-treated groups. All the serum, urine, and feces samples of the six groups were collected after 16 weeks of treatment. Biochemical and microcomputed tomography (μCT) parameters were also acquired. Coupled with orthogonal partial least-squares discrimination analysis, one dimensional nuclear magnetic resonance (NMR) was used to monitor serum metabolic alterations. A total of twenty-two biomarkers, including lipids, amino acids, polyunsaturated fatty acids, glucose, and so on were identified for the different components-treated groups. Through pathway analysis, it is indicated that glyoxylate and dicarboxylate metabolism, glycine, serine, and threonine metabolism, alanine, aspartate, and glutamate metabolism, d-glutamine, and d-glutamate metabolism were the major intervened pathways. Levels of these biomarkers shifted away from the model group and were restored to normal after treatment with the four components. In addition, 16S rDNA sequencing demonstrated that the abundance of Anaerofilum, Rothia, and Turicibacter bacteria was positively correlated with an anti-osteoporosis effect, whereas the abundance of Oscillospira was negatively correlated. The osteoprotective effect of the polysaccharide components of crude and salt-processed AB is related to the regulation of the abundance of these gut microbiota.

Comparison of Clinical Balance and Visual Dependence Tests in Patients With Chronic Dizziness With and Without Persistent Postural-Perceptual Dizziness: A Cross-Sectional Study.

BackgroundThe diagnosis of persistent postural-perceptual dizziness (PPPD) is primarily based on medical history taking. Research on the value of clinical balance and visual dependence tests in identifying PPPD is scarce.Objectives(1) to contrast clinical balance and visual dependence tests between PPPD patients, dizzy non-PPPD patients, and healthy persons; and (2) to evaluate whether these clinical tests can help to identify PPPD in patients with chronic dizziness.MethodsConsecutive patients with chronic dizziness (38 PPPD and 21 non-PPPD) and 69 healthy persons underwent Static Balance tests, the Timed Up and Go test, the Tandem Gait test, and the Functional Gait Assessment (FGA). Visual dependence tests included the Visual Vertigo Analog Scale (VVAS), the Rod-and-Disc test (RDT), and postural sway while facing rotating dots. Groups were compared using ANOVA with post-hoc Tukey, or independent samples t-tests. The value of the clinical tests for PPPD identification was evaluated through logistic regression and Partial Least Squares Discriminant (PLS-DA) analyses.ResultsPPPD patients had significantly higher VVAS scores than dizzy non-PPPD patients (p = 0.006). Facing rotating dots, PPPD and dizzy non-PPPD patients had increased postural sway compared to healthy persons (PPPD vs. healthy: center of pressure (COP) velocity p < 0.001, and COP area p < 0.001; but non-PPPD vs. healthy: COP velocity p = 0.116 and COP area p = 0.207). PPPD patients had no significantly increased postural sway compared to dizzy non-PPPD patients. PPPD and dizzy non-PPPD patients also scored significantly worse on balance tests compared to healthy persons (PPPD vs. healthy: for all balance tests p < 0.001; non-PPPD vs. healthy: FGA p < 0.001, for all other tests p < 0.05). Differences were insignificant in balance scores between PPPD and dizzy non-PPPD patients, or in RDT scores between the three study groups. In patients with chronic dizziness, a higher VVAS score was most associated with PPPD [odds ratio 1.04; 95% CI (1.01; 1.07); p = 0.010]. The cross-validated (CV) PLS-DA model with all clinical tests included, had fair discriminative ability (CVerror = 47%).ConclusionPPPD patients were more visually dependent, but did not have worse postural balance compared to dizzy non-PPPD patients. Elevated VVAS scores characterized PPPD most in patients with chronic dizziness.

Determination of Terpenes in Traditional Chinese Medicine (TCM) by Comprehensive Two-Dimensional Gas Chromatography – Mass Spectrometry (GC × GC) Coupled to High-Resolution Quadrupole Time-of-Flight Mass Spectrometry (QTOFMS) with Orthogonal Partial Least Squares – Discrimination Analysis (OPLS-DA)

With a variety of pharmacological effects, the genus Elsholtzia and Mosla of Lamiaceae contain volatile components, including monoterpenes, sesquiterpenes, and their oxygenated derivatives that are widely used in traditional Chinese medicine (TCM). In this work, the volatile terpenes were studied by a more efficient technique based on headspace solid-phase micro-extraction (HS-SPME) combined with comprehensive two-dimensional gas chromatography and quadrupole time-of-flight mass spectrometry (GC × GC-QTOFMS). A total of 91 terpenes consisting of 7 monoterpenes, 34 monoterpene oxides, 39 sesquiterpenes, and 11 sesquiterpene oxides were identified. From the retention time, 4,8,8-trimethyl-2-methylene-4-vinylbicyclo[5.2.0] nonane was identified to be the dominant volatile component in E. blanda, M. chinensis for the first time with 8% prevalence, but further verification is needed. 10 volatile terpenes, including 2-isopropyl-5-methyl-3- cyclohexen-1-one, isoegomaketone, α-muurolene, linalool oxide, ethyl2-(5-methyl- 5-vinyltetrahydrofuran-2-yl)propan-2-ylcarbonate, perilla ketone, elsholtzia ketone, alloaromadendrene oxide, artemisia ketone, longifolene-(V4) were identified by multivariate orthogonal partial least-squares discrimination analysis (OPLS-DA). Because these terpenes have significant pharmacological effects, such as anti-anxiety, anti-oxidation, anti-bacteria, and anti-virus properties, the identification of these terpenes may result in new information related to their pharmacological activities.

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

Different soybean varieties vary greatly in their nutritional value and composition. Screening for superior varieties is also essential for the development of the soybean seed industry. The objective of the paper was to analyze the feasibility of terahertz (THz) frequency-domain spectroscopy and chemometrics for soybean variety identification. Meanwhile, a grey wolf optimizer-support vector machine (GWO-SVM) soybean variety identification model was proposed. Firstly, the THz frequency-domain spectra of experimental samples (6 varieties, 270 in total) were collected. Principal component analysis (PCA) was used to analyze the THz spectra. After that, 203 samples from the calibration set were used to establish a soybean variety identification model. Finally, 67 samples from the test set were used for prediction validation. The experimental results demonstrated that THz frequency-domain spectroscopy combined with GWO-SVM could quickly and accurately identify soybean varieties. Compared with discriminant partial least squares (DPLS) and particles swarm optimization support vector machine, GWO-SVM combined with the second derivative could establish a better soybean variety identification model. The overall correct identification rate of its prediction set was 97.01%.

Pretreatment optimization of tissue metabolomics in colorectal cancer.

To optimize the pretreatment method of colorectal cancer tissue samples for metabolomics research based on solid-phase nuclear magnetic resonance (NMR). The mucosal tissues of colorectal cancer were classified into five groups with a volume of 0.2 cm*0.2 cm*0.2 cm. The pretreatment methods for each group were as follows: I. Preservation with liquid nitrogen alone. Samples were also treated with liquid nitrogen for 10 (II), 20 (III), and 30 min (IV), respectively, immediately after isolation and then transferred to a -80℃ refrigerator; V. Only -80℃ refrigerator storage. No more than 30 minutes should pass between isolation and pretreatment of tumor samples. The tissue sample testing process was carried out on Bruker AVII-600 NMR Spectrometer. NMR signals were collected and analysed using partial least-squares discrimination analysis (PLS-DA) to explore the effects of different pretreatment methods on the metabolic changes of samples. The levels of pelargonic acid, stearic acid, D-Ribose, heptadecanoic acid, pyruvic acid, succinate, sarcosine, glycine, creatine, and L-lactate in the group I (only liquid nitrogen) were significantly lower than the other groups (p<0.05); the content of glycerophosphocholine in the group I (only liquid nitrogen) was lower than that in the other groups (p=0.055). These indicated that the glucose and choline phospholipid metabolism levels of the liquid nitrogen group were significantly lower than those of the other four groups. Liquid nitrogen storage can stop the metabolic process of glucose and choline phospholipid in colorectal cancer tissue samples in vitro, thus maintaining the metabolic state of tissue samples in vivo as much as possible.

Qualitative Discrimination of Intact Tobacco Leaves Based on Near-Infrared Technology

To explore the application of near-infrared (NIR) technology to the quality analysis of raw intact tobacco leaves, a nondestructive discrimination method based on NIR spectroscopy is proposed. A “multiregion + multipoint” NIR spectrum acquisition method is developed, allowing 18 NIR diffuse reflectance spectra to be collected from an intact tobacco leaf. The spectral characteristics and spectral preprocessing methods of intact tobacco leaves are analyzed, and then different spectra (independent or average spectra) and different algorithms (discriminant partial least-squares (DPLS) and Fisher’s discriminant algorithms) are used to construct discriminant models for verifying the feasibility of intact leaf modeling and determining the optimal model conditions. Qualitative discrimination models based on the position, green-variegated (GV), and the grade of intact tobacco leaves are then constructed using the NIR spectra. In the application and verification stage, a multiclassification voting mechanism is used to fuse the results of multiple spectra from a single tobacco leaf to obtain the final discrimination result for that leaf. The results show that the position-GV discrimination model constructed using independent spectra and the DPLS algorithm and the grade discrimination model constructed using independent spectra and Fisher’s algorithm achieve optimal results with intact leaf NIR wavenumbers from 5006–8988 cm−1 and the first-derivative and standard normal variate transformation preprocessing method. Finally, when applied to new tobacco leaves, the position-GV model and the grade model achieve discrimination accuracies of 95.18% and 92.77%, respectively. This demonstrates that the two models have satisfactory qualitative discrimination ability for intact tobacco leaves. This study has established a feasible method for the nondestructive qualitative discrimination of the position, GV, and grade of intact tobacco leaves based on NIR technology.

Metabolomics Identifies Biomarker Signatures to Differentiate Pancreatic Cancer from Type 2 Diabetes Mellitus in Early Diagnosis.

Methods Plasma metabolic profiles in 26 PC patients, 27 DM patients, and 23 healthy volunteers were examined using an ultraperformance liquid chromatography coupled with tandem mass spectrometry platform. Differential metabolite ions were then identified using the principal component analysis (PCA) model and the orthogonal partial least-squares discrimination analysis (OPLS-DA) model. The diagnosis performance of metabolite biomarkers was validated by logistic regression models. Results We established a PCA model (R2X = 23.5%, Q2 = 8.21%) and an OPLS-DA model (R2X = 70.0%, R2Y = 84.9%, Q2 = 69.7%). LysoPC (16 : 0), catelaidic acid, cerebronic acid, nonadecanetriol, and asparaginyl-histidine were found to identify PC, with a sensitivity of 89% and a specificity of 91%. Besides, lysoPC (16 : 0), lysoPC (16 : 1), lysoPC (22 : 6), and lysoPC (20 : 3) were found to differentiate PC from DM, with higher accuracy (68% versus 55%) and higher AUC values (72% versus 63%) than those of CA19-9. The diagnostic performance of metabolite biomarkers was finally validated by logistic regression models. Conclusion We succeeded in screening differential metabolite ions among PC and DM patients and healthy individuals, thus providing a preliminary basis for screening the biomarkers for the early diagnosis of PC.

Comparison of the ability of UV-Vis and UPLC-Q-TOF-MS combined with chemometrics to discriminate recycled and virgin polyethylene

A growing attention is attracted to the use of recycled plastics as food contact materials, and its chemical safety research and discrimination approach are indispensable. In current study, ultraviolet-visible spectrometry (UV-Vis) and ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) were used to provide spectral and mass fingerprinting for polyethylene (PE). Coupling with chemometrics, two methods were developed to discriminate recycled and virgin PE. UV-Vis combined with chemometrics could be a more accessible, simpler and faster approach. 237–331 nm in UV spectrum was regarded as marker region selected by orthogonal partial least-squares discrimination analysis (OPLS-DA) and the accuracy of both calibration and validation set could reach 100% in linear discrimination analysis (LDA) based on this region. Besides, 2314 ions were detected by UPLC-Q-TOF-MS and processed by MS-DIAL. 48 candidate chemicals were identified, including ketone, esters, carboxylic acid, alcohols and phenols, amine, nitriles, aldehydes and others. Possible origins of these compounds could be classified as plastic, food, drug, cosmetics and pesticide related. Many of these compounds are highly toxic, especially pesticide related, indicating that recycling in closed loop or sorting by the recycled plastic articles is very necessary if the recycled PE is going to be used as food contact material.

Early Detection of Powdery Mildew Disease and Accurate Quantification of Its Severity Using Hyperspectral Images in Wheat

Early detection of the crop disease using agricultural remote sensing is crucial as a precaution against its spread. However, the traditional method, relying on the disease symptoms, is lagging. Here, an early detection model using machine learning with hyperspectral images is presented. This study first extracted the normalized difference texture indices (NDTIs) and vegetation indices (VIs) to enhance the difference between healthy and powdery mildew wheat. Then, a partial least-squares linear discrimination analysis was applied to detect powdery mildew with the combined optimal features (i.e., VIs &amp; NDTIs). Further, a regression model on the partial least-squares regression was developed to estimate disease severity (DS). The results show that the discriminant model with the combined VIs &amp; NDTIs improved the ability for early identification of the infected leaves, with an overall accuracy value and Kappa coefficient over 82.35% and 0.56 respectively, and with inconspicuous symptoms which were difficult to identify as symptoms of the disease using the traditional method. Furthermore, the calibrated and validated DS estimation model reached good performance as the coefficient of determination (R2) was over 0.748 and 0.722, respectively. Therefore, this methodology for detection, as well as the quantification model, is promising for early disease detection in crops.

Differentiation Between Chaenomelis Fructus and its Common Adulterant, Guangpi Mugua.

The dried fruit of Chaenomeles speciosa, known as Chaenomelis Fructus or Zhoupi Mugua, is a type of traditional Chinese medicine (TCM) that is widely used to treat many diseases. Guangpi Mugua, the dried fruit of the Chaenomeles sinensis, is its most commonly known adulterant. To establish a robust approach for the quality control and identification of Chaenomelis Fructus. Thin-layer chromatography (TLC) was optimized and used to discriminate Chaenomelis Fructus from Guangpi Mugua. High-performance liquid chromatography (HPLC) combined with fingerprint analysis and partial least-squares (PLS) discrimination analysis (DA) was employed to study the chemical differences between Chaenomelis Fructus and Guangpi Mugua. The single standard to determine multi-components (SSDMC) method, with credible precision, repeatability, stability, and durability, was developed for quantitative analysis of the abundant markers. The developed TLC and HPLC methods were effective in the authentication of Chaenomelis Fructus. Moreover, oleanolic acid, ursolic acid, pomolic acid, corosolic acid, 3-O-acetylpomolic acid, and one unknown compound were identified to be critical markers for the discrimination of Chaenomelis Fructus from Guangpi Mugua. Adulteration has always been a challenge in the development of TCM. This study presents useful insights that may help solve the problem of adulteration during the preparation of Chaenomelis Fructus. The present study provides a systematic method for the quality control of Chaenomelis Fructus. This is therefore the first step towards solving the problem of adulteration to improve the clinical safety and effectiveness of Chaenomelis Fructus.

Regional differences of nucleosides and amino acids in Elaphuri Davidiani Cornu

In order to reveal the regional characteristics of nucleosides and amino acids in Elaphuri Davidiani Cornu,39 samples of Elaphuri Davidiani Cornu collected from 4 different regions were analyzed by UPLC-QTRAP ~®/MS2 method followed by orthogonal partial least-squares discrimination analysis( OPLS-DA) and cluster analysis( CA). The results showed all the samples contained abundant nucleosides and amino acids,with the total content of 45. 09 μg·g~(-1) and 634. 80 μg·g-1,respectively. The samples presented significant regional differences in the contents of individual components,and the main differential components included Ura,Hpro,Thr,Glu,G5 P,2&apos;-dG,Adeno,Met,Ade,Gln,Orni,Phe,2&apos;-dA,Hit,Lys,and Ile. Among them,Ura,Met,Glu,and Ile had the highest content in the samples from Dafeng in Jiangsu,Qinhu in Jiangsu,Beijing,and Shishou in Hubei,respectively. OPLS-DA and CA demonstrated that all the samples of Elaphuri Davidiani Cornu could be divided into three categories,reflecting the regional characteristics. The results indicated that the accumulation of nucleosides and amino acids in Elaphuri Davidiani Cornu was closely related to its habitat,providing a useful reference for the research on the quality formation,quality evaluation and control,as well as the comprehensive utilization of Elaphuri Davidiani Cornu. The findings suggested that the content factors of Ura,Met,Glu,and Ile could be included into the quality standard system of Elaphuri Davidiani Cornu as the characteristics of medicinal materials from different regions.

Geographical origin discrimination of Amomi fructus using an ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based metabolomics approach combined with antioxidant activity analysis

Background: Amomi fructus as edible and medicinal food can be used as a flavoring in the daily cooking and herbal tea, the consumption of A. fructus is gradually aggregate in recent years. The quality of A. fructus from Yangchun city of Guangdong province in China is familiar as the best and the value is higher than other areas, the flavor and pharmacological activities is mostly affected by its quality. However, geographical origin confusion often occurs in the market, so it is obligatory to develop technology to differentiate the geographical origin of A. fructus. Objectives: The objective of this study is to assess the antioxidant activity of A. fructus and discriminate the geographical origin of A. fructus using nontarget metabolomics technology. Materials and Methods: Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was used for acquired data in positive ionization modes, principal component analysis, and orthogonal partial least-squares discrimination analysis. The online analysis website Metabo Analyst 4.0 ( https://www.metaboanalyst.ca/ ) was used for the ensuing analysis. A reducing power test, chelating power of ferrous ions test, 1,1-diphenyl-2-picryl-hydrazyl-hydrate assay, and hydroxyl radicals scavenging ability assays were used to assess the differences in antioxidant activity between the samples from the two geographical locations. Results: Eleven differential metabolites were attained, among which eight were established as marker compounds with the greatest contribution to the discernment between the two production areas. The antioxidant activity happens obvious differences due to dissimilar geographical origin. Conclusion: Untargeted metabolomics combined with multivariate statistical analysis is a commanding strategy to extricate the samples from the same species and to comprehend the quality differences from the viewpoint of overall metabolic profile.

Urine metabolomics analysis based on ultra performance liquid chromatography-high resolution mass spectrometry combined with osmolality calibration sample concentration variability

尿液是代谢组学研究中主要关注的体液样本之一。尿液样本中的代谢物浓度受饮食、疾病等因素影响变异较大,这极大阻碍了高质量组学数据的获取和可靠生物标志物的鉴定。研究为克服尿液样本的浓度变异性,在原始数据采集前,根据样本渗透压的大小,针对性地调整进样量或者稀释样本,从而确保代谢组学分析样本的渗透压与进样量的乘积相当,再经超高效液相色谱-高分辨质谱技术(UPLC-HRMS)分析,采用总离子丰度或总有用峰面积(MSTUS)对数据集进行归一化处理。研究利用临床样本及其梯度稀释的溶液,对该方法与现有研究普遍使用的方法进行了比较,随后通过先天性肾积水患者及健康志愿者的尿液样本做了进一步的方法学验证。数据集经校正后,峰面积RSD<30%的提取峰数量增加,主成分分析结果较校正前有更高的组内聚集和组间分群效应,正交偏最小二乘判别分析的统计模型更不易过拟合。与肌酐比较,渗透压值与质谱信号间呈现了更好的线性关系。以上结果表明,数据采集前通过样本渗透压进行校正,能有效消除因样本本身代谢物浓度变化引起的组内差异,提高方法的重复性和统计模型的可靠度。以渗透压为基准的校正策略,比肌酐校正法适用范围更广,结果也更准确。研究可对后续各类来源的尿液代谢组学研究提供数据归一化的指导和参考。

Reconstruction and analysis of correlation networks based on GC–MS metabolomics data for hypercholesterolemia

Background and aimsHypercholesterolemia is characterized by the elevation of plasma total cholesterol level, especially low-density lipoprotein (LDL) cholesterol. This disease is usually caused by a mutation in genes such as LDL receptor, apolipoprotein B, or proprotein convertase subtilisin/kexin type 9. However, a considerable number of patients with hypercholesterolemia do not have any mutation in these candidate genes. In this study, we examined the difference in the metabolic level between patients with hypercholesterolemia and healthy subjects, and screened the potential biomarkers for this disease. MethodsAnalysis of plasma metabolomics in hypercholesterolemia patients and healthy controls was performed by gas chromatography-mass spectrometry and metabolic correlation networks were constructed using Gephi-0.9.2. ResultsFirst, metabolic profile analysis confirmed the distinct metabolic footprints between the patients and the healthy ones. The potential biomarkers screened by orthogonal partial least-squares discrimination analysis included l-lactic acid, cholesterol, phosphoric acid, d-glucose, urea, and d-allose (Variable importance in the projection > 1). Second, arginine and methionine metabolism were significantly perturbed in hypercholesterolemia patients. Finally, we identified that l-lactic acid, l-lysine, l-glutamine, and l-cysteine had high scores of centrality parameters in the metabolic correlation network. ConclusionPlasma l-lactic acid could be used as a sensitive biomarker for hypercholesterolemia. In addition, arginine biosynthesis and cysteine and methionine metabolism were profoundly altered in patients with hypercholesterolemia.

Baked red pepper (Capsicum annuum L.) powder flavor analysis and evaluation under different exogenous Maillard reaction treatment

To improve pepper powder product flavor, headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) was used to detect flavor substances in pepper powder, including dried pepper powder, baked pepper powder (BPP) and Maillard treated pepper powder (MTPP). Fifty-three flavor compounds were identified, mainly comprising alcohols, esters, aldehydes, ketones, etc. Volatile organic compound type and content in BPP and MTPPs differed from dried pepper powder, with more types present in MTPPs than BPP. Partial least-squares discrimination analysis and principal component analysis were employed to identify relationships between flavor substances and sample type, and evaluate different exogenous Maillard's flavoring reaction affects. Dried pepper powder was positively related with most flavor compounds, and Maillard reaction substrate combinations of lysine-xylose, lysine-lactose, valine-fructose, and valine-xylose exhibited high correlation with several characteristic compounds, whereas no characteristic compounds exhibited high correlations in BPP. Thus, adding exogenous Maillard reaction substrate offers a mechanism to improve BPP flavor quality.

“Modeling−Prediction” Strategy for Deep Profiling of Lysophosphatidic Acids by Liquid Chromatography−Mass Spectrometry: Exploration Biomarkers of Breast Cancer

Lysophosphatidic acids (LPAs) are important bioactive phospholipids consisting of various species involved in a wide array of physiological and pathological processes. However, LPAs were rarely identified in untargeted lipidomics studies because of the incompatibility with analytical methods. Moreover, in targeted studies, the coverages of LPAs remained unsatisfactorily low due to the limitation of reference standards. Herein, a "modeling-prediction" workflow for deep profiling of LPAs by liquid chromatography-mass spectrometry was developed. Multiple linear regression models of qualitative and quantitative parameters were established according to features of fatty acyl tails of the commercial standards to predict the corresponding parameters for unknown LPAs. Then 72 multiple reaction monitoring (MRM) transitions were monitored simultaneously and species of LPA 14:0, LPA 16:1, LPA 18:3, LPA 20:3 and LPA 20:5 were firstly characterized and quantified in plasma. Finally, the workflow was applied to explore the changes of LPAs in plasma of breast cancer patients compared with healthy volunteers. Multi-LPAs indexes with strong diagnostic ability for breast cancer were identified successfully using Student's t- test, orthogona partial least-squares discrimination analysis (OPLS-DA) and logistic regression- receiver operating characteristic (ROC) curve analysis. The proposed workflow with high sensitivity, high accuracy, high coverage and reliable identification would be a powerful complement to untargeted lipidomics and shed a light on the analysis of other lipids.

Triple Negative Breast Cancer Detection Using LC-MS/MS Lipidomic Profiling.

Breast cancer (BC) is a heterogeneous malignancy that is responsible for a great portion of female cancer cases and cancer-related deaths in the United States. In comparison to other major BC subtypes, triple negative breast cancer (TNBC) presents with a relatively low survival rate and a high rate of metastasis. This has led to a strong, though largely unmet, need for more sensitive and specific methods of early-stage TNBC (ES-TNBC) detection to combat its high-grade pathology and relatively low survival rate. The current study employs a liquid chromatography-tandem mass spectrometry assay capable of targeted, highly specific, and sensitive detection of lipids to propose two diagnostic biomarker panels for TNBC/ES-TNBC. Using this approach, 110 lipids were reliably detected in 166 human plasma samples, 45 controls, and 121 BC (96 non-TNBC and 25 TNBC) subjects. Univariate and multivariate analyses allowed the construction and application of a 19-lipid biomarker panel capable of distinguishing TNBC (and ES-TNBC) from controls, as well as a 5-lipid biomarker panel capable of differentiating TNBC from non-TNBC and ES-TNBC from ES-non-TNBC. Receiver operating characteristic curves with notable classification performances were generated from the biomarker panels according to their orthogonal partial least-squares discrimination analysis models. TNBC was distinguished from controls with an area under the receiving operating characteristic curve (AUROC) = 0.93, sensitivity = 0.96, and specificity = 0.76 and ES-TNBC from controls with an AUROC = 0.96, sensitivity = 0.95, and specificity = 0.89. TNBC was differentiated from non-TNBC with an AUROC = 0.88, sensitivity = 0.88, and specificity = 0.79 and ES-TNBC from ES-non-TNBC with an AUROC = 0.95, sensitivity = 0.95, and specificity = 0.87. A pathway enrichment analysis between TNBC and controls also revealed significant disturbances in choline metabolism, sphingolipid signaling, and glycerophospholipid metabolism. To the best of our knowledge, this is the first study to propose a diagnostic lipid biomarker panel for TNBC detection. All raw mass spectrometry data have been deposited to MassIVE (dataset identifier MSV000085324).

Application of terahertz spectrum and interval partial least squares method in the identification of genetically modified soybeans.

Genetically modified soybeans are the world's most important genetically modified agricultural product. At present, the traditional methods for identifying genetically modified and non-transgenic soybeans are time-consuming, costly, and complicated to operate, which cannot meet the needs of practical applications. Therefore, it is necessary to discover a fast and accurate method for identifying transgenic soybeans. Terahertz (THz) time domain spectra were collected in sequence from 225 transgenic and non-transgenic soybean samples. Fourier transform was used to convert the terahertz time domain spectrum into a THz frequency domain spectrum with a frequency range of 0.1-2.5THz. Firstly, the interval partial least squares (iPLS) method was used to remove interference spectral bands and select appropriate spectral intervals. Secondly, 168 samples were selected as the calibration set. Discriminant partial least squares (DPLS), Grid Search support vector machine (Grid Search-SVM) and principal component analysis back propagation neural network (PCA-BPNN) were used to establish a qualitative identification model. Afterwards, 57 test set samples were predicted. By comparing the experimental results, it was found that iPLS could effectively screen and remove the interference THz band, which was more helpful to improve the efficiency and accuracy of the identification model. After the iPLS and mean center pre-treatment technology, the Grid Search-SVM identification model had the best identification effect, with a total accuracy rate of 98.25% (transgenic identification rate was 96.15%, non-transgenic identification rate was 100%). This study shows that after selecting spectra from iPLS, THz spectroscopy combined with chemometrics can more accurately, quickly, and efficiently identify transgenic and non-transgenic soybeans.