Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis

Near infrared reflectance spectroscopy （NIRS）, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis （PLS-DA） to discriminate the transgenic （TCTP and mi166） and wild type （Zhonghua 11） rice. Furthermore, rice lines transformed with protein gene （OsTCTP） and regulation gene （Osmi166） were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.

Near-Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis Applied to Identification of Liquor Brands

The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for the liquor brands with the same flavor and the same alcohol content is essential. However, it is also difficult because the components of such liquor samples are very similar. Near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was applied to identification of liquor brands with the same flavor and alcohol content. A total of 160 samples of Luzhou Laojiao liquor and 200 samples of non-Luzhou Laojiao liquor with the same flavor and alcohol content were used for identification. Samples of each type were randomly divided into the modeling and validation sets. The modeling samples were further divided into calibration and prediction sets using the Kennard-Stone algorithm to achieve uniformity and representativeness. In the modeling and validation processes based on PLS-DA method, the recognition rates of samples achieved 99.1% and 98.7%, respectively. The results show high prediction performance for the identification of liquor brands, and were obviously better than those obtained from the principal component linear discriminant analysis method. NIR spectroscopy combined with the PLS-DA method provides a quick and effective means of the discriminant analysis of liquor brands, and is also a promising tool for large-scale inspection of liquor food safety.

Near-Infrared Spectroscopy Combined with Absorbance Upper Optimization Partial Least Squares Applied to Rapid Analysis of Polysaccharide for Proprietary Chinese Medicine Oral Solution

Near-infrared (NIR) spectroscopy was applied to reagent-free quantitative analysis of polysaccharide of a brand product of proprietary Chinese medicine (PCM) oral solution samples. A novel method, called absorbance upper optimization partial least squares (AUO-PLS), was proposed and successfully applied to the wavelength selection. Based on varied partitioning of the calibration and prediction sample sets, the parameter optimization was performed to achieve stability. On the basis of the AUO-PLS method, the selected upper bound of appropriate absorbance was 1.53 and the corresponding wavebands combination was 400 - 1880 & 2088 - 2346 nm. With the use of random validation samples excluded from the modeling process, the root-mean-square error and correlation coefficient of prediction for polysaccharide were 27.09 mg·L-1 and 0.888, respectively. The results indicate that the NIR prediction values are close to those of the measured values. NIR spectroscopy combined with AUO-PLS method provided a promising tool for quantification of the polysaccharide for PCM oral solution and this technique is rapid and simple when compared with conventional methods.

Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study:principal components analysis vs.partial least squares

With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.

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.

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

A multivariate partial least squares approach to joint association analysis for multiple correlated traits

Many complex traits are highly correlated rather than independent. By taking the correlation structure of multiple traits into account, joint association analyses can achieve both higher statistical power and more accurate estimation. To develop a statistical approach to joint association analysis that includes allele detection and genetic effect estimation, we combined multivariate partial least squares regression with variable selection strategies and selected the optimal model using the Bayesian Information Criterion(BIC). We then performed extensive simulations under varying heritabilities and sample sizes to compare the performance achieved using our method with those obtained by single-trait multilocus methods. Joint association analysis has measurable advantages over single-trait methods, as it exhibits superior gene detection power, especially for pleiotropic genes. Sample size, heritability,polymorphic information content(PIC), and magnitude of gene effects influence the statistical power, accuracy and precision of effect estimation by the joint association analysis.

Characteristic wavelength selection of volatile organic compounds infrared spectra based on improved interval partial least squares

As important components of air pollutant,volatile organic compounds(VOCs)can cause great harm to environment and human body.The concentration change of VOCs should be focused on in real-time environment monitoring system.In order to solve the problem of wavelength redundancy in full spectrum partial least squares(PLS)modeling for VOCs concentration analysis,a new method based on improved interval PLS(iPLS)integrated with Monte-Carlo sampling,called iPLS-MC method,was proposed to select optimal characteristic wavelengths of VOCs spectra.This method uses iPLS modeling to preselect the characteristic wavebands of the spectra and generates random wavelength combinations from the selected wavebands by Monte-Carlo sampling.The wavelength combination with the best prediction result in regression model is selected as the characteristic wavelengths of the spectrum.Different wavelength selection methods were built,respectively,on Fourier transform infrared(FTIR)spectra of ethylene and ethanol gas at different concentrations obtained in the laboratory.When the interval number of iPLS model is set to 30 and the Monte-Carlo sampling runs 1000 times,the characteristic wavelengths selected by iPLS-MC method can reduce from 8916 to 10,which occupies only 0.22%of the full spectrum wavelengths.While the RMSECV and correlation coefficient(Rc)for ethylene are 0.2977 and 0.9999 ppm,and those for ethanol gas are 0.2977 ppm and 0.9999.The experimental results show that the iPLS-MC method can select the optimal characteristic wavelengths of VOCs FTIR spectra stably and effectively,and the prediction performance of the regression model can be significantly improved and simplified by using characteristic wavelengths.

Comparison of Calibration Curve Method and Partial Least Square Method in the Laser Induced Breakdown Spectroscopy Quantitative Analysis

The Laser Induced Breakdown Spectroscopy (LIBS) is a fast, non-contact, no sample preparation analytic technology;it is very suitable for on-line analysis of alloy composition. In the copper smelting industry, analysis and control of the copper alloy concentration affect the quality of the products greatly, so LIBS is an efficient quantitative analysis tech- nology in the copper smelting industry. But for the lead brass, the components of Pb, Al and Ni elements are very low and the atomic emission lines are easily submerged under copper complex characteristic spectral lines because of the matrix effects. So it is difficult to get the online quantitative result of these important elements. In this paper, both the partial least squares (PLS) method and the calibration curve (CC) method are used to quantitatively analyze the laser induced breakdown spectroscopy data which is obtained from the standard lead brass alloy samples. Both the major and trace elements were quantitatively analyzed. By comparing the two results of the different calibration method, some useful results were obtained: both for major and trace elements, the PLS method was better than the CC method in quantitative analysis. And the regression coefficient of PLS method is compared with the original spectral data with background interference to explain the advantage of the PLS method in the LIBS quantitative analysis. Results proved that the PLS method used in laser induced breakdown spectroscopy was suitable for simultaneous quantitative analysis of different content elements in copper smelting industry.

Partial Least Squares(PLS)Methods for Abnormal Detection of Breast Cells

Breast cancer is one of the malignant tumors having high incidence in women,the incidence of breast cancer has increased in all parts of the world since twentieth century,but its etiology is not yet completely clear,so it is very important to detect breast cells.In this paper,we built a regression model to detect breast cells,and generated a method for predicting the formation of benign and malignant breast cells by training the model,then we used the 10 features of breast cells to predict it,the results reaching upto 93.67%accuracy,it was very effective to predict and analyse whether the breast cells getting cancer,It had an important role in the diagnosis and prevention of breast cancer.

基于GC-QTOF-MS对物理回收的食品接触用再生高密度聚乙烯中迁移物的非靶向筛查

以3家企业提供的21?种食品接触用再生高密度聚乙烯(recycled high-density polyethylene,rHDPE)样品在60℃条件下与两种代表性食品模拟物(95%乙醇、4%乙酸溶液)接触10 d作为迁移实验条件,利用气相色谱-串联四极杆飞行时间质谱检测迁移到食品模拟物中的物质。被筛查出的161种物质根据其毒性进行分级(由低至高分为Ⅰ~Ⅳ级),其中毒性Ⅲ和Ⅳ级的有59种,且其预测辛醇/水分配系数大于毒性Ⅰ、Ⅱ级的物质。被筛查的物质中苯及取代衍生物占比最高。邻苯类增塑剂、抗氧剂降解产物以及多环芳烃等物质需要特别关注。使用正交偏最小二乘判别分析迁移物在不同阶段样品中的迁移量变化,发现终产品相较母粒样品中物质的迁移量有所提升。该研究可以为食品接触用r HDPE中迁移物的分析及安全风险评估提供理论基础。

Discriminant Analysis of Liquor Brands Based on Moving-Window Waveband Screening Using Near-Infrared Spectroscopy

Partial least squares discriminant analysis (PLS-DA) with integrated moving-window (MW) waveband screening was applied to the discriminant analysis of liquor brands with near-infrared (NIR) spectroscopy. Luzhou Laojiao, a popular liquor with strong fragrant flavor, was used as the identified liquor brand (160 samples, negative, 52 vol alcoholicity). Liquors of 10 other brands with strong fragrant flavor were used as the interferential brands (200 samples, positive, 52 vol alcoholicity). The Kennard-Stone algorithm was used for the division of modeling samples to achieve uniformity and representativeness. Based on the MW-PLS-DA, a simplified optimal model set with 157 wavebands was further proposed. This set contained five types of wavebands corresponding to the NIR absorption bands of water, ethanol, and other micronutrients (i.e., acids, aldehydes, phenols, and aromatic compounds) in liquor for practical choice. Using five selected simple models with 4775 - 4239, 7804 - 6569, 6264 - 5844, 9435 - 7896, and 12066 - 10373 cm-1, the validation recognition rates were obtained as 99.3% or higher. Results show good prediction performance and low model complexity, and also provided a valuable reference for designing small dedicated instruments. The proposed method is a promising tool for large-scale inspection of liquor food safety.

Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Brand Identification of Wine

High-end wine brand is made through the use of high-quality grape variety and yeast strain, and through a unique process. Not only is it rich in nutrients, but also it has a unique taste and a fragrant scent. Brand identification of wine is difficult and complex because of high similarity. In this paper, visible and near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was used to explore the feasibility of wine brand identification. Chilean Aoyo wine (2016 vintage) was selected as the identification brand (negative, 100 samples), and various other brands of wine were used as interference brands (positive, 373 samples). Samples of each type were randomly divided into the calibration, prediction and validation sets. For comparison, the PLS-DA models were established in three independent and two complex wavebands of visible (400 - 780 nm), short-NIR (780 - 1100 nm), long-NIR (1100 - 2498 nm), whole NIR (780 - 2498 nm) and whole scanning (400 - 2498 nm). In independent validation, the five models all achieved good discriminant effects. Among them, the visible region model achieved the best effect. The recognition-accuracy rates in validation of negative, positive and total samples achieved 100%, 95.6% and 97.5%, respectively. The results indicated the feasibility of wine brand identification with Vis-NIR spectroscopy.

UPLC/Q-TOF-MS特征图谱结合化学计量学评价秋海棠属5种药材质量

目的:采用UPLC/Q-TOF-MS技术结合化学计量学方法综合评价秋海棠、中华秋海棠、掌裂叶秋海棠、长柄秋海棠和柔毛秋海棠的质量。方法:采用ACQUITY UPLC HSS T3 C_(18)(2.1 mm×100mm,1.8μm)色谱柱,以乙腈-0.1%甲酸为流动相进行梯度洗脱,柱温30℃,流速0.2 mL·min^(-1),检测波长254 nm;采用电喷雾离子源(ESI)正负离子同时扫描,以MSE方式进行采集,建立秋海棠属5种药材UPLC/Q-TOF-MS特征图谱,并对它们各自共有峰进行确认和归属,运用主成分分析法(PCA)和偏最小二乘判别分析法(PLS-DA)对数据进行统计分析。结果:筛选出秋海棠、中华秋海棠、掌裂叶秋海棠、长柄秋海棠和柔毛秋海棠药材各自的共有特征峰分别为15、13、12、12、15个,且共鉴定出29个化合物;秋海棠属5种药材质量存在较大差异,其中,柔毛秋海棠与其他4种药材的化学成分差异显著,秋海棠与中华秋海棠药材之间化学成分差异较小,掌裂叶秋海棠与长柄秋海棠药材之间化学成分差异较小;还筛选出对该5种药材区分贡献显著的8个化学标志物,原花青素B2、表儿茶素、葫芦素D或其同分异构体、葫芦素D葡萄糖苷和葫芦素B为潜在鉴别特征性化学标志物,原花青素B1、儿茶素和芦丁为潜在含量差异性化学标志物。结论:该方法直观、准确地反映了此5种药材的整体质量及化学成分差异情况,并对于建立科学、合理的秋海棠属药材质量评价方法和安全用药具有重要的指导意义,也为其整体质量评价和控制及标准修订提供参考。

A partial least-squares regression approach to land use studies in the Suzhou-Wuxi-Changzhou region

In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically independent. But in fact, they have the tendency to be dependent, a phenomenon known as multicollinearity, especially in the cases of few observations. In this paper, a Partial Least-Squares （PLS） regression approach is developed to study relationships between land use and its influencing factors through a case study of the Suzhou-Wuxi-Changzhou region in China. Multicollinearity exists in the dataset and the number of variables is high compared to the number of observations. Four PLS factors are selected through a preliminary analysis. The correlation analyses between land use and influencing factors demonstrate the land use character of rural industrialization and urbanization in the Suzhou-Wuxi-Changzhou region, meanwhile illustrate that the first PLS factor has enough ability to best describe land use patterns quantitatively, and most of the statistical relations derived from it accord with the fact. By the decreasing capacity of the PLS factors, the reliability of model outcome decreases correspondingly.

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.

采用GC×GC-TOFMS分析3种脂肪含量牛乳中挥发性化合物

采用顶空固相微萃取-全二维气相色谱-飞行时间质谱(headspace solid phase microextraction in combination with comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry,HS-SPME-GC×GC-TOFMS)技术对全脂牛乳(whole milk,WM)、低脂牛乳(low-fat milk,LFM)和脱脂牛乳(non-fat milk,NFM)3种牛乳样品进行挥发性化合物分析,结果表明:共检测到49种挥发性化合物,其中2-壬酮、2-十一酮等奇数碳链的甲基酮构成WM的主要风味化合物;偏最小二乘法判别分析表明,其模型可以很好地区分3种牛乳样品,并且有较好的方差和交叉验证预测能力;通过变量投影重要性>1、P≤0.05且含量≥1%筛选出9种化合物,被认定为关键香气差异化合物,这些化合物可能是导致3种牛乳风味不同的主要因素;聚类热图结果表明,NFM因异味化合物(如十六醛)的存在可能导致不良感官表现,而WM和LFM存在更多的香气化合物,令其在感官方面具有饱满丰富的香气。本研究建立了HS-SPME-GC×GC-TOFMS分析牛乳的研究方法,为乳制品风味改进和乳制香精调配提供了理论指导。

基于UPLC-Q-TOF-MS技术的不同产地肺形草化学成分的研究

目的 分析不同产地肺形草中化学成分的差异。方法 采用超高效液相-四极杆-飞行时间串联质谱法(UPLC-Q-TOF-MS)技术,色谱条件为Welchrom HPLC C18色谱柱(2.1 mm×150 mm,3μm),乙腈(A)-0.1%甲酸水溶液(B)为流动相,梯度洗脱(0~2min,5%A;2~20 min,5%~12%A;20~35 min,12%~40%A;35~38 min,40%A;38~48 min,40%~80%A;48~50 min,80%A),流速为0.3 mL·min^(-1),进样室温度为15℃,柱温为35℃,进样量为2μL。电喷雾离子源(ESI),在负离子模式下扫描,扫描范围m/z 100~1250。采用自建数据库匹配、文献参照对不同产地肺形草化学成分进行鉴定,对所得质谱数据经MarkView 1.2.1处理,使用SIMCA 14.1进行主成分分析(PCA)和正交偏最小二乘法-判别分析(OPLS-DA),得到变量重要性投影(VIP)值> 1的化合物信息,综合筛选分析不同产地肺形草的分组趋势、相关性和含量差异性化学成分。结果 在负离子模式下共鉴别出51个化合物,其中黄酮8个、呫吨酮10个、环烯醚萜6个、生物碱3个、醌类4个、三萜3个、苯丙素2个、酚酸7个、酯类3个、脂肪酸2个、其他类3个。PCA及OPLS-DA分析结果显示,不同产地肺形草清晰地聚成3类,t检验发现P <0.05且VIP> 1的化合物有11个。结论 本法可以系统、精准地鉴定肺形草中含有的化学成分。不同产地肺形草成分差异明显,同一成分含量也不相同,以此可作为区分不同产地肺形草的重要依据之一。

Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis

Powdery mildew （Blumeria graminis） is one of the most destructive crop diseases infecting winter wheat plants, and has devastated millions of hectares of farmlands in China. The objective of this study is to detect the disease damage of powdery mildew on leaf level by means of the hyperspectral measurements, particularly using the continuous wavelet analysis. In May 2010, the reflectance spectra and the biochemical properties were measured for 114 leaf samples with various disease severity degrees. A hyperspectral imaging system was also employed for obtaining detailed hyperspectral information of the normal and the pustule areas within one diseased leaf. Based on these spectra data, a continuous wavelet analysis （CWA） was carried out in conjunction with a correlation analysis, which generated a so-called correlation scalogram that summarizes the correlations between disease severity and the wavelet power at different wavelengths and decomposition scales. By using a thresholding approach, seven wavelet features were isolated for developing models in determining disease severity. In addition, 22 conventional spectral features （SFs） were also tested and compared with wavelet features for their efficiency in estimating disease severity. The multivariate linear regression （MLR） analysis and the partial least square regression （PLSR） analysis were adopted as training methods in model mildew on leaf level were found to be closely related with the development. The spectral characteristics of the powdery spectral characteristics of the pustule area and the content of chlorophyll. The wavelet features performed better than the conventional SFs in capturing this spectral change. Moreover, the regression model composed by seven wavelet features outperformed （R2=0.77, relative root mean square error RRMSE=0.28） the model composed by 14 optimal conventional SFs （R2---0.69, RRMSE--0.32） in estimating the disease severity. The PLSR method yielded a higher accuracy than the MLR method. A combination of CWA and PLSR was found to be promising in providing relatively accurate estimates of disease severity of powdery mildew on leaf level.

Improvement of quantitative analysis of molybdenum element using PLS-based approaches for laser-induced breakdown spectroscopy in various pressure environments

An experimental setup has been designed and realized in order to optimize the characteristics of laser-induced breakdown spectroscopy system working in various pressure environments. An approach combined the normalization methods with the partial least squares(PLS) method are developed for quantitative analysis of molybdenum(Mo) element in the multi-component alloy,which is the first wall material in the Experimental Advanced Superconducting Tokamak. In this study, the different spectral normalization methods(total spectral area normalization,background normalization, and reference line normalization) are investigated for reducing the uncertainty and improving the accuracy of spectral measurement. The results indicates that the approach of PLS based on inter-element interference is significantly better than the conventional PLS methods as well as the univariate linear methods in the various pressure for molybdenum element analysis.