基于无机元素的花椒产地溯源和品种聚类分析

采用电感耦合等离子体原子发射光谱法,对红花椒（陕西韩城、四川汉源、四川茂汶、甘肃武都）和青花椒（云南昭通、贵州关岭、四川金阳、四川汉源、重庆江津）9 大主产地的80 个样品中21 个无机元素含量进行测定。采用主成分分析（principal component analysis,PCA）和偏最小二乘判别分析（partial least squaresdiscrimination analysis,PLS-DA）对红花椒和青花椒中无机元素进行综合评价,PCA和PLS-DA将80 个花椒聚为9组,PLS-DA分类效果更佳,并能将红花椒和青花椒有效区分,从元素组成角度揭示了红花椒和青花椒的亲缘关系和地域分布特征。研究证明多元素分析结合PLS-DA可作为一种花椒品种和产地识别的有效工具,对于产地溯源和品种鉴定具有重要意义。     

基于近红外技术和偏最小二乘判别法对无花果成熟度的快速判别

以无花果为试验对象,对其进行近红外光谱采集,并对其糖度、单果重、纵径、横径、硬度5个指标进行K-均值聚类;根据光谱数据、主成分分析确定最优聚类效果的成分和各类别的指标分布构建偏最小二乘判别分析(PLS-DA)模型进行聚类判别,以实现对果实成熟度(幼果期、成长期、成熟期)分类的准确、快速、无损伤鉴别。结果表明,3种成熟阶段的无花果样品的糖度、单果重和硬度均具有显著性差异,成熟果和成长果与幼果的纵径和横径间具有显著性差异。根据PLS-DA判别模型累计训练集的分类正确率为99.59%,测试集的分类正确率为99.15%。说明主成分分析与光谱数据所建立的PLS-DA模型性能较好,对无花果成熟度的快速鉴别是有效且可行的。     

近红外光谱技术结合偏最小二乘判别分析检测三七品质

目的 利用近红外光谱技术实现不同等级三七样品的快速鉴别。方法 采集等级A（20头）、等级B（30头）、等级C（40头）、等级D（60头）四种不同等级三七样品的近红外光谱,构建偏最小二乘判别分析（partial least squares discriminant analysis, PLS-DA）分类器模型鉴别四种等级的三七样品,同时为了减近红外光谱中的冗余波长变量,进一步优化模型的判别结果,利用竞争自适应重加权采样（competitive adaptive reweighted sampling, CARS）算法提取近红外光谱中的特征变量。结果 所构建的PLS-DA分类器模型对等级C和等级D的三七样品,鉴别准确率达到100%,但是对于等级A和等级B的三七样品因为存在误判,鉴别准确率仅为0%和20%。经过CARS算法提取近红外光谱特征变量后,光谱变量数大幅减少,从1557个变量下降到78个变量。以优选后的特征变量构建的CARS-PLS-DA分类器模型更加简化,对四种等级三七样品的预测均方根误差均明显下降,说明模型的预测分类变量更接近真实的分类变量,鉴别结果更加准确。同时,对四种等级三七样品的鉴别准确率显著上升,其中对于等级C和等级D的鉴别准确率为100%,对于等级B的鉴别准确率从20%提升到100%,等级A鉴别准确率从0%提升到75%。结论 所构建的CARS-PLS-DA分类器模型对四种等级的三七样品具有更好的鉴别效果,可以实现不同等级三七的品质鉴定。     

低聚糖和多酚结合偏最小二乘判别分析鉴别油菜蜜产地

采用气相色谱-串联质谱技术测定油菜蜜中6 种低聚糖成分,高效液相色谱-串联质谱技术测定油菜蜜中18 种酚酸物质,并结合偏最小二乘判别分析（partial least square-discrimination analysis,PLS-DA）对来自湖北钟祥市、江苏盐城市、青海刚察县3 个具有显著地理、气候、环境差异的51 个油菜蜜样本进行产地鉴别。方差分析结果显示：3 个产地油菜蜜中松二糖含量具有显著性差异,且青海刚察油菜蜜低聚糖含量相对偏高;油菜蜜的18 种多酚类物质中大多数具有显著的地理差异性且湖北钟祥的油菜蜜中多酚含量相对偏高。多元统计分析结果显示多酚具有显著的地理特征性。油菜蜜中低聚糖和多酚的含量结合PLS-DA产地鉴别的预测精度可达到97%。     

高效液相色谱结合偏最小二乘判别分析对硫熏天麻识别的研究

目的 构建基于天麻高效液相色谱(high performance liquid chromatography, HPLC)指纹图谱法结合化学计量学分析对硫磺熏蒸的天麻进行识别。方法 参照《中华人民共和国药典》一部(2015年版), 采用高效液相色谱-梯度洗脱法测定硫磺熏蒸天麻的指纹图谱, 通过中药色谱指纹图谱相似度评价系统软件(2012版)进行指纹图谱信息统计, 将信息输入SIMCA-P14.0软件进行主成分分析法和偏最小二成判别分析。结果 该方法能较好的分离天麻的活性成分, 依据检测结果确定了18个共有指纹峰, 由于未硫熏组和硫熏组天麻样品的指纹图谱存在差异性, 利用化学计量学方法可以正确识别硫熏天麻。结论 利用高效液相指纹图谱结合化学计量分析可准确识别硫磺熏蒸的天麻。     

基于正交偏最小二乘法判别分析宁夏和河北产区市售马瑟兰葡萄酒理化特征和抗氧化能力

随着中国葡萄酒产业的不断发展,产区之间的同质化问题日趋严重。马瑟兰是近年来在我国各个产区均得到较好表现的优质品种。为解析宁夏和河北产区马瑟兰市售干红葡萄酒特征与差异,进而揭示马瑟兰在不同产区间的不同风格质量表现,对宁夏和河北产区共27款市售酒的理化性质、有机酸、花色苷和其他酚类物质含量及抗氧化能力进行检测,并结合正交偏最小二乘法判别分析(orthogonal partial least squares-discriminant analysis, OPLS-DA)和相关性分析等方法进行分析。结果显示,两地区样品检测中除总酸含量外,其他基础理化指标含量都具有显著差异。有机酸中,琥珀酸是两地区含量差异最大的有机酸;河北地区马瑟兰酒样花青素-3-O-葡萄糖苷含量较高(0.001 1 g/L),而二甲花翠素-3-O葡萄糖苷比宁夏地区酒样低0.033 g/L。基于单体酚含量建立的OPLS-DA模型区分效果最佳,其中11种单体酚对该模型的建立起关键作用。抗氧化能力与相关性分析的结果显示,河北地区马瑟兰酒样抗氧化能力显著高于宁夏地区,且总酚和单体酚类物质对此有重要贡献,特别是表儿茶酸没食子酸酯和龙胆...     

基于氢核磁共振和偏最小二乘法- 判别分析建立鹿龟酒质量控制方法的研究

目的：建立一种基于氢核磁共振(1H-NMR)和偏最小二乘法-判别分析的鹿龟酒质量控制新方法。方法：基于1H-NMR技术测定样品的全成分信息,并转化成数据矩阵,利用SIMCA-P软件进行偏最小二乘法-判别分析(PLS-DA)。结果：鹿龟酒与不同的缺味样品及仿制品在所得的散点图中能得到明显区分。结论：氢核磁共振偏最小二乘法-判别分析法是一种操作简便,能够更全面体现其质量情况的鹿龟酒质量控制新方法。     

基于偏最小二乘回归的填充型烤烟优化施肥研究

为防止过量施肥导致环境污染及烟叶品质下降,通过大田试验,建立了氮磷钾肥与烤烟产量及烟叶主要化学成分的偏最小二乘回归施肥模型。结果表明,氮磷钾肥与烤烟产量及烟叶化学成分均有显著的回归关系,一次项系数表明了氮磷钾肥对烤烟影响的主次关系;交互项和二次项系数表明了氮磷钾肥的施用比例存在一定的临界值,氮磷钾肥在临界值内表现为协同促进的作用,高于临界值则表现为拮抗作用。若以获得最大经济效益为目的,最佳氮磷钾比例为1:1.6:2.1;若以改善烤烟烟叶香气、燃烧性及吸烟的安全性为目的,最佳氮磷钾比例为1:1.3:2.9。     

基于多元素分析的冬枣产地鉴别方法

为了探讨利用产地间差异性元素进行产地判别的可行性,测定了不同产地冬枣样本中10种元素的含量,并对数据进行了差异性分析、聚类分析、Fisher判别分析和偏最小二乘判别分析（partial least squares discrimination analysis,PLS-DA）。结果表明,不同产地冬枣中Mg、B、Mn、Fe、Zn元素存在显著差异,是具有产地特征的指纹元素。R型系统聚类分析也证实B、Mn、Fe和Zn元素具有共同特征。基于产地特征元素和Q型聚类、Fisher判别和PLS-DA建立的冬枣产地鉴别模型正确率均高于基于全部元素的分析结果,其中利用特征元素建立的PLS-DA模型鉴别正确率最高,回代检验和交叉检验正确率均为94.0%,Q型聚类模型的判别能力最差,最高的判别正确率为84.06%。本研究证实了产地间差异性元素是有效的产地判别因子,具有监督模式的Fisher判别和PLS-DA算法准确率远高于无监督模式的系统聚类法,更适于产地鉴别分析。      

近红外光谱结合不同偏最小二乘法快速检测镇江香醋的浑浊度

利用近红外光谱(4000cm-1～10000cm-1)结合化学计量学方法快速检测了镇江香醋中的浑浊度。首先,用近红外光谱仪采集香醋样本的近红外光谱数据以及用离心法测定样本的浑浊度值;然后,采用间隔偏最小二乘法(iPLS)、反向区间偏最小二乘法(biPLS)、联合间隔偏最小二乘算法(siPLS)优选光谱特征区间;最后,采用全光谱(4000cm-1～10000cm-1)偏最小二乘法(PLS)对优选出来的区间建立香醋浑浊度近红外光谱模型。结果表明,采用siPLS将全光谱均匀划分30个子区间,选择4个子区间[4 10 18 27]联合时,建立的模型预测效果最佳,其RMSECV和RMSEP分别为0.173和0.208,校正集和预测集相关系数分别为0.9337和0.9004。因此,利用近红外光谱技术快速检测香醋中的浑浊度是可行的。     

近红外光谱结合不同偏最小二乘法无损检测食醋中总酸含量

探讨了快速、无损检测食醋中总酸含量的建模方法,利用近红外光谱法分别结合间隔偏最小二乘法(iPLS)、反向区间偏最小二乘法(BiPLS)、联合间隔偏最小二乘算法(SiPLS)进行建模,对各算法在不同划分区间数及区间选择时对建立模型的影响进行比较.结果表明:BiPLS、SiPLS(2,3,4区间联合)建模效果较好于iPLS所建立的模型,其中BiPLS在选择43个子区间,5个子区间联合(3,4,6,7,16)最佳,其RMSECV和RMSEP分别为0.2876和0.2726,校正集和预测集相关系数分别为0.9343和0.938;SiPLS在选择3个区间联合,49个区间数(3、5、7区间联合)最佳,其RMSECV和RMSEP分别为0.2607和0.2802,校正集和预测集相关系数分别为0.9463和0.9371;iPLS在选择22个子区间,第三个子区间,主因子数为4时最佳,其RMSECV和RMSEP分别为0.2998和0.2977,校正集和预测集相关系数分别为0.928和0.9213.不同偏最小二乘算法所选取区域大多集中于5500～6000 cm-1范围内,证明该波数范围应该是总酸的相应特征区间.     

偏最小二乘方法在不同食醋判别分析中的应用

目的:建立一种由氢核磁共振技术（1H-NMR）和偏最小二乘回归分析（PLS）结合的方法来对不同种类食醋进行判别分析。方法:由1H-NMR技术测定的样本化学信息,经MestRe Nova软件转化成数字信息,将数据导入SIMCAP13.0软件中采用PLS进行建模和预测,统计其判别的准确率。结果:山西陈醋、镇江香醋和白醋在PLS得分图中能明显分开,并利用内部和外部的数据模型进行了验证,得出的预测准确率较高,所获得的RMSEE和RMSEP值也说明PLS模型对判别不同种类食醋具有较强的可预测性。结论:本分析方法在不同种类食醋的判别中具有较高的准确性,可为不同食醋的判别提供一种简单方法。      

Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis

Lameness causes decreased animal welfare and leads to higher production costs. This study explored data from an automatic milking system (AMS) to model on-farm gait scoring from a commercial farm. A total of 88 cows were gait scored once per week, for 2 5-wk periods. Eighty variables retrieved from AMS were summarized week-wise and used to predict 2 defined classes: nonlame and clinically lame cows. Variables were represented with 2 transformations of the week summarized variables, using 2-wk data blocks before gait scoring, totaling 320 variables (2 × 2 × 80). The reference gait scoring error was estimated in the first week of the study and was, on average, 15%. Two partial least squares discriminant analysis models were fitted to parity 1 and parity 2 groups, respectively, to assign the lameness class according to the predicted probability of being lame (score 3 or 4/4) or not lame (score 1/4). Both models achieved sensitivity and specificity values around 80%, both in calibration and cross-validation. At the optimum values in the receiver operating characteristic curve, the false-positive rate was 28% in the parity 1 model, whereas in the parity 2 model it was about half (16%), which makes it more suitable for practical application; the model error rates were, 23 and 19%, respectively. Based on data registered automatically from one AMS farm, we were able to discriminate nonlame and lame cows, where partial least squares discriminant analysis achieved similar performance to the reference method.     

Estimation of olive oil acidity using FT-IR and partial least squares regression

Olive oil characteristics are directly related to olive quality. Information about olive quality is of paramount importance to olive and olive oil producers, in order to establish its price. Real-time characterization of the olives avoids mixtures of high quality with low quality fruits, and allows improvement of olive oil quality. This work describes an indirect determination of olive acidity and that allows a rapid evaluation of olive oil quality. The applied method combines chemical analysis (30 min Soxhlet olive pomace extraction) in tandem with a spectroscopic technique (FT-IR) and multivariate regression (PLS1). The most suitable calibration model found used SNV pre-processing and was built with 4 Latent Variables giving a RMSECV of 8.7% and a Q² of 0.97. This accurate calibration model allows the estimation of olive acidity using a FT-IR spectrum of the corresponding Soxhlet oil dry extract and therefore is a suitable method for indirect determination of FFA in olives.     

基于近红外光谱与组合间隔偏最小二乘法的稻米镉含量快速检测

采用近红外光谱(near infrared spectroscopy,NIRS)结合组合间隔偏最小二乘法(synergy interval partial least squares,siPLS)建立稻米镉含量快速检测的方法。收集并分析72个稻米样品的NIRS谱图。对光谱前处理方法进行优化,确定平滑、多元散射校正与均值中心化处理为最优方法。采用siPLS法筛选特征波数,建立稻米镉含量的定量模型。稻米镉siPLS模型交叉验证均方根(RMSECV)与预测均方根(RMSEP)值分别为0.247与0.261,训练集相关系数(Rc)与预测集相关系数(Rp)值分别为0.919与0.895。结果表明:运用siPLS法选择特征波长后,不但可以降低模型的复杂度,同时还能够提高预测精度。NIRS作为一种快速、无损与便捷的初筛方法,可用于检测稻米中镉含量是否超标。     

A comparison of partial least squares (PLS) and sparse PLS regressions in genomic selection in French dairy cattle

Genomic selection involves computing a prediction equation from the estimated effects of a large number of DNA markers based on a limited number of genotyped animals with phenotypes. The number of observations is much smaller than the number of independent variables, and the challenge is to find methods that perform well in this context. Partial least squares regression (PLS) and sparse PLS were used with a reference population of 3,940 genotyped and phenotyped French Holstein bulls and 39,738 polymorphic single nucleotide polymorphism markers. Partial least squares regression reduces the number of variables by projecting independent variables onto latent structures. Sparse PLS combines variable selection and modeling in a one-step procedure. Correlations between observed phenotypes and phenotypes predicted by PLS and sparse PLS were similar, but sparse PLS highlighted some genome regions more clearly. Both PLS and sparse PLS were more accurate than pedigree-based BLUP and generally provided lower correlations between observed and predicted phenotypes than did genomic BLUP. Furthermore, PLS and sparse PLS required similar computing time to genomic BLUP for the study of 6 traits.     

利用ICP-MS/MS测定白酒中无机元素的主成分分析及聚类分析

建立电感耦合等离子体串联质谱(inductively coupled plasma tandem mass spectrometry,ICP-MS/MS)法测定18种白酒中14种无机元素的含量,研究白酒中无机元素含量的分布规律及其与白酒品种之间的关系。白酒样品经简单稀释后直接进样,采用ICP-MS/MS测定其中Mg、Al、Cr、Mn、Fe、Co、Ni、Cu、Zn、As、Se、Sr、Ba、Pb的含量,在MS/MS模式下,分别选择O2和NH3/He为反应气,消除质谱干扰,将分析结果进行标准化处理后利用SPSS主成分分析和聚类分析对无机元素分布进行评价。各元素的检出限为1.34~25.8 ng/L,加标回收率为93.0%~105%,相对标准偏差(RSD)≤3.84%,方法的检出限低、准确度好、稳定性和精密度高。主成分分析结果显示,积累方差的80.003%来自前5个主成分,18种白酒的特征元素为Al、Fe、Ba;聚类分析将18种白酒划分为4类,可实现白酒品种的初步判别,不同产地白酒中无机元素的含量具有明显差异性。     

基于主成分与聚类分析的35个苹果品种多酚综合评价

为了探讨苹果品种间多酚的差异,本研究采集了35个苹果品种为试材进行多酚种类和含量的测定,采用相关性分析、主成分分析和聚类分析对不同苹果品种和多酚的关系进行综合评价。结果表明:35个不同苹果品种的多酚组成和含量差异明显,部分多酚之间存在显著相关关系;依据主成分解释总变量和碎石图提取了4个主成分反应原变量75%的信息,第一主成分(Principal Component 1,PC1)主要包括金丝桃苷、原花青素B2,第二主成分(Principal Component 2,PC2)主要包括根皮苷,结合得分图直观地显示了苹果品种和多酚间关系,秦阳、嘎啦一系、嘎啦二系分布在PC1和PC2的正向区间,属于早熟品种,短枝华冠、华星、华脆分布在PC2负向区间,属于中熟品种,黄元帅、富士、秦冠分布在PC1负向区间,属于晚熟品种。聚类分析将35个苹果品种分为5类,聚类结果与主成分的得分图结果基本一致。本实验对不同苹果品种中多酚进行的综合评价为苹果原材料的选择和多酚的综合利用提供理论支持。      

A comparison of artificial neural networks and partial least squares modelling for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectral fingerprints

A series of partial least squares (PLS) models were employed to correlate spectral data from FTIR analysis with beef fillet spoilage during aerobic storage at different temperatures (0, 5, 10, 15, and 20 °C) using the dataset presented by Argyri et al. (2010). The performance of the PLS models was compared with a three-layer feed-forward artificial neural network (ANN) developed using the same dataset. FTIR spectra were collected from the surface of meat samples in parallel with microbiological analyses to enumerate total viable counts. Sensory evaluation was based on a three-point hedonic scale classifying meat samples as fresh, semi-fresh, and spoiled. The purpose of the modelling approach employed in this work was to classify beef samples in the respective quality class as well as to predict their total viable counts directly from FTIR spectra. The results obtained demonstrated that both approaches showed good performance in discriminating meat samples in one of the three predefined sensory classes. The PLS classification models showed performances ranging from 72.0 to 98.2% using the training dataset, and from 63.1 to 94.7% using independent testing dataset. The ANN classification model performed equally well in discriminating meat samples, with correct classification rates from 98.2 to 100% and 63.1 to 73.7% in the train and test sessions, respectively. PLS and ANN approaches were also applied to create models for the prediction of microbial counts. The performance of these was based on graphical plots and statistical indices (bias factor, accuracy factor, root mean square error). Furthermore, results demonstrated reasonably good correlation of total viable counts on meat surface with FTIR spectral data with PLS models presenting better performance indices compared to ANN.