顶空固相微萃取-气相色谱-质谱/嗅闻仪结合电子鼻和多元统计方法对4种柠檬的风味差异分析

该研究旨在利用多种技术手段以及多元统计方法分析不同品种柠檬风味差异。采用气相色谱-质谱/嗅闻联用（Gas Chromatography-Mass Spectrometry/Olfactory,GC-MS/O）与电子鼻技术对不同品种柠檬中挥发性风味成分进行研究,并以主成分综合评价函数计算不同品种柠檬理化特性与香气的综合得分进行评价。结果显示：电子鼻PCA分析能较好的区分不同品种柠檬,GC-MS/O检测出65种挥发性风味组分,分别为烯类（46种）、醛类（6种）、醇类（5种）、酯类（5种）、酮类（2种）和酚类（1种）。不同品种柠檬中相对香气活度值（ROAV）大于1的风味组分有柠檬烯、合金欢烯、芳樟醇、桉叶油醇、辛醛、壬醛、柠檬醛以及癸醛8种,确定为其关键香气成分。安岳黄柠檬综合得分最高、香水柠檬次之,表明安岳黄柠檬理化特性以及香气较好。该研究可应用于柠檬溯源鉴定与柠檬品质分析,为柠檬综合利用提供理论支持。     

基于GC-IMS解析植物乳杆菌发酵对猕猴桃果汁挥发性物质的影响

以“红阳”猕猴桃为试材,通过气相迁移离子色谱（gas chromatography-ion mobility spectrometry,GC-IMS）技术分析比较灭菌后果汁（YL）、发酵对照组（CK）、植物乳杆菌发酵果汁（PL）挥发性风味物质组成,并采用偏最小二乘判别分析（Partial least squares-discrimination analysis,PLS-DA）筛选不同处理样品的差异物质,探讨乳酸菌发酵对“红阳”猕猴桃汁挥发性风味物质组成的影响。结果表明,该研究所选用菌株（Lactobacillus plantanum B-1）应用于猕猴桃果汁发酵在24 h内菌落数由6.21 lg CFU/mL快速上升至8.60 lg CFU/mL,总酸由原来的0.60%增加至1.33%,发酵性能良好。GC-IMS结合PLS-DA分析结果表明,植物乳杆菌发酵可提高(E)-2-己烯醛、己醇、芳樟醇、乙酸乙酯、丁酸乙酯,丙酮、4-庚酮、2-壬酮等含量,增加果汁果香味,减少1,8-桉叶素、(E)-2-己烯醇、己醛等猕猴桃特征风味物质的损失,抑制乙醇、糠醛等对风味产生不良影响的物质生成,从而提升猕猴桃果汁风味品质。该研究为植物乳杆菌在猕猴桃果汁产品中应用提供了理论支撑。     

Identification of metabolic content of selected Amarone wine

The metabolic characterisation of food and beverages nowadays has been largely employed in combination with multivariate data analysis. In this work, the valuable Amarone “Passito” dry red wine produced in Verona area (Italy) was investigated in order to find a possible correlation between metabolic content and vintage/ageing process. A total number of 46 wine samples from 11 different winemakers, three different vintages and ageing process were investigated without sample preparation. ¹H NMR spectra were analysed in combination with principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) protocols to investigate vintage and ageing effects on samples differentiation based on the metabolic content. The results indicated amino acids, sugars, and aromatic compounds the most relevant discriminant variables for Amarone wine samples.     

HRMAS-NMR spectroscopy and multivariate analysis meat characterisation

(1)H-high resolution magic angle spinning-nuclear magnetic resonance spectroscopy was employed to gain the metabolic profile of longissimus dorsi and semitendinosus muscles of four different breeds: Chianina, Holstein Friesian, Maremmana and Buffalo. Principal component analysis, partial least squares projection to latent structure - discriminant analysis and orthogonal partial least squares projection to latent structure - discriminant analysis were used to build models capable of discriminating the muscle type according to the breed. Data analysis led to an excellent classification for Buffalo and Chianina, while for Holstein Friesian the separation was lower. In the case of Maremmana the use of intelligent bucketing was necessary due to some resonances shifting allowed improvement of the discrimination ability. Finally, by using the Variable Importance in Projection values the metabolites relevant for the classification were identified.     

Discovery of subtle effects in a human intervention trial through multilevel modeling

Many benefits can be gained if multi-factorial diseases with a high incidence and prevalence are better understood. Sophisticated approaches like multilevel analyses are needed to discover subtle differences between healthy people and people at the onset of disease in these types of studies. Multilevel analysis generates different sub-models for each level of variation. For instance, within and between subject variation can be split and analyzed separately if the two factors are orthogonal (i.e., not confounded). In the present paper, the benefits of a multilevel approach in multi-way analysis (nPLS-DA) will be described for the analysis of metabolomics data of a double blinded, randomized, parallel intervention trial with twenty slightly overweight men, whom received a diclofenac or placebo treatment for nine days. Blood samples were taken on multiple time points on 5 treatment days.The cross-validated error rate for classifying subjects in the correct treatment group for the multilevel nPLS-DA was compared with the error rate from the ordinary nPLS-DA. 42.1% of the subjects were misclassified using ordinary nPLS-DA, whereas only 5% were misclassified using the multilevel approach. Metabolites which contributed in different ways to treatment group differences could be determined and used for biological interpretation.The multilevel multi-way technique turned out to be a much stronger tool for modeling differences between treatment groups than the ordinary method. The metabolites that contributed most to treatment differences were not only statistically, but also biologically relevant. The multilevel approach found the effects that were better interpretable, whereas the ordinary nPLS-DA failed to do so. The methodology that was described in this paper is not only limited to human intervention studies, but can be used also for studies with a similar data structure. The multilevel approach is able to investigate effects on all levels of variation of every well designed study, hence improving the interpretability of the results.     

不同类型酵母对精酿啤酒化学和感官特性的影响

为研究不同类型的酵母对精酿啤酒的化学和感官特性的影响,选取5株酿酒酵母（4株上面发酵酵母、1株下面发酵酵母）进行酿造发酵实验,采用顶空固相微萃取（HS-SPME）法以及离子色谱（IC）法提取并鉴定分析啤酒中的香气成分和有机酸成分,采用偏最小二乘法-判别分析（PLS-DA）进行挥发性化合物的定量与鉴别,并用电子舌对成品酒进行滋味评价。结果表明,4株上面啤酒酵母（A1-A4）与下面啤酒酵母L5相比,上面啤酒酵母的起发速度更快,发酵度更高,发酵液风味物质含量较高,酯香醇厚。综合发酵性能指标可以看出,菌株A2的发酵速度较快,最早达到发酵峰值,发酵液感官风味协调,是一株发酵性能优良的精酿啤酒酵母;菌株A3具有较高的发酵度为80.87%,乙酸乙酯的产量高达45.64 mg/L,是一株具有高发酵度的啤酒酵母。     

气相色谱-离子迁移谱法结合多元统计学分析不同陈酿时间白兰地的挥发性香气成分差异

目的 利用气相色谱-离子迁移谱（gas chromatography-ion mobility spectrometry, GC-IMS）结合化学计量学方法判别不同年份白兰地的差异,建立桶藏白兰地年份酒快速判别预测模型。 方法 基于GC-IMS技术,利用指纹图谱判别各年份白兰地差异和特征性挥发性物质,结合化学计量学方法对各物质吸收峰体积进行数据降维并且建立年份判别模型。 结果 共定性出50种挥发性风味物质,由指纹图谱可判断不同年份白兰地中VOCs含量及种类存在显著差异。利用多元统计方法对鉴定出的香味物质吸收峰数据进行降维分析和可视化操作,主成分分析（principal component analysis, PCA）累计贡献率可达85%,表明不同年份样品具有良好的聚类效果。通过偏最小二乘回归分析（partial least squares-discriminant analysis, PLS-DA）建立年份酒判别模型,得到不同年份酒样的特征性标记物,筛选出22种VIP值大于1的对陈酿白兰地年份判别贡献较大的香气成分。 结论 利用GC-IMS结合多元统计分析可判别年份白兰地中特征性呈味物质种类和含量差异并且建模效果良好,得到研究结果可为白兰地的年份鉴别和质量控制提供经验借鉴和应用思路。     

Simultaneous detection for adulterations of maltodextrin,sodium carbonate,and whey in raw milk using Raman spectroscopy and chemometrics

To achieve rapid on-site identification of raw milk adulteration and simultaneously quantify the levels of various adulterants, we combined Raman spectroscopy with chemometrics to detect 3 of the most common adulterants. Raw milk was artificially adulterated with maltodextrin (0.5–15.0%; wt/wt), sodium carbonate (10–100 mg/kg), or whey (1.0–20.0%; wt/wt). Partial least square discriminant analysis (PLS-DA) classification and a partial least square (PLS) regression model were established using Raman spectra of 144 samples, among which 108 samples were used for training and 36 were used for validation. A model with excellent performance was obtained by spectral preprocessing with first derivative, and variable selection optimization with variable importance in the projection. The classification accuracy of the PLS-DA model was 95.83% for maltodextrin, 100% for sodium carbonate, 95.84% for whey, and 92.25% for pure raw milk. The PLS model had a detection limit of 1.46% for maltodextrin, 4.38 mg/kg for sodium carbonate, and 2.64% for whey. These results suggested that Raman spectroscopy combined with PLS-DA and PLS model can rapidly and efficiently detect adulterants of maltodextrin, sodium carbonate, and whey in raw milk.     

基于红外光谱指纹和挥发性组分信息融合模型鉴别大米产地来源

为建立一种红外光谱指纹信息和挥发性组分信息融合鉴别模型,提高模型对大米产地的鉴别率。通过傅里叶红外光谱和气相色谱-质谱联用分析20?份盘锦大米、19?份射阳大米和15?份五常大米样品中红外光谱吸光度和挥发性组分含量,利用方差分析筛选出特征光谱和挥发性组分,结合偏最小二乘判别分析（partial least squares-discriminant analysis,PLS-DA）法建立融合这2?种指纹信息的鉴别方法。结果表明,信息融合模型的大米产地鉴别准确率为97.4%,与单一光谱指纹信息模型（92.9%）和挥发性指纹信息模型（88.9%）相比,分别提高了4.5%和8.5%。因此,信息融合技术提高了该模型鉴别效果,采用PLS-DA法信息融合模型对大米产地进行鉴别是可行有效的。