桉树杂交种与其亲本的近红外光谱判别

研究桉树控制授粉后目标性状的基因作用方式是探索其基因重组规律的重要内容。常规的数量统计分析精度往往不高,而DNA分析的专业要求高,且费时费力。该研究利用近红外光谱(NIRs)研究不同基因型桉树杂交种、亲本及杂交种与亲本间近红外光谱信息的关系,探索NIRs用于桉树杂交种与其亲本判别的可行性和准确性。以控制授粉的桉树亲本及其杂交F1代材料为对象,每种基因型从各自田间试验分别选取10个单株,采集树冠中上部新鲜健康叶片。用手持式近红外仪Phazir Rx(1624)采集桉树杂交种与其亲本叶片的NIRs信息。每单株选10片完全生理成熟的健康叶片,避开叶脉扫描其正面光谱5次,以50条NIRs信息的均值代表单个叶片的NIRs信息,最终每个基因型获得10条NIRs信息。对原始NIRs采用二阶多项式S.G一阶导数预处理。预处理后的NIRs用于多元统计分析,首先对桉树杂交亲本和子代样本进行主成分分析(PCA),直观展示不同基因型的分类情况。然后运用簇类独立软模式(SIMCA)和偏最小二乘判别分析(PLS-DA)两种有监督的判别模式验证NIRs用于桉树杂交种与其亲本树种的分类判别效果。PCA结果显示,不同的亲本间、杂交种间及杂交种与亲本间样本的主因子得分可以清晰地将各基因型分开。SIMCA模式判别分析中,桉树杂交种样本到亲本PCA模型的样本距离显示,待判别样本能够形成单独的聚类,且能直观反映两者的遗传相似。PLS-DA判别结果显示,桉树杂交亲本的PLS模型能通过预测其杂交子代的响应变量将其与亲本准确分开。结果表明,桉树叶片的NIRs信息可以准确地反映桉树杂交子代遗传信息的传递规律,NIRs判别模型可以准确地将各种基因型予以区分。因此,NIRs信息不仅可用于桉树杂交种和纯种的定性判别,还可以分析桉树基因重组过程中加性遗传效应的大小,从而为桉树遗传基础分析及其育种改良研究提供理论支撑。     

Exhaled breath condensate: Determination of non-volatile compounds and their potential for clinical diagnosis and monitoring. A review

Exhaled breath condensate is a promising, non-invasive, diagnostic sample obtained by condensation of exhaled breath. Starting from a historical perspective of early attempts of breath testing towards the contemporary state-of-the-art breath analysis, this review article focuses mainly on the progress in determination of non-volatile compounds in exhaled breath condensate. The mechanisms by which the aerosols/droplets of non-volatile compounds are formed in the airways are discussed with methodological consequences for sampling. Dilution of respiratory droplets is a major problem for correct clinical interpretation of the measured data and there is an urgent need for standardization of EBC. This applies also for collection instrumentation and therefore various commercial and in-house built devices are described and compared with regard to their design, function and collection parameters. The analytical techniques and methods for determination of non-volatile compounds as potential markers of oxidative stress and lung inflammation are scrutinized with an emphasis on method suitability, sensitivity and appropriateness. The relevance of clinical findings for each group of possible non-volatile markers of selected pulmonary diseases and methodological recommendations with emphasis on interdisciplinary collaboration that is essential for future development into a fully validated clinical diagnostic tool are given.     

Computational modelling of the antischistosomal activity for neolignan derivatives based on the MIA-SAR approach

Theoretical models for exploring the antischistosomal activity of a dataset of 18 synthetic neolignans are built using the multivariate image analysis applied to structure–activity relationships (MIA-SAR) approach. The obtained models were validated using the accuracy (Acc) in leave-one-out cross-validation, external validation and Y-randomization procedures, yielding correct classification superior to 80%, 70% and 60%, respectively. Additionally, a comparison was made of the models obtained from binary (black and white) and coloured images; the colours (pixel values) were selected to correspond to chemical properties. It was observed that the models obtained from coloured images with pixel values corresponding to electronegativity (known as the aug-MIA-SAR_colour approach) generally yielded superior statistical parameters compared with those obtained from binary images (MIA-SAR) and randomly coloured images (atoms are coloured according to their type) with atomic sizes corresponding to Van der Waals radius (aug-MIA-SAR), respectively. Mechanistic interpretation of the influence of different substituents on the antischistosomal activity revealed that methoxy substituents in the R1 (or R2) and R5 positions of the neolignan scaffold are indispensable for the antischistosomal activity. The obtained results provide knowledge of the possible structural modifications to yield novel neolignan compounds with antischistosomal activity.     

Distinguishing between Decaffeinated and Regular Coffee by HS-SPME-GC×GC-TOFMS,Chemometrics, and Machine Learning

Coffee, one of the most popular beverages in the world, attracts consumers by its rich aroma and the stimulating effect of caffeine. Increasing consumers prefer decaffeinated coffee to regular coffee due to health concerns. There are some main decaffeination methods commonly used by commercial coffee producers for decades. However, a certain amount of the aroma precursors can be removed together with caffeine, which could cause a thin taste of decaffeinated coffee. To understand the difference between regular and decaffeinated coffee from the volatile composition point of view, headspace solid-phase microextraction two-dimensional gas chromatography time-of-flight mass spectrometry (HS-SPME-GC×GC-TOFMS) was employed to examine the headspace volatiles of eight pairs of regular and decaffeinated coffees in this study. Using the key aroma-related volatiles, decaffeinated coffee was significantly separated from regular coffee by principal component analysis (PCA). Using feature-selection tools (univariate analysis: t-test and multivariate analysis: partial least squares-discriminant analysis (PLS-DA)), a group of pyrazines was observed to be significantly different between regular coffee and decaffeinated coffee. Pyrazines were more enriched in the regular coffee, which was due to the reduction of sucrose during the decaffeination process. The reduction of pyrazines led to a lack of nutty, roasted, chocolate, earthy, and musty aroma in the decaffeinated coffee. For the non-targeted analysis, the random forest (RF) classification algorithm was used to select the most important features that could enable a distinct classification between the two coffee types. In total, 20 discriminatory features were identified. The results suggested that pyrazine-derived compounds were a strong marker for the regular coffee group whereas furan-derived compounds were a strong marker for the decaffeinated coffee samples.     

Characterisation of Flavour Attributes in Egg White Protein Using HS-GC-IMS Combined with E-Nose and E-Tongue: Effect of High-Voltage Cold Plasma Treatment Time

Egg white protein (EWP) is susceptible to denaturation and coagulation when exposed to high temperatures, adversely affecting its flavour, thereby influencing consumers’ decisions. Here, we employ high-voltage cold plasma (HVCP) as a novel nonthermal technique to investigate its influence on the EWP’s flavour attributes using E-nose, E-tongue, and headspace gas-chromatography-ion-mobilisation spectrometry (HS-GC-IMS) due to their rapidness and high sensitivity in identifying flavour fingerprints in foods. The EWP was investigated at 0, 60, 120, 180, 240, and 300 s of HVCP treatment time. The results revealed that HVCP significantly influences the odour and taste attributes of the EWP across all treatments, with a more significant influence at 60 and 120 s of HVCP treatment. Principal component analyses of the E-nose and E-tongue clearly distinguish the odour and taste sensors’ responses. The HS-GC-IMS analysis identified 65 volatile compounds across the treatments. The volatile compounds’ concentrations increased as the HVCP treatment time was increased from 0 to 300 s. The significant compounds contributing to EWP characterisation include heptanal, ethylbenzene, ethanol, acetic acid, nonanal, heptacosane, 5-octadecanal, decanal, p-xylene, and octanal. Thus, this study shows that HVCP could be utilised to modify and improve the EWP flavour attributes.     

素食人群尿液1H NMR代谢轮廓的多因素方差分析

结合方差分析(ANOVA)和偏最小二乘法判别分析(PLS-DA)两种分析技术,对素食和普食人群的尿液1H NMR谱进行分析.利用ANOVA方法将数据矩阵分解为几个独立因素矩阵,滤除干扰因素后,再利用PLS-DA对单因素数据进行建模分析.实验结果表明,ANOVA/PLS-DA方法可以有效地减少饮食因素和性别因素之间的相互...     

PLS-DA优化模型的马铃薯黑心病可见近红外透射光谱检测

马铃薯黑心病是一种马铃薯主要内部缺陷,严重损害薯条、薯片、全粉等加工制品的质量和产率。目前对马铃薯的分级主要侧重于外部品质检测,针对内部缺陷检测的研究很少。旨在开发一种马铃薯黑心病的快速无损检测技术,为此搭建了马铃薯可见近红外透射光谱分析平台,分析健康与黑心病马铃薯的透射光谱特性并优化光谱判别模型参数。基于现有马铃薯分级线和复享PG2000高速光谱仪,采用左右透射方式（光源与光纤探头位于分级线果盘左右两侧）,采集470个马铃薯（其中健康薯234个、黑心薯236个）的透射光谱图,建立偏最小二乘判别模型（PLS-DA）,并利用主成分分析（PCA）与光谱形态特征相结合的方法选择特征波长,优化模型。分析发现,健康薯与黑心薯的可见近红外透射光谱在吸光度值和光谱形态特征方面均存在明显区别。黑心薯的平均光谱吸光度值高于健康薯（650～900 nm范围内）,但黑心薯的平均光谱曲线较为平缓,无明显吸收峰,而健康薯平均光谱曲线在665,732和839 nm附近有明显吸收峰,并且健康薯与黑心薯的平均光谱差值在705 nm处达到最大值。基于PLS-DA法建立了马铃薯黑心病判别模型,对黑心病的判别效果显著,分类器特性曲线（ROC）下面积（AUC）值为0.994 2,黑心薯识别总正确率能够达到97.16%,RMSE_CV和RMSE_P分别为0.28和0.26。此外,成功利用PCA与光谱形态特征相结合的方法对模型进行简化,最终得到由6个波长（658,705,716,800,816和839 nm）组成的特征波长组合,简化后的模型总正确率能够达到96.73%,接近全波段模型判别水平。研究表明,左右透射的方式能够准确识别黑心马铃薯,实现对马铃薯内部缺陷的快速无损检测。对我国马铃薯产业的发展起到一定的促进作用,为马铃薯内部缺陷在线检测技术的提高提供了重要的理论基础和实践依据。     

近红外光谱结合化学计量学的常见中国蜂蜜掺杂糖浆鉴别

目前我国蜂蜜市场掺假现象严重,研究一种快速、准确的方法用于市场流通领域掺假蜂蜜的鉴别具有重要的现实意义。采用近红外光谱（NIR）结合化学计量学方法对常见的天然蜂蜜以及掺假（掺杂常见糖浆）蜂蜜进行建模识别,并比较偏最小二乘-判别分析（PLS-DA）及支持向量机（SVM）对糖浆掺假蜂蜜鉴别模型的影响。首先,采集来自中国10个省份、20种常见蜂蜜的112个天然纯蜂蜜样品,以及6种常见糖浆样品按不同糖浆含量（10%,20%,30%,40%,50%,60%）配制的112个掺假蜂蜜样品,共计224个样品;通过近红外光仪器扫描获得所有样品的近红外光谱数据（波长范围400~2 500 nm）;然后,分别采用一阶导数（FD)、二阶导数（SD）、多元散射校正（MSC)、标准正态变化(SNVT)四种方式对原始光谱进行预处理;再结合PLS-DA和SVM建立天然蜂蜜和糖浆掺假蜂蜜的鉴别模型,比较不同预处理方法对两种不同建模算法建立的蜂蜜掺假鉴别模型效果。其中SVM算法的惩罚参数c和核函数参数g通过网格搜索法（GS）、遗传算法（GA）、粒子群算法（PSO）三种寻优算法进行优化。分析结果表明：光谱数据进行预处理后所建立的模型准确率均有明显提升,而对于SVM模型,惩罚参数c和核函数参数g对模型准确率的提升效果要比光谱预处理带来的提升效果更明显。在PLS-DA算法中,经FD光谱预处理后建立的模型效果最好,最佳PLS-DA模型准确率为87.50%;在SVM算法中,经MSC预处理后,再通过GS寻优,获得惩罚参数c为3.0314,核函数参数g为0.3298的条件下所建立的模型效果最好,最佳SVM模型准确率为94.64%。由此可见,非线性的SVM算法结合NIR光谱数据所建立的天然蜂蜜与糖浆掺假蜂蜜鉴别模型要优于线性的PLS-DA模型,同时表明NIR光谱结合化学计量学方法对常见糖浆掺杂的中国蜂蜜鉴别是可行的。     

基于高光谱成像技术的滩羊肉新鲜度快速检测研究

滩羊肉的新鲜度是其品质安全的一个重要衡量指标,也是肉品品质安全控制的关键环节。挥发性盐基氮（TVB-N）是表征肉品腐败过程主要的化学信息,能有效地评价出滩羊肉的新鲜度。然而,TVB-N的传统检测过程繁琐且人为影响因素大,检测结果缺乏客观性和一致性,不能满足当今肉品检测过程无损、快速、高效的需求。高光谱成像技术符合现代检测技术向多源信息融合方向发展的需求,已在食品安全领域得到广泛应用。利用可见/近红外高光谱成像技术（400~1 000 nm）结合动力学和化学计量学方法以及计算机编程技术,将同时实现滩羊肉贮存期内（15 ℃环境）TVB-N 浓度的快速检测和贮藏期的预测。研究中提取每个样品感兴趣区域的平均光谱数据,选用蒙特卡洛算法剔除异常样本。采用X-Y共生距离（SPXY）法划分为校正集和预测集,分别选用多元散射校正（multiplicative scatter correction, MSC）、卷积平滑（savitzky-golay, SG）、标准变量变换（standard normalized variate, SNV）、归一化（normalization）、基线校准（baseline）五种方法对原始光谱数据进行预处理,优选出最佳预处理方法。采用竞争性自适应重加权法（campetitive adaptive reweighted sampling, CARS）和连续投影算法（successive projections algorithm, SPA）分别提取了21个和6个特征波长。为优化模型并提高其模型精度,采用SPA算法对 CARS 所选特征波长进行二次提取,优选出14个特征波长。基于所提取的特征波长建立TVB-N浓度的PLSR模型,优选出 SNV-CARS-SPA-PLSR 模型具有较高的预测能力（R2_c=0.88,RMSEC=2.51, R2_p=0.65, RMSEP=2.11）。同时,建立了滩羊肉TVB-N变化与贮藏时间的动力学模型,并将优化后的光谱模型和动力学反应模型相结合建立了滩羊肉光谱吸光度值与贮藏时间的高光谱动力学模型,实现对贮藏时间的预测,并通过 PLS-DA判别模型对滩羊肉贮藏时间进行判别分析（校正集判别准确率为100%,预测集为97%）。研究表明,利用可见/近红外高光谱成像技术结合动力学和化学计量学方法以及计算机编程技术,可以有效地实现滩羊肉品质智能监控与质量安全快速无损分析,为开发实时在线检测装备提供理论参考。