High speed measurement of corn seed viability using hyperspectral imaging

Corn is one of the most cultivated crops all over world as food for humans as well as animals. Optimized agronomic practices and improved technological interventions during planting, harvesting and post-harvest handling are critical to improving the quantity and quality of corn production. Seed germination and vigor are the primary determinants of high yield notwithstanding any other factors that may play during the growth period. Seed viability may be lost during storage due to unfavorable conditions e.g. moisture content and temperatures, or physical damage during mechanical processing e.g. shelling, or over heating during drying. It is therefore vital for seed companies and farmers to test and ascertain seed viability to avoid losses of any kind. This study aimed at investigating the possibility of using hyperspectral imaging (HSI) technique to discriminate viable and nonviable corn seeds. A group of corn samples were heat treated by using microwave process while a group of seeds were kept as control group (untreated). The hyperspectral images of corn seeds of both groups were captured between 400 and 2500 nm wave range. Partial least squares discriminant analysis (PLS-DA) was built for the classification of aged (heat treated) and normal (untreated) corn seeds. The model showed highest classification accuracy of 97.6% (calibration) and 95.6% (prediction) in the SWIR region of the HSI. Furthermore, the PLS-DA and binary images were capable to provide the visual information of treated and untreated corn seeds. The overall results suggest that HSI technique is accurate for classification of viable and non-viable seeds with non-destructive manner.     

A new metabonomics method for simultaneous determination of EFAs and NEFAs in plasma using GC-MS and its application

A new metabonomics method was developed for simultaneous qualitative and quantitative analysis on esterified and nonesterified fatty acids(EFAs and NEFAs) in plasma.Merely 10μL of plasma was required.The pretreatment of the sample was simple without disposing the protein.After simple extraction and derivation,15 FAs in plasma were precisely quantified.Gas chromatography tandem mass spectrometry(GC-MS) was used in the study and the quantities of the analytes,which varied in abundance over three orders of ...     

NMR and chemometrics in tracing European olive oils: The case study of Ligurian samples

An NMR and chemometric analytical approach to classify extra virgin olive oils according to their geographical origin was developed within the European TRACE project (FP6-2003-FOOD-2-A, contract number: 0060942). Olive oils (896 samples) of three consecutive harvesting years (2005, 2006, and 2007) coming from Mediterranean areas were analyzed by ¹H NMR spectroscopy. Olive oil samples from Liguria, an Italian region, were chosen as a case study and PLS-DA and SIMCA modeling analyses were used to build up statistical models both to discriminate between Ligurian and non-Ligurian olive oils and to define the Ligurian olive oil class to confirm the declared provenience.     

A simple,rapid, and robust “on-the-go” identity testing of biotherapeutics using FTIR spectroscopy

The stunning rise of biotherapeutics as successful treatments of complex and hard-to-treat diseases, in particular cancer, has necessitated the development of a rapid analytical method capable of differentiating these otherwise significantly similar antibody-based products. The existing methods for product identification pose significant drawbacks in terms of the consumption of time and labor. Here, we present an FTIR spectroscopy-based simple, rapid, and robust method that is capable of differentiating between the biotherapeutics (both innovator products and biosimilars). The proposed approach uses partial least-squares-discriminant analysis to pinpoint subtle differences in the FTIR spectra of the samples, enabling us to not only identify the product but also calculate its concentration. This FTIR-based method can be used to fulfill the identity testing requirement of a pharmaceutical drug in its final packaged form set by the US Food and Drug Administration. Along with this, identity testing can also be deployed at multiple steps in the manufacturing process and can also be used by the appropriate regulatory or government agency for tackling counterfeits of biotherapeutic products.     

Geographical Discrimination of Bell Pepper (Capsicum annuum) Spices by (HS)-SPME/GC-MS Aroma Profiling and Chemometrics

Dried and ground red pepper is a spice used as seasoning in various traditional dishes all over the world; nevertheless, the pedoclimatic conditions of the diverse cultivation areas provide different chemical characteristics, and, consequently, diverse organoleptic properties to this product. In the present study, the volatile profiles of 96 samples of two different ground bell peppers harvested in diverse Italian geographical areas, Altino (Abruzzo) and Senise (Lucania), and a commercial sweet paprika, have been studied by means of headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS). The investigation of their volatile profile has led to the identification of 59 analytes. Eventually, a discriminant classifier, Partial Least Squares Discriminant Analysis (PLS-DA), was exploited to discriminate samples according to their geographical origin. The model provided very accurate results in external validation; in fact, it correctly classified all the 30 test samples, achieving 100% correct classification (on the validation set). Furthermore, in order to understand which volatiles contribute the most at differentiating the bell peppers from the different origins, a variable selection approach, Variable Importance in Projection (VIP), was used. This strategy led to the selection of sixteen diverse compounds which characterize the different bell pepper spices.     

激光诱导击穿光谱的高铁车轮钢晶粒度等级研究

我国高速铁路运行距离长,服役环境多变,对车轮钢的性能要求较高。车轮钢的晶粒尺寸直接影响着车轮钢的力学性能,且晶粒的特征和测量对材料科学有着重要的作用,因此为了保证高速列车的安全运行,对高铁车轮的晶粒度等级进行检测是十分必要的。利用激光诱导击穿光谱(LIBS)实验平台对5个不同晶粒度等级的ER8高速列车车轮钢样品(经过不同热处理得到不同晶粒度等级)进行击穿获取光谱信息,比较了基体元素Fe和合金元素(Cr, Mo, Co)的谱线强度与5个不同晶粒度等级的样品之间的相关性,发现均与样品晶粒度等级存在不同程度的相关性。利用此关系建立以谱线强度为变量的偏最小二乘判别分析(PLS-DA)模型,在建立模型前分别采用标准正态变量变换(SNV)、多元散射校正(MSC)和Savitzky-Golay卷积平滑方法进行预处理。通过比较各种预处理方法,得出采用SNV预处理后建立的模型效果最佳,建模集误判个数为4个,准确率为95.7%,预测集误判个数为3个,准确率为90%。在SNV预处理方法的基础上,分别选择竞争性自适应重加权算法(CARS)、连续投影算法(SPA)和CARS-SPA三种波长筛选方法进行波长筛选,...     

红外光谱结合化学计量学对不同采收期滇重楼的定性定量分析

傅里叶变换红外光谱(FTIR)结合偏最小二乘判别分析(PLS-DA)和偏最小二乘回归(PLSR)对不同采收期滇重楼(Paris polyphylla var. yunnanensis)分别进行定性鉴别与定量分析,以期为滇重楼合理采收和鉴别评价提供科学依据。采集46份不同采收期滇重楼样品的红外光谱,对光谱数据进行自动基线校正+纵坐标归一化+自动平滑+小波去噪(WD)预处理后进行PLS-DA分析;采用超高效液相色谱测定样品中重楼皂苷Ⅰ,Ⅱ,Ⅵ,Ⅶ的含量,将液相测定数据与红外光谱数据进行拟合,经自动基线校正+纵坐标归一化+自动平滑+一阶求导+正交信号校正(OSC)优化处理后,建立滇重楼中重楼皂苷Ⅰ,Ⅱ,Ⅵ,Ⅶ的快速预测模型。结果显示,(1)原始红外光谱中主要吸收区域在950~700,1 200~950,1 800~1 500和2 800~3 500 cm-1附近。(2)PLS-DA得分图可准确区分不同采收期滇重楼样品。(3)液相数据显示重楼总皂苷含量随着年限的增加先成倍增加,再逐渐减少,最后呈现缓慢增加的趋势。(4)重楼总皂苷含量定量模型的预测值与真实值间无显著性差异,表明模型预测效果好。FTIR结合化学计量学可准确区分不同采收期滇重楼并快速预测其皂苷含量,为不同采收期滇重楼的鉴别和皂苷含量预测提供一种新方法,同时为确定滇重楼的最佳采收期提供参考依据。     

可见近红外高光谱成像对灵武长枣定量损伤等级判别

利用可见近红外(Vis-NIR)高光谱成像技术对完好和损伤等级灵武长枣进行快速识别检测.采用定量损伤装置得到损伤Ⅰ,Ⅱ,Ⅲ,Ⅳ和Ⅴ级的灵武长枣,借助高光谱成像系统采集完好长枣和损伤长枣样本高光谱图像.提取感兴趣区域(region of interest,ROI)并计算样本平均光谱值.利用光谱-理化值共生距离算法(SPX...     

基于ELM和可见/近红外光谱的鲜枣动态分类检测

枣营养丰富且品种繁多,不同品种的内外部品质与市场价格均存在差异,为了实现鲜枣品种分类的快速无损检测,以产自同一地区的板枣、鸡心枣和相枣为研究对象,动态采集光谱数据。采用移动平滑(moving smoothing)与多元散射校正(multiplicative scatter correction, MSC)相结合的方法预处理光谱数据,对预处理后的光谱数据采用连续投影算法(successive projections algorithm, SPA)提取出11个特征波长分别为：980, 1 860, 1 341, 1 386, 2 096, 1 831, 1 910, 1 628, 441, 768, 601 nm,其重要程度依次递减。以所提取的特征波长作为输入变量,建立极限学习机(extreme learning machine,ELM)分类模型,进行预测判别,并与偏最小二乘判别分析(partial least squares discriminant analysis, PLS-DA)和最小二乘支持向量机(least squares support vector machines, LS-SVM)方法进行比较。结果表明：SPA-ELM方法所建校正模型的决定系数R2=0.972 38,校正均方根误差RMESC=0.018 724,SPA-ELM方法与SPA-PLS-DA和SPA-LS-SVM方法判别准确率均为100%,说明ELM是一种有效的分类判别方法。该研究为鲜枣品种分类检测提供了新的理论基础。     

Antioxidant and Antiglycation Effects of Cistus × incanus Water Infusion,Its Phenolic Components,and Respective Metabolites

Reactive oxygen and carbonyl species promote oxidative and carbonyl stress, and the development of diabetes, metabolic syndrome, cardiovascular diseases, and others. The traditional herb Cistus × incanus is known for its antioxidant properties; therefore, the current study aimed to assess how the chemical composition of a C. incanus water infusion corresponds with its antioxidative and antiglycative effects in vitro. The composition of infusions prepared from commercial products was analyzed with UHPLC-ESI-qTOF-MS. Total phenolics, flavonoids, and non-flavonoid polyphenols were determined. Antioxidant activity of infusions and selected polyphenols was investigated using DPPH, ABTS, and FRAP. Fluorometric measurements and methylglyoxal capture were performed to investigate the antiglycation activity. PCA and PLS-DA models were applied to explore the correlation between chemical and antioxidant results. The principal flavonoids in C. incanus were flavonols. In vitro tests revealed that a stronger antioxidant effect was demonstrated by plant material from Turkey rich in flavonoids, followed by Albania and Greece. Flavonols and ellagic acid displayed stronger antiradical and reducing power than EA-derived urolithins. Hyperoside was the most potent inhibitor of glycation. The results indicate that flavonoids are primarily responsible for rock rose antioxidant and antiglycation properties. PLS-DA modeling can be used to identify the origin of plant material with sensitivity and specificity exceeding 86%.