Multivariate optimization and comparison between conventional extraction (CE) and ultrasonic-assisted extraction (UAE) of carotenoid extraction from cashew apple

Carotenoids are an essential component of cashew and can be used in pharmaceuticals, cosmetics, natural pigment, food additives, among other applications. The present work focuses on optimizing and comparing conventional and ultrasound-assisted extraction methods. Every optimization step took place with a 1:1 (w:w) mixture of yellow and red cashew apples lyophilized and ground in a cryogenic mill. A Simplex-centroid design was applied for both methods, and the solvents acetone, methanol, ethanol, and petroleum ether were evaluated. After choosing the extractor solvent, a central composite design was applied to optimize the sample mass (59–201 mg) and extraction time (6–34 min). The optimum conditions for the extractor solvent were 38% acetone, 30% ethanol, and 32% petroleum ether for CE and a mixture of 44% acetone and 56% methanol for UAE. The best experimental conditions for UAE were a sonication time of 19 min and a sample mass of 153 mg, while the CE was 23 min and 136 mg. Comparing red and yellow cashews, red cashews showed a higher carotenoid content in both methodologies. The UAE methodology was ca. 21% faster, presented a more straightforward composition of extracting solution, showed an average yield of superior carotenoid content in all samples compared to CE. Therefore, UAE has demonstrated a simple, efficient, fast, low-cost adjustment methodology and a reliable alternative for other applications involving these bioactive compounds in the studied or similar matrix.     

Code2vect: An efficient heterogenous data classifier and nonlinear regression technique

The aim of this paper is to present a new classification and regression algorithm based on Artificial Intelligence. The main feature of this algorithm, which will be called Code2Vect, is the nature of the data to treat: qualitative or quantitative and continuous or discrete. Contrary to other artificial intelligence techniques based on the “Big-Data,” this new approach will enable working with a reduced amount of data, within the so-called “Smart Data” paradigm. Moreover, the main purpose of this algorithm is to enable the representation of high-dimensional data and more specifically grouping and visualizing this data according to a given target. For that purpose, the data will be projected into a vectorial space equipped with an appropriate metric, able to group data according to their affinity (with respect to a given output of interest). Furthermore, another application of this algorithm lies on its prediction capability. As it occurs with most common data-mining techniques such as regression trees, by giving an input the output will be inferred, in this case considering the nature of the data formerly described. In order to illustrate its potentialities, two different applications will be addressed, one concerning the representation of high-dimensional and categorical data and another featuring the prediction capabilities of the algorithm.     

Determining the Zn Content of Cherry in Field Using VNIR Spectroscopy

Visible and near-infrared(VNIR)spectroscopy is an eco-friendly method used for estimating plant nutrient deficiencies.The aim of this study was to investigate the possibility of using VNIR method for estimating Zn content in cherry orchard leaves under field conditions.The study was conducted in 3different locations in Isparta region of Turkey.Fifteen cherry orchards containing normal and Zn deficient plants were chosen,and 60 leaf samples were collected from each location.The reflectance spectra of the leaves were measured with an ASD FieldSpec HandHeld spectroradiometer and a plant probe.The Zn contents of leaf samples were predicted through laboratory analysis.The spectral reflectance measurements were used to estimate the Zn levels using stepwise multiple linear regression analysis method.Prediction models were created using the highest coefficient of determination value.The results show that Zn content of cherry trees can be estimated using the VNIR spectroscopic method(87.5相似文献   

基于向后逐步回归模型的我国农机需求特征及影响因素研究

以2004-2014年数据为样本,以农机总动力为因变量,以农机购置补贴、农机价格、农机作业服务价格、农户人均纯收入、农产品价格、农业劳动力数量和农作物播种总面积为自变量构建了我国农机需求影响因素的回归方程,向后逐步回归结果表明:(1)除农作物播种总面积、农机作业服务价格、农产品价格之外,其余变量对农机总动力的影响均统计上显著;(2)农机购置补贴、农机价格指数、农户人均纯收入及农业劳动力四个变量对农机需求的弹性系数分别为0.027、-0.004、0.457、0.474。最后根据研究结果提出加大与落实农机购置补贴、规范农机价格、培育新型经营主体、加速土地规范流转等政策建议。     

QSAR Study on Binding Affinity of PATs (Rodenticides) to the [3H]-Mepyramine-Labelled H1 Receptor In Rat and Guinea Pig Brain

Abstract

The binding of a series of PAT analogues (rodenticides) to the [³H]-mepyramine-labelled H₁ receptor in rat and guinea pig brain was investigated topologically using negentropy (N), molecular redundancy (MRI), first-order molecular connectivity (¹X _v ), Wiener (W), and Szeged (Sz) indices. Multiple regression analyses showed that MRI provided excellent results upon introduction of indicator parameters. Predictive ability of the proposed models was discussed using cross-validation parameters.     

Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.     

基于LIBS技术的水泥粉末在线成分分析

在工业现场生产水泥的过程中，各种成分的含量直接影响着水泥的质量，因此如何快速准确地监测水泥中各个成分的含量意义重大。采用的实验方式为，将不经过任何预处理的水泥粉末直接放入位于二维移动平台上的物料盒中，通过激光诱导击穿光谱(LIBS)直接对水泥粉末表面的不同位置进行激发检测，对得到的光谱数据首先进行归一化和主成分分析等预处理操作，然后针对水泥中Ca, Si, Al, Fe, Mg五种元素，分别建立偏最小二乘(PLS)和支持向量回归(SVR)两种定量分析模型进行方法比较。此外，对比了粉末状水泥与压片式水泥两种测量方式的结果。实验结果表明，采用粉末状水泥直接测量的方式下，针对水泥样品元素浓度与所得到的光谱中特征线强度的关系，SVR方法比PLS方法更具优势，粉末状水泥直接测量的精度接近压片式测量的精度，说明LIBS技术对水泥粉末状样品直接在线测量具有可行性。     

Successive stepwise evolution of host layer‐stacking framework upon the intercalation of mobile vapor guests within side‐chain layers

For the ordered phases of hairy‐rod semiconductive poly(2,5‐bis(3‐tetradecylthiophene‐2‐yl)thieno[3,2‐b]thiophene) (PBTTT) sandwiched in between crystalline platelets of hexamethylbenzene, the successive stepwise evolution of layer‐stacking framework upon guest intercalation has been studied in this research. The direct consequence of the guest intercalation into side‐chain layers is evaluated to cause the lateral shift of thiophene backbones along π–π stacking, resulting in stepwise shift of ultraviolet absorption wavelength. The thermal motions of vapor guests within disordering side‐chain layers subsequently cause progressive expansion of host stacking framework. With the increase in side‐chain length, thicker layers of disordering side chains in liquid crystals (LCs) accommodate additional vapor guests and larger amplitudes of thermal motions of guests, hence promoting the level of reversible d‐spacing change. The mixing between mobile vapor guests and aliphatic side chains is clarified as the mechanism of guest intercalation, which rationalizes successive guest intercalation during heating and the contribution of disordering side‐chain layers. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2017 , 55, 1448–1456     

Regression and ANN models for estimating minimum value of machining performance

Surface roughness is one of the most common performance measurements in machining process and an effective parameter in representing the quality of machined surface. The minimization of the machining performance measurement such as surface roughness (R_a) must be formulated in the standard mathematical model. To predict the minimum R_a value, the process of modeling is taken in this study. The developed model deals with real experimental data of the R_a in the end milling machining process. Two modeling approaches, regression and Artificial Neural Network (ANN), are applied to predict the minimum R_a value. The results show that regression and ANN models have reduced the minimum R_a value of real experimental data by about 1.57% and 1.05%, respectively.     

轮台白杏叶片铁锰浓度光谱估算模型

建立基于光谱分析的轮台白杏叶片铁(Fe)、锰(Mn)元素浓度估算模型,为快速建立轮台白杏树体微量营养元素诊断体系提供技术途经。采用Unispec-SC光谱仪测定土壤肥力存在显著差异条件下轮台白杏果实不同生育时期叶片的光谱反射率,通过分析叶片中Fe和Mn元素浓度与Rλ和f′(Rλ)的相关性,筛选出光谱指示波段,并采用线性回归模型建立光谱估算模型以估算叶片Fe和Mn营养元素浓度。结果表明:轮台白杏果实不同生育时期叶片Fe元素的光谱敏感波段各不相同,果实坐果期和硬核期敏感波段分别为873和874nm,375和437nm。果实成熟期敏感波段为836和837nm而果实收后期敏感波段为325和1 054nm;轮台白杏果实四个生育时期Mn元素的光谱敏感波段分别为913和1 129nm,425和970nm,390和466nm,423和424nm;轮台白杏叶片Fe和Mn元素浓度均与光谱反射率一阶微分f′(Rλ)相关性最强,与之建立的线性光谱估算模型拟合度最高,且达到了显著或极显著水平。表明果实不同生育时期,轮台白杏叶片Fe和Mn元素的光谱指示波段不同,可根据双波段f′(Rλ)采用线性模型估算白杏叶片Fe和Mn元素浓度。