Melamine (2,4,6-triamino-1,3,5-triazine) is a nitrogen-rich chemical implicated in the pet and human food recalls and in the global food safety scares involving milk products. Due to the serious health concerns associated with melamine consumption and the extensive scope of affected products, rapid and sensitive methods to detect melamine's presence are essential. We propose the use of spectroscopy data-produced by near-infrared (near-IR/NIR) and mid-infrared (mid-IR/MIR) spectroscopies, in particular—for melamine detection in complex dairy matrixes. None of the up-to-date reported IR-based methods for melamine detection has unambiguously shown its wide applicability to different dairy products as well as limit of detection (LOD) below 1 ppm on independent sample set. It was found that infrared spectroscopy is an effective tool to detect melamine in dairy products, such as infant formula, milk powder, or liquid milk. ALOD below 1 ppm (0.76 ± 0.11 ppm) can be reached if a correct spectrum preprocessing (pretreatment) technique and a correct multivariate (MDA) algorithm—partial least squares regression (PLS), polynomial PLS (Poly-PLS), artificial neural network (ANN), support vector regression (SVR), or least squares support vector machine (LS-SVM)—are used for spectrum analysis. The relationship between MIR/NIR spectrum of milk products and melamine content is nonlinear. Thus, nonlinear regression methods are needed to correctly predict the triazine-derivative content of milk products. It can be concluded that mid- and near-infrared spectroscopy can be regarded as a quick, sensitive, robust, and low-cost method for liquid milk, infant formula, and milk powder analysis. 相似文献
This study compares the performance of partial least squares (PLS) regression analysis and artificial neural networks (ANN) for the prediction of total anthocyanin concentration in red-grape homogenates from their visible-near-infrared (Vis-NIR) spectra. The PLS prediction of anthocyanin concentrations for new-season samples from Vis-NIR spectra was characterised by regression non-linearity and prediction bias. In practice, this usually requires the inclusion of some samples from the new vintage to improve the prediction. The use of WinISI LOCAL partly alleviated these problems but still resulted in increased error at high and low extremes of the anthocyanin concentration range. Artificial neural networks regression was investigated as an alternative method to PLS, due to the inherent advantages of ANN for modelling non-linear systems. The method proposed here combines the advantages of the data reduction capabilities of PLS regression with the non-linear modelling capabilities of ANN. With the use of PLS scores as inputs for ANN regression, the model was shown to be quicker and easier to train than using raw full-spectrum data. The ANN calibration for prediction of new vintage grape data, using PLS scores as inputs, was more linear and accurate than global and LOCAL PLS models and appears to reduce the need for refreshing the calibration with new-season samples. ANN with PLS scores required fewer inputs and was less prone to overfitting than using PCA scores. A variation of the ANN method, using carefully selected spectral frequencies as inputs, resulted in prediction accuracy comparable to those using PLS scores but, as for PCA inputs, was also prone to overfitting with redundant wavelengths. 相似文献
Laser-induced breakdown spectroscopy (LIBS) is an on-line, real-time technology that can produce immediate information about the elemental contents of tissue samples. We have previously shown that LIBS may be used to distinguish cancerous from non-cancerous tissue. In this work, we study LIBS spectra produced from chicken brain, lung, spleen, liver, kidney and skeletal muscle. Different data processing techniques were used to study if the information contained in these LIBS spectra is able to differentiate between different types of tissue samples and then identify unknown tissues. We have demonstrated a clear distinguishing between each of the known tissue types with only 21 selected analyte lines from each observed LIBS spectrum. We found that in order to produce an analytical model to work well with new sample we need to have representative training data to cover a wide range of spectral variation due to experimental or environmental changes. 相似文献
It has been proved that near-infrared (NIR) spectroscopy is a powerful analytical tool in the pharmaceutical industries1, especially in the quantitative analysis of the pharmaceu- tical tests during the last decades2-4. Currently, the quantitative analyti… 相似文献
Multi-linear regression analysis (MLR), radial basis function (RBF) and multilayer perceptron (MLP) of artificial neural network (ANN) with five inputs (temperature, concentrations of HCl, TOA, Cyanex 921, Zr (IV) and percentage of extraction (%E)) as only output were employed for the construction of models. It was observed that ANN (RBF and MLP) performed better as compared to the MLR model. Based on the models proposed, the extraction of Zr(IV) could be predicted under variable experimental conditions of concentrations of HCl, TOA (Tri-n-octylamine), Cyanex 921 (Tri-n-octyl phosphineoxide), Zr(IV) and temperature. The nonlinear and complex relation between the percentage of extraction and operating variables have been determined using two and three layered feed forward neural network with back-propagation of error learning algorithm. Uncertainties in data have been determined in terms of statistical parameters such as root mean-squared error and R-squared values to check the efficiency of the model for prediction. 相似文献
Two artificial neural network models (forward and inverse) are developed to describe ethylene/1‐olefin copolymerization with a catalyst having two site types using training and testing datasets obtained from a polymerization kinetic model. The forward model is applied to predict the molecular weight and chemical composition distributions of the polymer from a set of polymerization conditions, such as ethylene concentration, 1‐olefin concentration, cocatalyst concentration, hydrogen concentration, and polymerization temperature. The results of the forward model agree well with those from the kinetic model. The inverse model is applied to determine the polymerization conditions to produce polymers with desired microstructures. Although the inverse model generates multiple solutions for the general case, unique solutions are obtained when one of the three key process parameters (ethylene concentration, 1‐olefin concentration, and polymerization temperature) is kept constant. The proposed model can be used as an efficient tool to design materials from a set of polymerization conditions.
The effects of three structural parameters on flow field and power consumption of in-line high shear mixer (HSM) were investigated by large eddy simulation (LES). In addition, an artificial neural network (ANN) is used to predict the relationship between the structural parameters and the power consumption, and the effect of dimensionless structural parameters on the power number constant Poz and k1 is studied. The results show that the stator tooth thickness and the tooth tip-base distance have a slight effect on the flow field, and the shear gap width is a key parameter affecting the flow field. As the stator teeth thickness, the tooth tip-base distance and the shearing gap width increases, the power number Po decreases. There is a linear relationship between the constant k1 and the dimensionless structural parameters. With the increase of the dimensionless parameter Ts/Ds-o of the stator tooth thickness, the dimensionless parameter St/H of the tooth tip-base distance, and the dimensionless parameter Sg/DR-o of the shear gap width, the constant k1 decreases. With the increase of the parameter St/H, Sg/DR-o and Ts/Ds-o, the constant Poz first increases and then decreases. There is a linear relationship between the constant Poz and the parameter Ts/h. With the increase of the parameter Ts/h, the constant Poz increases. 相似文献
The objective of this study was to investigate the extraction efficiency of 9 natural deep eutectic solvents (NDES) with the assistance of ultrasound for phenolic acids, flavonols, and flavan-3-ols in muscadine grape (Carlos) skins and seeds in comparison to 75% ethanol. Artificial neural networking (ANN) was applied to optimize NDES water content, ultrasonication time, solid-to-solvent ratio, and extraction temperature to achieve the highest extraction yields for ellagic acid, catechin and epicatechin. A newly formulated NDES (#1) consists of choline chloride: levulinic acid: ethylene glycol 1:1:2 and 20% water extracted the highest amount of ellagic acid in the skin at 22.1 mg/g. This yield was 1.73-fold of that by 75% ethanol. A modified NDES (#3) consisting of choline chloride: proline: malic acid 1:1:1 and 30% water extracted the highest amount of catechin (0.61 mg/g) and epicatechin (0.89 mg/g) in the skin, and 2.77 mg/g and 0.37 mg/g in the seed, respectively. The optimal yield of ellagic acid in the skin using NDES #1 was 25.3 mg/g (observed) and 25.3 mg/g (predicted). The optimal yield of (catechin + epicatechin) in seed using NDES #3 was 9.8 mg/g (observed) and 9.6 mg/g (predicted). This study showed the high extraction efficiency of selected NDES for polyphenols under optimized conditions. 相似文献