十八烷基取代吲哚啉螺吡喃衍生物的合成及光谱性质

螺吡喃类化台物是一种重要的光响应分子,它在光信息存储技术中作为光盘材料具有广阔的应用前景。这类化合物在不同介质中的光谱、光电和光致变色性质引起了众多科学家的兴趣,并且已有大量研究工作报道。近年来,光致变色固体膜,特别是光致变色化合物的LB膜和超分子膜的特性引起了广泛的关注。因为在这种状态下,化合物分子具     

Comparison of indices and multivariate models to non-destructively predict the fruit chlorophyll by means of visible spectrometry in apple fruit

Non-destructive, rapid, instrumental tools in fruit production are required for predicting the optimum harvest window and monitoring fruit quality during shelf life. The degree of chlorophyll degradation is a sensitive indicator for fruit maturation and ageing. Adequate indices for chlorophyll prediction by means of non-destructive spectral analysis have been studied in the fields of photosynthesis research and remote sensing developments. However, an evaluation of these indices and multivariate linear regression models does not exist so far for predicting the fruit chlorophyll content.Spectral transmittance recordings in the visible wavelength range were carried out on apple fruit Malus domestica Borkh. ‘Elstar’ (n=99) and ‘Jonagold’ (n=117). The fruit chlorophyll a content of ‘Elstar’ apples was measured wet-chemically and predicted by means of the specific indices: NDVI, Tr₆₉₈/Tr₇₆₀, red-edge, TrII on Tr′(λ), TrII, and RVSI with the correlation coefficients of determination R²=0.84, 0.81, 0.75, 0.81, 0.15, and 0.67, respectively. Partial least-squares (PLS) calibration models were built using calibrated spectra (630-730 nm), first derivative of spectra and second derivative of spectra yielding multivariate correlation coefficients of determination R²=0.81, 0.86, and 0.92, respectively. Similar results were found for ‘Jonagold’ apple fruit. Linear regression of indices and PLS calibration models were empirically tested on the chlorophyll a content of apple fruit measuring the same cultivars grown in a different seasons and growing locations. In the case of ‘Elstar’ apple fruit, correlation coefficients of fruit chlorophyll a content analysed wet-chemically as well as fruit maturation measured as calendar weeks were similar to those in the calibration experiment. Application of linear regression equations of indices and PLS models on spectra of ‘Jonagold’ apple fruit led to less accurate results for those methods, which use wavelengths above 720 nm as indicative range.     

Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system

Because the shape of prawn is not round, spectroscopy instruments cannot measure the spectra of the whole prawn without containing background information. In this study, an online hyperspectral imaging system in the spectral region of 380–1100 nm was developed to determine the moisture content of prawns at different dehydrated levels. Hyperspectral images of prawns were acquired at different dehydration periods. The spectra of prawns then were extracted from hyperspectral images based on ‘Manual Prawn Mask’ and ‘Automatic Prawn Mask’, respectively. Spectral data were analyzed using partial least squares regression (PLSR) and least-squares support vector machines (LS-SVM) to establish the calibration models, respectively. Successive projections algorithm (SPA) was first applied for the optimal wavelength selection in the hyperspectral image analysis. Out of 482 wavelengths, only twelve wavelengths (428, 445, 544, 569, 629, 672, 697, 760, 827, 917, 958, and 999 nm) were selected by SPA as the optimum wavelengths for moisture prediction. Based on these optimum wavelengths, a multiple linear regression (MLR) calibration model was established and used to obtain the moisture distribution of each prawn. The overall results of this study revealed the potentiality of hyperspectral imaging as an objective and non-destructive method to obtain the content and distribution of moisture of prawns whose shapes are not round.     

Near-infrared spectra combined with partial least squares for pH determination of toothpaste of different brands

Near-infrared spectroscopy(NIR),which is generally used for online monitoring of the food analysis and production process, was applied to determine the internal quality of toothpaste samples.It is acknowledged that the spectra can be significantly influenced by non-linearities introduced by light scatter,therefore,four data preprocessing methods,including off-set correction, 1st-derivative,standard normal variate(SNV) and multiplicative scatter correction(MSC),were employed before the date analysis. The multivariate calibration model of partial least squares(PLS) was established and then was used to predict the pH values of the toothpaste samples of different brand.The results showed that the spectral date processed by MSC was the best one for predicting the pH value of the toothpaste samples.     

Determination of thiamine HCl and pyridoxine HCl in pharmaceutical preparations using UV-visible spectrophotometry and genetic algorithm based multivariate calibration methods

Simultaneous determination of binary mixtures pyridoxine hydrochloride and thiamine hydrochloride in a vitamin combination using UV-visible spectrophotometry and classical least squares (CLS) and three newly developed genetic algorithm (GA) based multivariate calibration methods was demonstrated. The three genetic multivariate calibration methods are Genetic Classical Least Squares (GCLS), Genetic Inverse Least Squares (GILS) and Genetic Regression (GR). The sample data set contains the UV-visible spectra of 30 synthetic mixtures (8 to 40 microg/ml) of these vitamins and 10 tablets containing 250 mg from each vitamin. The spectra cover the range from 200 to 330 nm in 0.1 nm intervals. Several calibration models were built with the four methods for the two components. Overall, the standard error of calibration (SEC) and the standard error of prediction (SEP) for the synthetic data were in the range of <0.01 and 0.43 microg/ml for all the four methods. The SEP values for the tablets were in the range of 2.91 and 11.51 mg/tablets. A comparison of genetic algorithm selected wavelengths for each component using GR method was also included.     

The use of MS classifiers and structure generation to assist in the identification of unknowns in effect-directed analysis

Structure generation and mass spectral classifiers have been incorporated into a new method to gain further information from low-resolution GC-MS spectra and subsequently assist in the identification of toxic compounds isolated using effect-directed fractionation. The method has been developed for the case where little analytical information other than the mass spectrum is available, common, for example, in effect-directed analysis (EDA), where further interpretation of the mass spectra is necessary to gain additional information about unknown peaks in the chromatogram. Structure generation from a molecular formula alone rapidly leads to enormous numbers of structures; hence reduction of these numbers is necessary to focus identification or confirmation efforts. The mass spectral classifiers and structure generation procedure in the program MOLGEN-MS was enhanced by including additional classifier information available from the NIST05 database and incorporation of post-generation ‘filtering criteria’. The presented method can reduce the number of possible structures matching a spectrum by several orders of magnitude, creating much more manageable data sets and increasing the chance of identification. Examples are presented to show how the method can be used to provide ‘lines of evidence’ for the identity of an unknown compound. This method is an alternative to library search of mass spectra and is especially valuable for unknowns where no clear library match is available.     

Multivariate analytical sensitivity in the determination of selenium, copper, lead and cadmium by stripping voltammetry when using soft calibration

A new approach to the multivariate sensitivity concept based on the determination of the capability of discrimination of a method of analysis is shown. Thus the analytical sensitivity is defined in this work by the analyte concentration that a analytical method is able to discriminate, which implies the estimation of the ‘false noncompliance’ and ‘false compliance’. In this approach the estimation of the multivariate analytical sensitivity is independent of scale factors and calibration models, and allows one to study the behavior of a analytical method for several concentrations and matrix. The estimation of this parameter in the simultaneous determination of selenium, copper, lead and cadmium by stripping voltammetry when using soft calibration is carried out, showing that different multivariate analytical sensitivities are obtained for each metal.     

Elimination of interference information by a new hybrid algorithm for quantitative calibration of near infrared spectra

A new hybrid algorithm is proposed to eliminate the interference information for multivariate calibration of near-infrared (NIR) spectra that includes noise, background and systemic spectral variation irrelevant to concentration. The method consists of two parts: approximate derivative based on continuous wavelet transform (CWT) and orthogonal signal correction (OSC). After the approximate derivative calculated by CWT, OSC was performed. It was successfully applied to real complex NIR spectral data to eliminate the interference information. Correction for the interference of NIR spectra resulted in a substantial improvement in the predicted precision, and a more concise calibration model was obtained. The proposed procedure also compared favourably with several pretreatment methods, and the new method appears to provide a high-performance pretreatment tool for multivariate calibration of NIR spectra. In addition, the strategy proposed here can be applied to various other spectral data for quantitative purposes as well.     

三线性直接分解法分析高维灰色体系

对于由多个两维测量数据组成的三维阵，本文提出一种新三线性直接分解方法。采用高维ＰＣＡ分解，从三维阵中直接提取抽象光谱和抽象浓度，再结合ＱＺ算法，唯一地确定混合物中各组分光谱的浓度。该方法可以排除其它未知组分的干扰，适用于高维灰色体系定性定量分析和多点校准。用模拟数据讨论了光谱分离度对该方法的影响，应用于混合维生素Ｂ１、Ｂ２和Ｂ６的荧光分析，求得的光谱和浓度与实验值吻合很好。     

Application of probabilistic neural networks in qualitative analysis of near infrared spectra: determination of producing area and variety of loquats

Near infrared (NIR) spectra of a sample can be treated as a signature, allowing samples to be grouped on basis of their spectral similarities. Near infrared spectroscopy (NIRS) combined with probabilistic neural networks (PNN) have been used to discriminate producing area and variety of loquats. Two varieties of loquats (‘Dahongpao’ and ‘Jiajiaozhong’) picked from two producing areas of ‘Tangxi’ and ‘Cunan’ in Zhejiang province were analyzed in this study. Principal component analysis (PCA) was applied before PNN modeling and the results indicated that the dimension of the vast spectral data can be effectively reduced. For each model, half samples were used to train the network and the remaining half were used to test the network. The results of the PCA-PNN models for discriminating the variety of samples from the same producing area or for discriminating the producing area of the same variety samples were much better than those of the PCA-PNN models for discriminating variety or producing area of all loquat samples. The results of this study show that NIRS combined with PCA-PNN is a feasible way for qualitative analysis of discriminating fruit producing areas and varieties.     

Microimaging FT-IR of head and neck tumors. V. Odontogenic cystic lesions

This study has been undertaken to investigate spectral features of cystic lesions of the jaw with the aim to understand their tumoral progression and to evidence initial signals of neoplastic changes.Three important groups (according to the World Health Organization classification) representing inflammatory (radicular) and developmental (orthokeratinized odontogenic, OOC) cysts as well as keratocystic odontogenic tumor (KCOT) of the jaw have been studied by microimaging infrared spectroscopy. From the spectral data analysed with the multivariate pattern recognition procedure, representative spectra were isolated and used to build correlation maps to localize the biochemical components in the tissue.The procedure enabled to better understand spectral features of these classes of cysts and to discriminate tumoral from non-tumoral spectra through the analysis of ‘vibrational markers’. In KCOT, the correlation of calcium derivatives (in metaplastic or displastic bones) with the tumor has been highlighted, too. The distribution of various biochemical components in the tissues, achieved through correlation maps of representative spectra, resulted in satisfactory agreement with the histopathological analysis. ‘Spectral isolation’ of micro tumorigenic zones in some normal OOC cysts, demonstrated the potentiality of infrared spectroscopy to evidence the early stage of a hidden lesion.     

Multicomponent determination of the pesticide naptalam and its metabolites in river water, by applying partial least squares calibration to the derivative spectrophotometric signals

A multivariate calibration method, partial least squares (types PLS-1 and PLS-2), was applied to the simultaneous determination of naptalam (N-(1-naphthyl) phthalamic acid) and its metabolites N-(1-naphthyl) phthalimide and 1-naphthylamine in mixtures by UV-visible absorption spectrophotometry. The absorption and first-derivative absorption spectra of mixtures were used to perform the optimization of the calibration matrices by the PLS method. Two different experimental designs for the three-component mixtures are assayed and the results are discussed. The proposed method with the derivative spectra was applied to the determination of these analytes in river water at the ppb level.     

Efficient use of pure component and interferent spectra in multivariate calibration

Partial Least Squares (PLS) is by far the most popular regression method for building multivariate calibration models for spectroscopic data. However, the success of the conventional PLS approach depends on the availability of a ‘representative data set’ as the model needs to be trained for all expected variation at the prediction stage. When the concentration of the known interferents and their correlation with the analyte of interest change in a fashion which is not covered in the calibration set, the predictive performance of inverse calibration approaches such as conventional PLS can deteriorate. This underscores the need for calibration methods that are capable of building multivariate calibration models which can be robustified against the unexpected variation in the concentrations and the correlations of the known interferents in the test set. Several methods incorporating ‘a priori’ information such as pure component spectra of the analyte of interest and/or the known interferents have been proposed to build more robust calibration models. In the present study, four such calibration techniques have been benchmarked on two data sets with respect to their predictive ability and robustness: Net Analyte Preprocessing (NAP), Improved Direct Calibration (IDC), Science Based Calibration (SBC) and Augmented Classical Least Squares (ACLS) Calibration. For both data sets, the alternative calibration techniques were found to give good prediction performance even when the interferent structure in the test set was different from the one in the calibration set. The best results were obtained by the ACLS model incorporating both the pure component spectra of the analyte of interest and the interferents, resulting in a reduction of the RMSEP by a factor 3 compared to conventional PLS for the situation when the test set had a different interferent structure than the one in the calibration set.     

Selectivity for glucose,glucose-6-phosphate,and pyruvate in ternary mixtures from the multivariate analysis of near-infrared spectra

Near-infrared spectroscopy offers the potential for direct in situ analysis in complex biological systems. Chemical selectivity is a critical issue for such measurements given the extent of spectral overlap of overtone and combination spectra. In this work, the chemical basis of selectivity is investigated for a set of multivariate calibration models designed to quantify glucose, glucose-6-phosphate, and pyruvate independently in ternary mixtures. Near-infrared spectra are collected over the combination region (4,000–5,000 cm⁻¹) for a set of 60 standard solutions maintained at 37 °C. These standard solutions are composed of randomized concentrations (0.5–30 mM) of glucose, glucose-6-phosphate, and pyruvate. Individual calibration models are constructed for each solute by using the partial least-squares (PLS) algorithm with optimized spectral range and number of latent variables. The resulting standard errors are 0.90, 0.72, and 0.32 mM for glucose, glucose-6-phosphate, and pyruvate, respectively. A pure component selectivity analysis (PCSA) demonstrates selectivity for each solute in these ternary samples. The concentration of each solute is also predicted for each sample by using a set of net analyte signal (NAS) calibration models. A comparison of the PLS and NAS calibration vectors demonstrates the chemical basis of selectivity for these multivariate methods. Selectivity of each PLS and NAS calibration model originates from the unique spectral features associated with the targeted analyte. Overall, selectivity is demonstrated for each solute with an order of sensitivity of pyruvate > glucose-6-phosphate > glucose. Figure Combination near-infrared spectroscopy allows selective analytical measurements for glucose, glucose-6-phosphate, and pyruvate in ternary mixtures owing to the uniqueness of the individual absorption spectra for each solute     

Rapid Determination of Metabolites in Bio‐fluid Samples by Raman Spectroscopy and Optimum Combinations of Chemometric Methods

The application of Raman spectroscopic techniques combined with multivariate chemometrics signal processing promise new means for the rapid multidimensional analysis of metabolites non‐destructively, with little or no sample preparation and little sensitivity to water. However, Rayleigh scattering, fluorescence and uncontrolled variance present substantial challenges for the accurate quantitative analysis of metabolites at physiological levels in biologically varying samples. Effective strategies include the application of chemometrics pretreatments for reducing Raman spectral interference. However, the arbitrary application of individual or combined pretreatment procedures can significantly alter the outcome of a measurement, thereby complicating spectral analysis. This paper evaluates and compares six signal pretreatment methods for correcting the baseline variances, together with three variable selection methods for eliminating uninformative variables, all within the context of multivariate calibration models based on partial least squares (PLS) regression. Raman spectra of 90 artificial bio‐fluid samples with eight urine metabolites at near‐physiological concentrations were used to test these models. The combination of multiplicative scatter correction (MSC), continuous wavelet transform (CWT), randomization test (RT) and PLS modeling presented the best performance for all the metabolites. The correlation coefficient (R) between predicted and prepared concentration reached as high as 0.96.     

Direct sampling tandem mass spectrometry (MS/MS) and multiway calibration for isomer quantitation

Direct sampling tandem mass spectrometry (MS/MS) was used for the quantitation of mixtures of the isomers 2-, 3- and 4-ethyl pyridine. The similarity between the analytes and the second-order nature of MS/MS data require the use of multivariate calibration techniques capable of handling multiway data. Multilinear PLS (N-PLS) was applied here, as well as the alternative technique of unfolding the data and using standard two-way PLS. Particular attention was paid to the optimal type of spectral preprocessing. Due to the presence of heteroscedastic noise the logarithmic transform of the spectra prior to calibration gives the best results. Predictions errors of the order of 10-15% were obtained, which compare well with other results found in the literature.     

An adaptive strategy for selecting representative calibration samples in the continuous wavelet domain for near-infrared spectral analysis

Sample selection is often used to improve the cost-effectiveness of near-infrared (NIR) spectral analysis. When raw NIR spectra are used, however, it is not easy to select appropriate samples, because of background interference and noise. In this paper, a novel adaptive strategy based on selection of representative NIR spectra in the continuous wavelet transform (CWT) domain is described. After pretreatment with the CWT, an extension of the Kennard–Stone (EKS) algorithm was used to adaptively select the most representative NIR spectra, which were then submitted to expensive chemical measurement and multivariate calibration. With the samples selected, a PLS model was finally built for prediction. It is of great interest to find that selection of representative samples in the CWT domain, rather than raw spectra, not only effectively eliminates background interference and noise but also further reduces the number of samples required for a good calibration, resulting in a high-quality regression model that is similar to the model obtained by use of all the samples. The results indicate that the proposed method can effectively enhance the cost-effectiveness of NIR spectral analysis. The strategy proposed here can also be applied to different analytical data for multivariate calibration.     

校正集样品特性对鼠药中毒鼠强近红外光谱分析的影响

根据市售鼠药样品成分各异且相对复杂,建立6种不同成分体系和9个不同样本容量的校正集,运用小波变换压缩鼠药的近红外透射光谱数据,结合BP反向神经网络算法对压缩的数据进行建模,考察校正集样品特性对模型预测能力的影响。试验结果表明:采用BP神经网络算法建立定量模型时,只要校正集样品中包含了与预测样品性质相似的样本,就能准确地对复杂样品进行近红外定量分析。当校正集容量分别为72和84时,模型预测结果趋于平稳。当校正集数量为96时,模型的最大相关系数为0.959 8,预测最小标准差和平均相对误差分别为1.893%和1.92%。     

Hyperspectral NIR imaging for calibration and prediction: a comparison between image and spectrometer data for studying organic and biological samples

A hyperspectral image in the near infrared contains thousands of position-referenced spectra. After imaging reference materials of known composition it is possible to build Partial Least Squares (PLS) regression models for predicting unknown compositions from new images or spectra. In this paper a comparison is made between spectra from a hyperspectral image and spectra from two spectrometers: a scanning grating instrument with rotating sample holders and an FT-NIR instrument utilizing a fiber-optic probe. The raw spectra and the quality of the PLS calibration models and predictions are compared. Two sample datasets consist of a set of 13 designed artificial mixtures of pure constituents and a selection of 13 sampled cheeses. The prediction error from the hyperspectral image spectra is between that of the two spectrometers. For a typical food sample, the average bias [and replicate standard deviation] was -0.6% [0.5%] for protein and -0.2% [1.3%] for fat. Comparable values for the best spectrometer were -0.2% bias for protein and -0.5% for fat. Some of the advantages of working with hyperspectral images are highlighted: the simultaneous exploration of representations of both spectral and spatial data, and the analysis of concentration profiles and concentration maps all contribute to better characterization of organic and biological materials.     

Isolation, structural elucidation and synthesis of a novel antioxidative pseudo-di-peptide, Hanasanagin, and its biogenetic precursor from the Isaria japonica mushroom

The entomogenous ‘Hanasanagitake’ mushroom, Isaria japonica, is used as a folk medicine and as a traditional health food choice in Japan. A search for naturally occurring antioxidative compounds from the mushroom led to the isolation of a novel pseudo-di-peptide, named Hanasanagin, and a possible biogenetic precursor. The structures of the pseudo-di-peptides were determined as (R)-3,4-diguanidinobutanoyl-(S)-DOPA and (R)-3,4-diguanidinobutanoyl-(S)-tyrosine by spectral analysis, chemical synthesis and enzymatic conversion.