Component reconstruction in the primary space of spectra and concentrations. Alternating regression and related direct methods

A set of methods that extract the spectral components in a chromatographic run is considered. The methods do not need libraries of previously known spectra or retention times. The methods have been developed for two-dimensional spectra but they can also be used for chromatographic analyses with a single-channel detector. The methods are direct; they do not use principal components as the starting point. Alternating regression (AR) remains in the primary space of spectra and concentrations during the calculations. Random numbers are used as the starting spectra. Regression is used to solve first for the concentrations, then for the spectra. The method uses two kinds of constraints: all spectra and concentrations are forced to be positive; and all concentration profiles are forced to a unimodal shape with a single local maximum. It is assumed that all observations are a linear sum of components. Compact alternating regression (CAR) is a new variant of the basic AR. The idea is to replace multiplication of a large matrix by two multiplications of smaller matrices. This typically speeds up the iterations by a factor of ten. AR and CAR have been successfully used with combined techniques such as gas chromatography—mass spectrometry and liquid chromatography with UV—visible detection. The reliability of the solution is checked by repeatedly injecting noise and performing the analysis several times. This produces estimates of confidence intervals. AR and CAR have recently been extended to handle single-dimensional signals. Examples are single-channel detectors such as the flame ionization detector in gas or liquid chromatography with a fixed-wavelength UV detector. A batch of samples is used as the observation matrix. As a result, one obtains both the concentrations and the elution shapes of individual chromatographic peaks.     

Environmental monitoring using a conventional photographic digital camera for multianalyte disposable optical sensors

The primary interest of this study concerns the use of an inexpensive photographic digital camera as the detection system, using its own flash as the source of light to present a new analytical procedure to measure disposable multianalyte optical sensors for potassium, magnesium, hardness and conventional pH test strips. The camera arrangement was designed in a fixed position over an optical board with controllable ambient conditions. After acquiring the digital image, the analytical information contained in each test zone is analyzed using theRGB colour space. Reflectance measurements were developed to study the colourimetric and spectral characteristics of the test zones. We obtained the following application ranges and precision in terms of relative standard deviation (RSD %): for potassium from 3.2 × 10⁻⁷ to 0.1 M with a precision between 3.3 and 4.0%, for magnesium from 2.7 × 10⁻⁶ to 1.5 M showing a precision between 4.7 and 7.8% and finally for hardness from 4.3 × 10⁻² to 200,000 mg L⁻¹ CaCO₃ and between 5.1 and 7.0%. Moreover, the analytical characteristics of several optical procedures were compared with the results presented here. The proposed method was statistically validated against a reference procedure using samples of water from different sources and beverages, indicating that there are no significant statistical differences at a 95% confidence level.     

Maintaining the predictive abilities of multivariate calibration models by spectral space transformation

In quantitative on-line/in-line monitoring of chemical and bio-chemical processes using spectroscopic instruments, multivariate calibration models are indispensable for the extraction of chemical information from complex spectroscopic measurements. The development of reliable multivariate calibration models is generally time-consuming and costly. Therefore, once a reliable multivariate calibration model is established, it is expected to be used for an extended period. However, any change in the instrumental response or variations in the measurement conditions can render a multivariate calibration model invalid. In this contribution, a new method, spectral space transformation (SST), has been developed to maintain the predictive abilities of multivariate calibration models when the spectrometer or measurement conditions are altered. SST tries to eliminate the spectral differences induced by the changes in instruments or measurement conditions through the transformation between two spectral spaces spanned by the corresponding spectra of a subset of standardization samples measured on two instruments or under two sets of experimental conditions. The performance of the method has been tested on two data sets comprising NIR and MIR spectra. The experimental results show that SST can achieve satisfactory analyte predictions from spectroscopic measurements subject to spectrometer/probe alteration, when only a few standardization samples are used. Compared with the existing popular methods designed for the same purpose, i.e. global PLS, univariate slope and bias correction (SBC) and piecewise direct standardization (PDS), SST has the advantages of implementation simplicity, wider applicability and better performance in terms of predictive accuracy.     

粤航一号稻米及其亲本抗性淀粉含量的测定与比较

为确定航天品种功能稻米——粤航一号抗性淀粉含量,给临床观察膳食干预后果判断提供实验参考数据,并了解航天育种对该品种大米抗性淀粉含量的影响,用Goni测定方法测定了粤航一号、富硒粤航一号、长丝占、粳籼89 4种稻米的抗性淀粉含量。结果表明,粤航一号、富硒粤航一号、长丝占、粳籼89 4种稻米的抗性淀粉含量分别是(0.30±0.11)%、(0.47±0.37)%、(0.31±0.66)%、(0.36±0.62)%。可见粤航一号与亲本长丝占,及与富硒品种富硒粤航一号,和非航天品种优质大米粳籼89在抗性淀粉含量上无差异,航天育种对该品种稻米抗性淀粉生成遗传特征无影响。