AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
Picktorial error1/8/2023 ![]() ![]() All analytical measurements are subject to errors, sometimes called noise, that contribute uncertainty to any type of analysis. The goal of chemometrics can simply be stated as the differentiation between chemical variance and the variance due to measurement error. Chemometrics, a sub-discipline of analytical chemistry, emerged from the need for more advanced multivariate data analysis methods capable of solving more complex chemical problems. ![]() With high-dimensional measurements becoming increasingly common in chemistry, the efficient extraction of meaningful information from chemical data has never been more important. Finally, proportional error as a heteroscedastic noise was highlighted as the most important source of variation in the error structure of the smartphone-based spectrophotometer. Afterwards, PCA and MCR-ALS were employed for the decomposition of the ECMs and resolved profiles were translated to the error types. In this contribution, a smartphone-based spectrophotometer was constructed integrating simple optical elements-a tungsten lamp as source and a piece of digital versatile disc (DVD) as a reflecting diffraction grating to investigate the error sources of the smartphone-spectrophotometer.įor this purpose, error covariance matrices (ECMs) were calculated using a series of replication capturing error information. Otherwise, error structure weights them for further data analysis. Error structure information values the objects/channels in a given data set and variables have the same importance when the noise has identical independent distribution (i.i.d). Although smartphones have widely been applied for sensing\biosensing purposes, the error structure/type of their outputs remained unexplored. After modification to a spectrophotometer (smart spectrophotometer), they can be utilized for the quantitative or qualitative applications. Smartphones are state-of-the-art devices with several interesting features which make them promising for analytical purposes. Experimental results employing both synthetic and real data show the effectiveness of the proposed method in recovering the signals and inferring the underlying graph. Moreover, a fast algorithm employing the proximal point method has been proposed to solve the problem efficiently. In the final step, an optimization problem is proposed to learn the underlying graph topology from the recovered signals. An optimal graph filter is also developed to recover the graph signals from noisy observations. A factor analysis model is applied to represent the graph signals in a latent space where the basis is related to the underlying graph structure. In this paper, a graph topology inference approach is proposed to learn the underlying graph structure from a given set of noisy multi-variate observations, which are modeled as graph signals generated from a Gaussian Markov Random Field (GMRF) process. Some examples are social networks, internet of things networks (intelligent IoT and industrial IoT), biological connection networks, sensor networks and traffic network patterns. Learning the topology of a graph from available data is of great interest in many emerging applications. The results were also compared with those achieved on the same dataset from a benchtop system in order to provide references analogous with those in the literature. We demonstrate how a careful evaluation of the sources of variability related to an experiment can help in the understanding of the system under study in order to obtain a more reliable development of the method and consciously choose the analytical parameters and strategies of analysis. A step-by-step methodology is presented to statistically address the different issues to consider in order to obtain realistic models when using miniaturized NIR spectrometers. Because of the high interest in real applications, a common type of hygroscopic powder sample was selected: forages. In this study, different statistical strategies were employed to understand the features and limitations of handheld NIR instruments. Indeed, analytical applications that include the use of miniaturized instrumentation are subject to several sources of variability that need to be known at the time of method development. Several applications and studies are typically presented by comparing results obtained with benchtop instrumentation even when the analytical strategies are substantially different. The use of miniaturized NIR spectrometers is spreading over the scientific literature with a particular focus on developing methods as rapid and easy-to-use as possible and following the philosophy of green analytical chemistry.
0 Comments
Read More
Leave a Reply. |