Microalgae Monitoring in Microscale Photobioreactors via Multivariate Image Analysis

Microscale photobioreactors for microalgae growth represent an interesting technology for fast data production and biomass characterization; however, the small scale poses severe monitoring challenges, as traditional methods cannot be used. Non-invasive techniques are therefore needed to quantify biomass concentration and other culture properties, for example, pigment composition. To this purpose, a soft sensing approach based on multivariate image regression is proposed to exploit RGB images and/or PAM-imaging chlorophyll fluorescence. Different PLS (Partial Least Squares) regression models are used to estimate: (a) biomass concentration from the features extracted by RGB indices and/or PAM-imaging chlorophyll fluorescence measurements; and (b) Chlorophyll a content per cell from the features extracted by RGB indices and biomass concentration measurements. Every single model is aimed at characterizing the microalgae culture at different light intensities during batch growth. Results show that the proposed monitoring approach is as accurate as traditional measurement approaches and may represent a promising methodology for fast and inexpensive monitoring of microscale photobioreactors.

Chemometric Approaches in Questioned Documents

Questioned documents comprehend analysis of identity theft, forged signatures or texts, documents alterations and falsification of security documents or banknotes. Questions involving inks or paper require chemical analysis, and multivariate analysis or chemometrics has been an emerging tool for data evaluation and interpretation after instrumental data collection in this area. The goal of this study is to identify previous articles that applied multivariate analysis within questioned documents for forensic purposes. The search for articles was performed in four databases (Google Scholar, Science Direct, Pubmed and Scopus). Sixty studies, published in the last ten years, were selected. Thirty-four articles described pen inks analysis; fourteen articles comprehended printed documents studies; eight articles evaluated paper analysis, and four articles included banknotes analysis. Spectroscopy, mass spectrometry, chromatography, thermo gravimetric analysis and multivariate image analysis were the analytical methods applied to collect chemical data. Chemometrics methods included mainly unsupervised pattern recognition techniques, regression methods, and supervised pattern recognition techniques, amongst other methods. This review summarized and discussed multivariate analysis techniques applied in different questioned documents sub-areas, highlighting the importance of this knowledge for forensic analysts. In addition, it shows new research topics such as different printing and pen inks, papers and security documents analysis herein not included.

An effective prediction of biomagnification factors for organochlorine pollutants

Biomagnification factor (BMF) is an important index of pollutants in food chains but its experimental determination is quite tedious. In this contribution, as the feature descriptors of molecular information, Tchebichef moments (TMs) were calculated from their structural images. Then stepwise regression was employed to establish the prediction model for the logBMF of organochlorine pollutants. The correlation coefficient with leave-one-out cross-validation (Rcv) was 0.9570; the correlation coefficient of prediction (Rp) and root mean square error (RMSEp) for external independent test set reached 0.9594 and 0.2129, respectively. Compared with traditional two-dimensional (2D) quantitative structure-property relationship (QSPR) and the reported augmented multivariate image analysis applied to QSPR (aug-MIA-QSPR), the proposed approach is more simple, accurate and reliable. This study not only obtained the model with better stability and predictive ability for the BMF of organochlorine pollutants, but also provided another effective approach to QSPR research.

An Effective Prediction of Biomagnification Factors for Organochlorine Pollutants

Biomagnification factor (BMF) is an important index of pollutants in food chains but its experimental determination is quite tedious. In this contribution, as the feature descriptors of molecular information, Tchebichef moments (TMs) were calculated from their structural images. Then stepwise regression was employed to establish the prediction model for the logBMF of organochlorine pollutants. The correlation coefficient with leave-one-out cross-validation (Rcv) was 0.9570 and the correlation coefficient of prediction (Rp) for external independent test set was 0.9594. Compared with traditional two-dimensional (2D) quantitative structure-property relationship (QSPR) and the reported augmented multivariate image analysis applied to QSPR (aug-MIA-QSPR), the proposed approach is more simple, accurate and reliable. This study not only obtained the model with better stability and predictive ability for the BMF of organochlorine pollutants, but also provided another effective approach to QSPR research.