This study describes two applications of a variant of the multivariate curve resolution alternating least squares (MCR-ALS) method with a correlation constraint. The first application describes the use of MCR-ALS for the determination of biodiesel concentrations in biodiesel blends using near infrared (NIR) spectroscopic data. In the second application, the proposed method allowed the determination of the synthetic antioxidant N,N'-Di-sec-butyl-p-phenylenediamine (PDA) present in biodiesel mixtures from different vegetable sources using UV-visible spectroscopy. Well established multivariate regression algorithm, partial least squares (PLS), were calculated for comparison of the quantification performance in the models developed in both applications. The correlation constraint has been adapted to handle the presence of batch-to-batch matrix effects due to ageing effects, which might occur when different groups of samples were used to build a calibration model in the first application. Different data set configurations and diverse modes of application of the correlation constraint are explored and guidelines are given to cope with different type of analytical problems, such as the correction of matrix effects among biodiesel samples, where MCR-ALS outperformed PLS reducing the relative error of prediction RE (%) from 9.82% to 4.85% in the first application, or the determination of minor compound with overlapped weak spectroscopic signals, where MCR-ALS gave higher (RE (%)=3.16%) for prediction of PDA compared to PLS (RE (%)=1.99%), but with the advantage of recovering the related pure spectral profile of analytes and interferences. The obtained results show the potential of the MCR-ALS method with correlation constraint to be adapted to diverse data set configurations and analytical problems related to the determination of biodiesel mixtures and added compounds therein.

A chemometrics course is offered to students in their fifth semester of the chemistry undergraduate program that includes an in-depth project. Students carry out the project over five weeks (three 8-h sessions per week) and conduct it in parallel to other courses or other practical work. The students conduct a literature search, carry out laboratory work, and write a technical report on a research subject of self-choice. In the project described here, the students worked on experimental design, using near-infrared spectroscopy and multivariate calibration, to develop methods to predict the properties of biodiesel and diesel blends. In addition to dealing with the chemometric tasks, the students synthesized the biodiesel sample and understood the importance as a renewable energy source.

A distillation device that acquires continuous and synchronized measurements of temperature, percentage of distilled fraction and NIR spectra has been designed for real-time monitoring of distillation processes. As a process model, synthetic commercial gasoline batches produced in Brazil, which contain mixtures of pure gasoline blended with ethanol have been analyzed. The information provided by this device, i.e., distillation curves and NIR spectra, has served as initial information for the proposal of new strategies of process modeling and multivariate statistical process control (MSPC). Process modeling based on PCA batch analysis provided global distillation trajectories, whereas multiset MCR-ALS analysis is proposed to obtain a component-wise characterization of the distillation evolution and distilled fractions. Distillation curves, NIR spectra or compressed NIR information under the form of PCA scores and MCR-ALS concentration profiles were tested as the seed information to build MSPC models. New on-line PCA-based MSPC approaches, some inspired on local rank exploratory methods for process analysis, are proposed and work as follows: a) MSPC based on individual process observation models, where multiple local PCA models are built considering the sole information in each observation point; b) Fixed Size Moving Window - MSPC, in which local PCA models are built considering a moving window of the current and few past observation points; and c) Evolving MSPC, where local PCA models are built with an increasing window of observations covering all points since the beginning of the process until the current observation. Performance of different approaches has been assessed in terms of sensitivity to fault detection and number of false alarms. The outcome of this work will be of general use to define strategies for on-line process monitoring and control and, in a more specific way, to improve quality control of petroleum derived fuels and other substances submitted to automatic distillation processes monitored by NIRS.

Concentrations of essential (Cu, Mn, and Zn) and toxic (Cr, Cd, and Pb) trace metals in 30 raw cow's milk samples were quantified using flame atomic absorption spectrometry. The samples were collected from the Nara-Awudarda, Tana-Abo, and Kosoye Amba-Rass sites in North Gondar, Ethiopia, preserved in a deep freezer (-20 °C), and then digested by Kjeldahl apparatus with HNO/HO (5:2; v/v) at 300 °C for 2.5 h. The data were subject to principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Overall hazard quotient (HQ) and carcinogenic risk (CR) values were also estimated to assess metal-related health risks. The mean concentrations of Cr, Mn, Cu, Zn, Cd, and Pb in the milk samples ranged 0.468-0.828, 1.614-2.806, 0.840-1.532, 1.208-5.267, ND-0.330, and ND-0.186 mg/kg, respectively. The lowest values were obtained for Kosoye Amba-Rass milk samples, while the highest were found for those collected from Nara-Awudarda milk samples, probably due to high mineral enrichment and metal leaching (especially Cd and Pb) from coal deposits. PCA revealed clustering of samples with respect to their geographic origin. Validation of PLS-DA model showed 100% classification efficiency using external validation samples and detected Cd and Cu as trace metal markers. The HQ and CR values were within the safe level; however, the former is close to the alert threshold level for Nara-Awudarda milk samples. Thus, further studies on common foodstuffs, constituting a higher proportion in the local diet, are required in this area to provide a complete risk assessment.

Formation of extractive-rich heartwood is a process in live trees that make them and the wood obtained from them more resistant to fungal degradation. Despite the importance of this natural mechanism, little is known about the deposition pathways and cellular level distribution of extractives. Here we follow heartwood formation in Larix gmelinii var. Japonica by use of synchrotron infrared images analyzed by the unmixing method Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). A subset of the specimens was also analyzed using atomic force microscopy infrared spectroscopy. The main spectral changes observed in the transition zone when going from sapwood to heartwood was a decrease in the intensity of a peak at approximately 1660 cm-1 and an increase in a peak at approximately 1640 cm-1. There are several possible interpretations of this observation. One possibility that is supported by the MCR-ALS unmixing is that heartwood formation in larch is a type II or Juglans-type of heartwood formation, where phenolic precursors to extractives accumulate in the sapwood rays. They are then oxidized and/or condensed in the transition zone and spread to the neighboring cells in the heartwood.

Funding from the European Community's Framework program for Research and Innovation Horizon 2020-2020 under grant agreement number 637232, related to the ProPAT project is acknowledged. Funding from Spanish government under the project CTQ2015-66254-C2-2-P is also acknowledged.

Purpose The current trend for continuous drug product manufacturing requires new, affordable process analytical techniques (PAT) to ensure control of processing. This work evaluates whether property models based on spectral data from recent Fabry-Pérot Interferometer based NIR sensors can generate a high-resolution moisture signal suitable for process control. Methods Spectral data and offline moisture content were recorded for 14 fluid bed dryer batches of pharmaceutical granules. A PLS moisture model was constructed resulting in a high resolution moisture signal, used to demonstrate (i) endpoint determination and (ii) evaluation of mass transfer performance.Results The sensors appear robust with respect to vibration and ambient temperature changes, and the accuracy of water content predictions (±13 % ) is similar to those reported for high specification NIR sensors. Fusion of temperature and moisture content signal allowed monitoring of water transport rates in the fluidised bed and highlighted the importance water transport within the solid phase at low moisture levels. The NIR data was also successfully used with PCA-based MSPC models for endpoint detection.Conclusions The spectral quality of the small form factor NIR sensor and its robustness is clearly sufficient for the construction and application of PLS models as well as PCA-based MSPC moisture models. The resulting high resolution moisture content signal was successfully used for endpoint detection and monitoring the mass transfer rate.KEY WORDS fluidised bed drying . mass transferresistance . MEMS Fabry-Pérot interferometer sensor . near infrared spectroscopy . online process monitoringElectronic supplementary material The online version of this article (https://doi.

Hyperspectral imaging (HSI) is a useful non-invasive technique that offers spatial and chemical information of samples. Often, different HSI techniques are used to obtain complementary information from the sample by combining different image modalities (Image Fusion). However, issues related to the different spatial resolution, sample orientation or area scanned among platforms need to be properly addressed. Unmixing methods are helpful to analyze and interpret the information of HSI related to each of the components contributing to the signal. Among those, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) offers very suitable features for image fusion, since it can easily cope with multiset structures formed by blocks of images coming from different samples and platforms and allows the use of optional and diverse constraints to adapt to the specific features of each HSI employed. In this work, a case study based on the investigation of cross-sections from rice leaves by Raman, synchrotron infrared and fluorescence imaging techniques is presented. HSI of these three different techniques are fused for the first time in a single data structure and analyzed by MCR-ALS. This example is challenging in nature and is particularly suitable to describe clearly the necessary steps required to perform unmixing in an image fusion context. Although this protocol is presented and applied to a study of vegetal tissues, it can be generally used in many other samples and combinations of imaging platforms.