Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational ...and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning-based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications.
Grana Padano (GP) cheese is a renowned PDO Italian cheese whose nutritional characteristics and market price are influenced by the ripening stage. In this work, it was demonstrated that the combined ...use of untargeted 1H NMR profiling and chemometric analysis can be used as a powerful tool to quantitatively characterize GP ripening and production, focusing on both aqueous and lipid fractions. An initial exploratory analysis revealed substantial variations in the aqueous fraction attributable to aging time, year and season of production. Multivariate analysis was adopted to show these differences, mainly attributable to amino acids. In contrast, the lipid fraction analysis highlighted the impact of production season on fatty acid unsaturation, influenced by feed variations. As regards the production process, this study focuses on the variations induced by bactofugation. In this respect, the aqueous fraction was found to be extensively influenced by this centrifugation step, affecting compounds crucial to organoleptic characteristics.
•Lipophilic and hydrophilic parts of the cheese metabolome reflect different aspects of cheese ripening•Scientific assessment of the effects on cheese composition made by bactofugation step•Development of a fast and simple sample preparation for both fractions, aqueous and lipid
Providing an easy explanation of the fundamentals, methods, and applications of chemometrics •Acts as a practical guide to multivariate data analysis techniques •Explains the methods used in ...Chemometrics and teaches the reader to perform all relevant calculations •Presents the basic chemometric methods as worksheet functions in Excel •Includes Chemometrics Add In for download which uses Microsoft Excel® for chemometrics training •Online downloads includes workbooks with examples
The author reviews his experiences teaching chemometrics for 25 + years, primarily in short course format. Teaching to live classes and also via remote learning are discussed. Challenges to learning ...are elucidated. Tips for successful classes are shared.
•Saffron as a high value spice is prone to mislabeling of origin.•Stable isotopes and bioelements are used to discriminate Iranian and Chinese saffron.•Chemometric models show good country and ...regional discrimination of saffron.
Origin verification of high-value saffron is essential for fair trade and to protect consumers' interests and rights. A traceability method using elemental content (% C and % N) and stable isotopes (δ13C, δ2H, δ18O, and δ15N) combined with chemometrics was developed to discriminate saffron from Iran and China and classify major domestic production areas in China. Results showed that Iranian samples had lower % C and % N contents but higher δ13C values than Chinese origin saffron, with δ13C acting as an important variable for origin discrimination. Moreover, δ2H and δ13C isotopes were found to be important variables to classify Chinese regional saffron origin. Two supervised pattern recognition models (PLS-DA) developed to classify Iranian and Chinese saffron, and regional Chinese saffron had a discrimination accuracy of 85.0 % and 80.2 %, respectively. These models provide the basis for a new regulatory inspection procedure to verify saffron origin and label claims, minimizing fraudulent mislabeling and adding value to saffron from specific regions.
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