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•An amine-selective sensor material has been developed by adsorbing a single BODIPY compound into a TLC plate.•The sensor exhibits structure-specific color changes in response to ...different amine vapors under both ambient and UV light.•Amine analysis can be conducted using hue data collected with a smartphone, eliminating the need for any additional analysis device.•A mobile application has been developed to analyze the collected data and provide analysis results.
Smartphone-assisted analysis has become widely utilized for detecting various species in recent years. In such studies, multiple dyes should be employed to ensure selectivity and analyte discrimination. In our research, we have demonstrated the capability of a specially synthesized dye to selectively detect and discriminate liquid amine vapors. The developed material employs meso-toluene-α,β,α’,β’-tetrabromoBODIPY immobilized on a thin-layer chromatography plate, exhibiting structure-specific color changes in response to amine vapors. The hue values of these colors, observed under both ambient and UV light, enable discrimination even among closely related amine structures. A mobile application has also been developed for the rapid interpretation of test results.
Cinnamon (Cinnamomum sp.) is a commonly used spice that is at risk of being counterfeited due to its intricate production process, which can compromise its integrity. This study aimed to develop a ...rapid detection method using Near Infrared (NIR) and Mid Infrared (MIR) spectroscopy, along with chemometrics, to identify fraud in genuine cinnamon powder. Various techniques, including exploratory, non-targeted, and targeted methods, were used to detect the presence of adulterants such as coffee husks and corn meal in cinnamon, with concentrations ranging from 10% to 50%. The exploratory and non-targeted models successfully identified the potential occurrence of fraud as a result of the addition of coffee husks and corn meal. The models' effectiveness was tested on commercial cinnamon samples, exposing a high proportion of potentially fraudulent samples. Adulteration percentages exceeded 60% for the presence of coffee husks and 50% for corn meal. The results show that NIR and MIR spectroscopy, when used with chemometrics, are effective for monitoring the quality of cinnamon powder.
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•NIR and MIR spectroscopy were effective in detecting fraud in cinnamon.•Exploratory models made it possible to identify fraud in cinnamon powder samples.•The PLS models successfully predicted the presence of adulterants in unknown samples.
► Infrared techniques are fast, accurate, and low-cost for biomass analysis. ► A comparison of infrared techniques and chemical method is made. ► Chemometric analaysis provides prediction model for ...composition analysis.
Current wet chemical methods for biomass composition analysis using two-step sulfuric acid hydrolysis are time-consuming, labor-intensive, and unable to provide structural information about biomass. Infrared techniques provide fast, low-cost analysis, are non-destructive, and have shown promising results. Chemometric analysis has allowed researchers to perform qualitative and quantitative study of biomass with both near-infrared and mid-infrared spectroscopy. This review summarizes the progress and applications of infrared techniques in biomass study, and compares the infrared and the wet chemical methods for composition analysis. In addition to reviewing recent studies of biomass structure and composition, we also discuss the progress and prospects for the applications of infrared techniques.
The development of chemometrics aims to provide an effective analysis approach for data generated by advanced analytical instruments. The success of existing analytical approaches in spectral ...analysis still relies on preprocessing and feature selection techniques to remove signal artifacts based on prior experiences. Data-driven deep learning analysis has been developed and successfully applied in many domains in the last few years. How to integrate deep learning with spectral analysis received increased attention for chemometrics. Approximately 20 recently published studies demonstrate that deep neural networks can learn critical patterns from raw spectra, which significantly reduces the demand for feature engineering. The composition of multiple processing layers improves the fitting and feature extraction capability and makes them applicable to various analytical tasks. This advance offers a new solution for chemometrics toward resolving challenges related to spectral data with rapidly increased sample numbers from various sources. We further provide a practical guide to the development of a deep convolutional neural network-based analytical workflow. The design of the network structure, tuning the hyperparameters in the training process, and repeatability of results is mainly discussed. Future studies are needed on interpretability and repeatability of the deep learning approach in spectral analysis.
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•Summarize the challenges of existing chemometric methods in spectral analysis.•Review the research progress on deep learning-based spectral analysis.•Provide a practical guide on deep learning approach in spectral analysis.
We report while understanding hydrogen uptake by organic based getters such as 1,4-bis(phenylethynyl)benzene (DEB) combined with a palladium(0)bis(dibenzylideneacetone) (Pd(dba)2) catalyst is ...essential, another crucial element to understand is the decomposition of the DEB, Pd(dba)2, and/or substrate material. The breakdown of these materials may create unwanted volatiles, which may interact with and lead to deterioration of sensitive materials. Moreover, it is critical to understand if different substrates cause the getter and/or catalyst to degrade in different manners. Utilizing comprehensive two-dimensional gas chromatography (GC×GC) with time-of-flight mass spectrometry (TOFMS), the presence of volatiles located in the headspace of various DEB/Pd(dba)2 getter substrates is examined. These samples include a getter infused silicone foam, a hydrogenated getter infused silicone foam, an activated carbon getter pellet, and a hydrogenated activated carbon getter pellet. Application of Fisher ratio (F-ratio) analyses lead to the identification of several compounds that are generated or consumed through the hydrogenation process. These include benzene derivatives such as bibenzyl, benzaldehyde, and vinyl benzoate in the activated carbon pellets and 1,5-diphenyl-3-pentanone, toluene, styrene, and 1–1'(2-pentene 1,5-diyl)bis benzene in the silicone foams, and alkane/alkene derivatives such undecane, 4-tridecene, and decane in the activated carbon pellets and 2,6-dimethyl undecane in the silicone foams. Further comparison of the different hydrogenated getter substrates (e.g. activated carbon pellet and silicone foam) indicates that the different substrates alter the decomposition products created from the degradation of the DEB and Pd(dba)2.
Chemometrics in forensic science Kumar, Raj; Sharma, Vishal
TrAC, Trends in analytical chemistry (Regular ed.),
August 2018, 2018-08-00, Volume:
105
Journal Article
Peer reviewed
This review represents a detailed discussion of the multivariate methods used in the examination of forensic exhibits; their advantages, disadvantages, and efficiency are compared. The last decade ...has seen the application of the chemometric methods combined with analytical techniques for characterization and discrimination of samples, which leads to the informative and representative examinations of the samples. Many research articles with reference to the use of chemometrics in forensic science have been published. This review has been divided into various sections which include chemometrics, its history, multivariate methods, and the application of chemometrics in various disciplines of forensic science. It is suggested that these new techniques and mathematical/statistical methods should be utilized in forensic science casework to get statistical confidence in the results.
•Chemometrics: Its history, types and application in various disciplines of forensic science.•Combined approach of analytical and multivariate methods; their advantages and disadvantages.•Chemometric methods are expedient due to their ease of interpreting results, reliability, and speed.•Advanced modeling methods such as SIMCA and SVM are gaining popularity.
The Raman spectra of dental tissues with periapical periodontitis have been analyzed. Chemometric analysis of the Raman spectra of hard dental tissues of healthy patients and the patients with ...periapical periodontitis has been carried out. The main spectral features of dental tissues with periapical periodontitis have been identified, which will further allow developing new methods of early detection of periapical periodontitis.
•21 metabolites were detected in honey produced in southeastern and southern Brazil.•Honey were discriminated by geographic and botanical origins using 1H NMR and PCA.•Eucalyptus spp., Hovenia ...dulcis, and Citrus spp. honey were discriminated.
This study applied qNMR spectroscopy and chemometrics to metabolically profile polyfloral honey samples collected in southeastern (São Paulo - SP) and southern (Santa Catarina - SC) Brazil, over the 2019–2020 and 2020–2021 harvest seasons. Both 1D and 2D NMR experiments were carried out to identify and quantify metabolites, followed by descriptive statistical analysis, heatmap, and principal component analysis (PCA), considering geographic and botanical origins. Twenty-one metabolites were detected, encompassing carbohydrates, amino acids, organic acids, ketones, alcohols, esters, and alkaloids. The heatmap and the PCA allowed for distinguishing the geographical and botanical origin. Regarding the botanical origin, three clusters were detected, i.e., Hovenia dulcis (SC), Citrus spp. (SP), and Eucalyptus spp. (SC and SP). The analytical approach proved to be effective for determining the geographic and botanical origins of Brazilian polyfloral honey, even though the country has a huge floral diversity and, accordingly, different types of honey.
In this tutorial, we focus on validation both from a numerical and conceptual point of view. The often applied reported procedure in the literature of (repeatedly) dividing a dataset randomly into a ...calibration and test set must be applied with care. It can only be justified when there is no systematic stratification of the objects that will affect the validated estimates or figures of merits such as RMSE or R2. The various levels of validation may, typically, be repeatability, reproducibility, and instrument and raw material variation. Examples of how one data set can be validated across this background information illustrate that it will affect the figures of merits as well as the dimensionality of the models. Even more important is the robustness of the models for predicting future samples. Another aspect that is brought to attention is validation in terms of the overall conclusions when observing a specific system. One example is to apply several methods for finding the significant variables and see if there is a consensus subset that also matches what is reported in the literature or based on the underlying chemistry.
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•The different approaches to validation are presented and discussed.•Data-driven vs hypothesis-oriented.•Illustration of the effects of adopting different strategies.
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•UV–Vis spectral fingerprint of protected green canephora coffees from Brazil.•Rondônia Robusta Amazônico and Espírito Santo Conilon are discriminated.•Coffee geographical origin and ...botanical variety are identified.•VIP scores and box plot of PLS-DA identifies discriminant wavelengths.•Coffee traceability can be achieved even in coffee before roasting.
Specialty green coffee beans have a higher commercial value and some of them have recently been classified in Brazil based on the indication of provenance and denomination of origin. In this context, the classification of the type of coffee bean is still a challenge, using traditional analytical techniques. Thus, alternative analytical techniques, such as ultraviolet–visible spectroscopy (UV–Vis), as non-target analysis can be applied as a quick and reliable method of coffee classification using data science, such as chemometric tools. In the present study, UV–Vis were evaluated as a new strategy for discrimination of green beans of Brazilian specialty canephora coffees with recognized geographical indications (Robusta Amazônico and Conilon from state of Espírito Santo), for the first time. Spectra obtained from the aqueous extract of 222 samples. The Principal Component Analysis (PCA) was performed and subsequently Partial Least Squares with Discriminant Analysis (PLS-DA) model developed. The PCA indicated tendency to group the samples in their respective classes, pointing to the similarities in the spectra of samples of the same origin. The PLS-DA model obtained showed figures of merit values starting at 89.3% in the test set. The VIP scores showed that the variables associated with chlorogenic acids, caffeine and chlorophyll are the most important for differentiating the studied coffees. The results obtained showed that UV–Vis fingerprint − non-targeted analysis associated with PLS-DA is appropriate for the discrimination of green beans of Brazilian specialty coffee from different origins, in a simple way, using common equipment in several laboratories.
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