Mass spectrometry-based untargeted metabolomics approaches for comprehensive structural annotation of bioactive metabolites from bushy cashew (Anacardium humile) fruits

Funding Information: The authors acknowledge financial support from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the institutional and financial support. Publisher Copyright: © 2023Peer reviewedPostprin

Multivariate calibration to determine phorbol esters in seeds of Jatropha curcas l. Using near infrared and ultraviolet spectroscopies

Link não abre. Favor verificar.The building of partial least squares (PLS) regression models using near infrared (NIR) and ultraviolet (UV) spectroscopies to estimate the concentrations of phorbol esters (PEs) in Jatropha curcas L. is presented. The models were built using two algorithms for variable selection, ordered predictors selection (OPS) and genetic algorithm (GA). Chromatographic analyses were performed to determine the concentrations of PEs. Spectral data were obtained from seeds and oil extract. The results of PLS models were performed by analyzing statistical parameters of quality such as root mean square error of prediction (RMSEP) and correlation coefficient of external predictions (Rp). The parameters obtained for NIR-PLS and UV-PLS models with OPS were respectively: RMSEP 0.48 and 0.22 mg g-1 and Rp 0.49 and 0.96. For GA were obtained, respectively: RMSEP 0.52 and 0.28 mg g-1 and Rp 0.12 and 0.95. The models built from seeds and oil extracts can be used respectively for screening and to accurately predict the PEs content. The OPS method provided simpler and more predictive models compared to those obtained by the selection of variables using the GA. Thus, the UV-PLS-OPS model can be used as an alternative method to quantification of PEs

New strategy for determination of anthocyanins, polyphenols and antioxidant capacity of Brassica oleracea liquid extract using infrared spectroscopies and multivariate regression

A new method was developed to determine the antioxidant properties of red cabbage extract (Brassica oleracea) by mid (MID) and near (NIR) infrared spectroscopies and partial least squares (PLS) regression. A 70% (v/v) ethanolic extract of red cabbage was concentrated to 9° Brix and further diluted (12 to 100%) in water. The dilutions were used as external standards for the building of PLS models. For the first time, this strategy was applied for building multivariate regression models. Reference analyses and spectral data were obtained from diluted extracts. The determinate properties were total and monomeric anthocyanins, total polyphenols and antioxidant capacity by ABTS (2,2-azino-bis(3-ethyl-benzothiazoline-6-sulfonate)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) methods. Ordered predictors selection (OPS) and genetic algorithm (GA) were used for feature selection before PLS regression (PLS-1). In addition, a PLS-2 regression was applied to all properties simultaneously. PLS-1 models provided more predictive models than did PLS-2 regression. PLS-OPS and PLS-GA models presented excellent prediction results with a correlation coefficient higher than 0.98. However, the best models were obtained using PLS and variable selection with the OPS algorithm and the models based on NIR spectra were considered more predictive for all properties. Then, these models provided a simple, rapid and accurate method for determination of red cabbage extract antioxidant properties and its suitability for use in the food industry

Temporal decomposition sampling and chemical characterization of eucalyptus harvest residues using NIR spectroscopy and chemometric methods

Near-infrared (NIR) spectroscopy and chemometric methods were used to predict the chemical properties of decomposing eucalyptus harvest residues to better understand the decomposition process of these materials. Leaves, twigs, branches, and bark from a decomposition experimental set up in commercial plantations were sampled for one year. The contents of carbon (C), nitrogen (N), extractives (EX), acid-soluble lignin (SL), Klason insoluble lignin (KL) and holocellulose (HC) were determined by the reference method in the collected samples. Principal component analysis (PCA) was employed to distinguish the types of harvest residues throughout the decomposition period. Multi-residue regression models were built from the NIR spectra using partial least squares regression (PLS). Two feature selection methods, i.e., ordered predictors selection (OPS) and genetic algorithm (GA), were applied and compared. The OPS and GA did not differ statistically; however, compared with the GA, OPS was more computationally efficient and selected fewer variables. Using the PLS-OPS models, the root mean square errors of prediction (RMSEP) for C, N, EX, SL, KL and HC were 19.70, 0.08, 0.74, 0.39, 28.13 and 33.99, respectively, and the prediction correlations (Rp) for these properties were 0.94, 0.99, 0.99, 0.99, 0.96 and 0.98, respectively. PLS-discriminant analysis (PLS-DA) was used to classify the samples over the decomposition time and provided a good separation. Some mismatches obtained in the modeled classes were explained by the differences in the decomposition rate and changes in the chemical composition of the different harvest residue components that were evaluated. The results showed the feasibility of NIR spectroscopy and chemometric methods to evaluate the chemistry of decomposing eucalyptus harvest residues, indicating that these methods can be used as rapid and inexpensive alternatives to conventional methods to help understand the decomposition process

Early prediction of sugarcane genotypes susceptible and resistant to Diatraea saccharalis using spectroscopies and classification techniques

The aim of this work was to use spectroscopic methods and partial least squares discriminant analysis (PLS-DA) for the early prediction of genotype resistance or susceptibility to sugarcane borer. The sugarcane leaf +1 was directly analyzed with no sample preparation by ultraviolet-visible-near-infrared (UV-VIS-NIR), middle-infrared (MID), and near-infrared (NIR) spectroscopies. Also, laser-induced breakdown spectroscopy (LIBS) was used to analyze pellets of dried and ground leaves and stalks of sugarcane. Classification models were built using PLS-DA. The models built using UV-VIS-NIR, MID or NIR spectra exhibited ideal sensitivity, specificity, and classification errors, i.e., 1 for both sensitivity and specificity and 0 for classification errors. Regarding the models built using LIBS spectra, those using spectra of pellets made from dried and ground leaves also presented ideal sensitivity, specificity, and classification errors; on the other hand, models built using the spectra of pellets made of dried and ground stalks did not present ideal values for these parameters. Thus, the models built, except for the one using LIBS of pellets made of stalks, showed excellent predictive capacity, making them suitable for predicting the resistance or susceptibility of sugarcane genotypes in the early stages of a plant's life

Study of chemical compound spatial distribution in biodegradable active films using NIR hyperspectral imaging and multivariate curve resolution

A study of spatial distribution of the four different plasticizers and sorbic acid incorporated in cellulose acetate biodegradable films using near-infrared hyperspectral imaging (NIR-HSI) and multivariate curve resolution-alternating least squares (MCR-ALS) is presented. A NIR-HSI was acquired for each film. MCR-ALS was applied to generate pure component distribution maps. A repeatability study was performed. The proposed method was able to recover the pure spectra of each film component accurately. The relative concentration vectors obtained by the MCR-ALS were rebuilt in matrices, and it was possible to analyze the homogeneity of the film constituents based on macropixel analysis and homogeneity index. The NIR-HSI imaging showed excellent repeatability. For the first time, a study detailing the distribution of chemical compounds incorporated into entire biodegradable films was possible by using NIR hyperspectral imaging combined with the MCR-ALS method341CAPES - Coordenação de Aperfeiçoamento de Pessoal e Nível SuperiorCNPQ - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPEMIG - Fundação de Amparo à Pesquisa Do Estado De Minas Geraissem informaçãosem informação00

LC-HRMS/MS-Based Metabolomics Approaches Applied to the Detection of Antifungal Compounds and a Metabolic Dynamic Assessment of Orchidaceae

The liquid chromatography–mass spectrometry (LC-MS)-based metabolomics approach is a powerful technology for discovering novel biologically active molecules. In this study, we investigated the metabolic profiling of Orchidaceae species using LC-HRMS/MS data combined with chemometric methods and dereplication tools to discover antifungal compounds. We analyze twenty ethanolic plant extracts from Vanda and Cattleya (Orchidaceae) genera. Molecular networking and chemometric methods were used to discriminate ions that differentiate healthy and fungal-infected plant samples. Fifty-three metabolites were rapidly annotated through spectral library matching and in silico fragmentation tools. The metabolomic profiling showed a large production of polyphenols, including flavonoids, phenolic acids, chromones, stilbenoids, and tannins, which varied in relative abundance across species. Considering the presence and abundance of metabolites in both groups of samples, we can infer that these constituents are associated with biochemical responses to microbial attacks. In addition, we evaluated the metabolic dynamic through the synthesis of stilbenoids in fungal-infected plants. The tricin derivative flavonoid- and the loliolide terpenoidfound only in healthy plant samples, are promising antifungal metabolites. LC-HRMS/MS, combined with state-of-the-art tools, proved to be a rapid and reliable technique for fingerprinting medicinal plants and discovering new hits and leads