We aimed to establish a rapid and precise method for identifying and quantifying multi-species vegetable oil (corn oil, olive oil (OLO), soybean oil, and sunflower oil (SUO)) adulterations in camellia oil (CAO), using soft ionization by chemical reaction in transfer–high-resolution mass spectrometry (SICRIT-HRMS) and machine learning methods. The results showed that SICRIT-HRMS could effectively characterize the volatile profiles of pure and adulterated CAO samples, including binary, ternary, quaternary, and quinary adulteration systems. The low m/z region (especially 100–300) exhibited importance to oil classification in multiple feature-selection methods. For qualitative detection, binary classification models based on convolutional neural networks (CNN), Random Forest (RF), and gradient boosting trees (GBT) algorithms showed high accuracies (98.70–100.00%) for identifying CAO adulteration under no dimensionality reduction (NON), principal component analysis (PCA), and uniform manifold approximation and projection (UMAP) strategies. The RF algorithm exhibited relatively high accuracy (96.25–99.45%) in multiclass classification. Moreover, the five models, including CNN, RF, support vector machines (SVM), logistic regression (LR), and GBT, exhibited different performances in distinguishing pure and adulterated CAO. Among 1093 blind oil samples, under NON, PCA, and UMAP: 10, 5, and 67 samples were misclassified by CNN model; 6, 7, and 41 samples were misclassified by RF model; 8, 9, and 82 samples were misclassified by SVM model; 17, 18, and 78 samples were misclassified by LR model; 7, 9, and 43 samples were misclassified by GBT model. For quantitative prediction, the PCA-CNN model performed optimally in predicting adulteration levels in CAO, especially with respect to OLO and SUO, exhibiting a high coefficient of determination for calibration ($R_C^2$, 0.9664–0.9974) and coefficient of determination for prediction ($R_p^2$, 0.9599–0.9963) values, low root mean square error of calibration (RMSEC, 0.9–5.3%) and root mean square error of prediction (RMSEP, 1.1–5.8%) values, and RPD (5.0–16.3) values greater than 3.0. These results indicate that SICRIT-HRMS combined with machine learning can rapidly and accurately identify and quantify multi-species vegetable oil adulterations in CAO, which provides a reference for developing non-targeted and high-throughput detection methods in edible oil authenticity.

This study aims to investigate the effects of selenium (Se) fortification on growth performance and the Se content in kale using Se fertilizer, and it determines the influences of Se fortification on the metabolic profile of kale using quasi-targeted metabolomics. The results showed that Se fortification increased the plant height and leaf weight of kale, up-regulated the total Se content and decreased the chlorophyll and total phenolic contents in kale leaf. Se fortification elevated selenate (Se(IV)), selenite (Se(VI)), selenocystine (SeCys₂), Se-methylselenocysteine (Se-MeSeCys) and selenomethionine (SeMet) contents, as well as total contents of Se in different forms in kale leaf. Se fortification also changed the metabolic profile of kale leaf, via six particular types of compounds (amino acid and its derivatives; organic acid and its derivatives; carbohydrates and its derivatives; lipids; flavonoids; organoheterocyclic compounds) and eight metabolic pathways (alanine, aspartate and glutamate metabolism; amino sugar and nucleotide sugar metabolism; sulfur metabolism; starch and sucrose metabolism; taurine and hypotaurine metabolism; glycolysis/gluconeogenesis; fructose and mannose metabolism; nitrogen metabolism). Moreover, 24 metabolic biomarkers were screened for kale leaf affected by Se fortification. Furthermore, correlations were observed between metabolic biomarkers and Se contents as well as speciation. These results indicate that Se fortification has a significant influence on the growth performance and nutritional compounds of kale, providing references for the future study on the production and bioactivity of Se-enriched kale.