Research Keyword: PLS-DA

Headspace Solid-Phase Microextraction Followed by Gas Chromatography-Mass Spectrometry as a Powerful Analytical Tool for the Discrimination of Truffle Species According to Their Volatiles

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This study analyzed the aromatic compounds in two types of Greek truffles to distinguish between them. Researchers used a technique called headspace solid-phase microextraction combined with gas chromatography to identify 45 different volatile compounds. The study found specific aromatic markers that uniquely identify each truffle species, demonstrating that this analytical approach can reliably differentiate between truffle types based on their smell.

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Mass Spectrometry-Based Untargeted Metabolomics and α-Glucosidase Inhibitory Activity of Lingzhi (Ganoderma lingzhi) During the Developmental Stages

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Scientists studied how the medicinal mushroom Lingzhi changes chemically as it grows from mycelium through various stages to mature fruiting bodies. They found that the mushroom contains many beneficial compounds, including special molecules called triterpenoids, that help block α-glucosidase, an enzyme involved in blood sugar control. Interestingly, the immature mushroom stage showed the strongest anti-diabetic activity, suggesting farmers should harvest at specific times depending on desired health benefits rather than always waiting for full maturity.

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Integrating Machine Learning and Molecular Methods for Trichophyton indotineae Identification and Resistance Profiling Using MALDI-TOF Spectra

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A new type of fungus called Trichophyton indotineae is causing stubborn skin infections that don’t respond well to standard antifungal treatments. Researchers used advanced laboratory techniques combined with computer analysis to better identify this fungus from MALDI-TOF spectra, which is a quick fingerprinting method for microorganisms. The study showed that machine learning could accurately distinguish this problematic fungus from similar species and found specific markers that could help clinics detect it faster, potentially improving patient treatment outcomes.

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Application of ATR-FTIR and FT-NIR spectroscopy coupled with chemometrics for species identification and quality prediction of boletes

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Researchers developed a fast and non-destructive method to identify different types of edible boletes and assess their nutritional quality by analyzing their amino acid content. Using special spectroscopy techniques combined with computer analysis, they achieved perfect accuracy in identifying five bolete species and could predict the amino acid content that contributes to flavor and nutrition. This breakthrough provides consumers with better protection against accidentally purchasing toxic mushroom species that look similar to edible ones, while helping food producers quickly assess quality without lengthy lab testing.

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