A chemometric strategy for accurately identifying illegal additive compounds in health foods by using ultra-high-performance liquid chromatography coupled to high resolution mass spectrometry

Abstract

This work provides a new cheometric strategy for accurately identifying unknown illegal additive compounds in complex health foods.

Diverse thermal desorption combined with self-aspirating corona discharge ionization for direct mass spectrometry analysis of complex samples

“…Rapid identification of drugs, pollutants, additives, and other chemical substances in real samples has always been an important and challenging task in public safety, environmental protection, food safety, and other fields. 1–3 Since the detection of specific substances can be affected by spectral interference and suppression among complex components, the samples often require appropriate pretreatment before instrumental analysis. 4 Common pretreatment processes include dissolution, extraction, filtration, etc , and chromatography is also widely used for the separation of various components.…”

Optimized identification of cheese products based on Raman spectroscopy and an extreme learning machine

“…The common quality and safety risks mainly come from illegal additives, microorganisms and heavy-metal pollution. 1,2 Therefore, the methods of identification and detection are mainly component analysis, for example, quantitative determination and analysis of sodium thiocyanate in milk by surface-enhanced Raman spectroscopy. [3][4][5] Such methods can effectively realize the qualitative and quantitative analysis of specific components, but also face the limitations of cumbersome and time-consuming preprossessing.…”

Discrimination of cis-diol-containing molecules using fluorescent boronate affinity probes by principal component analysis

“…Chemometrics provides a powerful tool to treat the data by statistical methods to help nd the differences among different classes, 26,27 particularly principal component analysis (PCA), which uses the idea of dimension reduction to transform multiple indicators into fewer comprehensive indicators on the premise of losing less information. 28 PCA can be used for treating various types of test data for target identication, e.g.…”