{"doi":"10.1021/acs.analchem.2c03488","title":"Optimization of the Sulfo-Phospho-Vanillin Assay for Total Lipid Normalization in Untargeted Quantitative Lipidomic LC–MS/MS Applications","abstract":"Liquid chromatography (LC)–mass spectrometry (MS)/MS lipidomic normalization is generally performed by equalizing pre-extraction sample materials or via DNA or protein pre-quantitation methods, which have known measurement inaccuracies. We propose the use of the sulfo-phospho-vanillin assay (SPVA), a total lipid colorimetric analysis, as a pre-quantitation method to normalize lipids in lipidomic LC–MS/MS applications. The assay has been applied to a 300 μL well volume in a 96-well plate and tested using Avanti total lipid standards of porcine brain and E. coli. Assay parameters for lipid sample volume, sulfuric acid, vanillin/phosphoric acid, post-reaction incubation time, and wavelength are optimized for robust application to biologically sourced lipid samples. Standard test samples were prepared using three concentrations covering approximately 100 μg/mL range. The optimized assay yielded test sample errors less than 10%, indicating a precise and accurate assay performance. The test samples were then analyzed by LC–MS/MS and normalized using SPVA pre-quantitation and pseudo-mass normalization. The detected lipids showed smaller standard deviations and greater relative concentration differences compared to the pseudo-mass normalized lipids, showing promise as a normalization method.","journal":"Analytical Chemistry","year":2022,"id":261623,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":15,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9591,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":916936,"name":"Dilip V. Prajapati","orcid":null,"position":1,"is_corresponding":false},{"id":361327,"name":"Kari B. Basso","orcid":"0000-0003-1268-6655","position":2,"is_corresponding":false},{"id":394124,"name":"Laura S. Bailey","orcid":null,"position":0,"is_corresponding":true}],"reference_count":51,"raw_metadata":null,"created_at":"2026-07-19T00:26:16.663144Z","pmid":"36520113","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}