— SIRIUS Solutions for
Cosmetics and personal care
SIRIUS helps cosmetic chemists characterize and authenticate botanical extracts, discover new natural actives, profile complete formulations, track degradation over shelf life, and screen for allergens, impurities and what migrates from packaging into the product. Molecular formulas, structures and compound classes come straight from your LC-MS/MS data, so the compounds no library covers still get a structural answer.
Why cosmetic chemists choose SIRIUS
A single botanical extract can hold thousands of compounds, a finished formulation sits in packaging that leaches its own chemistry, and fragrance ingredients oxidize into new sensitizers on the shelf. Target lists and spectral libraries only cover what someone has already catalogued, so most features in a cosmetic sample stay unnamed: the minor constituent that carries the claim, the oxidation product behind a skin reaction, the leachable nobody registered.
SIRIUS does not need a matching library entry. It reads the fragmentation spectrum to annotate the molecular formula, predict a molecular fingerprint, search public and in-house structure databases, and assign a compound class. When no database holds the answer, it generates candidate structures de novo.
Characterize
Profile botanical extracts and complete formulations down to the minor constituents, find the actives behind a claim, and elucidate novel compounds no library has seen.
Screen
Screen formulations, raw ingredients and packaging for allergens, impurities, extractables, leachables and degradation products, including the ones that sit outside any target list.
Verify
Confirm species, chemotype and batch-to-batch consistency of natural ingredients by comparing chemical fingerprints and elucidating the markers that set them apart.
BOTANICAL EXTRACTS & NATURAL ACTIVES
Find and elucidate the actives behind a claim
Plant extracts and bio-fermented ingredients are among the most chemically crowded samples a lab handles. The compounds responsible for anti-aging, antioxidant or anti-inflammatory activity are often minor constituents, closely related to dozens of isomers, and many are new by definition: unique botanical derivatives, fermentation products and novel analogs that no spectral library has ever seen. Traditional dereplication stops at the well-known flavonoids and phenolic acids.
SIRIUS ranks molecular formulas, searches against millions of structures in PubChem, natural product and in-house databases, and assigns compound classes to features no database explains. When no database holds the answer, it generates candidate structures de novo, so a bioactive fraction leads to a structural hypothesis before you isolate a single compound.
SIRIUS discoveries
Fractions of Baccharis trimera show potent anti-tyrosinase and anti-inflammatory activities, revealing key flavones as promising natural ingredients for anti-aging skincare.
Upcycling by-products: Coffee waste is a rich source of hydrophilic antioxidants and lipophilic sterols.
False daisies contain anti-inflammatory, antioxidant and anti-collagenase ingredients with anti-aging potential.
Molecular formula annotation from isotope pattern and fragmentation tree, refined by network-enhanced ranking across the dataset.
Molecular structure annotation checks candidates against PubChem, natural product databases and your own ingredient libraries.
Compound class prediction maps flavones, terpenoids, tannins and alkaloids across every feature, database-free.
De novo structure generation proposes structures for actives that exist in no database.
AUTHENTICATION, FORMULATION PROFILING AND SKIN EFFICACY
Verify ingredients and profile formulations from bottle to skin
Growing demand for natural and plant-derived beauty products puts more weight on raw material authenticity. Closely related species often share the same major constituents, so a quercetin or chlorogenic acid peak alone may not tell you which plant an extract came from. Markers that help detect a substitution, a different chemotype or a lower grade are often minor compounds, and many of them have no library spectrum. Finished products raise a similar challenge: a cream or serum combines actives, lipids, emollients, surfactants and preservatives, while targeted methods typically focus on the declared ingredients. The skin surface adds another layer, as ingredients, host and microbiome interact there.
SIRIUS can help dereplicate known constituents and propose compound classes for many features without a library match. This lets you compare raw materials, batches, process variants or competitor products on predicted fingerprints and compound classes, annotate candidate markers that distinguish them, follow those markers from raw ingredient into finished product, and explore changes in the skin-surface metabolome in clinical studies.
Structure database search combined with spectral library matching for fast dereplication of known flavonoids, polyphenols and terpenoids.
Compound class prediction sorts unknown features into families and highlights class-level differences between chemotypes.
Field example: pre- and postbiotic skincare
Participants with dry skin used a shower gel and body lotion with a triple-biotic complex or standard products for six weeks. Untargeted metabolomics with SIRIUS and CSI:FingerID annotation covered 3,508 features and showed that the skin metabolome shifted far more than the microbiome. Fatty acids, fatty acyls and dicarboxylic acids linked to barrier function and hydration were modulated most strongly.
More SIRIUS discoveries
Over 24,000 molecular features traced from four natural raw materials into finished tablets and drops, confirming consistent constituents across batches.
PHOTOPROTECTION
Discover natural UV-protective compounds
Algae, cyanobacteria and marine organisms protect themselves from UV radiation with mycosporine-like amino acids (MAAs) and related compounds, which makes them attractive candidates for sustainable sunscreen ingredients. These molecules are small, highly polar and structurally similar, and many analogues have never been described, so they rarely have reference spectra.
SIRIUS lets you search beyond public databases. Build a custom database of plausible analogues, score them against the predicted fingerprint, and use compound class prediction to find the whole family of related candidates across your samples.
Molecular structure annotation checks candidates against public databases and custom database of plausible analogues
Compound class prediction flags uncharacterized members of a compound family across field samples.
Custom structure databases turn combinatorial or literature-based candidate lists into a searchable space.
Substructure annotation links fragment peaks to parts of the candidate to check a structural hypothesis.
Structure Sketcher lets you modify a candidate and instantly re-score it against the predicted fingerprint.
SIRIUS discoveries
13 distinct UV-absorbing mycosporine-like amino acids identified in red macroalgae from the Peruvian coast.
A custom combinatorial database in SIRIUS corrected an earlier structural hypothesis and revealed algasporine-glycine, a new UV-protective natural product from a pine-bark alga.
Reduced nitrogen sources promoted the release of the microbial sunscreens shinorine and porphyra-334 by Microcystis.
Three previously unknown MAA structures found in the eyes of Baltic flatfish, with MAA levels correlating with ocular SPF.
ALLERGENS, SENSITIZERS AND SHELF-LIFE STABILITY
Track allergens, sensitizers and degradation
Light, oxygen, heat and interactions between ingredients keep changing a product long after it leaves the line. Fragrance terpenes oxidize on air exposure, preservatives and dyes break down in the formulation or on the skin, and retinoids, vitamins, antioxidants and UV filters lose efficacy. The products that form can be stronger sensitizers than the declared ingredient, and they are rarely in any database. Targeted methods and stability studies show that a parent compound is disappearing, but not what it turns into.
SIRIUS screens LC-MS/MS-amenable allergens and their transformation products in the same non-targeted run. Turn your allergen, preservative and active-ingredient inventories into custom structure databases, expand them with predicted oxidation, hydrolysis or photolysis products, and follow each product across time points and storage conditions.
Custom structure databases built from regulated allergen lists, preservatives and in-house ingredient inventories.
BioTransformer 3.0 integration generates expected biotransformation products for imported structure databases.
Reaction workflows and Reaction Sketcher apply custom reactions such as oxidation, hydrolysis or photolysis to extend the search space from each parent.
Field example
Researchers screened 594 human milk samples for chemical contaminants from our environment, food, and personal care products. Besides methyl, ethyl and propyl paraben, they found sulfated forms of these preservatives and four further parabens, including phenyl paraben and 2-ethylhexyl 4-hydroxybenzoate, which appeared only in the South African samples.
More SIRIUS discoveries
Online electrochemistry combined with LC-MS revealed potentially allergenic transformation products of tattoo pigments.
A UV filter among six exposure biomarkers significantly elevated in the urine of women with ovarian malignancies.
Personal care agents among 25 contaminants reported in tap water for the first time.
PACKAGING, EXTRACTABLES, LEACHABLES AND IMPURITIES
Identify leachables and impurities before they reach the skin
Creams, lotions and serums are often oil-rich and stored for months in plastic tubes, pumps and jars, ideal conditions for additives, oligomers and their breakdown products to migrate. Raw ingredients bring their own synthesis by-products and contaminants. Safety assessments under frameworks such as the EU Cosmetics Regulation need these substances characterized, yet non-intentionally added substances were never registered, and exact mass alone cannot separate a leachable from a formulation ingredient with the same molecular formula.
The fragmentation spectrum can. SIRIUS combines library matching with structure annotation, searches your E&L and additive inventories alongside public databases, and ranks every candidate with a confidence score so the leachables and impurities worth confirming surface first.
Molecular structure annotation tells isomers apart that share one molecular formula.
Network-enhanced formula annotation uses structurally related impurities in the same sample set to refine formula ranking.
Compound class prediction assigns unknown NIAS to classes such as phthalate esters or phenolic antioxidants without a database.
Custom structure databases from your extractables studies, additive lists and supplier declarations, plus your in-house E&L libraries.
Field example: Plastic-related contaminants
In 37 liquors packaged in plastic and glass, SIRIUS returned correct formulas for key plasticizers and bisphenols and distinguished ethylvanillin from the preservative ethylparaben, two compounds with the same formula that mass-based suspect screening could not separate.
More SIRIUS discoveries
114 chemicals migrating from plastic and paper packaging identified, including hazardous plasticizers and persistent NIAS.
Fenozan identified as a key biomarker for human exposure to synthetic phenolic antioxidants used in plastics, food and cosmetics.
Know how far to trust an annotation
An unexpected allergen or leachable can trigger a reformulation, a supplier audit or a safety assessment. Before a candidate goes further, you need to know how much weight it can carry and be able to show why.
Confidence score
rates how likely a structure hit is exactly correct, similar in spirit to a false discovery rate, so you can select the most confident identifications across thousands of features.
Substructure validation
links fragment peaks and neutral losses to parts of the candidate and highlights shared substructure explanations in analog hits, exportable as figures.
Identity and analog library matches
cross-checks against curated public spectra and your in-house libraries, and verifies shared structural cores of related compounds.
Protection against suspect blindness
shows the best library match and the best structure hit side by side, so a familiar ingredient name does not hide a better explanation.
Feature and spectral quality metrics
flag weak or ambiguous features before they reach a report.
From one extract to a full ingredient portfolio
Raw material qualification, stability studies and packaging programs produce recurring datasets across many matrices. SIRIUS scales from interactive inspection on a laptop to automated pipelines on a compute cluster, on the same project files.
GUI, CLI and API in one project
Automate runs via the command line or the REST API with its Python client, then inspect selected results in the GUI. All three share one persistence layer.
Zero-parameter LC-MS import
Import runs directly from mzML or mzXML with built-in feature detection, alignment and adduct detection, or bring features from your preferred pre-processing tool.
Your ingredients, your databases
Build custom structure databases from INCI inventories, allergen lists and supplier declarations, and add in-house reference spectra.
Integrates with your tools
Software integrations with tools such as MassHunter, mzmine and Compound Discoverer keep SIRIUS inside existing workflows.
Building your own pipeline? Follow the SIRIUS API tutorial.
See what SIRIUS finds in your samples
Millions of queries are processed every year in SIRIUS to characterize the compounds that spectral libraries can’t. Bring an extract, a formulation or a packaging study with peaks nobody can explain. We will show you what SIRIUS annotates from your own fragmentation data, and how much confidence stands behind each result.
Related solutions
Impurities, E&Ls and Contaminant Screening
Extractables, leachables and degradation products
Pharmaceuticals and Drug Discovery
Drug leads, metabolites and degradants
Environmental and Water Analysis
Pollutants and degradation products in water and soil
Metabolism and Biotransformations
Biotransformation routes across biological systems
Explore more on the blog
Browse related discoveries and application notes by topic.
For Research Use Only. Not for use in diagnostic procedures.