Compound identification using unknown electron ionization (EI) mass spectra in gas chromatography coupled with mass spectrometry (GC-MS) is challenging in untargeted metabolomics, natural product chemistry, or exposome research. While the total count of EI-MS records included in publicly or commercially available databases is over 900 000, efficient use of this huge database has not been achieved in metabolomics. Therefore, we proposed a "four-step" strategy for the identification of biologically significant metabolites using an integrated cheminformatics approach: (i) quality control calibration curve to reduce background noise, (ii) variable selection by hypothesis testing in principal component analysis for the efficient selection of target peaks, (iii) searching the EI-MS spectral database, and (iv) retention index (RI) filtering in combination with RI predictions. In this study, the new MS-FINDER spectral search engine was developed and utilized for searching EI-MS databases using mass spectral similarity with the evaluation of false discovery rate. Moreover, in silico derivatization software, MetaboloDerivatizer, was developed to calculate the chemical properties of derivative compounds, and all retention indexes in EI-MS databases were predicted using a simple mathematical model. The strategy was showcased in the identification of three novel metabolites (butane-1,2,3-triol, 3-deoxyglucosone, and palatinitol) in Chinese medicine Senkyu for quality assessment, as validated using authentic standard compounds. All tools and curated public EI-MS databases are freely available in the 'Computational MS-based metabolomics' section of the RIKEN PRIMe Web site ( http://prime.psc.riken.jp ).

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Ergosterol (ERG) is a sterol produced by most fungi, but not by most plants. Thus, measurement of ERG in cereals makes it possible to determine the presence of fungi in cereals that can cause quality problems, such as mycotoxin contamination. This study developed and performed a single-laboratory validation for a method to test for ERG in various cereals. ERG was extracted by refluxing samples for 1 h with methanol-sodium hydroxide. ERG was extracted from the extract with hexane and then purified using a silica gel cartridge column. ERG was then separated and detected by reverse-phase high-performance liquid chromatography (HPLC). 'Within-day' recoveries of ERG at low levels were 92-99% with relative standard deviations (RSDs) of 3.2-6.5%. 'Between-day' recoveries of ERG at low levels were 97% and RSDs were 4.2-10.2%, respectively. Average recoveries of ERG over the range from 1.0 to 100.0 mg kg(-1) were 81-105% and RSDs were 3.9-16.3%.

Patulin contamination is known in various fruit products, including apple products. In this study, a solid phase extraction clean-up method was developed and validated for patulin in various fruit juices. Patulin was extracted from samples with ethyl acetate and then diluted with hexane. Patulin was isolated with a silica gel cartridge column, then analysed by reverse phase liquid chromatography with UV detection. The detection and quantitation limits were 0.06 and 0.15 ng, respectively. Recoveries within a day, and between days, were determined. Within day recoveries of patulin (n=6) at 5.0 and 50.0 µg/kg were 96-105% and 89-95%, respectively, with relative standard deviations (RSD) of 2.4-6.9 and 0.7-1.7%, respectively. Between day recoveries at 5.0 and 50.0 µg/kg were 96-108% and 92-94%, respectively, with RSDs of 7.1-12.1 and 2.3-4.1%, respectively. Average recoveries of patulin in the range from 2.0 to 80.0 µg/kg were 114 to 93%.

Calibration-Curve-Locking Databases (CCLDs) have been constructed for automatic compound search and semi-quantitative screening by gas chromatography/mass spectrometry (GC/MS) in several fields. CCLD felicitates the semi-quantification of target compounds without calibration curve preparation because it contains the retention time (RT), calibration curves, and electron ionization (EI) mass spectra, which are obtained under stable apparatus conditions. Despite its usefulness, there is no CCLD for metabolomics. Herein, we developed a novel CCLD and semi-quantification framework for GC/MS-based metabolomics. All analytes were subjected to GC/MS after derivatization under stable apparatus conditions using (1) target tuning, (2) RT locking technique, and (3) automatic derivatization and injection by a robotic platform. The RTs and EI mass spectra were obtained from an existing authorized database. A quantifier ion and one or two qualifier ions were selected for each target metabolite. The calibration curves were obtained as plots of the peak area ratio of the target compounds to an internal standard versus the target compound concentration. These data were registered in a database as a novel CCLD. We examined the applicability of CCLD for analyzing human plasma, resulting in time-saving and labor-saving semi-qualitative screening without the need for standard substances.