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Inborn errors of metabolism, also known as inherited metabolic diseases (IMDs), are related to genetic mutations and cause corresponding biochemical metabolic disorder of newborns and even sudden infant death. Timely detection and diagnosis of IMDs are of great significance for improving survival of newborns. Here we propose a strategy for simultaneously detecting six types of IMDs via combining GC-MS technique with the random forest algorithm (RF). Clinical urine samples from IMD and healthy patients are analyzed using GC-MS for acquiring metabolomics data. Then, the RF model is established as a multi-classification tool for the GC-MS data. Compared with the models built by artificial neural network and support vector machine, the results demonstrated the RF model has superior performance of high specificity, sensitivity, precision, accuracy, and matthews correlation coefficients on identifying all six types of IMDs and normal samples. The proposed strategy can afford a useful method for reliable and effective identification of multiple IMDs in clinical diagnosis. [Display omitted] • A strategy was proposed for identifying multiple inherited metabolic diseases (IMDs). • We used combined GC-MS for acquiring metabolomics data with random forest for data handling. • The encouraging results implied the promise of the strategy for robust and reliable identification of multiple IMDs. [ABSTRACT FROM AUTHOR]

• Metabolomics coupled with chemometrics was described for stable biomarker screening. • Information-rich urinary metabolomic profiling was detected by GC–MS. • Chemometric method modeled urinary metabolomics data with desirable results. • A feasible stable biomarker screening system was developed for IMDs. Screening of stable metabolic biomarkers objectively measuring the underlying pathophysiological changes are vital to boost early diagnosis and treatment prognosis of inherited metabolic diseases (IMDs). In this study, gas chromatography-mass spectrometry (GC–MS) was invoked to perform urinary metabolomics analysis for IMDs and a comprehensive chemometric method was presented to screen stable metabolic biomarkers effectively. For this chemometric method, a novel hybrid perturbation framework based on partial least squares discriminant analysis (PLS-DA) was adopted, in which the data level utilized bootstrap (BS) method and the function level employed the strategy of multiple informative vector fusion (MIVF), forming an algorithm BS-MIVF-PLSDA. Investigated by two common IMDs (methylmalonic acidemia and propionic acidemia), GC–MS allowed the detection of a rich profile of metabolites, characterizing subtle differences between disease and healthy control groups. The proposed chemometric method BS-MIVF-PLSDA exhibited superiorities to simpler techniques in terms of identifying candidate metabolic biomarkers more biologically correlated to the metabolic mechanisms with desirable selection stability. Moreover, the chemometric method enabled good classification performance in discriminating between disease and healthy control groups using the identified top-ranked candidate metabolic biomarkers. All the results showed that GC–MS urinary metabolomics coupled with chemometrics was feasible to offer an efficient path to achieve stable metabolic biomarkers, supporting early diagnosis and guiding treatment in IMDs clinical practice. [ABSTRACT FROM AUTHOR]