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The altered document identification proficiency test of the China National Accreditation Service for Conformity Assessment (CNAS), initiated by the China Academy of Forensic Science in 2022, provided a specially prepared questioned document sample. This challenging case sample rendered conventional morphological examination methods virtually ineffective. As a result, only a low percentage of judicial appraisal institutions received the “satisfactory” rating. This work examined the characteristics of the inkjet printers used in the proficiency test from two independent perspectives, by combining volatile solvent composition analysis of printing inks using GC–MS creatively with conventional morphological examination. This work not only efficiently determined whether there was appended content via a secondary printing pass in the case sample, but also further identified the brands of the inkjet printers used to prepare the sample of the proficiency test. Finally, precautions for identifying the brands of inkjet printers were summarized. We hope this work will underscore the importance of incorporating physical and chemical analytical methods in questioned document examination and draw forensic examiners’ attention to its necessity. •Presents results of China Academy of Forensic Science 2022 altered document proficiency test.•Combined chemical and morphological analysis improves robustness.•GC–MS analysis of volatile solvents recommended and demonstrated.•Discusses limitations of morphological methods under current training system.

Achieving a reliable fault diagnosis for gears under variable operating conditions is a pressing need of industries to ensure productivity by averting unwanted breakdowns. In the present work, a hybrid approach is proposed by integrating Hu invariant moments and an artificial neural network for explicit extraction and classification of gear faults using time-frequency transforms. The Zhao-Atlas-Marks transform is used to convert the raw vibrations signals from the gears into time-frequency distributions. The proposed method is applied to a single-stage spur gearbox with faults created using electric discharge machining in laboratory conditions. The results show the effectiveness of the proposed methodology in classifying the faults in gears with high accuracy.