![]() The case study on peanut oil samples demonstrated the model accuracy of around 93%. Food olfactometry result was obtained by a machine learning method based on CNN using fingerprint image as the input. Then the fingerprint image generation program was applied for structuring the complex instrumental data. The fingerprint template was created by the data analysis of existing GC-MS spectrum dataset. Thus, a novel fingerprint modeling and profiling process was proposed based on several machine learning models including convolutional neural network (CNN). Gas chromatography-mass spectrometry/olfactometry (GC-MS/O) is widely used to solve the food flavor evaluation problem, but the olfactometry evaluation is unfeasible to be carried out in large batches and is unreliable due to potential issue of an operator or systematic laboratory effect. Food flavor quality evaluation is attracting continuous attention, but a suitable evaluation system is severely lacking.
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