# GC-MS Fingerprints Profiling Using Machine Learning Models for Food Flavor Prediction
###### Abstract  
Food flavor quality evaluation is attracting continuous attention, but a suitable evaluation system is severely lacking. Gas chromatography-mass spectrometry/olfactometry (GC-MS/O) is widely used to solve the food flavor evaluation problem, but the olfactory evaluation is unfeasible to be carried out in large batches and is unreliable due to potential issue of an operator or systematic laboratory effect. Thus, a novel fingerprint modeling and profiling process was proposed based on several machine learning models including convolutional neural network (CNN). The fingerprint template was created by the data analysis of existing GC-MS spectrum dataset. Then the fingerprint image generation program was applied for structuring the complex instrumental data. Food olfactory result was obtained by a machine learning method based on CNN using fingerprint image as the input. The case study on peanut oil samples demonstrated the model accuracy of around 93%. By structure optimization and further dataset expansion, the whole process has the potential to be utilized by sensory laboratories for aroma analysis instead of humans.  
GC-MS/O profiling; machine learning; convolutional neural network; fingerprint modeling; odor compounds +
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cheese aroma by GC-O analysis, applying principal components analysis (PCA) to divide the cheese samples by significant sensory odor attributes and partial least squares regression (PLSR) to make a correlation between sensory attributes and odor-active compounds. [PERSON] [6] introduced odor activity values (OAVs) and detection frequency analysis (DFA) methods to complete the identification of aroma-active compounds in Jiashi Muskmelon. These communications demonstrated that GC-MS/O analysis is a practical approach to evaluate the sensory attributes of typical food. However, olfactory measurement is highly expensive in labor costs and might have adverse effects on human health due to the harmful chemicals in samples [7]. Potential issues of an operator or systematic laboratory effect may also exist during the GC-MS/O result analysis [2], especially the process of compounds filtration of mass spectrometry. Additionally, the regression methods based on a multiple linear regression model are not able to make sufficient utilization of dataset, and the stability and accuracy of the result cannot be guaranteed.  
To make a relevant and valid prediction of the food sensory attributes, machine learning approaches are worth trying. Convolutional neural network (CNN) has been a hot discussion topic in recent years, such as GoogLeNet [8] proposed by Google Inc., Resnet [9] proposed by Microsoft Corporation. These CNN models are mature technologies for regression or classification, which extract features from images and map the features to a new space [10]. The unique patterns of GC-MS data in a specific sample can be obtained by fingerprint image generation process and recognized by a well-trained CNN model to forecast the odor of the sample and its retention time. A specific case study on peanut oil chemometrics analysis and sensory evaluation is performed. The peanut oil market has a huge consumption potential in China. Besides, the unique aroma of peanut oil is the main reason of the great popularity among Chinese people, and the odor compounds in peanut oil are receiving increasing attention from industry and research institutes [11].  
In this paper, the whole process of converting GC-MS spectrum into olfactometry results is introduced, including instrumental and sensory analysis, odor compounds' identification, GC-MS data structuring process by fingerprint image generation, and machine learning forecasting. Peanut oil samples are applied to demonstrate the characteristic of the model. In addition, the CNN model is optimized by structure design and the whole process can be applied in a laboratory.