Volatile Gas Detection Based on Electronic Nose Combined With a Feature Complementary Calculation Network to Identify the Adulterated Peanuts

ABSTRACT Peanuts are an important food ingredient, and quality adulteration may pose health risks to consumers. Therefore, a rapid, accurate, and effective method for detecting peanut adulteration should be developed. This paper proposes a peanut adulteration identification method based on an electronic nose (e‐nose) and a feature complementary calculation network (FCC‐Net). First, volatile organic compound gas data of peanuts with different adulteration ratios are collected using an e‐nose system. Then, leveraging the cross‐sensitivity and temporal dynamic characteristics of the e‐nose data, a feature complementary calculation module (FCCM) is proposed to extract deep gas features. Finally, based on the FCCM, a lightweight FCC‐Net is designed to identify peanuts with varying adulteration levels. Experimental results demonstrate that FCC‐Net outperforms classical lightweight deep learning models and state‐of‐the‐art gas classification methods in terms of accuracy (97.33%), precision (96.92%), and recall (97.61%), while maintaining extremely low parameters (0.0102 M) and computational cost (0.3700 M). The combination of the e‐nose system and FCC‐Net provides an efficient and lightweight solution for peanut quality inspection.