# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 4 Challenges  
The growing prominence of the e-nose for detection and classification has illuminated a complex web of challenges that span crucial domains. These challenges are intricate, encompassing key aspects that demand meticulous consideration to ensure the seamless implementation of e-nose technology. A. Data Challenges: in the era of burgeoning big data, the distribution of collected data has evolved into a diverse landscape due to varying experimental and analysis conditions. Acquiring labeled e-nose data remains labor-intensive and time-consuming. However, relying on a limited number of labeled data samples for classification purposes [206], particularly in large-scale scenarios, often results in suboptimal simplification and unreliable model performance. Furthermore, grappling with data drift correction algorithms, especially when dealing with data from disparate sources, presents a formidable challenge [207; 208]. The task of maintaining precise and consistent data collection across diverse domains remains intricate, influencing the dependability of classification models. B. Sample Size Complexities: the process of gas sample sensing revolves around the manipulation of the electrical resistance value of metal oxide semiconductors. This manipulation is influenced by various factors, including sensor sensitivity, environmental shifts, sensor lifespan, and instrumental fluctuations. Consequently, there is a possibility of the testing sample distributions drifting from their training counterparts. Such drift can render previously established regression or classification models ineffective. Achieving an optimal equilibrium concerning sample dataset sizes and compositions is critical in navigating this challenge. Furthermore, the attributes of the authentic dataset, its size, the number of samples, and potential distribution patterns, combined with contextual background information, can introduce variations in validation approaches. Notably, a model that relies on a limited number of samples is highly vulnerable to unpredictable outcomes [209]. C. Calibration and Validation: the limited temporal validity of multivariate calibration models poses a significant challenge to the practical use of electronic noses. Frequent recalibration of these systems is costly and time-consuming due to the need for numerous reference samples, which are often limited in availability. Various factors can invalidate these calibration models, including sensor drift, changes in sample composition, and the need to replace sensors or transfer calibration models between different sensor sets. To address these challenges, alternative methods such as drift correction and calibration update have been proposed:  
1. Drift correction involves modeling sensor drift using a series of measurements and using it to correct new data. 2. Calibration standardization aims to correct new measured data by establishing a relationship between two experimental conditions using a reduced set of samples measured under both conditions. 3. Calibration update involves incorporating new sources of variance into the calibration model by recalculating it using initial calibration samples and a reduced set of samples measured under new conditions, which can be either standard or unknown samples. For further insights into protocols or guidelines for calibrating and validating e-nose data, please refer to [PERSON] [210]. D. Feature Extraction Struggles: the process of feature extraction constitutes a pivotal phase in e-nose systems. It involves mapping high-dimensional data into a lower dimensional subspace that captures essential data structures while enhancing discriminative capabilities [211; 212; 213]. While deep learning approaches have been explored for extracting features from unlabeled gas samples [207], the challenge lies in uncovering the ideal combination of extracted features and preprocessing techniques.