## 1 Introduction  
Chili powder is a common commitment that many people around the world enjoy eating to enrich the flavor of foods. It is rich in substances such as capsaicin, flavonoids, and phenols [1], which have pharmacological activities and can be used clinically for the prevention and treatment of certain diseases. For example, studies have confirmed that capsaicin has anticancer, antibacterial, analgesic, and cardioprotective effects [2; 3]. Compared with bulky foods, powdered foods are much easier for unscrupulous sellers to adulterate in order to reduce the raw material costs. Currently reported adulterants in chili powder mainly include Sudan I-IV dyes [4; 5; 6; 7], brick powder [7; 8], red beetroot [9; 10], almond shell [8; 11], dried tomato peel [11], and starch [11]. These adulterants pose significant health risks to consumers. Therefore, it is very important to identify adulterated chili powder. In recent years, many advanced techniques have been applied in the detection of adulterants. For example, [PERSON] et al. [12] investigated the adulterants in chili powder by using a spectroscopic method. [PERSON] et al. [7] used machine-learning algorithms to classify the high-quality images of chili-brick powder adulterants. These methods showed good results in the detection of adulterants. However, the poor portability and the high cost of the spectroscopic equipment significantly limit its practical applications. [PERSON] et al. [9] marked plant-based adulterants in chili powder based on PCR (polymerase chain reaction) and SCAR (sequence characterized amplified regions) analyses. This method provided excellent detection accuracy for the adulterations with low concentrations, but it requires complex operating procedures and specialized skills, which makes it unsuitable for rapid detection. Electronic nose (E-nose) is a system that can detect different odors and identify the chemical components by optimizing its sensor array [13]. It has been widely used in the food industry [14; 15; 16]. For example, [PERSON] et al. [17] combined gas chromatography-mass spectrometry with an E-nose to analyze the grinding flavor of pepper and found that the aroma concentration of pepper ground at a low temperature was higher than the traditional milling method. [PERSON] et al. [18] achieved the accurate identification of fruit freshness based on fruity aromas by using E-nose. [PERSON] et al. [19] found that the E-nose technique can identify safron with an adulteration rate higher than 10%. Therefore, as an emerging, rapid, and nondestructive detection method, the E-nose is expected to be suitable for the detection of adulterants in chili powder. Extracting effective odor features from the response signals and then training models are the two key steps for odor identification by using the E-nose. At present, the commonly used ways to extract the odor features are time-domain features [18; 20; 21], frequency domain features [22; 23], curve fitting [24; 25; 26], and data compression [27; 28]. However, the effectiveness of the feature-extraction methods is affected by many factors, such as the target gases and the application scenarios. Therefore, it needs to be specially designed for different practical applications [13; 29]. In addition, most of the current models used in the E-nose are based on the neural network [30; 31]. Although a neural network model can achieve a high identification accuracy, it inevitably takes a relatively long computational time. This shortcoming limits its implementation in rapid identification and its application on portable devices [24; 32]. In this paper, an E-nose system was firstly designed and used to detect the botanical adulterants in chili powders. The odor features were then extracted by using the HOG algorithm based on the visualized E-nose response signals. Finally, an identification approach was proposed to identify the different adulterants (almond shell, red beetroot, and tomato peel) and their contents in chili powders.