## 1 Introduction  
Fish meal is a high protein feed made from one or more kinds of fish, which is deoiled, dehydrated, and crushed. With an increase in the aquaculture scale in China in recent years, the demand for aquatic feed is rapidly increasing, and the demand for fish meal shows sustained growth. Fish meal is a highly valued source of feed proteins because it is easily digested and has excellent essential amino acids, other essential nutrients, and DHA. Fish meal is an excellent source of vitamins (such as riboflavin, nicotinic acid, vitamin A, and vitamin D) and minerals (such as calcium, phosphorus, iron, zinc, selenium, and iodine) [1] and is the main animal-derived feed raw material in the feed industry. Therefore, the quality of fish meal directly affects the quality of feed products [2]. During the storage process of fish meal, the quality and nutritional components of fish meal vary by different degrees due to weather changes, temperature, humidity, mold, and other microorganisms, which affect the digestion and immunity of animals [3; 4]. Therefore, the detection of fish meal quality is an important technical link and means to ensure the quality of feed products. In case of odor detection, the traditional instruments, such as gas chromatography (GC), gas chromatography/chemiluminescence (GC/SCD), and gas chromatography/mass spectrometry (GC/MS) are mostly employed [5; 6]. Conventional physical and chemical analysis methods, such as chemical detection, sensory evaluation, and spectral analysis [7] are also employed. Unfortunately, they are destructive and time consuming for sample measurement. Furthermore, these instruments are very expensive and bulky. Bionic olfaction technology is a new detection technology that has been developed in recent years. Currently, this technology is prevalent. Some scholars have applied this technology to the quality inspection of agricultural products, including the quality inspection of food crops [8; 9; 10; 11; 12], fruit [13; 14; 15; 16; 17], meat [18], and other plant crops [19; 20; 21], and the discrimination is satisfactory. [PERSON] [22] et al. applied bionic olfaction technology to detect beer aging; [PERSON] [23; 24] et al. applied this technology to detect the freshness of fish. [PERSON] [25; 26; 27; 28] et al. applied bionic olfaction technology to colony detection, with high accuracy; [PERSON] [29; 30] et al. applied this technology to medical research. Therefore, the electronic nose has potential in detecting fish meal quality. Although many classification and prediction techniques, such as principal component analysis (PCA), support vector machines (SVM), are employed to process nonlinear measurement data from E-nose systems, the application in fish meal is still necessary. No information is available in the application of an E-nose in assessing fish meal quality. In the quality assessment by an E-nose, feature extraction and selection are of great importance. Because of large quantities of data, it would be more useful to segregate the irrelevant or redundant attributes from relevant and important ones, although the exact governing rules may not be known. Feature selection provides a means for choosing the features that were best for classification, based on various criteria. Many feature extraction methods have been reported, while none is relevant with E-nose feature vectors extraction, which could sufficiently represent the information of the fish meal quality. Most of the features selected in the literature are the integral value, but the integral value can only reflect the overall information of the sample, and cannot reflect the dynamic information, that is, the mainstream information of the sample. The objectives of this research are to determine whether it is possible to distinguish fish meal with different storage times by an E-nose, and whether the feature vectors chosen are effective in the discrimination of fish meal samples, with different storage times.