Mass spectrometry-based proteomics aims to identify peptides and proteins to give direct proofs of gene expressions, analyze structures and functions of proteins, study the relationship between proteins and diseases, and provide targeted treatment options. All these studies are based on the credibility of identified peptides and proteins. However, it is impossible to manually check all identified peptides because a large number of identifications can be collected from one mass spectrometry experiment. Thus, target-decoy approach (TDA) is proposed and always used to control the quality of identified peptides and proteins, and has been expanded to subclasses of peptides (including ordinary subclasses of peptides, variant peptides, and modified peptides) and cross-linking peptides. However, TDA still has two limitations: (1) the estimation of false discovery rate (FDR) is inaccurate and (2) validation of single identification cannot be supported. Thus, the identification results that passed the TDA-based FDR control need to be further validated and other validation methods which are used after TDA-FDR filtration (referred to as Beyond-TDA methods) have been developed to enhance peptide validation. This paper reviews TDA and its extensions as well as Beyond-TDA methods and discusses the advantages and disadvantages of each method. In the first part of this paper, we introduce the goal of proteomics, the process of mass spectrometry acquisition and analysis, the validation problem, and the early statistical methods to evaluate the identification credibility. Then, in the second part of this paper, we describe in detail the ordinary TDA-FDR method, including the assumption that random matches are equally likely to appear in target and decoy databases, the construction methods to generate the decoy database, and the computational formula of TDA-FDR. We also introduce the extensions of TDA-FDR on ordinary subclasses of peptides, variant peptides, modified peptides, proteogenomics peptides, cross-linking peptides, and glycopeptides. However, TDA cannot model the homologous incorrect peptides, thus TDA-FDR underestimates the actual false rate. So, after TDA-FDR filtration, it is necessary to use more strict validation methods, i.e., Beyond-TDA methods, which are reviewed in detail in the third part of this paper, to control validation credibility. In this part, four kinds of methods are introduced, including validation methods based on search space (trap database validation and open search validation), spectra similarity (synthetic peptide validation and theoretical spectra prediction), chemical information (retention time prediction and stable isotopic labeling validation) and machine learning technology (Percolator, pValid, and DeepRescore). Lastly, we summarize the content of this paper and discuss the future improvement directions of validation methods.