本文提出了Ｆ１组合评选的两种量化方法——最小平方和（ＬＳＳ）法与最小绝对值和（ＬＳＡ）法，分别以ＳＳ＝∑ｎｉｗｉ〔（Ｆ１ｉ－Ｉｉ）２／ｍａｘ（Ｆ１ｉ－Ｉｉ）２〕和ＳＡ＝∑ｎｉｗｉ（｜Ｆ１ｉ－Ｉｉ｜／ｍａｘ｜Ｆｌｉ－Ｉｉ｜）度量Ｆ１组合多个性状表现与育种目标的接近程度，ＳＳ或ＳＡ值越小，则Ｆ１组合越好。采用ＬＳＳ法、ＬＳＡ法对３２个小麦高产育种Ｆ１组合进行了分析，以Ｆ２实际选择情况进行了验证。结果表明，ＬＳＳ法和ＬＳＡ法都能很好地评价Ｆ１组合的优劣，进而可以对Ｆ１组合分级并淘汰一定量的组合。 In this paper, we present two quantitative methods of F1 portfolio selection: Least Squares Sum (LSS) and Least Absolutes (LSA), respectively. We use SS = Σniwi [(F1i-Ii) 2 / max 2] and SA = Σniwi (| F1i-Ii | / max | Fli-Ii |) to measure the closeness of multiple traits in F1 combination with the breeding target. The smaller the SS or SA value, the better F1 combination. LSS method and LSA method were used to analyze the F1 combinations of 32 high-yielding wheat cultivars, and the actual selection of F2 was verified. The results show that both the LSS and LSA methods can well evaluate the advantages and disadvantages of F1 combinations, and thus can rank F1 combinations and eliminate a certain amount of combinations.