Bye E, Grønnerød O, Vogt N B
Department of Occupational Hygiene, Institute of Occupational Health, Oslo, Norway.
Histochem J. 1989 Jan;21(1):15-22. doi: 10.1007/BF01002467.
The SIMCA (soft independent modelling of class analogy) method of pattern recognition has been used to classify four muscle fibre types: I, IIA, IIB and IIC. The samples were histochemically stained human skeletal sections from biopsy material. Disjoint (separate) class modelling gave information about variables, i.e., the combinations of alkaline, acidic and Ca2+-containing preincubation procedures with appropriate discrimination power, and showed satisfactory separation of the classes (fibre types). Two serial stained muscle sections represent a minimum for a proper classification of the four fibre groups. A comparison of biopsy samples from two different persons showed significant variation in the data structure between similar fibre types, probably caused by intermuscle variations. It is suggested that the introduction of computer-assisted classification by the application of such multivariate analytical techniques both facilitates the classification of muscle fibres and improves the precision and reliability of fibre typing.
模式识别的SIMCA（类类比软独立建模）方法已被用于对四种肌纤维类型进行分类：I型、IIA型、IIB型和IIC型。样本是来自活检材料的经组织化学染色的人类骨骼肌切片。不相交（单独）类建模给出了关于变量的信息，即碱性、酸性和含Ca2+预孵育程序的组合及其适当的判别能力，并显示出各类（纤维类型）的满意分离。两张连续染色的肌肉切片是对四个纤维组进行正确分类的最少数量。对来自两个不同人的活检样本的比较显示，相似纤维类型之间的数据结构存在显著差异，这可能是由肌肉间差异引起的。建议通过应用此类多变量分析技术引入计算机辅助分类，这既有助于肌纤维的分类，又能提高纤维分型的精度和可靠性。
