What type of cross validation to use if my data has 5 scans per sample to avoid having same sample in train and test set

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My data (150 samples) has 5 NIR scans per sample. I am not able to average the 5 scans because some of them were taken out as they were not valid. I am using Support Vector Machines form the Classification learner apps from Machine Learning and Deep Learning Matlab 2020b.
I used tgspcread to read my NIR files onto Matlab, normalised by standard deviation and only used the valid samples for my classification. Am I right to say that the samples are independent of each other or will the remaining samples (out of the 5 scans) be termed as same even after normalisation?
My second quaetion is, what type of cross validation will be ideal to avoid having the same samples in the training set and test set considering the fact that the classification app has only KFold and Holdout Cross Validation

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