Choosing the lowest RMSECV would lead to overfit the model and it is not good.

Before you perform PLSR, I suggest to understand the data first. Following steps may help you to analyze your data:

1. Analyzing its PCA to find some pattern or outlier identification

2. Analyzing the raw spectra to examine the most prominent peaks that has chemical information and can contribute to targeted y-reference. This helps to check with regression vector later to avoid an overfit model. Other thing, this may help to indicate, maybe, some strange spectra.

3. Try to see number of data. Is it possible to split the data as training and independent test set? rule of thumb to split is 2/3 for training and 1/3 for test. This is important as it allows you to see whether the model tends to overfit or not. Cross validation could be the last option if number of data is limited

4. In the case this research only can use cross validation, after building the model, see the revolution of RMSE. Most of model have a trend where the more LV used the lower RMSE will be but at certain point, it won't improve that significant, so, should stop on LV before that point.

5. Try to cross check with regression vector to see whether the model try include noise or only prominent peaks that responsible to targeted value. if the reg vector has less noise, it could be an indication that your model is robust.

Thank you so much for your clear, easy and complete explanation!

Anw, do you have any recommendation book or articles regarding to NIR chemometrics for newbie? I've tried to search them, but mostly is not so clear to understand for practical application.

Nurul Ainina General approach is "limiting the number of components or LV (as the risk of overfitting increases with the number of components/ LV).." Reference :Article Statistical analysis in metabolic phenotyping

But you can also do the the trial and error with RMSEE values