We developed a classification approach to correctly predict the environmental footprints of feed ingredients while penalizing the misclassification of the classes with the highest environmental footprint. We considered linear discriminant analysis (LDA) as a classifier, which is a well-established method in chemometrics to analyze spectral data . We computed a classification model for each considered environmental indicator (i.e., climate change and land stress with and without including the effect of land-use change, defined in equations ( ), (10) and in equations ( ), ( ) in the Supplemental Information), to discriminate feed ingredients coming from different countries of origin and measured in different locations. Accurate predictions (i.e. 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 close to 1) indicate that the models can be associated with life cycle impact assessment to predict the environmental footprint of feed ingredients.