The major cost (60?80%) of animal production is attributed to feed but most growers are yet to accept and adopt alternative materials like by-products due to their vast variations in nutrient components. Feed and animal production methods are currently considered as unsustainable -with environmental issues related to by-products disposal. Rapid and non-destructive models for quantifying sugars, organic acids, amino acids and other nutrients in alternative materials and a model for precision animal feed production were developed. Consequently, we investigated the nutrient components of by-products using line-scan hyperspectral imaging (HSI) technique. Hyperspectral images of by-products were acquired in the spectral range of 1000?2500 nm. The spectral data were extracted and preprocessed to develop a prediction model using partial least square regression (PLSR) analysis. The PLSR models developed resulted in the following acceptable prediction accuracies (R2p); sugars (0.76?0.94), organic acids (0.72?0.75), amino acids (0.55?0.84), and other nutrients content (0.69?0.96). The root means square error of predictions (RMSEP) obtained were sugars (0.076?0.524 mg/mL), organic acids (0.360?0.626 mg/ mL), amino acids (0.007?0.052 mg/mL), and other nutrients content (0.403?1.035 %). The results obtained from the PLSR models showed reliable performance for quantifying chemical components of different by-products. Further, the generated PLSR-based chemical-mapped images facilitated the visual assessment of the chemical concentration and distribution in byproducts. Thus, based on the results, the application of HSI in combination with multivariate analysis method of PLSR in a commercial setting may be feasible. This can ultimately enable costsaving in breeding by curtailing overfeeding and post-production losses and significantly mitigate environmental issues related to by-products disposal.