Modeling and optimization of laying hen manure drying process to reduce protein and ammonium-N losses by adding sodium bentonite and wheat straw.

Laying hen manure (LHM) is a major source of pollution due to its high nitrogen (N) and moisture content (MC). Therefore, reducing the MC of LHM is necessary to retain its recyclable value and reduce environmental pollution. One effective way is by incorporating sodium bentonite (SB) and wheat straw (WS) as amendments in the LHM.

Prediction and variability mapping of some physicochemical characteristics of calcareous topsoil in an arid region using Vis-SWNIR and NIR spectroscopy.

Site-specific management of soils needs continuous measurements of soil physicochemical characteristics. In this study, Vis-NIR spectroscopy with two spectroscopic instruments, including charge-coupled device (CCD) and indium-gallium-arsenide (InGaAs) spectrometers, was adopted to estimate some physicochemical characteristics of a calcareous topsoil in an arid climate. Partial least squares (PLS) as linear and artificial neural networks (ANN) as nonlinear multivariate techniques were utilized to enhance the accuracy of prediction.

Classification of Bitter Orange Essential Oils According to Fruit Ripening Stage by Untargeted Chemical Profiling and Machine Learning.

The quality and composition of bitter orange essential oils (EOs) strongly depend on the ripening stage of the citrus fruit. The concentration of volatile compounds and consequently its organoleptic perception varies. While this can be detected by trained humans, we propose an objective approach for assessing the bitter orange from the volatile composition of their EO.