The study aimed to define a grain-adapted quality score (GQS) to assess the fermentative pattern of ensiled high-moisture maize grain (EMG) based on organic acids, ammonia, and ethanol data of a lab-scale dataset. The GQS was validated by comparison with both the Flieg-Zimmer’s quality score (FQS) and a standardized quality score (SQS) by a received operating analysis. Compared with FQS and SQS, the cut-offs of poor/good samples for the proposed GQS were 47 (accuracy of 0.94) and 71 points (accuracy of 0.88) over 100, respectively. The relationship among indices was also tested in a farm-derived dataset by arranging a confusion matrix, which showed the higher predictive performance considering the lower cut-off. On the lab-scale dataset, a factorial discriminant analysis (FDA) assessed the most predictive chemical post-ensiled traits able to segregate EMG samples according to three fermentative quality classes of GQS. High-quality samples were accurately determined as having a positive correlation with lactate, while low- and middle-quality ones were partially overlapped and correlated with NH3-N, butyrate, and propionate. The validation of the FDA model in the blind farm-derived dataset confirms the effectiveness of the proposed GMS to rank between poorly- or well-preserved EMG.

Assessment of Fermentative Quality of Ensiled High-Moisture Maize Grains by a Multivariate Modelling Approach

Severino Segato;Giorgio Marchesini;Lorenzo Serva;Barbara Contiero;Luisa Magrin;Igino Andrighetto
2022

Abstract

The study aimed to define a grain-adapted quality score (GQS) to assess the fermentative pattern of ensiled high-moisture maize grain (EMG) based on organic acids, ammonia, and ethanol data of a lab-scale dataset. The GQS was validated by comparison with both the Flieg-Zimmer’s quality score (FQS) and a standardized quality score (SQS) by a received operating analysis. Compared with FQS and SQS, the cut-offs of poor/good samples for the proposed GQS were 47 (accuracy of 0.94) and 71 points (accuracy of 0.88) over 100, respectively. The relationship among indices was also tested in a farm-derived dataset by arranging a confusion matrix, which showed the higher predictive performance considering the lower cut-off. On the lab-scale dataset, a factorial discriminant analysis (FDA) assessed the most predictive chemical post-ensiled traits able to segregate EMG samples according to three fermentative quality classes of GQS. High-quality samples were accurately determined as having a positive correlation with lactate, while low- and middle-quality ones were partially overlapped and correlated with NH3-N, butyrate, and propionate. The validation of the FDA model in the blind farm-derived dataset confirms the effectiveness of the proposed GMS to rank between poorly- or well-preserved EMG.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/3416358
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