Search

Verification of beef production systems and authentication of origin is becoming increasingly important as consumers base purchase decisions on a greater number of perceived values including the healthiness and environmental impact of products. Previously Raman spectroscopy has been explored as a tool to classify carcases from grass and grain fed cattle. Thus, the aim of the current study was to validate Partial Least Squares Discriminant Analysis (PLS-DA) models created using independent samples from carcases sampled from northern and southern Australian production systems in 2019, 2020 and 2021. Validation of the robustness of discrimination models was undertaken using spectral measures of fat from 585 carcases which were measured in 2022 using a Raman handheld device with a sample excised for fatty acid analysis. PLS-DA models were constructed and then employed to classify samples as either grass or grain fed in a two-class model. Overall, predictions were high with accuracies of up to 95.7% however, variation in the predictive ability was noted with models created for southern cattle yielding an accuracy of 73.2%. While some variation in fatty acids and therefore models can be attributed to differences in genetics, management and diet, the impact of duration of feeding is currently unknown and thus further work is warranted., Competing Interests: Declaration of competing interest Funding was achieved by Stephanie Fowler with the assistance of David Hopkins. Stephanie Fowler designed the experiment, complied the first draft, was responsible for project administrated and collected the data along with Matthew Kerr. Data was analysed by Leigh Schmidtke. Edits were made by all authors and the final manuscript was prepared by Stephanie Fowler. The authors declare no conflict of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model's performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications. [ABSTRACT FROM AUTHOR]