Abstract
Quality of frozen pork is gained attractive attention around the world for its significance in international trade and national strategic reserves. This work proposed to explore the feasibility of predicting total volatile basic nitrogen (TVB-N) content in frozen pork samples without thawing using a developed portable near-infrared (NIR) spectroscopy. Various preprocessing techniques and variable selection algorithms have been applied to build the prediction model. Based on the final result achieved better performance was noted by using 2nd derivate as a preprocessing technique and efficient spectra variable selection algorithms. Variable selection algorithm random frog partial least squares (RF-PLS) showed the best prediction model of TVB-N content in frozen pork samples; with 0.9669 of coefficient of determination for the independent samples in prediction set. This study demonstrated that the fast, non-destructive, non-invasive, and accurate approach portable NIR spectroscopy could be successfully utilized for monitoring the quality of frozen pork coupled variable selection algorithms.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
