Effect of temperature and relative humidity in refrigerator on quality traits and storage characteristics of Pre-packed Hanwoo loin

et al. 2020

Animals

This study was conducted to investigate the effects of rumen-protected methionine and α-tocopherol (RPMT) on growth performance, carcass characteristics, and meat composition of late fattening Hanwoo steers in high-temperature seasons. Fourteen steers were randomly assigned to one of two diets; control (commercial concentrate) and treatment (commercial concentrate + 20 g of RPMT). Average daily gain was 34% higher in the treatment group than in the control group; however, there was no significant difference due to the small number under investigation. Feed conversion ratio was lower in the treatment group than in the control group (p < 0.05). Supplementation of RPMT had little effect on the fatty acid composition of longissimus muscle. Metmyoglobin in the longissimus muscle was significantly lower in treatment group compared to the control group at the ninth day of storage (p < 0.05). The redness of the longissimus muscle was higher in the treatment group than in the control group on day 9 of storage (p < 0.01). Thus, the results suggest that RPMT have positive effects on growth performance, and Commission Internationale de l’Eclairage color stability in the longissimus muscle of late fattening Hanwoo steers in high-temperature seasons.

Section: Resultsmentioning

confidence: 99%

Effect of Rumen Protected Methionine and α-Tocopherol on Growth Performance, Carcass Characteristics, and Meat Composition of Late Fattening Hanwoo Steer in High-Temperature Seasons

Park

Ahn

et al. 2020

Animals

“…Currently, standard techniques for the evaluation of meat freshness or its aging degree are mostly destructive, non-portable, expensive, and di cult to access by general users. 11,12 Thus, optical inspection techniques such as the re ectance of UV-Vis-NIR from meat surfaces have been investigated over several decades. [13][14][15] However, several approaches rely on extracting the degree of freshness without understanding the underlying mechanism related to speci c molecular changes in freshness, which has rendered the results unreliable and unreproducible.…”

Section: Introductionmentioning

confidence: 99%

Quantification for Food Inspection enabled by Hyperspectral Imaging System and Machine Learning

Lee

et al. 2023

Preprint

Several important sensory physical quantities exist but are difficult to quantify, including food freshness. With the aid of a hyperspectral imaging system (HIS) and machine learning (ML), meat freshness is converted into a measurable physical quantity, i.e., freshness index (F. I.), in this study. F. I. is defined from meat fluorescence, which has a strong correlation with bacterial density, using a line-scan-type HIS stimulated at 365 nm. Combined with ML techniques, hyperspectral images are processed more efficiently. By employing linear discriminant and quadratic component analyses for hyperspectral images, F. I. can be estimated from its decision boundary after hyperspectral images are taken at an unknown freshness state. Advanced sensing versatility utilized by computational sensing systems allows hyper-personalization and hyper-customization of human life with the aid of home appliances and smartphones.

Machine Vision with a CMOS-Based Hyperspectral Imaging Sensor Enables Sensing Meat Freshness

Lee,

Kim,

Kim

et al. 2024

ACS Sens.

Imaging spectral information of materials and analysis of its properties have become an intriguing tool for consumer electronics used for food inspection, beauty care, etc. Those sensory physical quantities are difficult to quantify. Hyperspectral imaging cameras, which capture the figure and spectral information simultaneously, can be a good candidate for nondestructive remote sensing. In this study, with the aid of a hyperspectral imaging system (HIS) and machine learning (ML) techniques, meat freshness is converted into a measurable physical quantity, i.e., the freshness index (FI). Herein, the FI is defined as meat fluorescence, which has a strong correlation with the bacterial density. Combined with ML techniques, hyperspectral data are processed more efficiently. By employing linear discriminant and quadratic component analyses, the FI can be estimated from its decision boundary after hyperspectral data are obtained in an unknown freshness state. We demonstrate that the HIS integrated with ML performs as the artificial eye and brain, which is advanced machine vision for consumer electronics, including refrigerators and smartphones. Advanced sensing versatility utilized by computational sensing systems allows hyper-personalization and hyper-customization of human life.