가시광선-근적외선 반사스펙트럼을 이용한 쇠고기의 신선도 평가

• 발행기관: 한국축산식품학회
• 저자명: 최창현 김종훈 김용주
• 간행물 정보: 『Food Science of Animal Resources』Korean Journal for Food Science of Animal Resources 제31권 제1호, 115~121쪽, 전체 7쪽
• 주제분류: 농수해양 > 식품과학
• 파일형태: PDF
• 발행일자: 2011.02.28

영문 초록

The objective of this study was to develop models to predict freshness factors (total viable counts (TVC), pH, volatile basic nitrogen (VBN), trimethylamine (TMA), and thiobarbituric acid (TBA) values) and the storage period in beef using a visible and near-infrared (NIR) spectroscopic technique. A total of 216 beef spectra were collected during the storage period from 0 to 14 d at a 10°C storage. A spectrophotometer was used to measure reflectance spectra from beef samples, and beef freshness spectra were divided into a calibration set and a validation set. Multi-linear regression (MLR) models using the stepwise method were developed to predict the factors. The MLR results showed that beef freshness had a good correlation between the predicted and measured factors using the selected wavelength. The correlation of determination (r2), standard error of prediction (SEP), and ratio of standard deviation to SEP (RPD) of the prediction set for TVC was 0.74, 0.64, and 2.75 Log CFU/cm2, respectively. The r2, SEP, and RPD values for pH were 0.43, 0.10, and 1.10; those for VBN were 0.73, 1.45, and 2.00 mg%; those for TMA were 0.70, 0.19, and 2.58 mg%; those for TBA values were 0.73, 0.13, and 2.77 mg MA/kg; and those for storage period were 0.77, 1.94, and 2.53 d, respectively. The results indicate that visible and NIR spectroscopy can predict beef freshness during storage.

키워드

beef freshness visible-near infrared reflectance spectra multi Linear regression stepwise