University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada.
Agriculture and Agri-Food Canada, Lacombe, AB T4L 1W1, Canada.
Meat Sci. 2018 Aug;142:1-4. doi: 10.1016/j.meatsci.2018.03.025. Epub 2018 Mar 30.
Pork bellies (n = 198) were scanned with dual energy X-ray absorptiometry (DXA). Visible and near-infrared reflectance (Vis-NIR) spectra were collected from the lean (latissimus dorsi), subcutaneous fat and intermuscular fat layers. Belly-flop angle and subjective belly scores were collected as measures of pork belly softness. Vis-NIR spectra from a single fat layer could explain between 72.7 and 81.1% of the variation in pork belly softness (43.6-72.4% in validation set). The combination of the lean and subcutaneous layers improved the calibration model fit to 79.7-99.9% (66.3-71.5% in validation set). The DXA estimates explained 62.3% of variation in pork belly softness (65.2% in validation set). Results indicated that DXA and NIR technologies could potentially be utilized for pork belly softness sorting in the pork industry.
对 198 块猪腹肉进行了双能 X 射线吸收测定法 (DXA) 扫描。从瘦肉（背最长肌）、皮下脂肪和肌间脂肪层采集可见和近红外反射光谱。采集猪腹肉松软度的衡量指标，包括“肚子贴地”角度和主观评分。单一脂肪层的 Vis-NIR 光谱可以解释猪腹肉松软度变化的 72.7%至 81.1%（验证集中为 43.6-72.4%）。将瘦肉和皮下脂肪层组合起来，可以将校准模型的拟合度提高到 79.7-99.9%（验证集中为 66.3-71.5%）。DXA 估计可以解释猪腹肉松软度变化的 62.3%（验证集中为 65.2%）。结果表明，DXA 和 NIR 技术有可能在猪肉行业中用于猪腹肉松软度的分类。
