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Portable vibrational spectroscopic methods can discriminate between grass-fed and grain-fed beef
Journal of Near Infrared Spectroscopy ( IF 1.8 ) Pub Date : 2021-11-19 , DOI: 10.1177/09670335211049506
Cassius EO Coombs 1 , Robert R Liddle 1 , Luciano A González 1
Affiliation  

The present study analysed the ability for portable near infrared reflectance (NIR) and Raman spectroscopy sensors to differentiate between grass-fed and grain-fed beef. Scans were made on lean and fat surfaces of 108 beef steak samples labelled as grass-fed (n = 54) and grain-fed (n = 54), with partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) used to develop discrimination models which were tested on independent datasets. Furthermore, PLS-DA was used to predict visual marbling score and days on feed (DOF). The NIR spectra accurately discriminated between grass- and grain-fed beef on both fat (91.7%, n = 92) and lean (88.5%, n = 96), as did Raman (fat 95.2%, n = 82; lean 69.6%, n = 68). Fat scanning using NIR spectroscopy moderately predicted DOF (r2val = 0.53), though Raman and NIR spectroscopy lean prediction models for DOF and marbling were less precise (r2val < 0.50). It can be concluded that portable NIR and Raman spectrometers can be used successfully to differentiate grass-fed from grain-fed beef and therefore aid retail and consumer confidence.



中文翻译:

便携式振动光谱方法可以区分草饲牛肉和谷饲牛肉

本研究分析了便携式近红外反射 (NIR) 和拉曼光谱传感器区分草饲牛肉和谷饲牛肉的能力。使用偏最小二乘判别分析 (PLS-DA) 和线性判别分析 (LDA)对标记为草饲 ( n = 54) 和谷物饲喂 ( n = 54)的 108 份牛肉牛排样品的瘦肉和脂肪表面进行扫描) 用于开发在独立数据集上进行测试的判别模型。此外,PLS-DA 用于预测视觉大理石花纹分数和饲料天数 (DOF)。NIR 光谱准确地区分了草饲牛肉和谷物喂养牛肉的脂肪 (91.7%, n = 92) 和瘦肉 (88.5%, n = 96),拉曼 (脂肪 95.2%, n = 96) 也是如此= 82; 精益 69.6%,n = 68)。使用 NIR 光谱的脂肪扫描适度预测 DOF (r 2 val = 0.53),尽管用于 DOF 和大理石花纹的拉曼和 NIR 光谱精益预测模型不太精确 (r 2 val < 0.50)。可以得出结论,便携式 NIR 和拉曼光谱仪可成功用于区分草饲牛肉和谷饲牛肉,因此有助于零售和消费者信心。

更新日期:2021-11-19
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