近红外光谱技术快速鉴别河南省地方品种羊肉

Rapid identification of local variety lamb in Henan Province by near infrared spectroscopy

中文关键词:  近红外光谱技术  河南省地方品种  羊肉  定性鉴别

英文关键词:near infrared reflectance spectroscopy  local breeds of Henan Province  mutton  qualitative identification

中文摘要:

      目的 利用近红外光谱技术和费歇尔(Fisher)判别法, 建立河南省地方品种羊肉定性判别模型和快速鉴别方法。方法 试验选择河南省地方羊品种5个和湖羊品种1个, 共计181个羊肉样品, 制成粉末干样, 在1400~2500 nm波长范围进行光谱扫描, 通过不同的预处理, 建立判别模型。结果 不同品种羊肉光谱图变化趋势一致, 原始光谱可以进行绵羊和山羊品种的鉴别, 正确率分别达到94.0%和85.0%以上; 通过平滑, 一阶求导, 多元散射校正(multiplicative scatter correction, MSC)和不同方法组合进行光谱处理后, 判别正确率均提高, 其中以一阶求导+MSC组合效果最好; 1400~2500 nm波长判别正确率达100.0%, 交叉验证率92.8%, 用该模型对6个品种羊肉进行鉴别, 校正集正确率均达到100.0%, 交叉验证正确率在87.5%以上, 预测集有4个品种正确率达到100.0%; 波长分段后, 1400~1620 nm预测准确性最好, 但个体鉴别准确性降低; 不同判别方法槐山羊的鉴别正确率均达到95.0%以上。结论 近红外光谱技术1400~2500 nm长波段可以准确鉴别羊肉品种, 其中以一阶求导+MSC预处理效果最好, 不同品种间以槐山羊判别正确率最高。

英文摘要:

      Objective To establish a qualitative identification model and rapid identification method of local mutton in Henan province by using near infrared spectroscopy and fisher method. Methods A total of 181 mutton samples were selected from 5 local sheep breeds and 1 Huyang breed in Henan Province, and dried powder samples were prepared. Spectral scanning was performed in the wavelength range of 1400–2500 nm, and discrimination models were established through different pretreatment. Results The results showed that the spectral patterns of different breeds of mutton showed the same trend, and the original spectra could be used to identify sheep and goats with an accuracy of 94.0% and 85.0%, respectively. After spectrum processing by smoothing, first-order derivation, multiplicative scatter correction (MSC) and other different methods, the discrimination accuracy was improved, among which the combination of first-order derivation+MSC had the best effect, the discrimination accuracy of 1400–2500 nm wavelength was 100.0%, and the cross-validation rate was 92.8%. When the model was used to identify 6 varieties of mutton, the accuracy of calibration set reached 100.0%, the cross-validation rate was above 87.5%, and the prediction set of 4 varieties reached 100.0%. After wavelength segmentation, the prediction accuracy for the range of 1400–1620 nm was the best, but the accuracy of individual identification decreased; For different discrimination methods, the identification accuracy of sophora goat was all above 95.0%. Conclusion In summary, near infrared spectroscopy technology in the long-wave range of 1400–2500 nm could accurately identify the variety of mutton, of which the first derivative+MSC preconditioning was the best. For the different varieties, the accuracy of sophora goats was the highest.