【摘要】：隨著畜禽肉和肉制品食用量的迅速增長,人們對肉品質(zhì)量提出了更高的要求;對于肉制品,消費者最為關心是肉品質(zhì)量,當前中國對肉品品質(zhì)在線檢測方面的研究和應用則相對較少,尚無針對肉品品質(zhì)在線無損檢測開發(fā)的設備。也沒能真正投入到肉品的生產(chǎn)加工過程。研究不同肉品脂肪的近紅外快速檢測模型。并采用標準化學方法進行差異分析。通過近紅外技術對豬肉、牛肉、羊肉進行掃描,采用國標法(索氏提取法)對鮮肉脂肪含量進行化學值的測定,以PLS(偏最小二乘法)作為建模方法,并通過不同的光譜預處理手段分別建立了豬牛羊肉的近紅外光譜參數(shù)與樣品的脂肪含量之間的對應關系模型。結(jié)果表明,對于豬肉來說,選擇4 260~6 014cm~(-1)波段+一階導+Norris所建的模型效果最好,其校正相關系數(shù)和預測相關系數(shù)分別為0.955 6和0.961 6;對于牛肉來說,選擇5 226~7 343cm~(-1)波段+一階導+S-G所建的模型效果最好,其校正相關系數(shù)和預測相關系數(shù)分別為0.923 5和0.942 7;對于羊肉來說,選擇5 207~7 362cm~(-1)波段+一階導+Norris所建的模型效果最好,其校正相關系數(shù)和預測相關系數(shù)分別為0.915 7和0.939 6;對于鮮肉來說,選選用波段為5 156~6 065cm~(-1)+二階導+S-G所建模型效果最好,其校正相關系數(shù)和預測相關系數(shù)分別為0.916 3和0.919 4。以上所有模型的校正相關系數(shù)均大于0.91,模型都具有較高的精密度,符合不同肉制品在實際生產(chǎn)的需求,具有分析速度快、檢測成本低、分辨率高、無損的優(yōu)點。
[Abstract]:With the rapid increase in the consumption of livestock and poultry meat and meat products, people have put forward higher requirements for meat quality. For meat products, consumers are most concerned about meat quality. At present, the research and application of meat quality online inspection in China is relatively few, and there is no equipment developed for meat quality online nondestructive testing. Also did not really invest in the meat production and processing process. The near infrared fast detection model of fat in different meat products was studied. The difference analysis was carried out by standard chemical method. The pork, beef and mutton were scanned by near-infrared technique. The chemical value of fresh meat fat was determined by Soxhlet extraction method. PLS (partial least square method) was used as modeling method. The model of the relationship between the near infrared spectrum parameters and the fat content of pig beef and mutton was established by different spectral pretreatment methods. The results showed that, for pork, the model with the first order guide Norris in the band of 4 260 0 014 cm ~ (-1) was the best, its corrected correlation coefficient and predictive correlation coefficient were 0.955 6 and 0.961 6, respectively, while for beef, the correlation coefficient of correction and prediction were 0.955 6 and 0.961 6, respectively. The model established by selecting the first order guide S-G in the band of 5226 ~ (7 343cm ~ (-1) was the best, the calibration correlation coefficient and the predictive correlation coefficient were 0.923 5 and 0.942 7, respectively. For mutton, the model of the first order guide Norris in the band of 52077 367 cm ~ (-1) was the best. The corrected correlation coefficient and predicted correlation coefficient were 0.915 7 and 0.939 6, respectively, and for fresh meat, the model with second-order derivative S-G was the best, the calibration correlation coefficient and prediction correlation coefficient were 0.916 3 and 0.919 4, respectively. The calibration correlation coefficient of all the above models is greater than 0.91.The model has higher precision and meets the needs of different meat products in actual production. It has the advantages of fast analysis, low detection cost, high resolution and nondestructive.
【作者單位】： 山西出入境檢驗檢疫局;中北大學;
【基金】：山西省科技攻關項目(20150313015)資助
【分類號】：O657.33;TS251.7

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