基于近红外光谱的猪肉水分在线检测与分级

摘要

In order to realize fast,nondestructive and real-time measurement of parameters (moisture) for pork on-line and divide pork into qualified and unqualified class according to GB/T 18394-2010 (pork moisture limit value ≤76.5％),an on-line detection and classification system was designed and developed based on near infrared reflectance spectra.To determine the detecting distance from the probe to the surface of the sample,optimizing platform was set up,which included spectrum acquisition unit,distance detection unit,light source unit,transmission unit and control unit.NIR spectra (349 ～1 761 nm) were obtained from 54 samples with different detecting distances in the range of 5 ～ 29 mm at approximately 2 mm intervals under static conditions.Moisture contents were determined by traditional methods and then related with the spectral information by partial least-squares (PLS) regression models with the first band (349 ～ 1 435 nm),the second band (1 037 ～ 1 761 nm) and double-band.The result showed that a detecting distance of 19 mm was the best to model.Based on optimal distance,spectra were obtained from 45 samples at static state and on-line by adjusting system of detection distance and multi points simultaneous detection,and then two PLS models were established.The optimal correlation coefficients of the model were 0.915 and 0.906,respectively.The two models results were almost the same.It verified the feasibility of predicting pork moisture on-line.In order to verify stability and precision of models for detecting online,NIR spectra were obtained from another 21 samples.The results showed that the NIR spectral range had an excellent ability to predict the content of moisture (R2 =0.8367) in pork online and classification accuracy was 90.48％.Results indicated that NIR spectroscopy was a promising technique to roughly predict moisture of intact fresh pork on-line.%基于近红外光谱法,优化光纤探头检测距离并通过检测距离调节系统和多点同时检测,设计了猪肉水分在线检测分级系统.首先,为确定光纤探头与生鲜猪肉样品表面间的最佳检测距离,在13个不同检测距离下(5～29 mm)采集了54个样品的光谱,采用多元散射校正方法对原始光谱进行预处理,分别建立了第1波段(349～1 435 nm)、第2波段(1037～1761 nm)和双波段结合3种情况的含水率偏最小二乘回归模型,分析了不同检测距离和不同波段的模型,确认19 mm为在线检测分级装备的最佳检测距离.然后,通过检测距离实时调节系统动态固定最佳检测距离,设计了双波段多点同时检测系统,采集45个猪肉样品在静态条件和在线条件下的光谱,通过比较分析,两种情况下预测结果相近,从而证实了所设计的在线系统能够预测猪肉水分,并且双波段融合建模效果优于单波段,预测结果为:校正集相关系数和校正均方根误差分别为0.906和0.598,验证集相关系数和预测均方根误差分别为0.836和0.402.最后,利用独立的21个猪肉样品验证猪肉预测分级模型精度及稳定性,结果判断正确率为90.48％,表明可见近红外光谱法结合多点检测能有效地在线检测猪肉水分并分级.