【摘要】：國內(nèi)三文魚市場(chǎng)魚龍混雜,假冒問題嚴(yán)重,但鑒別方法有限。采用紅外光譜技術(shù)結(jié)合偏最小二乘判別分析法(PLS-DA)研究了黑龍江大馬哈魚、淡水虹鱒、智利太平洋鮭三種魚肉對(duì)挪威三文魚的冒充問題。采用FITR光譜儀和KBr壓片法采集四種肉類的原始光譜,并對(duì)原始光譜分別進(jìn)行多元散射校正(MSC)、Savitzky-Golay平滑、一階導(dǎo)數(shù)(first derivative)、標(biāo)準(zhǔn)正則變換(SNV)、峰面積歸一化(peak area normalization)五種預(yù)處理來消除噪聲等干擾因素并確定最佳預(yù)處理方法。為建立PLS-DA鑒別模型,將四種魚肉的光譜分別賦予-3,-1,1和3四個(gè)參考分值,建模后通過預(yù)測(cè)檢測(cè)集魚肉得分來檢驗(yàn)?zāi)Ｐ蜏?zhǔn)確性。結(jié)果表明:采用峰面積歸一化法時(shí),PLS-DA檢測(cè)模型的效果最好,校正集和交叉驗(yàn)證集的決定系數(shù)分別為0.97和0.95。RMSEC和RMSECV分別為0.37和0.52。該模型能顯著區(qū)分四種魚肉、檢測(cè)集的預(yù)測(cè)分值分別聚集在各自的參考分值附近,在閾值為±1的判別條件下預(yù)測(cè)準(zhǔn)確度為96%。同時(shí)采用馬氏距離法進(jìn)一步對(duì)四種魚肉的光譜進(jìn)行分析,發(fā)現(xiàn)相互之間差異明顯,其中挪威三文魚與其品種差別最大的淡水虹鱒距離最大,與其比較接近的智利太平洋鮭的距離最小,紅外光譜信息能夠反映不同魚肉的品種、生活環(huán)境等差異。因此,采用紅外光譜技術(shù)結(jié)合PLS-DA法能夠準(zhǔn)確的鑒別出其他魚肉對(duì)挪威三文魚的冒充問題,同時(shí)對(duì)其他肉類檢測(cè)有一定借鑒意義。
[Abstract]:Domestic salmon market is mixed, counterfeit problem is serious, but the identification method is limited. Infrared spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was used to study the impersonation of salmon from Heilongjiang, freshwater rainbow trout and Chilean Pacific salmon to Norwegian salmon. The original spectra of four kinds of meat were collected by FITR spectrometer and KBr compression method, and the original spectra were smoothed by multielement scattering correction (MSC), Savitzky-Golay, respectively. The first derivative (first derivative), standard canonical transformation (SNV), peak area normalization (peak area normalization) five pretreatments to eliminate noise and other interference factors and to determine the best pretreatment method. In order to establish the PLS-DA discriminant model, the spectra of four kinds of fish were assigned to four reference scores of -1 and 3, respectively, and the veracity of the model was tested by predicting the fish meat score. The results show that when the peak area normalization method is used, the PLS-DA detection model has the best effect, and the determination coefficients of the calibration set and the cross-validation set are 0.97 and 0.37 and 0.52 for 0.95.RMSEC and RMSECV, respectively. The model can distinguish four kinds of fish significantly, the prediction scores of the detection set are clustered around their respective reference points respectively, and the prediction accuracy is 96 when the threshold value is 鹵1. At the same time, the spectrum of four kinds of fish was analyzed by Markov distance method, and it was found that there were obvious differences among them, among which the distance between Norwegian salmon and freshwater rainbow trout, which had the biggest difference in species, was the largest. The distance of Chilean Pacific salmon is the smallest, and the infrared spectrum information can reflect the difference of species and living environment of different fish. Therefore, the use of infrared spectroscopy combined with PLS-DA method can accurately identify other fish to Norway salmon impersonation, and at the same time for other meat detection has certain reference significance.
【作者單位】： 華南農(nóng)業(yè)大學(xué)工程學(xué)院 教育部南方農(nóng)業(yè)機(jī)械與裝備關(guān)鍵技術(shù)重點(diǎn)實(shí)驗(yàn)室 廣東省食品質(zhì)量安全重點(diǎn)實(shí)驗(yàn)室;仲愷農(nóng)業(yè)工程學(xué)院信息科學(xué)與技術(shù)學(xué)院 廣東省食品安全與智能控制工程技術(shù)研究中心;
【基金】：國家自然科學(xué)基金青年項(xiàng)目(61501531) 廣東省自然科學(xué)基金項(xiàng)目(2015A030313602) 廣東省科技計(jì)劃項(xiàng)目(2015A020209173) 廣州市產(chǎn)學(xué)研協(xié)同創(chuàng)新重大專項(xiàng)(201508010013,201704020030)資助
【分類號(hào)】：O657.33;TS254.7

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