本文關(guān)鍵詞： FT-NIR PLS-DA 阪崎腸桿菌 金葡萄球菌 大腸桿菌 牛奶　出處：《光譜學(xué)與光譜分析》2016年S1期 　論文類型：期刊論文

【摘要】：乳制品的質(zhì)量安全問(wèn)題受到廣泛的關(guān)注。為快速、準(zhǔn)確判定乳制品污染源,利用傅里葉變換近紅外光譜技術(shù)采集被阪崎腸桿菌、金葡萄球菌、大腸桿菌三種致病菌污染的牛奶樣品的近紅外透射光譜(NP),使用一階求導(dǎo)(FD),標(biāo)準(zhǔn)正態(tài)變量變換(SNV),多元散射校正(MSC)對(duì)光譜進(jìn)行預(yù)處理,結(jié)合偏最小二乘判別分析(partial least squares-discriminant analysis,PLS-DA)對(duì)三種細(xì)菌判別的可行性進(jìn)行探究。研究表明,利用FD與SNV預(yù)處理方法得到PLS-DA模型判別結(jié)果均比NP差,而利用MSC預(yù)處理后準(zhǔn)確率與NP一致達(dá)到了100%,且預(yù)測(cè)集相關(guān)系數(shù)(Rp)比NP高,預(yù)測(cè)均方根誤差(RMSEP)更小,表明模型在經(jīng)過(guò)MSC預(yù)處理后預(yù)測(cè)性能更理想。闡明利用FT-NIR技術(shù)結(jié)合化學(xué)計(jì)量學(xué)方法經(jīng)過(guò)合適預(yù)處理方法后能有效用于乳制品中微生物類別的鑒別。
[Abstract]:In order to determine the pollution sources of dairy products quickly and accurately, Enterobacter sakazakii and Staphylococcus aureus were collected by Fourier transform near infrared spectroscopy (FT-NIR). The near infrared transmission spectrum (NIR) of milk samples contaminated by three kinds of pathogenic bacteria of E. coli was pretreated by first order derivation, standard normal variable transform (SNV) and multivariate scattering correction (MSC). Combining partial least squares-discriminant analysis with partial least squares discriminant analysis (PLS-DA), the feasibility of three kinds of bacteria discrimination was explored. The results showed that the results of PLS-DA model were worse than that of NP by using FD and SNV pretreatment methods. The accuracy of preprocessing with MSC is 100% consistent with NP, and the correlation coefficient of prediction set (RP) is higher than that of NP, and the root-mean-square error (RMSEP) of prediction is lower than that of NP. It is shown that the model can be used to predict the performance of dairy products after pretreatment with MSC, and it is indicated that FT-NIR combined with chemometrics can be used to identify the microorganism in dairy products effectively.
【作者單位】： 福州大學(xué)電氣工程與自動(dòng)化學(xué)院;福州大學(xué)生物科學(xué)與工程學(xué)院;福建醫(yī)科大學(xué)醫(yī)學(xué)技術(shù)與工程學(xué)院;福建省出入境檢驗(yàn)檢疫局檢驗(yàn)檢疫技術(shù)中心;
【基金】：福建省自然科學(xué)基金項(xiàng)目(2016J01771)資助
【分類號(hào)】：O657.33;TS252.7

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