光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
SPECTROSCOPY AND SPECTRAL ANALYSIS
2015年
8期
2099-2102
,共4页
于舸%杨仁杰%吕爱君%谭恩忠
于舸%楊仁傑%呂愛君%譚恩忠
우가%양인걸%려애군%담은충
二维近红外-红外相关谱%多维偏最小二乘判别%掺杂牛奶%淀粉
二維近紅外-紅外相關譜%多維偏最小二乘判彆%摻雜牛奶%澱粉
이유근홍외-홍외상관보%다유편최소이승판별%참잡우내%정분
Two-dimensional NIR-IR correlation spectroscopy%Multi-way partial least squares discriminant analysis%Adultera-ted milk%Starch
为更快、更准确的判别掺杂牛奶和纯牛奶，将二维异谱 N IR‐IR相关谱与多维偏最小二乘判别（NPLS‐DA）相结合，建立了掺杂牛奶与纯牛奶 NPLS‐DA 模型。首先，准备并配置纯牛奶和浓度范围为0.01～1 g · L －1掺杂淀粉牛奶样品各36个，并在室温的条件下采集所有样品的一维近红外透射光谱和中红外衰减全反射光谱。接着，计算了所有样品在4200～4800和900～1700 cm －1范围的同步二维N IR‐IR相关谱，研究了其二维相关谱特性，并指出虽然该技术可提供更多的信息，但由于掺杂物微量，仍旧无法根据相关图谱直接对比判定牛奶是否掺杂，需要借助模式识别的方法进行判别。最后，将同步二维NIR‐IR相关谱与NPLS‐DA结合建立掺杂牛奶与纯牛奶的判别模型，该模型对校正集内部样品和预测集外部样品的判别正确率分别为95.8％和100％。此外，为了比较，分别建立了基于二维NIR和IR相关光谱的 NPLS‐DA模型，两模型对未知样品的判别正确率均为95.8％。研究结果表明：采用NIR‐IR相关谱的NPLS‐DA模型能提供更好判别结果。该方法可有效提取食品中掺杂物的特征信息，为检测掺杂食品提供了一个新的方法。
為更快、更準確的判彆摻雜牛奶和純牛奶，將二維異譜 N IR‐IR相關譜與多維偏最小二乘判彆（NPLS‐DA）相結閤，建立瞭摻雜牛奶與純牛奶 NPLS‐DA 模型。首先，準備併配置純牛奶和濃度範圍為0.01～1 g · L －1摻雜澱粉牛奶樣品各36箇，併在室溫的條件下採集所有樣品的一維近紅外透射光譜和中紅外衰減全反射光譜。接著，計算瞭所有樣品在4200～4800和900～1700 cm －1範圍的同步二維N IR‐IR相關譜，研究瞭其二維相關譜特性，併指齣雖然該技術可提供更多的信息，但由于摻雜物微量，仍舊無法根據相關圖譜直接對比判定牛奶是否摻雜，需要藉助模式識彆的方法進行判彆。最後，將同步二維NIR‐IR相關譜與NPLS‐DA結閤建立摻雜牛奶與純牛奶的判彆模型，該模型對校正集內部樣品和預測集外部樣品的判彆正確率分彆為95.8％和100％。此外，為瞭比較，分彆建立瞭基于二維NIR和IR相關光譜的 NPLS‐DA模型，兩模型對未知樣品的判彆正確率均為95.8％。研究結果錶明：採用NIR‐IR相關譜的NPLS‐DA模型能提供更好判彆結果。該方法可有效提取食品中摻雜物的特徵信息，為檢測摻雜食品提供瞭一箇新的方法。
위경쾌、경준학적판별참잡우내화순우내，장이유이보 N IR‐IR상관보여다유편최소이승판별（NPLS‐DA）상결합，건립료참잡우내여순우내 NPLS‐DA 모형。수선，준비병배치순우내화농도범위위0.01～1 g · L －1참잡정분우내양품각36개，병재실온적조건하채집소유양품적일유근홍외투사광보화중홍외쇠감전반사광보。접착，계산료소유양품재4200～4800화900～1700 cm －1범위적동보이유N IR‐IR상관보，연구료기이유상관보특성，병지출수연해기술가제공경다적신식，단유우참잡물미량，잉구무법근거상관도보직접대비판정우내시부참잡，수요차조모식식별적방법진행판별。최후，장동보이유NIR‐IR상관보여NPLS‐DA결합건립참잡우내여순우내적판별모형，해모형대교정집내부양품화예측집외부양품적판별정학솔분별위95.8％화100％。차외，위료비교，분별건립료기우이유NIR화IR상관광보적 NPLS‐DA모형，량모형대미지양품적판별정학솔균위95.8％。연구결과표명：채용NIR‐IR상관보적NPLS‐DA모형능제공경호판별결과。해방법가유효제취식품중참잡물적특정신식，위검측참잡식품제공료일개신적방법。
New approach for discriminant analysis of adulterated milk is proposed based on combining hetero‐spectral two‐dimen‐sional (2D) near‐infrared (NIR) and mid‐infrared (IR) correlation spectroscopy along with multi‐way partial least squares dis‐criminant analysis (NPLS‐DA) .Firstly ,36 pure milk samples were collected and 36 adulterated milk with starch samples (0.01 to 1 g · L -1 ) were prepared by adding appropriate mass of starch into pure milk .Then ,one‐dimensional NIR transmittance spectra and IR attenuated total reflection spectra of pure milk and adulterated milk with starch were measured at room tempera‐ture .And the synchronous 2D NIR‐IR (4 200~4 800 vs .900~1 700 cm -1 ) correlation spectra of all samples were calculated . Due to the trace of adulterants ,the synchronous 2D IR‐NIR correlation spectral differences between adulterated milk with starch and pure milk are very subtle .Consequently ,it was impossible to directly distinguish whether the sample was pure milk or adul‐terated milk .Finally ,2D IR‐NIR correlation spectra were to build a discriminant model to classify adulterated milk and pure milk .The classification accuracy rates of samples in calibration set and in prediction set were 95.8% and 100% respectively .Al‐so ,the NPLS‐DA models were built based on 2D NIR and 2D IR correlation spectra ,respectively .The classification accuracy rates of samples in prediction set were 95.8% .Comparison results showed that the NPLS‐DA model could provide better results using 2D NIR‐IR correlation spectra than using 2D NIR ,and 2D IR correlation spectra .The proposed method can not only effec‐tively extract the feature information of adulterants in milk ,but also explores a new perspective method for detection of adultera‐ted food .
