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
2141-2146
,共6页
王宁宁%申兵辉%关建军%赵中瑞%朱业伟%张录达%严衍录%郑煜焱%董成玉%康定明
王寧寧%申兵輝%關建軍%趙中瑞%硃業偉%張錄達%嚴衍錄%鄭煜焱%董成玉%康定明
왕저저%신병휘%관건군%조중서%주업위%장록체%엄연록%정욱염%동성옥%강정명
奶粉%近红外光谱%仿生模式识别%偏最小二乘回归%支持向量回归
奶粉%近紅外光譜%倣生模式識彆%偏最小二乘迴歸%支持嚮量迴歸
내분%근홍외광보%방생모식식별%편최소이승회귀%지지향량회귀
Milk powder%Near infrared spectrum%Biomimetic pattern recognition%Partial least squares regression%Support vec-tor regression
将蒙牛、伊利、完达山三个品牌的奶粉样品掺入不同量的淀粉构成32份实验样品。在跨度近两个月时间内,用JDSU微型近红外光谱仪,分五天重复5次采集这些样品的中波近红外漫反射光谱。采用仿生模式识别(BPR)算法对样品进行掺假识别定性分析,并研究了分析的可靠性与模型的稳健性。以90%作为评价分析结果(样品掺杂的正确识别率 CAR与正确拒识率 CRR)的阈值:将测试结果高于此阈值的所有样品中掺入淀粉的最低含量分别称为样品掺杂的正确识别限与正确拒识限。结果显示:三个品牌奶粉样品分别各自建模时,若用同一天测定的部分光谱数据建立模型,预测该天剩余光谱,样品掺杂的正确识别限与正确拒识限都可以达到0.1%。对于三种品牌奶粉合并后的纯奶粉及其淀粉掺杂样品混合建模时,若用同一天测定的光谱建模与测试,样品掺杂的正确识别限也可以达到0.1%,正确拒识限则为1%;若用不同时间采集的光谱进行交叉测试,正确识别限与正确拒识限都只有5%;若用四天的光谱数据联合建模,测试第五天的数据,正确识别限可以稳定达到1%,正确拒识限可以达到5%。应用两种算法对奶粉中淀粉含量进行定量分析比较,进一步验证了有关定性分析对样品掺杂正确识别限和正确拒识限的可靠性。
將矇牛、伊利、完達山三箇品牌的奶粉樣品摻入不同量的澱粉構成32份實驗樣品。在跨度近兩箇月時間內,用JDSU微型近紅外光譜儀,分五天重複5次採集這些樣品的中波近紅外漫反射光譜。採用倣生模式識彆(BPR)算法對樣品進行摻假識彆定性分析,併研究瞭分析的可靠性與模型的穩健性。以90%作為評價分析結果(樣品摻雜的正確識彆率 CAR與正確拒識率 CRR)的閾值:將測試結果高于此閾值的所有樣品中摻入澱粉的最低含量分彆稱為樣品摻雜的正確識彆限與正確拒識限。結果顯示:三箇品牌奶粉樣品分彆各自建模時,若用同一天測定的部分光譜數據建立模型,預測該天剩餘光譜,樣品摻雜的正確識彆限與正確拒識限都可以達到0.1%。對于三種品牌奶粉閤併後的純奶粉及其澱粉摻雜樣品混閤建模時,若用同一天測定的光譜建模與測試,樣品摻雜的正確識彆限也可以達到0.1%,正確拒識限則為1%;若用不同時間採集的光譜進行交扠測試,正確識彆限與正確拒識限都隻有5%;若用四天的光譜數據聯閤建模,測試第五天的數據,正確識彆限可以穩定達到1%,正確拒識限可以達到5%。應用兩種算法對奶粉中澱粉含量進行定量分析比較,進一步驗證瞭有關定性分析對樣品摻雜正確識彆限和正確拒識限的可靠性。
장몽우、이리、완체산삼개품패적내분양품참입불동량적정분구성32빈실험양품。재과도근량개월시간내,용JDSU미형근홍외광보의,분오천중복5차채집저사양품적중파근홍외만반사광보。채용방생모식식별(BPR)산법대양품진행참가식별정성분석,병연구료분석적가고성여모형적은건성。이90%작위평개분석결과(양품참잡적정학식별솔 CAR여정학거식솔 CRR)적역치:장측시결과고우차역치적소유양품중참입정분적최저함량분별칭위양품참잡적정학식별한여정학거식한。결과현시:삼개품패내분양품분별각자건모시,약용동일천측정적부분광보수거건립모형,예측해천잉여광보,양품참잡적정학식별한여정학거식한도가이체도0.1%。대우삼충품패내분합병후적순내분급기정분참잡양품혼합건모시,약용동일천측정적광보건모여측시,양품참잡적정학식별한야가이체도0.1%,정학거식한칙위1%;약용불동시간채집적광보진행교차측시,정학식별한여정학거식한도지유5%;약용사천적광보수거연합건모,측시제오천적수거,정학식별한가이은정체도1%,정학거식한가이체도5%。응용량충산법대내분중정분함량진행정량분석비교,진일보험증료유관정성분석대양품참잡정학식별한화정학거식한적가고성。
Three China trademarks of milk powder called Mengniu ,Yili ,Wandashan were taken as testing samples .Each of them mixed varied amount of starch in different gradient ,which were consisted of 32 adulterated milk powder samples mixed with starch ,was taken as standard samples for constructing predicted model .To those 32 samples ,the reflecting spectrum char‐acteristics in middle wave of near infrared spectrum with Near Infrared Spectrum Analyzer (Micro NIR 1700) produced by JDSU Ltd .USA were collected for five repeats in five different days .The time span was nearly two months .Firstly ,we build the model used the reflecting spectrum characteristics of those samples with biomimetic pattern recognition (BPR) arithmetic to do the qualitative analysis .The analysis included the reliability of testing result and stability of the model .When we took ninety percent as the evaluation threshold of testing result of CAR(Correct Acceptance Rate)and CRR (Correct Rejection Rate) ,the lowest starch content of adulterate milk powder in all tested samples which the tested result were bigger than that abovemen‐tioned threshold was designated CAR threshold (CAR‐T) and CRR threshold (CRR‐T) .CAR means the correct rate of accep‐ting a sample which is belong to itself ,CRR means correct rate of refusing to accept a sample which is not belong to itself .The results were shown that ,when we constructed a model based on the near infrared spectrum data from each of three China trade‐mark milk powders ,respectively ,if we constructed a model with infrared spectrum data tested in a same day ,both the CAR‐T and CRR‐T of adulterate starch content of a sample can reach 0 .1% in predicting the remainder infrared spectrum data tested within a same day .The three China trademarks of milk powder had the same result .In addition ,when we ignored the trade‐marks ,put the spectrum data of adulterate milk powder samples mixed with the same content of starch of three China trade‐marks milk powder together to construct a model ,the CAR‐T of mixed starch content of a sample may reach 0 .1% ,the CRR‐T can reach 1% ,if the model construction and predicting were performed with near infrared spectrum data tested in a same day . However ,the CAR‐T can just stably reach up to 5% and the CRR‐T have the same result ,if the model construction and predic‐ting were crossly performed with mixed near infrared spectrum data tested in different days .Furthermore ,the correct recogniz‐ing threshold mixed starch of a sample can stably reach up to 1% and the CAR‐T can reach 5% ,if the model construction was based on near infrared spectrum data combined the previous four days to predict the output of the another day .On the other hand ,we also engaged quantitative analysis to the starch content in milk power with two kinds of arithmetic (PLSR ,LS‐SVR) . In contrast with the testing outputs ,the reliability of both the CAR‐T and CRR‐T in qualitative analysis was further validated .