光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
SPECTROSCOPY AND SPECTRAL ANALYSIS
2014年
7期
1811-1815
,共5页
褚璇%王伟%张录达%郭浪花%Peggy Feldner%Gerald Heitschmidt
褚璇%王偉%張錄達%郭浪花%Peggy Feldner%Gerald Heitschmidt
저선%왕위%장록체%곽랑화%Peggy Feldner%Gerald Heitschmidt
最优波长%Fisher判别分析法%玉米颗粒%黄曲霉毒素%近红外高光谱图像
最優波長%Fisher判彆分析法%玉米顆粒%黃麯黴毒素%近紅外高光譜圖像
최우파장%Fisher판별분석법%옥미과립%황곡매독소%근홍외고광보도상
Optimum wavelengths%Fisher discrimination analysis%Corn kernels%Aflatoxin%Near-infrared hyperspectral imaging
黄曲霉毒素是广泛存在于玉米中且具有剧毒的一种代谢产物,以美国农业部农业研究署(USDA-ARS) Toxicology and Mycotoxin Research Unit提供的2010年先锋玉米为研究对象,验证了高光谱成像技术对玉米中黄曲霉毒素检测的可行性。以甲醇为溶剂制备四种不同浓度的黄曲霉毒素溶液,并将其逐一滴在等量的4组共120粒玉米颗粒表面,以未处理的30粒洁净玉米作为一组对照样本,将大小、形状相似的150个样品随机分为训练集103个,验证集47个;对获取的400~1000 nm波段范围内的高光谱图像,先进行标准正态变量变换(standard normal variate transformation ,SNV)预处理,然后引入基于 Fisher判别最小误判率的方法选择最优波长,并以所选波长作为Fisher判别分析法的输入建立判别模型,对玉米颗粒表面不同浓度的黄曲霉毒素进行识别,最后对模型判别正确率进行了验证。结果表明,选取四个最优波长(812.42,873.00,900.36和965.00 nm )时Fisher判别分析模型对训练集与验证集的准确率分别为87.4%和80.9%。该方法为含黄曲霉毒素玉米颗粒便携式检测仪器的开发,以及对田间霉变玉米自然代谢产生毒素的检测奠定了技术基础。
黃麯黴毒素是廣汎存在于玉米中且具有劇毒的一種代謝產物,以美國農業部農業研究署(USDA-ARS) Toxicology and Mycotoxin Research Unit提供的2010年先鋒玉米為研究對象,驗證瞭高光譜成像技術對玉米中黃麯黴毒素檢測的可行性。以甲醇為溶劑製備四種不同濃度的黃麯黴毒素溶液,併將其逐一滴在等量的4組共120粒玉米顆粒錶麵,以未處理的30粒潔淨玉米作為一組對照樣本,將大小、形狀相似的150箇樣品隨機分為訓練集103箇,驗證集47箇;對穫取的400~1000 nm波段範圍內的高光譜圖像,先進行標準正態變量變換(standard normal variate transformation ,SNV)預處理,然後引入基于 Fisher判彆最小誤判率的方法選擇最優波長,併以所選波長作為Fisher判彆分析法的輸入建立判彆模型,對玉米顆粒錶麵不同濃度的黃麯黴毒素進行識彆,最後對模型判彆正確率進行瞭驗證。結果錶明,選取四箇最優波長(812.42,873.00,900.36和965.00 nm )時Fisher判彆分析模型對訓練集與驗證集的準確率分彆為87.4%和80.9%。該方法為含黃麯黴毒素玉米顆粒便攜式檢測儀器的開髮,以及對田間黴變玉米自然代謝產生毒素的檢測奠定瞭技術基礎。
황곡매독소시엄범존재우옥미중차구유극독적일충대사산물,이미국농업부농업연구서(USDA-ARS) Toxicology and Mycotoxin Research Unit제공적2010년선봉옥미위연구대상,험증료고광보성상기술대옥미중황곡매독소검측적가행성。이갑순위용제제비사충불동농도적황곡매독소용액,병장기축일적재등량적4조공120립옥미과립표면,이미처리적30립길정옥미작위일조대조양본,장대소、형상상사적150개양품수궤분위훈련집103개,험증집47개;대획취적400~1000 nm파단범위내적고광보도상,선진행표준정태변량변환(standard normal variate transformation ,SNV)예처리,연후인입기우 Fisher판별최소오판솔적방법선택최우파장,병이소선파장작위Fisher판별분석법적수입건립판별모형,대옥미과립표면불동농도적황곡매독소진행식별,최후대모형판별정학솔진행료험증。결과표명,선취사개최우파장(812.42,873.00,900.36화965.00 nm )시Fisher판별분석모형대훈련집여험증집적준학솔분별위87.4%화80.9%。해방법위함황곡매독소옥미과립편휴식검측의기적개발,이급대전간매변옥미자연대사산생독소적검측전정료기술기출。
Aflatoxin is a toxic metabolite widely existing in corn .In the present paper ,the feasibility of detecting aflatoxin on corn kernel surface by hyperspectral imaging technology was verified .The corn called pioneer with the same shape is provided by Toxicology and Mycotoxin Research Unit .With methanol configuration ,four different concentrations of aflatoxin solutions were prepared and dripped on every 30 corn kernels .Also other clean 30 kernels without aflatoxin dripped were prepared to be the control samples .Among the 150 kernel samples ,103 training samples and 47 validation samples were prepared randomly .First-ly ,hyperspectral image in the range of 400 to 1 000 nm was collected .For eliminating the deviations in original spectrum ,stand-ard normal variate transformation (SNV) was adopted as pretreatment method .And then several optimum wavelengths were se-lected by the principle of minimum misdiagnosis rate .After that the selected optimum wavelengths were taken as the input of the Fisher discrimination analysis to discriminate the different concentrations of aflatoxin on the corn .Finally ,the discrimination model based on four optimum wavelengths (812.42 ,873.00 ,900.36 and 965.00 nm) was built and the accuracy of the model was tested .Results indicate that the classification accuracy of calibration and validation set was 87.4% and 80.9% respectively . This method provides basis for designing the corresponding portable instrument and distinguishing aflatoxin produced by natural-ly metabolism in corn .