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采用高效液相色谱测定干辣椒样品中的辣椒碱和二氢辣椒碱含量,利用透反射模式采集干辣椒样品的近红外光谱,采用偏最小二乘法(PLS)建立干辣椒样品的辣度评价的校正模型,并对模型用于预测。结果表明,筛选出建模光谱波段为9000~4000cm-1;通过残余方差(RVV)分析,确定了最适主成分数为8;干辣椒的辣度PLS评价模型的定标集和验证集的相关系数(RC,RV)分别为0.9871、0.9104,定标集和验证集的标准差(SEC,SEP)分别为2445、3976。研究发现,近红外光谱法作为一种快速而准确的定量分析手段,在干辣椒及其制品的辣度评价方面有广阔的应用前景。 相似文献
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油菜籽芥酸和硫甙含量近红外反射光谱测定技术的优化设置 总被引:6,自引:1,他引:6
以完整甘蓝型油菜籽为样品,研究了不同光谱预处理和回归统计方法在用近红外反射光谱(NIRS)分析油菜籽中芥酸和硫甙的含量时,对建立回归方程的影响。结果表明,光谱预处理对校正结果影响较大,不同光谱数学处理以2阶导数处理较好,在各阶导数处理中存在着交叉现象,对于芥酸和硫甙都可找到一些较好的处理组合。对于芥酸,除趋势变换法(De-trending)散射处理外,其余各处理均有明显效果。但散射处理对硫甙分析效果不如芥酸明显。回归统计方法对建立芥酸和硫甙的回归方程影响最为有效,两者表现一致,其中以改良偏最小二乘法(MPLS)效果最好。对于芥酸和硫甙,采用“标准正态变量转换(SNV) 趋势变换法(De-trending)/2,4,4,1/MPLS”的组合建立回归方程效果较好,检验平均偏差(Bias)分别小至-0.405和0.315,而检验决定系数(RSQ)分别高至0.982和0.972。本研究采用整粒小样品(3g)来分析,效果较好,可直接用于育种早世代选择。 相似文献
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为寻找一种简便易行的糜子籽粒水分、淀粉、粗蛋白、粗脂肪含量测定方法,以120个普通糜子品种为材料,采用近红外漫反射光谱(NIRS)法测定糜子籽粒水分、淀粉、粗蛋白、粗脂肪含量进行了研究。结果表明,采用一阶导数+减去一条直线、矢量归一化光谱预处理,分别建立水分、淀粉、粗蛋白、粗脂肪含量的校正模型,校正和预测效果最佳。模型的校正决定系数(R~2cal)分别为0.921 0、0.905 8,0.926 3和0.904 5,交叉验证决定系数(R~2cv)分别为0.921 0、0.900 2、0.940 0和0.881 2;外部验证决定系数(R~2val)分别为0.912、0.801、0.890和0.786。结果表明该模型代替化学分析法鉴定糜子品质是完全可行的。 相似文献
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甘薯蛋白质含量近红外反射光谱分析模型应用研究 总被引:9,自引:1,他引:8
以71份薯叶和170份薯块根样品为材料,应用近红外光谱技术(NIRS)和偏最小二阶乘法(PLs),建立甘薯蛋白质含量近红外反射光谱分析数学模型,并对模型预测结果的准确性进行了评价.结果显示.甘薯叶和块根的蛋白质含量的近红外光谱预测模型校正决定系数(R2cal)分别为O.996和0.993,校正均方差(RMSEE)分别为O.255和O.126,内部交叉验证决定系数(R2cv)分别为0.984和O.986,均方差(RMSECV)分别为0.448和O.178.模型对样品NIR的预测值与其相应的化学值有较好的相关性,此模型可用来预测甘薯蛋白含量.在甘薯优质育种和品质分析中具有广泛的应用价值. 相似文献
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Near infrared reflectance (NIR) spectroscopy was used to measure moisture, fat, and sucrose in powdered cocoa products. Spectra of a series of known samples were recorded and multiple linear regression techniques were used to relate the concentrations of each parameter to reflectance measurements at selected wavelengths. Precision and accuracy were estimated to evaluate the potential application of the NIR spectroscopy in the quality control of powdered cocoa products. Results showed that moisture, fat, and sucrose could be analyzed in powdered cocoa products by near infrared reflectance spectroscopy because good correlation coefficients and low standard errors were achieved in prediction study. 相似文献
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《Journal of dairy science》1987,70(5):1086-1091
Near infrared reflectance spectroscopy analysis of forages for CP, ADF, NDF, and minerals was compared with wet chemistry results. Infrared analysis was conducted with a NEO-Tech 51A where spectral equations had been transferred by the vendor from master calibrations. These calibrations were based upon microwave drying of feed samples and grinding through a cyclone mill. Our forage testing laboratory routinely tests by oven drying at 60°C and grinding with a Wiley mill through a 2-mm screen. Protein and ADF analyses were conducted on 66 corn silage, hay crop silage, and hay samples. Coefficients of determination for oven drying-wet chemistry and microwave-infrared, oven drying-wet chemistry and oven drying-infrared, and microwave-infrared and oven drying-infrared analysis were: .90, .93, and .98 for crude protein and .92, .94, and .96 for ADF. Infrared results were compared with wet chemistry CP and ADF for 265 corn silage, hay, and hay crop silage samples. Coefficients of determination were .97 for CP and .95 for ADF with standard errors of prediction of .92 and 1.98. The respective correlation coefficients for corn silage, hay crop silage, and hay were .94 and .73; .95 and .92; and .73 and .74. Although standard errors of prediction were low for all minerals, coefficients of determination were above .80 for Ca and K. 相似文献
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近红外光谱检测技术在肉类工业中的应用 总被引:2,自引:2,他引:2
近年来,近红外光谱技术(near infrared reflectance spectroscopy,NIRS)已经成为肉和肉产品品质检测中最为有效、先进的方法之一。本文回顾了应用NIRS技术对食品化学成分、物理性质和感官指标的检测,说明了NIRS技术在肉品分级制度中的应用,并讨论了NIRS在肉品工业上的应用前景。 相似文献
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A near infrared reflectance (NIR) spectrophotometric procedure was evaluated for potential use in assaying nutrient contents of vegetables. Dried ground samples of cauliflower, broccoli, Brussels sprouts, spinach, asparagus, peas, green beans, and squash were analyzed chemically for crude protein, crude fat, ash, and neutral detergent fiber (NDF) and then scanned to develop NIR prediction equations. For the 75 calibration foods samples, the respective correlation coefficients between actual and NIR predicted values of these food components were 0.97, 0.77, 0.91, 0.81. Predictions for 26 unknown samples, which included cabbage, carrots, chard, corn, onions and rhubarb, were based on NIR equations developed from the first 75 samples. The resultant correlations were 0.95, 0.49, 0.85, and 0.78, respectively, for crude protein, crude fat, ash and NDF. NIR prediction detected incorrect chemical values of crude protein in several samples. 相似文献
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选取来自内蒙同一品种普通公牛和淘汰母牛各部位肉各224个肉样,新疆同一品种普通公牛和淘汰母牛各部位肉130个肉样,分别经冷冻解冻和冷鲜状态下,应用便携式近红外光谱仪在1000~1799nm波长内对绞碎后的肉糜进行光谱扫描,并且测定了内蒙肉样的蛋白质、脂肪、水分和剪切力值(WBSF)。从中分别选择来自两个产地的154和91个肉样作为建模集,采用偏最小二乘判别分析法(PLS-DA)建立了两个产地普通公牛肉和淘汰母牛肉的鉴别模型。所建的两个模型对校正集和验证集样本的鉴别准确率均达100%,对剩余预测集的70和39个肉样进行鉴别分析的鉴别准确率分别为96%和90%。 相似文献