稻米储藏品质近红外光谱快速判定技术及仪器研发

采用近红外光谱(NIRS)分析技术和化学计量方法建立稻米脂肪酸值、品尝评分值和水分含量的近红外分析模型并对模型进行了预测准确性评价;在建立定标模型的过程中,分别探讨光谱散射和数学导数处理等优化对定标模型的影响。结果表明:偏最小二乘法是建立稻米脂肪酸值、品尝评分值和水分含量测定定标模型的最佳回归方法,所建立脂肪酸值、品尝评分值和水分含量模型的定标相关系数(RSQ)分别为0.961、0.9230和0.9999,定标标准偏差(SEC)分别为1.9205、2.5391和0.04。标准方法测定值与NIRS方法预测值之间的T检验结果显示两种方法无显著性差异,表明所建立的稻米脂肪酸值、品尝评分值和水分含量的NIRS数学模型有较好预测准确性。本试验研制了便携式粮食储藏品质测定仪。     

便携式近红外谷物分析仪快速测定小麦蛋白质的研究

收集中国小麦各个主产区不同品种样品104份,研究采用国产便携式近红外谷物分析仪快速测定小麦蛋白质含量,用化学计量方法建立了小麦蛋白质含量的近红外分析模型,并对模型进行了预测准确性评价。在建模过程中,分别探讨光谱散射和数学导数处理等优化对定标模型的影响。结果表明:采用偏最小二乘法(PLS)建立模型,光谱预处理最佳条件为:Savitzky-Golay平滑、Savitzky-Golay一阶导数、基线校正、均值中心化、主因子数为12。所建模型的定标标准偏差(SEC)和定标相关系数(RC)分别为0.177和0.988;外部验证的标准偏差(SEP)和相关系数(RP)分别为0.188和0.961。标准方法测定值与NIRS方法预测值之间的T检验结果为T=0.304(P<0.05),表明两种测定方法测定值之间无显著性差异,说明定标模型具有很好的预测准确性,可应用于优质小麦收购中蛋白质含量的快速测定。     

粳稻储藏食用品质近红外光谱快速测定技术研究

采用近红外光谱(NIRS)分析技术和化学计量方法建立了粳稻储藏品尝评分值的近红外分析模型,并对模型进行了预测准确性评价;在建立定标模型的过程中,探讨了不同光谱散射、数学等优化处理对定标模型的影响。结果表明:修正偏最小二乘法(MPLS)是建立粳稻储藏品尝评分值定标模型的最佳回归方法,所建立颗粒状和粉末状样品模型的定标相关系数(RSQ)分别为0.927 4和0.923 0,定标标准偏差(SEC)分别为2.347 9和2.539 1。定标模型具有较好的预测准确性。     

近红外法测定大豆脂肪酸值方法的研究

脂肪酸值是衡量大豆品质重要指标.将近红外光谱技术与化学计量方法结合,建立大豆样品脂肪酸值的定标方程,并对定标方程进行了验证,优化得到大豆脂肪酸值的定标方程,交互定标决定系数(1-VR)为0.948 2,外部验证决定系数(R2)为0.915 0,定标标准偏差(SEC)为1.205 8,交叉验证标准偏差(SECV)为1.591 2,现有数据预测标准偏差(SEP)为1.395.通过外部验证,表明该方法也可以应用于实际检测.     

发酵液中乙醇含量的近红外光谱NIRS定量分析与验证

应用近红外光谱技术(NIRS),采用偏最小二乘法(PLS)建立发酵液中乙醇含量定量分析预测模型,并比较发酵液原液和发酵液上清液NIRS预测模型对预测乙醇含量的效果差异。结果表明,发酵液原液所建立的乙醇含量和发酵上清液所建立的乙醇含量预测模型校正误差(SEC)、交叉检验标准误差(SECV)、校正相关系数(RSQ)、交叉验证相关系数(1-VR)皆有很好的关联性。由此可见,通过内部交叉检验和外部验证.用近红外光谱法测定发酵液中的乙醇含量具有很高的准确度,为近红外光谱法快速监测乙醇发酵提供了新的方法。     

近红外光谱快速测定稻谷水分含量的研究

收集我国不同地区、不同品种、不同储藏时间的稻谷样品144份,应用近红外光谱（NIRS）技术研究了稻谷水分含量快速测定方法,在建立定标模型的过程中,探讨了光谱散射处理、数学（导数）处理等优化处理对定标模型的影响。结果表明：修正偏最小二乘法是建立稻谷水分含量测定定标模型的最适合数学方法,所建立的定标模型的相关系数（R）为0．9999,定标标准偏差（SECV）为0．04;55份样品外部检验的相关系数（r）为0．996,检验标准差（SEP）为0．072,标准方法与NIRS方法测定的水分含量之间的T检验值为1．685（P〈0．05）,两种方法测定结果无显著性差异,预测值与实测值的平均绝对偏差为0．03,说明所建立的稻谷水分含量测定的NIRS数学模型具有很高的预测准确性,可应用于稻谷品质分析的快速检测。     

近红外光谱法检测小麦粉中的水分含量

以化学法测定67个小麦粉样品的水分含量,利用波通DA7200型近红外光谱分析仪采集样品近红外光谱,选择合适的光谱区间及光谱预处理方法,采用偏最小二乘法(PLS)和留一法内部交叉验证方式建立定标模型.50个定标样品的近红外光谱经一阶导数预处理,由PLS法获得的定标模型决定系数(R2)为0.984 3.利用17个验证集样品进行外部检验,预测值与真实值之间的相关系数(R2)为0.984 8,预测集标准偏差(SEP)为0.092 9.近红外光谱法具有方便、快速、准确、无损、无污染的特点,应用于小麦粉水分的测定是可行的.     

基于近红外光谱的玉米脂肪酸值快速测定研究

近红外光谱技术可以实现玉米中脂肪酸值的快速无损分析,运用修正偏最小二乘法建立玉米脂肪酸值测定定标模型,其中1-VR 值为0.929,RSQ值为0.947,SEC 值为5.252,SEP值为3.362。T 检验结果显示标准方法测定值与NIRS方法预测值无显著性差异,表明玉米脂肪酸值NIRS 数学模型具有较好的预测准确性。      

玉米籽粒直链淀粉含量的近红外透射光谱无损检测

以214份玉米样品为材料,利用近红外谷物分析仪对样品进行光谱扫描,并测定直链淀粉含量的参比数据,借助于WinISI软件,采用多种数学处理方法和不同的回归统计方法进行定标曲线的开发,优化得到了玉米籽粒直链淀粉含量测定的近红外定标方程,其中直链淀粉占总淀粉含量及直链淀粉占样品干重两个定标方程的定标标准偏差(SEC)、定标相关系数(RSQ)、交叉验证标准误差(SECV)和检验工作标准误差(SEP)分别为2.3201和1.2064、0.8860和0.8856、2.5896和1.3769、3.368和2.133。通过内部交叉验证和外部交叉验证及对其它的231份自交系、杂交种和高直链淀粉自交系进行预测,结果表明,近红外分析技术具有较高的准确度,能代替常规化学分析方法应用于玉米育种的早代材料直链淀粉含量的筛选,可作为高直链淀粉玉米育种的一种简便快速的无损筛选技术。     

应用近红外透射光谱法测定稻米胶稠度研究

以195份稻米为样品,利用近红外透射光谱分析仪,对样品进行光谱扫描,并利用化学法测定胶稠度.利用近红外定标软件,采用多种计量数学处理方法和不同的回归统计方法进行定标曲线的开发和比较,得到了稻米胶稠度测定的近红外分析数学模型,数学模型的定标标准偏差(SEC)、交叉检验标准误差(SECV)和定标决定系数(RSQ)分别为:10.35、10.51和0.827 9.内部交叉验证和外部验证结果表明近红外定量分析胶稠度有很高的准确度.     

Development of a predictive model to determine potato flour content in potato-wheat blended powders using near-infrared spectroscopy

Potato is a good source of dietary energy and several micronutrients, and the development of staple foods using potato-wheat blended powder has received much attention recently in China. A rapid and accurate method for determining the potato flour content in potato staple foods would be valuable to market regulation efforts. We developed a predictive model for the potato flour content in potato-wheat blended powders based on near-infrared spectroscopy (NIRS) analysis. The correction of the near-infrared optical path was carried out to eliminate optical path differences using multiplicative scatter correction (MSC) and smoothing of the spectra using the Savitzky-Golay smoothing first-orderderivative (S-G-1^stD).The prediction model was developed based on partial least squares (PLS) combined with cross-validation(CV) within the full spectrum(850–1100 nm). The results showed that the optimal main factors of potato flour content in potato-wheat blended powder were 9, and the coefficient of determination of calibration (R²c) and standard error of cross-validation (SECV) of the prediction model reached 0.9997 and 0.51, respectively, indicating a good correlation. The repeatability standard deviation (SDr) and repeatability coefficient of variation of cross-validation (CVr) in validated samples were 0.246 and 0.967, respectively, which indicated that the prediction model had good repeatability. The bias-corrected standard error of prediction (SEP) and correlation coefficient of validation (R²P) were 0.69 and 0.9995, respectively, demonstrating good accuracy and stability. The results of this study demonstrated that this prediction model based on NIRS could determine the potato flour content in potato-wheat blended powders accurately and quickly.     

近红外光谱技术快速检测小龙虾新鲜度

应用近红外光谱技术实现对小龙虾新鲜度的快速检测。利用化学计量学方法,通过对近红外品质分析仪采集的虾肉绞碎前后光谱（850～1 050 nm）调整不同预处理方法、偏最小二乘法和组合算法,建立一种基于总挥发性盐基氮（total volatile basic nitrogen,TVB-N）含量的小龙虾新鲜度定量预测模型。结果表明：采用标准正态变量变换与一阶导数结合的预处理方法模型预测效果最好,且绞碎后的虾肉光谱比绞碎前建模效果更好;为满足实际应用需要,对绞碎前的虾肉TVB-N含量预测模型进行分析,其交叉验证误差为3.123,交叉验证相关系数为0.947,用此模型对预测集24 个样品进行预测,预测值与实测值的交叉验证相关系数为0.951 4,在TVB-N含量超过20 mg/100 g（不新鲜）的检测准确率为100%。近红外光谱技术可应用于快速检测小龙虾新鲜度,所建模型具有较好的预测能力。     

番茄总糖含量的近红外光谱无损检测方法研究

根据偏最小二乘法建立番茄总糖含量的定量分析模型,比较原始光谱和平均光谱以及10 种光谱预处理方法对近红外光谱无损检测番茄总糖含量的影响。结果表明：平均光谱所建立的偏最小二乘法校正模型明显优于原始光谱所建模型,常数偏移消除最适合番茄总糖近红外光谱的预处理,其在11998.9～7497.9cm-1 和4601.3～4256.5cm-1优化光谱区内,所建偏最小二乘法定量分析模型的预测值和实测值的相关系数(R)为0.917,校正标准差(RMSEC)为0.263%,预测标准差(RMSEP)为0.236%。平均光谱和优化的光谱预处理方法可有效提高近红外光谱无损检测番茄总糖含量的准确性。     

基于近红外光谱技术的面粉水分无损检测模型的建立

直接对面粉样品进行近红外光谱扫描,采用105℃恒重法测定面粉中水分含量,在不同的光谱数据预处理方式下运用改进偏最小二乘法(MPLS)建立水分含量定标模型,通过比较模型预测效果以确定最佳预处理方法,随后用PCA、PLS、MPLS三种建模方法在最佳预处理方式下建模通过比较模型预测效果以确定最佳建模方法并用验证集对最优模型进...     

Rapid measurement and control of the moisture content of compost using near-infrared spectroscopy

In a compost fermentation of soybean-curd (tofu) refuse, the effects of the moisture content of the compost on the compost reaction were studied. The moisture content of the compost was a very important factor for good fermentation. Near-infrared spectroscopy (NIRS) was applied to the determination of the moisture content of the compost. The reflected rays in the wavelength range between 400 and 2500 nm were measured at 2 nm intervals. The absorption of water was observed at three wavelengths, 960, 1406 and 1888 nm. To formulate a calibration equation, a multiple linear regression analysis was carried out between the near-infrared spectral data at 960 nm (sample number, n = 50) and on the moisture content obtained using a drying method. The values of the simple correlation coefficient and the standard error of calibration were 0.987 and 1.33%, respectively. To validate the calibration equation obtained, the moisture content in the prediction sample set (n = 35) not used for formulating the calibration equation was calculated using the calibration equation, and compared with the values obtained using the drying method. Good agreement was observed between the results of the drying method and those of the NIRS method. The simple correlation coefficient and standard error of prediction were 0.979 and 1.85%, respectively. Then, the NIRS method was applied to a practical situation in which the moisture content was measured and controlled during the compost fermentation, and good results were obtained. The study indicates that NIRS is a useful method for measurement and control of the moisture content in the compost of soybean-curd refuse.     

近红外光谱法快速测定榨菜中亚硝酸盐含量

为研究利用傅立叶近红外光谱分析仪(NIRS)快速测定市售榨菜中亚硝酸盐的含量,先取榨菜样品按GB5009.33-2016测定其亚硝酸盐含量,再向榨菜样品中添加亚硝酸钠,制成亚硝酸钠浓度范围为0.122~39.0875 mg/kg,浓度梯度为0.66 mg/kg的60个样本校正集;与10个样本预测集采集对应的傅立叶近红外光谱曲线,将光谱信息与实际测量值相关联,利用TQ analyst建模软件进行计算分析。结果表明:建模最优预处理方法为一阶微分(1D)与Savitzky-Golay filter滤波平滑的组合预处理;比较分析偏最小二乘法(PLS)与主成分回归法(PCR)的亚硝酸盐样品建立的光谱模型,数据结果显示采用偏最小二乘法(PLS)的亚硝酸盐组分模型稳定性和预测能力更好;内部交叉验正均方差(RSMECV)、交叉验证决定系数(R_c)、外部预测均方根误差(RMSEP)、预测决定系数(R_P)相关系数(r)分别为0.0310、0.9925、0.0141、0.9720、0.9378。经F检验与t检验,与国标所测结果无显著性差异。NIRS检测快速,无损便捷,可用于市售榨菜中亚硝酸盐残留量的定量检测。     

近红外光谱分析技术测定芝麻水分含量的研究

建立了基于FOSS近红外谷物分析仪快速测定芝麻水分含量的模型,探讨了光学处理和数学处理等因素对模型的影响进行,并对模型进行了内部验证和外部检验.实验结果表明最佳的建模参数为:光学处理采用标准正常化处理(SNV only),数学处理技术采用"2,4,4,1".得到的定标方程的定标标准偏差(SEC)为0.0430,定标相关...     

Prediction of fatty acid content in rabbit meat and discrimination between conventional and organic production systems by NIRS methodology

To investigate the feasibility of using the NIRS methodology to analyse the fatty acid content of rabbit meat and to discriminate between conventional and organic production, the meat of a hind leg of 119 rabbits was scanned between 1100 and 2498 nm and 104 samples were sent to the laboratory for reference analysis of fatty acids by gas chromatography. A commercial spectral analysis program (WINISI-2, v. 1.04) was used to process the data and to develop chemometric models. The better calibration equation for each fatty acid, leading to a higher determination coefficient of cross-validation (r²) and low standard error of cross-validation (SECV) was retained. Prediction of linoleic, palmitic, palmitoleic and oleic acid content was excellent or good (r² between 0.90 and 0.70); prediction of arachidonic, stearic, α-linolenic and eicosatrienoic FA has r² between 0.69 and 0.50. However, miristic, vaccenic, icosaenoic and eicosadienoic FA are problematic to predict. When fatty acids were grouped, the r² of the calibration equations were: 0.85 for saturated FA, 0.83 for MUFA, 0.92 for PUFA and 0.91 for n − 6 FA, indicating excellent or good prediction. Prediction of α-linolenic FA (r² = 0.59) needs more precision. The obtained equations have been applied for predicting meat fatty acid composition of both groups of production systems, conventional and organic, for an other 52 rabbit meat samples (2 × 26). Meat of the organic source had lower (p = 0.000) monounsaturated FA (30.54% vs. 34.64%) and higher (p = 0.019) polyunsaturated FA (27.28% vs. 23.66%) than rabbit meat from the conventional system, while the saturated FA content was similar (42%) in both groups. The discriminant model correctly classified (98%) between conventional or organic produced rabbit meat.     

近红外漫反射光谱法测定小麦Zeleny沉降值

选用220份良种小麦品种作为原始样品集,基于漫反射基本原理,使用现代傅立叶变换近红外光谱仪扫描其近红外光谱。探讨以傅里叶近红外光谱法(FT-NIRS)预测小麦的Zeleny沉降值的可行性。以良好的常规实验数据为前提,通过内部交叉检验进行预测模型的建立,其校正决定系数(R2)和交叉检验均方误差(RMSECV)分别为0.9363和2.4,外部验证校正决定系数(R2)和预测均方误差(RMSEP)分别为0.9502和2.68。该法的建立证明了近红外漫反射光谱技术应用于小麦Zeleny沉降值测定的可行性。