毛白杨材性的近红外光谱分析研究

用近红外光谱仪采集毛白杨杂交子代各样品的谱图,用偏最小二乘法和完全交互验证方式建立化学测量值的光谱验证模型,并用其对未知样品进行了预测.结果发现毛白杨冷水抽出物、1%NaOH抽出物、综纤维素、Klasson木素NIR验证模型确定系数分别为93%、94.6%、94.73%、81.87%,校正标准误差值分别为0.171、0.402、0.409、0.232,光谱残差分别为3.78、4.31、4.36、2.35.预测结果表明:近红外光谱分析技术可以用于毛白杨材性的快速分析测定.     

近红外光谱技术在乳品中的应用

近红外光谱技术是一门发展迅猛的高新技术,具有快速、高效、操作简单和不损伤样品等特点,目前广泛应用于乳品行业中。综述了近红外光谱技术特点及在乳品中的应用,重点介绍了其在牛乳检测中的应用,并对进一步的研究进行了展望。     

红外及近红外光谱技术在食品分析检验中的应用进展

红外及近红外波谱技术,是利用物质对红外光区的电磁辐射的选择性吸收来进行结构分析及定性和定量分析,具有快速,方便,样品用量少,不损坏样品等优点。     

甘蓝型油菜粒色近红外光谱分析模型构建

以306份甘蓝型油菜种子为研究材料,通过计算机图像处理技术筛选出最能反映油菜籽粒色变化的RGB色彩空间参数,并随机选择246份材料对粒色信息中的R（红色）值建立近红外光谱分析模型。在油菜粒色数字信息中,R值最能体现油菜籽粒色性状的变化情况,不同粒色等级对应样品的R值为：0级小于55、1级在55～100之间、2级在100～125之间、3级在125～140之间、4级在140～150之间、5级大于150。对R值构建NIRS分析模型,12种数学及光谱处理组合中最优模型的内部交叉检验相关系数为0.900;外部检验相关系数为0.831,检验偏差为2.147。说明利用RGB色彩空间下的R值构建粒色近红外光谱分析模型是可行的,该模型具有较高的稳定性和准确性,可以作为油菜种子粒色的定性和定量分析的依据。     

现代近红外光谱分析在食品检测中的应用

综述了近红外光谱技术在食品分析中的应用,介绍了近红外光谱技术的特点,并分别详述了近红外光谱技术用于酿造食品、牛奶及其制品质量、肉与肉制品的检测,以及在其它食品检测中的应用。     

Short-wave near-infrared spectroscopy of milk powder for brand identification and component analysis

The aim of the present paper was to provide new insight into the short-wave near-infrared (NIR) spectroscopic analysis of milk powder. Near-infrared spectra in the 800- to 1,025-nm region of 350 samples were analyzed to determine the brands and quality of milk powders. Brand identification was done by a least squares support vector machine (LS-SVM) model coupled with fast fixed-point independent component analysis (ICA). The correct answer rate of the ICA-LS-SVM model reached as high as 98%, which was better than that of the LS-SVM (95%). Contents of fat, protein, and carbohydrate were determined by the LS-SVM and ICA-LS-SVM models. Both processes offered good determination performance for analyzing the main components in milk powder based on short-wave NIR spectra. The coefficients of determination for prediction and root mean square error of prediction of ICA-LS-SVM were 0.983, 0.231, and 0.982, and 0.161, 0.980, and 0.410, respectively, for the 3 components. However, there were less than 10 input variables in the ICA-LS-SVM model compared with 225 in the LS-SVM model. Thus, the processing time was much shorter and the model was simpler. The results presented in this paper demonstrate that the short-wave NIR region is promising for fast and reliable determination of the brand and main components in milk powder.     

Prediction of sensory attributes of cheese by near-infrared spectroscopy

The objective of this work is to evaluate by near infrared reflectance (NIR) spectroscopy different attributes, visual (presence of holes), taste (salty, buttery flavour), texture (hardness, chewiness, creamy), rancid flavour and other sensations (pungency, retronasal sensation), in cheeses elaborated with milk of variable composition (cow, ewe, goat), collected in winter and summer and subject to ripening times of 4 and 6 months, taking as reference data the evaluation of the sensorial properties obtained by a panel of eight trained experts.     

Prediction of sensory characteristics of beef by near-infrared spectroscopy

Sensory hardness, tenderness and juiciness of M. Longissimus dorsi muscles from 10 beef carcasses at three ageing stages were predicted by near-infrared (NIR) spectroscopic analysis in the reflection (NIRR) and transmission modes (NIRT) during 14 days ageing at 2°C. Predicting the sensory variables hardness and tenderness from NIRR measurements using principal component regression (PCR), yielded correlation coefficients in the range 0·80-0·90. The root mean square errors of prediction for the predictions of hardness and tenderness were in the range 0·5-0·7, given in sensory assessment units. Juiciness was not well predicted. Prediction of sensory variables from NIRT measurements did not give satisfactory results. Including samples from all carcasses, cows and young bulls in the models resulted in good predictions from NIRR measurements of frozen and thawed samples. However, the best prediction results were generally obtained from separate calibrations of the samples from the bulls. The potential of NIR spectroscopy in the prediction of sensory variables in whole meat needs to be further investigated on a larger number of samples with different breeds, animals and process treatments included.     

Detection of insect fragments in wheat flour by near-infrared spectroscopy

Insect fragments in commercial wheat flour are a major concern to the milling industry because consumers expect high quality and wholesome products at the retail level. Thus, the US Food and Drug Administration (FDA) has established a defect action level of 75 insect fragments per 50 g of flour. Millers routinely test their wheat flour to comply with this federal requirement and to deliver sound flour to their consumers. The current standard flotation method for detecting fragments in flour is expensive and labor intensive. Therefore, we examined the possible use of a rapid, near-infrared spectroscopy (NIRS) method for detecting insect fragments in wheat flour. We also compared the sensitivity and accuracy of the NIRS method with that of the current standard flotation method. Fragment counts with both techniques were significantly correlated with the actual number of fragments present in flour samples. However, the flotation method was more sensitive than the NIRS method with fragment counts below the FDA defect action level. We were unable to predict whether the number of fragments in a sample exceeded the FDA action level with our NIRS instrumentation. However, we were able to predict accurately whether flour samples contained less than or more than 130 fragments. Although current NIRS instruments are unable to detect insect fragments at the FDA action level, this method should be re-examined in the future because NIRS technology is rapidly improving.     

农作物品质的近红外光谱无损检测研究进展

简述近红外光谱分析原理及过程,归纳总结该技术在谷类作物、豆类作物、油料作物以及糖料作物品质检测中的应用,提出存在的问题,指出进一步的研究方向.     

利用近红外分析技术测定大豆水分含量方法的研究

建立了基于FOSS XDS近红外光谱分析仪快速测定大豆水分含量模型,对光学处理和数学处理手段等因素对模型的影响进行了探讨,对模型进行了内部和外部验证.实验结果表明最佳的建模参数为:光学处理选用标准正常化处理(SNV Only),数学处理选用1.4.4.1方法,大豆水分定标方程的交互定标决定系数(1-VR)为0.990 8,定标决定系数(R2)为0.993 9,定标标准误差(SEC)为0.096 7,交互定标标准误差(SECV)为0.127 3,现有数据预测标准偏差(SEP)为0.136.利用该模型对大豆水分含量进行检测,达到了代替常规标准方法的要求,可以应用于快速检测.     

Prediction of pork quality using visible/near-infrared reflectance spectroscopy

Near-infrared spectroscopy is a rapid screening technique that may be used to determine meat quality traits. While several calibrations on meat quality parameters have been published, the accuracy and robustness of a calibration has rarely been validated with independent samples. In this study, in 207 loin muscles from three independent batches of pigs of different breeds drip loss, colour values, pH and intramuscular fat were determined. Calibrations were made from each combination of two batches and validated with the third batch. Validations of pH, intramuscular fat, drip loss, and L(?), a(?), and b(?) colour values had on average 1.27 times the accuracy of the calibration. Breed did not influence the accuracy of the calibration. Intramuscular fat can be determined with good accuracy. Muscle pH and colour values are reasonably well predicted. Drip loss can not be determined quantitatively with sufficient accuracy, but classification of quality groups is possible.     

近红外光谱结合化学计量学方法在油脂检测中的应用

近红外光谱结合化学计量学技术由其独特的技术优势在油脂掺假分析领域已取得广泛应用。本文主要对近红外光谱结合化学计量学技术的原理及特点进行论述,并综述了近年来在油脂定性及定量分析方法中应用较为广泛、实用性较强的化学计量学方法,即聚类分析、主成分分析、支持向量机、人工神经网络、主成分回归以及偏最小二乘法结合近红外光谱在油脂掺假分析中的应用,并展望了该技术的发展前景。     

近红外光谱法测定菜籽油中水分含量

以菜籽油为试验材料,用干燥乙腈萃取菜籽油中水分,用傅里叶变换近红外光谱技术(FT-NIRS)结合偏最小二乘法(PLS)建立乙腈水分定量分析模型,并用乙腈水分模型间接预测菜籽油水分含量。结果表明:校正样品集的标准偏差(SEC)为0.014 6,预测样品集的标准偏差(SEP)为0.039,预测值与实测值的决定系数(R2)为0.921 6。菜籽油水分含量用FT-NIRS分析和卡尔费休法测定结果之间有良好的一致性,模型具有较高的预测能力,结果准确可靠,可代替常规分析方法。     

Nondestructive evaluation of jujube quality by visible and near-infrared spectroscopy

This study compared prediction ability of interactance, transmission measurements of visible and near-infrared (Vis/NIR) spectroscopy in detecting the soluble solids content (SSC) of jujubes. Calibration models relating Vis/NIR spectra to SSC were developed based on partial least squares regression (PLSR) with respect to the logarithms of the reciprocal absorbance (log (1/R)), its first and second derivatives (D₁log (1/R), D₂log (1/R)). The PLSR models for prediction samples resulted correlation coefficients (r_p) of 0.74-0.91 and root mean square error of prediction (RMSEP) of 2.018-3.200 °Brix for interactance; r_p of 0.63-0.73 and RMSEP of 3.517-3.863 °Brix for transmission, respectively. The results indicate that interactance displays an obvious advantage over transmission measurement.The reflectance measurement was used to access the discrimination potential in sorting external insect-infested jujubes from intact class. Stepwise discriminant analysis (SDA) was performed to identify the effective wavelengths that best discriminated the insect-infested jujubes from intact jujubes and to derive a discriminant function in classifying the jujubes showing external infestation and those that were free of infestation. The results showed that log (1/R) had better correct classification rate than D₁log (1/R), and D₂log (1/R) for classifying intact, insect-infested and stem-end classes.     

近红外光谱分析法快速检测奶粉的品质

研究利用近红外光谱分析技术快速测定多品牌、多类型配方奶粉中蛋白、脂肪及乳糖的方法.收集11个品牌的10多种配方奶粉,共80个样品,WQF-400N傅里叶变换近红外光谱仪采集漫反射光谱,光谱范围10000～3500 cm-1,分辨率为2 cm-1,扫描次数为32次.采用不同的预处理方法结合PLS建立预测模型.结果表明,通过近红外光谱分析可以快速检测多品牌、多类型配方奶粉中蛋白质、脂肪和乳糖的质量分数,预测相关系数分别为0.9458,0.9306和0.9618,预测均方根误差(σ1)分别为0.842,1.925和2.324.     

Application of near-infrared spectroscopy for screening the potato flour content in Chinese steamed bread

Food Science and Biotechnology - Near-infrared (NIR) spectroscopy combined with chemometrics was used as a technique to predict the potato flour content in Chinese steamed bread (CSB). The inner...     

近红外光谱技术在食品安全领域应用研究进展

近红外光谱技术作为一类新型的分析检测技术,在食品安全领域有着广阔的应用前景。综述了该技术在食品品种、产地和掺假真伪鉴别,致病菌、药物残留检测,污染物鉴别以及转基因食品鉴定等方面的应用研究进展,旨在为推动近红外光谱技术在食品安全检测方面的应用提供参考。     

壳聚糖生产过程中脱乙酰度的近红外光谱法现场监测

用固态CP/MAS NMR对壳聚糖的脱乙酰度进行测定,建立快速测定壳聚糖的脱乙酰度的近红外方法。该方法对水分、氢氧化钠、乙酸钠、蛋白质对脱乙酰度测定的影响进行了校正,最后建立的最优近红外测定方程的交叉验证标准误差(SECV)为0.6306,交叉验证相关系数(1-VR)为0.9987。用该方程对定标样品组进行验证,其工作标准误差(SEP)为0.4312,检验决定系数(R2)为0.9994,线性斜率(Slope)为0.9999。结果表明该方法准确、可靠、快速,可以用于壳聚糖生产过程中脱乙酰度的现场监测。     

Evaluation of near-infrared reflectance spectroscopy for predicting stover quality trait in semi-exotic populations of maize

Near-infrared reflectance spectroscopy (NIRS) analysis was investigated as a means of predicting quality parameters in semi-exotic maize stover. These parameters included crude protein (CP), neutral detergent fibre, acid detergent fibre and in vitro dry matter digestibility (IVDMD). Samples of semi-exotic maize stover were collected during three growing seasons (1989, 1990, 1991) from three locations in Catalonia, Spain. Calibration equations were obtained by multiple linear regression of conventional laboratory values on NIRS data from 84 samples and verified with 20 additional samples. Separate NIRS calibration were developed also within year (1989 and 1990, respectively). A Bran + Luebbe InfraAnalyzer model 450 was used for the study. In the multi-year calibration the coefficients of squared multiple correlation (R²) ranged from 0–81 for IVDMD to 0–92 for CP and the standard errors of calibration (SEC) ranged from 0–35 for CP to 1–46 for IVDMD. The study showed that NIRS analysis can be used to evaluate the quality of semi-exotic maize in breeding programmes.