Monitoring the staling of wheat bread using 2D MIR-NIR correlation spectroscopy

Staling of bread is a major source of food waste and efficient monitoring of it can help the food industry in the development of anti-staling recipes. While the staling fingerprint in the mid-infrared region is fairly well established this paper set out to find the most informative parts of the near-infrared spectra with respect to staling. For this purpose, two-dimensional correlation spectroscopy on near- and mid-infrared spectra of wheat bread crumb during aging was employed for the first time. The important mid-infrared absorption band at 1047 cm⁻¹ related to amylopectin retrogradation was found to correlate positively with increased bread hardness and to co-vary with the near-infrared band at 910 nm in the short wavelength region (r² = 0.88 to hardness), the near-infrared band at 1688 nm in the 1. overtone region (r² = 0.97 to hardness) and to the near-infrared band in the long wavelength region at 2288 nm (r² = 0.97 to hardness). The spectral information from the first principal component on near-infrared and the first principal component on mid-infrared was found to be highly correlated by a r² = 0.98. It is demonstrated that the major bread staling processes such as amylopectin retrogradation and water loss can be followed with both near- and mid-infrared spectroscopy.     

利用近红外光谱技术快速检测烤烟苗期病毒病

[目的]筛选苗期病毒快速检测的化学成分指标。[方法]采用摩擦接种方法对烟苗接种TMV病毒诱发病毒病,利用近红外光谱对健株和病株内在化学成分(还原糖、钾、氯、总氮、总糖和总烟碱)进行了定性和定量分析。[结果]接种TMV 10 d后,健株与病株在化学成分上差异较大,特别是在有机物含量上,各调查时期的处理均高于对照。[结论]可将有机物如还原糖、总糖和总烟碱等含量作为病毒检测的主要指标,并辅以无机成分含量作参考。     

利用近红外分析技术测定玉米子粒品质与进行品质育种的探讨

利用FOSS公司1255型近红外透射光谱分析仪,对2000～2001年的686份玉米自交系和2001～2004年的2517份杂交组合的子粒品质成分进行测定。结果表明,玉米子粒3种品质成分在不同材料间存在显著差异,粗脂肪和蛋白质变异幅度和变异系数较大,粗淀粉含量的变异幅度和变异系数较小,年份之间有差异。自交系中粗脂肪平均含量为4.24%～4.27%,蛋白质平均含量为11.75%～12.17%,粗淀粉平均含量为71.04%～71.10%。玉米杂交组合粗脂肪平均含量为4.45%～4.86%,粗蛋白平均含量为9.65%～10.05%,粗淀粉平均含量为72.53%～73.49%,并对玉米品质育种和近红外技术的利用作了探讨。     

Discriminant analysis of wood-based materials using near-infrared spectroscopy

 This study deals with the suitable discriminant techniques of wood-based materials by means of near-infrared spectroscopy (NIRS) and several chemometric analyses. The concept of Mahalanobis' generalized distance, K nearest neighbors (KNN), and soft independent modeling of class analogy (SIMCA) were evaluated to determine the best analytical procedure. The difference in the accuracy of classification with the spectrophotometer, the wavelength range as the explanatory variables, and the light-exposure condition of the sample were examined in detail. It was difficult to apply Mahalanobis' generalized distances to the classification of wood-based materials where NIR spectra varied widely within the sample category. The performance of KNN in the NIR region (800–2500 nm), for which the device used in the laboratory was employed, exhibited a high rate of correct answers of validation (>98%) independent of the light-exposure conditions of the sample. When employing the device used in the field, both KNN and SIMCA revealed correct answers of validation (>88%) at wavelengths of 550–1010 nm. These results suggest the applicability of NIRS to a reasonable classification of used wood at the factory and at job sites. Received: March 13, 2002 / Accepted: July 19, 2002 Acknowledgments The authors thank Gifu Prefectural Human Life Technology Research Institute and Kubota Co. for their support. We also thank Professor Dr. Shiro Kimura and Dr. Hideyuki Yokochi for their constructive discussions about the research.     

利用近红外光谱技术识别不同类别的茶叶

以龙井、碧螺春、祁红和铁观音4种中国名茶为对象,研究了采用近红外光谱结合K最近邻法(KNN)模式识别方法对茶叶进行识别与分类的可行性.选取6500～5500 cm-1(1538～1818 nm)波数范围内的光谱,通过标准正态变量变换(SNV)预处理后,利用KNN的模式识别方法建立识别模型.结果表明,4主成分因子建立的KNN判别模型最佳,模型对训练集与预测集中样本的识别率都达到100%.该结论为快速准确识别茶叶提供了一种新思路.     

基于近红外光谱的茶粕中茶皂素快速测定分析方法研究

[目的]研究近红外光谱技术在茶粕中茶皂素的快速检测应用.[方法]在确定了装样厚度的基础上,利用115个茶粕样品结合偏最小二乘方法,建立了茶粕中茶皂素含量的分析模型,为验证模型的实用性,对90个茶粕样品进行了预测,将预测结果与标准方法结果进行成对结果t检验.[结果]试验得出,该试验方法与江西出入境检验检疫局综合技术中心的标准操作规范JXSOP070-2013的结果不存在显著差异,并据此制定了茶粕中茶皂素近红外快速检测方法的标准操作规程(standard operation procedure SOP),经实际应用证明效果显著.[结论]利用近红外光谱检测方法快速分析茶粕中茶皂素含量具有快速、高效、环保的特点,可提高检测速度和检测效率.     

向日葵籽仁脂肪和脂肪酸含量近红外光谱模型的建立

为实现向日葵品质的快速无损检测,选取50份具有代表性的油用向日葵材料,采用偏最小二乘法（PLS）构建籽仁脂肪、亚油酸、油酸、硬脂酸和棕榈酸含量的近红外光谱（NIRS）模型。结果表明,脂肪、亚油酸、油酸含量模型校正和验证相关系数均大于0.96,且预测值与化学值相对误差均在10%以下,能够达到样品成分含量的快速测定。硬脂酸和棕榈酸含量模型校正相关系数分别为0.92和0.82,验证相关系数分别为0.83和0.74,预测值与化学值相对误差在4.66%~17.99%之间,可用于样品成分含量的初步预测。本研究构建的NIRS模型,有助于油用向日葵种质资源品质鉴定和快速筛选。     

近红外光谱快速检测技术在食用油领域的应用

近红外光谱技术由于其分析速度快、信息量大、不破坏样品、无污染、操作技术要求低等特点而受到人们越来越多的关注,在众多领域得到研究和应用。综述了近红外光谱技术在食用油领域的研究和应用进展,主要包括在食用油酸值、过氧化值、脂肪酸含量、种类及掺假判定、调和油配比等多个方面的研究。虽然食用油种类繁多、近红外光谱仪器和软件的专用性不足,是造成近红外光谱技术在食用油实际检测中应用的阻碍,但随着研究的进一步深入,该技术在食用油领域一定有着广阔的应用前景。     

燕麦蛋白质近红外定量模型的创建及其在育种中的应用

[目的]研究利用近红外光谱分析法定量分析燕麦完整籽粒粗蛋白含量的可行性,探讨不同地区种植的同一燕麦品种蛋白质含量的变化,以期为燕麦的营养品质育种提供参考依据。[方法]收集蛋白质含量变幅较大的124份代表性燕麦样品,利用近红外谷物品质分析仪进行光谱扫描,采用常规化学分析方法(GB/T 5009.5—2010)测定样品蛋白质含量,借助近红外定标软件Win ISI,采用偏最小二乘法(PLS)建立燕麦粗蛋白含量的定标模型。利用定标模型对14个地区219份(17个品种)燕麦完整籽粒粗蛋白含量进行测定,分析不同地区、不同类型燕麦样本间的差异。[结果]124份燕麦籽粒样品的粗蛋白含量为15.49%~23.77%,分布范围较广,具有较好的代表性。成功建立了燕麦蛋白质含量的定标模型,决定系数较高,标准误差较小,回归方程具有较高的准确性。因此,利用近红外光谱分析技术检测燕麦籽粒中粗蛋白含量是可行且可靠的,可替代化学测定方法。[结论]近红外光谱分析技术为检测燕麦籽粒粗蛋白质含量提供了一种新方法。