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。     

近红外光谱技术在茅台酒酒醅检测中的应用

采用近红外光谱技术结合茅台酒生产工艺特点及抽样检验的方法,确立了一套适用于茅台酒酒醅的分析模型和分析方法,提高了酒醅理化指标检测效率,优化了近红外光谱模型预测准确度,有助于及时有效地获得理化数据,为指导生产提供了数据支撑。     

近红外光谱法快速测定藜麦籽粒粗蛋白含量

目的:为了满足高蛋白质藜麦的选育、栽培和农业实践所需,实现藜麦籽粒粗蛋白含量快速、无损检测。方法:本研究应用近红外光谱技术对藜麦籽粒粗蛋白含量的快速检测进行系统研究,选用具有代表性的122份藜麦品种为试材,以其中94份为建模集,28份为验证集,扫描得到藜麦建模集的近红外原始光谱,用Unscrambler 10.4软件进行光谱预处理并使用偏最小二乘法(PLS)建立藜麦籽粒粗蛋白含量的定量预测模型。结果:经滤波拟合法(Savitzky-Golay,SG)+标准正态变量(Standard Normal Variate,SNV)预处理建立的模型预测值决定系数(R2)为0.9380,被测组分浓度分析误差(RMSE)为0.4823,表现最佳。用此模型对验证集28份样品进行预测,相关分析表明,预测值与国标法实测值决定系数为0.9416;单因素方差分析表明,国标法实测值和模型预测值之间无显著差异(P>0.05),建立的模型具有很高的准确性,预测效果良好。结论:近红外光谱法作为一种简单快速无损的检测手段,能够用于藜麦籽粒粗蛋白含量的检测,可以为优质藜麦育种、栽培和农业实践提供技术支持。     

Feasibility of FT–Raman spectroscopy for rapid screening for DON toxin in ground wheat and barley

Rapid detection of deoxynivalenol (DON) in cereal-based food and feed has long been the goal of regulators and manufacturers. As non-destructive approaches, infrared (IR) and near-infrared (NIR) spectroscopic techniques have been used for the prediction and classification of contaminated single-kernel and ground grain without any DON extraction steps. These methods, however, are hindered by the intense and broad spectral bands attributed to naturally occurring moisture. Raman spectroscopy could be an alternative to IR and NIR due to its insensitivity to water and fewer overlapped bands. This study explored the feasibility of the Raman technique for rapid and non-destructive screening of DON-contaminated wheat and barley meal. The advantages of this technique include the use of a 1064-nm NIR excitation laser that reduces interference from fluorescence of biological compounds in wheat and barley, the use of a simple intensity–intensity algorithm at two unique frequencies, plus the technique's ease of sample preparation. The results indicate that the simple algorithm, as well as principal component analysis applied to the Raman spectra, can be used to classify low from high DON grain.     

Feasibility of FT–Raman spectroscopy for rapid screening for DON toxin in ground wheat and barley

Rapid detection of deoxynivalenol (DON) in cereal-based food and feed has long been the goal of regulators and manufacturers. As non-destructive approaches, infrared (IR) and near-infrared (NIR) spectroscopic techniques have been used for the prediction and classification of contaminated single-kernel and ground grain without any DON extraction steps. These methods, however, are hindered by the intense and broad spectral bands attributed to naturally occurring moisture. Raman spectroscopy could be an alternative to IR and NIR due to its insensitivity to water and fewer overlapped bands. This study explored the feasibility of the Raman technique for rapid and non-destructive screening of DON-contaminated wheat and barley meal. The advantages of this technique include the use of a 1064-nm NIR excitation laser that reduces interference from fluorescence of biological compounds in wheat and barley, the use of a simple intensity-intensity algorithm at two unique frequencies, plus the technique's ease of sample preparation. The results indicate that the simple algorithm, as well as principal component analysis applied to the Raman spectra, can be used to classify low from high DON grain.     

储粮害虫检测技术研究进展

储粮害虫是危害粮食安全的主要因素之一,害虫检测技术对于粮食综合管理有非常重要的意义。根据国内外学者对储粮害虫检测技术的研究报道,本文归纳和总结了各种检测技术,分别介绍传统类、新型物理及生物类检测法。传统检测方法较为成熟并已被标准化,常用方法有直观检查法、取样筛检法、探管诱捕法;物理检测法较为新颖,以快速无损型检测方法为主,包括近红外及高光谱法、软X射线、电导率法、声测法、微波法、图像识别法、电子鼻等七种;生物检测法多为辅助性方法,常与其他方法联用以达到较优检测效果。本文对各种方法进行深入比较,并探讨国内外最新害虫检测技术,以期为将来研发合适的检测方法和仪器提供参考。     

Evaluation of sampling units and sampling plans for adults of Cryptolestes ferrugineus (Coleoptea: Laemophloeidae) in stored wheat under different temperatures, moisture contents, and adult densities

Development and evaluation of optimum size and number of sample units is required for cost-effective management of stored grain beetles. In this study, we evaluated the sampling parameters and accuracy of insect density detection and estimation, developed the optimum size and number of sample units, and conducted a feasibility study of the insect detection and density estimation. The measured insect densities in 92% of random samples were less than the introduced insect densities and 67.4 ± 10.8% of random samples did not contain adults when the introduced insect density was 0.1 A/kg (adult/kg). If the random sampling technique was used and 15% of the stored wheat bulk was sampled, 72% of determined means of insect densities of the sampling sets were lower than the introduced insect densities. Increasing the size of sample units did not improve the accuracy of the estimation of insect densities; however, it did considerably increase the probability of insect detection when insect densities were lower than 1.0 A/kg. We recommend at least 7 kg per sample unit for insect detection (especially when insect densities < 0.1 A/kg) and the optimum number of sample units with 15 kg grain per unit should be >24 for a fixed precision of 0.35 when insect densities < 0.1 A/kg. This might be a challenge for grain storage practice. Therefore, using sampling technique to estimate insect densities and detect insects at low insect densities (<0.1 A/kg) might not be practicable.     

小麦品种单籽粒性状的研究

小麦单籽粒鉴定法（ＳＫＣＳ）的发明使得小麦品种间和品种内单籽粒性状的研究成为可能,从而为小麦育种目标性状的制定,品种鉴定及品质评价带来了新的认识和信息,通过对１３个小麦品种（品系）单籽粒性状的研究认为,品种内单籽粒含量存在着较大的变化,单籽粒重量的变幅较为突出,且品种内的变化大于品种间的变化;个别品种籽粒宽度的变异系数较高;关中地区的小麦品种绝大多数为硬质麦,个别品种存在着籽粒质地不纯的现象,籽粒     

Evaluation of the effect of moisture content on cereal grains by digital image analysis

Physical appearance and kernel morphology significantly affect the grade of a harvested crop in addition to other factors such as test weight, percentage of foreign matter and constituent components. Moisture content of grain can potentially affect the physical appearance and kernel morphology. The objective of this study was to evaluate the effect of moisture content on the classification capability of colour, morphology and textural features of imaged grains. Colour images of individual kernels and bulk samples of three grain types, namely Canada Western Amber Durum (CWAD) wheat, Canada Western Red Spring (CWRS) wheat and barley were acquired using a machine vision system. The grain kernels were conditioned to 12%, 14%, 16%, 18% and 20% moisture contents before imaging. Previously developed algorithms were used to extract 123 colour, 56 textural features from bulk sample images and 123 colour, 56 textural, 51 morphological features from individual kernel images. The extracted features were analysed for the effect of moisture content. Statistical classifiers and a back propagation neural network model were used for classifying the grain bulk at different moisture contents. The colour and textural features of bulk grain images were affected by the moisture content more than that of the single kernel images.     

基于FT-NIR光谱技术结合KPCA-MD-SVM对白酒基酒的快速判别

为保证在摘酒过程中基酒分段的准确,研究通过利用傅里叶变换近红外设备对整个摘酒过程中的基酒样品进行光谱采集,使用支持向量机（SVM）对最优预处理的基酒光谱建立基酒分段模型,其模型训练集的正确率为93.02%,测试集判别率为90.08%。为减少建模时间和提高模型的可靠性,使用核主成分分析（KPCA）对基酒光谱数据降维,并对此建立基酒分段模型。其训练集正确率为94.81%,测试集判别率为90.75%,相比无KPCA分析时的分段模型训练集高1.79%,测试集高0.67%。为进一步提高模型的判别能力,使用马氏距离（MD）剔除了降维后的异常数据样品,创建的基酒分段模型训练集对基酒段数的正确率为98.72%,测试集正确率为98.75%。剔除异常样品后的分段模型的训练集正确率提高了3.91%,测试集判别率提高了8%。以上研究表明了KPCA+MD+ SVM基酒分段模型能对基酒进行快速判别,为近红外光谱在自动化摘酒方面提供了一种理论可能。     

Detection of External and Internal Insect Infestation in Wheat by Near-Infrared Reflectance Spectroscopy

Near-infrared spectroscopy (NIR) in the reflectance mode was investigated for the rapid, automatic and non-destructive detection of insect stored-grain pests external or internal to wheat kernels. Convincing calibration performance was obtained for external adult Oryzaephilus surinamensis (L) (saw-toothed grain beetle) in unmilled samples including varieties Beaver (soft wheat) and Mercia (hard wheat) at two moisture contents. With this large substrate variability, the method could differentiate between uninfested samples and samples with approximately 270 insects kg⁻¹ or more. Milling made no improvement. Large spectral differences were observed between uninfested kernels and kernels infested internally with Sitophilus granarius (L) (grain weevil) larvae or pupae, arising from both a changed chemical composition and physical structure. Single uninfested and infested kernels were discriminated by their second derivative (d²) spectra. For both external and internal infestation there was substantial evidence that insect protein and/or chitin and moisture were being detected. NIR should be useful as a rapid method of detection.     

Factors affecting the behaviour of beetle pests in stored grain, with particular reference to the development of lures

This review summarises the published information on the behaviour of beetles in grain bulks that could lead to the identification of additional factors with the potential for enhancing existing lures or developing new ones. The review concentrates mainly on the adult stage of the five beetle pest species Oryzaephilus surinamensis (L.), Sitophilus granarius (L.), Cryptolestes ferrugineus (Stephens), Typhaea stercorea (L.) and Ahasverus advena (Waltl). After an analysis of the grain store environment and the origins of storage pests, particular attention is paid to behaviour associated with beetle movement and distribution within grain bulks, and their response to food and other multi-species attractants. Potential sources of new semiochemicals are identified; as well as the grains themselves, these include attractive volatiles associated with hot spots in grain, mouldy grain, and alternative insect pest habitats such as mammals’ and birds’ nests. It might also be worth looking for new semiochemicals produced during the mass upward movements of beetles from grain often seen following disturbance, although these could be repellents rather than attractants. Other sources of repellent semiochemicals with potential as grain protectants could be predatory and parasitic species of insects. Consideration is given to behavioural influences that may conflict with the intended response to attractive lures; for example, the presence of damaged grains, moisture and temperature gradients in the grain bulk may act as rival attractants, while many species prefer to hide away from bright light. As population densities rise, inter- and intra-specific competition becomes increasingly important. Human activities associated with grain handling, drying, cooling and pest control, as well as the presence of natural enemies and pathogens, all have the potential to affect insect response to a lure. Opportunities for the development of more effective lures are highlighted.     

粮食真菌毒素污染的无损检测方法研究进展

粮食种植、加工和储运过程易受真菌毒素污染,严重威胁人畜健康。然而,目前传统的真菌毒素检测方法存在过程繁琐、时效性差或灵敏度低等缺点,难以满足现场快速准确检测的需要,日益成为制约粮食质量安全的瓶颈。现代无损检测技术作为一种新兴的检测技术,可以在不破坏样品的情况下对其进行质量评价,是粮食品质在线、实时检测的一个重要发展方向,在粮食真菌毒素污染的快速分析方面具有巨大的应用潜力。本文重点综述了近红外光谱、高光谱图像、电子鼻等典型无损检测技术在粮食真菌毒素污染检测分析中的最新研究进展,讨论了各种技术的优点及限制因素,并对其应用前景进行了展望。     

光谱技术在白酒质量控制中的研究进展

白酒质量控制中涉及检测样品成分复杂,采用传统检测方法存在效率较低和环境污染隐患等不足。因此,无损高效的光谱技术成为了分析白酒酿造过程中关键控制指标与质量监测控制的重要手段。本文综述了近红外光谱、拉曼光谱、荧光光谱和高光谱成像技术的特点,其中着重介绍光谱技术应用于酿酒原料关键物质组成、大曲理化指标、酒醅和白酒的无损检测,并讨论了光谱技术在白酒质量控制技术和提高白酒品质及安全性的重要意义。本文进一步提出,未来可扩展光谱技术在白酒检测中的应用范围,如在白酒酿造过程中建立其副产物（酒糟、黄水等）中理化指标的快速检测方法,或进行酱香型基酒三种典型体快速分类,将有益于白酒酿造行业的质量控制效果和提高经济效益。     

油茶果自然霉变程度的可见/近红外与中短波近红外光谱检测

分析利用可见/近红外光谱（400～1 000 nm）与中短波近红外光谱（900～1 700 nm）对不同自然霉变程度油茶果检测判别的可行性,实验同时采集不同霉变程度油茶果赤道阴面、阳面和接合面三点的两波段光谱,样品平均光谱的主成分分析（principal component analysis,PCA）发现不同霉变程度样品同组内具有一定聚类效果且PC1和PC2对于判别不同组间样品有效,全光谱偏最小二乘判别分析模型结果显示原始光谱已具有足够信息,建立的模型性能比预处理后全光谱更优。进一步进行特征波长选取,发现相比于PC载荷,连续投影法在两光谱范围选取波长建立的简化模型均为最优,预测集判别准确率与Kappa系数均为84.4%与0.766 7。结合预测集混淆矩阵发现,两光谱范围最优简化模型预测不同霉变组样品特异度相当,均在0.84以上,但900～1 700 nm中短波近红外光谱对于中等霉变程度的判别灵敏度（0.72）略高。本研究表明近红外光谱技术可用于油茶果的自然霉变程度检测,可见/近红外与中短波近红外光谱能力相当,考虑到仪器成本问题,可见/近红外光谱具有更好的实时检测应用前景。     

Fusarium damage assessment in wheat kernels by Vis/NIR hyperspectral imaging

Fusarium head blight is a fungal disease that affects the world’s small grains, such as wheat and barley. Attacking the spikelets during development, the fungus causes a reduction of yield and grain of poorer processing quality. Secondary metabolites that often accompany the fungus, such as deoxynivalenol (DON), are health concerns to humans and livestock. Conventional grain inspection procedures for Fusarium damage are heavily reliant on human visual analysis. As an inspection alternative, a near-infrared (NIR) hyperspectral image system (1000–1700 nm) was fabricated and applied to Fusarium-damaged kernel recognition. An existing extended visible (400–1000 nm) system was similarly used. Exhaustive searches were performed on the 144 and 125 wavelength pair images that, respectively, comprised the NIR and visible systems to determine accuracy of classification using a linear discriminant analysis (LDA) classifier. On a limited set of wheat samples the best wavelength pairs, either with visible or NIR wavelengths, were able to discriminate Fusarium-damaged kernels from sound kernels, both based on visual assessment, at an average accuracy of approximately 95%. Accuracy dropped off substantially when the visual contrast between the two kernel conditions became imperceptible. The NIR region was slightly better than the visible region in its broader array of acceptable wavelength pairs. Further, the region of interest (ROI) defined as the whole kernel was slightly better than ROIs limited to either a portion of the endosperm or the germ tip. For the NIR region, the spectral absorption near 1200 nm, attributed to ergosterol (a primary constituent in fungi cell membranes), was shown to be useful in spectral recognition of Fusarium damage.     

刀式混合研磨仪GM200在样品制备中的应用研究

目的建立蔬菜、水果、鱼类、肉类及粮食谷物类等样品前处理操作规范。方法采用前处理设备德国莱驰刀式混合研磨仪(RETSCH GRINDOMIX GM200),从各类食品样品着手,上机进行反复试验,找到最适的仪器操作参数对样品进行均一化处理。结果蔬菜、水果、鱼类、肉类及粮食谷物等经刀式混合研磨仪GM200研磨后均获得均一性样品,满足后续检测的实验要求(包括元素、农残、兽残等检测项目)。结论采用刀式研磨仪GM200对样品进行预处理,可以进一步地提高样品的均一化程度,更好地保留分析目标化合物,为食品样品中待测组分的提取、净化、浓缩,进行定量、定性分析检测做好充分准备,保证结果的准确性和重现性。     

一种探管式储粮害虫诱捕在线监测装置的设计研究

为实现对粮仓粮堆内害虫发生的早期监测,本研究基于探管式诱捕器和红外光电传感器研发了一种经济有效、可实时在线监测害虫发生的装置;基于该装置建立了9种主要储粮害虫成虫红外光电序列数据集;通过方差分析和数据可视化方法验证了其数据分布特性与害虫进入诱捕段的位置无关;对红外光电序列建立了高斯核支持向量机模型,通过实验室的测试,对蛀蚀性和粉食性两大害虫类别的判别达到了加权平均分类准确率87.7%、召回率87.5%和F1值87.6%。     

近红外光谱法快速检测山苍子果实主要品质指标的定量模型的研究

为实现山苍子果实精油、核仁油、蛋白质含量的快速检测,采用近红外光谱结合化学计量学方法对山苍子果实精油、核仁油、蛋白质含量的测定进行了研究。115个样本采用连续投影算法(SPA)筛选剔除冗余变量,以光谱-理化值共生距离(SPXY)算法划分为校正集和外部验证集。多种预处理方法对原始光谱进行优化后,分别确定二阶导数结合Detrend、导数SG结合Detrend、二阶导数为最优预处理方法,建立山苍子果实精油、核仁油、蛋白质含量定标模型,精油、核仁油、蛋白质定标模型外部验证集R²分别为0.910 7、0.966 4、0.911 0,结果表明,所建模型精度较好,可用于山苍子果实品质的快速检测。     

近红外光谱分析技术在鸡肉品质检测中的应用研究进展

全球的鸡肉产量和消费量逐年上升,鸡肉品质已成了消费者的重点关注对象。近红外光谱分析技术是一种无损、快速、环保以及实时的检测技术,目前已快速发展成为一种应用于食品分析领域的新方法,在肉品检测方面也展现出极大的分析和应用潜力,近年来,已有较多国内外研究探讨了其在鸡肉品质检测方面的可行性。本文通过对近红外检测技术在鸡肉颜色、p H值、持水力、剪切力及化学成分预测方面的定量分析,肉品动物来源、鸡肉产地溯源及品质等级划分的定性分析以及其他相关方面检测的研究进行了综述,总结了应用近红外检测各品质指标的研究现状,可为相关研究者提供参考和借鉴,并基于分析总结对今后近红外光谱分析在鸡肉品质检测应用的发展前景及发展方向做了展望。