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<正> 国标中规定小麦不完善粒的测定方法是:“在检验小样杂质的同时,按质量标准的规定拣出不完善粒称重(W1)”。结果计算公式为: 不完善粒(%)=(100-M)×W1/W式中:W1——为不完善粒重量,g W——试样重量,g M——大样杂质百分率上述的操作方法及计算公式均是只考虑 相似文献
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将同一小麦样品进行人工调节水分,然后在不同水分条件下,用小麦硬度指数仪进行测量小麦硬度指数,并对结果进行分析,探讨水分对小麦硬度指数的影响. 相似文献
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不完善粒含量是小麦质量的重要评价指标,当前检验以人工为主,检验时间长,检验结果受主观经验影响大。随着科学技术的发展,粮食质量检验的自动化程度不断提高,目前已有不完善粒分析仪投入使用。为全面分析不完善粒分析仪的应用效果,进行了单机和联机效果跟踪,验证分析其准确度、重复性、稳定性及检验时间。结果表明:不完善粒分析仪采用500 g样品量检验的结果与人工定值无显著差异,准确度、稳定性满足使用要求;一个样品的检验时间只需4 min,远远低于人工的15~20 min,大大提升了检验效率。 相似文献
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依据GB/T 21304—2007《小麦硬度测定硬度指数法》规定的测定方法,以小麦硬度指数测定技术为指导,使用JYDB100-40型小麦硬度指数测定仪,对混合小麦、软质小麦、硬质小麦和加麦2号四种样品,分别分析试样称量精度从(±0.01~±0.9)g对硬度指数测定值的影响。结果显示,同一品种的样品在不同试样质量所测得硬度指数数值的绝对误差不大于1的前提下,当混合小麦试样质量在(25.00±0.3)g时,软质小麦试样质量在(25.00±0.09)g时,硬质小麦试样质量在(25.00±0.9)g时,所测定的硬度指数变化范围均在允许误差范围内。综合结论为:试样在不大于±0.09g称量误差范围内,各品种小麦的硬度指数值都没有超出标准允许的误差范围。建议对GB/T 21304—2007规定的样品称量误差±0.01 g的精度要求,可以适当放宽到±0.09 g。 相似文献
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为了实现图像处理技术对小麦不完善粒的准确快速识别,研究了一种基于小麦不完善粒图像特征和BP神经网络的不完善粒识别方法。采集小麦不完善粒图像,对图像进行中值滤波、形态学运算、图像分割等处理后,针对每个小麦籽粒,提取其形态、颜色和纹理共3大类54个特征参数,采用主成分分析法提取8个主成分得分向量作为模式识别的输入,建立BP神经网络模型,实现对小麦不完善粒的检测识别。结果表明,该模型对完善粒、破损粒、病斑粒、生芽粒和虫蚀粒的判别正确率分别为93%、98%、100%、90%和85%,平均判别正确率达到93%,可有效对小麦不完善粒进行检测识别。 相似文献
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采用图像分析技术与自动控制技术,将人工智能技术应用于小麦不完善粒检测,研究开发了小麦不完善粒指标的自动快速无损检测仪器。通过验证该仪器检测小麦不完善粒的准确性、重复性、稳定性、台间差等相关性能参数,结果表明:该仪器检测性能稳定,准确性、重复性、稳定性、台间差均符合行业标准要求,操作简单,检测速度快,克服了人工检测主观性强、重复性差、不同人员间检验一致性较差等问题,可实现小麦不完善粒的自动快速无损检测,能够满足粮食收储企业、加工企业和检测机构的检测需要。 相似文献
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本文主要介绍了高光谱成像技术及其在小麦不完善粒检测中的应用,指出了现阶段在不完善粒检测中存在的主要问题,并对今后的研究方向进行了展望,以期推动高光谱成像技术在不完善粒检测中的应用发展。 相似文献
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针对现阶段酿酒企业检测高粱不完善粒效率较低和识别率不高等问题,结合市场上现有的粮食不完善粒检测仪器,开发了一套基于图像识别的高粱不完善粒快速检测仪,对图像的采集、关键硬件、机器视觉和深度学习等方面做了一系列研究,研究分别采用单一特征分析技术、基于机器学习的图像分类技术、基于深度学习的图像分类技术、细粒度图像分类技术对高粱图片进行分类识别分析,通过对比,最终利用Tensorrt部署技术将细粒度图像分类网络部署到设备中。结果表明,开发的高粱不完善粒快速检测仪的识别精度与人工检测的平均误差控制在1%以内;50 g高粱样品的检测时间控制在5min以内。相较于传统的人工检测,检测时间大大缩短,同时避免了人工检测主观上的偏差,对于酿酒企业的高粱不完善率检测鉴定具有重要意义。 相似文献
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Brett F Carver 《Journal of the science of food and agriculture》1994,65(1):125-132
While quantitative measurements of wheat (Triticum aestivum L) kernel hardness are important for market classification of cultivars, their genetic relationship to end-use quality in breeding populations is not well established. After verifying that divergent selection for hardness score (HS) based on near- infrared reflectance (NIR) spectroscopy was effective, the objective was to determine correlated selection responses in milling and flour quality of two hard red winter populations differing widely in parental origin. Selection was applied in the F3 generation using replicated field plots at two locations. Selection response was evaluated in the F4 generation at the same locations the following year. Selection for high HS (harder kernels) increased kernel protein concentration in both populations, while low HS selection decreased it. Selection for HS had no consistent and detectable impact on flour yield or physical dough properties (mixograph absorption, mixing time, and mixograph rating or tolerance). Selection for high HS decreased SDS sedimentation volume adjusted for flour protein concentration in both populations, but the magnitude of the response was small (?1.7 ml in actual units; ?0.3 ml after adjustment). Because correlative effects of NIR hardness were primarily expressed in protein quantity and not protein quality, milling and flour quality must be considered independently of NIR hardness if genetic improvement in those traits is desired. 相似文献
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小麦粉的质量受面筋值,稳定时间等多项指标影响,为指导小麦粉的质量控制,利用面筋测定仪和粉质仪对低筋、中筋、高筋3种馒头类小麦粉进行面筋值、粉质曲线进行测定,分析各项指标的规律。研究结果表明:3种馒头类小麦粉最适参数范围,低筋馒头粉面筋值为26%~28%,稳定时间为3~5 min;中筋馒头粉面筋值为28.0%~29.5%,稳定时间为4~6 min;高筋馒头粉面筋值为29.5%~31.5%,稳定时间为6~7 min。 相似文献
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Craig F Morris Kim Garland Campbell Garrison E King 《Journal of the science of food and agriculture》2005,85(11):1959-1965
Kernel texture is a key factor in the quality and utilization of soft wheat (Triticum aestivum L), yet the variation in kernel texture among US soft wheat cultivars is largely unknown. This study evaluated the following hypothesis: soft wheat cultivars differ in kernel texture due to minor genetic factor(s). Once identified, selected contrasting cultivars could serve as candidates for crop improvement and future genetic studies. To test the hypothesis, kernel texture (SKCS, Single Kernel Characterization System), NIR (near‐infrared reflectance) and Quadrumat break flour yield were evaluated for 30 cultivars drawn from the four major US soft wheat regions and sub‐classes (eastern and western soft white winter, soft red winter and Club). Cultivars were grown in replicated trials over 6 site‐years in Washington state. The results clearly indicated that relatively large, consistent genetic differences in kernel texture exist among US soft wheat cultivars. SKCS and NIR were fairly well correlated (r = 0.85) and tended to rank cultivars in the same order. However, individual cultivars deviated from this linear relationship and occasionally rankings changed substantially. Trends were observed among the geographical regions and sub‐classes, eg the first 13 hardest‐ranked positions (SKCS) were held by western cultivars (13 of the 16 total western cultivars). Quadrumat break flour yield provided an independent assessment of kernel texture and was not correlated with SKCS or NIR hardness. Four distinct cultivar groupings were made based on analysis of variance and two‐dimensional graphical assessment. Each group represented contrasting levels of kernel texture (SKCS or NIR) and break flour yield. Identification of the specific underlying gene(s) conferring kernel texture variation among US soft wheats awaits the next phase of research. Copyright © 2005 Society of Chemical Industry 相似文献