肉品质量无损检测技术研究进展

肉类和肉类食品品质与安全检测一直是肉类行业中关注的热点。本文综述了超声波、电磁特性、电子鼻与电子舌、计算机视觉、光谱分析等几种当前主要无损检测技术在肉品质量评价中的研究进展。其中,重点介绍了光谱分析中具有图谱合一、可同时获取样品内外部品质信息特性的高光谱成像技术及其在国内外肉品品质检测中的应用现状,提出了该技术在肉类品质检测研究方向的几点思考,并展望了无损检测技术在肉品品质检测中的研究前景,以期为后续研究提供参考。     

计算机视觉对青刀豆长度及弯曲度评价的初步研究

目的利用计算机视觉对青刀豆长度及弯曲度进行评价。方法本文以青刀豆为研究对象,利用计算机视觉系统获得青刀豆的表面图像,用Matlab 7.0对各个等级青刀豆图像进行处理分析,提取与青刀豆的长度及弯曲度参数,确定了计算机视觉对青刀豆长度和弯曲度的分级方法。结果计算机视觉对青刀豆的长度和弯曲度的综合分级准确率可达到95.6%。结论本研究为进一步开发青刀豆品质自动分级设备提供了参考。     

基于计算机视觉的牛肉质量分级研究进展

牛肉品质分级对于肉牛产业具有重要意义,为克服人工评级的弊端,客观、无损的自动分级技术成为研究热点。本文综述了国内外利用计算机视觉对牛肉大理石纹、生理成熟度、肉色和脂肪色这些指标进行分级预测的研究进展情况,讨论了研究过程中诸如图像分割、特征提取方面存在的困难,最后指出了计算机视觉技术在牛肉品质分级应用过程中存在的问题以及可能的发展方向。     

肉品检测技术的研究进展

目前肉类食品的检验正朝着更加安全和卫生的方向发展,检验质量不断提高.该文介绍了当代肉类品质检测技术,包括物理分析法中的计算机视觉技术、超声波技术、电磁学检测技术;仪器分析法的高效液相色谱、毛细管电泳安培法、近红外光谱分析技术、核磁共振波谱分析技术;现代分子生物学技术的核酸探针检测技术、生物芯片检测技术和微生物快速检验方法.     

基于计算机视觉的牛肉分级技术研究进展

自从主要的牛肉生产国相继颁布牛肉分级系统以来,计算机视觉牛肉分级技术一直就是牛肉分级领域中的研究重点.本文概述了目前世界上主要的牛肉分级体系,着重论述了国内外计算机视觉牛肉分级技术的发展情况,提出了现行的计算机视觉牛肉分级技术面临的主要问题及其发展方向.     

智能感官分析技术在肉类品质分析中的应用

本文介绍了常用的智能感官分析技术及其原理,概述了计算机视觉技术、电子鼻技术、电子舌技术和质构分析技术在肉类品质分析中的应用现状,提出了智能感官分析技术发展需解决的关键问题。     

高光谱成像技术在肉类安全无损检测的应用研究进展

高光谱成像技术在肉类安全品质预测及分选分级方面已取得了诸多成果。作者重点综述了其在肉类有毒有害物质检测、肉类掺假检测、肉类分选分级中的研究现状,讨论了其存在的不足及发展趋势,以期为肉类安全无损检测方法的研究提供参考。     

光谱技术在生鲜肉品质安全快速检测的研究进展

光谱技术作为无损快速检测技术在肉品行业中得到广泛应用.该技术能实现生鲜肉快速、在线、准确、无损检测,是各类生鲜肉品质安全分析的重要技术之一.文章综述了近红外光谱、拉曼光谱、高光谱成像技术、荧光光谱等光谱技术在生鲜肉品质检测和安全评定上的重要应用和研究进展.主要包括水分、蛋白质及脂肪等影响肉类营养品质的组成成分分析,肉品食用品质如嫩度、大理石花纹、肉色及新鲜度等指标的评价,肉品加工品质如保水性并由此实行肉类分级的检测以及生鲜肉在微生物污染等安全品质的评定.同时分析各种光谱技术的现状提出存在的问题,并针对目前发展趋势展望了该技术的前景:光谱技术通过与机器视觉技术等新型无损检测技术的有机融合,将实现在线检测评价生鲜肉品质安全的目标.     

基于计算机视觉技术的肉类色泽评价

近年来运用计算机视觉对肉类进行感官质量评价一直是热点的研究领域。本文主要综述了最近的一些应用研究成果,其中主要包括计算机视觉技术在肉类表征质量属性中一个重要指标——色泽上的感官评价上的应用。     

肉类品质无损检测技术研究现状与发展趋势

介绍了近年来国内外肉类品质无损检测技术,主要包括近红外光谱技术、计算机视觉技术和电子鼻技术,可实现对肉品水分、蛋白质、脂肪、pH、新鲜度、剪切力等多个指标的检测。但单一技术无法实现肉品品质的综合评价,多源感知信息融合技术和高光谱图像技术将多种信息融合,可以更好地反映肉品的综合性状,在肉品内外部品质检测方面具有独特的优势,是肉品品质检测的发展趋势。     

北京地区三元杂交猪胴体的分级优化

为了提高猪胴体分级的准确性,利用计算机视觉技术、图像处理技术及统计分析方法,对已建立的猪胴体分级标准及预测方程进行修订.结果表明:以左半胴体质量、臀中肌横长和臀中肌膘厚预测瘦肉率绝对误差小于4%;同时以瘦肉率、臀中肌膘厚、1/2横长处膘厚及6~7肋处膘厚等特征作为分级主要参数,使分级准确率达90%.将各处膘厚与瘦肉率相结合,并对猪胴体级别根据实际需求进行调整,可使分级工作更加合理,准确性也有提高.     

Surface Gloss Evaluation of Apples Based on Computer Vision and Support Vector Machine Method

This paper investigates computer vision applications for surface gloss evaluation to determine a quick surface gloss evaluation method for apples. “Red Fuji” apples were wax-coated with different concentrations of shellac solutions to obtain the apple samples with different levels of surface gloss. The surface gloss values and the color scales of the apple samples were detected using a pinhole gloss meter and a color meter. The apple sample images were captured and processed, and the color parameters of the high light areas were extracted. Support vector machine (SVM) regression and classification models were built to predict the surface gloss values and the surface gloss levels of apples, respectively. The results showed that to predict the surface gloss of apple samples, the correlation coefficients of the SVM regression model were 0.94 and 0.90 for the training and the testing groups, respectively. The classification accuracy rates of the SVM classification model for the training and the testing groups were 100 and 96.7%, respectively. Finally, apple surface gloss level classification software was developed, which showed good operating results for both classification accuracy rates and calculation speed. This paper provided a new surface gloss evaluation method based on computer vision for apples.     

Quality grading of Atlantic salmon (Salmo salar) by computer vision

ABSTRACT:  In this study, we present a promising method of computer vision-based quality grading of whole Atlantic salmon ( Salmo salar ). Using computer vision, it was possible to differentiate among different quality grades of Atlantic salmon based on the external geometrical information contained in the fish images. Initially, before the image acquisition, the fish were subjectively graded and labeled into grading classes by a qualified human inspector in the processing plant. Prior to classification, the salmon images were segmented into binary images, and then feature extraction was performed on the geometrical parameters of the fish from the grading classes. The classification algorithm was a threshold-based classifier, which was designed using linear discriminant analysis. The performance of the classifier was tested by using the leave-one-out cross-validation method, and the classification results showed a good agreement between the classification done by human inspectors and by the computer vision. The computer vision-based method classified correctly 90% of the salmon from the data set as compared with the classification by human inspector. Overall, it was shown that computer vision can be used as a powerful tool to grade Atlantic salmon into quality grades in a fast and nondestructive manner by a relatively simple classifier algorithm. The low cost of implementation of today's advanced computer vision solutions makes this method feasible for industrial purposes in fish plants as it can replace manual labor, on which grading tasks still rely.     

Automated Design of a Computer Vision System for Visual Food Quality Evaluation

Considerable research efforts in computer vision applied to food quality evaluation have been developed in the last years; however, they have been concentrated on using or developing tailored methods based on visual features that are able to solve a specific task. Nevertheless, today’s computer capabilities are giving us new ways to solve complex computer vision problems. In particular, a new paradigm on machine learning techniques has emerged posing the task of recognizing visual patterns as a search problem based on training data and a hypothesis space composed by visual features and suitable classifiers. Furthermore, now we are able to extract, process, and test in the same time more image features and classifiers than before. Thus, we propose a general framework that designs a computer vision system automatically, i.e., it finds—without human interaction—the features and the classifiers for a given application avoiding the classical trial and error framework commonly used by human designers. The key idea of the proposed framework is to select—automatically—from a large set of features and a bank of classifiers those features and classifiers that achieve the highest performance. We tested our framework on eight different food quality evaluation problems yielding a classification performance of 95 % or more in every case. The proposed framework was implemented as a Matlab Toolbox available for noncommercial purposes.     

我国屠宰企业猪胴体分级技术的研究进展与建议

文中叙述了研究猪肉品质的重要性,介绍了国内外的猪胴体分级标准,综述了多种检测猪肉品质的先进技术和方法,描述了利用计算机视觉技术获取多指标、多特征研究猪肉品质是我国屠宰企业猪胴体分级的发展趋势。     

Artificial intelligence techniques point out differences in classification performance between light and standard bovine carcasses

The validity of the official SEUROP bovine carcass classification to grade light carcasses by means of three well reputed Artificial Intelligence algorithms has been tested to assess possible differences in the behavior of the classifiers in affecting the repeatability of grading. We used two training sets consisting of 65 and 162 examples respectively of light and standard carcass classifications, including up to 28 different attributes describing carcass conformation. We found that the behavior of the classifiers is different when they are dealing with a light or a standard carcass. Classifiers follow SEUROP rules more rigorously when they grade standard carcasses using attributes characterizing carcass profiles and muscular development. However, when they grade light carcasses, they include attributes characterizing body size or skeletal development. A reconsideration of the SEUROP classification system for light carcasses may be recommended to clarify and standardize this specific beef market in the European Union. In addition, since conformation of light and standard carcasses can be considered different traits, this could affect sire evaluation programs to improve carcass conformation scores from data from markets presenting a great variety of ages and weights of slaughtered animals.     

Machine vision system for online inspection of freshly slaughtered chickens

A machine vision system was developed and evaluated for the automation of online inspection to differentiate freshly slaughtered wholesome chickens from systemically diseased chickens. The system consisted of an electron-multiplying charge-coupled-device (EMCCD) camera used with an imaging spectrograph and controlled by a computer to obtain line-scan images quickly on a chicken processing line of a commercial poultry plant. The system scanned chicken carcasses on an eviscerating line operating at a speed of 140 chickens per minute. An algorithm was implemented in the system to automatically recognize individual carcasses entering and exiting the field of view, to locate the region of interest (ROI) of each chicken, to extract useful spectra from the ROI as inputs to the differentiation method, and to determine the condition for each carcass as being wholesome or systemically diseased. The system can acquire either hyperspectral or multispectral images without any cross-system calibration. The essential spectral features were selected from hyperspectral images of chicken samples. The differentiation of chickens on the processing line was then carried out using multispectral imaging. The high accuracy obtained from the evaluation results showed that the machine vision system can be applied successfully to automatic online inspection for chicken processing. Mention of trade names or commercial products is solely for the purpose of providing specific information and does not imply endorsement or recommendation by the USDA.