• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

肉类成分的快速测定方法。

Rapid methods for determination of meat composition.

作者信息

McNeal J E

出版信息

J Assoc Off Anal Chem. 1987 Jan-Feb;70(1):95-9.

PMID:3558286
Abstract

Rapid analytical procedures are needed to determine the total protein, moisture, and fat content of meat and poultry products. During the past 5 years, the U.S. Department of Agriculture (USDA) has been studying various methods involving instrumentation that test for these constituents either in combination or separately. The studies are initiated on request of the Department or the instrumentation manufacturer and are conducted at the manufacturers' facilities using 2 sets of samples preanalyzed by conventional means. One set, with values, is used for calibration purposes, the other set is tested as unknown samples. The resultant data are evaluated statistically vs the conventional test results. Most studies show the usefulness of these rapid tests for product quality control, but some fall short of regulatory requirements because of unacceptable bias or variability. Typical within-product standard deviations obtained in rapid methods instrumentation tests have ranged from 0.47 to 0.67 for percent total protein, 0.73 to 1.71 for moisture, and 0.41 to 1.14 for fat. For conventional methods, the acceptable USDA performance criteria for repeatability are standard deviations of less than 0.24 for protein, 0.46 for moisture, and 0.63 for fat. Improvements in instrumentation are being made and studies continue.

摘要

需要快速分析程序来测定肉类和家禽产品的总蛋白、水分和脂肪含量。在过去5年里,美国农业部(USDA)一直在研究各种使用仪器的方法,这些方法可以联合或单独检测这些成分。这些研究是应美国农业部或仪器制造商的要求发起的,在制造商的设施中进行,使用两组预先通过传统方法分析过的样品。一组有已知值,用于校准目的,另一组作为未知样品进行检测。将所得数据与传统测试结果进行统计学评估。大多数研究表明这些快速检测方法对产品质量控制有用,但有些方法由于偏差或变异性不可接受而未达到监管要求。在快速方法仪器测试中获得的典型产品内标准偏差,总蛋白百分比为0.47至0.67,水分为0.73至1.71,脂肪为0.41至1.14。对于传统方法,美国农业部可接受的重复性性能标准是,蛋白质的标准偏差小于0.24,水分的标准偏差小于0.46,脂肪的标准偏差小于0.63。仪器正在改进,研究也在继续。

相似文献

1
Rapid methods for determination of meat composition.肉类成分的快速测定方法。
J Assoc Off Anal Chem. 1987 Jan-Feb;70(1):95-9.
2
Automated methods for determination of fat and moisture in meat and poultry products: collaborative study.肉类和家禽产品中脂肪和水分测定的自动化方法:协同研究。
J Assoc Off Anal Chem. 1985 Sep-Oct;68(5):876-80.
3
Homogeneity of meats prepared for analysis with a commercial food processor: collaborative study.
J Assoc Off Anal Chem. 1989 Sep-Oct;72(5):777-83.
4
Determination of total fat in meat and meat products by a rapid, dry column method.采用快速干式柱法测定肉类及肉制品中的总脂肪含量。
J Assoc Off Anal Chem. 1980 May;63(3):600-3.
5
Chemical analysis of meat products.肉类产品的化学分析。
J Assoc Off Anal Chem. 1987 Jan-Feb;70(1):77-80.
6
Dietary phosphorus restriction in dialysis patients: potential impact of processed meat, poultry, and fish products as protein sources.透析患者的膳食磷限制:加工肉类、家禽和鱼类产品作为蛋白质来源的潜在影响。
Am J Kidney Dis. 2009 Jul;54(1):18-23. doi: 10.1053/j.ajkd.2009.01.269. Epub 2009 Apr 19.
7
Comparison of automated method and improved AOAC Kjeldahl method for determination of protein in meat and meat products.
J Assoc Off Anal Chem. 1981 Jan;64(1):29-31.
8
Precision parameters of methods of analysis required for nutrition labeling. Part I. Major nutrients.营养标签所需分析方法的精密度参数。第一部分。主要营养素。
J Assoc Off Anal Chem. 1990 Sep-Oct;73(5):661-80.
9
Determination of fat, moisture, and protein in meat and meat products by using the FOSS FoodScan Near-Infrared Spectrophotometer with FOSS Artificial Neural Network Calibration Model and Associated Database: collaborative study.使用配备FOSS人工神经网络校准模型及相关数据库的FOSS FoodScan近红外分光光度计测定肉类和肉类产品中的脂肪、水分和蛋白质:协作研究。
J AOAC Int. 2007 Jul-Aug;90(4):1073-83.
10
Rapid identification of closely related muscle foods by vibrational spectroscopy and machine learning.通过振动光谱法和机器学习快速鉴定密切相关的肌肉类食品。
Analyst. 2005 Dec;130(12):1648-54. doi: 10.1039/b511484e. Epub 2005 Oct 26.