Verification of organic feed identity by fatty acid fingerprinting

The origin and authenticity of feed for laying hens is an important and fraud-susceptible aspect in the production of organic eggs. Chemical fingerprinting in combination with chemometric methods is increasingly used in conjunction with administrative controls to verify and safeguard the authenticity of food commodities. On the basis of fatty acid fingerprinting data of 36 organic and 60 conventional feeds, we have developed a chemometric classification model to discriminate between organic and conventional chicken feed. A two-factor partial least squares-discriminant analysis (PLS-DA) model was developed using 70% of the original data. External validation of the model with the remaining 30% of the data showed that all of the organic feeds and 90% of the conventional feeds (18 of 20) were correctly identified by the model. These results indicate that the PLS-DA model developed in this study could be routinely used to verify the identity of unknown or suspicious feed for laying hens.     

Near-Infrared Reflectance Spectroscopy as a Method for Determining Organic Carbon Concentrations in Soil

Near-infrared reflectance spectroscopy (NIRS) has the potential to be a reliable method for accurately quantifying soil organic carbon (SOC). The objective of this study was to evaluate NIRS as a method for predicting SOC. Partial least squares (PLS) regression was used to predict SOC from soil reflectance values or the first derivative of the reflectance values. Two model validation techniques were evaluated: One was a full cross-validation and in the other 30 percent of the samples were removed from the calibration data set and then tested using the calibrated model. Significant relationships were observed for predicted SOC when compared to laboratory-measured SOC for all models evaluated, regardless of validation technique. The prediction models using the first derivative of the reflectance values outperformed prediction models using the reflectance values alone. In conclusion, NIRS can be used as a quick and accurate method for measuring SOC.     

Near-infrared reflectance spectroscopy enables the fast and accurate prediction of the essential amino acid contents in soy, rapeseed meal, sunflower meal, peas, fishmeal, meat meal products, and poultry meal

Near-infrared reflectance spectroscopy (NIRS) calibrations were developed to enable the accurate and fast prediction of the total contents of methionine, cystine, lysine, threonine, tryptophan, and other essential amino acids, protein, and moisture in the most important protein-rich feed ingredients. More than 1000 samples of global origin collected over four years were analyzed on amino acids following the official methods of the United States and the European Union. Detailed data and graphics are given to characterize the obtained calibration equations. NIRS was validated with independent samples for soy and meat meal products and compared to the amino acid predictions using linear crude protein regressions. With a few exceptions, validation showed that 85-98% of the amino acid variance in the samples could be explained using NIRS. NIRS predictions compared to reference results agree excellently, with relative mean deviations below 5%. Especially for meat and poultry meals, NIRS can predict amino acids much better than crude protein regressions. By enabling the amino acid analysis of many samples to be completed in a short time, NIRS can improve the accuracy of feed formulation and thus the quality and production costs of mixed feeds.     

基于近红外光声光谱的土壤有机质含量定量建模方法

该研究的目的在于应用近红外光声光谱技术结合不同的定量分析方法实现5种不同类型土壤有机质含量的快速估测。对中国中、东部地区5种不同类型土壤风干样本进行光谱扫描,经过多元散射校正、一阶导数、二阶导数及平滑等预处理后,应用逐步多元回归(SMLR)、主成分分析(PCR)、偏最小二乘法(PLS)和偏最小二乘法-反向传播神经网络(PLS-BPNN)等方法建立土壤有机质含量的定量估测模型。结果显示,不同预处理方法对所建土壤有机质含量估测模型的预测精度有较大影响,总体表现为多元散射校正+Norris一阶导数>多元散射校正>Norris一阶导数>标准正态化>Norris二阶导数>吸光度>Savitzky-Golay平滑后一阶导数>Savitzky-Golay平滑后二阶导数。对于4种不同建模方法,均以多元散射校正+Norris一阶导数滤波平滑后的光谱建模精度最高,其中采用PLS-BPNN方法建模效果最好,其次是PLS、SMLR和PCR。采用PLS-BPNN建立有机质校正模型具有极高的预测精度,建模决定系数和均方根偏差分别为0.97和1.88,模型测试决定系数和均方根偏差分别为0.97和1.72。因此,基于多元散射校正+Norris一阶导数光谱建立的PLS-BPNN模型可能是土壤有机质含量估测建模的最优方法。     

Near-infrared reflectance spectroscopy (NIRS) enables the fast and accurate prediction of essential amino acid contents. 2. Results for wheat,barley, corn,triticale, wheat bran/middlings,rice bran,and sorghum

Further NIRS calibrations were developed for the accurate and fast prediction of the total contents of methionine, cystine, lysine, threonine, tryptophan, and other essential amino acids, protein, and moisture in the most important cereals and brans or middlings for animal feed production. More than 1100 samples of global origin collected over five years were analyzed for amino acids following the Official Methods of the United States and European Union. Detailed data and graphics are given to characterize the obtained calibration equations. NIRS was validated with 98 independent samples for wheat and 78 samples for corn and compared to amino acid predictions using linear crude protein regression equations. With a few exceptions, validation showed that 70-98% of the amino acid variance in the samples could be explained using NIRS. Especially for lysine and methionine, the most limiting amino acids for farm animals, NIRS can predict contents in cereals much better than crude protein regressions. Through low cost and high speed of analysis NIRS enables the amino acid analysis of many samples in order to improve the accuracy of feed formulation and obtain better quality and lower production costs.     

Prediction of model pools for a long‐term experiment using near‐infrared spectroscopy

Fourty‐one soil samples from the “Eternal Rye” long‐term experiment in Halle, Germany, were used to test the usefulness of near‐infrared spectroscopy (NIRS) to differentiate between C derived from C₃ and C₄ plants by using the isotopic signature (δ¹³C) and to predict the pools considered in the Rothamsted Carbon (RothC) model, i.e., decomposable plant material, resistant plant material, microbial biomass, humified organic matter, and inert organic matter. All samples were scanned in the visible‐light and near‐infrared region (400–2500 nm). Cross‐validation equations were developed using the whole spectrum (first to third derivative) and a modified partial least‐square regression method. δ¹³C values and all pools of the RothC model were successfully predicted by NIRS as reflected by RSC values (ratio between standard deviation of the laboratory results and standard error of cross‐validation) ranging from 3.2 to 3.4. Correlations analysis indicated that organic C can be excluded as basis for the successful predictions by NIRS in most cases, i.e., 11 out of 16.     

Prediction of carbon mineralization rates from different soil physical fractions using diffuse reflectance spectroscopy

Soil carbon (C) mineralization rate is a key indicator of soil functional capacity but it is time consuming to measure using conventional laboratory incubation methods. Recent studies have demonstrated the ability of visible-near infrared spectroscopy (NIRS) for rapid non-destructive determination of soil organic carbon (SOC) and nitrogen (N) concentration. We investigated whether NIRS (350-2500 nm) can predict C mineralization rates in physically fractionated soil aggregates (bulk soil and 6 size fractions, n=108) and free organic matter (2 size fractions, n=27) in aerobically incubated samples from a clayey soil (Ferralsol) and a sandy soil (Arenosol). Incubation reference values were calibrated to first derivative reflectance spectra using partial least-squares regression. Prediction accuracy was assessed by comparing laboratory reference values with NIRS values predicted using full hold-out-one cross-validation. Cross-validated prediction for C respired (500 days) in soil aggregate fractions had an R² of 0.82 while that of C mineralized (300 days) in organic matter fractions was 0.71. Major soil aggregate fractions could be perfectly spectrally discriminated using a 50% random holdout validation sample. NIRS is a promising technique for rapid characterization of potential C mineralization in soils and aggregate fractions. Further work should test the robustness of NIRS prediction of mineralization rates of aggregate fractions across a wide range of soils and spectral mixture models for predicting mass fractions of aggregate size classes.     

农产品产地加工与储藏工程技术分类

生鲜牛肉的含水率对其牛肉的加工、储藏、贸易与食用质量有重要影响,为了提高牛肉的经济价值和食用品质,需要研究牛肉含水率的无损检测技术。以取自不同超市的内蒙小黄牛和鲁西黄牛背最长肌为研究对象,有效样本86个,其中,75%的样本作为校正集,25%的样本作为验证集。采集牛肉新鲜切口处400～1170 nm波长范围内的漫反射光谱,用国标方法测定牛肉含水率。经过多元散射校正（multiplicative scatter correction, MSC）、变量标准化（standard normalized variate, SNV）和直接正交信号校正（direct orthogonal signal correction, DOSC）等方法预处理,在400～1170 nm范围内分别建立多元线性回归（multiple linear regression, MLR）模型、主成分回归（principal component Regression, PCR）模型和偏最小二乘回归（partial least squares regression, PLSR）模型。结果表明使用MSC预处理方法建立的模型预测效果最佳,其中用PLSR建模结果最好,校正集的相关系数和校正标准差分别是0.92和0.0069,验证集的相关系数和验证标准差分别是0.92和0.0047,外部验证的相关系数和验证标准差分别是0.85和0.0054。结果表明,可见/近红外光谱结合MSC预处理方法建立的PLSR模型,可以对牛肉含水率进行准确的快速无损评价,为生鲜牛肉含水率快速无损检测技术的应用提供理论参考。     

Rapid Prediction of Rice Quality Characteristics by Near‐Infrared Reflectance Spectroscopy for Breeding Programs

Breeding of high‐quality rice requires quick methods to evaluate the quality characteristics such as milling, grain appearance, nutritional, eating, and cooking qualities. Because routine measurements of these quality traits are time consuming and expensive, a rapid predictive method based on near‐infrared spectroscopy (NIRS) can be applied to measure these quality parameters. In this study, calibration models for measurement of grain quality were developed using a total of 570 brown and milled rice samples. The results indicated that the models developed from the spectra of brown rice for all the quality traits had the coefficient of determination for external validation (R²) larger than 0.64 except for gel consistency. The best model was developed for the protein content, with R² of 0.94 for external validation. The model for the total score of physicochemical characteristics (TSPC), a comprehensive index reflecting all other traits, had R² of 0.70 and SD/SEP of 1.70, which indicates that high or low TSPC for a given rice could be discriminated by NIRS. The models developed from brown rice were as accurate as those from milled rice. Results suggest that NIRS‐based predictions for rice quality traits may be used as indicator traits to improve rice quality in breeding programs.     

精料补充料中肉骨粉含量的近红外光谱检测

为了保证饲料安全,精料补充料中肉骨粉的检测是十分必要的。该文探讨了精料补充料中肉骨粉含量的近红外光谱分析方法,123个样品作为校正集,采用偏最小二乘法(PLS),分别对光谱进行散射校正和卷积平滑、一阶微分、二阶微分预处理建立校正模型,以最大的决定系数(R²)和最小的标准差(RMSEC)为选择依据,通过比较,以多元散射校正和卷积平滑处理与二阶微分相结合的处理效果最好,其预测值与测量值的决定系数(R²)和标准差(RMSEC)分别为0.9751和0.437。34个样品作为检验集进行外部验证,决定系数(r²)和标准差(RMSEP)分别为0.9749和0.420,平均绝对误差和相对误差分别为0.326和13.89％。结果表明,利用近红外分析技术可以检测精料补充料中肉骨粉的含量。     

Nondestructive determination of oil content and fatty acid composition in perilla seeds by near-infrared spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was used as a rapid and nondestructive method to determine the oil content and fatty acid composition in intact seeds of perilla [Perilla frutescens var. japonica (Hassk.) Hara] germplasms in Korea. A total of 397 samples (about 2 g of intact seeds) were scanned in the reflectance mode of a scanning monochromator, and the reference values for the oil content and fatty acid composition were measured by gravimetric method and gas-liquid chromatography, respectively. Calibration equations for oil and individual fatty acids were developed using modified partial least-squares regression with internal cross validation (n = 297). The equations for oil and oleic and linolenic acid had lower standard errors of cross-validation (SECV), higher R2 (coefficient of determination in calibration), and higher ratio of unexplained variance divided by variance (1-VR) values than those for palmitic, stearic, and linoleic acid. Prediction of an external validation set (n = 100) showed significant correlation between reference values and NIRS estimated values based on the standard error of prediction (SEP), r2 (coefficient of determination in prediction), and the ratio of standard deviation (SD) of reference data to SEP. The models for oil content and major fatty acids, oleic and linolenic acid, had relatively higher values of SD/SEP(C) and r2 (more than 3.0 and 0.9, respectively), thereby characterizing those equations as having good quantitative information, whereas those of palmitic, stearic, and linoleic acid had lower values (below 2.0 and 0.7, respectively), unsuitable for screening purposes. The results indicated that NIRS could be used to rapidly determine oil content and fatty acid composition (oleic and linolenic acid) in perilla seeds in the breeding programs for development of high-quality perilla oil.     

基于近红外光谱的脐橙产地溯源研究

为研究近红外光谱分析技术鉴别脐橙产地的可行性,该文采用江西、重庆和湖南3个产地脐橙样品1140～1170nm波段的近红外光谱经一阶导数(9点平滑)预处理,分别建立了簇类独立软模式法脐橙产地鉴别模型。在5%显著水平下,模型对3个产地训练集样品的识别率均为100%,拒绝率分别为85.7%、83.3%、100%;对验证集样品的识别率均为100%,拒绝率分别为100%、89.5%、100%,表明簇类独立软模式法模型基本能够判别脐橙产地。将江西、重庆和湖南3个产地的脐橙样品分别赋值0、1、?1,在全波段范围内建立原始光谱脐橙产地的偏最小二乘判别模型,其预测值与真实值的决定系数为0.973,校正标准差为0.110,预测标准差为0.159,模型对训练集和验证集样品的识别率达到100%。因此,应用近红外光谱分析技术可准确、快速地追溯脐橙产地来源。     

Effect of selenium-enriched probiotics on laying performance, egg quality, egg selenium content, and egg glutathione peroxidase activity

A 35-day experiment was conducted to evaluate the effect of selenium-enriched probiotics (SP) on laying performance, egg quality, egg selenium (Se) content, and egg glutathione peroxidase (GPX) activity. Five hundred 58-week-old Rohman laying hens were randomly allotted to 5 dietary treatments of 100 each. Each treatment had 5 replicates, and each replicate had 5 cages with 4 hens per cage. The SP was supplemented to a corn-soybean-meal basal diet at 3 different levels that supplied total Se at 0.2, 0.5, and 1.0 mg/kg. The basal diet served as a blank control, while the basal diet with supplemental probiotics served as a probiotics control. The results showed that dietary SP supplementation not only increased (p < 0.05) the rate of egg laying, day egg weight, mean egg weight, egg Se content, and egg GPX activity but also decreased (p < 0.05) the feed:egg ratio and egg cholesterol content. The egg Se content was gradually increased (p < 0.05) along with the increasing level of dietary Se. The SP supplementation also slowed down (p < 0.05) the drop of Haugh units (HU) of eggs stored at room temperature. The egg GPX activity had a positive correlation (p < 0.01) with egg Se content and a negative correlation (p < 0.01) with egg HU drop. These results suggested that Se contents, GPX activity, and HU of eggs were affected by the dietary Se level, whereas the egg-laying performance and egg cholesterol content were affected by the dietary probiotics. It was concluded that this SP is an effective feed additive that combines the organic Se benefit for hen and human health with the probiotics benefit for laying hen production performance. It was also suggested that the eggs from hens fed this SP can serve as a nutraceutical food with high Se and low cholesterol contents for both healthy people and patients with hyperlipidemia, fatty liver, or cardiovascular disease.     

蛋鸡福利化养殖模式及技术装备研究进展

自1999年欧洲福利法提出全面禁止传统笼养以来,世界各国开展了很多蛋鸡福利化养殖系统的研究,以保障蛋鸡福利,取得了很多方面的改进。然而,在世界上的主要蛋品生产国家中,福利化养殖设施所占蛋鸡养殖模式的比例仍然较低。随着人们对动物福利的重视和行业的可持续发展要求的提高,开发新型养殖设施的呼声日益高涨。根据国内养殖业建筑现状以及现代高产品种抗病力差的特点,发展福利化养殖装备提高蛋鸡鸡体本身的健康和福利,减少因药物使用带来的负面影响,中国开发福利化蛋鸡养殖设施以提高鸡体健康和福利以抵抗疾病,减少用药;同时促进舍内环境良好,创造适应现代鸡群的生存条件显得至关重要。该文通过概述世界现存的几种替代传统笼养的福利化养殖模式及装备,比较它们各自的特点以及它们在蛋鸡福利、生产性能及蛋品质、社会经济环境对它们产生的不同影响等方面的差异。提出并分析了不同养殖系统存在的主要问题。通过总结国外蛋鸡福利化养殖设施的优缺点,为国内蛋鸡福利化系统的开发提供参考。在改善行为福利的同时,还需要结合传统笼养鸡体与粪便分离、动物健康状况好、投资较低的优点,改变现有的福利化养殖装备系统设计。另外,该文展望了福利化养殖设施的发展方向,并提出了发展福利化养殖设施的新思路。     

基于近红外光谱技术的三种硬实种子无损鉴定

摘要：为探讨近红外光谱技术在鉴定种子硬实特性上的普遍性,该文采用近红外光谱法结合偏最小二乘法建立了大豆、苦豆子和决明子单粒种子硬实特性的定性分析模型,每种种子均选择120粒种子进行近红外定性分析,种子分为建模集、检验集2组,建模集80粒,检验集40粒,各组中硬实与非硬实种子的比例为1:1,比较了光谱重复次数、光谱范围以及不同建模样品的建模效果。结果表明：采用二次平均光谱所建模型的鉴别率优于单次光谱;大豆采用4 000～5 000 cm-1光谱范围,矢量校正预处理,主成分为8时,建模集与检验集鉴别率均在85%以上;决明子采用4 000～8 000 cm-1光谱范围,一阶导数预处理,主成分为4时,模型建模集与检验集鉴别率均在90%左右;苦豆子采用4 000～8 000 cm-1光谱范围,二阶导数预处理,主成分为8时,模型的建模集与检验集鉴别率均在95%以上。以上结果表明近红外光谱技术可以很好地应用于单粒种子硬实特性的判断,有助于硬实机理的深入研究。     

放养条件下密度和补饲量对蛋鸡生产性能及蛋黄胆固醇含量的影响

选择45周龄体重接近的健康本地鸡441只,随机分为7组,在山西省太谷县生态养鸡场进行2(补饲量)×3(密度)两因子放养试验,研究林下种植苜蓿不同放养密度与补饲量对蛋鸡生产性能和蛋黄胆固醇含量的影响。补饲量设自由采食量50%、70%两个处理,密度为每667m2 100只、250只、400只3个处理,以笼养全程自由采食为对照,每组3个重复,每重复3个小区用于轮牧,小区面积为62 m2;预试期7 d,正试期70 d。结果表明:补饲量和放养密度互作对平均产蛋率影响极显著(P0.01),对蛋重和料蛋比影响不显著(P0.05)。笼养+自由采食组(CK)与补饲量70%、100只·667m-2组蛋重、平均产蛋率及料蛋比差异不显著(P0.05),但产蛋率显著高于其他各组(P0.05),蛋重显著高于补饲量50%、100只·667m-2组(P0.05),料蛋比显著低于补饲量50%组(P0.05)。补饲量和放养密度互作对蛋黄重、蛋黄胆固醇含量和全蛋胆固醇含量影响不显著(P0.05);放养密度对全蛋胆固醇含量影响极显著(P0.01)。笼养+自由采食组蛋黄重极显著高于补饲量50%、100只·667m-2组(P0.01),蛋黄胆固醇含量和全蛋胆固醇含量显著高于100只·667m-2组(P0.05)。补饲量70%下,100只·667m-2放养密度对牧草的破坏性小于其他放养密度。结合产蛋性能、蛋黄胆固醇含量以及草地保护,以70%补饲量+100只·667m-2组养殖模式较好,效果较佳。     

Analysis of Fatty Acids in Kernel,Flour, and Oil Samples of Maize by NIR Spectroscopy Using Conventional Regression Methods

High cost and painstaking procedures associated with fatty acid analyses of maize kernel necessitate the use of alternative methods. NIR spectroscopy offers advantages in this respect for a variety of areas such as plant breeding, food and feed industries, and biofuel production, in which different forms of maize kernel (e.g., intact kernel, flour, or oil) are used as material. We investigated the possibility of estimating maize oil quality traits by using different samples (intact kernel, flour, and oil) and conventional regression methods (multiple linear regression [MLR] and partial least squares regression [PLSR]) applied to their NIR spectra. MLR and PLSR calibration models were developed for oleic acid, linoleic acid, oleic/linoleic acid ratios, total monounsaturated fatty acid, total polyunsaturated fatty acid (PUFA), and total saturated fatty acid by analyzing 120 maize samples. Robustness in terms of prediction accuracy of the models developed here was tested with a reserved set of samples (n = 30). The results suggested that fatty acids could be possibly estimated by calibrations developed from flour and oil samples with a high degree of accuracy, whereas intact samples did not offer satisfactory results. PLSR and MLR methods gave better results in flour and oil samples, respectively. PUFA was the trait that was most successfully estimated from both flour (for the PLSR model, standard error of the estimate [SEP] of 1.78%, relative performance to deviation [RPD] of 3.09, R² = 0.93) and oil (for the MLR model, SEP of 0.85%, RPD of 6.52, R² = 0.98) samples. We concluded that sample type and chemometric method should be handled as important factors in calibration development, and the effects of these factors may vary depending on the trait being analyzed.     

Rapid Determination of Carbon,Nitrogen, Silicon,Phosphorus, and Potassium in Sugar Mill By-products,Mill Mud,and Ash using Near Infrared Spectroscopy

This article describes a proof-of-concept exercise to examine the ability of near infrared spectroscopy (NIRS)–based methods to predict the major nutrient properties of sugar mill by-products, particularly mill mud, ash, and mixtures of mud and ash. Sixty mill mud, mixed mud/ash, and ash samples were subsampled three times and analyzed using traditional analytical techniques for carbon (C), nitrogen (N), silicon (Si), phosphorus (P), and potassium (K), and the NIR spectra were recorded. Two different partial least squares (PLS) regression models were constructed, one using all samples and the other without the ash samples included in the model development. Three mud, one mixed mud/ash, and two ash samples were retained for predictive purposes and were not included in the model development process. R² values in the range of 0.77 to 0.98 were obtained for all constituents across both sets of PLS models. The standard errors of prediction (SEP) were similar for both models for N (0.10 and 0.08), P (0.17 and 0.16), and K (0.05 and 0.05). However, the SEP obtained for Si (3.53 and 1.04) and C (1.92 and 1.00) varied between the two models. These preliminary results are very encouraging. Future research will extend to robust NIRS calibrations for these nutrients and develop applications for their use within laboratory or field situations to permit nutrient monitoring in various sugar mill by-products.     

Quantification of fatty acids in forages by near-infrared reflectance spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was evaluated as a possible alternative to gas chromatography (GC) for the quantitative analysis of fatty acids in forages. Herbage samples from 11 greenhouse-grown forage species (grasses, legumes, and forbs) were collected at three stages of growth. Samples were freeze-dried, ground, and analyzed by GC and NIRS techniques. Half of the 195 samples were used to develop an NIRS calibration file for each of eight fatty acids, with the remaining half used as a validation data set. Spectral data, collected over a wavelength range of 1100-2498 nm, were regressed against GC data to develop calibration equations for lauric (C12:0), myristic (C14:0), palmitic (C16:0), stearic (C18:0), palmitoleic (C16:1), oleic (C18:1), linoleic (C18:2), and alpha-linolenic (C18:3) acids. Calibration equations had high coefficients of determination for calibration (0.93-0.99) and cross-validation (0.89-0.98), and standard errors of calibration and cross-validation were < 20% of the respective means. Simple linear regressions of NIRS results against GC data for the validation data set had r2 values ranging from 0.86 to 0.97. Regression slopes for C12:0, C14:0, C16:0, C18:0, C16:1, C18:2, and C18:3 were not significantly different (P = 0.05) from 1.0. The regression slope for C18:1 was 1.1. The ratio of standard error of prediction to standard deviation was > 3.0 for all fatty acids except C12:0 (2.6) and C14:0 (2.9). Validation statistics indicate that NIRS has high prediction ability for fatty acids in forages. Calibration equations developed using data for all plant materials accurately predicted concentrations of C16:0, C18:2, and C18:3 in individual plant species. Accuracy of prediction was less, but acceptable, for fatty acids (C12:0, C14:0, C18:0, C16:1, and C18:1) that were less prevalent.     

Liquid chromatographic determination of fluoroquinolones in egg albumen and egg yolk of laying hens using fluorometric detection

A liquid chromatographic method was developed for the determination of ciprofloxacin, enrofloxacin, and sarafloxacin at 10-200 ppb in both egg yolk and egg albumen of laying hens. Egg yolk or albumen was acidified with 1 M phosphoric acid followed by deproteination with acetonitrile and centrifugation. The supernate was pipetted out, and the remaining protein pellet was extracted three times with acetonitrile. The supernates were combined and concentrated at 50 degrees C to <0.7 mL. The final volume was adjusted to 2 mL with 0.02 M potassium phosphate buffer, pH 2.5. Separation of the analytes was achieved using reversed-phase HPLC with fluorometric detection. The recoveries were >80% and coefficients of variation <20%. After validation, the method was applied for use in a national survey for fluoroquinolones in table eggs. Of the 276 eggs assayed, none was found positive for fluoroquinolones. The findings suggest that illegal use of fluoroquinolones in laying hens is not widespread.