在线凝胶渗透色谱-气相色谱-串联质谱法测定食用植物油中11种有机磷阻燃剂

目的 建立在线凝胶渗透色谱–气相色谱-串联质谱法(GPC-GCMS/MS)测定食用植物油中11种有机磷阻燃剂的分析方法。方法 0.5 g 食用油样品用(3：7)丙酮/环己烷溶液稀释定容至5 mL, GPC-GCMS/MS采用多反应监测模式(MRM)测定,外标法定量。结果 11种有机磷阻燃剂在1～50 μg. L_{^{－ 1}}范围内,线性关系良好,相关系数r>0.9970,方法检出限为1.0～3.0 μg. kg_{^{－ 1}},定量限为3.0～9.0 μg. kg_{^{－ 1}}。空白样品中添加3个水平质量浓度,回收率在65.6～129%之间,相对标准偏差RSD为0.2～13.9%。结论 该方法样品前处理简单,仪器自动化程度高,重现性较好,精密度较高,适用于食用植物油中有机磷阻燃剂的检测。     

QuEChERS-高效液相色谱-串联质谱法测定粮谷中啶磺草胺的残留

目的 建立了高效液相色谱-串联质谱法(HPLC-MS/MS)测定粮谷中啶磺草胺的分析方法。方法 样品经酸化乙腈(含0.5%(V/V)乙酸的乙腈)提取,优化的QuEChERS(400 mg C₁₈、400 mg无水硫酸镁)方法净化,净化液经离心后,直接过膜上机检测。HPLC-MS/MS方法以0.1%(V/V)甲酸水-乙腈为流动相,流速为0.3 mL/min梯度洗脱,采用C₁₈色谱柱进行分离,电喷雾正离子电离(ESI^₊),多反应监测(MRM)检测,基质匹配外标法定量。结果 在11种基质(大豆、大麦、大米、高粱、黑麦、苦荞、土壤、小麦粉、小麦、燕麦、玉米)中,啶磺草胺在0.001~0.03 μg/mL质量浓度与其对应的峰面积之间线性关系良好,R^₂均大于0.996,在0.005、0.01和0.05 mg/kg 3个添加水平下,啶磺草胺的平均回收率在90.3%~112.3%之间,相对标准偏差在1.1%~7.9%之间。结论 方法准确度、精密度和灵敏度均符合农药残留检测的要求。     

多壁碳纳米管固相萃取/超高效液相色谱-串联质谱测定烤烟中粉唑醇残留量

目的 建立了一种多壁碳纳米管固相萃取净化,超高效液相色谱-串联质谱法(Ultra performance liquid chromatography-tandem mass spectrometry, UPLC-MS/MS)测定烤烟中粉唑醇残留量的分析方法。方法 烤烟样品用0.1%甲酸乙腈溶液提取,NaCl盐析,多壁碳纳米管(MS-NANO-V)净化,并采用超高效液相色谱-串联质谱法(UPLC-MS/MS)检测,0.1%甲酸水溶液-甲醇溶液作为流动相,外标法定量。结果 粉唑醇在0.001~1 mg/L浓度范围内的相关系数R_²为0.9989,呈现良好的线性关系。在0.005、0.5、5 mg/kg三个添加水平下,粉唑醇在烤烟中的平均回收率范围为90~102%,相对标准偏差范围为4.9~9.6%。方法的定量限为0.005 mg/L。结论 该方法高效、准确,操作简便、精密度好,适用于烤烟中粉唑醇残留量的检测。     

羊乳婴幼儿配方粉中牛乳成分鉴别与测定模型的建立

为实现羊乳婴幼儿配方粉中牛乳成分的快速分析，建立羊乳婴幼儿配方粉中牛乳的液相色谱-串联质谱法(liquid chromatography-tandem mass spectrometry, LC-MS/MS)鉴别与测定模型。样品经胰蛋白酶水解，采用高分辨四极杆静电场轨道阱串联质谱（Quadrupole-Orbitrap Mass Spectrometer, Q Exactive, Thermo Scientific）结合蛋白数据库，筛选特征肽段。选择具有代表性的羊全脂乳粉、牛全脂乳粉和羊乳清粉、牛乳清粉，然后分别按不同的比例进行混合，LC-MS/MS测定，得到牛β-酪蛋白与牛全脂奶粉的换算系数k1为2.8343，牛β-乳球蛋白与牛全脂奶粉的换算系数k2为1.6542，牛β-乳球蛋白与牛乳清粉的换算系数k3为27.8598，通过换算系数构建特征肽与牛全脂奶粉和牛乳清粉的定量模型。结果表明，牛β-乳球蛋白和牛β-酪蛋白在20~1000 nmol/L范围内，线性关系良好，R2>0.999；方法的检出限为1.9 g/kg，定量限为6.4 g/kg；日内精密度为3.2%~8.1%，日间精密度为3.4%~7.3%，准确度为99.0%~103.0%；经三家实验室验证，方法的精密度为1.7%~6.9%；方法准确度为91.5%~103.0%；重复性相对标准偏差（r%）为4.3%~5.1%，再现性相对标准偏差（R%）为4.7%~5.7%。该模型可分别对羊乳婴幼儿配方粉中牛乳清粉和牛全脂乳粉进行鉴别与测定，两者之和即为牛乳含量。该模型极大的简化了前处理，具有操作简便、快速、方法实用性强等优点。     

高效液相色谱法测定小麦粉及其制品中间苯二酚

建立一种利用高效液相色谱测定小麦粉及其制品中间苯二酚含量的方法。样品经甲醇提取,亚铁氰化钾和乙酸锌沉淀,经C₁₈-Amide柱分离,甲醇/水为流动相洗脱,二极管阵列检测器与荧光检测器串联检测,外标法定量。二极管阵列和荧光检测器的定量结果均具备良好特异性,且在各自线性范围内r^₂＞0.999,定量限分别为1 mg/kg和0.05 mg/kg,回收率在73.0-108.6%之间,RSD<8.2%。建立的方法操作简单、灵敏度高、准确度好,适用于小麦粉及其制品中间苯二酚的测定。     

QuEChERS-气相色谱–串联质谱法测定海南淡水鱼中有机氯农药残留

目的 以海南常见5种淡水鱼为研究对象,建立改良的QuEChERS方法结合气相色谱－串联质谱(GC-MS/MS)检测鱼肉中12种有机氯农药(OCPs)残留的快速检测方法。方法 样品采用丙酮+正己烷(1+1)超声提取,经0.4 g PSA、0.4 g C18、1.2 g无水硫酸镁和40 mg 二氧化锆净化,GC-MS/MS检测。结果 在1.6～40 ng/mL范围内线性良好,Ｒ_²≥0.9985;3个添加水平(n = 6)平均回收率在90.8%～121.6%范围内,RSD在1.3%～7.5%范围内;检出限在0.04～0.72 μg/kg之间,定量限在0.13～2.43 μg/kg之间。结论 该方法灵敏度高、快速、准确,能够满足鱼肉中多种OCPs的检测要求。QuEChERS与二氧化锆净化,结合GC-MS/MS可以应用于鱼肉中OCPs的快速检测。     

分散固相萃取结合超高效液相色谱-串联高分辨质谱法快速测定羊乳中磺胺类药物

目的 建立基于多孔石墨烯复合多壁碳纳米管纳米材料(MWCNTs@PG)的分散固相萃取结合超高效液相色谱串联高分辨质谱的羊乳中10种磺胺类药物快速分析方法。方法 样品经QuEChERS处理,分散固相萃取后采用C₁₈色谱柱分离,正离子模式下高分辨数据依赖采集。研究了吸附剂种类、pH、洗脱溶剂种类、吸附剂用量、洗脱溶剂体积、吸附时间对磺胺提取效率与检测准确性的影响。结果 10种磺胺物质在各自的线性范围相关系数均大于0.99,检出限为0.01~0.25 μg/kg,回收率和精密度分别为75%~109%和0.6%~9.3%。结论 基于MWCNTs@PG分散固相萃取对羊乳中磺胺药物有较高的回收率和净化效果,超高效液相色谱串联高分辨质谱可实现快速分离、准确定性和精准定量,适合于复杂基质中兽药残留的快速准确分析。     

凝胶渗透色谱-高效液相色谱法测定火麻仁油中Δ9-四氢大麻酚含量

目的 建立使用凝胶渗透色谱(GPC)净化,高效液相色谱(HPLC)分析测定火麻仁油中Δ⁹-四氢大麻酚(Δ⁹-THC)的检测方法。方法 样品经环已烷和乙酸乙酯溶解,经GPC净化分离,再以Agilent Eclipse XDB-C₁₈色谱柱(150 mm×4.6 mm,3 μm)分离,以乙腈-水流动相洗脱,二级管阵列检测器分析,外标法定量。结果 该方法在0～500 μg/L浓度内,线性相关系数(r)＞0.999,方法检测限(LOD)为0.05 mg/kg,定量限(LOQ)为0.17 mg/kg。加标回收率在84.6%～101.8%之间,相对标准偏差为3.2%～4.7%(n=6)。在实际样品检测中,10种不同品牌的火麻仁油中Δ⁹-THC含量在0.40～5.82 mg/kg之间。结论 该方法稳定性好,灵敏度高,适用于火麻仁油样品中Δ⁹-THC的检测分析。     

双重净化法结合液质联用法快速测定鸡蛋中的磺胺类药物残留

目的 通过双重净化建立鸡蛋中磺胺类药物残留的液相色谱-质谱检测方法。方法 样品经乙腈提取,通过式吸附净化和阳离子交换净化,液相色谱分离,四极杆-静电场轨道阱质谱定性定量测定,平行反应监测模式检测,内标法定量。结果 磺胺类药物保留时间分布在3~7 min,在0.5~20 μg.kg_^-1范围内呈现良好的线性关系,日内、日间精密度均小于20 %,回收率稳定在85~115 %之间,定量限为0.5 μg.kg_^-1。结论 此方法快速、准确且灵敏度高,为监测磺胺类药物在鸡蛋中的残留监控提供了快速准确的技术手段。     

LC-MS/MS测定大豆油中苯并烯氟菌唑残留量

本文建立了液相色谱－串联质谱(LC-MS/MS)法测定大豆油中苯并烯氟菌唑残留量的方法。选择QuEChERS(Quick、Easy、Cheap、Effective、Rugged、Safe)方法进行样品提取、净化,经C18色谱柱分离,以乙腈和0.1%甲酸水为流动相进行梯度洗脱,选择负离子多反应监测模式检测,外标法定量。结果表明5-100μg/kg范围内线性关系良好,相关系数R₂^>0.99。方法的定量限为5μg/kg。在空白大豆油基质中添加5μg/kg,10 μg/kg,50 μg/kg三个浓度进行加标回收实验,回收率在72.1-92.6%范围内, 相对标准偏差在5.3-12.1%范围内。该检测方法简便、快速、准确、灵敏度高,适用于大豆油中苯并烯氟菌唑残留量的测定。     

Effect of leaving milk trucks empty and idle for 6 h between raw milk loads

The US Pasteurized Milk Ordinance (PMO) allows milk tanker trucks to be used repeatedly for 24 h before mandatory clean-in-place cleaning, but no specifications are given for the length of time a tanker can be empty between loads. We defined a worst-case hauling scenario as a hauling vessel left empty and dirty (idle) for extended periods between loads, especially in warm weather. Initial studies were conducted using 5-gallon milk cans (pilot-scale) as a proof-of-concept and to demonstrate that extended idle time intervals could contribute to compromised raw milk quality. Based on pilot-scale results, a commercial hauling study was conducted through partnership with a Pacific Northwest dairy co-op to verify that extended idle times of 6 h between loads have minimal influence on the microbiological populations and enzyme activity in subsequent loads of milk. Milk cans were used to haul raw milk (load 1), emptied, incubated at 30°C for 3, 6, 10, and 20 h, and refilled with commercially pasteurized whole milk (load 2) to measure cross-contamination. For the commercial-scale study, a single tanker was filled with milk from a farm known to have poorer quality milk (farm A, load 1), emptied, and refilled immediately (0 h) or after a delay (6 h) with milk from a farm known to have superior quality milk (farm B, load 2). In both experiments, milk samples were obtained from each farm's bulk tank and from the milk can or tanker before unloading. Each sample was microbiologically assessed for standard plate count (SPC), lactic acid bacteria (LAB), and coliform counts. Selected isolates were assessed for lipolytic and proteolytic activity using spirit blue agar and skim milk agar, respectively. The pilot-scale experiment effectively demonstrated that extended periods of idle (>3 h) of soiled hauling vessels can significantly affect the microbiological quality of raw milk in subsequent loads; however, extended idle times of 6 h or less would not measurably compromise milk quality in subsequent loads in commercial tankers. Current tanker sanitation practices appear to be sufficient for maintaining raw milk SPC, LAB, and coliform levels, which are important measures of milk quality.     

羊乳及其奶酪中掺入牛乳检测的研究

羊乳及由其制作的传统奶酪,越来越受到国内人们的青睐。而在国内外,羊乳及其奶酪中掺入牛乳的掺假行为,却常有发生。所以,迫切需要一种准确的检测方法,来避免这种掺假行为对我国的进出口贸易造成损失。文中主要综述了电泳,色谱,免疫化学,DNA技术等方法检测羊乳及其奶酪中掺入牛乳的研究状况。     

Within-milking variation in milk composition and fatty acid profile of Holstein dairy cows

Changes in milk composition during a milking are well characterized, but variation in milk fatty acid (FA) profile is not well described and may affect the accuracy of in-line milk composition analyzers and could potentially be used for selective segregation of milk. Within-milking samples were collected from 8 multiparous high-producing Holstein cows (54.86 ± 6.8 kg of milk/d; mean ± standard deviation). A milk-sampling device was designed to allow collection of multiple samples during a milking without loss of vacuum or interruption of milk subsampling. Milk was collected during consecutive morning and afternoon milkings (12-h intervals) and was replicated 1 wk later. Each sample represented approximately 20% of the milking and was analyzed for fat, true protein, and lactose concentration and FA profile. Milk fat concentration markedly increased over the course of milk let down (4.4 and 4.2 percentage units at the a.m. and p.m. milking, respectively), whereas milk fat globule size did not change. Milk protein and lactose concentration decreased slightly during milking. Modest changes in milk FA profile were also observed, as milk de novo and 16-C FA concentrations increased approximately 10 and 8%, respectively, whereas the concentration of preformed FA decreased about 7% during the milking. In agreement, mean milk FA chain length and unsaturation modestly decreased during milking (0.59 and 0.014 U, respectively). The observed changes in milk fat concentration during a milking are consistent with previous reports and reflect the dynamic nature of milk fat secretion from the mammary gland. Changes in milk FA profile are not expected to practically affect the accuracy of spectroscopy methods for determination of milk fat concentration. Furthermore, the small variation in FA profile during a milking limits the use of within-milking milk segregation to tailor milk FA profile.     

UHT奶的质量与原料奶的关系

主要论述了原料奶中的微生物尤其是嗜热性芽孢，低温性细菌及蛋白质的稳定性对ＵＨＴ奶的影响。     

Presence of Campylobacter and Arcobacter species in in-line milk filters of farms authorized to produce and sell raw milk and of a water buffalo dairy farm in Italy

The objectives of this study were to investigate the presence of Campylobacter spp. and Arcobacter spp. in dairy herds authorized for the production and sale of raw milk and in a water buffalo dairy farm, and to test the antimicrobial susceptibility of the isolates. A total of 196 in-line milk filters were collected from 14 dairy farms (13 bovine and 1 water buffalo) for detection of Campylobacter spp. and Arcobacter spp. by microbiological culture. For each farm investigated, 1 isolate for each Campylobacter and Arcobacter species isolated was tested using the Etest method (AB Biodisk, Solna, Sweden) to evaluate the susceptibility to ciprofloxacin, tetracycline, chloramphenicol, ampicillin, erythromycin, and gentamicin. A total of 52 isolates were detected in 49 milk filters in 12 farms (85.7%) out of 14 and the isolates were identified as Campylobacter jejuni (6), Campylobacter hyointestinalis ssp. hyointestinalis (8), Campylobacter concisus (1), Campylobacter fetus ssp. fetus (1), Arcobacter butzleri (22), and Arcobacter cryaerophilus (14). The small number of isolates tested for antimicrobial susceptibility precludes any epidemiological consideration but highlights that all Campylobacter isolates were susceptible to macrolides, which are the first-choice drugs for the treatment of campylobacteriosis, and that resistance to fluoroquinolones and tetracycline was detected; for Arcobacter isolates, resistance to ampicillin and chloramphenicol was detected. The sale of raw milk for human consumption by self-service automatic vending machines has been allowed in Italy since 2004 and the presence of C. jejuni in in-line milk filters confirms that raw milk consumption is a significant risk factor for human infection. The high occurrence of emerging Campylobacter spp. and Arcobacter spp. discovered in dairy farms authorized for production and sale of raw milk represents an emerging hazard for human health.     

Nonbovine milk and its products as sources of probiotics delivery: An overview of its viability,functionality and product quality characteristics

Dairy products are the most predominant food carriers for probiotics, providing adequate therapeutic and functional benefits to the host when sufficient probiotics are maintained. Bovine milk currently dominates the global probiotic food market, but there is an increasing trend of applying nonbovine milk from other dairy animals as probiotic carrier food matrices as described in this review. Nonbovine dairy products can be considered suitable food matrices for probiotic delivery due to their excellent probiotic viability (mostly >log 7 cfu/mL or g) during shelf life, functional properties and product quality characteristics, being considered desirable and novel dairy products.     

Effect of incomplete milking on milk production rate and composition with 2 daily milkings

The primary aim of this study was to assess the effects of incomplete milking on milk secretion and milk composition at the quarter level. Twelve cows were enrolled beginning at 5 d in milk and remained on study through 47 d in milk. Half of each contralateral udder was incompletely milked (treatment), detaching the teat cup early to leave approximately 30% of the total milk yield behind. This target milk remaining in the gland was based on weekly calibration milking measurements of quarter total milk yield. Control quarters were milked completely until milk flow had decreased to 0 kg/min based on visual assessment. Harvested milk yield was measured twice daily at each milking, and milk components (fat, protein, lactose, solids nonfat, milk urea nitrogen) and somatic cell count, were measured twice weekly at the quarter level. The experimental unit in this design was the half-udder, and a mixed-model approach was used to assess the main and interactive effects of experiment week and treatment on milk production rate, milk remaining in the gland, and milk composition. The effect of treatment on milk production rate was significant, with the average control half-udder producing 0.97 kg/h and the treatment half-udder 0.73 kg/h. The effect of week on milk production rate and the interaction of week × treatment were also significant. The effect of treatment on milk remaining in the gland was significant, illustrating that an increase in milk remaining in the cisternal compartment had been achieved. We detected a significant decrease in milk lactose percentage in treatment half-udders, and a significant increase in somatic cell count (log₁₀). The increase was relatively small, from a geometric mean of 26,300 cells/mL in control quarters to 48,300 cells/mL in treatment quarters. The decrease in milk production rate in treatment half-udders supports current knowledge about how mammary epithelial cell secretion, proliferation, and apoptosis are modulated by autocrine-paracrine factors.     

The effect of different precooling rates and cold storage on milk microbiological quality and composition

The objective of this study was to measure the effect of different milk cooling rates, before entering the bulk tank, on the microbiological load and composition of the milk, as well as on energy usage. Three milk precooling treatments were applied before milk entered 3 identical bulk milk tanks: no plate cooler (NP), single-stage plate cooler (SP), and double-stage plate cooler (DP). These precooling treatments cooled the milk to 32.0 ± 1.4°C, 17.0 ± 2.8°C, and 6.0 ± 1.1°C, respectively. Milk was added to the bulk tank twice daily for 72 h, and the tank refrigeration temperature was set at 3°C. The blend temperature within each bulk tank was reduced after each milking event as the volume of milk at 3°C increased simultaneously. The bacterial counts of the milk volumes precooled at different rates did not differ significantly at 0 h of storage or at 24-h intervals thereafter. After 72 h of storage, the total bacterial count of the NP milk was 3.90 ± 0.09 log₁₀ cfu/mL, whereas that of the precooled milk volumes were 3.77 ± 0.09 (SP) and 3.71 ± 0.09 (DP) log₁₀ cfu/mL. The constant storage temperature (3°C) over 72 h helped to reduce bacterial growth rates in milk; consequently, milk composition was not affected and minimal, if any, proteolysis occurred. The DP treatment had the highest energy consumption (17.6 ± 0.5 Wh/L), followed by the NP (16.8 ± 2.7 Wh/L) and SP (10.6 ± 1.3 Wh/L) treatments. This study suggests that bacterial count and composition of milk are minimally affected when milk is stored at 3°C for 72 h, regardless of whether the milk is precooled; however, milk entering the tank should have good initial microbiological quality. Considering the numerical differences between bacterial counts, however, the use of the SP or DP precooling systems is recommended to maintain low levels of bacterial counts and reduce energy consumption.     

Invited review: Corrected milk: Reconsideration of common equations and milk energy estimates

Corrected milk equations were developed in attempts to bring milk weights to a standardized basis for comparison by expressing the weight and composition of milk as corrected to the energy content of milk of a specific composition. Expressed as milk weights familiar on farm and in commerce, this approach integrates energy contributions of the dissimilar components to make the mass units more comparable. Such values are applied in evaluating feed efficiency, lactation performance, and global milk production, as functional units for lifecycle assessments, and in translation of research results. Corrected milk equations are derived from equations relating milk gross energy to milk composition. First, a milk energy equation is used to calculate the energy value of the milk composition to correct to (e.g., 0.695 Mcal/kg for milk with 3.5% fat, 3.05% true protein, and 4.85% lactose). That energy value is divided into the energy equation to give the corrected milk equation. Confusion has arisen, as different equations purport to correct to the same milk composition; their differences are based on uses of different energy equations or divisors. Accuracy of corrected milk equations depends on the accuracy of the energy equations used to create them. Energy equations have evolved over time as different milk component analyses have become more available. Inclusion of multiple milk components more accurately predicts milk energy content than does fat content alone. Omission of components from an equation requires the assumption that their content in milk is constant or highly correlated with an included component. Neither of these assumptions is true. Milk energy equations evaluated on a small data set of measured milk values have demonstrated that equations that incorporate protein, fat, and lactose contents multiplied by the gross energy of each component more closely predict milk energy than equations containing fewer components or regression-derived equations. This provides a tentative recommendation for using energy equations that include the 3 main milk components and their gross energy multipliers for predicting milk energy and deriving corrected milk equations. Accuracy of energy equations is affected by the accuracy of gross energy values of individual components and variability of milk composition. Lactose has consistent reported gross energy values. In contrast, gross energy of milk fat and protein vary as their compositional profiles change. Future refinements could assess accuracy of milk fat and protein gross energy and whether that appreciably improves milk energy predictions. Fat gross energy has potential to be calculated using the milk fatty acid profile, although the influence on gross energy may be small. For research, direct reporting of milk energy values, rather than corrected milk, provides the most explicit, least manipulated form of the data. However, provision of corrected milk values in addition to information on components can serve to translate the energy information to a form familiar to and widely used in the field. When reporting corrected milk data, the corrected milk equation, citation for the energy equation used, and composition and energy contents of the corrected milk must be described to make clear what the values represent.