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QuEChERs方法结合SERS技术检测猪肉中氨基糖苷类抗生素残留
基金项目:国家自然科学基金(编号:８１７０１８２５);江苏省社会发展基金(编号:B E ２０１８６８４)作者简介:杨海帆,女,扬州大学在读本科生.通信作者:丁莉(１９８８ ),女,扬州大学附属医院主管技师,扬州大学在读硕士研究生.E Ｇm a i l :m １３９５２７２５３９６＠１６３．c o m收稿日期:２０２３Ｇ０８Ｇ１７㊀㊀改回日期:２０２３Ｇ１２Ｇ２６D O I :１０．１３６５２/j ．s p j x ．１００３．５７８８．２０２３．８０７９５[文章编号]１００３Ｇ５７８８(２０２４)０３Ｇ００６８Ｇ０７Q u E C h E R s 方法结合S E R S 技术检测猪肉中氨基糖苷类抗生素残留D e t e c t i o no f a m i n o g l y c o s i d ea n t i b i o t i c r e s i d u e s i n p o r kb yQ u E C h E R sm e t h o dc o m b i n e dw i t hS E R S t e c h n i qu e 杨海帆１,２Y A N G H a i f a n １,２㊀丁㊀莉２D I N GL i ２㊀徐妙文１X U M i a o w e n １㊀沈㊀康１S H E N K a n g １㊀王煦博１WA N G Xu b o １(１．扬州大学医学院,江苏扬州㊀２２５００１;２．扬州大学附属医院,江苏扬州㊀２２５００１)(１．Y a n g z h o uU n i v e r s i t y S c h o o l o f M e d i c i n e ,Y a n g z h o u ,J i a n gs u ２２５００１,C h i n a ;２．A f f i l i a t e d H o s p i t a l o f Y a n g z h o uU n i v e r s i t y ,Y a n g z h o u ,J i a n gs u ２２５００１,C h i n a )摘要:目的:实现猪肉中氨基糖苷类抗生素残留的快速㊁定量和高通量检测.方法:以P P 合成纸为衬底制备基于金纳米花(A u N F s )的方阵排列S E R S 基底.通过Q u E C h E R s 方法对猪肉样品进行前处理,并对其进行S R E S 检测.结果:采用４Ｇ巯基苯甲酸为S E R S 探针分子,基底表现出良好的均一性㊁S E R S 增强效应㊁重现性和稳定性.４７５,６１９c m －１特征峰处的S E R S 信号强度分别与硫酸庆大霉素和硫酸新霉素浓度的对数具有良好的线性关系(R ２分别为０．９９１６,０．９９０７),最低检测限分别低至１ˑ１０－９,１ˑ１０－８m o l /L ,并成功应用于猪肉中氨基糖苷类抗生素的快速㊁定量和高通量检测.结论:试验方法为S E R S 技术应用于真实肉类样品中抗生素残留检测提供了一种经济㊁高效㊁省时和高灵敏的途径.关键词:表面增强拉曼散射;金纳米花;氨基糖苷类抗生素;硫酸庆大霉素;硫酸新霉素;猪肉A b s t r a c t :O b je c t i v e :T o a c h i e v er a p i d ,q u a n t i t a t i v ea n d h i g h Ｇt h r o u g h p u td e t e c t i o n of a m i n og l y c o s i d e a n t i b i o t i c r e s i d u e si n p o r k ．M e th o d s :S q u a r e Ｇa r r a y a li g n e dS E R Ss u b s t r a t e sb a s e do n A un a n o f l o w e r s (A u N F s )w e r e p r e p a r e d u s i n g P P s y n t h e t i c p a p e r a s a s u b s t r a t e ．P o r k s a m p l e s w e r e p r e Ｇt r e a t e d b yQ u E C h E R sm e t h o da n ds u b j e c t e dt oS R E S ．R e s u l t s :U s i n g ４Ｇm e r c a pt o b e n z o i c a c i da s t h eS E R S p r o b em o l e c u l e ,t h e s u b s t r a t e e x h i b i t e d g o o d h o m o g e n e i t y ,S E R S e n h a n c e m e n t e f f e c t ,r e p r o d u c i b i l i t y a n ds t a b i l i t y ．t h eS E R Ss i g n a l i n t e n s i t i e sa tt h e c h a r a c t e r i s t i c p e a k s a t ４７５,６１９c m －１s h o w e d g o o d l i n e a rr e l a t i o n s h i p s w i t h t h e l o g a r i t h m s o f t h e c o n c e n t r a t i o n s o f g e n t a m i c i n s u l p h a t ea n dn e o m y c i ns u l p h a t e ,r e s p e c t i v e l y (R ２o f ０．９９１６a n d０．９９０７,r e s p e c t i v e l y )．T h el i m i t so fd e t e c t i o n s (L O D s )w e r e a s l o wa s １ˑ１０－９,１ˑ１０－８m o l /L ,r e s p e c t i v e l y,a n dw e r e s u c c e s s f u l l y a p p l i e d t o t h e r a p i d ,q u a n t i t a t i v e a n dh i gh Ｇt h r o u g h p u td e t e r m i n a t i o no fa m i n o g l y c o s i d ea n t i b i o t i c s i n p o r k ．C o n c l u s i o n :T h ee x pe r i m e n t a l m e t h o d p r o v i d e sa ne c o n o m i c a l ,ef f i c i e n t ,t i m e Ｇs a v i ng a n dhi g h l y s e n s i t i v ew a y f o r t h e a p p l i c a t i o n o f S E R S t e c h n o l o g y f o r t h e d e t e c t i o no f a n t i b i o t i c r e s i d u e s i n r e a l m e a t s a m pl e s ．K e yw o r d s :s u r f a c e Ｇe n h a n c e dR a m a n s c a t t e r i n g ;A un a n o f l o w e r s ;a m i n o g l y c o s i d e a n t i b i o t i c ;g e n t a m i c i n s u l f a t e ;n e o m yc i n s u l f a t e ;po r k 硫酸庆大霉素和硫酸新霉素是众多氨基糖苷类抗生素家族中的一员,具有广谱的抗菌作用[１－２],在畜牧业中常与硫酸结合使用[３－４].尽管目前养殖业更多使用毒性较小的抗生素,但氨基糖苷类抗生素因低成本和高效的优势仍被广泛应用于禽畜疾病的预防和治疗,然而长期使用可能会导致动物组织中抗生素残留.人类长期摄入抗生素含量超标的食物不仅会诱导细菌产生耐药性[５],甚至会导致过敏反应㊁耳毒性和肾毒性[６].G B３１６５０２０１９规定牛㊁猪肌肉组织中庆大霉素和新霉素的最大残留限量分别为１００,５００m g /k g.目前,氨基糖苷类抗生素的检测方法主要有液相色谱法[７]㊁液相色谱串联质谱法[８]㊁免疫层析法[１,９]和酶联免疫吸附分析法等[１０－１２].色谱法和免疫层析法的灵敏度高,但耗时㊁成本高,难以实现现场化㊁快速化和规模化检测;而酶联免疫吸附分析８６F O O D &MA C H I N E R Y 第４０卷第３期总第２６９期|２０２４年３月|法易发生交叉反应,其假阳性率高.表面增强拉曼散射(S E R S)是一种快速㊁无损㊁不受水分干扰的检测方法,结合拉曼光谱与表面材料技术,利用局域表面等离子体共振(L S P R)和化学吸附的方法使得S E R S信号显著提升,高灵敏地检测低浓度分析物[１３－１４].当检测某些超低浓度的残留物时,纳米材料产生的S E R S增强十分关键.金纳米花(A u N F s)具有粗糙的表面和许多分支,在材料表面形成大量的 热点 ,为待测分子提供特殊的吸附位点,从而显著增强S E R S效应[１５].因此,A u N F s被广泛用作S E R S活性基底的制备.P P合成纸是一种典型的疏水材料,可使纳米颗粒在其表面聚集,增强S E R S效应.有研究[１６]表明,采用还原氧化石墨烯 金复合纳米材料检测氧氟沙星,检测限为０．３n g/m L.W a t t a n a v i c h e a n等[１７]以银纳米棒作为基底检测恩诺沙星㊁土霉素和新霉素,检测限分别为０．５,２．０,１００．０μm o l/L,但未应用于食品中抗生素残留的检测.食品样品成分复杂,传统样品前处理耗时长㊁精度低,引入误差大[１８－１９],而样品前处理技术直接关系到整个检测过程的准确性和精密性.Q u E C h E R s方法是目前动物抗生素残留检测领域备受关注的前处理技术[２０],具有快速㊁简单㊁廉价㊁有效㊁可靠㊁耐用和安全的优点,可极大提高样品前处理效率[２１].目前,Q u E C h E R s方法已成功应用于S E R S技术检测食品中抗生素残留[２２－２３],但将该方法与S E R S技术结合检测动物中氨基糖苷类药物残留的研究尚未见报道.研究拟以疏水性P P合成纸为衬底制备方阵排列S E R S基底,通过Q u E C h E R s方法对样品进行前处理,并对猪肉中氨基糖苷类抗生素残留进行定量分析,旨在为猪肉中氨基糖苷类抗生素残留提供快速㊁定量和高通量的检测方法.１㊀材料与方法１．１㊀材料与试剂氯金酸㊁盐酸多巴胺㊁无水乙醇㊁４Ｇ巯基苯甲酸(４ＧM B A)㊁乙腈㊁乙酸㊁氯化钠㊁硫酸镁㊁氯化镁:分析纯,上海阿拉丁生化科技有限公司;硫酸庆大霉素㊁硫酸新霉素:北京索莱宝科技有限公司;P P合成纸:上海天成纸品纸业合作公司;试验用水为超纯水(电阻率１８．２MΩ c m).１．２㊀仪器与设备场发射扫描电子显微镜:SＧ４８００Ⅱ型,日本日立公司;显微共焦拉曼光谱仪:R e n i s h a w i n V i a型,英国R e n i s h a w公司;透射电子显微镜:T e c n a i１２型,荷兰P h i l i p s公司;透射电子显微镜:F E IT e c n a iG２F２０SＧTW I N型,美国F E I公司;显微拉曼成像光谱仪:D X R x i型,美国T h e r m o f i s h e r公司;紫外 可见分光光度仪:C a r y U VＧ５０００型,扬州贝欧生物科技有限公司.１．３㊀试验方法１．３．１㊀A u N F s的合成㊀取０．４m LH A u C l４溶液(５０m m o l/L)加入到含１０m L超纯水的烧杯中,剧烈搅拌混匀,加入０．８m L盐酸多巴胺溶液(５３mm o l/L),６０ħ水浴搅拌３０m i n,３０００r/m i n离心１０m i n,去除上清液,向沉淀中加入１m L超纯水,混匀得A u N F s溶液,４ħ贮藏备用.１．３．２㊀S E R S基底的制备㊀将P P合成纸切割成２c mˑ２c m小块,并按３ˑ３排列,在其表面滴５０m LA u N F s溶液,自然干燥即得研究使用的方阵排列S E R S基底.１．３．３㊀样品前处理㊀取１０g冷冻猪肉,研磨,倒入５０m L离心管中.将１３．５m L乙腈和３００,１５０,７５,１５m L乙酸滴入离心管中,得到的乙酸体积分数分别为２％,１％,０．５％,０．１％,并用超纯水定容到１５m L.加入脱水剂,涡旋振荡３m i n,８０００r/m i n离心５m i n,取上清液于４ħ贮藏备用.称取一定量的硫酸庆大霉素和硫酸新霉素,加入猪肉提取液并超声溶解.使用超纯水梯度稀释,最终得到浓度为１ˑ１０－１０~１ˑ１０－３m o l/L的猪肉提取液加标样品,并将样品滴加到制备好的基底表面,选择激发波长７８５n m,曝光时间１０s和光源强度５mW进行S E R S检测.增强因子(E F)是S E R S检测领域的一个重要参数,表示信号分子与纳米结构表面相互作用的S E R S信号的放大倍数.E F的计算需要仔细评估S E R S和正常拉曼条件下的信号强度和分子数量,并按式(１)进行计算.F E＝I S E R S I R S,(１)式中:F E 增强因子;I S E R S ４ＧM B A标记的方阵排列S E R S基底在当前浓度(C S E R S)测量的S E R S强度;I R S 仅有４ＧM B A的P P合成纸在当前浓度(C R S)下的S E R S强度.１．３．４㊀S E R S检测条件的优化㊀分别考察乙酸体积分数(２．０％,１．０％,０．５％,０．１％)㊁脱水剂种类(N a C l㊁M g S O４和M g C l２)及脱水剂添加量对１ˑ１０－６m o l/L氨基糖苷类抗生素浓度的猪肉提取液加标样品S E R S光谱图的影响.１．３．５㊀数据分析㊀样本的拉曼光谱均采集３次,取平均值进行数据分析.２㊀结果与分析２．１㊀A u N F s的表征由图１可知,A u N F s表面有许多不规则的突起,大小９６|V o l．４０,N o．３杨海帆等:Q u E C h E R s方法结合S E R S技术检测猪肉中氨基糖苷类抗生素残留均一,颗粒大小约为５００n m.晶面是晶体具有一定空间角度的一系列平行平面,用垂直于平面的矢量表示.A u N F s的晶面间距为０．２４n m,表明A u N F s优先在(１１１)晶面生长.A u N F s溶液在４７３n m处有一个吸收峰.综上,通过一步合成法,简单㊁快速合成了大小均一,形貌均匀的A u N F s.２．２㊀方阵排列S E R S基底的表征由图２可知,P P合成纸表面有高密度的A u N F s,呈多层排列,可增强基底的S E R S效应.S E R S映射的颜色在１０７７c m－１处的特征峰较为规则,说明以P P合成纸为衬底的方阵排列S E R S基底有良好的均匀性.４ＧM B A标记的方阵排列S E R S基底有很强的S E R S信号.当C S E R S 为１ˑ１０－６m o l/L,C R S为１m o l/L时,E F为３．９ˑ１０７,高于金纳米星的[２４],表明A u N F s有很强的S E R S表面增强效应.使用４个不同批次的方阵排列S E R S基底分别检测４ＧM B A,结果如图２(d)所示.４次S E R S光谱波形相似且在１０７７c m－１处R S D为７．０１％.因此,该基底表现出良好的重现性.将制备好的S E R S基底于４ħ静置１,７,１４d后,S E R S光谱的波形和强度相似,且在１０７７c m－１特征峰处,贮藏１４d的S E R S强度与贮藏１d的相比仅下降了９．９３％,说明A u N F s基底有较好的稳定性,具有疏水特性的P P合成纸阻止了水性A u N F s溶液吸收,并使A u N F s均匀地保留在P P合成纸表面,因此方阵排列S E R S基底表现出良好的性能.２．３㊀硫酸庆大霉素和硫酸新霉素的S E R S光谱图硫酸庆大霉素和硫酸新霉素的结构式如图３和表１所示.图１㊀A u N F s的结构表征图F i g u r e１㊀S t r u c t u r a l c h a r a c t e r i z a t i o nd i a g r a mo fA u N Fs图２㊀方阵排列S E R S基底的表征图F i g u r e２㊀C h a r a c t e r i z a t i o nm a p o f s q u a r eＧa r r a y a l i g n e dS E R Ss u b s t r a t e s０７安全与检测S A F E T Y&I N S P E C T I O N总第２６９期|２０２４年３月|㊀㊀为了进一步定量检测猪肉中硫酸庆大霉素和硫酸新霉素残留,对其特征峰进行分析.由图４可知,硫酸庆大霉素和硫酸新霉素在９９７,１０７４,１５７２c m －１处表现出明显的特征峰,分别由H N H 摇摆振动㊁C O 拉伸振动和N H 的弯曲振动引起[２５],因此将这些特征峰归属图３㊀硫酸庆大霉素和硫酸新霉素的结构式F i g u r e ３㊀S t r u c t u r a l f o r m u l a o f g e n t a m i c i n s u l f a t e a n d表１㊀硫酸庆大霉素结构组成T a b l e １㊀S t r u c t u r a l c o m po s i t i o no f g e n t a m i c i n s u l f a t e 化合物R １R ２R ３硫酸庆大霉素C １C H ３H C H ３硫酸庆大霉素C ２H H C H ３硫酸庆大霉素C １a H HH 硫酸庆大霉素C ２aH C H ３H 于氨基糖苷类抗生素分子的S E R S 特征峰.而硫酸庆大霉素和硫酸新霉素的S E R S 光谱图分别在４７５,６１９c m －１处表现出微弱且独有的特征峰,是由H N H 的扭转振动和N H 的弯曲振动引起.因此,选择４７５,６１９c m －１处的峰作为硫酸庆大霉素和硫酸新霉素定性分析的特征峰,并以此处S E R S 信号强度确定最佳试验条件,对猪肉中氨基糖苷类抗生素残留进行定量分析.２．４㊀S E R S 检测条件的优化２．４．１㊀提取液㊀由图５可知,当乙酸体积分数为１％时,硫酸庆大霉素和硫酸新霉素的特征峰明显.因此,采用９０％乙腈(１％乙酸)水溶液作为提取液.２．４．２㊀脱水剂种类㊀由图６可知,加入N a C l 时,氨基糖苷类抗生素特征峰明显,因此,选择作为脱水剂.图４㊀猪肉提取液的S E R S 光谱图F i g u r e ４㊀S E R Ss e c t r a o f o r ke x t r a c t s 图５㊀乙酸体积分数对硫酸庆大霉素和硫酸新霉素S E R S 光谱图的影响F i g u r e ５㊀E f f e c t o f a c e t i c a c i dv o l u m e f r a c t i o no n t h eS E R Ss p e c t r o g r a m s o f g e n t a m i c i n s u l f a t e a n dn e o m yc i n s u l f a t e １７|V o l ．４０,N o ．３杨海帆等:Q u E C h E R s 方法结合S E R S 技术检测猪肉中氨基糖苷类抗生素残留２．４．３㊀脱水剂添加量㊀由图７可知,当N a C l 添加量为２g 时,硫酸庆大霉素和硫酸新霉素分别在４７５,６１９c m －１特征峰处S E R S 强度最大,说明此时的脱水效果最佳.因此,选择２g N a C l 作为脱水剂.图脱水剂种类对硫酸庆大霉素和硫酸新霉素S E R S 光谱图的影响F i g u r e ６R Ss p e c t r o m s o f g e n t a m i c i n s u l f a t e a n dn e o m yc i n l f a t e 图添加量对硫酸庆大霉素和硫酸新霉素强度的影响F i g u r e ７㊀E f f e c t o fN a C l a d d i t i o no nS E R S i n t e n s i t y o f g e n t a m i c i n s u l f a t e a n dn e o m yc i n s u l f a t e ２．５㊀猪肉中氨基糖苷类抗生素的定量分析由图８可知,随着加标浓度的增加,硫酸庆大霉素和硫酸新霉素对应特征峰S E R S 强度逐渐增强.硫酸庆大霉素加标浓度为１ˑ１０－１０m o l /L 的样品在１０７４,１５７２c m －１处有特征峰,但由于氨基糖苷类抗生素特征峰的共性,仅能将其定性为氨基糖苷类抗生素残留,不能定性为硫酸庆大霉素残留;而在４７５c m －１处未表现出明显的特征峰,因此选择１ˑ１０－９m o l /L 作为硫酸庆大霉素的最低检测限(L O D ).硫酸新霉素加标浓度为１ˑ１０－９m o l /L的样品在６１９c m －１处未表现出明显的特征峰,因此选择１ˑ１０－８m o l /L 作为硫酸新霉素的L O D .在４７５c m －１特征峰处建立猪肉提取液硫酸庆大霉素加标样品的S E R S强度与浓度(１ˑ１０－９~１ˑ１０－３m o l /L )的对数的标准曲线,线性回归方程为y ＝３９５．９８x ＋４８４５．３７,相关系数(R ２)为０．９９１６.在６１９c m －１特征峰处建立猪肉提取液硫酸新霉素加标样品的S E R S 强度与浓度(１ˑ１０－８~１ˑ１０－３m o l /L )的对数的标准曲线,线性回归方程为y ＝１０８０．７８x ＋９２８０．７２,R ２为０．９９０７.因此,方阵排列S E R S 基底可实现猪肉提取液加标样品的定量检测.㊀㊀由表２可知,试验采用的方阵排列S E R S 基底检测灵敏度高,仅次于间接竞争性化学发光酶免疫分析,且检测速度快,有较好的稳定性,因此S E R S 技术对氨基糖苷类抗生素的检测具有较高的应用前景.２．６㊀实际样品中氨基糖苷类抗生素的测定在最佳检测条件下,猪肉样品中未检出氨基糖苷类抗生素残留,说明该猪肉符合国家检测标准.为验证S E R S 方法的可行性,对１ˑ１０－６m o l /L 氨基糖苷类抗生素加标浓度的猪肉提取液样品进行检测,结果见表３.由表３可知,试验方法的检测回收率和酶联免疫法的相近,结果无显著性差异(P ＞０．０５),且R S D 均＜８％,表明基于方阵排列S E R S 基底对猪肉中氨基糖苷类抗生素残留的检测方法具有较高的准确性和可行性.２７安全与检测S A F E T Y &I N S P E C T I O N 总第２６９期|２０２４年３月|图８㊀猪肉提取液硫酸庆大霉素和硫酸新霉素加标样品的光谱图F i g u r e８㊀S E R Ss p e c t r a o f g e n t a m i c i n s u l f a t e a n dn e o m y c i n s u l f a t e s p i k e d s a m p l e s f r o m p o r ke x t r a c t s表２㊀基于不同方法检测氨基糖苷类抗生素T a b l e２㊀D e t e c t i o no f a m i n o g l y c o s i d e a n t i b i o t i c sb a s e do nd i f f e r e n tm e t h o d s抗生素检测方法单位L O D文献硫酸庆大霉素免疫层析检测m g/k g１．４９[１]超高效液相色谱 串联质谱法m g/k g１０[８]微生物抑制法m g/L５０[１２]酶联免疫吸附法n g/m L０．５２[２６]间接竞争化学发光酶免疫分析法n g/m L０．００２[２７]S E R S检测m o l/L１ˑ１０－９硫酸新霉素㊀超高效液相色谱 串联质谱法m g/k g１０[８]酶联免疫吸附法m g/k g５[２８]侧流免疫测定n g/m L０．１[２９]S E R S检测m o l/L１ˑ１０－８表３㊀实际样品中氨基糖苷类抗生素残留的检测结果T a b l e３㊀D e t e c t i o n r e s u l t s o f a m i n o g l y c o s i d e a n t i b i o t i c r e s i d u e s i na c t u a l s a m p l e s抗生素加标浓度/(m o l L－１)试验方法检测浓度/(m o l L－１)回收率/％R S D/％酶联免疫吸附法检测浓度/(m o l L－１)回收率/％R S D/％硫酸庆大霉素１ˑ１０－６０．９６ˑ１０－６９６６．４３０．９４ˑ１０－６９４６．８２硫酸新霉素㊀１ˑ１０－６０．９２ˑ１０－６９２７．２９０．９５ˑ１０－６９５６．２９３㊀结论研究以疏水性P P合成纸为衬底,制备了一种基于A u N F s的方阵排列S E R S基底.通过与Q u E C h E R S方法结合,快速㊁定量和高通量检测猪肉中氨基糖苷类抗生素残留.基于P P合成纸的疏水特性,A u N F s聚集更加紧密,使该基底具有良好的均一性㊁稳定性和S E R 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[17]国家认证认可监督管理委员会.化学分析中测量不确定度评估指南:RB/T030 2020[S].北京:中国标准出版社,2020:1Ｇ96.National Certification and Accreditation Administration.Guidance of quantifying measurement uncertainty in chemical analysis:RB/ T030 2020[S].Beijing:Standards Press China,2020:1Ｇ96.(上接第７４页)[23]XU L L,WU R M,GENG X,et al.Rapid detection of sulfonamide antibiotics residues in swine urine by surfaceＧenhanced Raman spectroscopy[J].Spectrochim Acta A Mol Biomol Spectrosc,2022, 267:120570.[24]ZHANG Y S,WANG Y,LIU A R,et al.Fabrication of flexibleSERS substrate based on Au nanostars and PDMS for sensitive detection of thiram residue in apple juice[J].Spectrochim Acta A Mol Biomol Spectrosc,2023,297:122721.[25]BALAN C,POP L C,BAIA M.IR,Raman and SERS analysis of amikacin combined with DFTＧbased calculations[J].Spectrochim Acta A Mol Biomol Spectrosc,2019,214:79Ｇ85.[26]JIN Y,JAN J W,HAN C H,et al.Development of ELISA andimmunochromatographic assay for the detection of gentamicin[J].J Agric Food Chem,2005,53(20):7639Ｇ7643.[27]DAI P,ZHANG Y,HONG Y P,et al.Production of high affinitymonoclonal antibody and development of indirect competitivechemiluminescence enzyme immunoassay for gentamicin residuein animal tissues[J].Food Chem,2023,400:134067.[28]WANG S,XU B,ZHANG Y,et al.Development of enzymeＧlinkedimmunosorbent assay(ELISA)for the detection of neomycinresidues in pig muscle,chicken muscle,egg,fish,milk and kidney [J].Meat Sci,2009,82(1):53Ｇ58.[29]HENDRICKSON O D,BYZOVA N A,ZVEREVA E A,et al.Sensitive lateral flow immunoassay of an antibiotic neomycin in foodstuffs[J].J Food Sci Technol,2021,58(1):292Ｇ301.１８|V o l．４０,N o．３杨㊀韵等:液相色谱 串联质谱法测定蜂蜜中４种喹诺酮类药物残留量的不确定度评定。
中国食物成分表-畜肉类及制品第二册P58-59
维生素C mg
mg
4.m4g7
0.74 0.2 0.16 0.21
1.11
0.41 0.71
0.09 0.29 1.05 0.85 0.17
0.18 0.32
维生素E To
维生素E Tota1
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mg
0.m4g 0
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3.m3g 9
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0.m6g8
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11
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钠
镁
mg
mg
mg
mg
256
278
1540.7
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mg
0.15 0.13 0.18 0.34 0.29 0.08 0.07 0.06 0.06 0.09 0.09 0.07 0.11 0.43 0.12 0.10 0.15 0.11 0.18 0.14 0.07 0.71 0.31
烟酸
mg
3.93 1.20
3.22 3.17 3.10 10.00 7.40 10.70 3.00 12.60 5.70 11.30 2.30 3.80 3.10 1.00 4.40 1.90 2.60 13.40 0.40 0.10
100
33.5
433
1795
081408
红果肠
100
51.4
260
1085
081409
火腿肠
100
57.4
212
888
081410
腊肠
100
8.4
584
2421
081411
松江肠
100
30.4
402
1674
081412
蒜肠
100
52.5
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An Approach to Connection Admission Control in Single-hop Multi-service Wireless Networks w
An Approach to Connection Admission Control in Single-hop Multi-service Wireless Networks with QoS RequirementsTara Javidi and Demosthenis TeneketzisDepartment of Electrical Engineering and Computer ScienceUniversity of MichiganAnn Arbor,MI48109taraj@ teneket@AbstractWe formulate a resource allocation problem in single-hop multi-service networks with quality of service re-quirements.We present a decomposition of the problem into two analytically tractable subproblems.We illustrate the approach for the case where the QoS requirement is expressed in terms of outage probability.We establish a sufficient condition for the optimality of the greedy policy in the above resource allocation problem.I.I NTRODUCTION-M OTIVATIONThe scarcity of available resources,such as limited bandwidth and low capacity,as wellas the interference among users result in serious challenges in the design for wireless net-works.An important network layer design problem is the efficient allocation of those limitedresources.An efficient allocation must optimize a performance objective while satisfying thequality of service(QoS)required by each type of service and connection(expressed in termsof signal to noise plus interference ratio(SNIR),and outage probability,latency,etc.).In this paper we present a systematic approach to connection admission control(CAC)insingle-hop multi-service wireless networks with QoS requirements.The approach consists ofthe decomposition of the resource allocation problem into two sub problems:()the specifi-cation of an admission region which guarantees the QoS requirements for each connected user,independently of the admission policy;and()the determination of a connection admis-sion policy that is optimal within the class of policies restricted to the admission region .such a decomposition results in tractable problems and creates a conceptual framework for understanding the interaction among the different layers of the wire less communication networks.The remainder of the paper consists of four parts.In Part1,we()formulate the CAC insingle hop multi-service wireless networks with QoS requirements;()discuss the nature ofthis problem and the need for alternative tractable methods to solve it;and()propose theaforementioned decomposition.In Part2,we()present an approach to defining probability of outage as a system-wide QoS measure for cellular systems;and()construct an admission region where,independently of admission strategies,the requirements on probability of outage are satisfied.In Part3,we address the CAC problem under a constraint described by the admission region.In Part4,we present conclusion and reflections.Part1.Outage-based Admission regionIn a wireless system the desirable resource allocation is achieved through two separate mechanisms of power-rate assignment(PRA)and connection admission control(CAC).In systems where rate of transmission of any user and the power control mechanism isfixed and known,CAC is the only mechanism to guarantee a certain level of service while max-imizing total revenue over horizon.Mathematically the question can be formulated assuch that where is the rate of revenue generated by a user of type,is the number of connections of type present at the system at time and is a function of CAC strategy,is the vector of number of users,is a vector of QoS measures for user of typeunder CAC strategy which is a function of the sequence of.is such function and its form depends on the physical layer and the power assignment rule.Notice that quality of service for a connection is a dynamic variable whose statistics depend on the chosen admission strategy.Hence,a key feature of this optimization problem is that there is a two-way coupling between the constraints resulting from the QoS requirements and the admission policy.Such a two-way coupling results in a computationally challenging and analytically intractable optimization problem.In this paper we propose the two step decomposition described in the introduction.Such a decomposition results in a one-way coupling between the constraints present in the resource allocation problem and the determination of an optimal allocation policy.Though,in gen-eral,our approach results in a suboptimal solution for the original problem,it reduces the complexity of the problem to a great extent.Furthermore,it creates a conceptual framework for understanding the interaction among different layers of wireless communication systems, such as physical layer concerns,QoS requirements,and network layer resource allocation. In other words,the admission region conceptual-ize the physical channel and QoS requirements;and the optimization problem is reduced toPart2.Outage-based Admission regionOutage probability is an important performance measure in cellular networks.In a cellular scenario low SNIRcan increase bit error rate,but more importantly if this ratio remains low for a long enough duration,it can cause an outage in an ongoing service(due to loss of synchro-nization,etc).This will result in disconnection of an admitted call.In most common scenarios, this is considered a more severe form of low performance than blocking(which occurs when a new call is denied admission to the cell,hence the network).As a result,outage probability is considered a main perfomance measure for traditional cellular networks.We describe an outage by two parameters:the SNIR threshold;and a minimum duration.An outage occurs when the SNIR remains below the threshold for a period longer than or equal to.In most of the currently available literature(e.g.see[9],[12]),an outage is assumed to occur when the SNIR falls below a threshold.We believe that this is not sufficient to capture the essence of an outage,since it ignores statistical correlation or burstiness in the incoming traffic stream.It is intuitively expected that traffic streams with high level of burstiness are more probable to cause an outage than non-bursty or iid streams with the same level of instantaneous instantaneous interference.Similarly,the memory present in shadowing channels directly affects how long the impairment will last,hence it affects the occurance of an outage.In other words,the drop in the SNIR below does not result in an outage instantaneously;an outage results in when the SNIR is low for an extended period of time,i.e.a time period that exceeds a minimum duration.With this definition,the occurance of outage events strictly depend on the second order statistics of the interference and/or shadowing.A characterization of outage both in terms of the threshold and the time duration has appeared only in[10]and[20].One key feature of[10]and[20]is that the effect of other users on the outage probability is not taken into account.That is,the effect of the(random)number of active users and the statistical variation of their channels on the probability of outage is ignored. Attention in both[10]and[20]is restricted on one user and on the effect of its physical channel on the outage probability.In general,the performance of a wireless system critically depends on two factors:the condition of the physical channel;and the interference created by other users.Indeed,we show that by incorporating the effect of multiple access interference into our approach,we are able to relate the outage probability to the number and type of users present in the system and,therefore,to determine an admission region associated with the maximum acceptable outage probability for each type of users.The salient features of our approach are the following:We model the statistical varia-tion of the physical channel by a Markov Chain(as in[20]).We consider several types of users in terms of their statistical activity,and QoS requirements.Wefix the total number of users admitted by the system,and we assume that the status of each user switches between “active”and“inactive”according to a Markov rule(independent of).The status of a particular user is not necessarily independent of that of another user.As a result of the aforementioned features,we can construct a model which allows us to define,for any multiple access scheme,the SNIR ratio and hence,determine for any parameters and the probability of outage as a function of thefixed number of users present in thesystem.This in turn allows us to analytically determine the capacity of the system(described in terms of an admission region)associated with maximum acceptable probability of outage. Therefore,we achieve two main goals in this part of the paper:the development of an approximate statistical model for outage and calculation of the outage probability;andthe analytic determination of an admission region based the on the desired performance of the system with regard to outage probability.This part is organized as follows:In Section2.I,we construct a stochastic model,analytically calculate the probability of outage,and provide a procedure to construct an outage-based ad-mission region.In Section2.II we present examples illustrating the modeling and results in Section2.I.I.O UTAGE-BASED A DMISSION R EGION FOR M ULTIUSER S YSTEMS WITH M ARKOVC HANNELSA.Philosophy of Our ApproachWe address the issue of outage within the context of QoS requirements.A user in the system encounters an outage event when its received SNIR at the base station falls below a threshold for an extended period of time.Hence,an outage is experienced by each user individually. Therefore,the key conceptual issue is how to analytically describe an outage event as a system-wide QoS criterion.We address this issue by introducing afictitious observer/user and by defining an outage incurring during this user’s service time.To guarantee that the outage-based QoS requirements are satisfied for every type of user that may be admitted by the system we proceed as follows:We consider a separatefictitious observer/user for each type of traffic. Such a user is always active and is identical to the actual users of the same type in terms of the statistics of the physical channel,SNIR threshold,and minimum outage duration.Each fictitious observer/user does not create any interference in the system,hence has no effect on the performance of the system.The outage probability for such a user is a conservative bound on the outage probability of each user of the same type.The system-wide QoS requirement in terms of outage probability is met if and only if the probability or the frequecy of outage for each of the aforementionedfictitious users is below a prespecified value(that depends on the type of user)which reflects the QoS requirement.In this section,we construct the outage-based admission region following the above philosophy.B.Outage Formulation for a Given Observer/User in the Presence of a Fixed Number of Users Wefix the number of admitted users,and then develop an approach to defining and comput-ing outage probability for afictitious observer/user,whose channel statistics,SNIR thresh-old,and minimum outage duration are given.In a wireless setting the received SNIR of an observer/user depends on two decoupled factors:1)the effect of physical channel in the absence of other users;this captures events like additive noise,fading,and/or shadowing(in the presence or absence of power-control mechanisms).2)the effect of the presence,power,and channel statistics of the other active users admitted in the system.Therefore,to determine the probability of outage,we need:tomodel the channel degradation;to model the interference of other admitted users;andto construct a“Super Markov Chain”combining and in order to describe the receivedSNIR of.For a detailed description of a model that completely accounts for the effect of these phenomena see[6].In this paper we consider only the worst case scenario for,wherethe channel of all the other users are in their best realization.Furthermore,we assume thatusers of similar type with similar channel realization are assigned similar transmission power.In this situation the received SNIR of depends simply on the effect of the physical channel and the presence of other active users.It is very common to model the effect of the channel on SNIR in the absence of other usersas a Markov chain.The validity of such model has been extensively studied and confirmed inthe literature(see[19]).The most commonly used example of this kind is the Gilbert Channel. In general,such a MC is defined by its state-space,and its transitionmatrix Prob Note that in the case of an idealpower control mechanism,the state-space is reduced to a singleton,hence;in case of power control with quantized error,we have.We assumethat channel states of individual users are mutually independent.To model the interference of other admitted users,we assume that there are types of users in terms of QoS requirements,transmission Power,and the activity factor[14],and there are users admitted to the system(not including).At any time slot,each admitted user can be active(“on”)or inactive(“off”).Since only active users interfere with the received signal of,we need tofind an appropriate model to describe the evolution of the users’“on”periods.In this paper,we assume that active and inactive periods for a user of type evolve according to a-order Markov chain.Consequently the number of type active users can be modeled by a Markov chain whose state is denoted by an integer. In general we assume that the activity of all users can be correlated.Based on the above we can express the state of the number of active users by the the random vector.By construction,this array evolves according to a known Markov rule.Let be the transition ma-trix for this Markov chain,i.e.ProbNote that is a square matrix of dimension.To describe the received SNIR of,we construct a“super Markov chain”(SMC)which represents the variation of the physical channel for and the number of the admitted users. The states of this SMC are vectors of form.where is the state of the channel between user and the base-station,and,,as mentioned before,denotes the number of type active user.The state-space of this SMC is.Since by assumption,the state of the physical channel for a user isindependent of the number of the other users and their channel state,the transition probabilityfor this SMC can be easily obtained by(1) where is the transition matrix of the Markov physical channel between observer/user and the base-station,and denotes the Kronecker product of the matrices and.If we denote by,the size of set,then the size of matrix is.To define an outage event mathematically,we must specify the received SNIR of observer/userat each state.This SNIR is a function.The exact form of depends on the dynamics of multiple access interference,and possibly the power control mech-anism.For instance,for a CDMA system where users of the same class have a common trans-mitted power and there is no power control,the form of function is:where is the noise power(that includes the expected total interference from the adjacent cells),is the the total number of active users of type,is the spreading gain for user ,is the channel gain between and the base station,and is the common transmitted power for all type-users.This form can extend to CDMA systems with power control where each class of users has a common targeted power,and where represents the error of the power control mechanism.After specifying the SNIR of user at each state,we define the set of“bad states”as(3) Based on the above classification of states we can now formally define the following:Definition1:An outage is an event where the state of the SMC enters and stays in for at least units of time.Definition2:The probability of an outage is defined to be the probability that a randomly selected time slot belongs to an outage event and is denoted by.C.Outage Analysis for a Given Observer/User in the Presence of a Fixed Number of Users The probability and frequency of an outage event in the constructed SMC can be studied in the framework of[20].Consider the constructed SMC and the associated transition matrix with it.We follow [20]to establish the necessary equations and relations that describe the probability of outage. Note that the SMC is mathematically equivalent to the physical Markov Channel studied in [20],even though the SMC,in general,has a much larger state space,and it has a very spe-cific structure due to its construction.Hence,after introducing the appropriate notation and definitions we can use results provided from[20]for the analysis of the probability of outage.C.1DefinitionsLet the row-vector denote the stationary distribution of SMC.Define asifotherwise(4) Define as the matrix with entriesifif(5) C.2ResultsWe establish an analytical expression for probability of outage.For that matter we need the following result from[20].Fact.1:The probability of outage is given as(6) Based on Fact1we establish an alternative analytical expression for the probability of out-age.The new expression is easier to compute as it involves neither inversion of a matrix nor calculation of vector.For the proof of Proposition1see[6].Proposition1:D.Construction of an Outage-Based Admission RegionWe now discuss how to use the results obtained in Section2.I-C to construct an admission region when the probability of outage is the QoS requirement under consideration.An admis-sion region is the set of all combinations of admitted users such that if connection admissionsare restricted to a subset of its interior,the probability of an outage encountered by afictitious observer/user of type is less than a prespecified threshold for all.The formulation of probability of outage presented in Section2.I-B and the analysis of Section2.I-C provide an expression for the probability of outage of afictitious user of type ()as a function of the vector of admitted usersthenrep-resent thefixed number of users of type admitted to the system.Letifififotherwisewhere is a column vector whose elements are all zero except for the element which is1, is the activation rate of each inactive user of type,is the probability of that an active1010101010100P r o b a b i l i t y o f O u t a g e Fig.1.Left)vs.,for cases in 2.II-A.Right)Admission Regions for cases in 2.II-A.user becomes inactive,,and .Note that,(is the time slot duration and is the fading cycle),dB,and the spreading gain is .Fig.1.Left)shows the result of such acalculation for CDMA systems when:1)the channel follows a Gilbert model with average burst lengths of 4,with steady-state probability of the bad-channel-state equal to and (the value recommended by ITU-T [13]);1’)channel is similar to one of (1),and ;2)the channel is an appropriate approximation to a Rayleigh fading channel with the maximum Doppler frequency of 100Hz as given by [19]and ;2’)the channel is similar to (2)and ;3)an ideal power control mechanism is implemented and ;3’)the channel is similar to (3)and ;and 4)power control is applied with error of 5%and .4’)the channel is similar to (4)and ;Now we study the outage problem for the same CDMA system when the traffic consists of two classes of users with different activity factors,spreading gains,and outage parameters;these parameters are,,,,dB,and dB.We set the maximum acceptable probability of outage to beequal to .Under this specification,Fig.1.Right)shows the admission region,when:1)the channel is described by a Gilbert model similar to the one in Section 2.II-A and ;2)the channel is described by a Gilbert model similar to the one in Section 2.II-A and ;and 3)there is an ideal power control mechanism and ;and 4)there is an ideal power control mechanism and .B.DiscussionFig.1illustrates that for all cases discussed in Section 2.II-A is an increasing function of .Similar plots are provided in [6].These plots,like Fig.1,show that ,,is increasing in and .This implies that the region defined by (8)is coordinate convex,hence for the examples studied.Based on these result we propose the following conjecture:Conjecture1:In any cellular system,(P)(for the proof,see[6]).Section3.II includes a brief discussion of a further extension of the CAC problem.I.T HE G ENERALIZED S TOCHASTIC K NAPSACK P ROBLEM WITH T WO C LASSES OFC ONNECTIONSThe generalized stochastic knapsack problem with two classes of users can be formulated as follows:Problem(P);each unit of time there is at most one new connection arrival to the system.Each arriving connection can be admitted to the knapsack if the resulting number of connections is in.If a request for connection is rejected,the connection is lost.An admitted connection remains in the knapsack until its service is completed.Without any loss of generality and for clarity,we assume that arrivals and departures within the time slot from time to occur in the open interval;furthermore,departures occur at the end of a time slot whereas arrivals occur at the beginning of a time slot.Thus,if we define and asis the time after the arrival time of newconnection requests and admission decisions in time slotis the time before the completion timeof any connection whose service ends in time slotwe have.Each admitted connection of type generates a revenue of rate while being served in the knapsack.The goal is tofind an optimal admission strategy that maximizes the total expected revenue over horizon,where may be infinite. Remark:As a result of our formulation,a packet of type may be admitted in the system at,complete service at,and result in a revenue.The main result of this section is summarized by the following theorem:Theorem2:If(11)then the policy that follows the greedy rule at all times is optimal.We note that as increases the sufficient conditions,described by(11),for optimality of the greedy admission policy become increasingly weak.Part4.ConclusionIn this paper we presented an approach to the connection admission control for a single-hop multi-service wireless network with QoS requirements.In general,a connection admission control strategy creates a complicated two-way coupling between the physical layer,i.e.QoS, and the network layer,i.e.the optimal resource allocation.Our approach proposes a decom-position of the problem in two subproblems:admission region construction and generalized knapsack scheduling.The result of such decomposition is reducing the interaction of the two layer into a one-way coupling between the physical layer(QoS)and the network layer(CAC). To demonstrate the methodology,we,then,constructed an outage-based admission region.Si-multaneous consideration of QoS requirements such as outage probability,average bit error rate,delay,etc.,can be incorporated into the admission control problem by taking the intersec-tion of the corresponding admission regions resulting from the above QoS requirements.Such an intersection defines the admission region for a generalized knapsack problem.We inves-tigated a generalized knapsack problem and established conditions sufficient to guarantee the optimality of the greedy admission policy.A CKNOWLEDGMENTThis research was supported in part by ARO Grant DAAH04-96-1-0377and NSF Grant ECS-9979347.R EFERENCES[1]S.Asmussen.Applied Probability and Queues.John Wiley&Sons,1987.[2] C.Barnhart,J.Wieselthier,and A.Ephremides.Admission-control policies for multihop wireless networks.WirelessNetworks,1:373–387,1995.[3]J.S.Evans and D Everitt.Effective bandwidth-based admission control for multiservice CDMA cellular networks.IEEETransactions on Vehicular Technology,48(1):36–46,January1999.[4]G.J Foschini,B.Gopinath,and J.F.Hayes.Optimum allocation of servers to two types of competing costumers.IEEETransactions on Communications,29(7):1051–1055,July1981.[5] A.Gavious and Z.Rosberg.A restricted complete sharing policy for a stochastic knapsack problem in B-ISDN.IEEETransactions on Communications,41(7):2375–2379,JULY1994.[6]T.Javidi and D.Teneketzis.Outage,QoS,and admission region in a single cell.Control Group Report CGR-01-07,University of Michigan,EECS Department,RM.4230,EECS BLDG.Ann Arbor,MI48109-2122USA,March2001.[7]S.Jordan and P.P.Varaiya.Control of multiple service,multiple resource communication networks.IEEE Transactionson Communications,42(11):2979–2988,November1994.[8]T.E.Lee and G.T.Oh.The assymptotic value-to-capacity ratio for the multi-class stochastic knapsack problem.Euro-pean Journal of Operational Research,103:584–594,1997.[9]J.Lin,W.Kao,Y.T.Su,and T.Lee.Outage and coverage consideration for micro-cellular mobile radio systems in ashadowed-Rician/shadowed-Nakagami environment.IEEE Transactions on Vehicular Technology,48(1):66–75,January 1999.[10]N.B.Mandayam,P.Chen,and J.M.Holtzman.Minimum duration outage for CDMA cellular systems:A level crossinganalysis.Wireless Personal Communication,(7):135–146,1998.[11]S.Martello and P.Toth.Knapsack Problems.J.Wiley&Sons,1990.[12]S.Oh and K.M.Wasserman.Dynamic spreading gain control in multiservice CDMA networks.IEEE Journal onSelected Areas in Communication,17(5):918–927,May1999.[13]R.O.Onvural.Assynchronous transfer mode networks:Performance issues.Artech House,1994.[14]T.S.Rappaport.Wireless Communications:Principle&Practice.Prentice Hall,1996.[15]K.W.Ross.Multiservice Loss Models for Broadband Telecommunication Networks.Springer,1995.[16]J.Sullivan and A.Mendelson.Personal communication services:Bringing new quality and clarity to the enterprise.InfoTech:PCS Reports1,Phillips,Fall1997.[17] D.Tse,,and S.Hanly.Effective bandwidths in wireless networks with multiuser receivers.In Proceedings of the17thAnnual IEEE Conference on Computer Communications(INFOCOM),volume1,page3542,1998.[18] D.Tse and S.V.Hanly.Linear multiuser receivers:Effective interference,effective bandwidth and user capacity.IEEETransactions on Information Theory,45(2):641–657,March1999.[19]H.S.Wang and N.Moayeri.Finite-state Markov chain:A useful model for radio communication channels.IEEETransactions on Vehicular Technology,44(1):163–171,February1995.[20]M.Zorzi.Outage and error events in bursty channels.IEEE Transactions on Communications,46(3):349–356,March1998.。
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我市出台《意见》推进“三旧”改造工作
商住项目商业比例提至50%
2009年至今，我市已完成"三旧"改造150宗，面积12990亩。
通过"三旧"
改造，增加建筑面积820万平方米，增加就业人口3 万人，增加年度税收
1.8亿元。
记者吴飞雄摄
“三旧”改造商住项目中商业比例提高，商业比例从不超过45%提高至不超过50%；“三旧”改造项目鼓励发展第三产业和公共设施配套；同时“三旧”改造项目转功能为商业用途增加了财政的返还力度。
在昨日下午召开的全市“三旧”改造工作现场会议上，市委常委、常务副市长邓小兵表示，市政府已经研究出台了《关于进一步推进我市“三旧”改造工作的
意见》，希望各镇区大胆用政策效力推进“三旧”改造顺利实施。
市长助理黄永林参加现场会。
据了解，2009年至今，我市已完成“三旧”改造150宗，面积12990亩，其中旧城镇3827亩、旧厂房5099亩，旧村庄4064亩，用地面积相当于新增建设用地约70%。
通过“三旧”改造，增加建筑面积820万平方米，增加就业人口3万人，增加年度税收1.8亿元。
通过改造为公共绿地、提高容积率、复垦为农用地等节地途径，节约土地6202亩。
其中2010年完成或实施改造的“三旧”改造项目有32个项目，设计改造面积10015亩，投入资金261亿元，由于成效显著，我市荣获全省2010年度节约集约用地一等奖与“三旧”改造工作三等奖。
邓小兵表示，我市“三旧”总体改造进度不快，改造任务艰巨，时间非常紧迫。
经省确认，中山“三旧”改造总面积为10.26万亩，其中涉及完善历史用地手续的“三旧”用地有5.6万亩。
截至11月底，各镇区共上报完善用地手续的地块只有265宗，面积3309亩，报批工作进度非常缓慢，邓小兵表示，完善历史用地报批工作要在明年9 月份之前完成报批，因为“三旧”改造是一项时效性很强的政策，其中涉及完善历史用地手续等政策只适用到2012年底，中山要在余下1年里完成5万多亩的“三旧”用地报批工作，时间非常紧、任务也很重。
市政府研究出台《关于进一步推进我市“三旧”改造工作的意见》，其中明确规定提高“三旧”改造商住项目的商业比例。
据了解，原政策是对居住用地的配件商业建筑面积占地块总建筑面积的比例可适当提高，但不得超过45%，现在提高到不超过50%，鼓励改造利用“三旧”资源重点发展第三产业，但是“三旧”改造商住项目中的商业比例应小于50%，否则用地性质发生改变，与控规不符。
同时，加大“三旧”改造项目转功能为商业用途的优惠力度。
由工业、住宅、商住等其他用途改变为商业用途的“三旧”改造项目，无论建筑上建筑物是否已取得房产证，在完善缴费手续后，市级扣除2万元/亩收取办证费外，土地属中心城区范围的返还50%，土地属镇区的全额返还。
新意见鼓励建设公共停车位及立体式公共人行通道，容积率反映了地块的开发强度，按规范地上建筑都要计入，否则不能真实反映地块开发状况。
为促进“三旧”项目建设，鼓励建设公交及慢行系统，降低“三旧”项目建设成本，保证实际容积率的增加在合理范围内。
对提供公共使用超过500车位以上的配件停车场（库），且在改造范围内配置有“港湾式公共汽车站场”等公交设施的或立体式人行公共通道设施的，项目配件车位可按规定的80%计算。
建设地面以上停车库的可不计容积率，但车位数不得超过总配建车位的30%，道路退让标准必须按有关规定执行。
而原政策在“三旧”项目配建车位需符合相关规范要求，地上车位计入容积率。
来源：中山日报作者：本报记者李丹丹字数：1331。