基于矿质元素差异的宁夏不同小产区葡萄酒的判别分析

崔泽方1,开建荣2,张媛珂1,刘娜3,姜文广3,葛谦2,马婷婷4, 5,房玉林1,4*,孙翔宇1,5*

1(西北农林科技大学 葡萄酒学院,陕西 杨凌,712100)2(宁夏农产品质量标准与检测技术研究所,宁夏 银川,750002)3(宁夏张裕龙谕酒庄有限公司,宁夏 银川 750002)4(陕西省葡萄与葡萄酒重点实验室,陕西 杨凌,712100)5(陕西省特色果品定向设计加工工程技术研究中心,陕西 杨凌,712100)

摘 要 通过检测分析宁夏不同核心小产区葡萄酒的矿质元素含量,结合多元统计分析方法,构建基于矿质元素的省(自治区)内地域尺度葡萄酒产区判别模型。该研究采集了宁夏银川、贺兰、永宁、青铜峡和红寺堡5个小产区的赤霞珠葡萄,使用同一方法酿酒,获得78个赤霞珠酒样,采用电感耦合等离子体质谱仪测定了B、Bi、Be等57种矿质元素含量,对这57种矿质元素进行主成分分析,将提取的主成分作为线性判别分析的输入变量,构建产地判别模型。结果显示,在葡萄酒中检测到的57种矿物质元素中,有46种元素含量在宁夏不同产区间具有显著差异(P<0.05)。通过主成分分析法,从57种矿质元素中成功提取了13个主成分,总方差贡献度达到了86.931%。基于Fisher判别分析方法建立了葡萄酒产区识别模型,回代检验与交叉验证的正确判别率为100%和98.7%,实现了宁夏自治区内地域尺度不同小产区葡萄酒的判别,证明了矿质元素溯源技术可以用于省(自治区)内地域尺度内的葡萄酒产区鉴别。

关键词 矿质元素;葡萄酒;产区鉴别;电感耦合等离子体质谱仪;贺兰山东麓

宁夏贺兰山东麓葡萄酒产区是我国重要的葡萄酒生产区域之一,其得天独厚的气候条件,造就了宁夏赤霞珠葡萄酒独一无二的品质,比如酒体饱满、色泽鲜亮、果香浓郁、花香突出等[1-2]。随着人民对高品质生活的追求,葡萄酒的真实性越来越受到消费者的关注,比如产地、品种和年份等,这些都会影响消费者的选择,也是部分不法商贩造假的方向。我国葡萄酒行业起步较晚,GB/T 15037—2006《葡萄酒》明确提出了产区葡萄酒,但并未明确提出其产区属性的标准。由于葡萄酒产区溯源体系尚不完善,葡萄酒造假的现象层出不穷,比如伪造地理标准农产品标签来误导消费者等,由此导致我国葡萄酒市场较为混乱,同时影响了宁夏葡萄酒产业的健康发展。因此,为规范葡萄酒市场,开展葡萄酒产区溯源体系研究迫在眉睫。

矿质元素指纹图谱被认为是追溯农产品产地来源的可靠技术之一,目前已经广泛应用于多种农产品的溯源,比如蜂蜜[3-4]、茶叶[5-6]、苹果[7-8]、耗牛肉[9]等。同时矿物元素已被确立为当地地理环境的显著化学指标[10-11],而葡萄内的矿质元素不能自身合成,必须从周围的土壤环境吸收富集,因此葡萄酒中矿质元素的含量与组成取决于葡萄生长的土壤等环境条件,且具有地域性特征,而且不受贮藏环境、加工条件等因素的影响[12]。因此,利用矿质元素差异追溯葡萄酒来源符合科学机制,切实可行[13-14]。PASVANKA等[15]通过矿质元素指纹技术实现了希腊6个产区葡萄酒的溯源;李彩虹等[16]、吕真真等[17]、程文娟等[18]通过矿质元素指纹技术实现了我国不同产区葡萄酒的溯源。AZCARATE等[19]通过测量葡萄酒中5种微量矿质元素,实现了阿根廷4个产区葡萄酒的溯源。以上研究均为针对不同地区、不同品种葡萄酒进行大范围的研究,而针对小尺度地域、单一品种的研究报道较少。本研究拟在前人研究的基础上,通过在宁夏贺兰山东麓不同核心小产区随机采样试验,探究宁夏自治区内地域尺度下矿质元素对赤霞珠葡萄酒产区溯源的可行性。本研究以期为葡萄酒产区鉴别提供理论依据的同时,减少葡萄酒仿冒现象的发生,有效保护消费者权益和宁夏贺兰山东麓葡萄酒品牌效益,为葡萄酒产业健康可持续发展贡献力量。

1 材料与方法

1.1 材料与试剂

选自宁夏贺兰山东麓不同核心小产区26个酒庄,每个酒庄采集3个赤霞珠葡萄样本,银川产区(n为样品数量,n=15,5个酒庄)、永宁产区(n=15,5个酒庄)、青铜峡产区(n=18,6个酒庄)、贺兰产区(n=15,5个酒庄)、红寺堡产区(n=15,5个酒庄),采用相同的酿造工艺,共酿造78个酒样(表1)。

表1 酒样产地信息及编号
Table 1 Information and number of the origin of wine samples

类别采样地点葡萄品种样品数样品编号1宁夏贺兰赤霞珠15HL1-HL152宁夏银川赤霞珠15YC1-YC153宁夏永宁赤霞珠15YN1-YN154宁夏青铜峡赤霞珠18QTX1-QTX155宁夏红寺堡赤霞珠15HSP1-HSP15

标准溶液为4组57种元素混合标液,包括金(Au)、铪(Hf)、铱(Ir)、钯(Pd)、铂(Pt)、钌(Ru)、锑(Sb)、锡(Sn),硼(B)、锗(Ge)、钼(Mo)、铌(Nb)、磷(P)、铼(Re)、钽(Ta)、钛(Ti)、钨(W)、锆(Zr),银(Ag)、铝(Al)、砷(As)、钡(Ba)、铍(Be)、铋(Bi)、钙(Ca)、镉(Cd)、钴(Co)、铬(Cr)、铯(Cs)、铜(Cu)、铁(Fe)、镓(Ga)、锂(Li)、镁(Mg)、锰(Mn)、钠(Na)、镍(Ni)、铷(Rb)、硒(Se)、锶(Sr)、铊(Tl)、铀(U)、锌(Zn),铈(Ce)、铒(Er)、铕(Eu)、钆(Gd)、钬(Ho)、钕(Nd)、镨(Pr)、钐(Sm)、铽(Tb)、钍(Th)、铥(Tm)、钇(Y)、镱(Yb),美国Perkin Elmer公司;汞(Hg)的单元素标准溶液,中国计量科学研究院;质控标准物质为生物成分分析用的标准物质胡萝卜GBW10047,中国地质科学院地球物理地球化学勘查研究所;硝酸(优级纯),德国Merck公司;使用的水为实验室一级用水。

1.2 仪器与设备

ELAN DRC-e型,美国 Perkin Elmer公司;Mars 6 Xpress 微波消解仪,美国CEM公司;赶酸仪(24位),莱伯泰科公司;AL 104 型万分之一电子天平,梅特勒-托利多公司。

1.3 实验方法

1.3.1 葡萄酒酿造

葡萄酒酿造工艺流程如下:

发酵罐准备→原料采收→葡萄分选→除梗破碎机破碎→添加SO2→装罐(5 L罐,每罐装4 L)→果胶酶处理→皮渣分离→满罐→添加SO2,15 ℃静置澄清1个月→倒罐→灌装打塞保存

首先准备若干个5 L的干净玻璃发酵罐,H2SO3以1 mL/L的标准添加到发酵罐中,加入少许水并盖上盖子,熏罐1 h,1 h后将发酵罐中的液体倒掉,待H2SO3完全挥发后,盖上盖子备用。

采取新鲜葡萄果实除梗破碎后放入5 L发酵罐中,添加SO2并搅拌均匀,按照SO2 60 mg/L计,即H2SO3 1 mL/L,待H2SO3入罐1 h后,按照20 mg/L标准加入果胶酶,24 h后按照200 mg/L接种酵母,接种酵母后,用双层纱布盖在罐口,每天固定时间压帽2次,测定温度和比重,当比重下降至0.992~0.994,并维持不变3~4 d,终止发酵,提前准备好硫熏过的发酵罐,用纱布或者压榨机皮渣分离,添加SO2,按照60 mg/L SO2计,即1 mL/L H2SO3,搅拌均匀,盖上盖子并在盖子外缠上保鲜膜,满罐密封贮藏,15 ℃下静置澄清1个月后,将酒液倒入硫熏过的酒瓶,添加SO2,按照SO2 30 mg/L计,即0.5 mL/L H2SO3,最后灌装打塞保存。自酿酒样理化指标见表2,均符合GB/T 15037—2007《葡萄酒》。

表2 葡萄酒理化指标
Table 2 Physicochemical and chemical indicators of wine

采样地点酒度/%总糖/(g/L)可滴定酸/(g/L)pH干浸出物/(g/L)银川11.72±0.61a3.20±0.30a5.29±0.52b3.52±0.06b24.08±1.25b贺兰12.30±0.73a3.21±0.30a5.39±0.21ab3.58±0.03a26.40±1.27a永宁11.87±1.09a3.25±0.36a5.31±0.24b3.55±0.04ab25.98±2.24a青铜峡12.01±0.90a3.28±0.37a5.03±0.40c3.59±0.08a24.33±3.04b红寺堡11.71±0.38a3.15±0.18a5.60±0.30a3.42±0.05c23.38±1.20b

注:不同小写字母代表差异显著(P<0.05)(下同)。

1.3.2 样品前处理

称取葡萄酒样品5 g(精确至0.01 g)于微波消解管中,置于赶酸仪上130 ℃加热使酒精和水挥发近干,晾凉后加入10 mL硝酸,加塞盖好盖子浸泡过夜,将微波消解管置于微波消解仪,按照程序升温进行消解,消解完成后,微波消解管放至室温后,打开盖子和内塞,然后将消解管移至赶酸仪,120 ℃赶酸2 h。赶酸完成后,待微波消解管放至室温后,用一级水将消解试样少量多次洗至50 mL聚四氟乙烯刻度管中,定容,摇匀,同时做试剂空白。

1.3.3 ICP-MS工作条件

ICP-MS测定元素采用标准模式。优化后的工作条件为:发生器功率1 300 W;检测器模拟阶电压-2 350 V;离子透镜电压6.00 V;雾化器流量0.98 L/min;等离子炬冷却气流量17.0 L/min;辅助器流量1.20 L/min。上机测定葡萄酒样品及质控品中57种元素,每个样品重复测量3次。

1.3.4 方法学验证

各元素的标准曲线的相关性系数均大于0.999,检出限介于0.000 1 μg/L至3.66 mg/L。GBW10047标准物质中各元素的测定值都符合标准参考值。表3详细列出了相关元素的标准曲线、检测限和定量限。

表3 ICP-MS仪器测定矿质元素标准曲线、检出限和定量限
Table 3 Standard curve, detection limit, and quantification limit of mineral elements determined by ICP-MS

元素工作方程相关系数检出限/(mg/L)定量/(mg/L)Al27Y=22.849 2x0.999 721.26×10-24.20×10-2As75Y=4.209 3x0.999 905.00×10-31.67×10-2B11Y=3.986 5x0.999 643.10×10-21.00×10-1Ba138Y=32.081x0.999 981.12×10-43.73×10-4Be9Y=3.955 56x0.999 487.80×10-32.60×10-2Bi209Y=30.170 9x0.999 997.80×10-52.60×10-5Cd114Y=13.523 8x0.999 926.60×10-52.20×10-4Ce140Y=23.650 2x0.999 998.60×10-62.90×10-5Co59Y=12.295 7x0.999 982.20×10-47.33×10-4Cr52Y=13.804 2x0.999 956.40×10-42.13×10-3Cs133Y=33.609 6x0.999 992.20×10-61.00×10-5Cu63Y=11.755 4x0.999 902.80×10-49.33×10-4Dy164Y=16.815 1x0.999 982.00×10-57.00×10-6Er166Y=27.982x0.999 951.00×10-53.40×10-5Eu153Y=35.385 6x0.999 973.00×10-61.00×10-5Gd133Y=12.423 4x0.999 983.40×10-51.13×10-4Ge74Y=7.887 32x0.999 917.80×10-42.60×10-3Hg202Y=134.757x0.993 436.80×10-72.30×10-6Ho165Y=140.913x0.999 871.30×10-74.00×10-7La139Y=16.892 8x1.000 001.00×10-43.33×10-4Li7Y=9.353 04x0.999 804.20×10-41.40×10-3Lu175Y=155.069x0.999 858.00×10-83.00×10-7Mg24Y=11.382 8x0.999 885.80×10-31.93×10-2Mn55Y=14.900 2x0.999 983.40×10-41.13×10-3Mo98Y=21.107 2x0.999 728.40×10-52.80×10-4Na23Y=58.550 3x0.998 472.60×10-38.67×10-3Nb93Y=70.750 8x0.999 791.10×10-63.70×10-6Nd142Y=18.718 8x0.999 972.60×10-58.67×10-5Ni60Y=4.879 06x0.999 916.00×10-32.00×10-2Pb208Y=17.102 2x0.999 992.80×10-59.33×10-5Pr141Y=18.756 2x0.999 9971.00×10-53.40×10-5Rb85Y=56.771 4x0.999 8021.90×10-66.47×10-6Sb121Y=4.434 08x0.999 9967.00×10-42.33×10-3Sc45Y=24.658x0.999 9083.80×10-41.27×10-3Se78Y=0.987 727x0.999 9341.14×10-13.79×10-1Sm152Y=7.149 82x0.999 9951.70×10-45.73×10-4Sn118Y=6.344 48x0.999 9894.80×10-41.60×10-3Sr88Y=110.688x0.999 5764.20×10-61.40×10-5Tb159Y=71.122 7x0.999 9676.00×10-71.90×10-6Ti48Y=11.888 8x0.999 9531.98×10-36.60×10-3Th232Y=52.208 3x0.999 9852.54×10-28.47×10-2Tl205Y=90.182 1x0.999 8421.50×10-64.93×10-6

续表3

元素工作方程相关系数检出限/(mg/L)定量/(mg/L)Tm169Y=136.841x0.999 8831.00×10-74.50×10-7U238Y=89.630 5x0.999 9631.00×10-63.47×10-6V51Y=17.996 9x0.999 9471.70×10-35.67×10-3Y51Y=97.176 9x0.999 7635.00×10-71.80×10-6Yb174Y=46.533 1x0.999 8761.50×10-65.10×10-6Fe57Y=2.397 89x0.998 4622.157.17Zn66Y=6.887 32x0.999 6258.40×10-32.80×10-2P31Y=0.659 051x0.999 9583.6612.19Ta181Y=134.574x0.999 8618.00×10-72.73×10-6Pt195Y=10.265 1x0.999 9681.14×10-43.80×10-4W184Y=22.252 1x0.999 951.58×10-55.27×10-5Hf180Y=45.878 8x0.999 8982.80×10-69.33×10-6Pd106Y=7.593x0.999 9674.40×10-41.47×10-3Ga69Y=18.820 3x0.999 9291.40×10-44.67×10-4Zr90Y=32.594 9x0.999 8732.40×10-58.00×10-5

1.4 数据处理

使用SPSS 25.0软件处理葡萄酒样品理化基本指标以及矿质元素的数据,并进行方差分析,将筛选出的元素进行主成分分析和判别分析,利用SPSS 25.0软件绘制图谱。

2 结果与分析

2.1 宁夏不同产区葡萄酒中矿质元素含量差异性分析

由表4可知,从葡萄酒样品中共检测出57种矿质元素,其中Mg、Ca、P元素含量均大于100 mg/L,其次是Na和B元素含量处于10~100 mg/L,紧而次之的是Al、Mn、Fe、Rb、Sr元素含量介于1~10 mg/L,接着是Ti和Li两者的元素含量在0~1 mg/L,最后检测出的其他矿质元素含量均为μg/L级别。

表4 不同产区葡萄酒矿质元素含量差异
Table 4 Differences of mineral element content in wines from different producing areas

元素贺兰产区银川产区永宁产区青铜峡产区红寺堡产区P值Al6.25±1.39c5.92±1.24c7.14±1.24bc7.83±0.76b9.92±2.12a<0.001As*6.79±0.86ab4.99±1.06ab7.85±4.84a7.96±5.02a3.73±0.93b0.001B14.16±2.41bc10.85±2.66c16.91±6.54ab20.53±9.82a12.59±1.74bc<0.001Ba0.073±0.23b0.094±0.028a0.080±0.011b0.068±0.018b0.081±0.015ab0.004Be*0.29±0.31a0.24±0.20a0.24±0.29a0.37±0.48a0.30±0.22a0.726Bi*0.000.001 5±0.001 3b0.000 29±0.000 60b0.002 4±0.005 6a0.003 7±0.005 9b0.040Cd*0.004 2±0.004 3a0.008 9±0.005 7a0.005 2±0.005 8a0.002 6±0.004 4a0.013±0.018a0.599Ce*0.71±0.45bc0.43±0.23c0.77±0.36bc1.18±0.27b1.71±1.48a<0.001Co*1.62±0.36b1.97±0.65ab1.90±0.48ab1.64±0.31b2.08±0.36a0.012Cr0.033±0.040b0.035±0.006 2b0.035±0.010b0.045±0.010a0.034±0.006 8b<0.001Cs*4.32±1.87bc3.45±1.53bc5.87±3.37b2.00±0.81c9.87±4.62a<0.001Cu0.075±0.033a0.067±0.045a0.060±0.029a0.051±0.024a0.070±0.042a0.314Dy*0.060±0.035ab0.039±0.020b0.048±0.015b0.093±0.022a0.08±0.06ab0.002Ca141.82±27.11a128.76±28.67a141.74±13.66a145.96±16.85a142.63±9.63a0.177Eu*0.026±0.008 2a0.027±0.006 7b0.024±0.004 6a0.026±0.010a0.030±0.013a0.002Gd*0.092±0.024ab0.077±0.022b0.092±0.018ab0.11±0.047ab0.12±0.074a0.017Ge*0.032±0.046a0.011±0.018a0.046±0.090a0.054±0.053a0.05±0.08a0.167Hg*1.74±3.21a0.029±0.008 4b0.11±0.067b0.052±0.037b0.062±0.032b0.003Ho*0.005 1±0.002 7a0.005 4±0.001 0b0.005 3±0.001 0a0.007 6±0.003 4a0.004 8±0.003 6b<0.001Li0.068±0.024b0.058±0.024b0.12±0.030b0.21±0.11a0.25±0.10a<0.001Lu*0.002 1±0.001 0a0.002 1±0.001 0b0.001 6±0.001 0a0.002 1±0.001 0a0.001 8±0.001 0b<0.001Mg207.27±30.44b222.91±26.63ab250.18±43.12a248.48±51.73a251.64±35.61a0.006Mn2.01±0.47a2.19±0.53a2.31±0.80a2.29±0.75a2.08±0.47a0.615Mo*6.52±2.25a8.57±2.70a4.42±1.01a6.41±2.25a11.85±18.23a0.143Na13.11±6.11b13.66±7.06b19.26±6.23ab29.02±26.49a13.48±4.35b0.005Nb*0.32±0.20a0.35±0.13a0.46±0.50a0.29±0.14a0.30±0.03a0.345Nd*2.58±0.82bc2.03±0.72c2.42±0.76bc3.40±0.70ab4.19±2.11a<0.001Ni0.017±0.003 7a0.024±0.011ab0.019±0.001 8bc0.018±0.003 0c0.029±0.010a<0.001Pb*9.17±3.04b9.54±2.65b9.07±4.02b9.66±1.32b81.04±155.92a0.015Pr*0.14±0.030bc0.11±0.019c0.13±0.04bc0.16±0.033ab0.21±0.11a<0.001Rb1.20±0.36ab1.32±0.61ab1.43±0.75a0.82±0.15b1.20±0.58ab0.017Sb*0.85±0.22a1.19±0.29a1.27±0.64a1.20±0.30a1.60±1.93a0.280Sc*1.95±0.33b2.05±0.30b2.01±0.30b2.14±0.41b2.76±0.16a<0.001Se*3.81±0.27ab3.80±0.45ab4.0±0.25a3.64±0.26b3.66±0.22b0.009Sm*0.086±0.030b0.07±0.001 0ab0.080±0.001 0b0.083±0.023b0.10±0.034a<0.001Sn*1.13±0.83b1.29±0.18b0.92±0.38b1.00±0.26b2.44±2.86a0.013Sr1.65±0.33c1.71±0.45c2.26±0.57b2.72±0.79ab2.94±0.58a<0.001Tb*0.004 8±0.001 0ab0.005 4±0.002 1c0.004 0±0.001 8b0.006 7±0.003 5c0.006 4±0.002 5a<0.001Ti0.30±0.086a0.26±0.046ab0.26±0.046ab0.26±0.41ab0.23±0.028b0.011Th*0.029±0.007c0.057±0.022b0.026±0.010c0.030±0.010c0.063±0.010a<0.001Tl*0.039±0.001bc0.056±0.013b0.033±0.001 0c0.038±0.004 8c0.064±0.010a<0.001Tm*0.009±0.008a0.006 7±0.001 1b0.007 3±0.002 5a0.012±0.006 0a0.11±0.010b<0.001U*0.27±0.17a0.25±0.13a0.18±0.15a0.53±0.70a0.44±0.31a0.069V*6.19±2.03a5.66±1.29a5.76±2.28a5.46±4.07a6.17±0.63a0.889Y*0.69±0.25ab0.50±0.084b0.49±0.061b0.82±0.34a0.71±0.43ab0.004Yb*0.027±0.007 6ab0.033±0.016b0.040±0.018ab0.049±0.016a0.044±0.021ab0.004Fe5.96±2.10a5.49±1.83ab4.31±1.16b4.47±0.99b4.12±1.17b0.003Zn0.14±0.12abc0.10±0.073bc0.25±0.24a0.028±0.048c0.19±0.17ab0.001Ag*41.78±86.67a0.016±0.011b0.035±0.012b0.013±0.006 4b0.007 5±0.001 0b0.009P220.8±75.42a159.52±28.99b187.2±33.56ab212.13±61.58a141.6±28.58b<0.001

续表4

元素贺兰产区银川产区永宁产区青铜峡产区红寺堡产区P值Ta*0.24±0.060bc0.30±0.048ab0.23±0.037c0.31±0.13a0.34±0.038a<0.001Pt*0.67±0.094b0.43±0.22ab0.84±0.35a0.84±0.11ab0.93±0.17ab<0.001W*0.51±0.091b0.82±0.26a1.17±1.25ab0.72±0.15ab0.72±0.28a0.038Hf*0.025±0.012b0.068±0.019a0.04±0.01b0.051±0.040b0.064±0.024b0.003Pd*0.36±0.086b0.57±0.17a0.30±0.01b0.27±0.21b0.38±0.032a<0.001Ga*2.28±0.77b3.14±0.98a2.02±0.23b2.36±0.64b3.00±0.93b<0.001Zr*1.44±0.89b2.06±0.64a1.47±0.80b1.16±0.58b0.99±0.31b<0.001

注:元素右上角*表示元素含量为μg/L;元素右上角未标*表示元素含量为mg/L;表中数据均为“平均值±标准差”。

通过对宁夏银川、贺兰、永宁、青铜峡和红寺堡5个葡萄酒产区的赤霞珠单品种葡萄酒中57种元素含量进行方差分析,结果显示Al、As、B、Ba、Ce、Cr、Cs、Dy、Eu、Hg、Ho、Li、Lu、Mg、Na、Nd、Ni、Pr、Sc、Se、Sm、Sr、Tb、Th、Tl、Tm、Y、Yb、Fe、Zn、Ag、P、Ta、Pt、Hf、Pd、Ga和Zr共38种元素含量在宁夏5个葡萄酒产区间差异极显著(P<0.01),Bi、Co、Gd、Pb、Rb、Sn、Ti和W共8种元素含量在宁夏5个葡萄酒产区间差异显著(P<0.05),Be、Cd、Cu、Ca、Ge、Mn、Mo、Nb、Sb、U和V共11种元素含量在宁夏5个葡萄酒产区差异不显著(P>0.05)(表4)。红寺堡产区葡萄酒的Al、Cs、Ce、Pb、Pr、Sc、Sn、Th和Tl共9种元素含量显著高于其他4个产区,贺兰产区葡萄酒的Hg、Ti和Ag元素含量显著高于其他4个产区,贺兰产区的Co元素含量显著低于其他4个产区,银川产区葡萄酒的Ba、Pd、Zr元素含量显著高于其他4个产区,但银川产区葡萄酒的B、Eu、Pt元素含量显著低于其他4个产区,青铜峡产区葡萄酒的B、Bi、Cr元素含量显著高于其他4个产区,青铜峡产区葡萄酒的Ni元素含量显著低于其他4个产区。这表明宁夏不同产区的葡萄酒在矿质元素含量上存在显著差异,而单因素方差分析虽能揭示这些差异,但不足以对产区进行精确判别。

2.2 不同产区葡萄酒矿质元素含量主成分分析

主成分分析作为一种无监督的识别方法,不仅可以缩小复杂数据的比例,以提供准确的分类,同时可以将多个指标降维简化成少数综合性指标,并且保留原指标包含的主要信息[20-22]。遵循特征值>1的原则,共提取了13个主要成分,它们的累计方差贡献率总计为86.931%。由表5可知,主成分1的方差贡献率为19.970%,综合了Al、Cd、Ce、Dy、Eu、Gd、Ge、Mo、Nd、Pb、Pr、Sb、Sm、Sn、Y、Yb共16种矿质元素的信息,主成分2的方差贡献率为12.275%,综合了B、Ho、Lu、Tb、Tm、P共6种矿质元素的信息,主成分3的方差贡献率为8.302%,综合了Co、Cs、Li、Mg、Sc、Sr共6种矿质元素的信息,主成分4的方差贡献率为8.028%,综合了Zr、As、Bi、V、Pd共5种矿质元素的信息,主成分5的方差贡献率为6.747%,综合了Zr、Ba、Ni、Fe、Ga共5种矿质元素的信息,主成分6的方差贡献率为5.358%,综合了Hg、Ag共2种矿质元素的信息,主成分7的方差贡献率为4.635%,综合了Be、Mn、Na、U共4种矿质元素的信息,主成分8的方差贡献率为4.007%,综合了Cr、Ta共2种矿质元素的信息,主成分9的方差贡献率为3.909%,综合了Se、Tl共2两种矿质元素的信息,主成分10的方差贡献率为3.736%,综合了Cu、Pt共2种矿质元素的信息,主成分11的方差贡献率为3.536%,综合了W元素的信息,主成分12的方差贡献率为3.256%,综合了Mn、Hf、Zn共3种矿质元素的信息,主成分13的方差贡献率为3.173%,综合了Nb元素的信息。共筛选出Al、Cd、Ce、Dy、Eu、Gd、Ge、Mo、Nd、Pb、Pr、Sb、Sm、Sn、Y、Yb、B、Ho、Lu、Tb、Tm、P、Co、Cs、Li、Mg、Sc、Sr、Zr、As、Bi、V、Pd、Ba、Ni、Fe、Ga、Hg、Ag、Be、Mn、Na、U、Cr、Ta、Se、Tl、Cu、Pt、W、Hf、Zn、Nb共计53种葡萄酒的特征矿质元素。

表5 前13个主成分的载荷矩阵及方差贡献率
Table 5 Load matrix and variance contribution rate of the first 13 principal components

元素成分12345678910111213Al 0.731-0.0880.328-0.128 0.079 0.095 0.095 0.161 0.202 0.324-0.125-0.002 0.127Zr-0.0310.002-0.1540.5420.509-0.119-0.1860.1020.008-0.180-0.097-0.1460.448As0.0250.4130.0420.630-0.2370.043-0.1480.211-0.0190.0360.197-0.0570.440B0.0770.5230.3890.424-0.3650.0200.131-0.076-0.1530.056-0.0640.148-0.232Ba0.165-0.1810.020-0.1110.9140.012-0.054-0.036-0.055-0.0530.116-0.0890.064Be-0.0880.0180.005-0.1600.1450.1830.790-0.024-0.1250.012-0.271-0.042-0.228Bi0.0870.3230.4100.798-0.1210.017-0.0070.097-0.056-0.0300.0430.062-0.053Cd0.5630.080-0.024-0.146-0.0060.127-0.004-0.273-0.090-0.0190.1030.0240.010Ce0.9400.0240.099-0.0560.0920.0570.0640.0520.0260.164-0.0200.027-0.096Co-0.114-0.3060.534-0.1620.291-0.290-0.208-0.068-0.1330.0980.190-0.202-0.240Cr0.3440.2020.0870.061-0.0700.007-0.0090.730-0.0910.1800.164-0.0580.013Cs-0.021-0.4450.529-0.366-0.1220.030-0.175-0.250-0.0390.028-0.414-0.0530.118Cu0.4450.1080.038-0.014-0.028-0.041-0.061-0.4490.005-0.660-0.0510.226-0.107Dy0.7110.396-0.0990.0700.049-0.0610.0080.1920.1910.098-0.130-0.073-0.066Ca0.1340.3350.278-0.4900.3490.3000.1200.084-0.299-0.0490.0100.306-0.032Eu0.8440.357-0.069-0.002-0.0180.1020.053-0.128-0.020-0.0150.186-0.1450.092Gd0.741-0.0050.1680.3460.144-0.0390.0000.2670.2060.062-0.032-0.1280.026Ge0.6030.3050.061-0.335-0.012-0.158-0.0920.0700.141-0.266-0.035-0.3240.057Hg0.0080.055-0.082-0.044-0.0240.9690.002-0.0520.0200.031-0.017-0.040-0.009Ho0.0110.9480.035-0.015-0.0860.006-0.0250.0290.0380.1100.1270.0330.058Li0.1560.0240.6810.171-0.2530.030-0.0640.2950.1800.264-0.0450.215-0.137

续表5

元素成分12345678910111213Lu-0.0100.846-0.077-0.118-0.1660.097-0.1420.114-0.0120.066-0.153-0.1910.125Mg-0.0710.2200.8620.1280.061-0.021-0.018-0.041-0.032-0.1760.012-0.062-0.028Mn-0.0840.0820.4240.0690.2410.0260.516-0.077-0.044-0.150-0.037-0.620-0.046Mo0.913-0.150-0.1720.0880.142-0.0490.0320.024-0.026-0.1340.125-0.091-0.072Na-0.0140.312-0.057-0.234-0.066-0.2520.730-0.172-0.0070.1350.0590.1230.063Nb-0.0070.0260.0070.3930.096-0.034-0.1060.048-0.0070.028-0.0530.0990.875Nd0.8880.0240.224-0.083-0.107-0.0030.1230.1820.070-0.014-0.1630.029-0.033Ni0.188-0.4430.256-0.1390.5140.028-0.045-0.029-0.0660.2240.0180.1870.067Pb0.945-0.218-0.1190.0020.053-0.045-0.004-0.0270.008-0.0230.117-0.0520.002Pr0.9120.0830.186-0.0580.0610.0470.0200.0140.0750.096-0.0980.067-0.011Rb-0.136-0.1730.293-0.298-0.1100.095-0.144-0.314-0.479-0.362-0.340-0.0080.002Sb0.867-0.089-0.2030.0240.125-0.061-0.0170.014-0.0570.0870.1330.0090.241Sc0.224-0.3070.531-0.4470.172-0.013-0.1230.2570.1870.167-0.0320.104-0.223Se-0.200-0.052-0.0970.0830.056-0.1060.080-0.102-0.7540.108-0.073-0.084-0.033Sm0.752-0.1780.1240.1410.4020.0420.032-0.0420.159-0.165-0.0870.164-0.031Sn0.872-0.327-0.2330.035-0.075-0.098-0.060-0.021-0.0390.0210.072-0.016-0.098Sr0.0470.0180.8400.187-0.010-0.1000.0420.1160.1750.1870.007-0.0190.146Tb0.0150.7640.1280.401-0.2540.1310.063-0.072-0.1830.1380.0430.027-0.067Ti0.1120.4760.1460.0150.4410.3930.0480.017-0.228-0.316-0.1990.097-0.190Th0.204-0.5620.2880.1100.188-0.030-0.071-0.1140.4130.150-0.0050.3530.109Tl-0.003-0.5130.1640.119-0.009-0.110-0.111-0.1290.6650.159-0.187-0.057-0.139Tm0.0610.9200.014-0.054-0.040-0.0710.148-0.0110.075-0.025-0.0940.030-0.040U0.345-0.145-0.131-0.046-0.1630.0600.8440.145-0.0340.0190.000-0.079-0.004V0.091-0.0500.1820.821-0.193-0.023-0.224-0.0230.0220.0970.0950.1020.229Y0.7220.3770.1460.3140.2030.069-0.0970.2210.068-0.084-0.0170.186-0.098Yb0.5260.3280.0490.1080.124-0.0060.0640.291-0.2430.0940.4200.0710.017Fe0.0530.194-0.4920.0240.5670.4210.112-0.0580.1530.136-0.267-0.058-0.026Zn0.353-0.1810.211-0.238-0.0330.165-0.178-0.408-0.054-0.0950.355-0.514-0.085Ag0.0000.036-0.077-0.040-0.0200.9680.010-0.0420.0210.031-0.006-0.033-0.029P-0.0510.6360.1060.0570.2850.4830.008-0.148-0.110-0.102-0.0150.0570.099Ta0.067-0.1770.265-0.0440.068-0.300-0.1020.6380.356-0.040-0.0300.1930.134Pt0.3580.1660.200-0.081-0.1360.0400.0620.025-0.0200.797-0.0730.147-0.060W0.029-0.0880.019-0.0100.026-0.046-0.1480.0330.044-0.0260.881-0.024-0.022Hf-0.273-0.1670.3700.3000.088-0.1080.008-0.0660.161-0.1250.0330.5530.079Pd-0.021-0.356-0.0660.7330.187-0.058-0.1240.0050.070-0.150-0.1870.1490.175Ga0.436-0.199-0.059-0.0870.818-0.0960.0630.0680.047-0.0840.0420.067-0.023特征值11.9517.9365.9194.6533.7622.8392.6662.3831.981.621.4881.2481.105方差贡献率/%19.9712.2758.3028.0286.7475.3584.6354.0073.9093.7363.5363.2563.173累计方差贡献率/%19.9732.24440.54648.57455.32160.67965.31469.32173.22976.96680.50283.75886.931

2.3 基于矿质元素葡萄酒产地区分模型的建立及判别分析

为了更深入地研究各元素含量对葡萄酒原产地判别的影响,利用基于Fisher判别函数的通用判别方法,对葡萄酒产地进行了多变量判别分析。分析时以主成分分析得到的13个成分中的53个葡萄酒的特征矿质元素作为自变量,进行了逐步判别。结果显示,B、Be、Cd、Ce、Co、Cr、Cs、Eu、Gd、Hf、Hg、Ho、Li、Lu、Mg、Nb、Nd、Ni、P、Pd、Pt、Sb、Se、Sm、Sr、Ta、Tb、Tl、Tm、V、W、Zn、Zr共计33个元素,因其对产区判别有显著影响而被选入判别模型中,基于这些元素构建了判别宁夏不同小产区葡萄酒的判别方程,分别为:

Y(贺兰)=13.016B+132 674.233Be+3 295 132.876Cd-43 929.908Ce+58 940.104Co-6 837.601Cr+5 487.660Cs+1 495 503.260Eu-664 842.074Gd+568 700.191Hf-11 880.605Hg-7 798 771.072Ho+451.020Li+102 982 476.796Lu+2.016Mg+133 042.968 Nb+11 262.223 Nd+8 932.289 Ni-0.146P-256 197.766Pd+76 054.302Pt+32 437.362Sb+169 568.098Se+3 218 461.153Sm-119.781Sr-219 300.660Ta-72 156 684.367Tb+3 297 210.755Tl-16 900 110.652Tm+37 539.385V+65 258.278 W-306.697Zn-31 410.685Zr-671.841

Y(银川)=20.136B+238 252.444Be+1 640 745.337Cd-11 084.867Ce+148 612.637Co-11 146.248Cr+4 166.539Cs-3 446 918.179Eu-4 047 959.507Gd+741 069.915Hf+17 937.259Hg-3 288 593.776Ho+551.456Li+102 987 916.822Lu+4.210Mg-139 906.096 Nb+25 795.750 Nd+17 282.424 Ni-1.476P+28 348.201Pd-142 532.367Pt+186 287.467Sb+170 198.862Se+7 566 778.680Sm-240.570Sr-234 172.550Ta-121 577 682.748Tb+5 434 449.802Tl-22 904 927.966Tm+73 658.966V+69 009.910 W-449.811Zn+66 884.862Zr-1 537.385

Y(永宁)=27.862B+345 405.101Be+5 284 856.240Cd+78 321.845Ce-216.469Co-5 888.631Cr+28 536.586Cs+89 042.965Eu+965 204.918Gd+1 360 338.405Hf-58 626.122Hg+3 397 8751.172Ho+888.288Li+114 249 205.019Lu+1.913Mg+468 547.758 Nb-44 012.350 Nd+7 232.748 Ni-0.084P-566 830.680Pd+128 952.183Pt+75 338.123Sb+224 700.630Se+1 320 423.108Sm-72.240Sr-673 125.693Ta-112 496 260.326Tb+4 305 056.507Tl-33 646 722.104Tm+27 427.913V+153 623.852W-272.713Zn-94 990.487Zr-1 113.042

Y(青铜峡)=21.027B+268 559.098Be+6 112 379.694Cd+80 797.618Ce-8 758.961Co-1 503.153Cr+16 110.474Cs+1 304 568.696Eu+1 472 127.644Gd+964 010.283Hf-50 768.128Hg+39 536 153.662Ho+866.588Li+22 257 712.334Lu+1.052Mg+463 365.348 Nb-31 778.414 Nd+6 425.940 Ni+0.327P-690 238.311Pd-25 929.288Pt+74 611.976Sb+208 160.599Se-1 743 397.542Sm+14.328Sr-190 086.622Ta-69 455 150.859Tb+4 536 345.869Tl-25 627 513.879Tm+7 204.380V+61 297.027 W-369.513Zn-134 206.331Zr-809.833

Y(红寺堡)=32.836B+448 278.463Be+6 750 843.105Cd+146 736.913Ce+69 454.159Co-11 814.754Cr+23 634.623Cs+537 016.749Eu-118 0561.536Gd+1 878 133.812Hf-45 892.201Hg+24 555 887.982Ho+1 546.511Li+117 135 919.633Lu+4.173Mg+468 049.913 Nb-43 943.399 Nd+19 692.023 Ni-1.077P-848 514.379Pd-44 505.898Pt+163 041.563Sb+288 296.671Se+3 740 869.725Sm-152.677Sr-466 138.102Ta-163 792 849.096Tb+8 077 615.387Tl-39 371 075.920Tm+70 314.297V+111 426.093 W-690.889Zn-120 818.003Zr-2 220.872

采用57种特征元素作为自变量,产区作为分类标准,通过Fisher线性判别分析方法对5个产区的赤霞珠葡萄酒样本进行产地溯源。如图1所示,同产区的葡萄酒均聚集在一起,有明显的地域性。结果表明,通过33个元素指标,可以很好地将宁夏5个小产区的赤霞珠葡萄酒判别出来,回代检验的结果显示可以完全将不同产区的葡萄酒判别出来,且回代检验的正确判别率为100%,交叉检验的结果显示仅有红寺堡产区的一个赤霞珠酒样被误判到永宁产区,交叉检验的判别正确率为98.7%,推测是两个产区地理位置相邻的原因(表6)。由此看出,基于矿质元素技术,可以很好地判别宁夏不同核心小产区的赤霞珠葡萄酒。

图1 宁夏不同产区赤霞珠葡萄酒散点图
Fig.1 Scattered map of Cabernet Sauvignon wines from different regions of Ningxia

表6 不同产区赤霞珠葡萄酒的一般判别分析结果
Table 6 Results of general discriminant analysis of Cabernet Sauvignon wines from different regions

产区预测组成员信息贺兰银川永宁青铜峡红寺堡整体判别正确率/%回代检验/%贺兰(n=15)100.00000银川(n=15)0100.0000永宁(n=15)00100.000青铜峡(n=18)000100.00红寺堡(n=15)0000100.0100.0交叉检验/%贺兰(n=15)100.00000银川(n=15)0100.0000永宁(n=15)00100.000青铜峡(n=18)000100.00红寺堡(n=15)006.7093.398.7

3 结论与讨论

本文通过分析宁夏贺兰山东麓5个葡萄酒核心小产区——银川、贺兰、青铜峡、红寺堡和永宁的78个赤霞珠葡萄酒样品中的57种矿质元素含量,发现Al、As、B等38种元素含量在宁夏5个葡萄酒产区间差异极显著(P<0.01),Bi、Co、Gd、Pb、Rb、Sn、Ti和W共8种元素含量在宁夏5个葡萄酒产区间差异显著(P<0.05),通过主成分分析提取出13个主成分,综合了57种矿质元素的86.931%的总信息。Fisher判别分析确定了B、Be、Cd等33种葡萄酒产区判别的有效溯源元素指标,构建了宁夏贺兰山东麓不同产区葡萄酒判别模型,该模型能较好地判别出宁夏不同核心小产区的葡萄酒,且回代判别正确率达到了100%,交叉检验正确率达到了98.7%。因此,矿质元素技术结合多元统计方法是追溯省(自治区)内地域尺度葡萄酒产区的有效方法。

本研究的结果与前人将矿质元素指纹技术应用在枸杞[23]、大米[24]、红枣[25]等农产品上溯源的结果一致,同时本研究弥补了国内对于小尺度地域葡萄酒产区溯源研究的空白,但对于省(自治区)内地域尺度内不同产区不同品种的葡萄酒以及不同产区混酿葡萄酒的判别有待进一步分析,在后续的研究工作中,将加大样本量对建立的判别模型的准确性进行修正和验证,同时探究矿质元素指纹技术在判别不同产区不同品种的葡萄酒以及不同产区混酿葡萄酒上的可行性,为宁夏贺兰山东麓葡萄酒产区溯源提供理论支撑以及数据支持。

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Appellation identification of wines from different small production areas in Ningxia based on mineral elements

CUI Zefang1, KAI Jianrong2, ZHANG Yuanke1, LIU Na3, JIANG Wenguang3, GE Qian2,MA Tingting4,5, FANG Yulin1,4*, SUN Xiangyu1,5*

1(College of Enology, Northwest A&F University, Yangling 712100, China)2(Ningxia Research Institute of Quality Standards and Testing Technology of Agricultural Products, Yinchuan 750002, China)3(Ningxia Changyu Longyu Estate Co.Ltd., Yinchuan 750002, China)4 (Shaanxi Provincial Key Laboratory of Viti-Viniculture, Yangling 712100, China)5(Shaanxi Engineering Research Center of Characteristic Fruit Directional Design and Machining, Yangling 712100, China)

ABSTRACT By detecting and analyzing the mineral element content of wines in different core small producing areas of Ningxia, combined with multivariate statistical analysis method, a regional scale wine producing area identification model based on mineral elements in the province (autonomous region) was established.In this study, Cabernet Sauvignon grapes were collected from five small production areas of Yinchuan, Helan, Yongning, Qingtongxia and Hongsipu in Ningxia, and 78 cabernet sauvignon wine samples were obtained by using the same method.The contents of 57 mineral elements such as B, Bi and Be were determined by ICP-MS, and the 57 mineral elements were analyzed by principal component analysis.The extracted principal components were used as input variables for LDA statistical analysis, and the origin discrimination model was constructed.The results showed that among the 57 mineral elements detected in wines, the contents of 46 elements were significantly different between different production areas in Ningxia (P<0.05).Through principal component analysis(PCA), 13 principal components were successfully extracted from the 57 mineral elements, and the total variance contribution reached 86.931%.Based on Fisher discriminant analysis method, a wine region identification model was established.The correct discriminant rates of back generation test and cross-verification were 100% and 98.7%, which realized the discrimination of wines from different small production areas in the geographical scale within Ningxia, and proved that the traceability technology of mineral elements could be used for the identification of wine production areas in the geographical scale within the province (autonomous region).

Key words mineral elements;wine;appellation identification;inductively coupled plasma mass spectrometry(ICP-MS);east foot of Helan Mountains

DOI:10.13995/j.cnki.11-1802/ts.040597

引用格式:崔泽方,开建荣,张媛珂,等.基于矿质元素差异的宁夏不同小产区葡萄酒的判别分析[J].食品与发酵工业,2025,51(13):311-319.CUI Zefang, KAI Jianrong, ZHANG Yuanke, et al.Appellation identification of wines from different small production areas in Ningxia based on mineral elements[J].Food and Fermentation Industries,2025,51(13):311-319.

第一作者:硕士研究生(房玉林教授与孙翔宇教授为共同通信作者,E-mail:fangyuliin@nwafu.edu.cn;sunxiangyu@nwafu.edu.cn)

基金项目:国家重点研发计划项目(2023YFD2100304-4);银川市科技计划项目科技人才专项(2023KJRC05);陕西省重点研发计划项目(2023-YBNY-176,2024QCY-KXJ-087,2024QCY-KXJ-083)

收稿日期:2024-07-29,改回日期:2024-09-07