Current advances on post-translational processing and related essential domains of drug transporters
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摘要:
药物转运体介导种类不同、结构各异的药物跨越细胞膜，影响药物在各组织器官中的浓度以及系统暴露量，不但是影响药效的关键因素，也是重要的药物−药物相互作用位点。人体中关键的药物转运体属于ABC结合盒 (ATP-binding cassette) 超家族或溶质载体 (Solute carrier) 超家族，分别介导细胞对药物的外排和吸收，两者协同作用，共同决定细胞内的药物浓度。作为膜蛋白，药物转运体在翻译后需要经过一系列复杂而精细的调控才能到达作用位点，发挥功能。此外，人体在药物的摄取过程中需要作出快速应对，因此往往以翻译后修饰的方式进行响应；而病理条件下转运体的功能也可能因细胞中各翻译后调控机制的非常态化而受到影响。明确药物转运体的翻译后处置过程，对于解析转运体药物转运的分子机制、阐明遗传多态性造成的个体药物响应差异有重要意义。本文对目前药物转运体的翻译后加工和修饰的相关研究进行了综述，也对在这些调控过程中发挥关键作用的转运体基序和位点进行了总结。
Abstract:Drug transporters mediate different types of drugs with diverse structures across cell membranes, affecting concentration of drugs in various tissues and organs and the systemic exposure. They are not only key factors that determine drug efficacy, but also important sites for drug-drug interaction. The major human drug transporters belong to the ATP-binding cassette (ABC) superfamily or the solute carrier superfamily, which mediate the efflux and absorption of drugs, respectively. These two kinds of transporters coordinate with each other and work in concert to determine intracellular drug concentrations. As membrane proteins, drug transporters need to go through a series of precise and complicated post-translational modifications before arriving at the site of action. Additionally, the human body needs to respond promptly during the intake of drugs and post-translational regulations hence become the manner of choice in such a process; The function of transporters may be affected by the abnormality of various post-translational regulatory mechanisms under pathological conditions as well. Therefore, a comprehensive understanding of the post-translational processing of drug transporters is of great importance for investigating the molecular mechanism(s) of drug transport and clarifying the inter-individual variability of drug response caused by genetic polymorphisms. The present article reviewed current reports on post-translational processing and modification of drug transporters, and summarized essential motifs and/or sites in transporters that play key roles in these regulatory processes.
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优质土壤是维持土壤生态系统生物的生产力、保护环境质量以及保证动植物健康的基础能力[1]。随着现代工业的快速发展，人类活动对土壤质量的影响正在加剧[2-3]，寻找更加便捷的土壤质量调查方法已引起大量学者的关注。可见−近红外光光谱(Visible-near infrared spectrum，VNIRS)是一种利用物质的某些官能团(如O—H、N—H等)对光的选择性吸收而快速评估其中某一项或几项成分含量的技术，与传统化学分析方法相比，它具有分析速度快、稳定、准确、样品制备简单且量小、无破损、节能环保等优势，在医药、食品、石油化工等行业得到广泛应用[4-7]。在农业科学中，研究学者已利用VNIRS技术进行了植物细根木质素，籽粒淀粉、蛋白质和氨基酸含量测定等[8-10]。由于土壤中几乎所有有机物质的主要组成和结构都能吸收特定波长的可见−近红外光，研究学者利用VNIRS技术进行了土壤碳储量估算、元素分析[11-12]、质量评估[13-14]、土壤团聚体来源辨别[15]和土壤分类及溯源等多方面的研究[16]。
土壤有机质具有较高的光谱响应属性，而氮、磷、钾等大量营养元素能够通过影响土壤的理化特性，从而影响土壤对光的吸收和反射特性。因此，研究人员对土壤有机质含量以及氮、磷和钾全量与光谱的关系进行了大量探讨，但是此前光谱分析的定量分析多用于常量范围内的物质分析[17-18]。赵明松等[19]和陈秋宇等[20]分别研究了江苏中部水稻土和潮土以及互花米草入侵湿地的土壤有机质的预测模型。Qiao等[21]用近红外光谱(Near infrared spectrum)技术分析了山西种植大豆褐土的有机质和全量养分含量，并利用主成分分析和最小二乘支持向量机(Least squares support vector machine)建立了土壤有机质含量与氮、磷、钾全量的预测模型。随着光谱分析仪器测试精度的提升，研究人员也开始逐渐关注光谱与土壤速效养分的关系，以期更加科学、快速地指导施肥。光谱学手段可以通过芳香碳的含量表征土壤可溶性有机质的组成和结构[22]，且红外光谱能够识别可溶性有机质中的有机官能团[23]，碱解氮含有C—H+C—H、C—H+C—C和N—H键的组合[24]，而速效磷虽然没有直接相关的光谱吸收峰，但是可以通过与其他有吸收峰物质之间的相关性被光谱技术预测[25]。张欣跃等[16]分析了黑龙江、安徽和江苏3个省的黑土、白浆土、草甸土、沼泽土、红壤、砂浆黑土、水稻土、潮土、棕壤、黄棕壤多种土壤指标的光谱特征，如pH，有机质、全磷、全钾、速效磷含量，阳离子交换量等。方向等[26]和汪六三等[27]利用VNIRS技术分别对皖南地区黄红壤和宁夏地区水稻土的碱解氮含量进行预测。李伟等[18]运用偏最小二乘法和人工神经网络算法建立了预测黑土土壤碱解氮、速效磷和速效钾含量的近红外光谱分析模型。
现有研究主要关注北方和长江中下游土壤[19-20]，鲜见华南地区土壤光谱评估模型的研究资料。华南地区处于热带亚热带地区，广泛分布着红壤、黄壤、赤红壤和砖红壤以及由这些土壤发育成的水稻土[28]。由于土壤性质存在高度空间异质性，不同地区土壤性质存在较大差异，因此华南区域土壤光谱评估无法借鉴北方和长江流域土壤的预测模型。广东省是华南地区的重要省份，粤东、粤西、粤北、珠三角、粤西北地区耕地的种植模式多样、变更较快，土壤质量变化大。因此，利用VNIRS构建模型快速分析不同区域的土壤养分能够及时掌握区域耕地土壤质量状况，保障全省粮食安全。另外，国内土壤学领域关于VNIRS的研究主要集中在借助国际土壤参比与信息中心(International Soil Reference and Information Center)提供的全球土壤光谱库(Global soil spectral library)进行全球或者跨省尺度下的有机质和肥力的光谱分析[29-31]，或是田间试验的小尺度研究[19, 21]。而在省域尺度下，针对华南地区的土壤光谱特征，构建有机质和养分VNIRS模型的研究仍缺乏。
因此，本研究以广东省不同地区514个耕地土壤样点为研究对象，通过传统化学分析方法和VNIRS技术对土壤有机质、全氮、可溶性有机碳、碱解氮和速效磷含量进行分析，并利用偏最小二乘法和主成分分析建立VNIRS预测模型，并进行反向验证，评估利用光谱分析土壤全量和速效养分含量的可行性，以期为快速检测广东省各地区耕地土壤养分、综合评价土壤质量、未来土壤监测和溯源普查提供科学参考。
1. 材料与方法
1.1 采样点分布与采样方法
本研究以广东耕地土壤为研究对象，土壤样品采自粤北、粤东、珠江三角洲、粤西北和粤西5个地区。具体采样区概况如表1所示。采用五点取样法在每个样点采集表面0~20 cm土层1 kg左右的土壤样品。共采集514份土壤样品，装入自封袋后按地区进行编号。土样在实验室自然风干后，挑除植物残体，研磨，过1 mm筛后，均匀分成两部分，一部分用作VNIRS扫描，另一部分粉碎过0.15 mm筛供实验室分析。
表 1 采样区概况Table 1. Information about sampling pots地区
Region行政市
Administrative city样点数量
Sampling point number气候类型
Climate type土壤类型
Soil type粤北
North Guangdong韶关 100 亚热带季风气候 以红壤为主，风化程度深，富铁铝化作用明显，质地较黏重 粤东
East Guangdong梅州 102 亚热带季风气候 以赤红壤、黄壤为主 粤西
West Guangdong湛江 98 热带北缘季风气候 有赤红壤、砖红壤，土层深厚，质地黏重，呈强酸性反应，铁铝富集最为显著 粤西北
Northwest Guangdong肇庆 99 南亚热带季风气候 以赤红壤为主，农田土壤整体偏酸 珠三角
Pearl River Delta惠州 65 亚热带季风气候 有红壤、赤红壤和黄壤，以赤红壤类型分布最广，土壤呈酸性，阳离子交换量较低，矿质养分较贫乏 珠海 50 亚热带季风海洋性
气候有红壤和赤红壤，红壤面积较少，分布不广，300 m以上的丘陵台地多分布有红壤 1.2 土壤养分测试及光谱扫描
土壤有机质含量采用土壤有机碳含量换算得到，后者采用重铬酸钾容量法−外加热法测定；全氮含量采用半微量开氏法测定；碱解氮含量采用NaOH碱解扩散法测定；速效磷含量采用0.5 mol·L−1 NaHCO3浸提−钼锑抗比色法测定；可溶性有机碳含量采用重铬酸钾氧化−硫酸亚铁滴定法测定；土壤pH采用以水、土体积、质量比5∶1浸提的pH计(雷磁PHS-3C)法测定[32]；土壤VNIRS使用福斯多功能近红外分析仪NIRS DS 2500采集，设置波长范围为400~2 490 nm，采样间隔为2 nm，对每个土壤样品重复扫描2次，最终的光谱数据取2次扫描结果的平均值。
1.3 土壤光谱数据的预处理及定标模型的建立
将各地区的土壤样品根据各指标的实验室分析结果按从小到大的顺序排序，每5个点抽取1个样品用于反向检验模型，剩余的土壤样品用于建立定标方程模型，即80%的土壤样品用作建模集，剩下的20%用作验证集。将样品的光谱数据和实验室测定的理化数据导入WinISI软件进行预处理及定标模型的建立。使用主成分分析结合马氏距离(Principle component analysis-mahalanobis distance，PCA-MD)进行异常样本检查。采用SNV+Detrend和一阶微分变换进行预处理，SNV能够将光谱数据标准化，而Detrend能够去除光谱散射现象，一阶微分能将原始光谱吸收峰分离同时消除极限漂移。本研究采用主成分回归法结合改进偏最小二乘法建立多元回归模型，共建立5×5个定标方程模型。
使用WinISI软件建立的定标模型，其定标结果包括定标标准误差(Standard error of calibration，SEC)；定标相关系数(Coefficient of determination for validation，RSQ)，表示近红外预测数据和实测数据的相关系数；交互验证标准误差(Standard error of cross validation，SECV)，用来预测没有参与定标样品的近红外值与化学分析值的标准误差平均值；交叉验证相关系数(1 minus the variance ration，1-VR)，用来预测没有参加定标样品的近红外值与化学分析值相关系数的平均值。通过这4种统计数据对建立的定标模型进行评估，其中SECV和1-VR用来表征定标方程的优劣，SECV越低、1-VR越接近1的模型，即为最佳定标模型。
1.4 数据处理
采用Microsoft Excel 2010进行数据整理，用R语言进行数据分析及绘图，采用单因素方差分析比较不同地区土壤养分含量的差异，多重比较采用Duncan’s检验，剔除同地区样品中超过平均值200%的异常值，显著性水平为0.05，用R语言的ggplot2包进行绘图，通过在R语言中导入ADE-4软件包对不同地区土壤理化数据进行主成分分析及绘图。
2. 结果与分析
2.1 不同地区耕地土壤养分的差异
实验室分析所得广东省各地区土壤养分含量如图1所示。各地区土壤有机质含量的排序为粤西北 > 粤东 > 粤北 > 珠三角 > 粤西，分别为35.22、29.25、24.53、23.49和21.82 g·kg−1，其中粤北、粤西和珠三角的差异不显著，粤西北地区有机质含量显著高于其他地区(P < 0.05)，分别比粤西、粤东地区高61.44%和20.42% (图1A)。各地区土壤全氮含量排序与有机质含量相同，粤西北地区土壤的全氮含量高于其他地区，达到1.70 g·kg−1，粤西地区的全氮含量最低，只有1.02 g·kg−1，而粤东和粤西北以及粤北和珠三角之间的差异不显著(图1B)。不同地区土壤的可溶性有机碳含量排序为粤西 > 粤北 > 粤东 > 粤西北 > 珠三角，珠三角地区的土壤可溶性有机碳含量只有65.62 mg·kg−1，显著低于其他地区(P < 0.05)，比最高的粤西地区低50.14%，其他地区土壤可溶性有机碳含量为86.18~98.53 mg·kg−1，差异不显著(图1C)。不同地区土壤碱解氮含量的排序为粤西北 > 粤东 > 粤北 > 珠三角 > 粤西，变化范围为120.42~169.47 mg·kg−1，粤西和珠三角地区的碱解氮含量分别比粤西北地区低40.73%和36.47% (图1D)。不同地区土壤的速效磷含量差异较明显，变化范围为44.81~97.15 mg·kg−1，速效磷含量最高的粤西地区比含量最低的粤西北地区高116.81%，粤北、粤东和珠三角地区的速效磷含量分别比粤西北地区高69.34%、34.77%和92.15% (图1E)。
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图 1 广东省不同地区土壤养分含量的差异NG：粤北，EG：粤东，WG：粵西，NWG：粤西北，PRD：珠三角；各小图中不同小写字母表示不同地区间差异显著(P < 0.05，Duncan’s法)Figure 1. Differences of soil nutrient contents in different areas of Guangdong ProvinceNG: North Guangdong, EG: East Guangdong, WG: West Guangdong, NWG: Northwest Guangdong, PRD: Pearl River Delta; Different lowercase letters in each figure indicate significant differences among different areas (P < 0.05, Duncan’s method)根据广东省5个地区土壤养分含量的主成分分析结果，第一主成分(PC1)和第二主成分(PC2)累计方差贡献率达到65.0%，可以反映不同土壤养分的大部分信息(图2A)。其中PC1的贡献率为43.9%，其主要与有机质、全氮、碱解氮有关，而PC2的贡献率为21.1%，其主要与可溶性有机碳有关。从各地区的主成分得分图(图2B)可以看出，粤西北、粤东、粤北和粤西地区的得分差异主要体现在PC1上，粤西北的养分偏向有机质、全氮和碱解氮较高的方向，粤西偏向于速效磷较高的方向，粤东和粤北的各养分处于居中水平，珠三角地区偏向于可溶性有机碳的水平较低，其他养分含量处于中间水平。
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图 2 广东省各地区耕地土壤养分主成分分析SOM：土壤有机质；DOC：可溶性有机碳；TN：全氮；AN：碱解氮；AP：速效磷Figure 2. Principal component analyses of soil nutrients in different areas of Guangdong ProvinceSOM: Soil organic matter; DOC: Dissolved organic carbon; TN: Total nitrogen; AN: Available nitrogen; AP: Available phosphorus2.2 定标方程的评价
广东省各地区的红外−近红外光谱定标模型的评价参数如表2所示。有机质定标模型的总体效果较好，尤其是粤西北和珠三角的模型，定标相关系数RSQ分别达到0.831 1和0.817 2，交叉验证相关系数1-VR也分别达到0.735 8和0.729 5；而粤北和粤东地区的定标效果较差，其交叉验证相关系数1-VR只有0.406 5和0.337 7，低于其他地区。广东各地区土壤全氮的定标效果也较好，定标相关系数RSQ都在0.65以上；粤北、粤东和珠三角地区的RSQ都超过0.70，1-VR也在0.60以上，且交叉验证误差SECV都处于相对较低的水平，说明其定标模型稳定性较好，其中粤北的模型最佳(RSQ = 0.898 6，1-VR = 0.731 9)。对于可溶性有机碳而言，粤北地区的定标模型效果远超其他地区，RSQ和1-VR都远高于其他地区，SECV也低于其他地区；其他4个地区模型的RSQ都低于0.50，其中珠三角地区的RSQ低至0.132 6，1-VR也小于0。对于土壤碱解氮与速效磷的定标模型，珠三角地区的模型效果最好，RSQ分别为0.820 0和0.694 9，1-VR为0.685 0和0.546 2；粤北、粤东和粤西地区的效果较差，RSQ都低于0.50，1-VR也在0.40以下。
表 2 广东省各地区的土壤养分模型预测效果1)Table 2. Prediction performance of soil nutrient models in different areas of Guangdong Province指标
Index地区
AreaSEC RSQ SECV 1-VR 有机质
Soil organic matter粤北 4.260 2 0.662 2 5.666 3 0.406 5 粤东 10.191 2 0.476 4 11.699 5 0.337 7 粤西 3.803 4 0.837 8 7.071 8 0.595 1 粤西北 3.985 1 0.831 1 5.032 2 0.735 8 珠三角 5.057 6 0.817 2 6.123 1 0.729 5 全氮
Total nitrogen粤北 0.107 4 0.898 6 0.180 9 0.731 9 粤东 0.295 9 0.784 0 0.382 5 0.646 6 粤西 0.219 8 0.660 5 0.347 6 0.336 7 粤西北 0.245 3 0.704 7 0.310 4 0.540 2 珠三角 0.245 4 0.789 8 0.308 9 0.677 0 可溶性有机碳
Dissolved organic carbon粤北 24.437 4 0.694 1 26.896 4 0.641 1 粤东 31.206 8 0.141 4 34.763 2 0.043 9 粤西 25.987 2 0.471 5 30.433 7 0.324 5 粤西北 31.937 4 0.281 6 33.930 4 0.216 6 珠三角 27.896 4 0.132 6 30.310 7 −0.021 7 碱解氮
Available nitrogen粤北 61.912 7 0.108 7 72.340 4 −0.040 3 粤东 50.192 4 0.323 6 57.612 0 0.148 8 粤西 42.216 6 0.380 4 53.081 4 0.130 4 粤西北 19.314 8 0.731 4 25.338 4 0.536 7 珠三角 20.557 2 0.820 0 27.365 0 0.685 0 速效磷
Available phosphorus粤北 15.325 8 0.225 1 18.882 4 0.077 8 粤东 24.353 7 0.070 1 27.115 0 −0.040 5 粤西 31.471 9 0.482 7 34.417 0 0.374 3 粤西北 18.240 2 0.647 6 28.404 3 0.400 4 珠三角 28.844 6 0.694 9 35.797 0 0.546 2 1) SEC：定标标准误差；RSQ：定标相关系数；SECV：交互验证标准误差；1-VR：交叉验证相关系数
1) SEC: Standard error of calibration; RSQ: Coefficient of determination for validation; SECV: Standard error of cross validation; 1-VR: 1 minus the variance ration总体而言，不同地区和不同指标之间的定标模型效果存在较大差异。粤北地区的全氮和可溶性有机碳的红外−近红外光谱定标效果较好，碱解氮和速效磷的模型还有待提高；而粤东地区只有全氮的定标效果较佳，粤西地区的总体效果都不佳；粤西北地区有机质的定标模型优于其他指标，全氮、碱解氮和速效磷的效果都一般；珠三角地区除了可溶性有机碳的定标效果较差，其他养分的定标模型都具有较好效果。
2.3 定标模型的反向验证
广东省各地区土壤养分含量的实测值(x)与红外−近红外光谱模型预测值(y)的相关性如图3所示。各地区土壤有机质含量实测值和预测值的相关性较好，粤北、粤西北和珠三角地区的决定系数(R2)都达到0.65以上，而粤东和粤西地区效果稍差，验证结果与定标模型的SECV结果相似。全氮验证模型的总体效果一般，珠三角的全氮验证模型效果最佳，R2达到0.65，其次是粤东和粤北地区，R2分别为0.50和0.49，粤西和粤西北的验证效果较差，验证结果与定标模型的1-VR有相似趋势。而可溶性有机碳的验证模型跟定标模型结果相符，只有粤北地区的模型具有较好的效果。粤西北和珠三角地区的碱解氮验证模型的R2分别有0.63和0.62，具有较好的线性关系，跟定标模型的SECV和1-VR结果相符。在速效磷的验证模型中，不同地区差异较大，粤北、粤东和粤西北地区的速效磷含量主要集中在0~80 mg·kg−1，而粤西和珠三角地区的速效磷分散在0~200 mg·kg−1；其定标模型的总体效果一般，而验证模型也呈现相同的结果，粤北、粤东和粤西地区的R2都低于0.1。
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图 3 广东省不同地区土壤养分含量实测值与VNIRS预测值的相关性Figure 3. Correlation between measured and VNIRS predicted values of soil nutrient contents in different regions of Guangdong Province总体而言，利用红外－近红外光谱定标模型得到的预测值与土壤有机质的实测值之间具有较好的相关性，全氮、可溶性有机碳和碱解氮则呈现出地区差异，而速效磷预测值与实测值的相关性较小。
3. 讨论与结论
3.1 VNIRS光谱特征能够溯源不同地区土壤
本研究采用传统分析手段和VNIRS 2种方式进行不同地区土壤有机碳和养分含量的分析。传统化学分析手段能够明显区分广东省各地区间的土壤有机质、全氮、可溶性有机碳、碱解氮和速效磷含量的差异。进一步对其进行主成分分析能够辨识不同地区养分差异的总体特征与关键影响因素；粤西北和粤东地区耕地的养分含量总体较高与土壤有机质、全氮、碱解氮密切相关，粤西地区耕地养分含量总体较低，但较高的速效磷含量是其区别于其他地区土壤的关键因素。这可能与广东不同地区的土壤类型、气候条件、地形地貌、耕作制度以及山地、丘陵、平原的不同施肥措施有关[33]。在本研究中，广东省不同地区的VNIRS光谱特征也存在较大的差异。通过VNIRS分析能够区分省域内不同土壤的来源，这与张欣悦等[16]进行的安徽省内县级尺度上土壤溯源模型验证的研究结果一致。利用不同县土壤的光谱特征与化学成分分别建模的判别精度差异只有0.1，进一步表明利用VNIRS分析可以进行土壤溯源，未来其在土壤分类研究方面可能具有一定的潜力。
3.2 VNIRS对不同元素的预测模型效果及精度差异明显
目前的研究表明，应用光谱模型预测土壤有机质和全氮等成分含量是可行的，但是预测的效果和精度有所差异[29-33]。本研究结果与前期研究相似，利用VNIRS技术对土壤碳、氮2种全量养分进行预测具有较好的效果，RSQ最高分别达到0.837 8和0.898 6，利用模型对剩余样品进行预测，样品预测值与实测值之间的R2最高分别达到0.69和0.65。土壤有机质预测效果较好是由于有机质含有C—C、C—H、N—H键以及这些键的组合，如702 nm波长与有机基团羟基(ROH)和甲基(—CH3)有关，742 nm波长与有机基团次甲基(—CH)有关，1 062 nm波长与有机基团芳烃(ArCH)有关，在VNIRS波长范围内有直接相关性[24]。全氮的预测效果较好是因为含有N—H键及N—H键组合基团，在VNIRS波长范围内具有直接相关性[34]。Mouazen等[17]发现，利用多种数学建模方式结合，能够有效提高光谱模型的预测精度。另外，土壤类型的复杂性可能在一定程度上影响有机质和全氮测定的精确性。王昶[35]和Ben-dor等[36]进行单种类型土壤有机碳和全氮的光谱模型预测，获得了更高的预测精度。
由于大多数土壤速效养分在可见−红外光波段没有直接的吸收峰，目前关于速效养分的研究主要通过各种数学建模手段探究其与其他拥有吸收峰物质之间的关系，从而达到预测的目的[24, 26-27]。但是大多数研究的建模效果不佳，在本研究中，部分地区的速效养分的预测效果达到较好的水平。土壤对可见−近红外光的吸收主要依靠有机成分和矿物的作用，而土壤的复杂性和不稳定性导致地域间各种因素都可能对光谱的吸收产生影响。广东地区土壤风化淋溶作用强，耕作强度高，养分贫瘠，耕地有效养分的补充主要依靠施肥[37]。地区间施肥强度差异大可能是导致可溶性有机碳、碱解氮和速效磷3种有效态养分只在部分区域具有较好预测效果的重要原因。另外，有研究表明，当土壤速效养分含量超过一定阈值后，其光谱预测效果也随之提升[38]。同时，土壤有效态养分的测试精度跟土壤类型、前处理方式、试验分析手段和仪器精度也有重要关系，采用高标准的采样分析技术有利于建立更好的模型。
3.3 VNIRS分析在土壤质量监测、普查中的应用
广东是我国城市化水平较高、经济发展迅速的大省，人口、资源、环境与社会经济发展矛盾极其突出，高质量耕地土壤对我省粮食安全与生态安全极其重要。借助VNIRS分析手段，能够在短时间快速、无损地分析土壤养分含量，对不同时间，不同市域、县域、流域等中观尺度的土壤质量进行估算，有助于适时调控耕地土壤田间管理。同时，该技术也可以应用于土壤普查等相关工作。土壤普查常采取传统的土壤质量指标分析方法，这种分析方法虽然比较精准，但具有检测项目繁杂、成本高、实时性差、检测速度慢等不足，对于快速检测大范围的土壤需要耗费大量资源。通过采集较少样品进行VNIRS分析后，可以结合少量样品的传统分析结果建立模型，由此可估算其他样品在调查区域土壤养分及有机质含量等指标的现状及变化趋势。在此基础上，结合大数据分析和地理信息技术，可以全面掌握全省土壤质量等级，提升土壤资源保护和利用水平。
3.4 结论
本研究通过传统化学分析方法和VNIRS技术对广东典型地区的耕地土壤进行了分析，构建了土壤有机质、全氮、可溶性有机碳、碱解氮、速效磷含量的VNIRS预测模型，并评估了利用光谱分析预测土壤全量和速效养分的可行性。广东省各地区耕地土壤性质及光谱特征存在显著差异，有机质、全氮含量处于中等水平，碱解氮和速效磷含量较高，总体而言粤西北和粤东地区的养分含量较高，粤西地区较低；利用VNIRS定标模型能够较好地预测土壤有机质和全氮含量，但不同地区适用的预测模型有所差异；利用VNIRS技术对有效态养分含量进行预测在部分地区具有较好效果，建议未来通过提高样品质量和测试精度、完善模型的建立方法等提升预测效果。
致谢：感谢李永涛教授，孙岩博士，李明惠、崔莹莹、姜敏等研究生们在样品收集、前期样品制备中提供的帮助！
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