Geographic classification of spanish and Australian tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis

Visible (vis) and near-infrared (NIR) spectroscopy combined with multivariate analysis was used to classify the geographical origin of commercial Tempranillo wines from Australia and Spain. Wines (n = 63) were scanned in the vis and NIR regions (400-2500 nm) in a monochromator instrument in transmission. Principal component analysis (PCA), discriminant partial least-squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) based on PCA scores were used to classify Tempranillo wines according to their geographical origin. Full cross-validation (leave-one-out) was used as validation method when PCA and LDA classification models were developed. PLS-DA models correctly classified 100% and 84.7% of the Australian and Spanish Tempranillo wine samples, respectively. LDA calibration models correctly classified 72% of the Australian wines and 85% of the Spanish wines. These results demonstrate the potential use of vis and NIR spectroscopy, combined with chemometrics as a rapid method to classify Tempranillo wines accordingly to their geographical origin.     

基于光谱特征参数的温室番茄叶片叶绿素含量预测

为了快速、准确估测温室番茄叶片叶绿素含量,提升作物精细管理水平,利用光谱分析技术研究了温室番茄不同生长阶段叶绿素含量和响应光谱的相关性,在幼苗营养生长阶段叶片叶绿素含量呈增长趋势,到移植50天前后达到最大值,在此期间反射光谱的红边会向红外方向（长波）偏移,同时绿峰向蓝光（短波）方向偏移,绿峰幅值减小。从结果期开始叶绿素含量呈下降趋势,而红边、绿峰及绿峰幅值向相反方向变化。为了定量分析叶绿素含量和叶片反射光谱间的关系,从自定义的68个光谱特征参数中提取了7个能反映叶绿素含量变化的最优参量,并使用逐步回归、岭回归、主成分分量回归和偏最小二乘回归消除了最优参量的多重共线性,建立了叶绿素含量预测模型,其中岭回归模型精度最佳,均方根误差（RMSE）为0.406,决定系数(R2)为0.839。     

NIR‐FT/Raman Spectroscopy for Nutritional Classification of Cereal Foods

The classification of cereals using near‐infrared Fourier transform Raman (NIR‐FT/Raman) spectroscopy was accomplished. Cereal‐based food samples (n = 120) were utilized in the study. Ground samples were scanned in low‐iron NMR tubes with a 1064 nm (NIR) excitation laser using 500 mW of power. Raman scatter was collected using a Ge (LN₂) detector over the Raman shift range of 202.45~3399.89 cm^‐1. Samples were classified based on their primary nutritional components (total dietary fiber [TDF], fat, protein, and sugar) using principle component analysis (PCA) to extract the main information. Samples were classified according to high and low content of each component using the spectral variables. Both soft independent modeling of class analogy (SIMCA) and partial least squares (PLS) regression based classification were investigated to determine which technique was the most appropriate. PCA results suggested that the classification of a target component is subject to interference by other components in cereal. The Raman shifts that were most responsible for classification of each component were 1600~1630 cm^‐1 for TDF, 1440 and 2853 cm^‐1 for fat, 2910 and 1660 cm^‐1 for protein, and 401 and 848 cm^‐1 for sugar. The use of the selected spectral region (frequency region) for each component produced better results than the use of the entire region in both SIMCA and PLS‐based classifications. PLS‐based classification performed better than SIMCA for all four components, resulting in correct classification of samples 85~95% of the time. NIR‐FT/Raman spectroscopy represents a rapid and reliable method by which to classify cereal foods based on their nutritional components.     

Optimised use of data fusion and memory-based learning with an Austrian soil library for predictions with infrared data

Infrared spectroscopy in the visible to near-infrared (vis–NIR) and mid-infrared (MIR) regions is a well-established approach for the prediction of soil properties. Different data fusion and training approaches exist, and the optimal procedures are yet undefined and may depend on the heterogeneity present in the set and on the considered scale. The objectives were to test the usefulness of partial least squares regressions (PLSRs) for soil organic carbon (SOC), total carbon (C_t), total nitrogen (N_t) and pH using vis–NIR and MIR spectroscopy for an independent validation after standard calibration (use of a general PLSR model) or using memory-based learning (MBL) with and without spiking for a national spectral database. Data fusion approaches were simple concatenation of spectra, outer product analysis (OPA) and model averaging. In total, 481 soils from an Austrian forest soil archive were measured in the vis–NIR and MIR regions, and regressions were calculated. Fivefold calibration-validation approaches were carried out with a region-related split of spectra to implement independent validations with n ranging from 47 to 99 soils in different folds. MIR predictions were generally superior over vis–NIR predictions. For all properties, optimal predictions were obtained with data fusion, with OPA and spectra concatenation outperforming model averaging. The greatest robustness of performance was found for OPA and MBL with spiking with R² ≥ 0.77 (N), 0.85 (SOC), 0.86 (pH) and 0.88 (C_t) in the validations of all folds. Overall, the results indicate that the combination of OPA for vis–NIR and MIR spectra with MBL and spiking has a high potential to accurately estimate properties when using large-scale soil spectral libraries as reference data. However, the reduction of cost-effectiveness using two spectrometers needs to be weighed against the potential increase in accuracy compared to a single MIR spectroscopy approach.     

Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis–NIR spectral library

We need to determine the best use of soil vis–NIR spectral libraries that are being developed at regional, national and global scales to predict soil properties from new spectral readings. To reduce the complexity of a calibration dataset derived from the Chinese vis–NIR soil spectral library (CSSL), we tested a local regression method that combined geographical sub‐setting with a local partial least squares regression (local‐PLSR) that uses a limited number of similar vis–NIR spectra (k‐nearest neighbours). The central idea of the local regression, and of other local statistical approaches, is to derive a local prediction model by identifying samples in the calibration dataset that are similar, in spectral variable space, to the samples used for prediction. Here, to derive our local regressions we used Euclidean distance in spectral space between the calibration dataset and prediction samples, and we also used soil geographical zoning to account for similarities in soil‐forming conditions. We tested this approach with the CSSL, which comprised 2732 soil samples collected from 20 provinces in the People's Republic of China to predict soil organic matter (SOM). Results showed that the prediction accuracy of our spatially constrained local‐PLSR method (R² = 0.74, RPIQ = 2.6) was better than that from local‐PLSR (R² = 0.69, RPIQ = 2.3) and PLSR alone (R² = 0.50, RPIQ = 1.5). The coupling of a local‐PLSR regression with soil geographical zoning can improve the accuracy of local SOM predictions using large, complex soil spectral libraries. The approach might be embedded into vis–NIR sensors for laboratory analysis or field estimation.     

Effect of overexpression of fatty acid 9-hydroperoxide lyase in tomatoes (Lycopersicon esculentum Mill.).

To modify the flavor properties of tomato fruits, cucumber fatty acid hydroperoxide lyase (HPL), which can act on 9-hydroperoxides of fatty acids to form volatile C9-aldehydes, was introduced to tomato plants. Through enzyme assay, high activity of the introduced HPL could be found in either the leaves or fruits of transgenic tomatoes; however, the composition of volatile short-chain aldehydes and alcohols in the transgenic tomato fruits was little modified. This was unexpected because tomato fruits have high lipoxygenase activity to form 9-hydroperoxides. When linoleic acid was added to a crude homogenate prepared from the transgenic tomato fruits, a high amount of C9-aldehyde was formed, but the amount of C6-aldehyde was almost equivalent to that in nontransgenic tomatoes. Through quantification of fatty acid hydroperoxides, it has been revealed that 13-hydroperoxides of fatty acids are preferably formed from endogenous substrate, whereas 9-hydroperoxides are formed from fatty acids added exogenously. From these observations, possible mechanisms to regulate metabolic flow of the lyase pathway are discussed.     

利用近红外光谱与PCA-SVM识别热损伤番茄种子

为了研究近红外光谱技术用于热损伤种子快速无损识别的可行性,该文以120粒番茄种子为研究对象,其中60粒番茄种子通过高温加热处理的方式成为热损伤种子组,其他60粒番茄种子为正常种子组,利用实验室自主搭建的近红外光谱检测系统获取单粒番茄种子在980~1 700 nm范围内的光谱,分别采用偏最小二乘判别法(partial least squares discriminant analysis,PLS-DA)和支持向量机(support vector machines,SVM)建立了番茄种子热损伤的定性分析模型。试验结果表明:2种判别模型的验证集总正确率均大于96%,均可用于热损伤种子的判别。其中,基于主成分分析(principal component analysis,PCA)预处理的光谱数据构建的支持向量机模型的判别效果最好,其校正集和验证集的判别正确率均为100%,更适用于种子热损伤识别。因此,应用近红外光谱技术可快速无损识别热损伤番茄种子,为种子检验提供了一种新的方法。     

不同施肥情况下甘蔗叶片的光谱特征分析及叶绿素含量检测

In order to investigate the visible-NIR spectral features of the leaves of a sugarcane variety(ROC 22), the spectral reflectance and the chlorophyll content were measured in the laboratory, and their correlations were analyzed.Prediction models were built eventually.Results showed that the negative correlations with r greater than 0.8 was found in 527-578 nm and 701-731 nm between spectral reflectance and chlorophyll content.And the red edge position(REP) was also found having high correlation with chlorophyll content, with the highest r of 0.8442.In order to explore the most sensitive bands for normalized difference vegetation index(NDVI), 48471 NDVI values were computed with different wavebands for each sample and their correlations with chlorophyll were also analyzed.The distribution maps of NDVI and its correlations in a two-spectral-dimension space both indicated that the red bands had significant influence than the NIR bands.The suggested sensitive red range was 710-735 nm, especially 720-725 nm; and the sensitive NIR range was from 780-850 nm, which had the higher robustness.The chlorophyll predication model with NDVI(725 and 840 nm) in tillering stage had determination coefficients of 0.7386, and was recommended for guiding the subsequent ridging fertilization.     

不同施肥情况下甘蔗叶片的光谱特征分析及叶绿素含量检测（英文）

In order to investigate the visible-NIR spectral features of the leaves of a sugarcane variety(ROC 22),the spectral reflectance and the chlorophyll content were measured in the laboratory,and their correlations were analyzed.Prediction models were built eventually.Results showed that the negative correlations with r greater than 0.8 was found in 527-578 nm and 701-731nm between spectral reflectance and chlorophyll content.And the red edge position(REP)was also found having high correlation with chlorophyll content,with the highest r of 0.8442.In order to explore the most sensitive bands for normalized difference vegetation index(NDVI),48471 NDVI values were computed with different wavebands for each sample and their correlations with chlorophyll were also analyzed.The distribution maps of NDVI and its correlations in a two-spectral-dimension space both indicated that the red bands had significant influence than the NIR bands.The suggested sensitive red range was 710-735 nm,especially720-725 nm;and the sensitive NIR range was from 780-850 nm,which had the higher robustness.The chlorophyll predication model with NDVI(725 and 840 nm)in tillering stage had determination coefficients of 0.7386,and was recommended for guiding the subsequent ridging fertilization.     

近红外透反射光谱测定单粒稻种的蛋白质含量

为探索NIR光谱技术在水稻种子蛋白质含量分析中的应用,本研究细致分析了单粒稻种在不同光谱采集方式下的近红外光谱(NIRS)特征,并利用离子束诱变育种得到的水稻9311突变体库的种子,建立准确性较好的单粒糙米和单粒稻种的蛋白质定量模型。结果表明,与漫反射光谱采集方式下的单粒糙米蛋白质模型相比,透反射和透射光谱采集方式下能得到相关性较好的糙米蛋白质模型,其中单粒糙米蛋白质最优定量模型的决定系数(R²)为0.97,预测均方根误差(RMSEP)为0.27%。在单粒稻种中,由于种壳的反射作用,漫反射光谱采集方式下依然无法建立准确性高的蛋白质模型,透反射光谱采集方式下能够建立具有一定预测能力的蛋白质定量模型(RMSEP=0.81%),透射光谱采集方式下能够建立准确性高的蛋白质定量模型(R²=0.96,RMSEP=0.24%)。本研究结果为无损快速分析单粒稻种提供了一种解决方法。     

Usefulness of near-infrared reflectance (NIR) spectroscopy and chemometrics to discriminate fishmeal batches made with different fish species

Near-infrared reflectance (NIR) spectroscopy combined with chemometrics was used to identify and authenticate fishmeal batches made with different fish species. Samples from a commercial fishmeal factory (n = 60) were scanned in the NIR region (1100-2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), dummy partial least-squares regression (DPLS), and linear discriminant analysis (LDA) based on PCA scores were used to identify the origin of fishmeal produced using different fish species. Cross-validation was used as validation method when classification models were developed. DPLS correctly classified 80 and 82% of the fishmeal samples. LDA calibration models correctly classified >80% of fishmeal samples according to fish species The results demonstrated the usefulness of NIR spectra combined with chemometrics as an objective and rapid method for the authentication and identification of fish species used to manufacture the fishmeal.     

基于光谱特征分析的苹果树叶片营养素预测模型构建

该文旨在利用光谱分析技术建立高精度苹果叶片营养素预测模型,为苹果树的精细管理提供技术支持。在苹果树年度生长周期的坐果期、生理落果期和果实成熟期等重要物候期,采集了180个果树叶片样本并测量了果树叶片在可见光和近红外波段的反射光谱,同时在实验室采用化学方法获取了果树叶片的氮素以及叶绿素含量。对于聚类后样本,分别分析了果树叶片反射光谱以及经小波滤波后的反射光谱与叶绿素以及氮素之间的相关关系,而后利用偏最小二乘和支持向量机（SVM,support vector machine）方法分别建立了果树叶片叶绿素和氮素含量的回归模型。研究发现,随着生长阶段的推进,在可见光处的反射率逐渐升高,在近红外处的反射率逐渐降低,且基于小波滤波反射光谱的营养素SVM回归模型精度最高：建立的叶绿素回归模型,其测定系数R2达到0.9920,均方根误差 RMSE为0.0039,验证精度R2达到0.9036,RMSE为0.1979;建立的氮素回归模型,其测定R2和验证R2也达到0.74以上,模型的回归RMSE为0.0554,验证RMSE为0.1215。结果表明,采用支持向量机回归模型可以精确估计果树叶片叶绿素含量,对氮素含量的估计精度也达到了实用化水平。     

Assessment of the nutritional values of genetically modified wheat, corn, and tomato crops

The genetic modification in fruit and vegetables could lead to changes in metabolic pathways and, therefore, to the variation of the molecular pattern, with particular attention to antioxidant compounds not well-described in the literature. The aim of the present study was to compare the quality composition of transgenic wheat ( Triticum durum L.), corn ( Zea mays L.), and tomato ( Lycopersicum esculentum Mill.) to the nontransgenic control with a similar genetic background. In the first experiment, Ofanto wheat cultivar containing the tobacco rab1 gene and nontransgenic Ofanto were used. The second experiment compared two transgenic lines of corn containing Bacillus thuringiensis "Cry toxin" gene (PR33P67 and Pegaso Bt) to their nontransgenic forms. The third experiment was conducted on transgenic tomato ( Lycopersicum esculentum Mill.) containing the Agrobacterium rhizogenes rolD gene and its nontransgenic control (cv. Tondino). Conventional and genetically modified crops were compared in terms of fatty acids content, unsaponifiable fraction of antioxidants, total phenols, polyphenols, carotenoids, vitamin C, total antioxidant activity, and mineral composition. No significant differences were observed for qualitative traits analyzed in wheat and corn samples. In tomato samples, the total antioxidant activity (TAA), measured by FRAP assay, and the naringenin content showed a lower value in genetically modified organism (GMO) samples (0.35 mmol of Fe (2+) 100 g (-1) and 2.82 mg 100 g (-1), respectively), in comparison to its nontransgenic control (0.41 mmol of Fe (2+) 100 g (-1) and 4.17 mg 100 g (-1), respectively). On the basis of the principle of substantial equivalence, as articulated by the World Health Organization, the Organization for Economic Cooperation and Development, and the United Nations Food and Agriculture Organization, these data support the conclusion that GM events are nutritionally similar to conventional varieties of wheat, corn, and tomato on the market today.     

Fourier transform near-infrared spectroscopy application for sea salt quality evaluation

Near-infrared (NIR) spectroscopy in diffuse reflectance mode was explored with the objective of discriminating sea salts according to their quality type (traditional salt vs "flower of salt") and geographical origin (Atlantic vs Mediterranean). Sea salts were also analyzed in terms of Ca(2+), Mg(2+), K(+), alkalinity, and sulfate concentrations to support spectroscopic results. High concentrations of Mg(2+) and K(+) characterized Atlantic samples, while a high Ca(2+) content was observed in traditional sea salts. A partial least-squares discriminant analysis model considering the 8500-7500 cm(-1) region permitted the discrimination of salts by quality types. The regions 4650-4350 and 5900-5500 cm(-1) allowed salts classification according to their geographical origin. It was possible to classify correctly 85.3 and 94.8% of the analyzed samples according to the salt type and to the geographical origin, respectively. These results demonstrated that NIR spectroscopy is a suitable and very efficient tool for sea salt quality evaluation.     

Near-infrared spectroscopy for measurement of total dietary fiber in homogenized meals

Near-infrared (NIR) reflectance spectroscopy was investigated as a method for prediction of total dietary fiber (TDF) in mixed meals. Meals were prepared for spectral analysis by homogenization only (HO), homogenization and drying (HD), and homogenization, drying, and defatting (HDF). The NIR spectra (400-2498 nm) were obtained with a dispersive NIR spectrometer. Total dietary fiber was determined in HDF samples by an enzymatic-gravimetric assay (AOAC 991.43), and values were calculated for HD and HO samples. Using multivariate analysis software and optimum processing, partial least squares models (n = 114) were developed to relate NIR spectra to the corresponding TDF values. The HO, HD, and HDF models predicted TDF in independent validation samples (n = 37) with a standard error of performance of 0.93% (range 0.7-8.4%), 1.90% (range 2.2-18.9%), and 1.45% (range 2.8-23.3%) and r(2) values of 0.89, 0.92, and 0.97, respectively. Compared with traditional analysis of TDF in mixed meals, which takes 4 days, NIR spectroscopy provides a faster method for screening samples for TDF. The accuracy of prediction was greatest for the HDF model followed by the HD model.     

不同种类茶叶冲泡前后中红外光谱变化探析

为建立一种快速无损评价茶叶/茶汤化学品质和色泽的方法,本试验选取7种代表性茶叶样本为材料,利用傅里叶变换中红外光谱(FTIR)技术,以开水冲泡方式制备茶汤冻干粉,对茶叶与茶汤共14个样本进行FTIR光谱采集分析。通过聚类分析、光谱曲线拟合、定性分析,并结合常规化学法检测茶多酚、茶多糖、可溶性糖、游离氨基酸、主要儿茶素类和生物碱含量。结果表明,不同茶叶光谱明显不同,由于茶叶加工工艺造成的脂类、儿茶素类、咖啡碱、游离氨基酸及叶绿素含量差异,利用1 800~1 170 cm^-1狭窄区域的光谱特征,可实现茶叶分类。茶叶与对应茶汤的光谱也存在明显差异,体现了茶叶在冲泡过程中的物质变化。主要特征峰区(位)为:2 925/2 855 cm^-1代表脂类饱和烃链-CH₂振动,1 700 cm^-1峰位对应儿茶素类和咖啡碱,1 534 cm^-1对应着叶绿素脱镁过程,1 551/1 556 cm^-1和1 500/1 497 cm^-1为叶绿素中亚甲基桥的伸缩振动,1 516/1 517 cm^-1为木质素中的苯环C=C伸缩振动。本研究为市场中茶叶、新型速溶茶粉样品化学品质检测提供了一种有效、快速的无损分析方法。     

Nondestructive diagnostics of magnesium deficiency based on distribution features of chlorophyll concentrations map on cucumber leaf

Abstract

A new and nondestructive method for diagnosing magnesium (Mg) deficiency based on chlorophyll concentration distribution features of cucumber leaves was proposed. Mg deficient cucumber plants and Control plants were grown under non-soil conditions with special nutrient supply. Cucumber leaves were employed to collect hyperspectral images using a visible and near infrared (VIS/NIR) hyperspectral imaging system (400–900?nm) and determine reference chlorophyll concentrations using high performance liquid chromatography (HPLC). An optimal chlorophyll concentration calibration model (Rp = 0.9087) was constructed and used to detect chlorophyll distribution maps of Mg deficient leaves and Control leaves. Results shown that chlorophyll content distributed more unevenly on Mg deficient leaves than Control leaves. The Standard Deviation (SD) value of the chlorophyll content at all the pixels on a chlorophyll distribution map was calculated for Mg deficient diagnostics. An Mg deficiency diagnostics model with satisfied performance (diagnostic rate 93.33%) was obtained. The result indicated the SD value of chlorophyll concentrations on the whole cucumber leaf could be employed to diagnose Mg deficiency nondestructively.     

基质栽培番茄营养液中氮、钾最佳浓度研究

【目的】合理的氮、钾养分供给是提高番茄生长及果实品质的重要措施，本文研究了滴灌营养液中不同的氮、钾养分供给水平对基质栽培番茄生长及果实品质的影响，为优化基质栽培番茄的营养液配方，确定最佳的氮、钾养分浓度，实现养分供给的精量化管理提供科学依据。【方法】以沙∶珍珠岩比例为1∶2配置栽培基质，用于温室中番茄栽培，以番茄‘A20’为试材，进行了水培试验。采用2因素 (氮、钾) 5水平响应面中心复合设计，滴灌营养液中氮浓度的基础值为244 mg/L，试验设计步长为120 mg/L；钾浓度的基础值为313 mg/L，试验设计步长为150 mg/L。调查了番茄叶片叶绿素含量、净光合速率、单株产量、果实可溶性糖含量、可滴定酸含量、糖酸比、维生素C含量和番茄红素含量。【结果】随着滴管液中氮浓度从74 mg/L增加到414 mg/L，番茄产量、叶片叶绿素含量、叶片净光合速率、果实可溶性糖含量、糖酸比、番茄红素含量和果实维生素C含量均呈先增后减的趋势。随着营养液中钾浓度从101 mg/L增加到525 mg/L，果实可溶性糖含量、糖酸比和维生素C含量持续增加，番茄产量、叶绿素含量、净光合速率、番茄红素含量均先增后减。此外，通过建立各指标与氮钾二因子的二次回归方程发现，氮素是叶绿素含量、净光合速率和单株产量的主要影响因子，钾素是果实可溶性糖、可滴定酸、糖酸比、维生素C和番茄红素含量的主要影响因子。氮、钾互作显著影响番茄产量和叶片叶绿素含量；充足的钾营养供给可以促进植株对氮素的吸收与同化，提高叶片叶绿素含量，利于产量提高；适量的氮素供应有利于钾素的吸收与利用，促进产量的进一步提高。采用主成分分析方法对8种配施方案下番茄产量和品质的综合性能进行评价，结果显示营养液中氮、钾浓度分别为N 378 mg/L、K 391 mg/L时为最优配方方案，且番茄叶片净光合速率达到最大值。【结论】在沙子和珍珠岩1∶2 (v∶v) 为基质的番茄无土栽培条件下，滴灌营养液中氮、钾浓度分别为N 378 mg/L和K 391 mg/L时，番茄产量和品质的综合性能达到最优，该方案可在生产实践中为基质栽培番茄营养液精确管理提供一定的借鉴意义。     

基于可见/近红外反射光谱的梨树叶片钾含量的快速测定研究

利用可见/近红外反射光谱定量分析技术对梨树鲜叶钾素含量进行快速测定研究。对150个梨树叶片样本进行光谱扫描,其中120个做建模集,30个做验证集。通过对样品的可见/近红外光谱进行多种预处理,并建立钾素预测模型,探讨了可见/近红外光谱数据预处理对预测精度的影响。结果表明,通过原始光谱与S-G(3)平滑相结合的预处理方法,用17个主成分建立的偏最小二乘法模型最好,其交叉验证集和预测集模型的决定系数(R2)分别为0.722 7和0.679 1,交叉验证均方根误差(RMSECV)为1.171,预测的平均相对误差为6.81%,能高效、快速地预测梨树叶片钾素含量,为梨树钾素快速测定提供了新的手段。     

不同氮浓度对温室番茄生长发育和叶片光谱特性的影响

营养液培养法研究不同氮浓度对温室番茄的生长发育及叶片光谱特性的影响。结果表明,在01~5 mmol/L范围内,随着营养液中氮浓度的增加,番茄的叶面积指数、株高、叶片数、花穗数、产量和叶片的吸光度都增大,但是超过一定的范围,植株的生长和发育均会受到抑制,说明适宜的氮素促进了植株的生长和发育。其中,处理N15的叶面积指数达到了3.0,叶片数和花穗数分别是26.7和7.0,均高于其它处理。不同处理间番茄产量均存在极显著的关系,以N15处理最高,N5的最低。从叶片的光谱图中可提取出与叶绿素有关的特征波段。在本试验条件下,番茄水培时营养液适宜的氮浓度为15 mmol/L。