基于相关分析和偏最小二乘回归的黄绵土土壤全氮和碱解氮含量的高光谱预测

以采取植被恢复措施的陕西省吴起县为研究区,实地采集24个土壤剖面不同层次的黄绵土土样100个,在进行土壤样本全氮（TN）和碱解氮（AHN）含量及实验室反射光谱数据测量和分析的基础上,用相关分析（CA）结合偏最小二乘回归（PLS）方法建立黄绵土土壤TN和AHN含量的校正模型,并用独立样本对校正模型进行验证.结果表明： 利用6种光谱变换方式建立的校正模型中,微分光谱建立的校正模型是预测研究区土壤TN含量的最佳模型,校正和验证R²分别为0.929和0.935,均方根误差（RMSE）分别为0.045和0.047 g·kg^-1,相对预测偏差（RPD）为3.12;而归一化变换建立的校正模型是预测土壤AHN含量的最佳模型,校正和验证R²分别为0.873和0.773,RMSE分别为9.946和16.204 mg·kg^-1,RPD为1.538.所建立的全氮预测模型可以对0～40 cm土层的TN进行有效预测,而碱解氮的预测模型对同一深度只能进行粗略预测.本研究为采取植被恢复措施的退化生态系统区黄绵土土壤全氮的快速预测提供了一种较好的方法,但是对于碱解氮的准确、快速预测,需要进一步研究.     

不同林龄云南松林土壤有机碳和全氮积累特征

以贵州省盘县3种林龄(19、28和45年生)云南松林为对象,研究了林地土壤有机碳和全氮含量的垂直分布、积累特征及其与土壤容重的关系.结果表明: 不同林龄云南松林土壤剖面的有机碳和全氮含量变化规律一致,表层呈富集现象,随着土层的加深而逐渐减少.随着林龄的增加,林地土壤的有机碳和全氮储量增加,19、28和45年生林地土壤有机碳储量分别为96.24、121.65和148.13 t·hm^-2,全氮储量分别为10.76、12.96和13.08 t·hm^-2.土壤有机碳与全氮含量呈极显著正相关,二者均与土壤容重呈极显著负相关.不同生长阶段林地土壤有机碳和全氮的积累速率有所差异,其中19～28年生林地的土壤有机碳和全氮含量积累速率高于28～45年生林地.
     

霍林河流域湿地土壤碳氮空间分布特征及生态效应

对霍林河流域湿地土壤有机碳及全氮空问分布特征及其生态效应的研究表明,有机碳和全氮的水平分异和垂直分异都十分显著,干湿交替周期是引起分异的关键因子;表层土壤有机碳与全氮含量显著相关(r=0．977),土壤碳氮比基本沿湿度梯度变化;土壤pH值对土壤表层碳氮含量及碳氮比值影响显著;流域湿地土壤与流域草原土壤碳氮比与土壤碳氮含量的相关性差异显著;其生态效应主要表现在生产效应和净化效应两方面．     

若尔盖不同地下水位泥炭湿地土壤有机碳和全氮分布规律

以若尔盖典型泥炭湿地为研究对象,选择不同地下水位的样地,对各样地不同土壤剖面的土壤有机碳含量、全氮含量的分布规律及土壤容重、pH等影响因素进行了研究.结果表明:0 ～ 50 cm土层范围内,土壤有机碳含量和全氮含量与地下水位呈指数相关,随着水位的降低,土壤有机碳含量和全氮含量减少,且变异系数呈显著增加趋势,表明泥炭地排水、降低地下水位是泥炭地土壤有机碳和全氮丧失的重要原因.土壤有机碳和全氮含量间存在显著正相关,但与土壤pH和容重均呈极显著负相关.4个水位梯度下的土壤碳氮比的变动幅度在15.76 ～18.16,相对较低,表明在若尔盖气候条件下有机碳的分解速度较慢,而土壤碳氮比并没有受到地下水位和pH值的显著影响.     

基于DWT-GA-PLS的土壤碱解氮含量高光谱估测方法

以山东齐河县为研究区,实地采集土壤样本,在土样高光谱测试并进行一阶导数变换的基础上,先运用离散小波变换(DWT)对土壤光谱去噪降维,然后采用遗传算法(GA)筛选土壤碱解氮定量估测模型的参与变量,最后应用偏最小二乘(PLS)回归构建土壤碱解氮含量的估测模型.结果表明: 离散小波变换结合遗传算法和偏最小二乘法(DWT-GA-PLS)用于土壤碱解氮含量定量估测,不仅可压缩光谱变量、减少模型参与变量,而且可改善模型估测准确度;较之于采用土壤全谱,小波离散分解1～2层低频系数构建的模型在参与变量大幅减少的情况下,取得更准确或与之相当的预测结果,其中,基于第2层小波低频系数采用GA筛选变量构建的PLS模型的预测效果表现最好,预测R²达到0.85,RMSE为8.11 mg·kg^-1,RPD为2.53.说明DWT-GA-PLS用于土壤碱解氮含量高光谱定量估测的有效性.     

松嫩平原玉米带土壤碳氮储量的空间特征

利用第二次全国和县级土壤普查的382个典型土壤剖面资料和1∶50万数字化土壤图建立土壤剖面空间数据库,利用土壤类型法估算松嫩平原玉米带土壤碳、氮储量,分析土壤有机碳、氮密度的空间分布特征,探讨土壤有机碳、氮密度与土壤类型和土地利用类型之间的关系.结果表明:松嫩平原玉米带土壤有机碳、氮储量分别为(163.12±26.48)Tg和(9.53±1.75)Tg,土壤碳、氮储量主要集中在草甸土、黑钙土和黑土等土类中.土壤有机碳、氮密度分别为5.51～25.25和0.37～0.80kg·m-2,土壤C/N值大致在7.90～12.67.土壤有机碳、氮密度的空间分布均表现为东部和北部高、西部低.在不同土地利用类型中,旱田土壤的有机碳密度最高,为(19.07±2.44)kg·m-2;林地土壤的氮密度最高,为(0.82±0.25)kg·m-2;水田土壤的碳、氮密度均较低.     

盐城海滨湿地盐沼植被对土壤碳氮分布特征的影响

在盐城海滨湿地不同植被带下采集土壤样品,研究了土壤有机碳和全氮的空间分布特征,分析了盐沼植物对湿地土壤碳、氮分布的影响.结果表明：在盐城海滨湿地,表层土壤中有机碳和全氮含量分别介于1.71～7.92 g·kg^-1和0.17～0.36 g·kg^-1之间,变幅较大,不同植被带之间存在显著差异,且各植被带表层土壤中有机碳、全氮含量均高于光滩.垂直方向上,各植被带土壤中有机碳、全氮的分布均呈自表向下逐渐降低的趋势,15 cm以下其含量基本保持稳定.土壤有机碳与全氮、碳氮比呈显著正相关,但全氮与碳氮比无显著相关性.     

盐城海滨湿地盐沼植被对土壤碳氮分布特征的影响

在盐城海滨湿地不同植被带下采集土壤样品,研究了土壤有机碳和全氮的空间分布特征,分析了盐沼植物对湿地土壤碳、氮分布的影响.结果表明：在盐城海滨湿地,表层土壤中有机碳和全氮含量分别介于1.71～7.92 g·kg^-1和0.17～0.36 g·kg^-1之间,变幅较大,不同植被带之间存在显著差异,且各植被带表层土壤中有机碳、全氮含量均高于光滩.垂直方向上,各植被带土壤中有机碳、全氮的分布均呈自表向下逐渐降低的趋势,15 cm以下其含量基本保持稳定.土壤有机碳与全氮、碳氮比呈显著正相关,但全氮与碳氮比无显著相关性.     

北京城市绿地表层土壤碳氮分布特征

在北京中心城区及周边郊区(覆盖六环路范围),采集不同类型绿地表层(0—20cm)土壤样品490份,测定了土壤有机碳、无机碳、全碳和全氮含量,探讨了城市土壤碳氮分布特征。结果表明:城市不同类型绿地土壤中碳含量差异明显,行道树土壤的有机碳、无机碳和全碳含量均显著高于其他类型绿地,而其它类型土壤有机碳含量差异不显著;居住绿地、道路绿地、单位绿地和公园绿地土壤无机碳含量显著高于生产绿地、防护绿地;城市土壤有机碳、无机碳和全碳含量与距离城市中心距离呈显著的负相关关系;与郊区土壤相比,城区绿地土壤有机碳、无机碳含量有富集的趋势,且无机碳增加更加明显;与郊区农业土壤相比,城市绿地土壤中有机碳有明显地增加趋势,说明北京的城市化在一定程度上有利于土壤碳库的累积。不同类型绿地土壤全氮含量差异不显著,城郊之间全氮含量也无显著差异,土壤全氮质量分数和碳氮比有逐渐减小的趋势,城市化对土壤氮的影响需要进一步研究。     

土壤碳氮特征沿干旱梯度时空格局及对气候变化的响应——基于中国生态系统研究网络(CERN)长期定位监测数据

土壤碳氮含量及其化学计量特征是表征生态系统碳汇能力和土壤质量的重要指标，在支撑生态系统结构功能以及缓解气候变化中起着关键作用。利用中国生态系统研究网络(CERN)长期定位监测数据，分析了土壤碳氮特征沿干旱梯度的时空规律及其对气候变化的响应。结果表明：空间上，典型荒漠草原生态系统随着干旱加剧，土壤有机碳和全氮含量减少，土壤有机碳对干旱响应的敏感性降低，而土壤全氮对干旱响应的敏感性增加，土壤有机碳随土壤全氮含量的增加而增加。时间上，2005—2018年，荒漠草原生态系统土壤有机碳和全氮含量变化速率沿干旱梯度表现出由负转正的增加趋势，其中，干旱区呈减少趋势，半干旱和半湿润地区呈增加趋势，鄂尔多斯站和沙坡头站呈显著增加趋势。从影响因素来看，土壤碳氮特征对降水量增加的敏感性沿干旱梯度呈现出先增强后减弱的“上凸”抛物线趋势，温度变化对土壤碳氮特征的调控没有表现出明显的干旱梯度效应。土壤碳氮比、土壤有机碳含量、土壤全氮含量对降水量和平均温度变化响应的敏感性均依次降低。不同干旱梯度土壤碳氮特征的变化规律为未来气候变化下生态系统结构与功能预测提供科学依据。     

Profile distribution of organic carbon and nitrogen in major soil types in the middle of Qilian Mountains

This paper studied the distribution patterns of organic carbon (OC), total nitrogen (TN), NH4+ -N, and NO3- -N in the profiles of brown calcic soil, grey cinnamon soil, chestnut soil, and alpine meadow soil in the middle of Qilian Mountains. In all test soils, the contents of OC, TN, NH4+ -N, and NO3- -N decreased with increasing soil depth, and the accumulation and decomposition of OC and various N forms differed with soil types. The average content of OC in different soil profiles changed from 14.01 to 41.17 g x kg(-1), and was in the order of grey cinnamon soil > alpine meadow soil > chestnut soil > brown calcic soil; the average content of TN changed from 1.28 to 2.73 g x kg(-1), with a sequence of alpine meadow soil > grey cinnamon soil > chestnut soil > brown calcic soil. The C/N ratio was from 11.33 to 19.22, with the order of grey cinnamon soil > chestnut soil > alpine meadow soil > brown calcic soil. NH4+ -N content changed from 5.80 to 8.40 mg x kg(-1), and was in the order of brown calcic soil > alpine meadow soil > chestnut soil > grey cinnamon soil; NO3- -N content changed from 6.57 to 15.11 mg x kg(-1), being in the order of chestnut soil > alpine meadow soil > brown calcic soil > grey cinnamon soil. The ratio of NO3- -N to NH4+ -N was 1.00-2.69, with the sequence of grey cinnamon soil > chestnut soil > alpine meadow soil > brown calcic soil. The OC and N contents in the same soil types differed significantly with the conditions of climate, vegetation, and topography (e. g. , slope aspect and slope position). Correlation analysis showed that there were highly significant nositive correlations between OC, TN, and NH4+ -N, but these three items had no significant positive correlations with NO3- -N. Furthermore, there were highly significant positive correlations between available K, NH4+ -N, and NO3- -N and between available P and OC, significant positive correlations between available P, TN, and NH4+ -N, but no significant correlations between pH, total K, and total P and OC and N.     

Assessing near-infrared reflectance spectroscopy for the rapid detection of lipid and biomass in microalgae cultures

With intensification of interest in microalgae as a source of biomass for biofuel production, rapid methods are needed for lipid screening of cultures. In this study, near-infrared reflectance spectroscopy (NIRS) was assessed as a method for analysing lipid (specifically, total fatty acid methyl esters (FAME) obtainable from processing) and biomass in late logarithmic and stationary phase cultures of the green alga Kirchneriella sp. and the eustigmatophyte Nannochloropsis sp. Culture samples were filtered, scanned by NIRS and chemically analysed; by combining these sets of information, models were developed to predict total biomass, FAME content and FAME as a percentage of dry weight in samples. Chemically derived (actual) and NIRS-predicted data were compared using the coefficient of determination (R ²) and the ratio of the standard deviation (SD) of actual data to the SD of NIRS prediction (RPD). For Kirchneriella sp. samples, models gave excellent prediction (R ²?≥?0.96; RPD?≥?4.8) for all parameters. For Nannochloropsis sp., the model metrics were less favourable (R ²?=?0.84–0.94; RPD?=?2.5–4.2), though sufficient to provide estimations that could be useful for screening purposes. This technique may require further validation and comparison with other species, but this study shows the potential of the NIRS as a rapid screening method (e.g. up to 200 sample analyses per day) for estimating FAME or other microalgal constituents and encourages further investigation.     

石灰性土壤微生物量碳、氮与土壤颗粒组成和氮矿化势的关系

以黄土高原土壤类型和土壤肥力差异较大的25个农田石灰性耕层土壤为供试土样,研究了土壤微生物量碳（BC）、微生物量氮（BN）与土壤氮素矿化势（NO）、全氮（TN）、有机碳（OC）及土壤颗粒组成的关系．结果表明：BC、BN与TN、OC呈极显著正相关（P〈0．01）,表明BC、BN与土壤肥力关系密切,可作为评价土壤质量的生物学指标．BC、BN与NO均呈高度正相关,相关系数分别为0．665和0．741（P〈0．01）．BC、BN、TN、OC、NO与土壤物理性粘粒（〈0．01mm）呈显著或极显著正相关,而与物理性砂粒（〉0．01mm）呈显著或极显著负相关,与物理性粘粒和砂粒比值呈显著或极显著正相关,表明土壤有机质主要通过与土壤物理性粘粒复合而形成有机无机复合体．     

Measuring carbon dynamics in field soils using soil spectral reflectance: prediction of maize root density, soil organic carbon and nitrogen content

This paper reports the development of a proximal sensing technique used to predict maize root density, soil carbon (C) and nitrogen (N) content from the visible and near-infrared (Vis-NIR) spectral reflectance of soil cores. Eighteen soil cores (0?C60?cm depth with a 4.6?cm diameter) were collected from two sites within a field of 90-day-old maize silage; Kairanga silt loam and Kairanga fine sandy loam (Gley Soils). At each site, three replicate soil cores were taken at 0, 15 and 30?cm distance from the row of maize plants (rows were 60?cm apart). Each soil core was sectioned at 5 depths (7.5, 15, 30, 45, and 60?cm) and soil reflectance spectra were acquired from the freshly cut surface at each depth. A 1.5?cm soil slice was taken at each surface to obtain root mass and total soil C and N reference (measured) data. Root densities decreased with depth and distance from plant and were lower in the silt loam, which had the higher total C and N contents. Calibration models, developed using partial least squares regression (PLSR) between the first derivative of soil reflectance and the reference data, were able to predict with moderate accuracy the soil profile root density (r ²?=?0.75; ratio of prediction to deviation [RPD]?=?2.03; root mean square error of cross-validation [RMSECV]?=?1.68?mg/cm³), soil% C (r ²?=?0.86; RPD?=?2.66; RMSECV?=?0.48%) and soil% N (r ²?=?0.81; RPD?=?2.32; RMSECV?=?0.05%) distribution patterns. The important wavelengths chosen by the PLSR model to predict root density were different to those chosen to predict soil C or N. In addition, predicted root densities were not strongly autocorrelated to soil C (r?=?0.60) or N (r?=?0.53) values, indicating that root density can be predicted independently from soil C. This research has identified a potential method for assessing root densities in field soils enabling study of their role in soil organic matter synthesis.     

Prediction of alpaca fibre quality by near-infrared reflectance spectroscopy

Rapid and efficient methods to evaluate variables associated with fibre quality are essential in animal breeding programs and fibre trade. Near-infrared reflectance spectroscopy (NIRS) combined with multivariate analysis was evaluated to predict textile quality attributes of alpaca fibre. Raw samples of fibres taken from male and female Huacaya alpacas (n = 291) of different ages and colours were scanned and their visible–near-infrared (NIR; 400 to 2500 nm) reflectance spectra were collected and analysed. Reference analysis of the samples included mean fibre diameter (MFD), standard deviation of fibre diameter (SDFD), coefficient of variation of fibre diameter (CVFD), mean fibre curvature (MFC), standard deviation of fibre curvature (SDFC), comfort factor (CF), spinning fineness (SF) and staple length (SL). Patterns of spectral variation (loadings) were explored by principal component analysis (PCA), where the first four PC's explained 99.97% and the first PC alone 95.58% of spectral variability. Calibration models were developed by modified partial least squares regression, testing different mathematical treatments (derivative order, subtraction gap, smoothing segment) of the spectra, with or without applying spectral correction algorithms (standard normal variate and detrend). Equations were selected through one-out cross-validation according to the proportion of explained variance (R²CV), root mean square error in cross-validation (RMSECV) and the residual predictive deviation (RPD), which relates the standard deviation of the reference data to RMSECV. The best calibration models were accomplished when using the NIR region (1100 to 2500 nm) for the prediction of MFD and SF, with R²CV = 0.90 and 0.87; RMSECV = 1.01 and 1.08 μm and RPD = 3.13 and 2.73, respectively. Models for SDFD, CVFD, MFC, SDFC, CF and SL had lower predictive quality with R²CV < 0.65 and RPD < 1.5. External validation performed for MFD and SF on 91 samples was slightly poorer than cross-validation, with R² of 0.86 and 0.82, and standard error of prediction of 1.21 and 1.33 μm, for MFD and SF, respectively. It is concluded that NIRS can be used as an effective technique to select alpacas according to some important textile quality traits such as MFD and SF.     

Changes in Soil Carbon and Nitrogen following Land Abandonment of Farmland on the Loess Plateau,China

The revegetation of abandoned farmland significantly influences soil organic C (SOC) and total N (TN). However, the dynamics of both soil OC and N storage following the abandonment of farmland are not well understood. To learn more about soil C and N storages dynamics 30 years after the conversion of farmland to grassland, we measured SOC and TN content in paired grassland and farmland sites in the Zhifanggou watershed on the Loess Plateau, China. The grassland sites were established on farmland abandoned for 1, 7, 13, 20, and 30 years. Top soil OC and TN were higher in older grassland, especially in the 0–5 cm soil depths; deeper soil OC and TN was lower in younger grasslands (<20 yr), and higher in older grasslands (30 yr). Soil OC and N storage (0–100 cm) was significantly lower in the younger grasslands (<20 yr), had increased in the older grasslands (30 yr), and at 30 years SOC had increased to pre-abandonment levels. For a thirty year period following abandonment the soil C/N value remained at 10. Our results indicate that soil C and TN were significantly and positively correlated, indicating that studies on the storage of soil OC and TN needs to focus on deeper soil and not be restricted to the uppermost (0–30 cm) soil levels.     

Effects of Subsetting by Parent Materials on Prediction of Soil Organic Matter Content in a Hilly Area Using Vis–NIR Spectroscopy

Assessment and monitoring of soil organic matter (SOM) quality are important for understanding SOM dynamics and developing management practices that will enhance and maintain the productivity of agricultural soils. Visible and near-infrared (Vis–NIR) diffuse reflectance spectroscopy (350–2500 nm) has received increasing attention over the recent decades as a promising technique for SOM analysis. While heterogeneity of sample sets is one critical factor that complicates the prediction of soil properties from Vis–NIR spectra, a spectral library representing the local soil diversity needs to be constructed. The study area, covering a surface of 927 km² and located in Yujiang County of Jiangsu Province, is characterized by a hilly area with different soil parent materials (e.g., red sandstone, shale, Quaternary red clay, and river alluvium). In total, 232 topsoil (0–20 cm) samples were collected for SOM analysis and scanned with a Vis–NIR spectrometer in the laboratory. Reflectance data were related to surface SOM content by means of a partial least square regression (PLSR) method and several data pre-processing techniques, such as first and second derivatives with a smoothing filter. The performance of the PLSR model was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to parent materials). The results showed that the models based on the global calibrations can only make approximate predictions for SOM content (RMSE (root mean squared error) = 4.23–4.69 g kg⁻¹; R² (coefficient of determination) = 0.80–0.84; RPD (ratio of standard deviation to RMSE) = 2.19–2.44; RPIQ (ratio of performance to inter-quartile distance) = 2.88–3.08). Under the local calibrations, the individual PLSR models for each parent material improved SOM predictions (RMSE = 2.55–3.49 g kg⁻¹; R² = 0.87–0.93; RPD = 2.67–3.12; RPIQ = 3.15–4.02). Among the four different parent materials, the largest R² and the smallest RMSE were observed for the shale soils, which had the lowest coefficient of variation (CV) values for clay (18.95%), free iron oxides (15.93%), and pH (1.04%). This demonstrates the importance of a practical subsetting strategy for the continued improvement of SOM prediction with Vis–NIR spectroscopy.     

Sentinel-2 Satellite Imagery Application to Monitor Soil Salinity and Calcium Carbonate Contents in Agricultural Fields

The estuary tides affect groundwater dynamics; these areas are susceptible to waterlogging and salinity issues. A study was conducted on two fields with a total area of 60 hectares under a center pivot irrigation system that works with solar energy and belong to a commercial farm located in Northern Sudan. To monitor soil salinity and calcium carbonate in the area and stop future degradation of soil resources, easy, non-intrusive, and practical procedures are required. The objective of this study was to use remote sensing-determined Sentinel-2 satellite imagery using various soil indices to develop prediction models for the estimation of soil electrical conductivity (EC) and soil calcium carbonate (CaCO₃). Geo-referenced soil samples were collected from 72 locations and analyzed in the laboratory for soil EC and CaCO₃. The electrical conductivity of the soil saturation paste extract was represented by average values in soil dataset samples from two fields collected from the topsoil layer (0 to 15 cm) characteristic of the local salinity gradient. The various soil indices, used in this study, were calculated from the Sentinel-2 satellite imagery. The prediction was determined using the root mean square error (RMSE) and cross validation was done using coefficient of determination. The results of regression analysis showed linear relationships with significant correlation between the EC analyzed in laboratory and the salinity index-2 “SI2” (Model-1: R² = 0.59, p = 0.00019 and root mean square error (RMSE = 1.32%) and the bare soil index “BSI” (Model-2: R² = 0.63, p = 0.00012 and RMSE = 6.42%). Model-1 demonstrated the best model for predicting soil EC, and validation R² and RMSE values of 0.48% and 1.32%, respectively. The regression analysis results for soil CaCO₃ determination showed linear relationships with data obtained in laboratory and the bare soil index “BSI” (Model- 3: R² = 0. 45, p = 0.00021 and RMSE = 1.29%) and the bare soil index “BSI” & Normalized difference salinity index “NDSI” (Model-4: R² = 0.53, p = 0.00015 and RMSE = 1.55%). The validation confirmed the Model-3 results for prediction of soil CaCO₃ with R² and RMSE values of 0.478% and 1.29%, respectively. Future soil monitoring programs might consider the use of remote sensing data for assessing soil salinity and CaCO₃ using soil indices results generated from satellite image (i.e., Sentinel-2).     

Influence of wastewater composition on nitrogen and phosphorus removal and process control in A2O process

A bench-scale anaerobic–anoxic–oxic (A²O) bioreactor with steady denitrifying phosphorus removal performance was tested to determine the influence of influent C/N ratio (SCOD/TN) and C/P ratio (SCOD/TP) on biological nutrient removal for treating synthetic brewage wastewater; meanwhile, the spatial profiles of DO, pH and ORP sensors in such systems were investigated. The results showed that influent C/N ratio had significant effect on the TN, TP removal efficiencies and the ratio of anoxic to aerobic P uptake amount. The maximal TN and TP removal efficiencies could be achieved when influent C/N ratio was kept at about 7.1 and 5, respectively. Besides, the ratio of anoxic to aerobic P uptake amount was found to be linearly dependent on the influent C/N ratio with coefficient R ² of 0.685 when total recirculation ratio was constant at 3.5. Influent C/P ratio had an important effect on the TP removal efficiency, while it hardly affected TN removal efficiency. In addition, the TP removal efficiency reached the maximum for influent C/P ratio of 42. On the other hand, it was also found that the typical profiles of DO, pH and ORP sensors could be observed, and they have similar trends at the different influent C/N ratio and C/P ratio. It was suggested that the operational state could be well known according to the changes of simple on-line sensors.     

At-line near-infrared spectroscopy for monitoring concentrations in temperature-triggered glutamate fermentation

Rapid development in the glutamate fermentation industry has dictated the need for effective fermentation monitoring by rapid and precise methods that provide real-time information for quality control of the end-product. In recent years, near-infrared (NIR) spectroscopy and multivariate calibration have been developed as fast, inexpensive, non-destructive and environmentally safe techniques for industrial applications. The purpose of this study was to develop models for monitoring glutamate, glucose, lactate and alanine concentrations in the temperature-triggered process of glutamate fermentation. NIR measurements of eight batches of samples were analyzed by partial least-squares regression with several spectral pre-processing methods. The coefficient of determination (R ²), model root-mean square error of calibration (RMSEC), root-mean square error of prediction (RMSEP) and residual predictive deviation (RPD) of the test calibration for the glutamate concentration were 0.997, 3.11 g/L, 2.56 g/L and 19.81, respectively. For the glucose concentration, R ², RMSEC, RMSEP and RPD were 0.989, 1.37 g/L, 1.29 g/L and 9.72, respectively. For the lactate concentration, R ², RMSEC, RMSEP and RPD were 0.975, 0.078 g/L, 0.062 g/L and 6.29, respectively. For the alanine concentration, R ², RMSEC, RMSEP and RPD were 0.964, 0.213 g/L, 0.243 g/L and 5.29, respectively. New batch fermentation as an external validation was used to check the models, and the results suggested that the predictive capacity of the models for the glutamate fermentation process was good.