In Situ Measurement of Soil Chemical Composition by Near-Infrared Spectroscopy: A Tool Toward Sustainable Vineyard Management

This study evaluated an in-field near-infrared (NIR) instrument to predict the contents of total nitrogen (TN), organic carbon (OC), potassium (K), sulfur (S), phosphorus (P), pH, and electric conductivity (EC) in soil vineyard samples (n = 70) sourced from three wine regions of Australia. Samples were analyzed using a portable NIR spectrophotometer (ASD FieldSpec III, 350–1800 nm). Partial least squares (PLS) regressions yield a coefficient of determination in calibration (R2) and a standard error in cross validation (SECV) of 0.74 (0.03) for TN, 0.92 (2.19) for S, 0.81 (0.42) for OC, 0.70 (109.2) for K, 0.84 (0.03) for EC, 0.83 (0.44) for pH, and 0.69 (24.6) for P, respectively. This study showed that it is possible to measure soil chemical properties in the vineyard, and the main advantages of this approach will be the speed, low cost, and ability to better manage and monitor soil fertility.     

Near‐infrared spectroscopy can predict the composition of organic matter in soil and litter

The usefulness and limitations of near‐infrared reflectance spectroscopy (NIRS) for the assessment of several soil characteristics are still not sufficiently explored. The objective of this study was to evaluate the ability of visible and near‐infrared reflectance (VIS‐NIR) spectroscopy to predict the composition of organic matter in soils and litter. Reflectance spectra of the VIS‐NIR region (400–2500 nm) were recorded for 56 soil and litter samples from agricultural and forest sites. Spectra were used to predict general and biological characteristics of the samples as well as the C composition which was measured by ¹³C‐CPMAS‐NMR spectroscopy. A modified partial least‐square method and cross‐validation were used to develop equations for the different constituents over the whole spectrum (1st to 3rd derivation). Near‐infrared spectroscopy predicted well the C : N ratios, the percentages of O‐alkyl C and alkyl C, the ratio of alkyl C to O‐alkyl C, and the sum of phenolic oxidation products: the ratios of standard deviation of the laboratory results to standard error of cross‐validation (RSC) were greater than 2, the regression coefficients (a) of a linear regression (measured against predicted values) ranged from 0.9 to 1.1, and the correlation coefficients (r) were greater than 0.9. Satisfactorily (0.8 ≤ a ≤ 1.2, r ≥ 0.8, and 1.4 ≤ RSC ≤ 2.0) assessed were the contents of C, N, and production of DOC, the percentages of carbonyl C and aromatic C and the ratio of alkyl C to aromatic C. However, the N‐mineralization rate and the microbial biomass were predicted unsatisfactorily (RSC < 1.4). The good and satisfactory predictions reported above indicate a marked usefulness of NIRS in the assessment of biological and chemical characteristics of soils and litter.     

近红外光谱定量分析技术在烟草和烟气化学成分分析中的研究进展

近红外光谱定量分析技术以其高效和快速的优点已经在烟草行业发挥了重要作用。首先详细介绍了近红外光谱分析技术的特点,包括其优点和应用局限;其次对近红外光谱定量分析流程进行了归纳总结;然后总结了近红外定量分析计量学方法,包括光谱预处理方法和近红外定量分析的化学计量学算法;最后综述了近年来近红外光谱定量分析技术在烟草和烟气化学成分分析中的应用状况并对其进行了展望。     

利用近红外高光谱图像技术快速鉴别西瓜种子品种

为了研究采用近红外高光谱图像技术对西瓜种子品种快速无损鉴别的可行性,该文采用近红外高光谱图像技术,通过提取西瓜种子的光谱反射率,结合Savitzky-Golay (SG)平滑算法,经验模态分解算法（empirical mode decomposition,EMD）和小波分析（wavelet transform,WT）对提取出的光谱数据进行去除噪声处理,采用连续投影算法（successive projections algorithm,SPA）和遗传-偏最小二乘法（genetic algorithm-partial least squares, GA-PLS）进行特征波长选择。基于全波段光谱建立了偏最小二乘判别分析（partial least squares-discriminant analysis,PLS-DA）,基于特征波长建立了反向传播神经网络（back-propagation neural network,BP NN)判别模型和极限学习机（extreme learning machine,ELM）判别模型。试验结果表明,基于特征波长的BPNN模型和ELM模型的结果优于基于全部波长的PLS-DA模型,基于SG预处理光谱提取的特征波长建立的ELM模型取得最优的判别效果,建模集和预测集的判别正确率均为100%。结果表明应用近红外高光谱成像技术对西瓜种子品种鉴别是可行的,为西瓜种子的品种快速鉴别提供了一种新方法。     

山东省主推小麦品种品质性状的近红外光谱分析

摘 要：为给山东小麦品质育种亲本选配提供参考依据,本研究利用近红外光谱技术对山东近两年 25 个主推小麦品种的蛋白质含量、吸水率、面筋含量、形成时间和稳定时间进行检测,结果表明,蛋白质含量和面筋含量达到强筋麦标准的分别占 68% 和 64%,72.00% 小麦品种的形成时间和稳定时间达到中筋麦标准;10% 的小麦品种为强筋麦,90% 的小麦品种为中筋麦,没有检测到弱筋麦。     

近红外技术快速测定肉鸡粪便主要肥料成分含量的研究

该文探讨了利用近红外光谱分析技术(NIRS)快速测定肉鸡粪便主要肥料成分含量的可行性。在饲养试验过程中采集了肉鸡粪便样品183个，利用常规实验室分析方法测定了其中的总氮(TN)、总磷(TP)、总钾(TK)、铵态氮(AN)、有效磷(EP)含量，利用近红外光谱仪取得了样品在1421.5～2572.2 nm波段的光谱，并使用偏最小二乘法(PLS)结合交互验证(CV)和留一检验法(LOO)建立了肉鸡粪便肥料成分的近红外光谱定标模型，同时利用该模型对20个样品的总氮、总磷、总钾、铵态氮、有效磷的含量进行预测，得出的5种主要肥料成分的预测值和真实值(实验室经典化学分析方法测定值)之间具有显著的相关性，其相关系数分别为0.9574，0.9590，0.9870，0.9572和0.9650。预测标准差分别为0.0014，0.0012，0.0012，2.3041×10^-4和0.3814。结果表明，利用近红外光谱法对肉鸡粪便风干样品的主要肥料成分进行快速测定是可行的。     

江西烤烟淀粉近红外检测模型的建立

【目的】传统的碘显色法检测淀粉含量需要复杂耗时的前处理和实验准备,近红外检测手段不仅 操作简单、检测快速,且不损耗试剂、样品、标准品,大大降低了检测成本,旨在以江西烤烟样品为依托,建 立一种适用于江西烤烟的淀粉近红外检测模型。【方法】收集了来自江西 21 个区县共 650 个烤烟样品的近红外 光谱和对应的连续流动分析仪检测数据（碘显色法）,将光谱数据和化学分析数据一一对应,使用不同的回归 方法和数据处理方法进行建模,通过比较它们的均方根误差和相关系数确定最佳的模型参数。【结果】建立了 适用于江西烤烟的淀粉含量预报模型,校正均方根误差和预测均方根误差分别达到 0.407、0.490,相关系数为 96.52%,并用外部样品对模型进行了验证,95% 的样品误差在 10% 以内。【结论】该模型可用于快速检测江西 烤烟样品中的淀粉含量,并可在一定程度上取代传统的碘显色测淀粉法,降低了检测成本,提高了分析效率。     

Investigating the effects of simulated transport vibration on tomato tissue damage based on vis/NIR spectroscopy

Visible and near infrared (vis/NIR) spectroscopy combined with chemometrics were investigated to evaluate the effects of simulated transport vibration levels on damage of tomato fruit. A total of 280 tomato samples were randomly divided into 5 groups; each group was subjected to vibration at different acceleration levels. A total of 230 samples (46 from each group) were selected as a calibration set; whereas 50 samples (10 from each group) were selected as a prediction set. Raw spectra, differentiation (the first derivative) spectra, extended multiplicative scatter correction (EMSC) processed spectra and standard normal variant combined with detrending (SNV–DT) processed spectra were used for calibration models. SNV–DT processed spectra had the best performance using for partial least squares (PLS) analysis. The PLS analysis was implemented to calibrate models with different wavelength bands including visible, short-wave near infrared (SWNIR) and long-wave near infrared (LWNIR) regions. The best PLS model was obtained in the vis/NIR (600–1600 nm) region. Using a grid search technique and radial basis function (RBF) kernel, four least squares support vector machine (LS–SVM) models with different latent variables (7, 8, 9, and 10 LVs) were compared. The optimal model was obtained with 9 LVs and the correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for the best prediction by LS–SVM were 0.984, 0.137 and 0.003, respectively. The results showed that vis/NIR spectroscopy could be applied as a reliable and rapid method for predicting the effect of vibration levels on tissue damage of tomato fruit.     

Nondestructive detection of insect infested chestnuts based on NIR spectroscopy

Insect feeding is a significant postharvest problem for processors of chestnuts (Castanea sativa, Miller). In most cases, damage from insects is ‘hidden’, i.e. not visually detectable on the fruit surface. Consequently, traditional sorting techniques, including manual sorting, are generally inadequate for the detection and removal of chestnuts with hidden damage. For the most part, the only method currently used by processors is a flotation system, in which chestnuts are placed in salt water and those that float are discarded. Flotation is unreliable, and a more effective method for detection of insect damage would benefit industry and consumers alike. In this study, the feasibility of using NIR spectroscopy to detect hidden insect damage is demonstrated. Using a genetic algorithm for feature selection (from 2 to 6 wavelengths) in combination with a linear discriminant analysis routine, classification error rates as low as 16.81% false negative, 0.00% false positive, and 8.41% total error were achieved, with an AUC value of 0.952 and an Wilk's λ of 0.403 (P < 0.001). A Savitzky–Golay first derivative spectral pretreatment with 13 smoothing points was used. The optimal features corresponded to Abs [1582 nm], Abs [1900 nm], and Abs [1964 nm]. These results represent an average of 55.3% improvement over a traditional flotation sorting system.     

基于近红外光谱的沼液挥发性脂肪酸含量快速检测

挥发性脂肪酸(Volatile Fatty Acids,VFA)作为厌氧发酵过程的重要中间产物,其在厌氧反应器中的累积能够反映出产甲烷菌的不活跃状态或厌氧发酵条件的恶化。为了实现对农牧废弃物厌氧发酵进行过程分析和状态监控,将近红外光谱(Near Infrared Spectroscopy,NIRS)与偏最小二乘(Partial Least Squares,PLS)相结合构建玉米秸秆和畜禽粪便厌氧发酵液乙酸、丙酸和总酸含量快速检测模型。将竞争自适应重加权采样法(Competitive Adaptive Reweighted Sampling,CARS)与遗传模拟退火(Genetic Simulated Annealing,GSA)算法相结合构建CARS-GSA算法对沼液中的乙酸、丙酸和总酸进行特征波长优选,原始光谱数据1 557个波长点经预处理和波长优选后,得到乙酸、丙酸和总酸特征波长变量分别为135、101和245个,建立的回归模型验证决定系数分别为0.988、0.923和0.886,预测均方根误差(Root Mean Squared Error of Prediction,RMSEP...