Applicability of near infrared spectroscopy as an alternative to acid detergent analysis for cattle and swine manure compost

Abstract

Recently, acid detergent analysis has been reported to provide valid data to evaluate decomposition properties and to determine the available nitrogen (AVN) of organic materials, such as compost. However, this methodology requires complex procedures and creates considerable costs. As an alternative, near infrared spectroscopy (NIRS) was evaluated as a simple method to determine acid detergent fiber (ADF), acid detergent lignin (ADL) and acid-detergent-soluble organic matter (ADSOM), in order to evaluate the decomposition properties of cattle and swine manure compost. To establish an easy and accurate method of estimating AVN in cattle and swine manure compost, the accuracies of direct estimations of AVN by NIRS in incubation experiments and indirect estimations by NIRS based on acid-detergent-soluble nitrogen (ADSN) or total nitrogen (TN) were examined. The reflectance spectra of freeze-dried and milled compost samples were determined using a scanning monochromator. Second derivative spectra and multiple regression analysis were used to develop calibration equations for each constituent. The calibration equations for ADF, ADL and ADSOM were “successful” according to commonly applied criteria. Acid-detergent-soluble nitrogen was found to be more suitable than TN for estimating AVN in cattle and swine manure compost. As the accuracies of the estimations of ADSN and TN by NIRS were comparable, the estimation of AVN based on ADSN as determined by NIRS was more accurate than that based on TN determined by NIRS. The direct prediction of AVN through NIRS was not as accurate as the estimation of AVN based on ADSN determined by NIRS. We conclude that NIRS is a practicable alternative to the time-consuming acid detergent analysis of cattle and swine compost, and that ADSN as determined by NIRS is useful for estimating AVN in the compost.     

Applicability of near infrared spectroscopy as an alternative to acid detergent analysis for cattle and swine manure compost

Recently, acid detergent analysis has been reported to provide valid data to evaluate decomposition properties and to determine the available nitrogen (AVN) of organic materials, such as compost. However, this methodology requires complex procedures and creates considerable costs. As an alternative, near infrared spectroscopy (NIRS) was evaluated as a simple method to determine acid detergent fiber (ADF), acid detergent lignin (ADL) and acid-detergent-soluble organic matter (ADSOM), in order to evaluate the decomposition properties of cattle and swine manure compost. To establish an easy and accurate method of estimating AVN in cattle and swine manure compost, the accuracies of direct estimations of AVN by NIRS in incubation experiments and indirect estimations by NIRS based on acid-detergent-soluble nitrogen (ADSN) or total nitrogen (TN) were examined. The reflectance spectra of freeze-dried and milled compost samples were determined using a scanning monochromator. Second derivative spectra and multiple regression analysis were used to develop calibration equations for each constituent. The calibration equations for ADF, ADL and ADSOM were "successful" according to commonly applied criteria. Acid-detergent-soluble nitrogen was found to be more suitable than TN for estimating AVN in cattle and swine manure compost. As the accuracies of the estimations of ADSN and TN by NIRS were comparable, the estimation of AVN based on ADSN as determined by NIRS was more accurate than that based on TN determined by NIRS. The direct prediction of AVN through NIRS was not as accurate as the estimation of AVN based on ADSN determined by NIRS. We conclude that NIRS is a practicable alternative to the time-consuming acid detergent analysis of cattle and swine compost, and that ADSN as determined by NIRS is useful for estimating AVN in the compost.     

Development of NIRS equations for food grain quality traits through exploitation of a core collection of cultivated sorghum

A sorghum core collection representing a wide range of genetic diversity and used in the framework of a sorghum breeding and genetics program was evaluated by near-infrared reflectance spectroscopy (NIRS) to predict food grain quality traits: amylose content (AM), protein content (PR), lipid content (LI), endosperm texture (ET), and hardness (HD). A total of 278 sorghum samples were scanned as whole and ground grain to develop calibration equations. Laboratory analyses were performed on NIRS sample subsets that preserved the core collection racial distribution. Principal component analysis performed on NIRS spectra evidenced a level of structure following known sorghum races, which underlined the importance of using a wide range of genetic diversity. Performances of calibration equations were evaluated by the coefficient of determination, bias, standard error of laboratory (SEL), and ratio of performance deviation (RPD). Ground grain spectra gave better calibration equations than whole grain. PR equation (RPD of 5.7) can be used for quality control. ET, LI, and HD equations (RPD of 2.9, 2.6, and 2.6, respectively) can be used for screening steps. Even with a small SEL in whole sample analysis, a RPD of 1.8 for AM confirmed that this variable is not easy to predict with NIRS.     

Quantification of fatty acids in forages by near-infrared reflectance spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was evaluated as a possible alternative to gas chromatography (GC) for the quantitative analysis of fatty acids in forages. Herbage samples from 11 greenhouse-grown forage species (grasses, legumes, and forbs) were collected at three stages of growth. Samples were freeze-dried, ground, and analyzed by GC and NIRS techniques. Half of the 195 samples were used to develop an NIRS calibration file for each of eight fatty acids, with the remaining half used as a validation data set. Spectral data, collected over a wavelength range of 1100-2498 nm, were regressed against GC data to develop calibration equations for lauric (C12:0), myristic (C14:0), palmitic (C16:0), stearic (C18:0), palmitoleic (C16:1), oleic (C18:1), linoleic (C18:2), and alpha-linolenic (C18:3) acids. Calibration equations had high coefficients of determination for calibration (0.93-0.99) and cross-validation (0.89-0.98), and standard errors of calibration and cross-validation were < 20% of the respective means. Simple linear regressions of NIRS results against GC data for the validation data set had r2 values ranging from 0.86 to 0.97. Regression slopes for C12:0, C14:0, C16:0, C18:0, C16:1, C18:2, and C18:3 were not significantly different (P = 0.05) from 1.0. The regression slope for C18:1 was 1.1. The ratio of standard error of prediction to standard deviation was > 3.0 for all fatty acids except C12:0 (2.6) and C14:0 (2.9). Validation statistics indicate that NIRS has high prediction ability for fatty acids in forages. Calibration equations developed using data for all plant materials accurately predicted concentrations of C16:0, C18:2, and C18:3 in individual plant species. Accuracy of prediction was less, but acceptable, for fatty acids (C12:0, C14:0, C18:0, C16:1, and C18:1) that were less prevalent.     

光谱特征变量和BP神经网络构建油用牡丹种子含水率估算模型

为了进一步提高种子含水率的高光谱估算精度，该研究测定了156份油用牡丹种子的近红外吸收光谱及其对应的含水率值，分析了近红外吸收光谱、一阶微分光谱、水分吸收特征参数与含水率的相关关系，构建了基于特征波长吸收光谱、特征波长一阶微分光谱、水分特征吸收参数和BP神经网络的油用牡丹种子含水率估算模型，并对模型进行了验证；再结合一元线性回归（SLR，Single Linear Regression）、逐步多元线性回归（SMLR，Stepwise MultipleLinear Regression）、偏最小二乘回归（PLSR，Partial Least Squares Regression）模型与BP神经网络（BPNN，BP Neural Network）模型进行比较。结果表明：1）油用牡丹种子含水率的吸收光谱特征波长位于1 410、1 900、1990 nm，一阶微分光谱特征波长位于1 150、1 950、2 080 nm；2）以DF2080和AD2140为自变量建立的一元线性回归模型预测效果较优，在能够满足水分估算精度的情况下，是最优的选择方法。3）将优选的特征参数作为输入，实测含水率值作为输出，构建BP神经网络模型，其建模与验模R2分别为0.978和0.973，RMSE分别为0.22%和0.242%，而RPD值分别为6.478和5.889，与其他模型相比，BP神经网络模型的建模及预测精度均最高，是估算油用牡丹种子含水率的最优模型，其次为逐步多元线性回归模型。研究结果表明BP神经网络模型对种子含水率具有更好的预测能力，是估算油用牡丹种子含水率的有效方法。     

Near-infrared reflectance spectroscopy (NIRS) enables the fast and accurate prediction of essential amino acid contents. 2. Results for wheat,barley, corn,triticale, wheat bran/middlings,rice bran,and sorghum

Further NIRS calibrations were developed for the accurate and fast prediction of the total contents of methionine, cystine, lysine, threonine, tryptophan, and other essential amino acids, protein, and moisture in the most important cereals and brans or middlings for animal feed production. More than 1100 samples of global origin collected over five years were analyzed for amino acids following the Official Methods of the United States and European Union. Detailed data and graphics are given to characterize the obtained calibration equations. NIRS was validated with 98 independent samples for wheat and 78 samples for corn and compared to amino acid predictions using linear crude protein regression equations. With a few exceptions, validation showed that 70-98% of the amino acid variance in the samples could be explained using NIRS. Especially for lysine and methionine, the most limiting amino acids for farm animals, NIRS can predict contents in cereals much better than crude protein regressions. Through low cost and high speed of analysis NIRS enables the amino acid analysis of many samples in order to improve the accuracy of feed formulation and obtain better quality and lower production costs.     

Quantification of exopolysaccharide, lactic acid, and lactose concentrations in culture broth by near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) was used for the simultaneous prediction of exopolysaccharide (EPS; 0-3 g/L) and lactic acid (0-59 g/L) productions as well as lactose (0-68 g/L) concentration in supernatant samples from pH-controlled batch cultures of Lactobacillus rhamnosus RW-9595M in supplemented whey permeate medium. To develop calibration equations, the correlation between the second derivative of 164 NIRS transmittance spectra and concentration data obtained with reference methods was calculated at the wavelength between 1653-1770 and 2041-2353 nm, using a partial least-squares method (PLS). The lactic acid and lactose concentrations were measured by HPLC, and the EPS concentration was estimated by a new ultrafiltration method. The PLS correlation coefficient (R(2)) and the standard error of cross-validation for the calibrations were 91% and 0.26 g/L for EPS, 99% and 2.54 g/L for lactic acid, and 98% and 3.32 g/L for lactose, respectively. The calibration equations were validated with 45 randomly selected culture samples from 6 cultures that were not used for calibration. A high agreement between data of the reference methods and those of NIRS was observed, with correlation coefficients and standard errors of prediction of 99% and 1.64 g/L for lactic acid, 99% and 4.5 g/L for lactose, and 91% and 0.32 g/L for EPS. The results suggest that NIRS could be a useful method for rapid monitoring and control of EPS lactic fermentations.     

Rapid measurement and evaluation of the effect of drying conditions on harpagoside content in Harpagophytum procumbens (devil's claw) root

The effect of drying conditions on harpagoside (HS) retention, as well as the use of near-infrared spectroscopy (NIRS) for rapid quantification of the iridoids, HS, and 8-rho-coumaroyl harpagide (8rhoCHG) and moisture, in dried Harpagophytum procumbens (devil's claw) root was investigated. HS retention was significantly (P < 0.05) lower in sun-dried samples as compared to tunnel-dried (60 degrees C, 30% relative humidity) and freeze-dried samples. The best retention of HS was obtained at 50 degrees C when evaluating tunnel drying at dry bulb temperatures of 40, 50, and 60 degrees C and 30% relative humidity. NIRS can effectively predict moisture content with a standard error of prediction (SEP) and correlation coefficient (r) of 0.24% and 0.99, respectively. The HS and 8rhoCHG NIRS calibration models established for both iridoid glucosides can be used for screening purposes to get a semiquantitative classification of devil's claw roots (for HS: SEP = 0.236%, r = 0.64; for 8rhoCHG: SEP = 0.048%, r = 0.73).     

可见/近红外光谱分析秸秆-煤混燃物的秸秆含量

快速检测秸秆-煤混燃物对生物质混燃发电中补贴政策的制定具有重要意义。该研究采用可见/近红外光谱法定性判别秸秆、煤和秸秆-煤混燃物,定量分析秸秆-煤混燃物中秸秆含量。收集并制备秸秆样品80个（粒径小于80 mm）、煤样品9个（粒径小于10 mm）,制备秸秆质量分数为70%～99%的秸秆-煤混燃物样品120个（混燃物1）、秸秆分数含量为1%～30%的秸秆-煤混燃物样品120个（混燃物2）。使用FOSS NIRS DS 2500型光谱仪获取样品光谱。分别使用偏最小二乘判别法（PLS-DA）建立定性分析模型,使用改进的偏最小二乘法（MPLS）建立定量分析模型。结果显示,在秸秆和混燃物1之间进行判别,使用1100～2500 nm谱区,正确判别率为90.00%;在煤和混燃物2之间进行判别,使用400～2500 nm谱区,正确判别率为71.88%;定量分析混燃物1和混燃物2中秸秆含量,相对分析误差分别为2.32（400～2500 nm谱区）和1.48（400～1100 nm谱区）。研究结果表明,1100～2500 nm谱区较适合秸秆和混燃物1之间的判别,该谱区同样适合定量分析混燃物1中秸秆含量。400～1100 nm谱区较适合煤和混燃物2之间的判别,该谱区同样适合定量分析混燃物2中秸秆含量。可见/近红外光谱结合化学计量学是快速定性和定量分析大粒度秸秆-煤混燃物的可行方法。     

Quantification of kavalactones and determination of kava (Piper methysticum) chemotypes using near-infrared reflectance spectroscopy for quality control in vanuatu

Kava ( Piper methysticum Forst f., Piperaceae) has anxiolytic properties and the ability to promote a state of relaxation without the loss of mental alertness. The rapid growth of the nutraceutical market between 1998 and 2000 has been stopped by a ban in Europe and Australia because of some suspicion of liver toxicity. It is now important to develop a fast, cheap, and reliable quality test to control kava exports. The aim of this study is to develop a calibration of the near-infrared reflectance spectroscopy (NIRS) using partial least-squares (PLS) regression. Two hundred thirty-six samples of kava roots, stumps, and basal stems were collected from the Vanuatu Agricultural Research and Technical Centre germplasm collection and from four villages. These samples, representing 45 different varieties, were analyzed using NIRS to record their absorption spectra between 400 and 2500 nm. A set of 101 selected samples was analyzed for their kavalactone content using HPLC. The results were used for PLS calibration of the NIRS. The NIRS prediction of the kavalactone content and the dry matter were in agreement with the HPLC results. There were good correlations between these two series of results, and coefficients ( R (2)) were all close to 1. The measurements were reproducible and had repeatability on par with the HPLC method. The NIRS system has been calibrated for the six major kavalactone content measurements, and it is suggested that this method could be used for quality control in Vanuatu.     

Development and Validation of Prediction Models for Rice Surface Lipid Content and Color Parameters Using Near‐Infrared Spectroscopy: A Basis for Predicting Rice Degree of Milling

Degree of milling (DOM) of rice plays a key role in determining rice quality and value. Therefore, accurate, nondestructive, quick, and automated surface lipid content (SLC) measurement would be useful in a commercial milling environment. This study was undertaken to provide calibration models for commercial use to provide quick and accurate evaluation of milled rice SLC and Hunterlab color parameters (L,a,b) as indications of rice DOM. In all, 960 samples, including seven cultivars from seven southern United States locations, stored for 0, 1, 2, 3, and 6 months, were milled for four durations to obtain samples of varying DOM. The samples were used to develop calibration models of milled rice SLC and L,a,b values. Another sample set (n = 58) was commercially milled and used to validate the developed models. A DA 7200 diode array analyzer was used to scan milled rice samples in wavelength spectra of 950–1,650 nm. SLC and color parameters were measured using a Soxtec system and a HunterLab colorimeter, respectively. The partial least squares regression (PLS) method using the full near‐infrared spectra was used to develop prediction models for rice SLC and color parameters. Milled rice SLC was well fitted with a correlation of determination of predicted and measured values of (R² = 0.934). Color parameters were also successfully fitted for L (R² = 0.943), a (R² = 0.870), and b (R² = 0.855). Performance of the developed models to predict rice DOM was superior in predicting SLC and L,a,b values with R² predicted and measured values of 0.958, 0.836, 0.924, and 0.661, respectively.     

Rapid Prediction of Rice Quality Characteristics by Near‐Infrared Reflectance Spectroscopy for Breeding Programs

Breeding of high‐quality rice requires quick methods to evaluate the quality characteristics such as milling, grain appearance, nutritional, eating, and cooking qualities. Because routine measurements of these quality traits are time consuming and expensive, a rapid predictive method based on near‐infrared spectroscopy (NIRS) can be applied to measure these quality parameters. In this study, calibration models for measurement of grain quality were developed using a total of 570 brown and milled rice samples. The results indicated that the models developed from the spectra of brown rice for all the quality traits had the coefficient of determination for external validation (R²) larger than 0.64 except for gel consistency. The best model was developed for the protein content, with R² of 0.94 for external validation. The model for the total score of physicochemical characteristics (TSPC), a comprehensive index reflecting all other traits, had R² of 0.70 and SD/SEP of 1.70, which indicates that high or low TSPC for a given rice could be discriminated by NIRS. The models developed from brown rice were as accurate as those from milled rice. Results suggest that NIRS‐based predictions for rice quality traits may be used as indicator traits to improve rice quality in breeding programs.     

Quantitative Analysis of Chlorophyll and Protein in Alfalfa Leaves Using Fiber‐Optic Near‐Infrared Spectroscopy

The performance of near‐infrared (NIR) spectroscopy as a rapid technique for the estimation of chlorophyll and protein contents in alfalfa (Medicago sativa L.) was investigated. A fiber‐optic probe was employed directly on a total of 198 fresh leaves to measure spectra between 1100 and 2200 nm. Partial least squares (PLS) regression models were developed with a calibration set of 120 samples spanning a concentration range of 5.20–158.5 for the chlorophyll content index (CCI), 0.39–4.60 mg g^?1 (fresh weight) for the chlorophyll extracted with dimethylsulfoxide (DMSO), and 9.92–45.32% (dry matter) for protein content. The models obtained were validated with 78 independent samples. Standard errors of prediction of 12.49 were obtained for the CCI, 0.24 mg g^?1 for DMSO‐extracted chlorophyll, and 3.27% for the protein content. These results support the use of NIRS equipped with a fiber‐optic probe to monitor and assess the composition and quality of forages in a nondestructive way.     

Near-infrared reflectance spectroscopy enables the fast and accurate prediction of the essential amino acid contents in soy, rapeseed meal, sunflower meal, peas, fishmeal, meat meal products, and poultry meal

Near-infrared reflectance spectroscopy (NIRS) calibrations were developed to enable the accurate and fast prediction of the total contents of methionine, cystine, lysine, threonine, tryptophan, and other essential amino acids, protein, and moisture in the most important protein-rich feed ingredients. More than 1000 samples of global origin collected over four years were analyzed on amino acids following the official methods of the United States and the European Union. Detailed data and graphics are given to characterize the obtained calibration equations. NIRS was validated with independent samples for soy and meat meal products and compared to the amino acid predictions using linear crude protein regressions. With a few exceptions, validation showed that 85-98% of the amino acid variance in the samples could be explained using NIRS. NIRS predictions compared to reference results agree excellently, with relative mean deviations below 5%. Especially for meat and poultry meals, NIRS can predict amino acids much better than crude protein regressions. By enabling the amino acid analysis of many samples to be completed in a short time, NIRS can improve the accuracy of feed formulation and thus the quality and production costs of mixed feeds.     

Quantifying Surface Lipid Content of Milled Rice via Visible/Near-Infrared Spectroscopy

Visible/near-infrared calibrations were developed and tested for surface lipid content (SLC) of milled long-grain rice. Three rice varieties were divided into two sample sets, with one containing two variables (degree of milling and variety) and another containing three variables (degree of milling, variety, and kernel thickness). The reflectance calibration equation from the set with three variables was much more accurate in predicting SLC than was the calibration from the two-variable set. Optimal calibration and prediction were obtained by combining both visible and near-infrared wavelength ranges and using the modified partial least squares technique on spectra pretreated by standard normal variate and first derivative methods. The best calibration yielded a coefficient of determination (R²) of 0.99 and a standard error of prediction of 0.04% SLC, which was approximately 1.5 times the standard error of calibration and also 1.5 times the SLC measurement error.     

基于EPO算法去除水分影响的土壤有机质高光谱估算

野外进行土壤有机质的光谱快速预测时需考虑土壤含水量的影响。在室内设计人工加湿实验分别获取9个土壤含水量梯度(0~32%,间隔4%)的土壤光谱数据,分析土壤含水量变化对光谱的影响,再利用外部参数正交化法(external parameter orthogonalization,EPO)进行湿土光谱校正,并结合偏最小二乘回归和支持向量机回归分别建立土壤有机质预测模型。结果表明,土壤光谱反射率随着土壤含水量的增加呈非线性降低趋势,偏最小二乘回归模型的预测偏差比为1.16,模型不可用;经EPO算法校正后,各土壤含水量梯度之间的光谱差异性降低,能实现土壤有机质在不同土壤含水量梯度的有效估算,偏最小二乘回归和支持向量机回归模型的预测偏差比分别提高至1.76和2.15。研究结果可为田间快速预测土壤有机质提供必要参考。     

基于dbiPLS-SPA变量筛选的固态发酵湿度近红外光谱检测

为了提高基于近红外光谱技术的固态发酵关键过程参数——湿度快速检测的精度和稳定性,研究采用动态反向区间偏最小二乘（dbiPLS）法结合连续投影算法（SPA）进行最佳光谱子区间和特征组合变量的筛选,通过交互验证法确定偏最小二乘（PLS）模型的主成分因子数,并以预测均方根误差（RMSEP）和相关系数（Rp）作为模型的评价标准。试验结果显示,最佳dbiPLS-SPA模型筛选的组合变量个数为8,其RMSEP和Rp分别为1.1795%（质量分数）和0.9430。试验结果表明,dbiPLS-SPA是一个有效的波长组合变量筛选方法,可简化模型结构、增强模型精度和稳健性。     

Parent and harvest year effects on near-infrared reflectance spectroscopic analysis of olive (Olea europaea L.) fruit traits

The influence of parent and harvest year on the determination of oil, moisture, oleic acid, and linoleic acid contents in intact olive fruit was studied by near-infrared spectroscopy (NIRS). Spectral data from 400 to 1700 nm were recorded on 437 fruit samples collected in 1996 and 1997 from seedling plants derived from three different female parents. Partial least squares models were developed using samples for each year and for each female parent separately and were validated against the other groups. Calibration models were accurate enough to predict all constituents in new samples from a different female parent but were not transferable across years. However, a calibration equation of sufficient accuracy was obtained from the combined data set (r values of 0.94, 0.93, 0.84, and 0.88 and RMSECV values of 1.33, 1.88, 4.73, and 2.91 for oil, moisture, oleic acid, and linoleic acid contents, respectively). These results demonstrate the utility of NIRS as a selection tool in olive breeding programs.     

Authentication of Rice Using Near‐Infrared Reflectance Spectroscopy

The authentication of rice (Korean domestic rice vs. foreign rice) has been attempted using near‐infrared spectroscopy (NIRS). Two sample sets (n₁ = 280 and n₂ = 200) were used to obtain calibration equations and the spectral regions used for this study were 500–600 nm, 700–900nm, and 980–2,498 nm. Modified partial least square (MPLS) regression was used to develop the prediction model. The standard error of cross validation (SECV) and the r² were 0.165 and 0.91 respectively for 1st calibration set and 0.165 and 0.93 for 2nd calibration set respectively. The results of the independent validation (n₃ = 80) showed that all of 80 samples were identified correctly. Even though authentication of rice was performed successfully using NIRS, the calibration statistics in this study showed that further effort is needed for implementation of NIRS for authentication of rice for industry purposes.