Sugarcane by-products used as soil amendments on a sandy soil: Effects on sugarcane crop nutrition and yield

Agricultural by-products applied as soil amendments have the potential to improve crop production on low organic matter (OM) soils. This two-year study investigated the use of two readily available sugarcane (Saccharum spp.) milling by-products, mill mud, and mill ash, as soil amendments to improve first sugarcane crop (plant cane) and subsequent crop (first ratoon) grown on a sandy Spodosol. Sugarcane was grown in lysimeters receiving mill mud, mill ash, and a 50:50 (v:v) mill mud to mill ash mix. Amendments were applied at low, medium, and high application rates (5, 10, and 15 cm depths, respectively) and then incorporated 30 cm deep. Amendment effects on plant nutrition, soil characteristics, and crop yield were determined. High rate applied mill mud and mill ash had the highest soil OM content for both years and soil OM did not significantly change between crops for all treatments except for high rate mill mud, which increased the second year (ratoon crop). Leaf nutrient levels for nitrogen (N), iron (Fe), and copper (Cu) for all treatments both years were insufficient; nutrient levels for magnesium (Mg), manganese (Mn), and silicon (Si) were within marginal to sufficient range for all treatments both years. All amendments produced significantly higher biomass and sucrose yields for plant cane and first ratoon crops compared to the control. Mill ash treatments produced the greatest increase in sucrose and biomass yields from plant cane to ratoon crops, which corresponded with an increase in potassium (K) in leaf tissue. However, mid and high rates of mix produced the highest sugarcane biomass and sucrose yields for the both the plant cane and ratoon crops.     

近红外光谱法快速测定土壤碱解氮、速效磷和速效钾含量

运用偏最小二乘法(PLS)和人工神经网络(ANN)方法分别建立了0.9 mm筛分风干黑土土壤碱解氮、速效磷和速效钾含量预测的近红外光谱(NIRS)分析模型。使用偏最小二乘算法建立的碱解氮、速效磷和速效钾校正模型的决定系数R²分别为0.9520、0.8714和0.7300，平均相对误差分别为3.42%、13.40%和7.40%。人工神经网络方法建立的碱解氮、速效磷和速效钾校正模型的决定系数分别为0.9563、0.9493和0.9522，相对误差分别为2.67%、6.48%和2.27%，测试集仿真的相对误差分别为5.44%、16.65%和7.87%。结果表明，人工神经网络方法所建立的校正模型均优于偏最小二乘法所建模型；用近红外光谱分析法预测土壤碱解氮含量是可行的，而速效磷、速效钾模型的测试集样品仿真的相对误差较大，其预测可行性还需做进一步研究。     

Nondestructive prediction of total phenolics, flavonoid contents, and antioxidant capacity of rice grain using near-infrared spectroscopy

Phytochemicals such as phenolics and flavonoids, which are present in rice grains, are associated with reduced risk of developing chronic diseases such as cardiovascular disease, type 2 diabetes, and some cancers. The phenolic and flavonoid compounds in rice grain also contribute to the antioxidant activity. Biofortification of rice grain by conventional breeding is a way to improve nutritional quality so as to combat nutritional deficiency. Since wet chemistry measurement of phenolic and flavonoid contents and antioxidant activity are time-consuming and expensive, a rapid and nondestructive predictive method based on near-infrared spectroscopy (NIRS) would be valuable to measure these nutritional quality parameters. In the present study, calibration models for measurement of phenolic and flavonoid contents and antioxidant capacity were developed using principal component analysis (PCA), partial least-squares regression (PLS), and modified partial least-squares regression (mPLS) methods with the spectra of the dehulled grain (brown rice). The results showed that NIRS could effectively predict the total phenolic contents and antioxidant capacity by PLS and mPLS methods. The standard errors of prediction (SEP) were 47.1 and 45.9 mg gallic acid equivalent (GAE) for phenolic content, and the coefficients of determination ( r (2)) were 0.849 and 0.864 by PLS and mPLS methods, respectively. Both PLS and mPLS methods gave similarly accurate performance for prediction of antioxidant capacity with SEP of 0.28 mM Trolox equivalent antioxidant capacity (TEAC) and r (2) of 0.82. However, the NIRS models were not successful for flavonoid content with the three methods ( r (2) < 0.4). The models reported here are usable for routine screening of a large number of samples in early generation screening in breeding programs.     

Near-Infrared Reflectance Spectroscopy (NIRS) assessment of δ(18)O and nitrogen and ash contents for improved yield potential and drought adaptation in maize

The oxygen isotope composition (δ(18)O), accumulation of minerals (ash content), and nitrogen (N) content in plant tissues have been recently proposed as useful integrative physiological criteria associated with yield potential and drought resistance in maize. This study tested the ability of near-infrared reflectance spectroscopy (NIRS) to predict δ(18)O and ash and N contents in leaves and mature kernels of maize. The δ(18)O and ash and N contents were determined in leaf and kernel samples from a set of 15 inbreds and 18 hybrids grown in Mexico under full irrigation and two levels of drought stress. Calibration models between NIRS spectra and the measured variables were developed using modified partial least-squares regressions. Global models (which included inbred lines and hybrids) accurately predicted ash and N contents, whereas prediction of δ(18)O showed lower results. Moreover, in hybrids, NIRS clearly reflected genotypic differences in leaf and kernel ash and N contents within each water treatment. It was concluded that NIRS can be used as a rapid, cost-effective, and accurate method for predicting ash and N contents and as a method for screening δ(18)O in maize with promising applications in crop management and maize breeding programs for improved water and nitrogen use efficiency and grain quality.     

RAPID DIAGNOSIS OF NUTRIENT ELEMENTS IN FINGERED CITRON LEAF USING NEAR INFRARED REFLECTANCE SPECTROSCOPY

In order to provide references for leaf nutrition diagnosis of fingered citron, the technique of near infrared reflectance spectroscopy (NIRS) was introduced to analyze nitrogen (N), phosphorus (P), potassium (K), iron (Fe), manganese (Mn), zinc (Zn), and copper (Cu) in the dry-leaf samples of fingered citron. The best calibration model for N was developed with high RSQ_CAL (0.90), SD/SECV (2.73) and low SEC (1.06 mg g^?1), good calibration models were obtained for P, K, Fe and Mn, and no significant correlations were found between the spectra and the individual amounts of Zn and Cu. When tested using a validation set (n = 38), N was well predicted with low values of SEP (1.21 mg g^?1) and high RPD (2.5). The values of SEP and RPD were also acceptable for the external validation of P, Fe and Mn. Near-infrared spectroscopy analysis technique shows potential of diagnosing minerals in fingered citron, particularly for N, P, Fe and Mn.     

近红外光谱法测定玉米秸秆饲用品质

为了对玉米秸秆的饲用品质进行可靠、便捷、快速的分析和评价,该研究以不同品种、密度、氮肥和水分处理的不同发育时期和不同部位玉米秸秆为试验材料,应用近红外光谱（NIRS）技术和偏最小二乘法（PLS）,采用一阶导数+中心化+多元散射校正的光谱数据预处理方法,构建了玉米秸秆体外干物质消化率（IVDMD）、酸性洗涤纤维（ADF）、中性洗涤纤维（NDF） 和可溶性糖（WSC）含量的NIRS分析模型。所建立的IVDMD、ADF、NDF和WSC含量的NIRS校正模型决定系数（R2cal）分别为0.9906、0.9870、0.9931和0.9802,交叉验证决定系数（R2cv）分别为0.9593、0.9413 、0.9678和0.9342,外部验证决定系数（R2val）分别为0.9549、0.9353、0.9519和0.9191,各项标准差（SEC、SECV和SEP）为0.935～1.904,相对分析误差（RPD）均大于3。结果表明,各参数的NIRS分析模型可用于玉米秸秆饲用品质的分析和品种选育的快速鉴定。     

Distribution of Total,Water‐Unextractable,and Water‐Extractable Arabinoxylans in Wheat Flour Mill Streams

Arabinoxylans are a minor but important constituent in wheat that affects bread quality, foam stability, batter viscosity, and sugar snap cookie diameter. Therefore, it is important to determine the distribution of arabinoxylans in flour mill streams to better formulate flour blends. Thirty‐one genetically pure grain lots representing six wheat classifications common to the western U.S. were milled on a Miag Multomat pilot mill, and 10 flour mill streams were collected from each. A two‐way ANOVA indicated that mill streams were a greater source of variation compared to grain lots for total arabinoxylans (TAX), water‐unextractable arabinoxylans (WUAX), and water‐extractable arabinoxylans (WEAX). TAX and WUAX were highly correlated with ash at r = 0.94 and r = 0.94, respectively; while the correlation for WEAX and ash decreased in magnitude at r = 0.60. However, the 5th middlings mill streams exhibited disparity between TAX and ash content as well as between WUAX and ash content. This may indicate that TAX and WUAX in mill streams are not always the result of bran contamination. Cumulative extraction curves for TAX, WUAX and WEAX revealed increasing gradients of arabinoxylans parallel to extraction rate. Therefore, arabinoxylans may be an indicator of flour refinement.     

Near-infrared reflectance spectroscopy (NIRS) for protein, tryptophan, and lysine evaluation in quality protein maize (QPM) breeding programs

Quality protein maize (QPM) has approximately twice the tryptophan (Trp) and lysine (Lys) concentrations in protein compared to normal maize. Because several genetic systems control the protein quality of QPM, it is essential to regularly monitor Trp and/or Lys in breeding programs. Our objective was to examine the potential of near-infrared reflectance spectroscopy (NIRS) to enhance the efficiency of QPM research efforts by partially replacing more expensive and time-consuming wet chemistry analysis. More than 276 maize samples were used to develop NIRS models for protein content (PC), Trp, and Lys. The standard error of prediction (SEP) for the calibration and the coefficient of determination for validation (R(2)(v)) were 0.26 and 0.96 for PC, 0.005 and 0.85 for Trp, and 0.02 and 0.75 for Lys. When the NIRS models were used to evaluate 266 S2 lines from five QPM breeding populations, the coefficients of determination between NIRS and the chemical data were 0.94, 0.76, and 0.80 for PC, Trp, and Lys, respectively. Therefore, the NIRS models can be used to support the QPM breeding efforts.     

Quality evaluation of sugar beet (Beta vulgaris) by near-infrared spectroscopy

The legal method (polarimetric measurement) for the determination of sucrose content and the wet chemical analysis for the quality control of sugar beet uses lead acetate. Because heavy metals are pollutants, the law could forbid their use in the future. Therefore, near-infrared spectroscopy (NIRS) was evaluated as a procedure to replace these methods. However, there are alternatives to lead clarification, such as the use of aluminum salts, which have been applied at many sugar companies. The real advantage of NIRS is in speed and ease of analysis. The aim of this study was to determine simultaneously the concentration of several components which define the industrial quality of beets. The first objective was the determination of sucrose content, which determines the sugar beet price. The standard error of prediction (SEP) was low: 0.11 g of sucrose/100 g of fresh beet. NIRS was also able to determine other beet quality parameters: brix, marc, glucose, nitrogen, sodium, potassium, sugar in molasses (i.e. sucrose in molasses), and juice purity. The results concerning brix, marc, sugar in molasses, and juice purity were satisfactory. NIRS accuracy was lower for the other parameters. Nevertheless, RPD (ratio standard deviation of concentration/SEP) and RER (ratio concentration range/SEP ratio) show that NIRS might be used for the sample screening on nitrogen, potassium, sodium, and glucose content.     

Use of near‐infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest‐floor material in soils: usefulness of a genetic algorithm

Several algorithms exist for the calibration procedures of near‐infrared spectra in soil‐scientific studies, but the potential of a genetic algorithm (GA) for spectral feature selection and interpretation has not yet been sufficiently explored. Objectives were (1) to test the usefulness of near‐infrared spectroscopy (NIRS) for a prediction of C and N from char and forest‐floor Oa material in soils using either a partial least squares (PLS) method or a GA‐PLS approach and (2) to discuss the mechanisms of GA feature selection for the examined constituents. Calibration and validation were carried out for measured reflectance spectra in the visible and near‐IR region (400–2500 nm) on an existing set of 432 artificial mixtures of C‐free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals), and forest‐floor Oa material. For all constituents (total C and N, C and N from all coals and from the Oa material, C derived from mixed coal, charcoal, lignite, and anthracite), the GA‐PLS approach was superior over the full‐spectrum PLS method. The RPD values (ratio of standard deviation of the laboratory results to standard error of prediction) ranged from 2.4 to 5.1 in the validation and indicated a better category of prediction for three constituents: “approximate quantitative” instead of a “distinction between high and low” for C derived from mixed coal and “good” instead of “approximate quantitative” for C and N derived from all coals. Overall, this study indicates that the approach using GA may have a greater potential than the PLS method in NIRS.     

Estimating hydrogen cyanide in forage sorghum ( Sorghum bicolor ) by near-infrared spectroscopy

Hydrogen cyanide (HCN) is a toxic chemical that can potentially cause mild to severe reactions in animals when grazing forage sorghum. Developing technologies to monitor the level of HCN in the growing crop would benefit graziers, so that they can move cattle into paddocks with acceptable levels of HCN. In this study, we developed near-infrared spectroscopy (NIRS) calibrations to estimate HCN in forage sorghum and hay. The full spectral NIRS range (400-2498 nm) was used as well as specific spectral ranges within the full spectral range, i.e., visible (400-750 nm), shortwave (800-1100 nm) and near-infrared (NIR) (1100-2498 nm). Using the full spectrum approach and partial least-squares (PLS), the calibration produced a coefficient of determination (R(2)) = 0.838 and standard error of cross-validation (SECV) = 0.040%, while the validation set had a R(2) = 0.824 with a low standard error of prediction (SEP = 0.047%). When using a multiple linear regression (MLR) approach, the best model (NIR spectra) produced a R(2) = 0.847 and standard error of calibration (SEC) = 0.050% and a R(2) = 0.829 and SEP = 0.057% for the validation set. The MLR models built from these spectral regions all used nine wavelengths. Two specific wavelengths 2034 and 2458 nm were of interest, with the former associated with C═O carbonyl stretch and the latter associated with C-N-C stretching. The most accurate PLS and MLR models produced a ratio of standard error of prediction to standard deviation of 3.4 and 3.0, respectively, suggesting that the calibrations could be used for screening breeding material. The results indicated that it should be feasible to develop calibrations using PLS or MLR models for a number of users, including breeding programs to screen for genotypes with low HCN, as well as graziers to monitor crop status to help with grazing efficiency.     

Rapid prediction of acid detergent fiber, neutral detergent fiber, and acid detergent lignin of rice materials by near-infrared spectroscopy

A rapid predictive method based on near-infrared spectroscopy (NIRS) was developed to measure acid detergent fiber (ADF), neutral detergent fiber (NDF), and acid detergent lignin (ADL) of rice stem materials. A total of 207 samples were divided into two subsets, one subset (approximately 136 samples) for calibration and cross-validation and the other subset for independent external validation to evaluate the calibration equations. Different mathematical treatments were applied to obtain the best calibration and validation results. The highest coefficient of determination for calibration (R2) and coefficient of determination for cross-validation (1-VR) were 0.968 and 0.949 for ADF, 0.846 and 0.812 for NDF, and 0.897 and 0.843 for ADL, respectively. Independent external validation still gave a high coefficient of determination for external validation (r2) and a low standard error of performance (SEP) for the three parameters; the best validation results were SEP = 0.933 and r2 = 0.959 for ADF, SEP = 2.228 and r2 = 0.775 for NDF, and SEP = 0.616 and r2 = 0.847 for ADL, indicating that NIR gave a sufficiently accurate prediction of ADF and ADL content of rice material but a less satisfactory prediction for NDF. This study suggested that routine screening for these forage quality parameters with large numbers of samples is possible with NIRS in early-generation selection in rice-breeding programs.     

Amelioration of Acidic Soil using Two Modified Industrial By-products: Rice Productivity and Environmental Safety

In this study, two modified industrial by-products (modified flue gas desulfurization residue (M FGD) residue and modified white mud (M white mud) were applied to an acidic soil at the rate of 750 kg ha^?1. Growth of rice (Oryza sativa L.) significantly increased compared with the untreated control. Increasing soil pH was observed as a common mechanism of action for M FGD and M white mud. Furthermore, the concentrations of organic matter, calcium (Ca), magnesium (Mg), silicon (Si), phosphorus (P), and potassium (K) in the treated soil were significantly increased. Compared with the untreated control, the concentrations of lead (Pb), cadmium (Cd), mercury (Hg), and arsenic (As) in rice grain grown in the treated soil also significantly decreased and were far less than the tolerance limits set by the National Food Standards of China. The quality of rice grain was improved after the application of modified industrial by-products. These results demonstrated that the two modified industrial by-products can be applied to modify acidic soil safely and effectively.     

Use of near‐infrared reflectance spectroscopy to assess nitrogen concentration in different plant tissues of rapeseed

Abstract

An evaluation of the performance of near‐infrared reflectance spectroscopy (NIRS) in the analysis of nitrogen (N) concentration in different rapeseed (Brassica napus L.) tissues was made. A total of 228 samples from an N‐efficiency study corresponding to leaves and stems at flowering, fallen leaves, mature stems, and mature pod walls were oven dried, ground, and then analyzed by NIRS. The N concentration was determined by Dumas combustion. Two different calibration strategies were followed: (i) separate calibration equations were developed for each type of tissue, resulting in r² above 0.95 in crossvalidation for all tissues with the ratio of the standard error of crossvalidation (SECV) to the standard deviation of the population (SD) ranging from 0.10 to 0.22, and (ii) a NIRS calibration equation was developed from a set integrating 149 samples from the five groups of tissues. External . validation with a set containing 79 further samples from all the groups resulted in an r² of 0.99 and a ratio of the standard error of performance (SEP) to the SD of 0.08. External validation for each group separately resulted in r² from 0.91 to 0.99 and SEP/SD from 0.10 to 0.27. It was concluded that a universal NIRS calibration equation integrating samples from all the types of tissues is an adequate approach for the accurate analysis of N concentration in rapeseed. Based on our results, the NIRS technique can reliably replace the Kjeldahl or Dumas methods to determine the N concentration in investigations of the N efficiency in rapeseed.     

Response of Container-Grown Nursery Crops to Raw and Composted Paper Mill Sludges

Growth and nutrient uptake of three container grown nursery crops were compared using different potting media, including bark mixed with varying amounts, 0, 33, 67, and 100% by volume, of raw paper mill sludge and Phase I (fresh) and Phase II (aged) sludge composts. Species grown were: Tartarian dogwood (Cornus alba L.), Coral Beauty cotoneaster (Cotoneaster dammeri C. K. Schneid.), and Variegata Nana weigela [Weigela florida (Bunge) A.DC.]. Each compost consisted of ca. 40% paper mill sludge. Despite differences in response of species to the amount and source of sludge, plants grew well in media containing Phase I and II composts and produced plants of marketable size at harvest. Media with >33% raw sludge resulted in fewer marketable plants and had a greater volume reduction with increasing amounts of sludge. This was also accompanied by declining shoot and root dry weight. Media containing Phase I compost showed less volume reduction than those with raw sludge, and yielded growth comparable to that obtained with Phase II compost. Media containing Phase II compost showed only marginal volume reduction. Changes in leaf N, P, K, Ca, Mg, Fe, Mn, and Zn were small, or nonsignificant. All nutrients except N were related to growth or amount of raw sludge or compost, although all species did not show the same response with each nutrient. The results showed that up to 33% of raw paper mill sludge or any amount of sludge compost was an effective substitute for bark. Since growth was not affected substantially by the age of the compost, the additional time and cost of producing Phase II compost may be unwarranted.     

Performance comparison of UV and FT-Raman spectroscopy in the determination of conjugated linoleic acids in cow milk fat

The determination of conjugated linoleic acids (CLA) in cow milk fat was studied by using UV (210-250 nm) and Fourier transform (FT)-Raman (900-3400 cm (-1)) spectroscopy in order to determine the best spectrophotometric technique for routine analysis of milk fat. A collection of 57 milk fat samples was randomly divided into two sets, a calibration set and a validation set, representing two-thirds and one-third of the samples, respectively. All calculations were performed on the calibration set and then applied to the validation set. The CLA content ranged from 0.56 to 4.70%. A comparison of various spectral pretreatments and different multivariate calibration techniques, such as partial least-squares (PLS) and multiple linear regression (MLR), was done. This paper shows that UV spectroscopy is as reliable as FT-Raman spectroscopy to monitor CLA in cow milk fat. The best calibration for FT-Raman was given by a PLS model of seven factors with a standard error of prediction (SEP) of 0.246. For UV spectroscopy, PLS models were also better than MLR models. The most robust PLS model was constructed with only one factor and with SEP=0.288.     

Sugarcane bagasse amendment mitigates nutrient leaching from a mineral soil under tropical conditions

Large quantities of organic by-products are generated by the sugarcane industry during sugar extraction process. These by-products may be used as soil amendments to improve soil quality, as nutrient leaching is common in mineral soils of Florida in USA due to their sandy texture and frequent rain events. A soil column study was designed to evaluate the effects of bagasse application at 85 t ha^-1 of fresh bagasse, 170 t ha^-1 of fresh bagasse, and 170 t ha^-1 of fresh bagasse + nitrogen (N) on the leaching potential of carbon (C), N, phosphorus (P), and potassium (K). Bagasse was incorporated within the topsoil (0-15 cm) in outdoor soil columns exposed to natural conditions, with periodical irrigation during the experiment. After approximately 57 weeks, the distributions of C, N, P, and K were evaluated at three soil depths (i.e., 0-15, 15-30, and 30-53 cm) within the soil columns. Total organic C (TOC), N, and P in leachates decreased significantly from the soils amended with bagasse compared with the control with no bagasse and no N. Based on K content changes in the columns, bagasse could also be utilized as a potential source of K for plants. Overall, application of bagasse as a soil amendment might be an effective way to sustainably reutilize organic by-products while reducing concerns regarding major nutrients entering groundwater resources. The results of this study could assist in developing nutrient management plans of using sugarcane bagasse as a potential soil amendment in mineral soils.     

大米直链淀粉含量的近红外光谱分析

大米的直链淀粉含量是影响大米蒸煮和加工特性的最重要因素之一,常被用作蒸煮米质构特性评价指标。该文对不同粒度、不同类型大米样品进行了近红外光谱分析,建立了大米直链淀粉含量的预测模型,（精米样品）预测值与化学分析值的相关系数达0.95。预测标准差、平均相对误差分别为0.56和3.1%。     

Development of a methodology for calcium, iron, potassium, magnesium, manganese, and zinc quantification in teas using X-ray spectroscopy and multivariate calibration

In this study an analytical methodology for food analyses combining X-ray spectroscopy (XRS) with partial least-squares (PLS) data treatment was developed. Fifteen tea samples were purchased at a local market, and XRS spectra were obtained without sample pretreatment. For comparison of the metal concentrations, the samples were also mineralized, and six elements were determined using flame atomic absorption spectrometry (Ca, Fe, Mg, and Mn), flame atomic emission spectrometry (K), and thermospray flame furnace atomic absorption spectrometry (Zn). The spectral information obtained from XRS and the metal concentrations found using the alternative techniques were employed to generate six PLS models. The Ca and Mn models required four latent variables (LV), Fe, five LV, K, two LV, and Mg and Zn, three LV. The limits of quantification for these models were 614, 134, 761, 140, 85, and 1 mg kg(-1) for Ca, Fe, K, Mg, Mn, and Zn, respectively.     

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).