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.     

Determination of nitrate in forages by using selective ion electrode: collaborative study

Each of 10 collaborating laboratories analyzed 4 blind duplicate pairs of forage samples for nitrate, by using a potentiometric method. Two forage controls and a 100 000 mg KNO3/L standard were also provided. Nitrate was extracted into an aqueous Al2(SO4)3 solution containing 70 mg KNO3/L and quantitated with a nitrate-selective electrode. Standards were prepared using extracting solution as diluent. Nitrate concentrations in forage samples ranged from less than 0.50 to 4.35% KNO3. Repeatability coefficients of variation (CVo) ranged from 1.74 to 3.61%, and reproducibility coefficients of variation (CVx) ranged from 6.92 to 7.66%. Mean recovery of a 0.55% KNO3 spike was 94.5%. The method has been adopted official first action.     

Comparison of diffuse reflectance fourier transform mid-infrared and near-infrared spectroscopy with grating-based near-infrared for the determination of fatty acids in forages

Diffuse reflectance Fourier transform mid infrared (FTMIR) and near-infrared spectroscopy (FTNIR) were compared to scanning monochromator-grating-based near-infrared spectroscopy (SMNIR), for their ability to quantify fatty acids (FA) in forages. A total of 182 samples from thirteen different forage cultivars and three different harvest times were analyzed. Three calibration analyses were conducted 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. When all samples were used in a one-out partial least squares (PLS) calibration, the average R (2) were FTNIR (0.95) > SMNIR (0.94) > FTMIR (0.91). Constituents C18:2 and C16:0 had among the highest R (2) regardless of the spectroscopic method used. The FTNIR did better for C12:0, C14:0, and C18:3. The SMNIR did better for C16:0, C16:1, C18:0, C18:1, and C18:2. A second set of calibrations developed with half of the samples as the calibration set and the rest as the validation set showed that all the methods produce acceptable calibrations, with calibration R (2) above 0.9 for most constituents. However, the SMNIR had a better average calibration relative error than the FTNIR, which was slightly better than the FTMIR. A third set of calibration equations developed using 100 random PLS runs with the 182 samples split randomly also shows that the three spectral methods are satisfactory for predicting FA. It is not clear whether any of the spectral methods is distinctly better than another. Calibration R (2) and validation R (2) were higher for most FA with the SMNIR than the FTMIR and FTNIR.     

Determination of total dietary fiber of intact cereal food products by near-infrared reflectance

Near-infrared reflectance spectra of cereal food products were acquired with a commercial dual-diode-array (Si, InGaAs) spectrometer customized to allow rapid acquisition of scans of intact breakfast cereals, snack foods, whole grains, and milled products. Substantial gains in the performance of multivariate calibration models generated from these data were obtained by a computational strategy that systematically analyzed the performance of various spectral windows. The calibration model based on 137 cereal food products determined the total dietary fiber (TDF) content of a test set of 45 intact diverse cereal food products with root-mean-squared error of cross-validation of between 1.8 and 2.0% TDF, relative to the laborious enzymatic-gravimetric reference method. The calibration performance is adequate to estimate TDF over the range of values found in diverse types of cereal food products (0.7-50.1%). The method requires no sample preparation and is relatively unaffected by specimen moisture content.     

利用近红外光谱测定玉米非淀粉组分中纤维素及半纤维素含量

玉米非淀粉组分是可再生的生物质资源,为实现玉米皮渣中纤维素及半纤维含量的快速检测,该研究以偏最小二乘法(PLS)建立数学模型,探讨一阶导数及二阶导数平滑等预处理对建模的影响,建立玉米皮渣中纤维素及半纤维素近红外分析模型.研究结果表明,纤维素模型的定标集和验证集相关系数为0.9806和0.9799,定标集标准偏差(SEE...     

Energy from fat determined by near-infrared reflectance spectroscopy

The amount of energy derived from fat in foods is a requirement of U.S. nutrition labeling legislation and a significant factor in diet development by health professionals. Near-infrared (NIR) spectroscopy has been used to predict total utilizable energy in cereal foods for nutrition labeling purposes, and in the current study, was investigated as a method for evaluation of the amount of energy derived from fat. Using NIR reflectance spectra (1104-2494 nm) of ground cereal samples and reference values obtained by calorimetry and by calculation, modified PLS regression models were developed for the prediction of percent energy from fat and energy from fat/g. The models were able to predict the percent of utilizable energy derived from fat with SECV and R(2) of 1.86-1.89% of kcal (n = 51, range 0-43.0) and 0.98, respectively, and SEP and r(2) of 1.74% of kcal (n = 55, range 0-38.0) and 0.98, respectively, when used to predict independent validation samples. Results indicate that NIR spectroscopy provides useful methods for predicting the energy derived from fat in food products.     

Determination of crude protein in animal feeds, using block digestion followed by steam distillation: collaborative study.

A method consisting of digesting animal feeds in a block digestor and determining ammonia by steam distillation followed by titration has been evaluated and compared with the official final action Kjeldahl method, 7.016. Fifteen laboratories analyzed 5 feed samples and lysine monohydrochloride. Statistical analysis showed that results from the 2 methods were comparable. The distillation technique has been adopted as official first action as an alternative technique for ammonia determination from the digest of the official final action block digestor method, 7.B11.     

Rapid detection of kernel rots and mycotoxins in maize by near-infrared reflectance spectroscopy

Near-infrared (NIR) spectroscopy is a practical spectroscopic procedure for the detection of organic compounds in matter. It is particularly useful because of its nondestructiveness, accuracy, rapid response, and easy operation. This work assesses the applicability of NIR for the rapid identification of micotoxigenic fungi and their toxic metabolites produced in naturally and artificially contaminated products. Two hundred and eighty maize samples were collected both from naturally contaminated maize crops grown in 16 areas in north-central Italy and from ears artificially inoculated with Fusarium verticillioides. All samples were analyzed for fungi infection, ergosterol, and fumonisin B1 content. The results obtained indicated that NIR could accurately predict the incidence of kernels infected by fungi, and by F. verticillioides in particular, as well as the quantity of ergosterol and fumonisin B1 in the meal. The statistics of the calibration and of the cross-validation for mold infection and for ergosterol and fumonisin B1 contents were significant. The best predictive ability for the percentage of global fungal infection and F. verticillioides was obtained using a calibration model utilizing maize kernels (r2 = 0.75 and SECV = 7.43) and maize meals (r2 = 0.79 and SECV = 10.95), respectively. This predictive performance was confirmed by the scatter plot of measured F. verticillioides infection versus NIR-predicted values in maize kernel samples (r2 = 0.80). The NIR methodology can be applied for monitoring mold contamination in postharvest maize, in particular F. verticilliodes and fumonisin presence, to distinguish contaminated lots from clean ones, and to avoid cross-contamination with other material during storage and may become a powerful tool for monitoring the safety of the food supply.     

Analysis of fiber content in flax stems by near-infrared spectroscopy

The conventional means of measuring the fiber content of flax is time-consuming and laborious, and the results obtained vary with the analysis technique used. The plant tissues must first be "retted", a process by which the fibers are separated from the rest of the stem, either by indigenous organisms in the soil when the stems are left in the field or by water (anerobic bacteria) or enzymatic retting. The fiber content is then determined by mechanical or manual separation. In this study, fiber content of flax stems was measured rapidly and objectively by near-infrared spectroscopy (NIRS) using whole pieces of stem in a large cell, in reflectance mode. Compared to the conventional method, the standard error of performance of the NIRS method was between 0.96 and 1.45% (dry matter basis), depending on the model and data processing used. NIRS calibrations were generated by hand separation of fiber from water-retted specimens. The water retting procedure takes several days to complete and requires considerable trained labor to complete the hand separation step. The NIRS procedure was conducted on pieces of stem to simulate measurement in the field.     

Generic combustion method for determination of crude protein in feeds: collaborative study

Nine laboratories participated in a collaborative study on determination of crude protein in animal feeds to compare a generically described combustion method with the AOAC mercury catalyst Kjeldahl method (7.015). The combustion method was written in general terms of method principle, apparatus specifications, and performance requirements. The sample set comprised closely matched pairs of feed ingredients and mixed products ranging from 10 to 90% protein. Ten pairs ground to 0.5 mm were the focus of the study; 4 pairs were ground to 1.0 mm for comparison. Nicotinic acid and lysine monohydrochloride were included as standards. Collaborators were instructed to report their results for performance checks using materials supplied. Only one laboratory failed to meet the proposed limits. Seven laboratories used the LECO Model FP-228 analyzer and 2 used the LECO CHN 600 analyzer. For the 0.5 mm pairs, repeatability standard deviations (Sr) ranged from 0.09 to 0.58 for the Kjeldahl method and from 0.14 to 0.33 for the combustion method, with a pooled Sr value of 0.28 and relative standard deviation (RSDr) of 0.59%. Reproducibility standard deviations (Sg) ranged from 0.23 to 0.86 (Kjeldahl) and from 0.30 to 0.61 (combustion), with a pooled Sg value of 0.52 and RSDg of 1.10%. Grand means for the samples ground to 0.5 mm were 47.65% protein by the combustion method and 47.41% protein by the Kjeldahl method. For samples ground to 1.0 mm, corresponding values were 31.82 and 31.50% protein. The generic combustion method has been approved interim official first action.     

Acid detergent fiber analysis in oilseed Brassicas by near-infrared spectroscopy

The potential of near infrared spectroscopy (NIRS) for determining the acid detergent fiber (ADF) in the seed of oilseed Brassica (fam. Brassicaceae) was assessed. One hundred and fifty accessions belonging to the species Indian mustard (Brassica juncea L. Czern.& Coss.), Ethiopian mustard (B. carinata A. Braun) and rapeseed (B. napus L.) were scanned by NIRS as intact and ground seed, and their ADF values were regressed against different spectra transformations by modified partial least squares regression. The coefficients of determination in the external validation (r(2)) for intact and ground seed were 0.83 and 0.85, respectively. The standard deviation to standard error of prediction ratio and range to standard error of prediction ratio were 2.40 and 10.75 for intact seed and 2.62 and 11.76 for ground seed. No significant differences in the prediction were found for both sample presentations. Effects of the C-H and O-H groups of lipids and water, respectively, as well as protein and chlorophyll, were most important in modeling these equations.     

Prediction of Japanese green tea ranking by fourier transform near-infrared reflectance spectroscopy

A rapid and easy determination method of green tea's quality was developed by using Fourier transform near-infrared (FT-NIR) reflectance spectroscopy and metabolomics techniques. The method is applied to an online measurement and an online prediction of green tea's quality. FT-NIR was employed to measure green tea metabolites' alteration affected by green tea varieties and manufacturing processes. A set of ranked green tea samples from a Japanese commercial tea contest was analyzed to create a reliable quality-prediction model. As multivariate analyses, principal component analysis (PCA) and partial least-squares projections to latent structures (PLS) were used. It was indicated that the wavenumber region from 5500 to 5200 cm(-1) had high correlation with the quality of the tea. In this study, a reliable quality-prediction model of green tea has been achieved.     

Determination of total dietary fiber in foods and food products: collaborative study

A collaborative study was conducted to determine the total dietary fiber (TDF) content of food and food products, using a combination of enzymatic and gravimetric procedures. The method was basically the same as published earlier (J. Assoc. Off. Anal. Chem. (1984) 67, 1044-1052), with changes in the concentration of alcohol and buffers, time of incubation, sample preparation, and some explanatory notes, all with the intent of decreasing the coefficient of variation (CV) of the method. Duplicate blind samples of soy isolate, white wheat flour, rye bread, potatoes, rice, wheat bran, oats, corn bran, and whole wheat flour were analyzed by 9 collaborators. TDF was calculated as the weight of the residue minus the weight of protein and ash. CV values of the data from all laboratories for 7 of the samples ranged from 1.56 to 9.80%. The rice and soy isolate samples had CV values of 53.71% and 66.25%, respectively; however, each sample contained only about 1% TDF. The enzymatic-gravimetric method for determining TDF has been adopted official first action.     

Rapid estimation of taro (Colocasia esculenta) quality by near-infrared reflectance spectroscopy

The aim of the present study is to develop a methodology for the rapid estimation of taro (Colocasia esculenta) quality. Chemical analyses were conducted on 315 accessions for major constituents (starch, total sugars, cellulose, proteins, and minerals). NIRS calibration equations, developed on a calibration set composed of 243 accessions, showed high explained variances in cross-validation (r(2)(cv)) for starch (0.89), sugars (0.90), proteins (0.89), and minerals (0.90) but poor response for amylose (0.44) and cellulose (0.61). The predictions were tested on an independent set of 58 randomly selected accessions. The r(2)(pred) values for starch, sugars, proteins, and minerals were, respectively, of 0.76, 0.74, 0.85, and 0.85 with ratios of performance to deviation (RPD) of 3.41, 4.01, 3.78, and 3.64. New calibration equations developed on 303 accessions confirmed good RPD values for starch (3.30), sugars (4.13), proteins (3.61), and minerals (3.74). NIRS could be used to predict starch, sugars, proteins, and minerals contents in taro corms with reasonably high confidence.     

Determination of kavalactones in dried kava (Piper methysticum) powder using near-infrared reflectance spectroscopy and partial least-squares regression

Kava (Piper methysticum Forst F.), or àwa in the Hawaiian language, has been used for thousands of years by the people of the South Pacific Islands, in particular Fiji, Vanuatu, Tonga, and Samoa, for social and ceremonial occasions. Kava has the unique ability to promote a state of relaxation without the loss of mental alertness. Kava recently became part of the herbal pharmacopoeia throughout the United States and Europe because of its anxiolytic properties. The active compounds are collectively called kavalactones (or kava pyrones). The need for a less time-consuming and costly method to determine the concentration of kavalactones in dried kava is urgent. The combination of near-infrared reflectance spectroscopy (NIRS) and partial least-squares (PLS) methods has been found to be a convenient, versatile, and rapid analytical tool for determination of kavalactones in dried kava powder. Calibration equations were developed based on the analyses of 110 samples with variable physical and chemical properties collected over time from Hawaii kava growers and validated by analyses of a set of 12 samples with unknown kavalactones concentration. All six major kavalactones and the total kavalactones were measured using NIRS with accuracy acceptable for commercial use. The NIRS measurements are reproducible and have a repeatability on a par with HPLC methods.     

Determination of moisture in Cheddar cheese by near infrared reflectance spectroscopy

Near infrared reflectance (NIR) spectroscopy was used to determine the moisture content of Cheddar cheese. Through multiple linear regression analysis, a 3-wavelength calibration was developed for use with a commercial filter monochromator instrument. For a validation set of 47 samples, the correlation coefficient squared (r2) between the NIR and oven moisture methods was 0.92, with a standard error of performance (SEP) of 0.38%. Sample temperature was found to significantly affect the spectral response; therefore, it was necessary to equilibrate all samples to a uniform temperature prior to NIR analysis. Aging may also affect the NIR characteristics of cheese, although it was possible to develop a successful calibration that encompassed a wide range of aging times.     

Assessment and monitoring of soil quality using near-infrared reflectance spectroscopy (NIRS)

Soil degradation processes have dramatically increased in their extent and intensity over the last decades. Progressively, actions have been taken in order to evaluate and reduce the major threats that have already wreaked havoc on soil conditions. Efficient and standardized monitoring of soil conditions is thus required but soil quality research is facing an important technological challenge because of the number of properties involved in soil quality. The objective of the present review is to examine critically the suitability of near-infrared reflectance spectroscopy (NIRS) as a tool for soil quality assessment. We first detail the soil quality-related parameters (chemical, physical and biological) that can be predicted with NIRS through laboratory measurements. The ability of imaging NIRS (airborne or satellite) for mapping a minimum data set of soil quality is also discussed. Then we review the most recent research using soil reflectance spectra as an integrated measure of soil quality, from global site classification to the prediction of specific soil quality indices. We conclude that imaging NIRS enables the direct mapping of some soil properties and soil threats, but that further developments to solve several technological limitations identified are needed before it can be used for soil quality assessment. The robustness of laboratory NIRS for soil quality assessment allows its implementation in soil monitoring networks. However, its routine use requires the development of international soil spectral libraries that should become a priority for soil quality research.     

Determination of polarimetric parameters of honey by near-infrared transflectance spectroscopy.

NIR transflectance spectroscopy was used to determine polarimetric parameters (direct polarization, polarization after inversion, specific rotation in dry matter, and polarization due to nonmonosaccharides) and sucrose in honey. In total, 156 honey samples were collected during 1992 (45 samples), 1995 (56 samples), and 1996 (55 samples). Samples were analyzed by NIR spectroscopy and polarimetric methods. Calibration (118 samples) and validation (38 samples) sets were made up; honeys from the three years were included in both sets. Calibrations were performed by modified partial least-squares regression and scatter correction by standard normal variation and detrend methods. For direct polarization, polarization after inversion, specific rotation in dry matter, and polarization due to nonmonosaccharides, good statistics (bias, SEV, and R(2)) were obtained for the validation set, and no statistically (p = 0.05) significant differences were found between instrumental and polarimetric methods for these parameters. Statistical data for sucrose were not as good as those of the other parameters. Therefore, NIR spectroscopy is not an effective method for quantitative analysis of sucrose in these honey samples. However, NIR spectroscopy may be an acceptable method for semiquantitative evaluation of sucrose for honeys, such as those in our study, containing up to 3% of sucrose. Further work is necessary to validate the uncertainty at higher levels.     

Determination of poly(ethylene glycol)-binding to browse foliage, as an assay of tannin, by near-infrared reflectance spectroscopy

Nutritionists are interested in functional assays of tannins that do not require time-consuming and expensive extraction, such as the (14)C-labeled poly(ethylene glycol) (PEG)-binding (PEG-b) assay. This paper reports the application of near-infrared (NIR) spectroscopy to determine the percentage of PEG binding, in place of the (14)C-labeled PEG-b assay of tannin, in Mediterranean woodland vegetation. Calibration was done with 53 samples from 14 species and was validated on 25 samples from 10 species. PEG-b ranged between 1.4 and 20.7% in the samples. The calibration obtained by using the modified partial least-squares (MPLS) method, with all wavelengths in the 1100-2500 nm range combined, and the validation were reasonably linear (R (2) = 0.96 and 0.91, respectively). The accuracies, estimated from the standard errors of cross-validation and prediction, were +/-1.6 and +/-1.7% PEG-b, respectively. The NIRS-aided procedure proposed here can serve as an accurate, inexpensive, time-saving, and environment-friendly functional assay of tannin in Mediterranean browse.