Analysis of nitrogen fractions in silage by near-infrared spectroscopy

Total nitrogen, soluble nitrogen (SN), nonprotein nitrogen (NPN), and acid-detergent insoluble nitrogen (ADIN) were analyzed in grass silage by near-infrared (NIR) spectroscopy. A set of 144 samples was used to calibrate the instrument by modified partial least-squares regression, and the following statistical results were achieved: standard error of calibration (SEC) = 0.449 and square correlation coefficient (R (2)) = 0.98 for total nitrogen x 6.25, SEC = 0.425 and R (2) = 0.95 for SN x 6.25, SEC = 0.414 and R (2) = 0.94 for NPN x 6.25, and SEC = 0.139 and R (2) = 0.84 for ADIN x 6.25. To validate the calibration performed, a set of 48 silage samples was used. Standard errors of prediction were 0.76, 0.64, 0.63, and 0.25 for total nitrogen, SN, NPN, and ADIN (all of them multiplied by 6.25), respectively, and R (2) for the regression of measurements by reference method versus NIR analysis were 0.94, 0.92, 0.90, and 0.48 for total nitrogen, SN, NPN, and ADIN, respectively. To compare the results obtained by NIR spectroscopy with those obtained by the reference methods for total nitrogen, SN, and NPN of the validation set, linear regression and paired t tests were applied, and the results were not significantly different (p = 0.05). When mean square prediction error analysis was applied, it could be concluded that for total nitrogen, SN, and NPN, a robust calibration model was obtained and that the main error was unexplained error. Statistical data for ADIN were worse than those of the other parameters; as a result NIR spectroscopy is not an effective method for quantitative analyses of ADIN in silage; nevertheless, it may be an acceptable method for semiquantitative evaluation.     

Nondestructive determination of oil content and fatty acid composition in perilla seeds by near-infrared spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was used as a rapid and nondestructive method to determine the oil content and fatty acid composition in intact seeds of perilla [Perilla frutescens var. japonica (Hassk.) Hara] germplasms in Korea. A total of 397 samples (about 2 g of intact seeds) were scanned in the reflectance mode of a scanning monochromator, and the reference values for the oil content and fatty acid composition were measured by gravimetric method and gas-liquid chromatography, respectively. Calibration equations for oil and individual fatty acids were developed using modified partial least-squares regression with internal cross validation (n = 297). The equations for oil and oleic and linolenic acid had lower standard errors of cross-validation (SECV), higher R2 (coefficient of determination in calibration), and higher ratio of unexplained variance divided by variance (1-VR) values than those for palmitic, stearic, and linoleic acid. Prediction of an external validation set (n = 100) showed significant correlation between reference values and NIRS estimated values based on the standard error of prediction (SEP), r2 (coefficient of determination in prediction), and the ratio of standard deviation (SD) of reference data to SEP. The models for oil content and major fatty acids, oleic and linolenic acid, had relatively higher values of SD/SEP(C) and r2 (more than 3.0 and 0.9, respectively), thereby characterizing those equations as having good quantitative information, whereas those of palmitic, stearic, and linoleic acid had lower values (below 2.0 and 0.7, respectively), unsuitable for screening purposes. The results indicated that NIRS could be used to rapidly determine oil content and fatty acid composition (oleic and linolenic acid) in perilla seeds in the breeding programs for development of high-quality perilla oil.     

Determination of acid-detergent fiber and crude protein in forages by near-infrared reflectance spectroscopy: collaborative study

A collaborative study was conducted to determine the standard error of difference among laboratories for near-infrared reflectance spectroscopic (NIRS) determination of acid-detergent fiber (ADF) and crude protein in forages. The 6 participating laboratories were members of the USDA/ARS National Near-Infrared Reflectance Spectroscopy Forage Research Project. The NIRS calibration equations were developed in the Associate Referee's laboratory for crude protein and ADF and were transferred to the instrument in each of the other collaborating laboratories. The calibration set included over 650 diverse forage samples with crude protein and ADF calibration data; the validation set included 94 samples of bermudagrass. Among-laboratory reproducibility for the NIRS method, calculated as the relative standard deviation for reproducibility (RSDR), was 1.14% for ADF and 0.42% for crude protein. The variance component for among-laboratory variation (coefficient of variation) was 2.54% for ADF and 2.89% for crude protein. These results confirm that it is possible to calibrate, validate, and transfer (NIRS) equations and data among laboratories for the accurate determination of ADF and crude protein, and thereby demonstrate that NIRS can be used as a standard method for the analysis of forages. The method has been adopted official first action.     

Rapid prediction of solid wood lignin content using transmittance near-infrared spectroscopy

A rapid transmittance near-infrared (NIR) spectroscopic method has been developed to characterize the lignin content of solid wood. Using simple, multiple regression, and partial least-squares statistical analysis the lignin contents of wood wafers, taken from increment cores, and synthetic wood, prepared by blending milled wood lignin and holocellulose, were compared and quantified. Strong correlations were obtained between the predicted NIR results and those obtained from traditional chemical methods. In addition to the experimental protocol and method development, NIR results from wood samples with different particle sizes and various lignin contents are discussed.     

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.     

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.     

Nondestructive determination of total soluble solid content and firmness in plums using near-infrared reflectance spectroscopy

The fruit industry requires rapid, economical, and nondestructive methods for classifying fruit by internal quality, which can be built into the processing line. Total soluble solid content and firmness are the two indicators of plum internal quality that most affect consumer acceptance. These parameters are routinely evaluated using methods which involve destruction of the fruit; as a result, only control batches can be analyzed. The development of nondestructive analytical methods would enable the quality control of individual fruits. Near-IR spectroscopy (NIRS) was used to assess total soluble solid content (SSC, degrees Brix) and firmness (N) in intact plums. A total of 720 plums (Prunus salicina L. cv. 'African Pride', 'Black Diamond', 'Fortune', 'Laetitia', 'Larry Anne', 'Late Royal', 'Prime Time', 'Sapphire', and 'Songold') were used to obtain calibration models based on reference data and near-IR spectral data. Standard errors of cross-validation (SECV) and coefficients of determination for cross-validation (r(2)) were (0.77 degrees Brix; 0.83) for total soluble solids content and (2.54 N; 0.52) for firmness. Results suggest that NIRS technology enables fruit to be classified in terms of total soluble solid content and firmness, thus allowing increased sampling of each production batch and ensuring a given quality with greater precision and accuracy.     

Major components of honey analysis by near-infrared transflectance spectroscopy

NIR transflectance spectroscopy was used to analyze fructose, glucose, and moisture in honey. A total of 161 honey samples were collected during 1992 (46), 1995 (58), and 1996 (57). Samples were analyzed by instrumental, enzymatic (fructose and glucose), and refractometric (moisture) methods. Initially, different calibrations were performed for each of the 3 years of sampling. Good predictions were obtained for all three components with equations of the particular year. But good predictions were not always obtained when the equations calculated one year were applied to samples from another year. To perform a lasting calibration, unique calibration (121 samples) and validation (40 samples) sets were built; honeys of the 3 years were included in both sets. Good statistics (bias, standard error of validation (SEV), and R(2)) were obtained for all three components of the validation set. No statistically significant differences (p = 0.05) were found between instrumental and reference methods.     

Application of near-infrared reflectance spectroscopy (NIRS) to the evaluation of carotenoids content in maize

The importance of including antioxidant compounds in the diet is well recognized. These compounds remediate the detrimental activity on animal cells of the so-called reactive oxygen substances (ROS). Many papers have reported on the determination of both hydrophilic and hydrophobic antioxidant compounds present in a large number of vegetables, and all methods involve the extraction from the matrix of the compounds to be determined. Because some problems may arise, such as the completeness of the extraction and the stability of the extracted compound during the extraction steps, the possibility of analyzing these compounds in the native matrix would be useful. Here is reported the application of near-infrared spectroscopy (NIRS) to the determination of the content of carotenoids in maize, comparing the obtained data with those derived from high-performance liquid chromatography (HPLC) determination of the extract obtained from the same samples. Equations for predicting carotenoid content in maize were derived using scores from modified partial least-squares (MPLS) as independent variables. Cross-validation procedures indicated good correlations between HPLC values and NIRS estimates. The results show that NIRS, a well-established and widely applied technique, can be applied to determine the maize carotenoids and that samples are readily analyzed in minutes, the only required step being their grinding.     

Analysis of sugars in Chinese rice wine by Fourier transform near-infrared spectroscopy with partial least-squares regression

The feasibility of rapid analysis for oligosaccharides, including isomaltose, isomaltotriose, maltose, and panose, in Chinese rice wine by Fourier transform near-infrared (FT-NIR) spectroscopy together with partial least-squares regression (PLSR) was studied in this work. Forty samples of five brewing years (1996, 1998, 2001, 2003, and 2005) were analyzed by NIR transmission spectroscopy with seven optical path lengths (0.5, 1, 1.5, 2, 2.5, 3, and 5 mm) between 800 and 2500 nm. Calibration models were established by PLSR with full cross-validation and using high-performance anion-exchange chromatography coupled with pulsed amperometric detection as a reference method. The optimal models were obtained through wavelength selection, in which the correlation coefficients of calibration (r(cal)) for the four sugars were 0.911, 0.938, 0.925, and 0.966, and the root-mean-square errors of calibrations were 0.157, 0.147, 0.358, and 0.355 g/L, respectively. The validation accuracy of the four models, with correlation coefficients of cross-validation (r(cv)) being 0.718, 0.793, 0.681, and 0.873, were not very satisfactory. This might be due to the low concentrations of the four sugars in Chinese rice wine and the influence of some components having structures similar to those of the four sugars. The results obtained in this study indicated that the NIR spectroscopy technique offers screening capability for isomaltose, isomaltotriose, maltose, and panose in Chinese rice wine. Further studies with a larger Chinese rice wine sample should be done to improve the specificity, prediction accuracy, and robustness of the models.     

Method for determining frying oil degradation by near-infrared spectroscopy

A rapid near-infrared (NIR) spectroscopic method for measuring degradation products in frying oils, including total polar materials (TPMs) and free fatty acids (FFAs), has been developed. Calibration models were developed using both forward stepwise multiple linear regression (FSMLR) and partial least-squares (PLS) regression techniques and then tested with two independent sets of validation samples. Derivative treatments had limited usefulness, especially in the longer (1100-2500 nm) wavelength region. When using a wavelength region of 700-1100 nm, PLS models gave improved results compared to FSMLR models. The best correlations (r) between the NIR and wet chemical methods for TPM and FFA were 0.983 and 0.943, respectively. In the longer wavelength region (1100-2500 nm), FSMLR models were as good or better than PLS models. The best correlations for TPM and FFA obtained in this region were 0.999 and 0.983, respectively.     

Authentication of the botanical origin of honey by near-infrared spectroscopy

Fourier transform near-infrared spectroscopy (FT-NIR) was evaluated for the authentication of eight unifloral and polyfloral honey types (n = 364 samples) previously classified using traditional methods such as chemical, pollen, and sensory analysis. Chemometric evaluation of the spectra was carried out by applying principal component analysis and linear discriminant analysis. The corresponding error rates were calculated according to Bayes' theorem. NIR spectroscopy enabled a reliable discrimination of acacia, chestnut, and fir honeydew honey from the other unifloral and polyfloral honey types studied. The error rates ranged from <0.1 to 6.3% depending on the honey type. NIR proved also to be useful for the classification of blossom and honeydew honeys. The results demonstrate that near-infrared spectrometry is a valuable, rapid, and nondestructive tool for the authentication of the above-mentioned honeys, but not for all varieties studied.     

Analysis of protein structures and interactions in complex food by near-infrared spectroscopy. 2. Hydrated gluten

Hydrated gluten, treated with various salts, was analyzed by near-infrared (NIR) spectroscopy to assess the ability of this method to reveal protein structure and interaction changes in perturbed food systems. The spectra were pretreated with second-derivative transformation and extended multiplicative signal correction for improving the band resolution and removing physical and quantitative spectral variations. Principal component analysis of the preprocessed spectra showed spectral effects that depended on salt type and concentration. Although both gluten texture and the NIR spectra were little influenced by treatment with salt solutions of low concentrations (0.1-0.2 M), they were significantly and diversely affected by treatment with 1.0 M salt solutions. Compared to hydration in water, hydration in 1.0 M sulfate salts caused spectral effects similar to a drying-out effect, which could be explained by salting-out.     

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 chemical composition of commercial honey by near-infrared spectroscopy.

The feasibility of using near-infrared spectroscopy to determine chemical composition of commercial honey was examined. The influences of various sample presentation methods and regression models on the performance of calibration equations were also studied. Transmittance spectra with 1 mm optical path length produced the best calibration for all constituents examined. The regression model of modified partial least squares (mPLS) was selected for the calibration of all honey constituents except moisture, for which the optimal calibration was developed with PLS. Validation of the established calibration equations with independent samples showed that the spectroscopic technique could accurately determine the contents of moisture, fructose, glucose, sucrose, and maltose with squared correlation coefficients (R(2)) of 1.0, 0.97, 0.91, 0.86, and 0.93 between the predicted values and the reference values. The prediction accuracy for free acid, lactone, and hydroxymethylfurfural (HMF) contents in honey was poor and unreliable. The study indicates that near-infrared spectroscopy can be used for rapid determination of major components in commercial honey.     

At-line near-infrared spectroscopy for prediction of the solid fat content of milk fat from New Zealand butter

Near-infrared (NIR) spectroscopy calibrations that will allow prediction of the solid fat content (SFC) of milk fat extracted from butter by one measurement during manufacture were developed. SFC is a measure of the amount of the solid fraction of fat crystallized at a temperature expressed as a percentage (w/w). At-line SFC determinations are currently performed by nuclear magnetic resonance (NMR) spectroscopy, which involves a 16 h delay period for tempering of the milk fat at 0 degrees C prior to the SFC measurements, from 0 to 35 degrees C in a series of 5 degrees C increments. The NIR spectra (400-2500 nm) were obtained using a sample holder maintained at 60 degrees C. Accurate predictions for the SFC (%) were developed by principal component analysis (PCA) and partial least-squares (PLS) regression models to relate the NIR spectra to the corresponding NMR values. The independent validation samples (N = 22) had a standard error of prediction (SEP) of 0.385-0.762% for SFC between 0 and 25 degrees C, with SFC reference values ranging between 70.42 and 8.96% with a standard deviation range of 3.36-1.47. The low bias (from -0.351 to -0.025), the slopes (0.935-1.077), and the excellent predictive ability (R2; 0.923-0.978) supported the validity of these calibrations.     

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.     

Measurement of inorganic phosphorus in soybeans with near-infrared spectroscopy

This study explored the feasibility of near-infrared (NIR) quantitative and qualitative models for soybean inorganic phosphorus (Pi), which is complementary to phytic acid, a component of nutritional and environmental importance. Spectra, consisting of diffuse reflectance (1100-2500 nm) of ground meal and single-bean transmittance (600-1900 nm) of whole seed, were collected on 191 recombinant inbred soybean lines. Partial least-squares regression models were individually developed for soy meal diffuse reflectance, single-bean transmittance, and averaged (24 beans/line) whole seed transmittance data. The best performance was obtained with diffuse reflectance data, in which the standard errors (rmsd) were 263 and 248 mg/kg for cross-validation and validation sets, respectively. Model accuracy was lower for the 24-bean average transmittance spectra and still lower for single beans. Despite the overall poorer modeling ability of Pi with respect to the common macronutrient NIR regressions, such as those for protein and oil, this technique holds promise for use in breeding programs.     

Rapid detection and identification of bacterial strains by Fourier transform near-infrared spectroscopy

The use of Fourier transform near-infrared (FT-NIR) spectroscopy and multivariate pattern recognition techniques for the rapid detection and identification of bacterial contamination in liquids was evaluated. The complex biochemical composition of bacteria yields FT-NIR vibrational transitions (overtone and combination bands) that can be used for classification and identification. Bacterial suspensions (Escherichia coli HB101, E. coli ATCC 43888, E. coli 1224, Bacillus amyloliquifaciens, Pseudomonas aeruginosa, Bacillus cereus, and Listeria innocua) were filtered to harvest the cells and eliminate the matrix, which has a strong NIR signal. FT-NIR measurements were done using a diffuse reflection-integrating sphere. Principal component analysis showed tight clustering of the bacterial strains at the information-rich spectral region of 6000-4000 cm(-1). The method reproducibly distinguished between different E. coli isolates and conclusively identified the relationship between a new isolate and one of the test species. This methodology may allow for the rapid assessment of potential bacterial contamination in liquids with minimal sample preparation.     

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.