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 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 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.     

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.     

Quantification of chlorophyll content and classification of nontransgenic and transgenic tomato leaves using visible/near-infrared diffuse reflectance spectroscopy

Visible/near-infrared (vis/NIR) spectroscopy combined with multivariate analysis was used to quantify chlorophyll content in tomato leaves and classify tomato leaves with different genes. In this study, transgenic tomato leaves with antisense LeETR1 (n = 106) and their parent nontransgenic ones (n = 102) were measured in vis/NIR diffuse reflectance mode. Quantification of chlorophyll content was achieved by partial least-squares regression with a cross-validation prediction error equal to 2.87. Partial least-squares discriminant analysis was performed to classify leaves. The results show that differences between transgenic and nontransgenic tomato leaves do exist, and excellent classification can be obtained after optimizing spectral pretreatment. The classification accuracy can reach to 100% using the derivative of spectral data in the full and partial wavenumber range. These results demonstrate that vis/NIR spectroscopy together with chemometrics techniques could be used to quantify chlorophyll content and differentiate tomato leaves with different genes, which offers the benefit of avoiding time-consuming, costly, and laborious chemical and sensory analysis.     

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.     

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.     

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.     

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.     

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.     

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.     

Application of near-infrared reflectance spectroscopy to the evaluation of rutin and D-chiro-Inositol contents in tartary buckwheat

Tartary buckwheat [Fagopyrum tataricum (L.) Gaench] is rich in rutin and D- chiro-inositol (DCI), which have beneficial effects in the treatment of hemorrhagic diseases and insulin-resistant diseases, respectively. The current methods of extraction and detection of rutin and DCI are complex and time-consuming; a simple way of analyzing these compounds in the native matrix would be desirable. In this work, near-infrared reflectance spectroscopy (NIRS) was applied to determine the contents of rutin and DCI in tartary buckwheat. The spectral data were compared with those determined from high-performance liquid chromatography (HPLC) methods. Models for predicting rutin and DCI contents in buckwheat were developed using a partial least-squares algorithm. Cross-validation procedures indicated good correlations between HPLC data and NIRS predictions (R2 = 0.76 for rutin and R2 = 0.86 for DCI). The rutin content ranged from 0.998 to 1.75%, while the DCI content covered 0.179-0.200%. The results showed that NIRS, a well-established and widely applied technique, could be applied to determine rutin and DCI in tartary buckwheat rapidly and nondestructively.     

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.     

Near-infrared reflectance spectroscopy for predicting amino acids content in intact processed animal proteins

Near-infrared calibrations were developed for the instantaneous prediction of amino acids composition of processed animal proteins (PAPs). Two sample presentation modes were compared (ground vs intact) for demonstrating the viability of the analysis in the intact form, avoiding the need for milling. Modified partial least-squares (MPLS) equations for the prediction of amino acids in PAPs were developed using the same set of samples (N = 92 PAPs) analyzed in ground and intact form and in three cups differing in the optical window size. The standard error for cross validation (SECV) and the coefficient of determination (1-VR) values yielded with the calibrations developed using the samples analyzed in the intact form showed similar or even better accuracy than those obtained with finely ground samples. The excellent predictive ability (1-VR > 0.90; CV < 3.0%) obtained for the prediction of amino acids in intact processed animal proteins opens an enormous expectative for the on-line implementation of NIRS technology in the processing and marketing of these important protein feed ingredients, alleviating the costs and time associated with the routine quality controls.     

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.     

Rapid determination of free fatty acids in poultry feed lipid extracts by SB-ATR FTIR spectroscopy

A simple, rapid, and reproducible method has been developed for the quantitative determination of free fatty acid (FFA) content in lipids extracted from poultry feeds by Fourier transform infrared (FTIR) spectroscopy with the use of a single-bounce attenuated total reflectance (SB-ATR) accessory. An FTIR calibration curve was prepared by gravimetrically adding oleic acid (15-37%) to pure refined, bleached, and deodorized (RBD) canola oil and measuring the area of the COOH absorption band at 1710 cm-1. The oil from each of 12 poultry feed formulations was extracted using conventional Soxhlet extraction, and after evaporation of the solvent, the FFA content was determined by the conventional AOCS titrimetric procedure and by the SB-ATR/FTIR method. The SB-ATR/FTIR FFA predictions were related to those determined by the AOCS titrimetric method by linear regression, producing an R value of 0.999 and a SD of +/-0.28% FFA. Time-course spectra collected as lipids extracted into hexane indicated that a 15 min extraction was adequate to obtain a representative sample for FFA determination, with further extraction resulting in little, if any, change in the proportion of FFA in the lipid extract. Only a small volume of the hexane extract ( approximately 20 mL) yielded sufficient material for the SB-ATR/FTIR analysis. Thus, by shortening the extraction time and taking a small sample so as to reduce solvent removal time, the SB-ATR/FTIR procedure provides a very simple and rapid means of determining the FFA content of poultry feed lipids.     

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.     

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 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.     

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.