Determination of starch and lipid in pea flour by near infrared reflectance analysis. the effect of pea genotype on starch and lipid content

Equations were derived for predicting starch and lipid content of pea varieties from near infrared data measured on a filter instrument (Technicon InfraAlyzer 400). The accuracy of the predictions were tested against separate data sets and were found to be better than ±2.6% for starch (range 20–45%) and ±0.3% for lipid (range 1.4–3.3%). Together with the previous work on the near infrared determination of protein it is possible to analyse rapidly for three components important to a plant breeding programme. The study has also demonstrated a clear-cut delineation between round and wrinkled pea genotypes.     

The application of near infrared reflectance analysis to rapid flour testing

Calibrations are presented for the analysis of flour for protein, moisture, particle size, colour and starch damage by means of near infrared (NIR) reflectance analysis. A commonly used NIR instrument was subjected to a thorough evaluation for its accuracy in flour testing for these parameters, by comparison of NIR with existing methods of analysis over an extensive period of time. The accuracy and precision of NIR for the parameters studied were satisfactory for quality control purposes and therefore the technique has an important place in the rapid flour testing.     

Measurement of the components in meat patties by near infrared reflectance spectroscopy

Near infrared calibrations have been derived and used routinely in the measurement of fat, moisture, protein, collagen free protein and starch in meat patties. The lower standard error of prediction (SEP) values for moisture, protein, fat and starch content determination were recorded with the first derivative calibration than with those of the second derivative treatment. The prediction for the moisture and protein content determinations with first derivative transforms were satisfactory, the correlation coefficients (r) being 0.99 and 0.98, respectively. Determining the fat content with both first and second derivative data showed excellent results, r amounting to more than 0.99. The result obtained for the starch and collagen free protein (CFP) content determination with the first derivative calibration, as well as with the second derivative treatment, showed a deviation from the chemical data and r was less than 0.97 in both cases. It is recommended that a sample preparation, such as demoisturizing or defatting, is needed to get a high correspondence with reference methods for starch and hydroxyproline determination in meat patties.     

Determination of protein in pea flour by near infrared analysis

Equations were derived for predicting crude protein content (total nitrogen × 6.25 or Kjeldahl nitrogen × 6.25) of pea varieties from near infrared data measured on a filter instrument (Technicon Infra Analyzer 400). The accuracy of the predictions were tested against different data sets and found to be better than ±1.5% (P=0.95), and adequate for use in a plant breeding programme.     

Measurement of fat and moisture in air-dried bread by near infrared reflectance

Near infrared calibrations have been derived and used routinely for a year in the measurement of fat and moisture in air-dried bread. First and second derivative calibrations were obtained using a Pacific Scientific mark II scanning spectrophotometer on samples sent from all over South Africa to the Wheat Board for analysis. Prediction analysis performed on further bread samples gave standard errors of prediction (s.e.p.) of 0.12% fat and 0.13% moisture.     

可见/近红外反射光谱法检测马铃薯抗性 淀粉含量的研究

目的利用可见/近红外反射光谱技术无损检测新鲜马铃薯茎块中抗性淀粉的含量。方法使用光谱仪获取新鲜马铃薯在345~1100 nm波段范围内的漫反射光谱;分别使用Savitzky–Golay(S-G)平滑处理、多元散射校正(MSC)法和一阶导数法(1st-D)对反射光谱进行预处理;对(S-G)反射光谱、MSC处理光谱和1st-D光谱使用逐步回归法判别法选择最优波长组合,建立多元线性回归模型,使用全交叉验证法验证模型。结果结果表明,可见/近红外反射光谱经过一阶导数处理后,确定的8个最优波长(370、569、576、866、868、886、922和963 nm)组合建立模型的校正和验证结果最好:模型的校正结果为相关系数R=0.996,标准差SEC=0.521%;模型交叉验证相关系数Rcv=0.982,验证标准差SECV=0.791%。结论可见/近红外反射光谱技术可以较好地预测新鲜马铃薯茎块的抗性淀粉含量,本研究可为可见近红外光谱技术在马铃薯功能成分的快速检测提供一定的技术基础。     

Discriminant analysis of black tea by near infrared reflectance spectroscopy

A new method for the discrimination of commercial black tea samples using near infrared (NIR) reflectance spectroscopy has been investigated. NIR data at four wavelengths (1660, 1720, 2050 and 2300 nm) corresponding to maximum variation in the intensity of absorption bands in the spectra of black teas were used to calculate Mahalanobis distances. A cut-off point in the values of these distances was determined by means of which two sets of black teas with differing sensory properties could be discriminated with a 91% success rate.     

Collaborative evaluation of universal calibrations for the measurement of protein and moisture in flour by near infrared reflectance

Calibrations which had been previously determined for the analysis of protein and moisture in U.K. and Irish milled straight run white flour were set into nine near infrared (NIR) reflectance instruments of the same model situated in different laboratories. One set of samples was circulated to adjust the bias (intercept) which has a component specific to each instrument then a second set was circulated 2 months later to evaluate the adjusted calibrations. Kjeldahl proteins were determined on the first set by seven of the laboratories and on the second set by one reference laboratory for direct comparison of that method with the NIR method and in order to assess the accuracy of NIR as well as precision.
On the second set of samples the mean NIR results for the different laboratories varied from 10.92 to 11.30% compared with a mean for the Kjeldahl of 11.03%. The accuracy of NIR calculated separately for each laboratory as the standard deviation (s.d.) of differences between NIR and Kjeldahl from the reference laboratory was between 0.13-0.33% while the precision or repeatability (within-laboratory) was 0.07%. For comparison the accuracy of Kjeldahl, calculated separately for each laboratory in the same way as for NIR, was 0.10% -0.27% and the precision (within-laboratory) was 0.04%.
In the case of moisture, the NIR results for the second set of samples had mean values ranging from 13.08 to 13.28% compared with a mean for oven drying of 13.13%. The accuracy of NIR was 0.20-0.29% with no significant biases and the precision was 0.05%.     

Measurement of fat and sucrose in dry cake mixes by near infrared reflectance spectroscopy

A feasibility study was carried out to assess the potential of near infrared (NIR) reflectance spectroscopy for the measurement of fat and sucrose in dry cake mixes. The calibration of the NIR instrument was carried out in a research laboratory and then assessed under quality control conditions in the factory laboratory. It was possible to measure fat with an accuracy (±2σ) of ±3.4% for products with a fat content of 8–25% compared with +0.76% for the precision of the Soxhlet procedure. In the case of sucrose the accuracy was ±5.4% for products with a sucrose content of 10–40% compared with ±2.0% for a high pressure liquid chromatography (hplc) method. It must be concluded, therefore, that while NIR offers a quicker, simpler method of quality control, this is at the expense of accuracy.     

Measurement of levels of bread improvers in concentrates by means of near infrared reflectance spectroscopy

The simultaneous analysis of ascorbic acid, azodicarbonamide and L-cysteine in bread improver mix concentrates at levels of 0–10% in a starch diluent has been achieved. The use of near infrared reflectance spectroscopy enabled the measurements to be obtained in less than one minute with accuracies of about ± 1% at the 95% confidence interval.     

Quantitative analysis of stevioside in the leaves of Stevia rebaudiana by near infrared reflectance spectroscopy

Stevioside, a diterpenic glycoside extracted from the leaves of Stevia rebaudiana (Bert) Bertoni, is a natural sweetener 300 times sweeter than sucrose. HPLC is routinely used to measure stevioside content in Stevia leaves. Our objectives were to investigate the use of near infrared reflectance spectroscopy (NIRS) to analyse stevioside concentration in Stevia leaves. The NIRS calibrations were developed from 64 samples covering the range normally found in Stevia leaves (4-13%). Another 30 samples were used for validation. Standard error of calibration and coefficient of multiple determination (R²) were 1.47 and 0.90 respectively, using the first derivative mathematical treatment. The validation results, although less accurate, suggest that NIRS is a precise and simple method for routine stevioside determination in Stevia leaves.     

Quality evaluation of processing potatoes by near infrared reflectance

The percentage dry matter and reducing sugar content are prime quality parameters for assessing the potential of potato tubers to produce acceptable yields of processed products possessing good textural and colour quality attributes. A study was conducted to develop a near infrared (NIR) calibration for dry matter which would be unaffected by storage time and crop year, and to assess whether NIR could be used to screen tubers with high reducing sugar content from tubers with more acceptable levels. Tubers from commercial growers of French fry processing potatoes were sampled over two years for dry matter and one year for sugars. Tubers used for development of the NIR calibration were stored at 8°C, while those for sugars were stored at 8, 5 and 2°C. A small coin of potato flesh was excised from each tuber, its NIR spectrum obtained on a scanning spectrophotometer and its specific gravity and dry matter, or its reducing sugar content, determined. Correlation plots indicated that wavelengths that correlated well for specific gravity correlated well for dry matter. The standard error of performance for NIR determination of percentage dry matter varied from 1.3 to 1.5%. Calibrations for dry matter were robust and were not affected by time or conditions of storage nor by changeover from one crop year to another. It appears, however, that adjustments to the NIR calibration would be required at harvest time. Coefficients of variation (CV) in determining dry matter by NIR were 5–6%. Spectral averaging appears to have potential for improving on this CV. NIR in reflectance mode was unable to act as a screening tool for unacceptable sugar content in tubers. © 1999 Society of Chemical Industry     

Prediction of sheep responses by near infrared reflectance spectroscopy

Prediction of animal response from near infrared reflectance spectra of feeds was compared with predictions from chemical analyses. Sixty samples of pure and mixed forage-based diets were obtained from sheep intake and digestion trials. Sheep responses measured were digestible energy, dry matter intake, and calculated intake of digestible energy. Diets were analyzed chemically for protein, neutral detergent fiber, and in vitro dry matter disappearance. Coefficients of multiple determination and standard errors for fitting the sheep responses to these 60 diverse diets by regression equations developed from chemical analyses (.62 to .70) or spectra (.63 to .72) were similar. The 60 diets were divided into two sets of 30; one set was used to develop calibration equations for each sheep response, and the second set was used to test the equations. Calibration and errors of prediction were similar. When wavelengths chosen for each of the laboratory measurements were used to fit the sheep responses, standard errors were higher than when responses of sheep were predicted directly from spectra. The scanning instrument has the capability of predicting laboratory analyses and shows potential for predicting animal response as accurately as animal response can be predicted from laboratory analyses.     

Determination of moisture in white flour,ground wheat and whole wheat by near infrared reflectance using a single calibration

A single calibration for the determination of moisture in white flour, ground wheat and whole wheat by near infrared (n.i.r.) reflectance analysis is presented. The predictive accuracy of the calibration compared with moisture determined by oven drying on 68 samples was 0.22%.     

Identification of some wheat varieties by near infrared reflectance spectroscopy

Variety identification by electrophoresis is not applicable to routine control in industry. In the present work, the feasibility of near infrared reflectance (n.i.r.) analysis was investigated. Two hundred and two wheat samples including 66 samples of six known varieties were collected and their n.i.r. spectra were recorded. Spectral data were mathematically corrected in order to reduce the effect of granularity on n.i.r. spectra, then Principal Component Analysis and Multiple Discriminant Analysis (MDA) were applied to the corrected data. MDA allowed an efficient identification of the genetic origin of unknown samples: on a prediction set, 87% of samples were correctly identified. The computerised identification procedure needed less than 20 records for one sample. Further studies are necessary before recommending n.i.r. as a routine screening method.     

Prediction of the nutritive value of forages by near infrared reflectance photometry

A filter-type near infrared photometer equipped with 19 filters was used to predict crude protein, in-vitro digestibility of organic matter, and in-vivo and from in-vitro predicted metabolisable energy in forages. The investigation was performed using pure species of grasses and legumes, grass/legume mixtures and grasses preserved by different methods. The need for specific calibrations is demonstrated. Results obtained using combinations of six filters selected during the calibration procedure were compared with those obtained using the six standard filters recommended for grain analysis. There were indications that selected filter combinations will only give accurate results if the composition of the test samples is very similar to that of the calibration samples, i.e. of the same species or family as the calibration samples. The correlation coefficient between manual methods and n.i.r. reflectance varied from 0.83 to 1.00 for crude protein, 0.48 to 0.91 for rumen digestible organic matter and 0.35 to 0.92 for metabolisable energy. When samples of grass silage were analysed using grass/legume mixtures for calibration of the equipment, the correlations were poor. Although the technique is satisfactory for determinating crude protein in forages utilised in practical farming, the need for specific calibrations when assessing in-vivo organic matter digestibility or in-vivo metabolisable energy renders it inferior to chemical/biological methods.     

Estimation of moisture in undried wheat and barley by near infrared reflectance

The potential of near infrared reflectance spectroscopy for estimating moisture in undried wheat and barley was investigated. Calibrations were developed for use with wholegrain (13–20% moisture) and coarsely-ground grain (13–26% moisture) for both grain types. The performance (accuracy and precision) during the 1983 harvest of the calibrations involving coarsely-ground grain (13–26% moisture) was superior to that of three moisture meters currently in commercial use in Ireland.     

Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS)

Near infrared reflectance spectroscopy (NIRS) was evaluated as a tool to segregate different types of bovine meat and predict several chemical fractions on samples from two breeds, three muscles and six grading (Chilean system) categories. Samples previously minced, frozen and thawed, were scanned (400–2500 nm) and then analyzed for dry matter, crude protein, ether extract, total ash and collagen content, after freeze drying. Discriminant analysis using a partial least squares regression technique and cross validation, correctly identified breed and muscle type for most samples, but carcass grades, with the exception of samples from calves, were not successfully predicted. Best calibrations for chemical composition tested by cross-validation, showed R² and standard errors of cross validation of 0.77 and 0.58% (dry matter), 0.82 and 0.48% (crude protein), 0.82 and 0.44% (ether extract). Calibrations for total ash showed a poor, and for collagen, a very poor prediction ability.     

Feasibility of estimating peanut essential minerals by near infrared reflectance spectroscopy

The use of near infrared reflectance spectroscopy (NIRS) to evaluate the nutritional quality of peanut kernels has potential applications in plant breeding as a rapid, non-destructive tool for seed/plant selection, and in quality control. We investigated the feasibility of applying NIRS to the estimation of essential mineral composition in peanut kernels using two sample sets: A, comprising 56 diverse genotypes (N = 163); and B, comprising nine genotypes grown in five distinct environments (N = 156). Essential mineral composition was analyzed by inductively coupled plasma-optical emission spectroscopy (ICP-OES) and -mass spectrometry (ICP-MS). Calibration models were developed by partial least squares (PLS) regression, and explored a variety of data pre-treatments. Models allowing approximate estimation of K (RPD_CV 2.25, r_CV² 0.800, RPD_P 2.22) and Mg (RPD_CV 2.24, r_CV² 0.786, RPD_P 1.74), and to a lesser extent Ca (RPD_CV 1.85, r_CV² 0.649, RPD_P 1.52) and P (RPD_CV 1.77, r_CV² 0.634, RPD_P 1.65), were developed for Set B, but poorer calibrations were obtained for Set A. This level of accuracy does not allow accurate prediction, but permits approximate quantification that may be useful in plant improvement programs for screening breeding populations. The results are remarkable because NIRS is rarely applied to analytes present at such low concentrations, especially inorganic constituents that are not inherently NIR-absorbent. Further analysis of more diverse peanut samples is warranted to confirm batch-to-batch accuracy and to improve the robustness of calibrations.