Standard Error Of Cross-validation Research Articles

Determination of Total Phenolic Content and Antioxidant Activity of Garlic (Allium sativum) and Elephant Garlic (Allium ampeloprasum) by Attenuated Total Reflectance–Fourier Transformed Infrared Spectroscopy

The total phenolic contents and antioxidant activities of garlics from California, Oregon, Washington, and New York were determined by Fourier transform infrared (FT-IR) spectroscopy (400-4000 cm(-1)). The total phenolic content was quantified [Folin-Ciocalteu assay (FC)] and three antioxidant activity assays, 2,2-diphenyl-picrylhydrazyl (DPPH) assay, Trolox equivalent antioxidant capacity (TEAC) assay, and ferric reducing antioxidant power (FRAP), were employed for reference measurements. Four independent partial least-squares regression (PLSR) models were constructed with spectra from 25 extracts and their corresponding FC, DPPH, TEAC, and FRAP with values for 20 additional extracts predicted (R > 0.95). The standard errors of calibration and standard error of cross-validation were <1.45 (TEAC), 0.36 (FRAP), and 0.33 μmol Trolox/g FW (DPPH) and 0.55 mg gallic acid/g FW (FC). Cluster and dendrogram analyses could segregate garlic grown at different locations. Hydroxyl and phenolic functional groups most closely correlated with garlic antioxidant activity.

Near infrared reflectance spectroscopy (NIRS) could be used for characterization of soil nematode community

Studying soil nematofauna provides useful information on soil status and functioning but requires high taxonomic expertise. Near infrared reflectance (NIR) spectroscopy (NIRS) has been reported to allow fast and inexpensive determination of numerous soil attributes. Thus the present study aimed at assessing the potential of NIRS for determining the abundance and diversity of soil nematodes in a set of 103 clayey topsoil samples collected in 2005 and 2006 from agricultural soils in the highlands of Madagascar. The morphological characterization of soil nematofauna involved extraction through elutriation then counting under binoculars and identification at family or genus level using microscopy, on ca. 150-g fresh soil samples. Taxa were assigned to five trophic groups, namely bacterial feeders, fungal feeders, obligate plant feeders, facultative plant feeders, and omnivores and predators (together). In addition, four ecological indexes were calculated: the Enrichment index, Structure index, Maturity index, and Plant parasitic index. Oven-dried (40 °C) < 2-mm sieved 5-g soil subsamples were scanned in the NIR range (1100–2500 nm), then spectra were fitted to nematofauna data using partial least square regression. Depending on the sample set considered (year 2005, year 2006, or both years), NIRS prediction of total nematode abundance was accurate (ratio of standard deviation to standard error of cross validation, i.e. RPD ≥ 2) or acceptable (RPD ≥ 1.6). Predictions were accurate, acceptable, or quasi-acceptable (RPD ≥ 1.4) for several of the six most abundant taxa, and to a larger extent, for most trophic groups (except facultative plant feeders); but they could not be made for taxa present in a small number of samples or at low abundance. By contrast, NIRS prediction of relative abundances (in proportion of total abundance) was poor in general, as was also the prediction of ecological indexes (except for the 2006 set). On the whole, these results were less accurate than NIRS predictions of soil attributes often reported in the literature. However, though not very accurate, NIRS predictions were worthwhile considering the labor-intensity of the morphological characterization. Most of all, NIRS analyses were carried out on subsamples that were probably too small (5 g) to allow representative sampling of nematofauna. Using larger samples for NIRS (e.g. 100 g) would likely result in more accurate predictions, and is therefore recommended. Scanning un-dried samples could also help improve prediction accuracy, as morphological characterization was carried out on samples not dried after sampling. Examining wavelengths that contributed most to NIRS predictions, and chemical groups they have been assigned to, suggested that NIRS predictions regarding nematofauna depended on constituents of both nematodes and preys’ food. Predictions were thus based on both nematofauna and soil organic properties reflected by nematofauna.

Nondestructive assessment of amino acid composition in rapeseed meal based on intact seeds by near-infrared reflectance spectroscopy

The ability of near-infrared spectroscopy (NIRS) was tested for estimating individual and total amino acid contents in rapeseed meal. Twelve different amino acids (aspartic acid, threonine, serine, glutamic acid, glycine, isoleucine, leucine, tyrosine, phenylalanine, lysine, histidine and arginine) in rapeseed meal could be predicted. The R2 ranged from 0.89 to 0.98, 1−VR (1 minus the ratio of unexplained variance to total variance) ranged from 0.86 to 0.97 and the ratio of sample standard deviation (SD) to the standard error of cross-validation (SECV) ranged from 2.69 to 5.90. The equations for alanine showed better agreement between reference value and spectra (R2=0.84, 1−VR=0.82 and SD/SECV=2.15). The calibration model for proline (R2=0.81 and 1−VR=0.78), methionine (R2=0.80 and 1−VR=0.78), cysteine (R2=0.76 and 1−VR=0.74), and valine (R2= 0.62 and 1−VR=0.58) however explained less variation. The NIRS prediction equation for total amino acid also showed high coefficient of determination (R2=0.93) and SD/SECV (3.87), and low SECV (17.01g/kg). Equations of 9 amino acids (aspartic acid, glutamic acid, glycine, alanine, valine, leucine, lysine, histidine and arginine) were developed for relative contents of total amino acid and deemed useful for prediction with R2 values from 0.80 to 0.95, 1−VR from 0.70 to 0.95 and SD/SECV from 1.83 to 3.95 and reasonably low SECV values. These results demonstrated that NIRS is a reliable tool for nondestructive assessment of variation in amino acid contents, increasing the efficiency of breeding and accelerating the selection process in rapeseed.

A novel approach for development of a multivariate calibration model using a Doehlert experimental design: Application for prediction of key gasoline properties by Near-infrared Spectroscopy

Alternative methods for quality control in the petroleum industry have been obtained using Near-infrared Spectroscopy (NIRS) combined with multivariate techniques such as PLS (Partial Least-Square). The process of development and refinement of PLS models usually follows a nonsystematic and univariate procedure. The Standard Error of Cross Validation (SECV), the Standard Error of Prediction (SEP) and the determination coefficient (r 2 regr.) are usually the only guides used in pursuit of the best model. In the present work, a novel approach was proposed using a Doehlert experimental design with three input variables (wavenumber range, preprocessing technique and regression/validation technique) varied at 5, 7 and 3 levels, respectively. Besides SECV, SEP and r 2 regr., some additional response variables, such as the slope, r 2 and p value from the external validation, as well as the number of PLS factors, were simultaneously assessed to find the optimum conditions for PLS modeling. The optimum setting for each input variable was simultaneously defined through a multivariate approach using a desirability function. With the proposed approach, the main and interaction effects could also be investigated. The methodology was successfully applied to obtain PLS models to monitor the gasoline quality through the process of product loading in trucks. To prevent product contamination or adulteration, fast prediction of key properties was obtained from FT-NIR spectra within the 7300–3900 cm − 1 region with SECV in the range 0.04–0.63% w/w for composition (Aromatics, Saturates, Olefins and Benzene) and 0.0008 for Relative Density 20/4 °C. Each optimized PLS model was obtained with less than 40 modeling runs, demonstrating the efficiency of the proposed approach.

Multicomponent Cleaning Verification of Stainless Steel Surfaces for the Removal of Dairy Residues Using Infrared Microspectroscopy

The application of infrared microspectroscopy (IRMS) technology, combined with multivariate analysis, was evaluated to develop sensitive and robust methods to assess cleanability of stainless steel surfaces for the removal of dairy food residues. UHT milk samples (skim, 1%, 2%, and whole) were analyzed for total nitrogen (Kjeldahl) and fat (Babcock) contents. The coupons were manually soiled with serially diluted milk samples resulting in soils ranging from 0.1 to 428.1 μg/cm(2) for protein and 0.1 to 374.17 μg/cm(2) for fat, and then autoclaved to simulate a heated equipment surface. Reflectance spectra were collected from stainless steel coupons by using IRMS, and multivariate analysis was used to develop calibration models based on cross-validated partial least squares regression (PLSR). Statistical analysis for the prediction of protein and fat showed a standard error of cross-validation (SECV) of 0.5 and 0.4 μg/cm(2) for prediction of protein and fat, respectively, and correlation coefficients (rVal) > 0.99. To improve the sensitivity, swabbing and concentration steps were used prior to IRMS analysis obtaining SECV of 0.04 and 0.01 μg/cm(2) for the prediction of protein and fat, respectively, and rVal > 0.99. The PLSR models accurately predicted the levels of protein and fat on autoclaved stainless steel coupons soiled with milk. A simple, reliable, and robust protocol based on IRMS and multivariate analysis was developed for multicomponent characterization of stainless steel surfaces that can contribute to more efficient cleaning verification with regard to contamination on surfaces of processing equipment. We report the application of Fourier transform infrared microspectroscopy (FTIR) for the validation of CIP cleaning efficiency that would provide a basis for better understanding of the mechanisms involved in the removal of physical soil and food residues from different types of equipment surfaces commonly utilized in the biotech, pharmaceutical, and food industries. Reliable calibration models were generated that showed the ability to predict the amounts of dairy soils on the surface of stainless steel coupons. Including a swabbing step of the coupons before infrared spectral acquisition provided improved sensitivity and reproducibility for multicomponent cleaning verification. Results from this research project would allow designing experiments to rapidly evaluate different materials and finishes, the effects of process variables, the influence of food components, and the development of reliable and robust cleaning validation protocols to ensure the safety and quality of the product.

Predictive power of LDA to discriminate abnormal wine fermentations

AbstractWine fermentation is a critical step of winemaking. Unfavorable conditions can seriously affect the quality of the final product; however, it is difficult to anticipate these abnormal behaviors. In this study, the predictive power of stepwise linear discriminant analysis (SLDA) was evaluated to discriminate the behavior of wine fermentation. Information on different chemical concentrations from 18 industrial wine fermentations of Cabernet Sauvignon was used in this study. The statistical procedure consisted of curve fitting with exponential curve, and Stepwise LDA applied to the parameters of the curve. This methodology was applied to different times between the beginning and the end of fermentation (72, 95, 100, 150, 200 and 400 h). The results revealed that between seven and eight, of the 28 variables studied, minimized the Standard Error of Cross‐Validation (SECV) for the different times. In almost all times studied, correlation coefficient of alcoholic degree, initial concentration of glucose, initial density and correlation coefficient of tartaric acid were the variables more discriminant, and they indicated some differences between a normal and an abnormal fermentation, which need to be corroborated with more information. In this work, before 95 h, it was not possible to minimize the prediction error and find the most discriminant variables. Copyright © 2011 John Wiley &amp; Sons, Ltd.

On-Site NIR Spectroscopy to Control the Shelf Life of Pork Meat

Control of meat shelf-life includes the time that it remains in the exhibitor of sale (such as the supermarket) until its rejection for the consumer, or withdrawal due to expiry date. Near infrared spectroscopy (NIRS) is one of the most promising techniques for large-scale meat quality control. This study investigated the potential of on-site NIRS portable instrumentation-based models to predict three microbiological parameters to establish if pork meat is acceptable or not for consumption (aerobic Mesophilous microorganisms, Enterobacteriaceae, and lactic acid bacteria) and pH to quality control food preservation and shelf-life extension on intact slices of pork meat packaged under two different modified atmospheres. NIR calibrations were developed by using an on-site Phazir instrument (Polychromix, Wilmington, MA, USA) in the range 1,600–2,400 nm. A total of 252 samples of pork meat slices were directly scanned twice in reflectance mode on trays, once before and another one after removing the film cover at 1, 3, 5, 7, 9, 12, and 15 days of storage. Results showed that spectra of meat acceptable or not for consumption have marked differences around 1,660 nm. NIRS quantitative prediction models showed r 2 values between 0.19 and 0.65 for the microbiological parameters assayed. The developed NIRS methodology makes possible on-site prediction of microbiological status of pork meat with a standard error of cross-validation around 1 log cfu/g. Results have shown that modified atmosphere packaging has no influence on calibration statistics.

Laser light backscattering to monitor moisture content, soluble solid content and hardness of apple tissue during drying

Continuous monitoring of food properties during drying is of stark relevance for the food processing industry. The main task of this research is to apply laser backscattering technology to simultaneously predict variations in hardness, moisture content and soluble solids content (SSC) of apples during drying. A laser diode with a wavelength of 635 nm was used to non-destructively analyze photon migration in the apple tissue. The backscattering area in pixel numbers, representing the illuminated area after laser light injection, and light luminescence measured by grey values were used for estimating changes in internal quality parameters during drying. A decrease in moisture content caused a reduction in photon scattering determined by diminished light propagation in the fruit tissue. Additionally, scattering images showed less photon migration as the SSC increased due to the light absorption of light at 635 nm by fruit compounds. To estimate moisture content, calibration models gave the highest coefficients of determination ( R 2 = 0.8 and 0.89) with standard errors of cross validation (SECV) of 11.6 and 9.8 for backscattering area and light luminescence, respectively. However, predictions obtained for SSC were slightly lower. Laser light measurement at 635 nm was found to be adequate for predicting changes in moisture content and SSC of apple during drying over different stages. On the contrary, photon scattering at 635 nm is not recommended as estimator of change in hardness during apple drying, based on the results.

NIR Spectroscopy Applications in the Development of a Compacted Multiparticulate System for Modified Release

The purpose of this study was to utilize near-infrared spectroscopy and chemical imaging to characterize extrusion-spheronized drug beads, lipid-based placebo beads, and modified release tablets prepared from blends of these beads. The tablet drug load (10.5-19.5 mg) of theophylline (2.25 mg increments) and cimetidine (3 mg increments) could easily be differentiated using univariate analyses. To evaluate other tablet attributes (i.e., compression force, crushing force, content uniformity), multivariate analyses were used. Partial least squares (PLS) models were used for prediction and principal component analysis (PCA) was used for classification. The PLS prediction models (R (2) >0.98) for content uniformity of uncoated compacted theophylline and cimetidine beads produced the most robust models. Content uniformity data for tablets with drug content ranging between 10.5 and 19.5 mg showed standard error of calibration (SEC), standard error of cross-validation, and standard error of prediction (SEP) values as 0.31, 0.43, and 0.37 mg, and 0.47, 0.59, and 0.49 mg, for theophylline and cimetidine, respectively, with SEP/SEC ratios less than 1.3. PCA could detect blend segregation during tableting for preparations using different ratios of uncoated cimetidine beads to placebo beads (20:80, 50:50, and 80:20). Using NIR chemical imaging, the 80:20 formulations showed the most pronounced blend segregation during the tableting process. Furthermore, imaging was capable of quantitating the cimetidine bead content among the different blend ratios. Segregation testing (ASTM D6940-04 method) indicated that blends of coated cimetidine beads and placebo beads (50:50 ratio) also tended to segregate.

Determining Weight and Moisture Properties of Sound and Fusarium‐Damaged Single Wheat Kernels by Near‐Infrared Spectroscopy

ABSTRACTSingle kernel moisture content (MC) is important in the measurement of other quality traits in single kernels because many traits are expressed on a dry weight basis. MC also affects viability, storage quality, and price. Also, if near‐infrared (NIR) spectroscopy is used to measure grain traits, the influence of water must be accounted for because water is a strong absorber throughout the NIR region. The feasibility of measurement of MC, fresh weight, dry weight, and water mass of single wheat kernels with or without Fusarium damage was investigated using two wheat cultivars with three visually selected classes of kernels with Fusarium damage and a range of MC. Calibration models were developed either from all kernel classes or from only undamaged kernels of one cultivar that were then validated using all spectra of the other cultivar. A calibration model developed for MC when using all kernels from the wheat cultivar Jagalene had a coefficient of determination (R2) of 0.77 and standard error of cross validation (SECV) of 1.03%. This model predicted the MC of the wheat cultivar 2137 with R2 of 0.81 and a standard error of prediction (SEP) of 1.02% and RPD of 2.2. Calibration models developed using all kernels from both cultivars predicted MC, fresh weight, dry weight, or water mass in kernels better than models that used only undamaged kernels from both cultivars. Single kernel water mass was more accurately estimated using the actual fresh weight of kernels and MC predicted by calibrations that used all kernels or undamaged kernels. The necessity for evaluating and expressing constituent levels in single kernels on a mass/kernel basis rather than a percentage basis was elaborated. The need to overcome the effects of kernel size and water mass on single kernel spectra before using in calibration model development was also highlighted.

Development of a Single Kernel NIR Barley Protein Calibration and Assessment of Variation in Protein on Grain Quality

In this paper, we describe results from a preliminary experiment on the development of a near infrared reflectance (NIR) calibration for single kernel (SK) % protein content in barley. The SKNIR calibration was developed using kernels from breeding lines and commercial barley varieties with a range in protein content of 7.3% to 16.6% as is . The calibration model produced an R2 = 0.903, while the validation set had a R2 = 0.837 with a standard error of cross validation and a standard error of prediction of 0.8% for both the calibration and validation sets respectively. The calibration was then used to estimate the variation in % protein of 4,000 single kernels from a commercial variety (Gairdner at 9.3% protein) by segregating kernels into six sub-groups ( 10.5% as is ). These sub-groups then had additional grain quality tests carried out including grain size, thousand kernel weight and NIR estimates of % protein, starch, hardness and barley hot water extract (HWE). The results showed an increase in grain size, and a decrease in HWE from the low to high % protein sub-groups. While only a single variety was used in the SKNIR protein segregation study, the results suggested SKNIR could be used to screen for the variation in grain quality traits based on variation in protein content.

Reducing Sample Quantity and Maintaining High Prediction Quality of Grassland Biomass Properties with near Infrared Reflectance Spectroscopy

Broad-scale ecological research often suffers from insufficient spatial and temporal replication. Near infrared (NIR) reflectance spectroscopy offers the opportunity for rapid and cheap measurements of many chemical constituents in organic materials. However, standard NIR instrumentation requires a certain amount of sample material which strongly restricts the fields of application for the NIR technique. Therefore, we tested if reliable predictions from NIR spectra can be obtained utilising a device that reduces the amount of required sample material by more than 95% compared to standard equipment. For large and small sample quantities, we present two sets of calibration models for C, N, P, K, Ca and Mg concentrations as well as fibre components such as neutral detergent fibre (NDF), acid detergent fibre (ADF) and acid detergent lignin (ADL) in above-ground grassland community biomass. Coefficients of multiple determination ( R2) of calibration models based on spectral data derived from standard equipment for C, N, P, K, Ca, Mg, NDF, ADF and ADL were 0.78, 0.98, 0.78, 0.92, 0.87, 0.89, 0.95, 0.94 and 0.87, respectively. Except for C and P, the ratio of standard deviation of the reference values to the standard error of cross validation and ratio of performance deviation indicated acceptable to high model precision. The application of NIR spectroscopy for C and P measurements was limited due to low variation in concentrations and/or low concentrations in the analysed above-ground grassland biomass. As compared to the deviation of duplicate reference measurements, the standard error of prediction was less than two times higher for C, N, NDF, ADF, ADL and K and up to three times higher for P, Ca and Mg. Prediction models based on the spectral data recorded with a small sample cell (volume of sample material less than 0.25 cm3) were of similar precision. The significant reduction of sample material required for NIR analysis and, at the same time, maintaining (high) precision of calibration models is an important advance towards the wider adoption of the NIR technique in ecological research.

Developing Robust Faecal near Infrared Spectroscopy Calibrations to Predict Diet Dry Matter Digestibility in Cattle Consuming Tropical Forages

Various studies, mainly from temperate areas, have reported calibrations developed from the near infrared (NIR) spectra of faeces (F.NIRS) for predicting diet digestibility in ruminants and there has been substantial variation in predictive accuracy as indicated by calibration and validation statistics. The present study was conducted to develop and examine the reliability and robustness of F.NIRS calibration equations to estimate dry matter digestibility (DMD) of forage diets ingested by cattle grazing in the rangelands of northern Australia. A large and diverse calibration data set of matched diet–faecal pairs was obtained over 10 years using three sampling methods: (1) grazed pasture with diet samples collected from oesophageal fistulated steers and faeces collected from resident cattle; (2) in vivo digestibility experiments with penned cattle fed forage hays; and (3) penned cattle fed pasture freshly harvested from the field. Estimated in vivo DMD reference values were determined using pepsin–cellulase in vitro analysis of diet samples. The final calibration set of 1052 samples represented 264 diets with DMD ranging from 38% to 75%. Calibration statistics for DMD% were: standard error of calibration = 1.87, standard error of cross validation = 1.91, and the coefficient of determination, r2 = 0.90. Factors of particular importance, with regard to the accuracy of DMD reference values, are identified and discussed and recommendations made for minimising reference errors. A comprehensive series of independent validation tests was conducted by selecting validation sample sets from the entire sample set according to a range of criteria. Each validation sub-set was tested using the calibration calculated from the remainder of the sample set. These tests showed that sampling method and experimental site often had important effects on calibration statistics and performance and also that the standard error of performance of the overall calibration would likely be &lt;2.5 DMD percentage units when applied to samples sourced from regions and pasture types represented in the calibration. Despite the large size and diversity of the calibration data set it was concluded that robustness would likely be improved by expansion of the calibration data set.

Near-Infrared Spectrometer for a Head-Feeding Combine for Measuring Rice Protein Content

We have developed a near-infrared (NIR) spectrometer mountable on a head-feeding combine for measuring rice protein in real time while harvesting. The developed sensor employs reflectance optics instead of the more usual transmittance optics because (1) it operates under severe vibration and dust conditions; (2) it performs measurements in high moisture contents, low fluidity of rough rice; and (3) because of low light transmittance due to absorption by husks. The light source was a tungsten halogen lamp, with a diffusion cylinder installed so that uniform light would illuminate the sample. An Si-CCD measured the spectrum from 740 nm to 1140 nm with a post-dispersive grating. We made a calibration curve of brown rice protein from a spectrum of rough rice examined in a laboratory. The calibration curve accuracy was r= 0.87 and SECV (Standard Error of Cross-Validation) =0.47%. In the adopted measurement method, the sensor loaded the rough rice into a wide sample chamber by gravity and analyzed the loaded grain at the bottom using a reflected signal. The developed sensor was able to measure the protein content of brown rice from spectra of rough rice taken under severe conditions, e.g., a high-vibration, high-dust harvesting environment. In addition to the protein content, the rice weight and moisture content could be displayed on the monitoring terminal in real time. The accuracy of the protein content measurements in these field examinations was r=0.65 and SEP (Standard Error of Prediction) =0.22%. The SEP was far better than the SECV of the calibration, but the protein content fell in a narrow range in the field examination. Thus, we concluded that the actual accuracy was the same as the calibration.

Non-destructive measurement of grapevine water potential using near infrared spectroscopy

Background and Aims: Near infrared (NIR) spectroscopy techniques are not only used for a variety of physical and chemical analyses in the food industry, but also in remote sensing studies as tools to predict plant water status. In this study, NIR spectroscopy was evaluated as a method to estimate water potential of grapevines. Methods and Results: Cabernet Sauvignon, Chardonnay and Shiraz leaves were scanned using an Integrated Spectronic (300–1100 nm) or an ASD FieldSpec® 3 (Analytical Spectral Devices, Boulder, Colorado, USA) (350– 1850 nm) spectrophotometer and then measured to obtain midday leaf water potential using a pressure chamber. On the same shoot, the leaf adjacent the one used for midday leaf water potential measurement was used to measure midday stem water potential. Calibrations were built and NIR showed good prediction ability (standard error in cross validation (SECV) <0.24 MPa) for stem water potential for each of the three grapevine varieties. The best calibration was obtained for the prediction of stem water potential in Shiraz (R = 0.92 and a SECV = 0.09 MPa). Conclusion: Differences in the NIR spectra were related to the leaf surface from which the spectra were collected, and this had an effect on the accuracy of the calibration results for water potential. We demonstrated that NIR can be used as a simple and rapid method to detect grapevine water status. Significance of the Study: Grapevine water potential can be measured using NIR spectroscopy. The advantages of this new approach are speed and low cost of analysis. It may be possible for NIR to be used as a non-destructive, in-field tool for irrigation scheduling.

Influence of Soil Particle Size on the Measurement of Sodium by Near-Infrared Reflectance Spectroscopy

This study evaluates the effect of soil particle size (SPS) on the measurement of exchangeable sodium (Na) (EXC-Na) by near-infrared reflectance (NIR) spectroscopy. Three hundred thirty-two (n = 332) top soil samples (0–10 cm) were taken from different locations across Uruguay, analyzed by EXC-Na using emission spectrometry, and scanned in reflectance using a NIR spectrophotometer (1100–2500 nm). Partial least squares (PLS) and principal component regression (PCR) models between reference chemical data and NIR data were developed using cross validation (leaving one out). The coefficient of determination in calibration (R2) and the root mean square of the standard error of cross validation (RMSECV) for EXC-Na concentration were 0.44 (RMSECV: 0.12 mg kg–1) for soil with small particle size (SPS-0.053) and 0.77 (RMSECV: 0.09 mg kg–1) for soils with particle sizes greater than 0.212 mm (SPS-0.212), using the NIR region after second derivative as mathematical transformation. The R2 and RMSECV for EXC-Na concentration using PCR were 0.54 (RMSECV: 0.07 mg kg–1) and 0.80 (RMSECV: 0.03 mg kg–1) for SPS-0.053 and SPS-0.212 samples, respectively.

Near Infrared Reflectance Spectroscopy (NIRS) for determination of chemical entities of natural pasture from Ethiopia

In the mixed crop-livestock farming system of Ethiopia, natural pasture production is related to seasonal rainfall distribution which is restricted to only a few months of the year. Livestock owners in this country experience periods of excess forage in the rain season where preserving it through haymaking became tradition as a strategy for the periods of scarcity, particularly the dry season of a year. Forage can also be stored as standing hay, the cheapest way of conserving forage, in a place like pastoral and agro-pastoral production system where a decline in quality can be tolerated. Mixed grass-legume pasture samples from different agro-ecology were collected and portion of the samples were analyzed conventionally to determine their chemical composition (dry matter, ash, crude protein, neutral detergent fiber, acid detergent fiber, lignin) and in vitro dry matter digestibility to validate the NIRS calibration. NIRS was evaluated by the coefficient of determination in calibration (R 2 ), standard error of calibration (SEC), and standard error of cross validation (SECV). The results showed NIRS is extremely useful for predicting nutrient contents as compared to conventional chemical analysis methods which are not difficult to conduct, but they are expensive in terms of time and resources.

Rapid determination of the fatty acid profile in pork dry-cured sausages by NIR spectroscopy

The feasibility of using near infrared reflectance spectroscopy to evaluate the generation of relevant and accurate nutritional information through a rapid determination of the fatty acid profiles in Iberian pork dry-cured sausages was analysed. Individual fatty acids contents together with the saturated (SFA), monounsaturated (MUFA) and polyunsaturated fatty acid (PUFA) groups were determined in 86 samples of Iberian dry-cured sausages. NIR calibrations were developed using modified partial least squared regressions. Selected models for the estimation of major constituents (C14:0, C16:0, C16:1, C18:0, C18:1 and C18:2) exhibited coefficients of determination for cross-validation which ranged between 0.41 and 0.84 with standard errors of cross-validation (SECV) between 0.07 and 1.51. Calibrations developed for SFA, MUFA and PUFA showed coefficients of determination of 0.86, 0.53 and 0.61, and SECV values of 0.98, 1.47 and 0.88, respectively. The results obtained suggest that in general, NIR spectroscopy could provide an exceptional opportunity for the quality control of dry-cured sausages, since it allows an estimation of the major constituents and/or a classification based on their fatty acid profiles rapidly whilst avoiding the generation of chemical residues. This information could be used to estimate shelf life of products and to include extra nutritional information associated to consumers’ health in their labelling.

The development of near-infrared spectroscopy (NIRS) calibration for prediction of ash content in legumes on the basis of two different reference methods

The aim of this study was to develop optimal NIRS calibration for ash content prediction in legumes by using the thermogravimetric (TGA) and gravimetric (GA) analytical methods. The calibration was performed on the basis of whole and structured sample sets ( n = 143 and n = 99, respectively). Samples were scanned using a Rapid Content Analyzer in reflectance mode (400–2500 nm). Different mathematical treatments of the spectra preceded modified partial least squares (MPLS) regression analyses. The performance of the models was assessed by cross validation and external validation ( n = 44). Models developed for the whole sample set on the basis of the TGA and GA methods were characterised by standard error of calibration (SEC) ranged from 0.28 to 0.50, standard error of cross validation (SECV) ranged from 0.43 to 0.60, coefficient of determination ( R 2) ranged from 0.97 to 0.89, explained variance (1 − VR) ranged from 0.94 to 0.85 and residual predictive deviation (RPD) ranged from 4.23 to 2.68, respectively. Models developed for the structured sample set on the basis of the TGA and GA methods were characterised by standard error of calibration (SEC) ranged from 0.32 to 0.42, standard error of cross validation (SECV) ranged from 0.53 to 0.56, coefficient of determination ( R 2) ranged from 0.97 to 0.94, explained variance (1 − VR) ranged from 0.91 to 0.89 and residual predictive deviation (RPD) ranged from 3.52 to 2.98, respectively. The obtained results showed the potential of NIRS method to accurately predict the ash content of legume grass samples that correspond to ash content determined by the TGA and GA methods.

Methodology for cork plank characterization (Quercus suber L.) by near-infrared spectroscopy and image analysis

The procedures used today to characterize cork plank for the manufacture of cork bottle stoppers continue to be based on a traditional, manual method that is highly subjective. Furthermore, there is no specific legislation regarding cork classification. The objective of this viability study is to assess the potential of near-infrared spectroscopy (NIRS) technology for characterizing cork plank according to the following variables: aspect or visual quality, porosity, moisture and geographical origin. In order to calculate the porosity coefficient, an image analysis program was specifically developed in Visual Basic language for a desktop scanner. A set comprising 170 samples from two geographical areas of Andalusia (Spain) was classified into eight quality classes by visual inspection. Spectra were obtained in the transverse and tangential sections of the cork planks using an NIRSystems 6500 SY II reflectance spectrophotometer. The quantitative calibrations showed cross-validation coefficients of determination of 0.47 for visual quality, 0.69 for porosity and 0.66 for moisture. The results obtained using NIRS technology are promising considering the heterogeneity and variability of a natural product such as cork in spite of the fact that the standard error of cross validation (SECV) in the quantitative analysis is greater than the standard error of laboratory (SEL) for the three variables. The qualitative analysis regarding geographical origin achieved very satisfactory results. Applying these methods in industry will permit quality control procedures to be automated, as well as establishing correlations between the different classification systems currently used in the sector. These methods can be implemented in the cork chain of custody certification and will also provide a certainly more objective tool for assessing the economic value of the product.