Near-infrared Reflectance Measurements Research Articles

NIR Reflectance Spectroscopy for Nondestructive Moisture Content Determination in Peanut Kernels

There are some commercial instruments available that use near-infrared (NIR) radiation measurements to determine the moisture content (MC) of a variety of grain products, such as wheat and corn, without the need of any sample grinding or preparation. However, to measure the MC of peanuts with these instruments, the peanut kernels have to be chopped into smaller pieces and filled into the measuring cell. This is cumbersome, time consuming, and destructive. An NIR reflectance method is presented here by which the average MC of about 100 g of whole kernels could be determined rapidly and nondestructively. The MC range of the peanut kernels tested was between 8% and 26%. Initially, NIR reflectance measurements were made at 1 nm intervals in the wavelength range of 1000 to 1800 nm, and the data were modeled using partial least squares regression (PLSR). The predicted values of the samples tested in the above range were compared with the values determined by the standard air-oven method. The predicted values agreed well with the air-oven values, with an R2 value of 0.93 and a standard error of prediction (SEP) of 1.18. Using the PLSR beta coefficients, five key wavelengths were identified, and MC predictions were made using multiple linear regression (MLR). The R2 and SEP values of the MLR model were 0.91 and 1.09, respectively. Both methods performed satisfactorily and, being rapid, nondestructive, and noncontact, may be suitable for continuous monitoring of MC of grain and peanuts as they move on conveyor belts during their processing.

Near-infrared digital photography to estimate snow correlation length for microwave emission modeling

The study is based on experimental work conducted in alpine snow. We made microwave radiometric and near-infrared reflectance measurements of snow slabs under different experimental conditions. We used an empirical relation to link near-infrared reflectance of snow to the specific surface area (SSA), and converted the SSA into the correlation length. From the measurements of snow radiances at 21 and 35 GHz, we derived the microwave scattering coefficient by inverting two coupled radiative transfer models (the sandwich and six-flux model). The correlation lengths found are in the same range as those determined in the literature using cold laboratory work. The technique shows great potential in the determination of the snow correlation length under field conditions.

Determining snow specific surface area from near-infrared reflectance measurements: Numerical study of the influence of grain shape

Determining the specific surface area of snow from reflectance measurements in the near infrared domain represents a promising technique to rapidly and quantitatively acquire snow stratigraphic profiles in the field. In this paper, we develop a ray tracing model that simulates the albedo of snowpacks composed of geometric crystals (spheres, cubes, cylinders, etc) and model simulations are exploited to study the influence of the grain shape on the SSA-albedo relationship. The results clearly show that the relationship depends on the grain shape at 1310 nm: Cubic (resp. cylindrical) grains reflect about 40% (resp. 20%) more than spherical grains at equal SSA. Depth-hoar modeled as a collection of hollow cubes is found to reflect exactly as much as cubes. None of the tested shapes (including concave and hollow shapes) reflects more than cubes. These results suggest that determining SSA from albedo measurement is uncertain when the snow grain shape is unknown. This uncertainty reaches ± 20% considering that spherical and cubic grains are the two extreme cases in terms of reflexion. This large value is probably over-pessimistic for practical applications as only perfect crystals are considered in this theoretical study and natural snow is always a mixture of curved and plane faces. Therefore, further experimental studies should focus on jointly measuring SSA and albedo in order to assess the influence of the grain shape (or snow type) on the SSA-albedo relationship in natural snows.

Localization of adipose tissue embedded biliary tree vessels by use of near-infrared diffuse photon propagation models: a computational feasibility study

Biliary tree structures are embedded in adipose tissue and, therefore, cannot be visualized directly by the surgeon during cholecystectomy operations. This can lead to inadvertent injuries with serious complications for the patient. Computational studies were performed to assess the feasibility of noninvasively localizing these structures from spectrally resolved near-infrared reflectance measurements. Methodologies were developed for vessel localization, both on the adipose tissue surface and depthwise, by use of semi-infinite and two-layer models of diffuse photon propagation in tissues, respectively. The simulation results, along with some preliminary experimental measurements on tissue-simulating phantoms, prove the feasibility of these methods and show promise for their future clinical application.

A method for determination of COD in a domestic wastewater treatment plant by using near-infrared reflectance spectrometry of seston

This paper proposes a method for determination of chemical oxygen demand (COD) in domestic wastewater. The proposed method is based on near-infrared reflectance (NIRR) measurements of seston collected from wastewater samples by filtration. The analysis does not require any special reagent, catalyst or solvent. Inherent baseline and noise features present in NIRR spectra are removed by a Savitzky–Golay derivative procedure followed by wavelet denoising. The resulting wavelet approximation coefficients are used for partial-least-squares modelling and subsequent prediction of COD values in new samples. The model is calibrated by using COD values obtained according to the American Public Health Association (APHA) reference method. The proposed method is applied to effluent samples from the anaerobic ponds of the Mangabeira municipal wastewater treatment plant in the city of João Pessoa (Paraíba, Brazil). By comparing the NIRR prediction results with the APHA reference values, a root-mean-square error of prediction (RMSEP) of 19 mg O 2 L −1 and a correlation of 0.97 were obtained. Such results are deemed adequate in view of the joint estimate of the standard error of the reference method, which was calculated as 21 mg O 2 L −1.

Prediction of sensory properties of espresso from roasted coffee samples by near-infrared spectroscopy

Thirty-five representative and suitably selected roasted coffee samples were characterised by near-infrared (NIR) spectroscopy and used to prepare the corresponding espresso samples to be subsequently subjected to sensory evaluation by trained panellists. The main purpose was to investigate the relationships between certain crucial sensory attributes of espresso coffees, including perceived acidity, mouthfeel, bitterness and aftertaste, and near-infrared spectra of original roasted coffee samples, in such a way that non-destructive near-infrared reflectance measurements would be used to predict all these sensory properties with a decisive influence from a quality assurance standpoint. Separate calibration models based on partial least squares regression (PLS), correlating NIR spectral data of roasted coffee samples with each sensory attribute of espresso samples studied, were developed. Wavelength selection was also performed applying iterative predictor weighting–PLS (IPW–PLS) in order to take into account only significant and characteristic spectral features, in an attempt to improve the quality of the final regression models constructed. Using IPW–PLS regression, prediction of the four sensory responses modelled was performed with high accuracy, with root mean square errors of the residuals in cross-validation (RMSECV) ranging from 4.7 to 7.0%. Thus, the results provided by the high-quality calibration models proposed in the present study, comparable in terms of accuracy to the evaluations provided by a trained sensory panel, are promising and prove the feasibility of using a similar methodology in on-line or routine applications to predict the sensory quality of unknown espresso coffee samples via their respective NIR roasted coffee spectra.

In vivo detection of gastric cancer in rats by electron paramagnetic resonance imaging.

Electron paramagnetic resonance imaging (EPRI) enables noninvasive spatial mapping of free radical metabolism and has recently been shown to provide in vivo physiologic information regarding alterations in the redox state of tumors and neoplastic tissues. With the use of nitroxide spin probes, it has been shown that certain tumors possess a highly reduced state. To determine whether EPRI can be used for early detection and visualization of gastric carcinoma based on its altered redox metabolism, studies were performed in a rat gastric cancer model induced by 1-methyl-3-nitro-1-nitrosoguanidine. Using a specialized 750 MHz resonator and EPRI instrument, a technique was developed for imaging nitroxide radicals in the whole stomach. In vivo three-dimensional EPRI of the stomach of rats with continuous intravenous administration of nitroxide 3-carboxamido-2,2,5,5-tetramethylpyrrolidine-N-oxyl (3-carbamoyl-proxyl) [3-CP] was performed. Whereas electron paramagnetic resonance images from untreated controls provide a uniform visualization of the stomach mucosa and wall, in the treated rats with gastric cancer, holes were present in the image at the locations of tumors. With localized spectroscopy, it was confirmed that the tumor regions were devoid of signal, and this was largely due to the presence of a more reduced state with rapid reduction of nitroxide. Pharmacokinetic studies indicated that 3-CP in tumors was rapidly reduced to an undetectable level, whereas the 3-CP levels in normal stomach tissue persisted. Near-infrared reflectance measurements of indocyanine green dye uptake indicated that there were no significant differences in tumor versus normal mucosal perfusion. From these results, we concluded that gastric cancer tumors could be distinguished from normal tissue based primarily on the marked difference in their rate of radical metabolism. Because alterations in cellular redox state and radical metabolism are of critical importance in tumor biology and treatment, this methodology should provide an important new tool for the study and visualization of gastric carcinoma and may also be of use in other cancer models.

Optical measurements of absorption changes in two-layered diffusive media

We have used Monte Carlo simulations for a two-layered diffusive medium to investigate the effect of a superficial layer on the measurement of absorption variations from optical diffuse reflectance data processed by using: (a) a multidistance, frequency-domain method based on diffusion theory for a semi-infinite homogeneous medium; (b) a differential-pathlength-factor method based on a modified Lambert–Beer law for a homogeneous medium and (c) a two-distance, partial-pathlength method based on a modified Lambert–Beer law for a two-layered medium. Methods (a) and (b) lead to a single value for the absorption variation, whereas method (c) yields absorption variations for each layer. In the simulations, the optical coefficients of the medium were representative of those of biological tissue in the near-infrared. The thickness of the first layer was in the range 0.3–1.4 cm, and the source–detector distances were in the range 1–5 cm, which is typical of near-infrared diffuse reflectance measurements in tissue. The simulations have shown that (1) method (a) is mostly sensitive to absorption changes in the underlying layer, provided that the thickness of the superficial layer is ∼0.6 cm or less; (2) method (b) is significantly affected by absorption changes in the superficial layer and (3) method (c) yields the absorption changes for both layers with a relatively good accuracy of ∼4% for the superficial layer and ∼10% for the underlying layer (provided that the absorption changes are less than 20–30% of the baseline value). We have applied all three methods of data analysis to near-infrared data collected on the forehead of a human subject during electroconvulsive therapy. Our results suggest that the multidistance method (a) and the two-distance partial-pathlength method (c) may better decouple the contributions to the optical signals that originate in deeper tissue (brain) from those that originate in more superficial tissue layers.

The Use of Visible and Near-Infrared Reflectance Measurements to assess Sensory Changes in Carrot Texture and Sweetness during Heat Treatment

The use of non-destructive near-infrared reflectance (NIR) measurements to predict the change in sensory quality of carrot discs during heat treatment was investigated. Near-infrared reflectance measurements were performed on five different batches of carrots, cooked for 0, 1, 2, 5, 10, or 15 min, with a spectrophotometer with a wavelength range of 400–2500 nm. Per combination of batch and heat treatment, 20 carrot discs with a height of 8 mm and a diameter of 25 mm were tested. As instrumental reference measurements the dry matter content (DM), soluble solids content (SSC), compressive strength F C and tensile strength F t were recorded on carrots from the same batches and heat treatments. Sensory profiling was carried out on three of the five batches. The sensory attributes considered were hardness, crispness, juiciness, sweet smell, and sweet taste. The shape of the NIR spectra was determined by the water absorption bands and the absorption by carotenoids in the visual light region. Partial least-squares regression (PLSR) was used to predict the average sensory scores for each batch and cooking time from the NIR spectra obtained from individual carrot discs. Second-order derivative pre-processing combined with meancentring, provided a better prediction capability than first-order derivation or multiplicative scatter correction. The root mean square errors of prediction (RMSEP) amounted to 1·13, 1·20, 0·89, 0·78, and 1·06 for hardness, crispness, juiciness, sweet smell and sweet taste, respectively, in sensory units (scale 0 to 15). To obtain the best possible prediction, it appeared to be important to include batch-specific information in the model. Compared with a PLSR model based on the four physico-chemical reference measurements, the NIR model provided a better estimation of hardness, crispness, and sweet smell. Measurements for one batch on xylem and phloem parts of the carrot discs separately, showed that a further improvement of the model calibration could be obtained by taking the NIR spectrum on the phloem part. The average physico-chemical results for a specific batch and heat treatment, could be estimated from a PLSR model based on the NIR spectra, with an average RMSEP of 7–13% of the measurement range under consideration. The results suggest that NIR measurements could be successfully used to control sensory quality during heat treatment.

Measurement of the percentage volume particle size distribution of powdered microcrystalline cellulose using reflectance near-infrared spectroscopy.

This is the first reported method for determining the percentage volume particle size distribution of a powder (microcrystalline cellulose) by near-infrared (NIR) reflectance spectroscopy. A total of 113 samples of powdered microcrystalline cellulose were used from six different commercially available grades, with different moisture contents (range: 0.9-4.8% m/m). NIR reflectance measurements of these samples were made in narrow soda glass vials. Reference particle size data for the samples were acquired by laser diffraction. The NIR data were then calibrated to measure particle size by partial least squares regression. The effects of a range of different NIR data pre-treatments on calibration and prediction precision were investigated. Overall, simple absorbance data were found to produce regression models with the best predictive ability (root mean square error of prediction = 0.90%). The method was also found to be insensitive to moisture content.

Freshness Assessment of Thawed and Chilled Cod Fillets Packed in Modified Atmosphere Using Near-infrared Spectroscopy

Near-infrared reflectance (NIR) spectra was recorded of 105 samples of cod mince prepared from chill stored thawed cod fillets of varying quality in modified atmosphere packaging (MAP). Traditional chemical, physical, microbiological and sensory quality methods developed for assessing fresh fish products were determined on the same cod fillets. The purpose was to evaluate the potential of NIR spectroscopy for estimating (i) frozen storage temperature, (ii) frozen storage period and (iii) chill storage period of thawed-chilled MAP Barents Sea cod fillets. Furthermore, the potential for measuring of selected quality attributes as drip loss, water holding capacity and content of dimethylamine by NIR was evaluated. The results of the investigation were presented using multivariate modelling methods such as partial least-squares regression (PLSR) and discriminant partial least-squares regression (DPLSR). Systematic differences in the NIR measurements on minced cod fillets were primarily due to the chill storage duration (days at 2°C) on thawed-chilled MAP fillets. PLSR models based on wavelengths selected by a new Jack-knife method resulted in a correlation coefficient of 0.90 between measured and predicted duration of chill storage period (days at 2°C). The root-mean-square error of cross-validation (RMSECV) was 3.4 d at 2°C. NIR measurements provided promising results for evaluation of freshness for thawed-chilled MAP cod fillets completing the traditionally quality methods. However, it is necessary to study the effect of e.g. sample preparation, season, fishing ground and cod size together with more sophisticated pre-treatments of NIR spectra before the NIR method can be integrated as a method for evaluation of thawed-chilled MAP cod fillets.

Freshness Assessment of Thawed and Chilled Cod Fillets Packed in Modified Atmosphere Using Near-infrared Spectroscopy

Near-infrared reflectance (NIR) spectra was recorded of 105 samples of cod mince prepared from chill stored thawed cod fillets of varying quality in modified atmosphere packaging (MAP). Traditional chemical, physical, microbiological and sensory quality methods developed for assessing fresh fish products were determined on the same cod fillets. The purpose was to evaluate the potential of NIR spectroscopy for estimating (i) frozen storage temperature, (ii) frozen storage period and (iii) chill storage period of thawed-chilled MAP Barents Sea cod fillets. Furthermore, the potential for measuring of selected quality attributes as drip loss, water holding capacity and content of dimethylamine by NIR was evaluated. The results of the investigation were presented using multivariate modelling methods such as partial least-squares regression (PLSR) and discriminant partial least-squares regression (DPLSR). Systematic differences in the NIR measurements on minced cod fillets were primarily due to the chill storage duration (days at 2°C) on thawed-chilled MAP fillets. PLSR models based on wavelengths selected by a new Jack-knife method resulted in a correlation coefficient of 0.90 between measured and predicted duration of chill storage period (days at 2°C). The root-mean-square error of cross-validation (RMSECV) was 3.4 d at 2°C. NIR measurements provided promising results for evaluation of freshness for thawed-chilled MAP cod fillets completing the traditionally quality methods. However, it is necessary to study the effect of e.g. sample preparation, season, fishing ground and cod size together with more sophisticated pre-treatments of NIR spectra before the NIR method can be integrated as a method for evaluation of thawed-chilled MAP cod fillets.

Comparison of near-infrared transmittance and reflectance measurements using dispersive and Fourier transform spectrophotometers

A spectrophotometer based on an integrating sphere coupled to a commercial Fourier Transform Infrared (FTIR) instrument has been constructed for regular spectral transmittance and reflectance measurements over the 1 µm to 18 µm wavelength region. Despite the large number of sources of error that often limit the radiometric accuracy of FTIR measurements, we demonstrate uncertainties similar to established dispersive instrumentation. We performed near-normal-incidence reflectance and transmittance measurements on a series of samples over the 1 µm to 2.5 µm wavelength range using both FTIR and dispersive spectrophotometers. The results are compared, taking into account any differences in measurement geometry among the various systems and the combined measurement uncertainty. We find agreement within the combined uncertainties over most of the measured spectral region.

Using miniature diode array NIR spectrometers for analysing wood chips and bark samples in motion

The chemical and physical properties of the wood chips entering the Kraft pulping process are of high interest for many different reasons. The most important one is the possibility to establish an effective and dynamic forward process control. The same is valid for one of the by-products, bark, which is burned in a solid fuel furnace for energy recovery. In the present study, NIR (near-infrared) reflectance measurements with an inexpensive silicon diode array spectrometer operating in the 800–1100 nm range are evaluated as a tool for on-line analysis of wood chips and bark transported on a conveyor belt moving at 1 m s−1. The objective of this work was to investigate the determination of water content, extractive content, Klason lignin, carbohydrate monomers and size distribution of wood chips together with water content, wood content and coarseness of bark. All these parameters could be measured with the exception of the carbohydrates, for which it was only possible to satisfactorily measure the sum of pentoses (arabinose + xylose) and the sum of hexoses (glucose + galactose + mannose). FWEFA (fixed window evolving factor analysis) was used for automatic identification of outliers. Copyright © 2000 John Wiley & Sons, Ltd.

Use of Near-Infrared Spectroscopy to Predict Potential Mushroom (Agaricus Bisporus) Yield of Phase II Compost

The yield of fruiting bodies of Agaricus bisporus from compost is variable due to differences in available nutrients and cultural conditions during production. The objective of this study was to evaluate the use of near-infrared (NIR) reflectance spectroscopy for assessing potential compost productivity. Three compost comparative trials, carried out over the past six years under controlled experimental conditions, have generated a large sample set (48 × 3 = 144) with yield data. Visible and NIR reflectance measurements of the calibration samples were made over the range of 400–2498 nm. A partial least-squares (PLS) algorithm was used to generate the predictive models using the information in the spectrum. A model developed with the spectral region 1100–2498 nm could explain 87% of the variation in mushroom yields obtained from the three trials. The accuracy of the NIR yield model has been confirmed in a validation compost comparative trial. A combined calibration equation ( r2 = 0.93) using the spectral data (1100–2498 nm) from the four trials was developed with a standard error of cross-validation of 18.6 kg/tonne.

Intercomparison of industrial near-infrared diffuse reflectance measurements

An intercomparison of diffuse reflectance measurements in the near-infrared region was carried out. Fourteen participants from industry and academia measured a number of different reflectance standards, calibrated at the NPL, over the wavelength range from 800 to 2500 nm. Measurements were made in the integrating sphere geometries, i.e. specular included and specular excluded and in the 0/45 geometry. The results of the intercomparison are presented. The raw data results showed significant variations in the value of diffuse reflectance measured across industry, in excess of 80% difference from NPL reflectance values in some cases. Renormalisation of the data to the NPL near-infrared diffuse reflectance scale significantly improved the agreement in measured values between participants, most being within 5% of the NPL values. Integrating sphere errors were present in many of the instruments used.

Estimation of surface snow properties using combined millimeter-wave backscatter and near-infrared reflectance measurements

Knowledge of surficial snow properties such as grain size, surface roughness, and free-water content provides clues to the metamorphic state of snow on the ground, which in turn yields information on weathering processes and climatic activity. Remote sensing techniques using combined concurrent measurements of near-infrared passive reflectance and millimeter-wave radar backscatter show promise in estimating the above snow parameters. Near-infrared reflectance is strongly dependent on snow grain size and free-water content, while millimeter-wave backscatter is primarily dependent on free-water content and, to some extent, on the surface roughness. A neural-network based inversion algorithm has been developed that optimally combines near-infrared and millimeter-wave measurements for accurate estimation of the relevant snow properties. The algorithm uses reflectances at wavelengths of 1160 nm, 1260 nm and 1360 nm, as well as co-polarized and cross-polarized backscatter at a frequency of 95 GHz. The inversion algorithm has been tested using simulated data, and is seen to perform well under noise-free conditions. Under noise-added conditions, a signal-to-noise ratio of 32 dB or greater ensures acceptable errors in snow parameter estimation.

Near Infrared Reflectance Spectroscopy in the Prediction of Sensory Properties of Beef

Near infrared (NIR) spectroscopy in the prediction of sensory hardness, tenderness and juiciness of bovine M. Longissimus dorsi muscles has been studied. Principal component regressions (PCR) of sensory variables from NIR reflectance measurements on frozen/thawed beef of 120 heat treated samples yielded multivariate correlation coefficients of cross-validation of 0.74, 0.70 and 0.61 for hardness, tenderness and juiciness, respectively. The corresponding correlation coefficients for NIR measurements of fresh (non-frozen) samples were approximately 0.1 units lower for all sensory variables. Predicting Warner Bratzler (WB) shear press values from NIR measurements gave a correlation coefficient similar to that for prediction of sensory hardness. The univariate correlation coefficient between sensory hardness and WB shear press values was 0.90.

Radiative Transfer Model for Relating Near-infrared and Micronaire Measurements of Cotton Fibers

Assessing cotton fiber quality has become increasingly important. Spectrophotometric reflectance measurements have shown promise in assessing fiber fineness and maturity. A technique is proposed herein in which cotton sample reflectance is related to electromagnetic theory with a radiative transfer approach. Cotton fibers are assumed to be cylindrical in shape. All calculations are performed assuming a wavelength of 2000 nm. It is shown that reflectance can be approximated as a function of the scattering and extinction coefficients of cotton. These coefficients are related to the more fundamental parameters of fiber radius, refractive index, and absorption index by way of the Lorenz-Mie theory. Different values of radius, refractive index, and absorption index are input to an electromagnetic wave model to predict reflectances. Comparing calculated results and spectrophotometric data is limited because the scattering phase function of cotton fibers is unknown at present. This is left to another study. However, results hold that most of the variation in reflectance emanates from changes in absorption index, while some variation comes from changes in radius. Uncertainties in refractive index have very little influence on reflectance. The effect of change in radius is heightened at low values of absorption index. Assuming that absorption by cotton fibers increases with maturity, it was concluded that the high correlations between near-infrared reflectance (NIR) measurements and Micronaire measurements are much more related to maturity than to fiber fineness. The radiative transfer approach proved quite useful in exposing some of the complex physical relationships involved.

Near-infrared reflectance and Fourier transform infrared analysis of instant coffee mixtures

Determination of the actual coffee content of coffee mixtures by wet analytical methods is lengthy and tedious. An alternative method was used, where coffee mixtures were prepared from instant coffee and instant coffee substitute within a range of 0–100% of actual coffee, 0–28.78 mg g–1 of caffeine and 0–25.77 mg g–1 of chlorogenic acid. The actual coffee, caffeine and chlorogenic acid contents were determined by near-infrared reflectance (NIR) and Fourier transform infrared (FT–IR) spectroscopic methods. Calibration was used to find the relationship between the spectra obtained and the coffee, caffeine and chlorogenic acid content. The integrated intensity in the range of 1900–1500 cm–1 in the analytical infrared region was recorded and values estimated by use of multiple linear regression and the partial least squares (PLS) method, in the case of NIR measurements, provided the basis for the calculations. The developed FT–IR and NIR methods were found to be reliable, rapid and exact for determining the actual coffee, caffeine and chlorogenic acid content of instant coffee mixtures within an acceptable level of accuracy.