The ability of a handheld near-infrared spectrometer to do a rapid quality assessment of bovine colostrum, including the immunoglobulin G concentration

Portable infrared-based instruments have made important contributions in different research fields. Within the dairy supply chain, for example, most of portable devices are based on near-infrared spectroscopy (NIRS) and are nowadays an important support for farmers and operators of the dairy sector, allowing fast and real-time decision-making, particularly for feed and milk quality evaluation and animal health and welfare monitoring. The affordability, portability, and ease of use of these instruments have been pivotal factors for their implementation on farm. In fact, pocket-sized devices enable nonexpert users to perform quick, low-cost, and nondestructive analysis on various matrixes without complex preparation. Because bovine colostrum (BC) quality is mostly given by the IgG level, evaluating the ability of portable NIRS tools to measure antibody concentration is advisable. In this study we used the wireless device SCiO manufactured by Consumer Physics Inc. (Tel Aviv, Israel) to collect BC spectra and then attempt to predict IgG concentration and gross and fine composition in individual samples collected immediately after calving (<6 h) in primiparous and pluriparous Holstein cows on 9 Italian farms. Chemometric analyses revealed that SCiO has promising predictive performance for colostral IgG concentration, total Ig concentration, fat, and AA. The coefficient of determination of cross-validation (R2CV) was in fact ≥0.75). Excellent accuracy was observed for dry matter, protein, and S prediction in cross-validation and good prediction ability in external validation (R2CV ≥ 0.93; the coefficient of determination of external validation, R2V, was ≥0.82). Nonetheless, SCiO's ability to discriminate between good- and low-quality samples (IgG ≥ vs. < 50 g/L) was satisfactory. The affordable cost, the accurate predictions, and the user-friendly design, coupled with the increased interest in BC within the dairy sector, may boost the collection of extensive BC data for management and genetic purposes in the near future.

Neural network enhanced aging time measurements of diary product remaining with infrared spectroscopy

The determination of the drying degree of food residues on surfaces is an important step before efficient cleaning can be achieved. To accomplish this goal, a rapid evaluation based on a neural network and non-invasive measurement technique is introduced. Two common starch-based products and various yogurts from different manufacturers are used as example contaminants to determine the aging time of dried food residue. Near-infrared spectroscopy serves as a modern and fast measurement technique for investigating food compositions. Two analysis methods were compared for processing the measured near-infrared spectral data. The raw data were analyzed using partial least squares regression in conjunction with necessary preprocessing steps. As an alternative method, three different types of neural networks are employed. The aim of this approach is to compensate for the filtering steps before regression, which are typically necessary for multivariate regression. The challenge is to measure three different types of food and obtain a reliable prediction of moisture content in order to draw conclusions about the drying time. The experiments have shown that simple flat neural networks have similar accuracy compared to conventional regression. The use of a convolutional layer in advance demonstrates a significant improvement in prediction compared to other neural networks and even manages to surpass the accuracy of PLS regression. A network with a convolutional layer can also compensate for the sometimes strong variations between food types.

Applications of optical property measurement for quality evaluation of agri-food products: a review

Spectroscopic techniques coupled with chemometric approaches have been widely used for quality evaluation of agricultural and food (agri-food) products due to the nondestructive, simple, fast, and easy characters. However, these techniques face the issues or challenges of relatively weak robustness, generalizability, and applicability in modeling and prediction because they measure the aggregate amount of light interaction with tissues, resulting in the combined effect of absorption and scattering of photons. Optical property measurement could separate absorption from scattering, providing new insights into more reliable prediction performance in quality evaluation, which is attracting increasing attention. In this review, a brief overview of the currently popular measurement techniques, in terms of light transfer principles and data analysis algorithms, is first presented. Then, the emphases are put on the recent advances of these techniques for measuring optical properties of agri-food products since 2000. Corresponding applications on qualitative and quantitative analyses of quality evaluation, as well as light transfer simulations within tissues, were reviewed. Furthermore, the leading groups working on optical property measurement worldwide are highlighted, which is the first summary to the best of our knowledge. Finally, challenges for optical property measurement are discussed, and some viewpoints on future research directions are also given.

Enhanced light focusing inside scattering media with shaped ultrasound

Light focusing is the primary enabler of various scientific and industrial processes including laser materials processing and microscopy. However, the scattering of light limits the depth at which current methods can operate inside heterogeneous media such as biological tissue, liquid emulsions, and composite materials. Several approaches have been developed to address this issue, but they typically come at the cost of losing spatial or temporal resolution, or increased invasiveness. Here, we show that ultrasound waves featuring a Bessel-like profile can locally modulate the optical properties of a turbid medium to facilitate light guiding. Supported by wave optics and Monte Carlo simulations, we demonstrate how ultrasound enhances light focusing a factor of 7 compared to conventional methods based on placing optical elements outside the complex medium. Combined with point-by-point scanning, images of samples immersed in turbid media with an optical density up to 15, similar to that of weakly scattering biological tissue, can be reconstructed. The quasi-instantaneous generation of the shaped-ultrasound waves, together with the possibility to use transmission and reflection architectures, can pave the way for the real-time control of light inside living tissue.

Investigation of the Possibility of Using Microspectrometers Based on CMOS Photodiode Arrays in Small-Sized Devices for Optical Diagnostics

The article considers the potential applicability of C12880MA and C11708MA Hamamatsu microspectrometers, which are characterized by an extremely compact design, occupying a small volume of several cubic centimeters, in portable spectrometric equipment with spatial resolution for monitoring the optical properties of condensed scattering media. The development of methods for determining the reduced scattering and absorption spectral coefficients of radiation from various scattering materials and products allows us to speak about the possibility of real-time control of the volume concentration of optically active components included in them, for example, fat and water in dairy products. For this, it is necessary to provide sufficiently accurate spectra of diffusely reflected broadband light radiation at different distances between the points of radiation entrance and registration. The aim of the manuscript is to assess the possibility of using the considered microspectrometers in compact devices for optical diagnostics and control of the optical properties of condensed scattering media. The features of the connection diagram of these microspectrometers and the necessary methods for correcting the initially obtained spectral dependencies of diffusive reflection, which will be of interest to developers of spectral diagnostic equipment, are considered in detail. The need to eliminate the influence of the inhomogeneity of dark counts of a CMOS photodiode array is shown. The hardware functions of the C12880MA and C11708MA Hamammatsu microspectrometers, as well as the AvaSpec 2048L fiber-optic spectrometer, were experimentally measured and compared. Methods for correcting the nonlinearity of their reading scales and light characteristics, as well as improving their equivalent spectral resolution using digital Wiener filtering, are described. It is shown that the equivalent spectral resolution of C12880MA and C11708MA microspectrometers can be improved by about 40% when recording smooth spectra, subject to the condition that the resulting side oscillations are small. It is pointed out that in order to reduce the level of side oscillations in the corrected spectra with improved resolution, it is necessary to ensure the smoothness of the original spectra and a good signal-to-noise ratio. A conclusion is made about the possibility of using the considered microspectrometers in portable spectrometric equipment with careful consideration of their characteristics, the features of their switching circuit, and the necessary software.

Feasibility of 830 nm laser imaging for vein localization in dark skin tissue-mimicking phantoms

Accessing blood vessels by medical professionals has been a challenge in healthcare centers worldwide. The main objective of this work is to investigate the localization of blood vessels in dark skin based on near infrared laser imaging. An 830 nm diode laser was used as a light source to irradiate dark skin mimicking optical phantoms. Phantoms were constructed to simulate dark skin with embedded polymer tubes filled with human blood to mimic subcutaneous veins. Appropriate image processing techniques were also used to enhance the detection and depth resolved differentiation of the vein phantoms. Results show that a linear regression model can represent the relation between the grey level in subcutaneous vein images and the depth of vessels down to 3 mm or deeper (n = 15, R² = 0.88, P < 0.001). The effect of laser power on the system performance is also discussed. Analysis of the collected images demonstrates the feasibility of 830 nm laser imaging for differentiating vein depths under dark skin surface. The proposed method would enhance the localization of invisible subcutaneous veins. This, in turn, would further improve the success rate of related medical procedures such as blood sampling, drawing, in the dark skin population.

Bridging the gap between measurement-based and simulation-based metamodels for deriving bulk optical properties from spatially-resolved reflectance profiles: effect of illumination and detection geometry

Non-invasive determination of the optical properties is essential for understanding the light propagation in biological tissues and developing optical techniques for quality detection. Simulation-based models provide flexibility in designing the search space, while measurement-based models can incorporate the unknown system responses. However, the interoperability between these two types of models is typically poor. In this research, the mismatches between measurements and simulations were explored by studying the influences from light source and the incident and detection angle on the diffuse reflectance profiles. After reducing the mismatches caused by the factors mentioned above, the simulated diffuse reflectance profiles matched well with the measurements, with R2 values above 0.99. Successively, metamodels linking the optical properties with the diffuse reflectance profiles were respectively built based on the measured and simulated profiles. The prediction performance of these metamodels was comparable, both obtaining R2 values above 0.96. Proper correction for these sources of mismatches between measurements and simulations thus allows to build a simulation-based metamodel with a wide range of desired optical properties that is applicable to different measurement configurations.

Solid-State Thin-Film Broadband Short-Wave Infrared Light Emitters

Solid-state broadband light emitters in the visible have revolutionized today's lighting technology achieving compact footprints, flexible form factors, long lifetimes, and high energy saving, although their counterparts in the infrared are still in the development phase. To date, broadband emitters in the infrared have relied on phosphor-downconverted light emitters based on atomic optical transitions in transition metal or rare earth elements in the phosphor layer resulting in limited spectral bandwidths in the near-infrared and preventing their integration into electrically driven light-emitting diodes (LEDs). Herein, phosphor-converted LEDs based on engineered stacks of multi-bandgap colloidal quantum dots (CQDs) are reported as a novel class of broadband emitters covering a broad short-wave infrared (SWIR) spectrum from 1050-1650 nm with a full-width-half-maximum of 400 nm, delivering 14 mW of optical power with a quantum efficiency of 5.4% and power conversion efficiency of 13%. Leveraging the electrical conductivity of the CQD stacks, further, the first broadband SWIR-active LED is demonstrated, paving the way toward complementary metal-oxide-semiconductor integrated broadband emitters for on-chip spectrometers and low-cost volume manufacturing. SWIR spectroscopy is employed to illustrate the practical relevance of the emitters in food and material identification case studies.

Relative retinal flow velocity detection using optical coherence tomography angiography imaging

Optical coherence tomography angiography (OCTA) imaging is a valuable tool for the visualization of retinal vasculature at an unprecedented level of details. However, due to relatively long time-interval between repeated scans in the conventional OCTA scanning protocol, the OCTA flow signal suffers from low dynamic range and loss of velocity-intensity correlation. The ability to distinguish fast and slow flow in the retina may provide a powerful tool for the assessment of early-stage retinal diseases such as vein occlusion. Here, we report a method to detect relative flow velocity in human retina using a 67.5 kHz spectral-domain OCTA device. By adapting the selection of A-scan time-intervals within a single OCTA acquisition and combining the resulting OCTA images, we expand the detectable velocity range. After a quantitative validation of this method performing microchannel flow experiments with varying flow velocities, we demonstrate this approach on human eyes using CIRRUS HD-OCT 5000 with AngioPlex (ZEISS, Dublin, CA) through a prototype scanning pattern.

Milk as a Complex Multiphase Polydisperse System: Approaches for the Quantitative and Qualitative Analysis

Milk is a product that requires quality control at all stages of production: from the dairy farm, processing at the dairy plant to finished products. Milk is a complex multiphase polydisperse system, whose components not only determine the quality and price of raw milk, but also reflect the physiological state of the herd. Today’s production volumes and rates require simple, fast, cost-effective, and accurate analytical methods, and most manufacturers want to move away from methods that use reagents that increase analysis time and move to rapid analysis methods. The review presents methods for the rapid determination of the main components of milk, examines their advantages and disadvantages. Optical spectroscopy is a fast, non-destructive, precise, and reliable tool for determination of the main constituents and common adulterants in milk. While mid-infrared spectroscopy is a well-established off-line laboratory technique for the routine quality control of milk, near-infrared technologies provide relatively low-cost and robust solutions suitable for on-site and in-line applications on milking farms and dairy production facilities. Other techniques, discussed in this review, including Raman spectroscopy, atomic spectroscopy, molecular fluorescence spectroscopy, are also used for milk analysis but much less extensively. Acoustic methods are also suitable for non-destructive on-line analysis of milk. Acoustic characterization can provide information on fat content, particle size distribution of fat and proteins, changes in the biophysical properties of milk over time, the content of specific proteins and pollutants. The basic principles of ultrasonic techniques, including transmission, pulse-echo, interferometer, and microbalance approaches, are briefly described and milk parameters measured with their help, including frequency ranges and measurement accuracy, are given.

Optical Methods Based on Ultraviolet, Visible, and Near-Infrared Spectra to Estimate Fat and Protein in Raw Milk: A Review

The present manuscript focuses on reviewing the optical techniques proposed to monitor milk quality in dairy farms to increase productivity and reduce costs. As is well known, the quality is linked to the fat and protein concentration; in addition, this issue is crucial to maintaining a healthy herd and preventing illnesses such as mastitis and ketosis. Usually, the quality of the milk is carried out with invasive methods employing chemical reagents that increase the time analysis. As a solution, several spectroscopy optical methods have been proposed, here, the benefits such as non-invasive measurement, online implementation, rapid estimation, and cost-effective execution. The most attractive optical methods to estimate fat and protein in cow's milk are compared and discussed considering their performance. The analysis is divided considering the wavelength operation (ultraviolet, visible, and infrared). Moreover, the weaknesses and strengths of the methods are fully analyzed. Finally, we provide the trends and a recent technique based on spectroscopy in the visible wavelength.

A high-throughput quantification of resin and rubber contents in Parthenium argentatum using near-infrared (NIR) spectroscopy

BACKGROUND

Guayule (Parthenium argentatum A. Gray), a plant native to semi-arid regions of northern Mexico and southern Texas in the United States, is an alternative source for natural rubber (NR). Rapid screening tools are needed to replace the current labor-intensive and cost-inefficient method for quantifying rubber and resin contents. Near-infrared (NIR) spectroscopy is a promising technique that simplifies and speeds up the quantification procedure without losing precision. In this study, two spectral instruments were used to rapidly quantify resin and rubber contents in 315 ground samples harvested from a guayule germplasm collection grown under different irrigation conditions at Maricopa, AZ. The effects of eight different pretreatment approaches on improving prediction models using partial least squares regression (PLSR) were investigated and compared. Important characteristic wavelengths that contribute to prominent absorbance peaks were identified.

RESULTS

Using two different NIR devices, ASD FieldSpec^®3 performed better than Polychromix Phazir™ in improving R² and residual predicative deviation (RPD) values of PLSR models. Compared to the models based on full-range spectra (750-2500 nm), using a subset of wavelengths (1100-2400 nm) with high sensitivity to guayule rubber and resin contents could lead to better prediction accuracy. The prediction power of the models for quantifying resin content was better than rubber content.

CONCLUSIONS

In summary, the calibrated PLSR models for resin and rubber contents were successfully developed for a diverse guayule germplasm collection and were applied to roughly screen samples in a low-cost and efficient way. This improved efficiency could enable breeders to rapidly screen large guayule populations to identify cultivars that are high in rubber and resin contents.

A New Method for Total Fat Detection in Raw Milk Based on Dual Low-Coherence Interferometer

The present work experimentally demonstrates a multimode fiber optic sensing setup for total fat detection in raw milk samples. The optical fiber arrangement incorporates a low-coherence Fabry-Perot cavity operating in dual response. The system provides a phase modulation for a total fat range from 0.97% to 4.36%. Here, the protein remains constant at 3%. The data indicate that maximum sensitivity close to 616 pm/%fat could be achieved at optimal wavelength operation (500 nm). In addition, the system presented a minimal repeatability error measurement of 0.08%, cross-sensitivity between protein and fat of 0.134, and a regression coefficient of r2=0.9763. A thermal analysis was also performed, which indicate the temperature immunity of the system. The proposed method represents a low-cost alternative to detect minimal fat variations in raw cow milk.

Estimation of Particle Size Distribution from Bulk Scattering Spectra: Validation on Monomodal Suspensions

A particle size distribution (PSD) estimation method based on light-scattering properties was validated on experimental visible/near-infrared scattering spectra of polystyrene suspensions, with a nominal particle size ranging from 0.1 to 12 μm in diameter. On the basis of μ_s and g spectra extracted from double integrating sphere measurements, good PSD estimates were obtained for particles ≥1 μm. The particle volume fraction estimates in the case of μ_s were close to the target concentrations, although influenced by small baseline fluctuations on the spectra. For submicrometer particles, on the other hand, the non-oscillating μ_s spectra lack discriminating power, resulting in erroneous PSD estimates. The reduced scattering coefficient spectra (μ_s') were found less useful for particle size estimation as they lack a characteristic shape, causing an over- or underestimation of the distribution width. In summary, the estimation routine proved to deliver PSD estimates in line with the reference measurements for micrometer-sized or larger particles based on their μ_s and g scattering spectra. Additional validation on more polydisperse samples forms the next step before going to bimodal PSD estimates.

Optical properties of human milk

With human milk being the most important source of infant nutrition, the protection and support of breastfeeding are essential from a global health perspective. Nevertheless, relatively few objective methods are available to investigate human milk composition and lactation physiology when a mother experiences breastfeeding problems. We argue that optics and photonics offer promising opportunities for this purpose. Any research activity within this new application field starts with a thorough understanding on how light interacts with human milk. Therefore, the aim of this study was to investigate the full set of optical properties for human milk and the biological variability therein. Using a novel approach that combines spatially resolved diffuse reflectance spectroscopy (SR-DRS) and spectroscopic optical coherence tomography (sOCT) between 450 and 650 nm, we quantified the absorption coefficient µa , scattering coefficient µs , reduced scattering coefficient µs', anisotropy g and backscattering coefficient µb,NA of mature human milk from 14 participants released at different stages during a breastfeed (foremilk, bulk milk and hindmilk). Significant correlations were found between µa , µs , µs' and µb,NA and the biochemically determined fat concentration per sample (Rs = 0.38, Rs = 0.77, Rs = 0.80, Rs = 0.44 respectively). We explained the observed variations in the optical properties of human milk using Mie theory and the biological variability in both the concentration and size distribution of milk fat globules. In conclusion, we have provided a full set of optical properties for human milk, which can hopefully serve as a starting point for future biophotonic studies on human milk and the milk containing lactating breast.

Measuring light transport properties using speckle patterns as structured illumination

The measurement of light absorption and scattering properties of biological materials has several diagnostic and therapeutic applications. We can measure these properties for skin without contact using structured illumination and imaging. However, building simple handheld devices remains challenging due to motion artefacts and moving targets. To overcome this limitation, we project random speckle patterns instead of discrete spatial frequencies on the target. Since random patterns are spatially broadband, they capture more information per image, enabling frame-by-frame analysis. In this paper, we describe the statistics of objective speckles and demonstrate how the optical system is designed for spatially bandlimited illumination. Next, we use a diverse set of liquid tissue phantom to validate the method. We successfully demonstrate that a calibrated instrument can independently predict the two primary light transport properties of a homogeneous turbid system. This work is a starting point for analysing skin and other heterogeneous biological media in the future.

Beyond scattering and absorption: Perceptual unmixing of translucent liquids

Is perception of translucence based on estimations of scattering and absorption of light or on statistical pseudocues associated with familiar materials? We compared perceptual performance with real and computer-generated stimuli. Real stimuli were glasses of milky tea. Milk predominantly scatters light and tea absorbs it, but since the tea absorbs less as the milk concentration increases, the effects of milkiness and strength on scattering and absorption are not independent. Conversely, computer-generated stimuli were glasses of “milky tea” in which absorption and scattering were independently manipulated. Observers judged tea concentrations regardless of milk concentrations, or vice versa. Maximum-likelihood conjoint measurement was used to estimate the contributions of each physical component—concentrations of milk and tea, or amounts of scattering and absorption—to perceived milkiness or tea strength. Separability of the two physical dimensions was better for real than for computer-generated teas, suggesting that interactions between scattering and absorption were correctly accounted for in perceptual unmixing, but unmixing was always imperfect. Since the real and rendered stimuli represent different physical processes and therefore differ in their image statistics, perceptual judgments with these stimuli allowed us to identify particular pseudocues (presumably learned with real stimuli) that explain judgments with both stimulus sets.

Estimation of particle size distributions from bulk scattering spectra: sensitivity to distribution type and spectral noise

A shape dependent method for particle size distribution (PSD) estimation based on bulk scattering properties was elaborated. This method estimates the parameters of a particle size distribution with predefined shape from the bulk scattering spectra. The estimation routine was validated on simulated data of polystyrene in water suspensions. To investigate the effect of measurement errors on PSD estimates, a sensitivity analysis was performed. The influence of spectral resolution and range was rather limited. Good PSD estimations were obtained on noise-free spectra, spectra with limited random noise and for estimations on μs or μs' in case of a multiplicative baseline. However, the PSD estimation deteriorated if an incorrect value for the refractive index of the particle relative to the medium was used as input parameter. Deviations caused by an incorrect distribution type were smaller for more narrow PSDs than for broader ones. Overall, this study showed the potential to estimate PSDs from bulk scattering spectra and indicated the factors affecting the accuracy.

Non-linearity correction in NIR absorption spectra by grouping modeling according to the content of analyte

To correct the non-linearity caused by light scattering in quantitative analysis with near infrared absorption spectra, a new modeling analysis method was proposed: grouping modeling according to the content of analyte. In this study, we tested the proposed method for non-invasive detection of human hemoglobin (Hb) based on dynamic spectrum (DS). We compared the prediction performance of the proposed method with non-grouping modeling method. Experimental results showed that the root mean square error of the prediction set (RMSEP) by the proposed method was reduced by 9.96% and relative standard deviation of the prediction set (RSDP) was reduced by 4.73%. The results demonstrated that the proposed method could reduce the effects of non-linearity on the composition analysis by spectroscopy. This research provides a new method for correcting the non-linearity stemming from light scattering. And the proposed method will accelerate the pace of non-invasive detection of blood components into clinical application.

Accuracy and application of milk fatty acid estimation with diffuse reflectance near-infrared spectroscopy

Near infrared spectroscopy (NIRS) has the potential to estimate contents of fatty acids (FA) in milk frequently at-farm or during daily milking routine. In this study, a total of 738 raw milk spectra collected from 33 Holstein cows over a period of 30 weeks were recorded. Reference data on FA composition in milk and in milk fat were analysed in laboratory. Calibration models were calculated for single FA and groups of FA in milk and in milk fat. Validation resulted in sufficient Ratio of Prediction to Deviation (RPD) values for some single FA and in higher RPD values for groups of FA when concentrations of FA in milk were predicted. Since the concentrations of most FA in milk are highly correlated with milk fat content, the prediction of FA contents in milk fat is more meaningful when independent predictions are intended. The accuracy of predicting single FA concentrations in milk fat is rather poor for most FA but still comparable to alternative analysing methods such as MIR analysis. The estimation of different groups of FA in milk fat resulted in an improved accuracy based on higher RPD values, which was sufficient to mirror the development in the different lactation phases. The course of cow individual long chain fatty acid (LCFA) concentration in the early lactation stage can be an indicator for body fat mobilisation. The accurate estimation of the extent and duration of body fat mobilisation in cow individuals was rather difficult with NIR predicted LCFA concentrations and would require a higher measuring frequency than applied in this study.

Spatially Resolved Lateral Transmission Measurements to Characterize Changes in the Scattering Coefficient and the Anisotropy Factor

A new setup is described to characterize the scattering coefficient and the scattering phase function of liquid media. The setup utilizes the basic idea of a spatially resolved reflectance measurement combined with a sophisticated illumination geometry. The sample is illuminated parallel and close to the interface of the sample and a glass window to get information from single scattered and multiple scattered light. By illuminating the sample with a fiber orientated with the axis parallel to the glass surface, small distances to the source can be examined unimpeded by the illumination beam. The derived information is, for example, not only sensitive to the concentration of the scatterers but also to the size of the scattering particles. We present the setup including the theory to describe the light propagation in the whole configuration using Monte Carlo simulations. The validation has been done with polystyrene microsphere dispersions with different scattering coefficients. As application for the developed setup, we show measurements of different milk samples which vary in concentration of fat, protein, and in fat droplet size during homogenization process. By measuring milk, we show the ability of the sensor to determine information about the scattering phase function without diluting the sample. For sensors in the dairy industry, a measurement with no pre-processing and no diluting of the sample is worthwhile, because this can be used to determine the fat and protein concentration on-line.

Determination of hemicellulose, cellulose and lignin content using visible and near infrared spectroscopy in Miscanthus sinensis

Lignocellulosic components including hemicellulose, cellulose and lignin are the three major components of plant cell walls, and their proportions in biomass crops, such as Miscanthus sinensis, greatly impact feed stock conversion to liquid fuels or bio-products. In this study, the feasibility of using visible and near infrared (VIS/NIR) spectroscopy to rapidly quantify hemicellulose, cellulose and lignin in M. sinensis was investigated. Initially, prediction models were established using partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function neural network (RBF_NN) based on whole wavelengths. Subsequently, 23, 25 and 27 characteristic wavelengths for hemicellulose, cellulose and lignin, respectively, were found to show significant contribution to calibration models. Three determination models were eventually built by PLS, LS-SVM and ANN based on the characteristic wavelengths. Calibration models for lignocellulosic components were successfully developed, and can now be applied to assessment of lignocellulose contents in M. sinensis.

Anisotropic light propagation in bovine muscle tissue depends on the initial fiber orientation, muscle type and wavelength

The effects of fiber orientation on vis/NIR light propagation were studied in three bovine muscles: biceps brachii, brachialis and soleus. Broadband light was focused onto the sample and the diffuse reflectance spot was captured using a hyperspectral camera (470-1620 nm), after which rhombuses were fitted to equi-intensity points. In samples with fibers running parallel to the measurement surface, the rhombus' major axis was oriented perpendicular to the fiber direction close to the point of illumination. However, at larger distances from the illumination spot, the major axis orientation aligned with the fiber direction. This phenomenon was found to be muscle dependent. Furthermore, the rhombus orientation was highly dependent on the sample positioning underneath the camera, especially when the muscle fibers ran parallel to the measurement surface. The bias parameter, indicating the deviation from a circular shape, was higher for samples with the fibers running parallel to the measurement surface. Moreover, clear effects of wavelength and distance from the illumination point on this parameter were observed. These results show the importance of fiber orientation when considering optical techniques for measurements on anisotropic, fibrous tissues. Moreover, the prediction of muscle fiber orientation seemed feasible, which can be of interest to the meat industry.

Colorimetric detection of melamine in milk by using gold nanoparticles-based LSPR via optical fibers

A biosensing system with optical fibers is proposed for the colorimetric detection of melamine in liquid milk samples by using the localized surface plasmon resonance (LSPR) of unmodified gold nanoparticles (AuNPs). The biosensing system consists of a broadband light source that covers the spectral range from 200 nm to 1700 nm, an optical attenuator, three types of 600 μm premium optical fibers with SMA905 connectors and a miniature spectrometer with a linear charge coupled device (CCD) array. The biosensing system with optical fibers is low-cost, simple and is well-proven for the detection of melamine. Its working principle is based on the color changes of AuNPs solution from wine-red to blue due to the inter-particle coupling effect that causes the shifts of wavelength and absorbance in LSPR band after the to-be-measured melamine samples were added. Under the optimized conditions, the detection response of the LSPR biosensing system was found to be linear in melamine detection in the concentration range from 0μM to 0.9 μM with a correlation coefficient (R²) 0.99 and a detection limit 33 nM. The experimental results obtained from the established LSPR biosensing system in the actual detection of melamine concentration in liquid milk samples show that this technique is highly specific and sensitive and would have a huge application prospects.

Broadband Optical Properties of Milk

Dairy products play an important role in our daily nutrition. As a turbid scattering medium with different kinds of particles and droplets, each alteration of these components changes the scattering properties of milk. The goal of this work is the determination of the amount of main scattering components, the fat droplets and the casein micelles, by understanding the light propagation in homogenized milk and in raw milk. To provide the absolute impact of these milk components, the geometrical and optical properties such as the size distribution and the refractive index (RI) of the components have to be examined. We determined the reduced scattering coefficient [Formula: see text] and the absorption coefficient [Formula: see text] from integrating sphere measurements. By use of a collimated transmission setup, the scattering coefficient [Formula: see text] was measured. Size measurements were performed to validate the influence of the fat droplet size on the results of the scattering properties; also, the RI of both components was determined by the said coefficients. These results were used to determine the absolute impact of the milk components on the scattering behavior. By fitting Mie theory calculations on scattering spectra [Formula: see text] and [Formula: see text] from different raw milk samples, it was possible to get reliable values for the concentrations of fat and casein and for the size of the fat droplets. By destroying the casein micelles, it was possible to separate the influence of the different scattering components on scattering behavior.

Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than the PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varieties in Miscanthus.

Computational optimization of the configuration of a spatially resolved spectroscopy sensor for milk analysis

A global optimizer has been developed, capable of computing the optimal configuration in a probe for spatially resolved reflectance spectroscopy (SRS). The main objective is to minimize the number of detection fibers, while maintaining an accurate estimation of both absorption and scattering profiles. Multiple fibers are necessary to robustify the estimation of optical properties against noise, which is typically present in the measured signals and influences the accuracy of the inverse estimation. The optimizer is based on a robust metamodel-based inverse estimation of the absorption coefficient and a reduced scattering coefficient from the acquired SRS signals. A genetic algorithm is used to evaluate the effect of the fiber placement on the performance of the inverse estimator to find the bulk optical properties of raw milk. The algorithm to find the optimal fiber placement was repeatedly executed for cases with a different number of detection fibers, ranging from 3 to 30. Afterwards, the optimal designs for each considered number of fibers were compared based on their performance in separating the absorption and scattering properties, and the significance of the differences was tested. A sensor configuration with 13 detection fibers was found to be the combination with the lowest number of fibers which provided an estimation performance which was not significantly worse than the one obtained with the best design (30 detection fibers). This design resulted in the root mean squared error of prediction (RMSEP) of 1.411 cm(-1) (R(2) = 0.965) for the estimation of the bulk absorption coefficient values, and 0.382 cm(-1) (R(2) = 0.996) for the reduced scattering coefficient values.

Robust metamodel-based inverse estimation of bulk optical properties of turbid media from spatially resolved diffuse reflectance measurements

Estimation of the bulk optical properties of turbid samples from spatially resolved reflectance measurements remains challenging, as the relation between the bulk optical properties and the acquired spatially resolved reflectance profiles is influenced by wavelength-dependent properties of the measurement system. The resulting measurement noise is apparent in the estimation of the bulk optical properties. In this study, a constrained inverse metamodeling approach is proposed to overcome these problems. First, a metamodel has been trained on a set of intralipid phantoms covering a wide range of optical properties to link the acquired spatially resolved reflectance profiles to the respective combinations of bulk optical properties (absorption coefficient and reduced scattering coefficient). In this metamodel, the wavelength (500 - 1700 nm) is considered as a third input parameter for the model to account for the wavelength dependent effects introduced by the measurement system. Secondly, a smoothness constraint on the reduced scattering coefficient spectra was implemented in the iterative inverse estimation procedure to robustify it against measurement noise and increase the reliability of the obtained bulk absorption and reduced scattering coefficient spectra. As the estimated values in some regions may be more reliable than others, the difference between simulated and measured values as a function of the evaluated absorption and scattering coefficients was combined in a 2D cost function. This cost function was used as a weight in the fitting procedure to find the parameters of the µ(s)' function giving the lowest cost over all the wavelengths together. In accordance with previous research, an exponential function was considered to represent the µ(s)' spectra of intralipid phantoms. The fitting procedure also provides an absorption coefficient spectrum which is in accordance with the measurements and the estimated parameters of the exponential function. This robust inverse estimation algorithm was validated on an independent set of intralipid® phantoms and its performance was also compared to that of a classical single-wavelength inverse estimation algorithm. While its performance in estimating µ(a) was comparable (R2 of 0.844 vs. 0.862), it resulted in a large improvement in the estimation of µ(s)' (R2 of 0.987 vs. 0.681). The change in performance is more apparent in the improvement of RMSE of µ(s)', which decreases from 10.36 cm(-1) to 2.10 cm(-1). The SRS profiles change more sensitively as a function of µ(a). As a result, there is a large range of µ(s)' and a small range of µa resulting in a good fit between measurement and simulation. The robust inverse estimator incorporates information over the different wavelengths, to increase the accuracy of µ(s)'estimations and robustify the estimation process.