Quantitative determination of fat and total protein in milk based on visible light scatter

LED-Based Desktop Analyzer for Fat Content Determination in Milk

In dairy, there is a growing request for laboratory analysis of the main nutrients in milk. High throughput of analysis, low cost, and portability are becoming critical factors to provide the necessary level of control in milk collection, processing, and sale. A portable desktop analyzer, including three light-emitting diodes (LEDs) in the visible light region, has been constructed and tested for the determination of fat content in homogenized and raw cow's milk. The method is based on the concentration dependencies of light scattering by milk fat globules at three different wavelengths. Univariate and multivariate models were built and compared. The red channel has shown the best performance in prediction. However, the joint use of all three LED signals led to an improvement in the calibration model. The obtained preliminary results have shown that the developed LED-based technique can be sufficiently accurate for the analysis of milk fat content. The ways of its further development and improvement have been discussed.

Analysis of Milk Microstructure Using Raman Hyperspectral Imaging

Optical spectroscopic analysis of the chemical composition of milk in its natural state is complicated by a complex colloidal structure, represented by differently sized fat and protein particles. The classical techniques of molecular spectroscopy in the visible, near-, and mid-infrared ranges carry only bulk chemical information about a sample, which usually undergoes a destructive preparation stage. The combination of Raman spectroscopy with confocal microscopy provides a unique opportunity to obtain a vibrational spectrum at any single point of the sample volume. In this study, scanning confocal Raman microscopy was applied for the first time to investigate the chemical microstructure of milk using samples of various compositions. The obtained hyperspectral images of selected planes in milk samples are represented by three-dimensional data arrays. Chemometric data analysis, in particular the method of multivariate curve resolution, has been used to extract the chemical information from complex partially overlaid spectral responses. The results obtained show the spatial distribution of the main chemical components, i.e., fat, protein, and lactose, in the milk samples under study using intuitive graphical maps. The proposed experimental and data analysis method can be used in an advanced chemical analysis of natural milk and products on its basis.

Multi-Reflectance-Spectroscopy, Part I: Theory and Calculations Using Simulated Milk Samples

Optical methods are appropriate for monitoring of constituents in suspensions and emulsions. A simple multi-wavelength, multi-reflectance spectroscopic technique, called MRS-Technology, is introduced. Two different signals of a sample are measured: the reflectance from a small and from a large measuring volume corresponding to the reduced scattering coefficient μSCA' and to the sum of μSCA' and the absorption coefficient μ_ABS, respectively. Analytical relations between the MRS reflectance and μ_ABS as well as μSCA' are derived. The investigations on MRS method are carried out using milk as an example. For this purpose "virtual" milk samples are defined. μ_ABS and μSCA' are calculated by means of the Mie scattering theory in the ultraviolet-visible-shortwave near-infrared (UV-Vis-SWNIR) spectral range. Using this data analytical reflectances can be calculated based on MRS theory as well as numerical reflectances obtained by Monte Carlo (MC) simulation. Analytical and numerical results are compared and investigated. The spectral behavior of the analytical reflectances is very similar to that of the numerical MC reflectances in the case of medium and low absorptions. By means of simple multilinear regression techniques (MLR), simple correlations between fat and protein volume fractions and reflectances could be generated with acceptable root mean square error (RMSE) values. Each correlation shows that best results will be achieved by using reflectances at sample-specific wavelengths for small and large measuring volumes of a sample indicating the potential of the MRS-Technology.

Evaluation of genetic models for COD and TSS estimation in wastewater through its spectrophotometric response

In an urban wastewater treatment plant (WWTP), early knowledge of the pollutant load levels throughout the plant is key to optimize its processes and achieve better purification levels. Molecular spectrophotometry has begun to gain prominence in this wastewater characterization process, as it is a simple, fast, inexpensive and non-invasive technique. In this research work, different mathematical models based on genetic algorithms have been developed for the estimation of chemical oxygen demand (COD) and total suspended solids (TSS) from the spectral response of the samples, measured in the 380-700 nm range by means of a light-emitting diode (LED) spectrophotometer developed by the researchers. A field campaign was carried out in Mapocho-Trebal WWTP (Chile), where 550 samples were obtained in three different parts of the plant: at the inlet (raw wastewater), at the outlet (secondary treated wastewater) and at the outlet of the primary clarifier. A total of 18 estimation models have been calculated by mean of HeuristicLab software, which have presented a high accuracy, with a Pearson's coefficient between 80 and 90% in most cases. In order to achieve the most accurate models possible to characterize each part of the plant, specific models have also been developed, as well as combined models that are valid for all types of wastewater.

Implementing Early Warning Systems in WWTP. An investigation with cost-effective LED-VIS spectroscopy-based genetic algorithms

Measuring how the pollution load evolves in real time along sewer networks is key for proper management of water resources and protecting the environment. The technique of molecular spectroscopy for water characterization has increasingly widespread use, as it is a non-invasive technique that leads to the correlation of the physical-chemical conditions of wastewater with spectroscopic surrogates by a series of mathematical estimation models. In the present research work, different symbolic regression models obtained with evolutive genetic algorithms are evaluated for the estimation of chemical oxygen demand (COD); five-day biochemical oxygen demand (BOD₅); total suspended solids (TSS); total phosphorus (TP); and total nitrogen (TN), from the spectral response of samples measured between 380 and 700 nm and without the use of chemicals or pre-treatment. Around 650 wastewater samples were used in the campaign, from 43 different wastewater treatment plants (WWTP) in which both, raw/influent and treated/effluent, were examined through 18 models composed of Classical Genetic Algorithm (CGA), the Age-Layered Population Structure (ALPS), and Offspring Selection (OS) by mean of HeuristicLab software, to make a comparison among them and to determine which models and wavelengths are most suitable for the correlation. Models are proposed considering both raw and treated samples together (15) and only with tertiary treated wastewater reclaimed for agriculture irrigation effluent (3). The Pearson correlation coefficients were in the range of 67-91% for the test data in the case of the combined models. The results conform the first steps for a real-time monitoring of WWTP.

Developing Multisensory Approach to the Optical Spectral Analysis

This article presents an overview of research aimed at developing a scientific approach to creating multisensor optical systems for chemical analysis. The review is mainly based on the author's works accomplished over the recent 10 years at Samara State Technical University with broad international cooperation. It consists of an introduction and five sections that describe state of the art in the field of optical sensing, suggested development methodology of optical multisensor systems, related aspects of experimental design and process analytical technology followed by a collection of practical examples in different application fields: food and pharmaceutical production, medical diagnostics, and ecological monitoring. The conclusion summarizes trends and prospects of the multisensory approach to optical spectral analysis.

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.

Rapid detection and quantification of sucrose adulteration in cow milk using Attenuated total reflectance-Fourier transform infrared spectroscopy coupled with multivariate analysis

Adulteration of milk to gain economic benefit has become a common practice in recent years. Sucrose is illegally added in milk to reconstitute its compositional requirement by improving the total solid contents. The present study is aimed to use FTIR spectroscopy in combination with multivariate chemometric modelling for the differentiation and quantification of sucrose in cow milk. Pure milk and adulterated milk spectra (0.5-7.5% w/v) were observed in the spectral region 4000-400 cm^-1. Principal component analysis (PCA) was used for the discrimination of pure milk and adulterated milk. Soft independent modelling of class analogy (SIMCA) was able to classify test samples with a classification efficiency of 100%. Partial least square regression (PLS-R) and principle component regression (PCR) models were established for normal spectra, 1st derivative and 2nd derivative for the quantification of sucrose in milk. PLS-R model (normal spectra) in the combined wavenumber range of 1070-980 cm^-1 showed the best prediction based on parameters like coefficient of determination (R²) (Cal: 0.996; Val: 0.993), RMSE (Cal: 0.15% w/v; Val: 0.20% w/v), RE% (Cal: 4.9% w/v; Val: 5.1% w/v) and RPD (13.40). This method has a detection level of 0.5% w/v sucrose adulteration.

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.

Review of on-line and near real-time spectroscopic monitoring of processes relevant to nuclear material management

Spectroscopic chemometric based on-line monitoring of used nuclear fuel (UNF) reprocessing solutions and characterization of legacy nuclear waste (LNW) stored at Hanford is discussed in this manuscript. Utilizing on-line and near real-time monitoring, as opposed to traditional off-line monitoring, can significantly reduce the cost, risk and improve the efficiency of characterizing UNF and LNW processing streams. Specifically, this manuscript will highlight the benefits of spectroscopy-based monitoring approaches, which generally include the ability to collect data non-destructively. Furthermore, significant literature precedence supports the use of various real-time analysis methods, including chemometric analysis, that enable near-instantaneous conversion of spectroscopic data into information useable by process operators. This approach can accurately quantify and qualify nuclear material in near-real time enabling immediate condition characterization and potential diversion detection within UNF reprocessing streams and LNW. The ability to be applied in a real reprocessing plant and in an actual Hanford waste tank/transfer pipe has been demonstrated by applying this technique to accurately quantify analytes in real UNF streams and LNW samples. The future development of spectroscopy-based on-line monitoring is also discussed in this manuscript.

Recent developments on rapid detection of main constituents in milk: a review

Milk is a good source of quality fats, proteins, carbohydrates, minerals, and vitamins. Determining milk constituents is very important in dairy production and is usually conducted by means of physical or chemical processes in laboratories. These methods are time-consuming and cannot satisfy the need in practice. Developing simple, quick, cost-effective, reliable, and sensitive methods on the detection of main constituents in milk is useful for dairy farmers, manufacturers and consumers. In last decades, many rapid detection techniques such as chromatography, spectroscopy, dielectric properties, and sensors, have emerged and shown great potential in the detection of main constituents in liquid milk. In this review, the rapid detection techniques applied to determine the main constituents in milk have been reviewed. Meanwhile, the potential advantages and limitations of these techniques and recommendations for future research have also been proposed.

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.

Accuracy Improvement of In-line Near-Infrared Spectroscopic Moisture Monitoring in a Fluidized Bed Drying Process

An exploratory analysis of a large representative dataset obtained in a fluidized bed drying process of a pharmaceutical powder has revealed a significant correlation of spectral intensity with granulate humidity in the whole studied range of 1091.8–2106.5 nm. This effect was explained by the dependence of powder refractive properties, and hence light penetration depth, on the water content. The phenomenon exhibited a close spectral similarity to the well-known stochastic variation of spectral intensities caused by the process turbulence (the so-called “scatter effect”). Therefore, any traditional scatter-corrective preprocessing incidentally eliminates moisture-correlated variance from the data. To preserve this additional information for a more precise moisture calibration, a time-domain averaging of spectral variables has been suggested. Its application resulted in a distinct improvement of prediction accuracy, as compared to the scatter-corrected data. Further improvement of the model performance was achieved by the application of a dynamic focusing strategy when adjusting the model to a drying process stage. Probe fouling was shown to have a minor effect on prediction accuracy. The study resulted in a considerable reduction of the root-mean-square error of in-line moisture monitoring to 0.1%, which is close to the reference method's reproducibility and significantly better than previously reported results.

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.

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.

Reference-free spectroscopic determination of fat and protein in milk in the visible and near infrared region below 1000nm using spatially resolved diffuse reflectance fiber probe

New technique of diffuse reflectance spectroscopic analysis of milk fat and total protein content in the visible (Vis) and adjacent near infrared (NIR) region (400-995nm) has been developed and tested. Sample analysis was performed through a probe having eight 200-µm fiber channels forming a linear array. One of the end fibers was used for the illumination and other seven - for the spectroscopic detection of diffusely reflected light. One of the detection channels was used as a reference to normalize the spectra and to convert them into absorbance-equivalent units. The method has been tested experimentally using a designed sample set prepared from industrial raw milk standards with widely varying fat and protein content. To increase the modelling robustness all milk samples were measured in three different homogenization degrees. Comprehensive data analysis has shown the advantage of combining both spectral and spatial resolution in the same measurement and revealed the most relevant channels and wavelength regions. The modelling accuracy was further improved using joint variable selection and preprocessing optimization method based on the genetic algorithm. The root mean-square errors of different validation methods were below 0.10% for fat and below 0.08% for total protein content. Based on the present experimental data, it was computationally shown that the full-spectrum analysis in this method can be replaced by a sensor measurement at several specific wavelengths, for instance, using light-emitting diodes (LEDs) for illumination. Two optimal sensor configurations have been suggested: with nine LEDs for the analysis of fat and seven - for protein content. Both simulated sensors exhibit nearly the same component determination accuracy as corresponding full-spectrum analysis.

Diagonal designs for a multi-component calibration experiment

Modern spectroscopic and sensor technologies combined with multivariate modelling are increasingly used for the quantitative analysis of complex mixtures. Their performance depends directly on the data design chosen for model training and validation. A well-balanced calibration experiment with the fewest samples possible presents additional challenges when several mixture components (factors) need to be calibrated on the same dataset and subsequently quantified from the same multivariate measurement. This practically important problem stays poorly addressed by the theory of experimental design. This theoretical work systematically formulates the requirements to an optimal calibration/validation dataset and introduces a new family of calibration designs, where the samples are placed along the diagonals of an experimental space that is a hypercube. Such placement is appropriate due to reasonable assumptions about the linear nature of analytical response. Suggested filling schemes allow economical diagonal designs with intrinsic validation to be built for multiple factors presented in as many levels as the number of samples. The most important practical cases of two and three factors are considered in detail, and generalization to higher dimensions is outlined. Diagonal designs of any complexity can be generated using a simple geometrical scheme or with a supplied script.

Building global models for fat and total protein content in raw milk based on historical spectroscopic data in the visible and short-wave near infrared range

A large set of fresh cow milk samples collected from many suppliers over a large geographical area in Russia during a year has been analyzed by optical spectroscopy in the range 400-1100 nm in accordance with previously developed scatter-based technique. The global (i.e. resistant to seasonal, genetic, regional and other variations of the milk composition) models for fat and total protein content, which were built using partial least-squares (PLS) regression, exhibit satisfactory prediction performances enabling their practical application in the dairy. The root mean-square errors of prediction (RMSEP) were 0.09 and 0.10 for fat and total protein content, respectively. The issues of raw milk analysis and multivariate modelling based on the historical spectroscopic data have been considered and approaches to the creation of global models and their transfer between the instruments have been proposed. Availability of global models should significantly facilitate the dissemination of optical spectroscopic methods for the laboratory and in-line quantitative milk analysis.

A sample-effective calibration design for multiple components

The experimental design of mixtures for multivariate calibration is introduced. The idea of this design is based on uniform distribution of experimental points in a concentration hypercube. Unlike the already reported uniform designs this one is pretty simple and not computationally demanding. The suggested approach does not employ the concept of fixed "levels" and allows for designs with any number of experimental mixtures and any number of components depending on "time and money" considerations for each particular calibration experiment. The performance of the design is assessed with a UV-Vis spectroscopic experiment for simultaneous quantification of four inorganic components in complex mixtures. The performance of the PLS regression models derived from the design is compared with that of cyclic permutation and Kennard-Stone designs. The suggested approach allows for comparable or higher prediction accuracy with the lower number of experimental points.

Determination of fat and total protein content in milk using conventional digital imaging

The applicability of conventional digital imaging to quantitative determination of fat and total protein in cow's milk, based on the phenomenon of light scatter, has been proved. A new algorithm for extracting features from digital images of milk samples has been developed. The algorithm takes into account spatial distribution of light, diffusely transmitted through a sample. The proposed method has been tested on two sample sets prepared from industrial raw milk standards, with variable fat and protein content. Partial Least-Squares (PLS) regression on the features calculated from images of monochromatically illuminated milk samples resulted in models with high prediction performance when analysed the sets separately (best models with cross-validated R(2)=0.974 for protein and R(2)=0.973 for fat content). However when analysed the sets jointly with the obtained results were significantly worse (best models with cross-validated R(2)=0.890 for fat content and R(2)=0.720 for protein content). The results have been compared with previously published Vis/SW-NIR spectroscopic study of similar samples.

Approach to on-line monitoring of PUREX process using chemometric processing of the optical spectral data

Optical spectroscopic measurements in the UV-Vis and IR ranges were performed in model solutions of aqueous and organic phases of the PUREX process for spent nuclear fuel (SNF) reprocessing. Chemometric processing of the spectral data with PLS (partial least squares) regression allowed simultaneous quantification of several key components (uranium, neptunium, plutonium, nitric acid) in these mixtures in an effective and elegant way. The content of all key components was quantitively determined with mean relative errors not exceeding 10%. It was shown that the employment of the whole spectra or their certain continuous regions for a PLS calibration enables to decrease the analytical errors compared to the use of a single wavelength in an ordinary least squares approach. The results of this research imply that the development of on-line techniques for SNF reprocessing monitoring is fully possible and can be based on optical spectroscopy methods combined with multivariate data processing techniques.