Determination of fat, protein, and total solids in ovine milk by near-infrared spectroscopy.

Analysis by near-infrared spectroscopy (NIRS) was investigated as a means of predicting quality parameters of ovine milk. Calibration equations were developed with samples of ovine milk obtained from a flock of Manchega and Lacaune dairy ewes at different stages of lactation for a wide variation in milk composition. Prediction equations for milk protein, fat, and total solids content were developed by use of reflection or transflection methods to measure absorbance values. Accuracies of measurements were compared. R2 (squared multiple correlation coefficient) values were satisfactory in most cases. The highest R2 value for milk protein content (0.92) was obtained in transflectance mode with unhomogenized milk. The highest R2 values for fat (0.99) and total solids (0.98-0.96) content were obtained in both a transflectance mode without sample conditioning and in a transflectance mode with milk homogenized at 40 degrees C. To validate the calibration, an independent set of 40 milk samples was used. The best r2 (simple correlation coefficient) values for protein, fat, and total solids were 0.92, 0.97, and 0.92, respectively. The study showed that NIRS is a potentially useful technique for evaluating the composition of unhomogenized ovine milk.     

Use of NIRS technology with a remote reflectance fibre-optic probe for predicting major components in cheese

In the present work the potential of near infra-red spectroscopy technology (NIRS) together with the use of a remote reflectance fibre-optic probe for the analysis of fat, moisture, protein and chlorides contents of commercial cheeses elaborated with mixtures of cow's, ewe's and goat's milk and with different curing times was examined. The probe was applied directly, with no previous sample treatment. The regression method employed was modified partial least squares (MPLS). The equations developed for the cheese samples afforded fat, moisture, protein, and chloride contents in the range 13-52%, 10-62%, 20-30%, and 0.7-2.9%, respectively. The multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEP (C)) obtained were respectively 0.97 and 0.995% for fat; 0.96% and 1.640% for moisture; 0.78% and 0.760% for protein, and 0.89% and 0.112% for chlorides.     

Mid-infrared spectrometry of milk for dairy metabolomics: a comparison of two sampling techniques and effect of homogenization

Milk production is a dominant factor in the metabolism of dairy cows involving a very intensive interaction with the blood circulation. As a result, the extracted milk contains valuable information on the metabolic status of the cow. On-line measurement of milk components during milking two or more times a day would promote early detection of systemic and local alterations, thus providing a great input for strategic and management decisions. The objective of this study was to investigate the potential of mid-infrared (mid-IR) spectroscopy to measure the milk composition using two different measurement modes: micro attenuated total reflection (μATR) and high throughput transmission (HTT). Partial least squares (PLS) regression was used for prediction of fat, crude protein, lactose and urea after preprocessing IR data and selecting the most informative wavenumber variables. The prediction accuracies were determined separately for raw and homogenized copies of a wide range of milk samples in order to estimate the possibility for on-line analysis of the milk. In case of fat content both measurement modes resulted in an excellent prediction for homogenized samples (R(2)>0.92) but in poor results for raw samples (R(2)<0.70). Homogenization was however not mandatory to achieve good predictions for crude protein and lactose with both μATR and HTT, and urea with μATR spectroscopy. Excellent results were obtained for prediction of crude protein, lactose and urea content (R(2)>0.99, 0.98 and 0.86 respectively) in raw and homogenized milk using μATR IR spectroscopy. These results were significantly better than those obtained by HTT IR spectroscopy. However, the prediction performance of HTT was still good for crude protein and lactose content (R(2)>0.86 and 0.78 respectively) in raw and homogenized samples. However, the detection of urea in milk with HTT spectroscopy was significantly better (R(2)=0.69 versus 0.16) after homogenization of the milk samples. Based on these observations it can be concluded that μATR approach is most suitable for rapid at line or even on-line milk composition measurement, although homogenization is crucial to achieve good prediction of the fat content.     

Determination of the percentage of milk (cow's, ewe's and goat's) in cheeses with different ripening times using near infrared spectroscopy technology and a remote reflectance fibre-optic probe

In the present work we studied the use of near infrared spectroscopy (NIRS) technology employing a remote reflectance fibre-optic probe (with a 5 cm × 5 cm quartz window) for the analysis of the percentage of milk (cow's, ewe's and goat's) used in the elaboration of cheeses with different ripening times. To do so, cheeses with known and varying percentages of cow's, ewe's and goat's milk were elaborated (112 samples with milk collected in winter and 112 samples with milk collected in summer) and used as reference material, and ripening controls were performed over 6 months. The method allows immediate control of the cheese without prior sample treatment or destruction by direct application of the fibre-optic probe to the sample. The regression method employed was modified partial least squares (MPLS). Of all the samples (224), 200 formed to so-called calibration set and the other 24 were used for external validation. The calibration results obtained using 200 samples of cheese allowed the percentage of cow's, ewe's and goat's milk to be measured. The multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEP(C)) obtained were respectively, 0.834 and 11.6% for cow's milk; 0.871 and 9.8% for goat's milk; 0.880 and 10.6% for ewe's milk. The ratio performance deviation (RPD) values obtained indicate that the NIRS equations can be applied to unknown samples.     

On-Site Measurement of Fat and Protein Contents in Milk Using Mobile NMR Technology

Robust and easy-to-use NMR sensor technology is proposed for accurate, on-site determination of fat and protein contents in milk. The two parameters are determined using fast consecutive ¹H and ³⁵Cl low-field NMR experiments on milk samples upon the 1:1 addition of a low-cost contrast solution. Reliable and accurate measurements are obtained without tedious calibrations and the need for extensive database information and may readily be conducted by non-experts in production site environments. This enables on-site application at farms or dairies, or use in laboratories harvesting significant reductions in costs and time per analysis as compared to wet-chemistry analysis. The performance is demonstrated for calibration samples, various supermarket milk products, and raw milk samples, of which some were analyzed directly in the milking room. To illustrate the wide application range, the supermarket milk products included both conventionally/organically produced, lactose-free milk, cow’s, sheep’s and goat’s milk, homogenized and unhomogenized milk, and a broad nutrient range (0.1–9% fat, 1–6% protein). Excellent agreement between NMR measurements and reference values, without corrections or changes in calibration for various products and during extensive periods of experiment conduction (4 months) demonstrates the robustness of the procedure and instrumentation. For the raw milk samples, correlations between NMR and IR, NMR and wet-chemistry, as well as IR and wet-chemistry results, show that NMR, in terms of accuracy, compares favorably with the other methods.     

Portable Analyzer for Rapid Analysis of Total Protein, Fat and Lactose Contents in Raw Milk Measured by Non-dispersive Short-wave Near-infrared Spectrometry

A novel portable analyzer for raw milk quality control during the material purchase at dairy plants was developed, by which the percentages(mass fraction) of main components including total protein, fat, and lactose of an unhomogenized milk sample could be determinated in 1 min with the help of non-dispersive short-wave near-infrared (NDSWNIR) spectrometry in a wavelength range from 600 nm to 1100 nm and multivariate calibration. The analyzer was designed with a single-beam optical system, which comprised a temperature control module, a multi-channel narrow-band light source(16 wavelengths), a glass absorption cell with 15 mm sample thickness, a silicon photodiode detector, several compound lenses and a recorder module. A total of 80 raw milk samples were collected at a dairy farm twice a month for 4 months. The samples were scanned with a common UV-Vis-NIR spectrometer and analyzed according to China GB standard methods. The uninformative variables elimination(UVE) method was carried out on the spectrum data and the percentages of main components of all the samples to choose the peak emitting wavelength of each channel of the light source. Another 90 raw milk samples were collected from the same dairy farm thrice a month for 3 months. The samples were analyzed according to China GB standard methods and with the proposed analyzer. The percentages of the main components and the NDSWNIR absorption data of the samples were used for the construction and validation of the multivariate calibration model with partial least squares(PLS) method. The root-mean-square errors of prediction(RMSEP) of total protein, fat and lactose were 0.201, 0.172 and 0.247 and the coefficients of correlation(R) were 0.932, 0.981 and 0.933, respectively.     

Calibration of somatic cell count in milk based on near-infrared spectroscopy

A PLS model for prediction of somatic cell count (SCC) based on near-infrared (NIR) spectra of unhomogenized milk is presented in the study. Samples of raw milk were collected from cows in the early lactation period (from 7th to 29th day after parturition). The NIR spectra were measured in the region 400–1100 nm. As reference method a fluoro-opto-electronic method was applied. Different preprocessing methods were investigated. The robust version of PLS regression was applied to handle outliers present in the dataset and the uninformative variable elimination–partial least squares (UVE–PLS) method was used to eliminate uninformative variables. The final model is acceptable for prediction of SCC in raw milk.     

Structure, dynamics, and optical properties of concentrated milk suspensions: an analogy to hard-sphere liquids

A systematic study of the effects of volume fraction increment on the optical properties, the structure, and the dynamics of the casein micelles and fat droplets in milk was performed using diffusing wave spectroscopy. Four types of milk were investigated, NIDO full fat milk, fat-free milk, whey and fat-free milk, and finally lactose and fat-free milk. Independent measurements to calculate the dependence of the viscosity and the index of refraction of the milk serum and casein micelles as a function of the volume fraction were also performed. We compare the experimentally determined quantities photon transport mean free path (l*) and self-diffusion coefficient D(s) with the predictions from theoretical calculations using classical colloidal models such as a hard-sphere fluid. We demonstrate that all types of milk with and without fat content behave, structurally, like colloidal hard-sphere systems up to volume fractions well over 45%. In the case of dynamic measurements, both lactose- and fat-free and whey- and fat-free milk behave also like hard-sphere systems whereas fat-free milk and fat-containing NIDO milk deviate slightly at volume fractions over 35%. Finally, a comparative measurement and theoretical calculation of the casein micelle's size was performed.     

Effects of milk protein type and pre-heating on physical stability of whipped and frozen emulsions

Various milk protein mixtures were used to manufacture complex emulsions differing by their total protein concentration (1, 2.25 and 3.5%) and by their weight proportion of casein (from 0 to 75%) or whey proteins (WP) (containing from 10 to 80% β-lactoglobulin). Beside those changes in protein concentration and composition, impact of milk protein heat treatment, which was applied before emulsification, was also investigated. The resulting structuration effects on the corresponding emulsions were determined through changes in the particle size distribution and in the amount of adsorbed protein at the fat globule surface. Furthermore, fat destabilisation under a whipping and freezing steps was underlined as functions of the processing parameters (protein concentration and composition, and application or not of an additional heat treatment), and it was discussed in terms of the resulting amount of displaced protein from the oil–water interface.     

Determination of total selenium and selenium distribution in the milk phases in commercial cow’s milk by HG-AAS

A procedure has been developed for determining the selenium in cows milk using hydride generation–atomic absorption spectrometry (HG-AAS) following microwave-assisted acid digestion. The selenium distributions in milk whey, fat and micellar casein phases were studied after separating the different phases by ultracentrifugation and determining the selenium in all of them. The detection limits obtained by HG-AAS for the whole milk, milk whey and micellar casein were 0.074, 0.065 and 0.075 g l^–1, respectively. The accuracy for the whole milk was checked by using a Certified Reference Material CRM 8435 whole milk powder from NIST, and the analytical recoveries for the milk whey and casein micelles were 100.9 and 96.9%, respectively. A mass balance study of the determination of selenium in the different milk phases was carried out, obtaining values of 95.5–100.8%. The total content of selenium was determined in 37 milk samples from 15 different manufacturers, 19 whole milk samples and 18 skimmed milk samples. The selenium levels found were within the 8.5–21 g l^–1 range. The selenium distributions in the different milk phases were studied in 14 whole milk samples, and the highest selenium levels were found in milk whey (47.2–73.6%), while the lowest level was found for the fat phase (4.8–16.2%). A strong correlation was found between the selenium levels in whole milk and the selenium levels in the milk components.     

Determination of fat, moisture, and protein in meat and meat products by using the FOSS FoodScan Near-Infrared Spectrophotometer with FOSS Artificial Neural Network Calibration Model and Associated Database: collaborative study

A collaborative study was conducted to evaluate the repeatability and reproducibility of the FOSS FoodScan near-infrared spectrophotometer with artificial neural network calibration model and database for the determination of fat, moisture, and protein in meat and meat products. Representative samples were homogenized by grinding according to AOAC Official Method 983.18. Approximately 180 g ground sample was placed in a 140 mm round sample dish, and the dish was placed in the FoodScan. The operator ID was entered, the meat product profile within the software was selected, and the scanning process was initiated by pressing the "start" button. Results were displayed for percent (g/100 g) fat, moisture, and protein. Ten blind duplicate samples were sent to 15 collaborators in the United States. The within-laboratory (repeatability) relative standard deviation (RSD(r)) ranged from 0.22 to 2.67% for fat, 0.23 to 0.92% for moisture, and 0.35 to 2.13% for protein. The between-laboratories (reproducibility) relative standard deviation (RSD(R)) ranged from 0.52 to 6.89% for fat, 0.39 to 1.55% for moisture, and 0.54 to 5.23% for protein. The method is recommended for Official First Action.     

Classification and determination of total protein in milk powder using near infrared reflectance spectrometry and the successive projections algorithm for variable selection

This paper proposes a methodology for the classification and determination of total protein in milk powder using near infrared reflectance spectrometry (NIRS) and variable selection. Two brands of milk powder were acquired from three Brazilian cities (Natal-RN, Salvador-BA and Rio de Janeiro-RJ). The protein content of 38 samples was determined by the Kjeldahl method and NIRS analysis. Principal component regression (PCR) and partial least squares (PLS) multivariate calibrations were used to predict the total protein. Soft independent modeling of class analogy (SIMCA) was also used for full-spectrum classification, resulting in almost 100% classification accuracy, regardless of the significance level adopted for the F-test. Using this strategy, it was feasible to classify powder milk rapidly and nondestructively without the need for various analytical determinations. Concerning the multivariate calibration models, the results show that PCR, PLS and MLR-SPA models are good for predicting total protein in powder milk; the respective root mean square errors of prediction (RMSEP) were 0.28 (PCR), 0.25 (PLS), 0.11 wt% (MLR-SPA) with an average sample protein content of 8.1 wt%. The results obtained in this investigation suggest that the proposed methodology is a promising alternative for the determination of total protein in milk powder.     

PLS-BP法近红外光谱技术同时测定鲜乳中四种主成分

基于近红外光谱技术,将偏最小二乘法(Partial Least Squares,PLS)和单隐层的反向传播网络(Back-Propagation Network,BP)联用并测定了鲜乳中4种主成分含量.用PLS法将原始数据压缩为主成分,取前3个主成分的14个数据输入网络,以Kolmogorov定理为依据,经过实验确定中间层的神经元个数为29,初始训练迭代次数为1000,建立了脂肪、蛋白质、乳糖、牛乳总固体4种主成分含量的预测校正模型.PLS-BP模型对样品4个组分含量的预测决定系数(R2)分别为:0.961、0.974、0.951、0.997;本研究为近红外光谱技术在鲜乳多组分快速检测提供了新思路.     

Determination of moisture, starch, protein, and fat in common beans (Phaseolus vulgaris L.) by near infrared spectroscopy

The presence of moisture, starch, protein, and fat was determined in common beans (Phaseolus vulgaris L.) by near infrared (NIR) spectroscopy without any previous sample pretreatment except grinding. A set of 96 samples was used to calibrate the instrument by modified partial least-squares regression. The following statistical results were achieved: standard error of calibration (SEC) = 0.31 and square correlation coefficient (R2) = 0.96 for moisture; SEC = 0.76 and R2 = 0.92 for starch; SEC = 0.39 and R2 = 0.98 for protein; and SEC = 0.14 and R2 = 0.80 for fat. To validate the calibration, a set of 25 bean samples was used. Standard errors of prediction were 0.39, 0.90, 0.56, and 0.13 for moisture, starch, protein, and fat, respectively, and R2 for the regression of measurements by the reference method versus NIR analysis were 0.94, 0.88, 0.94, and 0.74 for moisture, starch, protein, and fat, respectively. To compare the results obtained for all 4 components of the validation set by NIR spectroscopy with those obtained by the reference methods, linear regression and paired t tests were applied, and the methods did not give significantly different results, P = 0.05.     

Rapid determination of moisture/solids and fat in dairy products by microwave and nuclear magnetic resonance analysis

A peer-verified method is presented for the determination of percent moisture/solids and fat in dairy products by microwave drying and nuclear magnetic resonance (NMR) analysis. The method involves determining the moisture/solids content of dairy samples by microwave drying and using the dried sample to determine the fat content by NMR analysis. Both the submitting and peer laboratories analyzed various dairy products by using a CEM SMART system (moisture) and the SMART Trac (fat). The samples included milks, creams, ice cream mix, sour cream, yogurt, cream cheese, and mozzarella, Swiss, and cheddar cheeses. These samples represented a range of products that processors deal with in daily plant operations. The results were compared with moisture/solids and fat values derived from AOAC-approved methods.     

Solid-state characterization of spray-dried ice cream mixes

The main physicochemical properties of spray-dried ice cream mixes (i.e. surface composition, wettability, flowability and microstructure) were analyzed. Emulsions contained 19-44% milk fat on a dry basis and included mixes with no added emulsifier and/or sucrose. The time necessary for complete wetting of the powders correlated with the amount of surface free-fat measured by means of solvent extraction. Non-micellar casein (sodium caseinate) showed to be a better co-encapsulant than micellar casein (skim milk) as demonstrated by surface fat coverage measured by electron spectroscopy for chemical analysis (ESCA). Emulsifiers influenced the fat surface composition of the powders by reducing the amount of surface protein due to their lower interfacial tension. Surface fat caused an initial overestimation of the particle size of the powders due to fat-related caking. Powders showed no flow before and after surface fat extraction which was attributed to fat-related caking and very small particle size (<80 microm), respectively.     

Influence of dietary composition on the carbon, nitrogen, oxygen and hydrogen stable isotope ratios of milk

The stable isotope ratios ((13)C/(12)C, (15)N/(14)N, (18)O/(16)O, D/H) of animal feed and milk were investigated, considering cows stabled in two farms and fed with diets made up of different kinds of C(3) plants and different amounts of maize. Maize was characterised by delta(13)C, delta(18)O and deltaD values significantly higher than those of the C(3) plants, while, for the C(3) plants, Festuca arudinacea had significantly higher content of (13)C and (15)N. The delta(13)C and delta(18)O values of the overall diet and the delta(13)C of milk casein and lipids were shown to be significantly correlated with the percentage of maize in the animal diet. On the other hand, the delta(18)O values of milk water and the delta(18)O, deltaD and delta(15)N values of casein were shown to be only slightly influenced by the amount of maize in the feed, being probably more closely correlated with the geo-climatic and pedological characteristics of the area of origin and with the presence of fresh plant or silage in the ration. The delta(13)C value of casein was shown to be a suitable parameter for evaluating the amount of maize in the diet: each 10% increase in the maize content corresponded to a shift of 0.7 per thousand to 1.0 per thousand in the delta(13)C of casein. A threshold value of -23.5 per thousand for delta(13)C in milk casein, above which it is not possible to exclude the presence of maize in the diet, was suggested. The results obtained could be useful for determining mislabelling of dairy products declared to have been produced by pastured animals or of PDO cheeses with an established amount of maize in the diet and for verifying the unpermitted addition of exogenous components to milk.     

Production of Coconut Protein Powder from Coconut Wet Processing Waste and its Characterization

Virgin coconut oil (VCO) has been gaining popularity in recent times. During its production, byproducts such as coconut skim milk and insoluble protein are obtained which are underutilized or thrown away to the environment at present. This study deals with utilization of these byproducts to obtain a value-added product, namely, coconut protein powder. When coconut milk was subjected to centrifugation, three phases, namely, fat phase (coconut cream), aqueous phase (coconut skim milk), and solid phase (insoluble protein) were obtained. The coconut skim milk and insoluble protein were mixed and homogenized before spray drying to obtain a dehydrated protein powder. The proximate analysis of the powder showed high protein content (33?% w/w) and low fat content (3?% w/w). Protein solubility was studied as a function of pH and ionic content of solvent. Functional properties such as water hydration capacity, fat absorption capacity, emulsifying properties, wettability, and dispersibility of coconut protein powder were evaluated along with morphological characterization, polyphenol content, and color analysis. Coconut protein powder has shown to have good emulsifying properties and hence has potential to find applications in emulsified foods. Sensory analysis showed high overall quality of the product, indicating that coconut protein powder could be a useful food ingredient.     

An efficient approach for the prediction of ion channels and their subfamilies

Ion channels are integral membrane proteins that are responsible for controlling the flow of ions across the cell. There are various biological functions that are performed by different types of ion channels. Therefore for new drug discovery it is necessary to develop a novel computational intelligence techniques based approach for the reliable prediction of ion channels families and their subfamilies. In this paper random forest based approach is proposed to predict ion channels families and their subfamilies by using sequence derived features. Here, seven feature vectors are used to represent the protein sample, including amino acid composition, dipeptide composition, correlation features, composition, transition and distribution and pseudo amino acid composition. The minimum redundancy and maximum relevance feature selection is used to find the optimal number of features for improving the prediction performance. The proposed method achieved an overall accuracy of 100%, 98.01%, 91.5%, 93.0%, 92.2%, 78.6%, 95.5%, 84.9%, MCC values of 1.00, 0.92, 0.88, 0.88, 0.90, 0.79, 0.91, 0.81 and ROC area values of 1.00, 0.99, 0.99, 0.99, 0.99, 0.95, 0.99 and 0.96 using 10-fold cross validation to predict the ion channels and non-ion channels, voltage gated ion channels and ligand gated ion channels, four subfamilies (calcium, potassium, sodium and chloride) of voltage gated ion channels, and four subfamilies of ligand gated ion channels and predict subfamilies of voltage gated calcium, potassium, sodium and chloride ion channels respectively.     

Quantitative determination of alpha(s2)- and alpha(s1)-casein in goat's milk with different genotypes by capillary electrophoresis

A capillary electrophoresis (CE) method has been applied for the quantitative determination of alpha(s1)- and alpha(s2)-CN in goat's milk. Several analytical parameters were evaluated showing the reliability of this CE method. Coefficients of determination (R2) greater than 99% were obtained and determination limits of 1.23 and 0.98 mg/ml were achieved for alpha(s1)- and alpha(s2)-CN, respectively. The analytical parameters studied in terms of accuracy, precision and recovery were within acceptable limits. Among 18 samples of 4 different genotypes (BB, EE, BF and FF) for alpha(s1)-CN were analysed, different amounts were obtained from the genotypes.