Infrared Spectroscopy Technique for the Nondestructive Measurement of Fat Content in Milk Powder

The recent advances of near-infrared spectroscopy in dairy production-a review

One of the major issues confronting the dairy industry is the efficient evaluation of the quality of feed, milk and dairy products. Over the years, the use of rapid analytical methods in the dairy industry has become imperative. This is because of the documented evidence of adulteration, microbial contamination and the influence of feed on the quality of milk and dairy products. Because of the delays involved in the use of wet chemistry methods during the evaluation of these products, rapid analytical techniques such as near-infrared spectroscopy (NIRS) has gained prominence and proven to be an efficient tool, providing instant results. The technique is rapid, nondestructive, precise and cost-effective, compared with other laboratory techniques. Handheld NIRS devices are easily used on the farm to perform quality control measures on an incoming feed from suppliers, during feed preparation, milking and processing of cheese, butter and yoghurt. This ensures that quality feed, milk and other dairy products are obtained. This review considers research articles published in reputable journals which explored the possible application of NIRS in the dairy industry. Emphasis was on what quality parameters were easily measured with NIRS, and the limitations in some instances.

Application of hyperspectral imaging and chemometric calibrations for variety discrimination of maize seeds

Hyperspectral imaging in the visible and near infrared (VIS-NIR) region was used to develop a novel method for discriminating different varieties of commodity maize seeds. Firstly, hyperspectral images of 330 samples of six varieties of maize seeds were acquired using a hyperspectral imaging system in the 380–1,030 nm wavelength range. Secondly, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of the spectral data. Thirdly, three optimal wavelengths (523, 579 and 863 nm) were selected by implementing PCA directly on each image. Then four textural variables including contrast, homogeneity, energy and correlation were extracted from gray level co-occurrence matrix (GLCM) of each monochromatic image based on the optimal wavelengths. Finally, several models for maize seeds identification were established by least squares-support vector machine (LS-SVM) and back propagation neural network (BPNN) using four different combinations of principal components (PCs), kernel principal components (KPCs) and textural features as input variables, respectively. The recognition accuracy achieved in the PCA-GLCM-LS-SVM model (98.89%) was the most satisfactory one. We conclude that hyperspectral imaging combined with texture analysis can be implemented for fast classification of different varieties of maize seeds.

Direct determination of unsaturation level of milk fat using Raman spectroscopy

We have demonstrated the potential of visible Raman spectroscopy in combination with chemometric analysis as a fast and simple tool for the determination of the unsaturation level of milk fat. The Raman measurements have been performed directly on liquid milk and on fat extracted from liquid milk. The Raman spectra taken from the extracted fat showed a higher resolution. The spectra directly obtained from the milk samples had some fluorescence background but nevertheless yielded the desired information. For calibration purposes, the iodine value (IV) was determined in all cases in order to evaluate the unsaturation level of the investigated samples. Two separate calibration models have been constructed; one for the milk samples and the second one for the extracted fat. The accuracy of these calibration models was estimated using the root mean square error of calibration and validation (RMSE) and the coefficient of determination (R(2)) between actual and predicted values.

Near-infrared and mid-infrared investigations of Na-dodecylbenzenesulfate intercalated into hydrocalumite chloride (CaAl-LDH-Cl)

Hydrocalumite (CaAl-LDH-Cl) belongs to layered double hydroxides (LDHs). The intercalation of Na-dodecylbenzenesulfate (SDBS) into CaAl-LDH-Cl has been investigated by X-ray diffraction (XRD), mid-infrared (MIR) spectroscopy and near-infrared (NIR) spectroscopy. The mid-infrared spectra indicated that SDBS could be intercalated into CaAl-LDH-Cl, with the same lattice structure to that of CaAl-LDH-Cl, and the interlayer distance of resultant product was expanded to 2.78 nm as confirmed by XRD. The near-infrared spectra (9200-4000 cm(-1)) showed that a special spectral range from 6200 to 5600 cm(-1) and prominent bands of CaAl-LDH-Cl intercalated with SDBS around 8300 cm(-1). This band was assigned to the second overtone of the first fundamental of C-H stretching vibrations of SDBS, and can be used to determinate the result of CaAl-LDH-Cl modified by anionic surfactants. The bands of water stretching vibrations and -OH groups shifted to higher wavenumbers when CaAl-LDH-Cl was intercalated by SDBS, and their intensity of MIR and NIR spectra became lower in intensity.

Near-infrared spectroscopy and partial least-squares regression for determination of arachidonic acid in powdered oil

Near-infrared (NIR) spectroscopy was evaluated as a rapid method of predicting arachidonic acid content in powdered oil without the need for oil extraction. NIR spectra of powdered oil samples were obtained with an NIR spectrometer and correlated with arachidonic acid content determined by a modification of the AOCS Method. Partial Least-Squares regression was applied to calculate models for the prediction of arachidonic acid. The model developed with the raw spectra had the best performance in cross-validation (n = 72) and validation (n = 21) with a correlation coefficient of 0.965, and the root mean square error of cross-validation and prediction were both 0.50. The results show that NIR, a well-established and widely applied technique, can be applied to determine the arachidonic acid content in powdered oil.

Exploring near and midinfrared spectroscopy to predict trace iron and zinc contents in powdered milk

Near infrared (NIR) and mid-infrared (MIR) spectroscopy were investigated to predict iron and zinc contents in powdered milk. A hybrid variable selection method, namely, uninformative variable elimination (UVE) combined with successive projections algorithm (SPA), was applied to select the most effective wavenumber variables from full 2756 NIR and 3727 MIR variables, respectively. Finally, 18 NIR and 18 MIR variables were selected for iron content prediction, and 17 NIR and 12 MIR variables for zinc content prediction. The obtained effective wavenumber variables were input into partial least-squares (PLS) and least-squares-support vector machines (LS-SVM), respectively. The selected MIR variables obtained much better results than NIR to predict both iron and zinc contents in both the PLS and LS-SVM models. The iron content prediction results based on LS-SVM with 18 MIR spectra were as follows: coefficient of determination (r(2)) was 0.920, residual predictive deviation (RPD) was 3.321, and root-mean-square error of prediction (RMSEP) was 1.444. The zinc content prediction results based on LS-SVM with 12 selected MIR spectra were as follows:r(2) was 0.946, RPD was 4.361, and RMSEP was 0.321. The good performance shows that UVE-SPA is a powerful variable selection tool. The overall results indicate that MIR spectroscopy incorporated to UVE-SPA-LS-SVM could be applied as an alternative fast and accurate method to determine trace mineral content in powdered milk, such as iron and zinc.

Application of a hybrid variable selection method for determination of carbohydrate content in soy milk powder using visible and near infrared spectroscopy

Visible and near-infrared (Vis-NIR) spectroscopy was investigated to fast determine the carbohydrate content in soy milk powder. A hybrid variable selection method was proposed. In this method, a simulate annealing (SA) algorithm was first operated to search the optimal band (OB) in the wavelet packet transform (WPT) tree. The OB with 47 variables was further selected by SA (WTP-OB-SA). Finally, the number of variables was reduced from 47 to 20. The best partial least-squares prediction with a high residual predictive deviation (RPD) value of 12.2242 was obtained using these 20 variables with the correlation coefficient (r) and root-mean-square error of prediction (RMSEP) being 0.9967 and 0.1669, respectively. The results indicated that Vis-NIR spectroscopy could efficiently determine the carbohydrate content in soy milk powder. The WPT-OB-SA selection method eliminated redundant variables and improved the prediction ability.

Hot topic: application of support vector machine method in prediction of alfalfa protein fractions by near infrared reflectance spectroscopy

The object of this study was to explore the potential for support vector machine (SVM) to improve the precision of predicting protein fractions by near infrared reflectance spectroscopy (NIRS). Generally, most protein fractions determined in Cornell Net Carbohydrate and Protein System (CNCPS), especially the neutral detergent insoluble protein (NDFCP) and acid detergent insoluble protein (ADFCP), could not be accurately predicted by the commonly used partial least squares (PLS) method. A recently developed chemometric method, SVM, was applied in NIRS prediction of alfalfa protein fractions in this study. Two hundred thirty alfalfa samples were scanned on a near infrared reflectance spectrophotometer, and analyzed for crude protein (CP), true protein precipitated in tungstic acid (TCP), borate-phosphate buffer-insoluble protein (BICP), NDFCP, and ADFCP. These 5 laboratory proteins and the CNCPS protein fractions A, B1, B2, B3, and C were predicted by NIRS using the PLS and SVM methods. According to PLS-NIRS regression, CP, TCP, BICP, A, and B2 obtained the determination coefficient of prediction (R(p)(2)) of 0.96, 0.91, 0.94, 0.94, and 0.93, and the ratios of standard deviation of prediction samples: standard error of prediction samples (RPD) values were 5.07, 3.31, 3.98, 3.96, and 3.91. Neutral detergent insoluble protein, ADFCP (fraction C), B1, and B3 were predicted with R(p)(2) of 0.75, 0.83, 0.30, and 0.62, and RPD values of 1.98, 2.42, 1.20, and 1.62; Calibrated by the SVM-NIRS method, R(p)(2) values of CP, TCP, BICP, NDFCP, ADFCP(C), A, and B2 achieved 0.99, 0.97, 0.97, 0.90, 0.93, 0.97, and 0.97, respectively. The RPD values of those fractions were 8.68, 8.26, 6.11, 3.08, 3.69, 5.97, and 5.81, respectively. The R(p)(2) and RPD values of fractions B1 and B3 were 2.67 and 0.87 (B1) and 2.51 and 0.75 (B3) directly predicted by SVM-NIRS model. In this study, the chemical analysis results of B1 and B3 were also correlated with calculated results from TCP-BICP and NDFCP-ADFCP, which were predicted by SVM-NIRS models. The B1 protein fraction achieved R(p)(2) and RPD values of 0.87 and 3.61, whereas values for B2 were 0.75 and 2.00. Data suggested that use of SVM methods in NIRS technology could improve the accuracy of predicting protein fractions. This study showed the potential of increasing the NIRS prediction accuracy to a level of practical use for all protein fractions, except B3.

Short-wave near-infrared spectroscopy of milk powder for brand identification and component analysis

The aim of the present paper was to provide new insight into the short-wave near-infrared (NIR) spectroscopic analysis of milk powder. Near-infrared spectra in the 800- to 1,025-nm region of 350 samples were analyzed to determine the brands and quality of milk powders. Brand identification was done by a least squares support vector machine (LS-SVM) model coupled with fast fixed-point independent component analysis (ICA). The correct answer rate of the ICA-LS-SVM model reached as high as 98%, which was better than that of the LS-SVM (95%). Contents of fat, protein, and carbohydrate were determined by the LS-SVM and ICA-LS-SVM models. Both processes offered good determination performance for analyzing the main components in milk powder based on short-wave NIR spectra. The coefficients of determination for prediction and root mean square error of prediction of ICA-LS-SVM were 0.983, 0.231, and 0.982, and 0.161, 0.980, and 0.410, respectively, for the 3 components. However, there were less than 10 input variables in the ICA-LS-SVM model compared with 225 in the LS-SVM model. Thus, the processing time was much shorter and the model was simpler. The results presented in this paper demonstrate that the short-wave NIR region is promising for fast and reliable determination of the brand and main components in milk powder.

Short-wave near-infrared spectroscopy analysis of major compounds in milk powder and wavelength assignment

In this study, short-wave near-infrared (NIR) spectroscopy at 800-1050 nm region was investigated for the analysis of main compounds in milk powder. Through quantitative analysis, the feasibility is further demonstrated for the simultaneous measurement of fat, proteins and carbohydrate in milk powder. Two models, partial least-squares and least-squares support vector machine, were compared and utilized for regression coefficients and loading weights. The affect of standard normal variate spectral pretreatment to model performance was evaluated. Based on the resulted coefficients and loading weights, interesting wavelength regions of nutrition in milk powder are screened and the assignment of all specific wavelengths is firstly proposed in the details associated with chemical base. Instead of the whole short-wave NIR spectral data, these assigned wavelengths which can be reliably exploited were used for the content determination. Compared with other spectroscopy technique, assigned short-wave NIR spectral wavelengths did a good work. Determination coefficients for prediction are 0.981, 0.984, and 0.982, respectively for three components. The proposed wavelength assignment in the short-wave NIR region could be used for the component contents determination of milk powder, and could be as a guidance to interpret the spectra of milk powder.