Butter is a dairy product made by churning cream or milk to separate butterfat from buttermilk. Per standard regulations, the only fat butter can contain is butterfat in the form of an emulsion of fat and water. It is composed of 80% to 90% butterfat, milk proteins, up to 16% water, and can contain salt as well. Butter is typically light yellow and has a variety of uses, such as a spread on bread products, a condiment on cooked vegetables, a dipping sauce for bread and some types of seafood, and cooking uses like pan frying and baking. Butter can be cultured or non-cultured, depending on if bacteria are added to induce fermentation and produce lactic acid. Production and consumption of butter are high in Europe and substantial in Asia and North America. The global butter market is expected to grow at an estimated CAGR of 4.2% from 2017-2023. There are numerous factors projected to contribute to this growth. Consumer consumption of fast, processed, and convenience foods is increasing, and butter is one of the key ingredients in such foods. Sale of butter is driven by its nutritional value and diverse applications across the food industry. Improvements in butter manufacturing that increase the nutritional value and flavor are helping propel market growth. Technological advances that result in improved processing and increased shelf-life of butter are contributing to growth as well.
Measuring Butter Using NIR Spectroscopy
Fat content is the most important parameter in butter, but current methods for testing fat are often time-consuming and involve expensive wet chemistry methods. In the case of Solid Fat Content (SFC), an excellent indicator of the functional characteristics of milk fat and an important parameter in many dairy products, the current reference method is Nuclear Magnetic Resonance (NMR) spectroscopy. This method requires over sixteen hours of sample preparation and is expensive, making it impractical for real-time analysis. Water is another critical component in butter that must be monitored. Adulteration is a significant issue in the dairy industry, and monitoring butter for adulteration is of key importance. Different methods of adulteration are always emerging, and testing methods for detecting adulterants must continue to evolve as well. Current methods for testing these parameters are expensive, laborious, and time-consuming, especially when implemented in a process setting. There is a need for fast, cost-effective, and real-time monitoring of parameters at all stages of the butter manufacturing process. One such method that has been examined is NIR spectroscopy.
- Solid Fat Content (SFC)
- Tallow Adulteration
Summary of Published Papers, Articles, and Reference Materials
Measurement of chemical parameters in major constituents of butter for quality control purposes has been studied using NIR spectroscopy. The results of most studies have been promising. One study examined measuring fat and moisture in butter, considered the two most important parameters not only in the manufacturing process but for monitoring for adulteration as well. Despite a limited sample set for such a study, results were good and proved the feasibility of measuring these two parameters using NIR spectroscopy. Another study examined measuring the Solid Fat Content (SFC), defined as the amount of solid fraction of fat crystallized at a specific temperature in terms of weight percentage. SFC is an essential indicator of the functional characteristics of milk fat and the current reference method for testing it is very time-consuming. Calibration models showed excellent results, showing the potential to replace the current reference method with NIR spectroscopy. Another study examined determining the presence of tallow adulterant in butter as well as quantitatively measuring the percent adulterant. Tallow is animal fat that can be melted down and used as an adulterant in butter. Various modeling algorithms were used for identification and quantification of tallow in the butter samples, all showing excellent results and proving NIR spectroscopy as a valid method for finding tallow adulterant in butter.
Scientific References and Statistics
Butter Quality Parameters
Comparison of Butter Quality Parameters Available on the Czech Market with the Use of FT-NIR Spectroscopy – Dvorak, Luzova, Sustova, Mljekarstvo 66 (1), 73-80 (2016)
The two most important quality parameters in butter are fat and water. They are not only important in terms of the manufacturing process but are crucial in butter adulteration as well. A common form of butter contamination is adding vegetable fats at the expense of butterfat, which according to regulations will make the substance undefinable as butter since butter must contain only butterfat, water, and a small portion of other components. Current methods for determining these parameters are expensive, time-consuming and impractical for real-time, on-line analysis. NIR spectroscopy was examined as a method for determining fat and moisture in butter available on the Czech market. Twenty-six samples of butter were procured for the study. Thirteen were manufactured in the Czech Republic, and thirteen came from abroad. Samples were prepared by cutting the butter into 1 cm thick slices from the center of the whole cube. NIR spectra were collected using an FT-NIR spectrometer in reflectance mode. Eighty scans were collected and averaged per spectrum using 4 cm-1 resolution. Each sample was scanned twice for a total of fifty-two spectra. Values for fat and moisture were obtained using standard reference methods. The NIR spectra and reference values were used to create Partial Least Squares (PLS) regression models correlating the spectra to fat and moisture.
|Fat||R2 = 0.947||RMSEP = 0.859%|
|Moisture||R2 = 0.956||RMSEP = 1.34%|
Both models showed high correlation coefficients and low prediction error. Independent validation of the models was performed by using cross-validation, a process where samples are removed from the model and the spectra of these samples are used to predict values for each parameter of interest using the calibrations. This process is repeated until all samples have predicted values from the NIR spectra and models. Those results are then compared to values obtained from the reference method. Cross-validation confirmed the validity of the models, and results should improve with a larger sample set. Twenty-six samples are a small number of samples to build regression models, but the results are decent considering the sample size. The results here prove the feasibility of monitoring fat and moisture using NIR spectra and calibration models.
Solid Fat Content of Milk Fat Analysis
At-Line Near-Infrared Spectroscopy for Prediction of the Solid Fat Content of Milk Fat from New Zealand Butter – Meagher, Holroyf, Illingworth, et al., Journal of Agricultural and Food Chemistry, 2007, 55, 2791-2796
Solid Fat Content (SFC) is an important parameter in dairy products and especially in butter. It is a measure of the amount of solid fraction of crystallized fat in terms of weight percentage and is a good indicator of the functional characteristics of milk fat. Cream is a water and oil emulsion, and when subject to agitation by churning, fat globule membranes can rupture, and the fat will agglomerate. An optimum crystallization pattern in the fat for this process is a function of temperature, and direct knowledge of the SFC in the cream can help the butter maker determine the proper conditions. Functional characteristics of cream-based products, such as texture and spreadability, are largely dependent on SFC. The current at-line American Oil Chemists’ Society (AOCS) approved a method for determining SFC is nuclear magnetic resonance (NMR) spectroscopy. NMR involves a sixteen-hour delay period for tempering the fat at 0°C before measurement and subsequent analysis from 0°C to 35°C in 5°C increments, rendering this method impractical for real-time measurements.
NIR spectroscopy was examined as a method for determining SFC in butter samples. Seventy-six samples were procured for the study. The selected samples were representative of the major dairy producing regions in New Zealand in terms of both geographical distribution and volume of butter production. Eight different plants provided samples and samples were acquired from two separate production seasons, comprising two years of production during the spring, summer, and fall. NMR reference testing was first performed on each sample, and a portion was set aside for NIR analysis. Each sample was held overnight at 0°C, and then two replicates of each sample were equilibrated for forty-five minutes at each temperature (0°C to 35°C in 5°C increments) before measurement. The mean of the two replicate sample values was used as the SFC reference value for the NIR calibration models. A portion of each replicate sample was equilibrated at 60°C before NIR spectra were obtained. Samples were scanned from 400 nm to 2500 nm at 2 nm intervals. Thirty-two scans were collected in reflectance mode and averaged into one spectrum. This process was repeated ten times for each sample, and these spectra were averaged as well. Random samples were also scanned over multiple days throughout data collection. In total, one hundred forty-nine spectra were collected. Spectra were first analyzed using Principle Component Analysis (PCA), and after examining various pre-treatments of the data, Partial Least Squares (PLS) regression models were created for each temperature used during NMR analysis from the NIR spectra and reference values for SFC.
|Range of R2 In Models from 0°C to 30°C||0.923 to 0.978|
|Range of RMSEP In Models from 0°C to 30°C||0.385% to 0.762%|
Visual examination of the NIR spectra and initial modeling work showed that only the wavelength range from 540 nm to 2250 nm was relevant for contributing to the calibration and this range was used for the PLS models. Various pre-treatments were applied to the spectra, and Standard Normal Variate (SNV) and 1st Derivative Transformation with Detrend Scatter Correction showed the best results during initial modeling assessment. The models for each temperature from 0°C to 30°C all showed excellent results that are good enough for use in a real-time setting. In the case of the 35°C model, results were much worse, but this is because of a minimal range of values. Each model from 0°C to 20°C had about a 10% range in the values for SFC. 25°C and 30°C models had about a 5% range in SFC. The 35°C model range was less than 2%, and this contributed to a low correlation. Further analysis of this work could include classification analysis to choose the proper range of SFC and then analyzing the actual value from the proper PLS model or creating one universal PLS model for all temperatures, which would likely require a much larger sample set to be robust enough to work in a real-time setting. The results here show promise for real-time analysis of SFC in butter using NIR spectra and PLS calibration models, replacing the expensive and time-consuming NMR method that is currently used.
Methods of Adulteration in Clarified Butter Samples
Robust New NIRS Coupled with Multivariate Methods for the Detection and Quantification of Tallow Adulteration in Clarified Butter Samples – Mabood, Abbas, Jabeen, et al., Food Additives & Contaminants: Part A, 35:3, 404-411
Food adulteration has become a big problem on a global scale during recent years. Increased population, higher supply, and demand for food, and less detectable methods of adulteration have all contributed to the problem. Food authenticity and detection of adulteration have become a priority for both food producers and consumers, as adulteration results in reduced profits, bad publicity, and in some cases, presents a health risk to the public. Dairy products are no exception to the adulteration issue, and one potential adulterant in butter is tallow, an animal fat material which causes increased serum cholesterol and triglycerides levels when consumed. Tallow is even used to make candles and soap and is an unsuitable substitute for butter at any concentration. Visual examination is difficult for determining the presence of adulterants, and wet chemistry methods are time-consuming and expensive. NIR spectroscopy was examined as a fast method requiring little sample preparation for determining the presence of tallow adulterant in butter. Nine portions of pure butter samples with no tallow adulteration were set aside. Tallow was prepared by melting animal fat and collecting the oil portion poured out from the solid residue. Nine samples of each of the following tallow concentrations by weight in butter were prepared: 1%, 3%, 5%, 7%, 9%, 11%, 13%, 15%, 17%, and 20%. Including the nine samples with no tallow adulteration, a total of ninety-nine samples were used for the study. NIR spectra were collected in reflectance mode from 10000 cm-1 to 4000 cm-1 using 2 cm-1 resolution. A transflectance sample accessory with a total pathlength of 0.5 mm was used for collection. Various pre-treatments were performed on the NIR spectral data, after which Principle Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Partial Least Squares (PLS) chemometric methods were used to build the models.
|Predict Value (0 = no tallow, 1 = presence of tallow)||R2= 0.95||RMSEP = 0.062|
|Tallow %||R2= 0.973||RMSEP = 1.537%|
The results presented were excellent and proved the feasibility of using NIR spectroscopy to determine the presence of tallow adulterant in butter as well as quantitatively measure the amount of tallow with reasonable prediction error. After various pre-treatments were performed, the wavenumber ranges from 7500 cm-1 to 4000 cm-1 using the first derivative with fifteen smoothing points was used for the calibration models. Examination of the scores plot after PCA showed clear classification and separation between each group of samples, proving that changes to the NIR spectra occur as more tallow is added to the samples. PLS-DA uses the arbitrary values of 0 and 1 for classification purposes between two groups. The model generates a number based on NIR spectra. A number less than 0.5 classifies as the group assigned to 0 and a number greater than 0.5 classifies as the group assigned to 1. In this case, the RMSEP of 0.062 is more than accurate enough to classify the sample as non-adulterated or adulterated. PLS predicts a quantitative value from NIR spectra and a calibration model. The RMSEP shows a prediction accuracy with error slightly greater than 1.5% tallow, which is accurate enough for real-time use. To properly validate the model, 30% of the samples were removed from the PLS model, a new model was created without those samples, and the NIR spectra of those samples were used with the new model to predict the percentage of tallow adulteration. These results proved the validity of the model and all predictions showed an error of less than 2% tallow. More samples over the range of values encompassing different types of butter will improve the results and make the model robust enough for universal application for tallow adulteration screening of butter.