Applications

Yogurt Analysis

Over the past few years, yogurt brands have focused their efforts on removing artificial additives and preservatives from products.

Introduction

Yogurt is a dairy product produced by bacterial fermentation of milk by the cultures Lactobacillus bulgaricus and Streptococcus thermophilus. Lactose in the milk is fermented to produce lactic acid, which acts on milk protein to give yogurt its texture and tart flavor. Milk is adjusted to optimize fat and solids content, pasteurized and homogenized, cooled, fermented, and cooled again once fermentation has brought the lactic acid to the desired level. Stabilizers, flavors, and fruit may be added at various points during the process. The worldwide industrial production of yogurt and fermented milks reached about 35 million tons in 2012. The market in North America is expected to have a 3.0% CAGR by 2024, with a value increase from $11.2 billion in 2015 to $14.6 billion in 2024. Factors driving market growth include consumer awareness of dairy products as having health benefits, probiotics content being a known factor in stomach health, niche products and a variety of choices in flavors, textures, and fruit content, and attractive packaging which especially helps the yogurt market for kids. Proper quality control at all stages of the yogurt manufacturing process is essential for a good finished product. Parameters of interest include microbial quality, degree of pasteurization, sugar, and acidity. Microbial quality is determined by a dye reaction test and a count that is too high makes the milk unsuitable for manufacturing.

Degree of pasteurization is measured by an enzyme known as phosphatase and performing this test is required before fermentation may proceed. In the case of sugar and acidity, both are essential guidelines of taste in the finished product, which can have different desired levels based on the type of yogurt produced. As is the case with many food and dairy products, adulteration is a major issue and different types of non-milk protein adulteration have been reported to be found in yogurt.

While no alternative methods currently exist for testing microbial quality and degree of pasteurization, sugar, acidity, and protein measurements are all known parameters that can be measured using NIR spectroscopy. Current methods for testing these parameters of interest in yogurt are expensive, laborious, and time-consuming, especially when implemented in a process setting. In-line monitoring is impractical and sometimes impossible. There is a need for fast, cost-effective, and real-time monitoring of parameters at all stages of the yogurt manufacturing process. One such method that has been examined is NIR spectroscopy.

Analytes

  • Sugar (°Brix)
  • Acidity (pH)
  • Non-Milk Protein Adulteration

Summary of Published Papers, Articles, and Reference Materials

Measurement of chemical parameters in major constituents of yogurt for quality control purposes has been studied using NIR spectroscopy. The results of most studies have been promising. Two separate but similar studies examined measuring sugar and acidity content in yogurt samples purchased from a commercial market. Results were good and should improve with a larger sample set with more varieties of yogurt, especially yogurts that extend the range of values for the parameters of interest. Another study examined protein adulteration in yogurt, a problem that has been reported in publications. Samples of yogurt were adulterated with three different types of non-milk proteins: edible gelatin, industrial gelatin, and soy protein powder. Results were good and determined that the spectroscopic method could determine the presence of 1% edible gelatin, 2% industrial gelatin, and 2% soy protein powder or higher concentrations. The potential of NIR spectroscopy as an untargeted detection tool for determining the presence of non-milk protein adulterants in yogurt was demonstrated.

Scientific References and Statistics

Fast Measurement of Sugar Content of Yogurt Using VIS/NIR Spectroscopy – He, Wu, Feng, Li, International Journal of Food Properties, 10: 1-7, 2007

Many different brands of yogurt are manufactured and one of the most important quality parameters is the sugar content. During the fermentation process, lactose in milk ferments to produce lactic acid, which acts on milk protein to give yogurt its texture and characteristic tart flavor. Sugar is a main guideline of taste in yogurt. Current reference methods for determining sugar content in yogurt are time-consuming and expensive, as well as being impractical for real-time measurement. NIR spectroscopy was examined as a method for determining the sugar content in yogurt. Five different brands of yogurt were procured from a local market for the study. They were stored in a refrigerator after purchase before being used in the study. Thirty-two samples of each brand of yogurt were used for a total of one hundred sixty samples. Samples were scanned in reflectance mode from 350 nm to 1075 nm at 1.5 nm intervals using a sensitivity of 3.5 nm. Thirty scans were collected per measurement and averaged into one spectrum. Immediately after each NIR spectrum was collected, the sample was measured using a sugar content meter for °Brix. The NIR spectra were pre-processed using a smoothing function to reduce noise and after smoothing, it was determined that the best wavelength range to use for chemometric modeling was 400 nm to 1000 nm. The reference values for sugar and NIR spectra were used to create a Partial Least Squares (PLS) regression model correlating the spectra to the sugar content.

Sugar R2= 0.934 RMSEP=0.389 °Brix

The PLS model for sugar showed excellent results and a high correlation coefficient. In order to verify the validity of the model, twenty-five samples were removed from the calibration and a new model was created without those samples. The NIR spectra from the removed samples were then used with the model to perform independent predictions, which showed error low enough to use the calibration in a real-time setting. The sample-set was limited and since yogurt can get manufactured using many different flavors and textures, more types of samples would be needed to use the model on a universal basis.
https://www.tandfonline.com/doi/pdf/10.1080/10942910600575658

Measurement of Yogurt Internal Quality Through Using Vis/NIR Spectroscopy – Shao, He, Feng, Food Research International 40 (2007) 835-841

Sugar and acidity are two important quality parameters in yogurt and must be tested as the target contents of both vary in different types of yogurt. Current reference methods for determining sugar and acidity content in yogurt are time-consuming and expensive, as well as being impractical for real-time measurement. NIR spectroscopy was examined as a method for determining the sugar and acidity content in yogurt. Five different brands of yogurt were procured from a local market for the study. They were stored in a refrigerator after purchase before being used in the study. Thirty-two samples of each brand of yogurt were used for a total of one-hundred sixty samples. Samples were scanned in reflectance mode from 350 nm to 1075 nm at 1.5 nm intervals. Thirty-two scans were collected per measurement and averaged into one spectrum. Immediately after each NIR spectrum was collected, the sample was measured using a sugar content meter for °Brix and a pH meter for acidity. The NIR spectra were first pre-processed using a smoothing function to reduce noise and after smoothing, it was determined that the best wavelength range to use for chemometric modeling was 400 nm to 1000 nm. Other processes were applied to the data for model optimization. The reference values for sugar and acidity were used with the NIR spectra to create Partial Least Squares (PLS) regression models correlating the spectra to the sugar content.

Sugar R2= 0.92 RMSEP= 0.36 °Brix
Acidity R2= 0.91 RMSEP= 0.04 pH

The PLS models for sugar and acidity showed excellent results and high correlation coefficients. In order to verify the validity of the models, thirty-five unknown samples were scanned and the NIR spectra were used with the model to perform independent predictions, which showed error low enough to use the calibrations in a real-time setting. The sample-set was limited and since yogurt can be manufactured using many different flavors and textures, more types of samples would be needed to use the models on a universal basis.
https://www.sciencedirect.com/science/article/pii/S0963996907000294

The Feasibility of Using Near-Infrared Spectroscopy and Chemometrics for Untargeted Detection of Protein Adulteration in Yogurt: Removing Unwanted Variations in Pure Yogurt – Xu, Yan, Cai, et al., Journal of Analytical Methods in Chemistry, Volume 2013, Article ID 201873

In recent years, scandals involving dairy product adulteration have led to the development of new targeted analytical methods to detect the presence of adulterants. Melamine has especially been examined as a milk adulterant but new types of adulterants are being reported all the time. One such adulterant is different types of non-milk proteins in yogurt. Because of the ever-evolving nature of adulterants, new types of untargeted analyses are needed to determine whether or not a product is pure and unadulterated, with the focus being on not necessarily identifying the specific adulterant but rather determining the presence of one. If a product is determined to be impure, further testing can be done to identify the adulterant. NIR spectroscopy was examined as a method for protein adulteration identification in yogurt. Yogurt was manufactured from milk and bacteria cultures using the standard method specifically for the study. The yogurt was divided into nineteen portions. Three portions were kept pure with no adulterant. Six portions were adulterated with edible gelatin ranging from 1% to 8% by weight. Five portions were adulterated with industrial gelatin ranging from 0.5% to 5% by weight. Five portions were adulterated with soy protein powder ranging from 0.5% to 5% by weight. In order to keep the thickness uniform in all the samples, pure water was added which is the common practice in protein adulteration. NIR spectra of both the pure and adulterated samples were collected from 12000 cm-1 to 4000 cm-1 in diffuse reflectance mode. Spectral resolution was 8 cm-1, scanning interval was 3.857 cm-1, and sixty-four scans were collected per reading and averaged into one spectrum. In total after dividing the portions, sixty spectra of pure samples and one hundred ninety-seven spectra of adulterated samples were collected. Spectrum of pure water was collected by averaging five measurements of water film on the reflectance background. In order to remove the influence of water variation, all NIR spectra of the pure and adulterated yogurt samples were orthogonally projected (OP) on the complement space of water spectrum using an algorithm, minimizing the influence of the water difference on the classification. Standard Normal Variate (SNV) processing was used as well to reduce scattering effects and correct interference caused by variations. The groups were divided into a training set and test set and the OCPLS class modeling algorithm was used to classify samples using the following sets of NIR spectra: raw spectra, OP, and SNV.

OCPLS Classification:
Raw Spectra Test Set 17/20
Raw Spectra Training Set 163/197
SNV Spectra Test Set 18/20
SNV Spectra Training Set 181/197
OP Spectra Test Set 18/20
OP Spectra Training Set 187/197

The results showed that both pre-processing methods had a positive effect on the models, with the OP spectra classification showing slightly better results than the SNV spectra classification. Because water has such a strong absorbance in the NIR wavelength range, it is possible that without pre-processing, the results for the raw spectra and SNV may be classifying based on the differences in water and not the presence of an adulterant. Careful analysis of the wavelength ranges used to determine the classification will show this, but such analysis was not performed in this study. One purpose of analyzing the classification in this manner was to determine the minimum threshold for each adulterant that could be detected from the NIR spectra. None of the non-adulterated samples were incorrectly classified as having an adulterant present. For the samples that were incorrectly classified as being pure while having an adulterant present, the concentration was 0.5% for edible gelatin, 1% for industrial gelatin, and 1% for soy protein powder. All samples with an adulterant concentration of 1% edible gelatin, 2% industrial gelatin, and 2% soy protein powder (or higher) were correctly classified. These can be considered the safe thresholds of detection that the models can accurately use to detect protein adulteration in yogurt. The potential was demonstrated to use NIR spectroscopy as a method for protein adulteration in screening of yogurt and further work with a larger sample set at lower concentrations of protein should improve the results.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3697415/

Commercial Reference

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