Query: milk Adulteration

title: Simultaneous determination of goat milk adulteration with cow milk and their fat and protein contents using NIR spectroscopy and PLS algorithms
abstract: This work employed NIR spectroscopy and PLS algorithms for the identification and quantification of goat milk adulteration by adding cow milk, besides the determination of their fat and protein contents. Since cow milk can represent a health risk to allergic consumers regardless its amount, PLS DA was able to identify cow milk additions in goat milk as low as 1.0154 g/100g, likewise the non adulterated goat and cow milk samples, achieving a 100% of correct classification. For quantification purposes, the Successive Projections Algorithm for interval selection in PLS (iSPA PLS) provided the best results for the determination of both adulteration and fat contents, while PLS gave better results for the protein quantification. Despite the great similarity of both natural dairy matrices and their intrinsic variability, the prediction results provided suitable values with high correlation coefficients and low RMSEP and REP values, with RPD values higher than 3. Therefore, the proposed methodology proved to be a useful, fast and non destructive tool for screening the quality of goat milk in terms of its adulteration with cow milk, in addition to the quantification of its fat and protein contents.

title: A novel approach to detect milk adulteration based on the determination of protein content by smartphone based digital image colorimetry
abstract: A novel alternative for the detection of milk adulteration by dilution based on the determination of the protein content is proposed. The proposed method involves the precipitation of the proteins by the salting out effect of copper sulfate and the proportional adsorption of Cu(II) on the precipitate. Subsequently, photometric measurements of the remaining amount of Cu(II) by smartphone based digital colorimetry are conducted to determine the protein content. The proposed procedure is rapid (sample throughput of 32 assays/h) precise (coefficient of variation 3.0% n 20) environmentally friendly, and allows the detection of as low as 1.0% v/v water in adulterated milk. The main species used to mask the protein content do not interfere, thus permitting the direct detection of adulteration. The procedure was successfully applied for the classification of milk samples as compliant and non compliant as well as for the indirect determination of the protein content in milk. The quantitative results agreed with those obtained by the reference method (near infrared spectroscopy) at a 95% confidence level.

title: A qualitative analysis of the machine learning focus on milk adulteration detection methods in food adultery: a
abstract: A qualitative analysis of the machine learning focus on milk adulteration detection methods in food adultery: a Food adultery

title: Smart Milk Adulteration Detection
abstract: Adulteration is extremely basic today and the most normally adulterated nourishment item is milk. The most widely recognized explanation behind adulteration is the utilization by producers of undeclared materials that are less expensive than the right and pronounced ones. Adulteration brings down the nature of nourishment and now and then, harmful synthetic substances are likewise added which can be dangerous to health. Adulteration of products either in liquid or solid form can harm or pose serious health risk issues to the consumer. The present investigation was arranged with the primary goal of recognizing purchasing practices of homemakers and their degree of awareness identified with chosen food items. Being considerate of the health betterment of the society we have planned a project which will allow the customer to know about the level of purity of product which they are buying and being aware of it in

title: A new formaldehyde optical sensor: Detecting milk adulteration.
abstract: A sensor consisting of an optical fibre with the exposed tip coated with the polyoxometalate salt (C4H9)4N]4H[PMo10V2O40] specially designed to be insoluble in water, which UV Vis spectrum changed in contact with formaldehyde, is presented. The sensor limit of detection for formaldehyde was 0.2 mg L 1, and the limit of quantification was 0.6 mg L 1, which were close to the conventional spectrophotometric method values of 0.2 mg L 1 and 0.5 mg L 1, respectively, and lower than the tolerable limit for ingested food. The sensor was tested for formaldehyde quantification in milk, as its deliberate addition is a matter of concern. The results obtained analysing formaldehyde in milk samples by the optical sensor and by the conventional method were not statistically different (a 0.05)

title: Milk Adulteration and Detection
abstract: Milk adulteration is a worldwide apprehension that adversely affects progressing countries like India. Milk is a healthy drink that consists of several macro and micro nutrients which offer gamut of health benefits and is consumed by people of all age groups. Addition of extraneous matter like starch, sugar, caustic soda, ammonia refined vegetable oil, common detergent etc into the milk lowers its nutritious value there by posing risk to health. Hence it is very important to test the purity of milk before consumption as synthetic milk and pure milk more or less look the same and have a same taste. This paper describes a detection system that consumes less time to determine the parameters like Nacl, urea, pH and water percentage contained in the milk with the help of sensors incorporated in the system.

title: On the utilization of deep and ensemble learning to detect milk adulteration
abstract: BackgroundFraudulent milk adulteration is a dangerous practice in the dairy industry that is harmful to consumers since milk is one of the most consumed food products. Milk quality can be assessed by Fourier Transformed Infrared Spectroscopy (FTIR) a simple and fast method for obtaining its compositional information. The spectral data produced by this technique can be explored using machine learning methods, such as neural networks and decision trees, in order to create models that represent the characteristics of pure and adulterated milk samples.ResultsThousands of milk samples were collected, some of them were manually adulterated with five different substances and subjected to infrared spectroscopy. This technique produced spectral data from the milk samples composition, which were used for training different machine learning algorithms, such as deep and ensemble decision tree learners. The proposed method is used to predict the presence of adulterants in a binary classification problem and also the specific assessment of which of five adulterants was found through multiclass classification. In deep learning, we propose a Convolutional Neural Network architecture that needs no preprocessing on spectral data. Classifiers evaluated show promising results, with classification accuracies up to 98.76% outperforming commonly used classical learning methods.ConclusionsThe proposed methodology uses machine learning techniques on milk spectral data. It is able to predict common adulterations that occur in the dairy industry. Both deep and ensemble tree learners were evaluated considering binary and multiclass classifications and the results were compared. The proposed neural network architecture is able to outperform the composition recognition made by the FTIR equipment and by commonly used methods in the dairy industry.

title: Rapid detection of cow milk adulteration/contamination in goat milk by a lateral flow colloidal gold immunoassay strip
abstract: to detect cow milk adulteration or accidental contamination of goat milk are both laborious and time consuming. The aim of this technical research communication was to develop a simple, rapid, specific and sensitive method for quantitative detection of cow milk in goat milk. A competitive lateral flow immunoassay (LFIA) strip was developed using a specific monoclonal antibody (mAb) labeled with colloidal gold nanoparticles (GNPs) for specifically binding to cow milk casein. The detection limit of this rapid detection was 0.07% of cow milk in goat milk, providing equal specificity and higher sensitivity when compared with a commercial enzyme linked immunosorbent assay (ELISA) These result suggest that the established rapid GNPs LFIA strip could be used for monitoring cow milk adulteration/contamination of goat milk.

title: Potentiality of using front face fluorescence spectroscopy for quantitative analysis of cow milk adulteration in buffalo milk.
abstract: In current study, synchronous front face fluorescence spectroscopy together with partial least squares regression (PLSR) is used to predict the adulteration of cow and buffalo milk quantitatively. Fresh (unprocessed milk) samples of cow and buffalo were collected from local dairy farms. Fluorescence emission from milk samples mixed in different concentrations, show intensity variations at wavelengths 370 380 nm, 410 nm, 442 nm and 520 560 nm. Among them, the emissions at band position of 442 nm and 525 nm are highly selective between the two species and could help in finding adulteration of cow milk in buffalo milk and vice versa. The emissions at these wavelength positions correspond to fat soluble vitamin A as well as b carotene. PLS regression is used as a statistical prediction model, which is developed by training with the emission spectra of milk samples having known level of adulterations. The developed model predicts the unknown level of adulterations by means of their spectral data. The goodness of the model is determined by the correlation coefficient R square (r2) value, which in our case is 0.99. Furthermore, the model root mean square error in cross validation (RMSECV) and in prediction (RMSECP) remains 1.16 and 6.24 respectively. This approach can effectively be applied to determine milk adulterations among other species as well as in detecting external agents (fraudulent) added into milk and other dairy products by further studies.

title: Detection of buffalo milk adulteration with cow milk by capillary electrophoresis analysis.
abstract: The addition of cow milk during the production of buffalo mozzarella is a common fraud in dairy industries because of the lower price and greater availability of cow milk throughout the year. The aim of this study was to develop a new, rapid, and robust capillary electrophoresis method for detecting and quantifying cow milk in buffalo milk by exploiting cow a lactalbumin as a marker of adulteration. In particular, a linear calibration curve was generated, using a training set of calibrators consisting of 7 series of 17 buffalo/bovine whey mixtures, obtained after casein precipitation, with increasing percentages of cow whey. The capillary electrophoresis method showed high linearity (R2 0.968) repeatability [relative standard deviation (RSD) 2.11, 3.02, 4.38, and 1.18% respectively for 5, 10, 20, and 50% of buffalo/bovine whey mixtures] and intermediate precision (RSD 2.18, 2.49, 5.09, and 3.19% respectively, for 5, 10, 20, and 50% buffalo/bovine whey mixtures) Moreover, the minimum amount of detectable fraudulent cow milk was 1% and the limit of quantification was 3.1%