Identification of Possible Milk Adulteration Using Physicochemical Data and Multivariate Analysis

Milk is one the most widely consumed foods in the world, with an average annual production of 723 million tons in the second half of the past decade. However, to increase milk’s profitability, some actors in the dairy chain may adulterate it, altering its chemical composition, and reducing the nutritional value of this food. The quality of milk is therefore assessed through chemical and physical analyses such as total dry matter, total Kjeldahl nitrogen, ash, acidity, fat, reducing sugars, depression of freezing point, and relative density. In this work, we have used Principal Components Analysis (PCA) and supervised methods (PLS-DA, SIMCA, kNN, and SVM-DA) to explore physicochemical data from milk and to classify and discriminate between samples that were compliant or not to the parameters set in the Brazilian Regulation for the Inspection of Animal Products and other national regulations. Classification results regarding specificity and selectivity for PLS-DA, SIMCA, kNN, and SVM-DA for noncompliant samples in the test group were, respectively, the following: 91, 88; 100, 97; 78, 67; 78, and 71%.     

Meat quality and lipid profiles in crossbred lambs finished on clover-rich pastures

Meat quality traits were compared in Texel x Polwarth and Texel x Corriedale ram lambs, with live weight of 25 kg, finished on pastures rich in white clover. The two crossbred groups showed similar results (P > 0.05) for pH, moisture, protein, meat color, individual SFA and total SFA. Fat content was higher and cholesterol lower in Texel x Polwarth lambs (P < 0.05), which also had higher amounts of MUFA (P < 0.05) and lower levels of total PUFA and n− 6 PUFA (P < 0.01). Differences in meat quality among the two crossbred groups were minor, and possibly not perceptible from the consumer standpoint. However, both groups of lambs produced lean meat with high amounts of PUFA (> 16%), possibly due to the ingestion of white clover in the finishing period. Overall, these results indicate that a differentiated product can be obtained in these conditions, with higher nutritional value than conventional lamb meat.     

Fuzzy logic for identifying pigments studied by Raman spectroscopy

Fuzzy logic and linguistic variables are used for the automatic interpretation of Raman spectra obtained from pigments found in cultural heritage art objects. Featured bands are extracted from a Raman spectrum of a reference pigment and the methodology for constructing the library is illustrated. An unknown spectrum is then interpreted automatically and a process for identifying the corresponding pigment is described. A reference library consisting of 32 pigments was built and the effectiveness of the algorithm was tested by the Raman spectroscopic analysis of 10 pigments that are known to have been extensively used in Byzantine hagiography. Binary mixtures of these pigments were also tested. The algorithm's level of identification was good even though extra peaks, noise, and background signals were encountered in the spectra.