Detection of heat treatment of honey with near infrared spectroscopy

Heat treatment of honey is usually applied due to crystallized form is not preferred by the consumers and makes handling harder to producers and beekeepers. Our aim is to find a method that can detect heat treatment of honey even at lower levels. In the study honeys were heated at 40°C, 60°C, 80°C, 100°C for one, two, three and four hours. Moisture, pH, electrical conductivity, HMF, color were determined, and spectra of honeys were recorded with NIRS. Results showed that NIRS could distinguish the honeys heated at 40°C while HMF was able to detect higher than 60°C treatment at significant level

Quantification of multiple adulterants in beef protein powder by FT-NIR

With its adverse effects on the health of consumers and the global economy, food adulteration is considered to be one of the pressing issues of our times. Beef Protein powders (BPP) are particularly among the most fraud-prone products mainly due to their ever-growing consumption. For the purpose of our study, FT-NIR spectroscopy was applied to quantify four commonly used adulterants: Melamine (M), Urea (U), Glycine (G) and Taurine (T) in BPP. Analysis with chemometric tools proved the efficiency of the aforementioned technique as a rapid nondestructive analytical tool for the detection of Beef protein powder adulteration

Spectroscopy as a rapid detecting paprika powder adulteration

Paprika powder, a spice known for imparting flavour, colour and aroma in foods has recently become compromised by fraudulent activities involving diverse adulterants such as corn flour and has prompted quality assurance (QA) measures. Near infrared spectroscopy (NIRS) is a non-destructive method gaining grounds in QA applications. The study applied NIRS to detect paprika powder adulterated with corn flour. Chemometric evaluation spectra showed that NIRS could discriminate cornflour adulteration with 100% classification accuracy. Adulteration was also predicted with high accuracies coefficient of determination (R2CV) between 0.97 and 0.99 and low errors (0.72 g/100g), proving the future QA applications of NIRS