Comparison of hyperspectral imaging and spectrometers for prediction of cheeses composition.

Artisanal cheeses are part of the heritage and identity of different countries or regions. In this work, we investigated the spectral variability of a wide range of traditional Brazilian cheeses and compared the performance of different spectrometers to discriminate cheese types and predict compositional parameters. Spectra in the visible (vis) and near infrared (NIR) region were collected, using imaging (vis/NIR-HSI and NIR-HSI) and conventional (NIRS) spectrometers, and it was determined the chemical composition of seven types of cheeses produced in Brazil. Principal component analysis (PCA) showed that spectral variability in the vis/NIR spectrum is related to differences in color (yellowness index) and fat content, while in NIR there is a greater influence of productive steps and fat content. Partial least squares discriminant analysis (PLSDA) models based on spectral information showed greater accuracy than the model based on chemical composition to discriminate types of traditional Brazilian cheeses. Partial least squares (PLS) regression models based on vis/NIR-HSI, NIRS, NIR-HSI data and HSI spectroscopic data fusion (vis/NIR + NIR) demonstrated excellent performance to predict moisture content (RPD > 2.5), good ability to predict fat content (2.0 < RPD < 2.5) and can be used to discriminate between high and low protein values (∼1.5 < RPD < 2.0). The results obtained for imaging and conventional equipment are comparable and sufficiently accurate, so that both can be adapted to predict the chemical composition of the Brazilian traditional cheeses used in this study according to the needs of the industry.

Near-infrared techniques for fraud detection in dairy products: A review.

The dairy products sector is an important part of the food industry, and their consumption is expected to grow in the next 10 years. Therefore, the authentication of these products in a faster and precise way is required for the sake of public health. This review proposes the use of near-infrared techniques for the detection of food fraud in dairy products as they are faster, nondestructive, environmentally friendly, do not require sample preparation, and allow multiconstituent analysis. First, we have described frequent forms of food fraud in dairy products and the application of traditional techniques for their detection, highlighting gaps and counterproductive characteristics for the actual global food chain, as longer sample preparation time and use of reagents. Then, the application of near-infrared spectroscopy and hyperspectral imaging for the detection of food fraud mainly in cheese, butter, and yogurt are described. As these techniques depend on model development, the coverage of different dairy products by the literature will promote the identification of food fraud in a faster and reliable way.

Effects of edible coatings of Chlorella sp. containing pomegranate seed oil on quality of Spondias tuberosa fruit during cold storage.

Spondias tuberosa is a highly perishable fruit that is difficult to transport, which limits its consumption in local areas. Thus, for economic reasons, there is a need for technologies to extend post-harvest shelf life of these fruits. The aim of this study was to evaluate the life of Spondias tuberosa fruit using edible coatings composed of Chlorella sp. and pomegranate seed oil (PSO) during cold storage. Coated and uncoated S. tuberosa fruits were stored for 12 days at 14 ± 2 °C and 85 ± 5% RH and evaluated every 3 days. Chlorella sp. + PSO coatings retarded ripening, maintained firmness, mass, and a greener color when compared to control treatment - since by default controls are not treated. Coatings in association with the best quality fruit presented 2.0% of Chlorella sp.