Chemometric techniques for the prediction of milk composition from MIR spectral data: A review.

Chemometrics; use of statistical models to characterise and understand complex chemical systems/samples, is an advancing field. In the dairy industry, the accurate prediction of milk composition involves combining mid-infrared spectroscopy with chemometric techniques for the evaluation of major constituents of milk. The increased interest in determination of detailed composition of dairy products, alongside emerging and more-widespread use of chemometric methodologies, have generated continuous improvement in predictive models for this application.

Rapid detection of viable microbes with 5-cyano-2,3-di-(p-tolyl)tetrazolium chloride and 5(6)-carboxyfluorescein diacetate using a fibre fluorescence spectroscopy system.

Aims: To assess the efficacy of two commercially available viability dyes, 5-cyano-2,3-di-(p-tolyl)tetrazolium chloride (CTC) and 5(6)-carboxyfluorescein diacetate (CFDA), in reporting on viable cell concentration and species using an all-fibre fluorometer.

Methods And Results: Four bacterial species (two Gram-positive and two Gram-negative) commonly associated with food poisoning or food spoilage (Escherichia coli, Salmonella enterica, Staphylococcus aureus, and Bacillus cereus) were stained with CTC or CFDA and the fibre fluorometer was used to collect full fluorescence emission spectra. A good correlation between concentration and fluorescence intensity was found for Gram-negative bacteria between 107 and 108 colony-forming units (CFU) ml-1.

Support vector regression for prediction of stable isotopes and trace elements using hyperspectral imaging on coffee for origin verification.

The potential of using rapid and non-destructive near-infrared - hyperspectral imaging (HSI-NIR) for the prediction of an integrated stable isotope and multi-element dataset was explored for the first time with the help of support vector regression. Speciality green coffee beans sourced from three continents, eight countries, and 22 regions were analysed using a push-broom HSI-NIR (700-1700 nm), together with five isotope ratios (δC, δN, δO, δH, and δS) and 41 trace elements. Support vector regression with the radial basis function kernel was conducted using X as the HSI-NIR data and Y as the geochemistry markers.

Continuous separation of bacterial cells from large debris using a spiral microfluidic device.

With the global increase in food exchange, rapid identification and enumeration of bacteria has become crucial for protecting consumers from bacterial contamination. Efficient analysis requires the separation of target particles (e.g.

Near-infrared reflectance spectroscopy accurately predicted isotope and elemental compositions for origin traceability of coffee.

Stable isotope ratios and trace elements are well-established tools that act as signatures of the product's environmental conditions and agricultural processes; but they involve time, money, and environmentally destructive chemicals. In this study, we tested for the first time the potential of near-infrared reflectance spectroscopy (NIR) to estimate/predict isotope and elemental compositions for the origin verification of coffee. Green coffee samples from two continents, 4 countries, and 10 regions were analysed for five isotope ratios (δC, δN, δO, δH, and δS) and 41 trace elements.