Hyperspectral imaging (HSI) combines Near-infrared (NIR) spectroscopy and digital imaging to give information about the chemical properties of objects and their spatial distribution. Protein content is one of the most important quality factors in wheat. It is known to vary widely depending on the cultivar, agronomic and climatic conditions. However, little information is known about single kernel protein variation within batches. The aim of the present work was to measure the distribution of protein content in whole wheat kernels on a single kernel basis, and to apply HSI to predict this distribution. Wheat samples from 2013 and 2014 harvests were sourced from UK millers and wheat breeders, and individual kernels were analysed by HSI and by the Dumas combustion method for total protein content. HSI was applied in the spectral region 980-2500nm in reflectance mode using the push-broom approach. Single kernel spectra were used to develop partial least squares (PLS) regression models for protein prediction of intact single grains. The protein content ranged from 6.2 to 19.8% ("as-is" basis), with significantly higher values for hard wheats. The performance of the calibration model was evaluated using the coefficient of determination (R2) and the root mean square error (RMSE) from 3250 samples used for calibration and 868 used for external validation. The calibration performance for single kernel protein content was R2 of 0.82 and 0.79, and RMSE of 0.86 and 0.94% for the calibration and validation dataset, enabling quantification of the protein distribution between kernels and even visualisation within the same kernel. The performance of the single kernel measurement was poorer than that typically obtained for bulk samples, but is acceptable for some specific applications. The use of separate calibrations built by separating hard and soft wheat, or on kernels placed on similar orientation did not greatly improve the prediction ability. We simulated the use of the lower cost InGaAs detector (1000-1700nm), and reported that the use of proposed HgCdTe detectors over a restricted spectral range gave a lower prediction error (RMSEC=0.86% vs 1.06%, for HgCdTe and InGaAs, respectively), and increased R2 value (Rc2=0.82 vs 0.73).

Increasing awareness about the use of compounds obtained from natural sources exerting health-beneficial properties, including antimicrobial and antioxidant effects, led to increased number of research papers focusing on the study of functional properties of target compounds to be used as functional foods or in preventive medicine. Pomegranate has shown positive health properties due to the presence of bioactive constituents such as polyphenols, tannins, and anthocyanins. Punicalagin is the major antioxidant, abundantly found in pomegranate’s peel. Research has shown that pomegranate polyphenols not only have a strong antioxidant capacity but they also inhibit the growth of pathogenic bacteria like V. cholera, P. aeruginosa and S. aureus, B. cereus, E. coli, and S. virulence factor, and inhibits fungi such as A. Ochraceus, and P. citrinum. Compounds of natural origin inhibit the growth of various pathogens by extending the shelf life of foodstuffs and assuring their safety. Therefore, the need to find compounds to be used in combination with antibiotics or as new antimicrobial sources, such as plant extracts. On the basis of the above discussion, this review focuses on the health benefits of pomegranate, by summarizing the current body of research focusing on pomegranate bioactive constituents and their therapeutic potential against some pathogenic microbes.

Virgin olive oil (VOO) has unique chemical characteristics among all other vegetable oils which are of paramount importance for human health. VOO constituents are also responsible of its peculiar flavor, a complex sensation due to a combination of aroma, taste, texture, and mouthfeel or trigeminal sensations. VOO flavor depends primarily on the concentration and nature of volatile and phenolic compounds present in olive oil which can change dramatically depending on agronomical and technological factors. Another aspect that can change the flavor perception is linked to the oral process during olive oil tasting. In fact, in this case, some human physiological and matrix effects modulate the flavor release in the mouth. The present review aims to give an overview on VOO flavor, with particular emphasis on the mechanisms affecting its production and release during a tasting.

Hyperspectral imaging (1000–2500 nm) was used for rapid prediction of moisture and total lipid content in intact green coffee beans on a single bean basis. Arabica and Robusta samples from several growing locations were scanned using a “push-broom” system. Hypercubes were segmented to select single beans, and average spectra were measured for each bean. Partial Least Squares regression was used to build quantitative prediction models on single beans (n = 320–350). The models exhibited good performance and acceptable prediction errors of ∼0.28% for moisture and ∼0.89% for lipids.