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Coffee aroma is critical for consumer liking and enables price differentiation of coffee. This study applied hyperspectral imaging (1000–2500 nm) to predict volatile compounds in single roasted coffee beans, as measured by Solid Phase Micro Extraction-Gas Chromatography-Mass Spectrometry and Gas Chromatography-Olfactometry. Partial least square (PLS) regression models were built for individual volatile compounds and chemical classes. Selected key aroma compounds were predicted well enough to allow rapid screening (R2 greater than 0.7, Ratio to Performance Deviation (RPD) greater than 1.5), and improved predictions were achieved for classes of compounds - e.g. aldehydes and pyrazines (R2 ∼ 0.8, RPD ∼ 1.9). To demonstrate the approach, beans were successfully segregated by HSI into prototype batches with different levels of pyrazines (smoky) or aldehydes (sweet). This is industrially relevant as it will provide new rapid tools for quality evaluation, opportunities to understand and minimise heterogeneity during production and roasting and ultimately provide the tools to define and achieve new coffee flavour profiles.

The special nature of hyperspectral imaging (HSI) data requires special image analysis treatments using mathematical, statistical, and software programming approaches. These operations are crucial in building an automatic computer-integrated HSI system qualified for nondestructive assessment of various food quality traits. The theory, fundamentals, and principles of such a system and all accompanying methods associated with the development of robust image processing algorithms of hyperspectral images are explored and reported in this chapter. The quality of the acquired hyperspectral images, the way of extracting spectral fingerprints, and methods of data modeling have substantial effects on the outcomes of the analyses and processing. Fundamental image analysis operations experienced on hyperspectral images during food quality evaluation processes are the cornerstone of this technique. The explored methodologies will have positive impacts not only for food engineers and scientists but also for the food industry willing to adopt this technology in their premises. The strategy applied for image processing for analyzing and visualizing the final results is extremely important to identify the proper decision in detection, classification, quantification, and/or prediction processes. The applications of HSI systems in different sorts of agrifood products were reported with specific examples to demonstrate the potential of such systems in a wide range of analytical tasks. At the end of this chapter, the reader can realize the great capabilities of HSI systems as a novel emerging technique for noninvasive estimation of quality parameters, which proofs why this technology received great acceptance from scientific communities and gained a rapid interest from researchers and food industries. Therefore the state of the art for HSI is expected to gain more and more applications in food analysis and characterization.

Molecular gastronomy is a novel discipline within the food science area. Its main difference with the traditional food science and technology studies is its focus on kitchen restaurant and home kitchen levels. The collaboration among food scientists (food chemists, food engineers, sensory scientists, etc.) and innovative chefs led to the implementation of a new approach to cooking, often referred to as “science-based cooking” or “molecular cooking.” This implies implementing new techniques, tools, or ingredients borrowed from scientific laboratories. In parallel, a closer look at the kitchen led scientists to investigate phenomena or methods that are often ignored by food scientists. The difference between molecular gastronomy and conventional food science has been discussed in this chapter, with some examples related to studies on olive oil, sous-vide cooking, the use of liquid nitrogen and ultrasound treatment, as well as the technique called “spherification.” The importance of food pairing in haute-cuisine restaurants and for researchers in the area of sensory science has been highlighted, with the presentation of the theoretical/computational approach based on the so-called flavor network and reporting some results based on empirical laboratory-based studies. The negative outcome of these investigations proves the difficulties of simplifying such a complex system, in which odor, taste-active compounds, texture, and other factors interact, and additional complexity is added by cooking itself. Also, the final consumers’ experience depends on other factors such as the dish presentation and their general expectations.

Hyperspectral imaging (HSI) is a novel technology for the food sector that enables rapid non-contact analysis of food materials. HSI was applied for the first time to whole green coffee beans, at a single seed level, for quantitative prediction of sucrose, caffeine and trigonelline content. In addition, the intra-bean distribution of coffee constituents was analysed in Arabica and Robusta coffees on a large sample set from 12 countries, using a total of 260 samples. Individual green coffee beans were scanned by reflectance HSI (980–2500 nm) and then the concentration of sucrose, caffeine and trigonelline analysed with a reference method (HPLC-MS). Quantitative prediction models were subsequently built using Partial Least Squares (PLS) regression. Large variations in sucrose, caffeine and trigonelline were found between different species and origin, but also within beans from the same batch. It was shown that estimation of sucrose content is possible for screening purposes (R2 = 0.65; prediction error of ~ 0.7% w/w coffee, with observed range of ~ 6.5%), while the performance of the PLS model was better for caffeine and trigonelline prediction (R2 = 0.85 and R2 = 0.82, respectively; prediction errors of 0.2 and 0.1%, on a range of 2.3 and 1.1% w/w coffee, respectively). The prediction error is acceptable mainly for laboratory applications, with the potential application to breeding programmes and for screening purposes for the food industry. The spatial distribution of coffee constituents was also successfully visualised for single beans and this enabled mapping of the analytes across the bean structure at single pixel level., Graphical abstract Image 1, Highlights • NIR hyperspectral imaging was applied to green coffee beans on a single seed basis. • Sucrose, caffeine and trigonelline were analysed from single beans using HPLC-MS. • PLS regression models were built to quantify for the coffee constituents. • Prediction models were applied to single pixels to visualise the compound distribution. • HSI was shown to be useful for rapid non-destructive screening of single coffee beans.