Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging

Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results indicated that a linear regression model based on partial least squares outperformed neural networks in this particular data modelling task. There existed a positive relationship between the number of samples in a calibration model and prediction performance, with a local optimum at a calibration set size of ~40% of the total data. The inclusion of samples from several years and locations could clearly improve the predictions of the investigated nutrient traits at small calibration set sizes. It should be stated that the expansion of calibration models with additional samples is only useful as long as they are able to increase trait variability. Models obtained in a certain environment were only to a limited extent transferable to other environments. They should therefore be successively upgraded with new calibration data to enable a reliable prediction of the desired traits. The presented results will assist the design and conceptualization of future hyperspectral imaging projects in order to achieve reliable predictions. It will in general help to establish practical applications of hyperspectral imaging systems, for instance in plant breeding concepts

An overview on the application of chemometrics in food science and technology: An approach to quantitative data analysis

Cozzolino, D ORCiD: 0000-0001-6247-8817During the last 30 years, food scientists and technologists all over the world are dealing with massive amounts of data derived from the use of different measuring devices (e.g. instrumental and sensory data), the integration of different analytical techniques and processes during the analysis and production of foods. Therefore, complementary disciplines and tools to the traditional ones used in food science such as statistics, experimental design and chemometrics have become essential in modern sciences and are an integral component in the day-to-day analysis of foods and derived products. The aim of this paper is to introduce as well as provide with an overview of different concepts, methods, techniques and general steps used in the quantitative analysis of foods when chemometrics or multivariate analytical methods are applied